CN117037457A - Landslide monitoring and early warning method - Google Patents
Landslide monitoring and early warning method Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data
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- 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
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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- Y02A50/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE in human health protection, e.g. against extreme weather
Abstract
The invention relates to the technical field of geological disaster monitoring, and discloses a landslide monitoring and early warning method, which comprises the steps of acquiring first mountain data and first environment data of a mountain to be monitored, and acquiring second mountain data and second environment data of the mountain to be monitored again within preset time; performing data processing on the first mountain data, the first environment data, the second mountain data and the second environment data to obtain data to be input; inputting data to be input into a landslide prediction model, outputting a landslide prediction value, and judging whether a landslide risk exists in the mountain to be monitored based on the landslide prediction value; when the mountain landslide risk to be monitored is judged, the intelligent monitoring of the mountain to be monitored can be realized by sending out early warning signals of different grades according to the landslide predicted value, the monitoring accuracy and the real-time performance are ensured, and by sending out early warning of different grades, targeted preventive measures can be further taken, so that the loss caused by the landslide is avoided or reduced.
Description
Technical Field
The invention relates to the technical field of geological disaster monitoring, in particular to a landslide monitoring and early warning method.
Background
Landslide is a collapse phenomenon of rock under the action of gravity, which often occurs in steep mountains. When landslide occurs, smoke and dust permeate along with the bang, rocks rapidly collapse and segregate, and the low-phase position collapses. Landslide can cause a significant disaster. The stones and soil blocks after landslide occur largely fall into the river channel and also block the river to form flood disasters, so that the mountain landslide monitoring and early warning method has important practical significance.
The existing landslide monitoring method comprises a gravity measurement method, an underground water level monitoring method and the like, and is relatively single in considered factors, so that all factors of landslide occurrence are not comprehensively considered, the existing landslide monitoring method cannot adjust monitoring period and monitoring accuracy according to actual conditions of the landslide, landslide risks exist and landslide risks do not exist, the data quantity obtained by monitoring is the same, and dynamic monitoring of the landslide cannot be achieved.
Therefore, how to provide a method for monitoring and early warning mountain in real time is a technical problem to be solved at present.
Disclosure of Invention
The embodiment of the invention provides a landslide monitoring and early warning method, which is used for solving the technical problems that intelligent monitoring and early warning cannot be carried out on a mountain to be monitored and monitoring accuracy and real-time cannot be guaranteed in the prior art.
In order to achieve the above purpose, the invention provides a landslide monitoring and early warning method, comprising the following steps:
acquiring first mountain data of a mountain to be monitored and first environment data of the mountain to be monitored, and acquiring second mountain data of the mountain to be monitored and second environment data of the mountain to be monitored again within preset time;
performing data processing on the first mountain data, the first environment data, the second mountain data and the second environment data to obtain data to be input;
inputting the data to be input into a landslide prediction model, outputting a landslide prediction value of the mountain to be monitored based on the landslide prediction model, and judging whether the mountain to be monitored has a landslide risk based on the landslide prediction value;
and when judging that the mountain landslide risk exists in the mountain to be monitored, sending out early warning signals of different grades according to the landslide predicted value.
In one embodiment, when performing data processing on the first mountain data, the first environment data, the second mountain data and the second environment data to obtain data to be input, the method includes:
calculating the mountain data variation of the mountain to be monitored according to the first mountain data and the second mountain data, wherein the first mountain data comprises a first rock stratum offset M1 and a first rock stratum offset angle N1 of the mountain to be monitored, and the second mountain data comprises a second rock stratum offset M2 and a second rock stratum offset angle N2 of the mountain to be monitored;
calculating the mountain data change rate of the mountain to be monitored according to the mountain data change quantity;
；
wherein X is a mountain data change rate of a mountain to be monitored, Δn is a mountain data change amount of the mountain to be monitored, Δn= { (M2-M1) a1+ (N2-N1) a2}, a1 is a formation offset conversion coefficient, a2 is a formation offset angle conversion coefficient, K is an influence coefficient of the mountain data change amount, hy is a formation offset direction parameter, y=1, 2,3, N, Δw is a formation offset direction change amount, p is a formation offset direction influence coefficient, 0<K is equal to or less than 1,0 is equal to or less than or equal to hy1, 0 is equal to or less than or equal to p is equal to 1;
Calculating the mountain environment variation of the mountain to be monitored according to the first environment data and the second environment data, wherein the first environment data is the water content Q1 of the mountain to be monitored, and the second environment data is the water content Q2 of the mountain to be monitored;
calculating the mountain environment change rate of the mountain to be monitored according to the mountain environment change quantity;
W=ΔQⅩβ;
wherein, W is the mountain environment change rate of the mountain to be monitored, deltaQ is the mountain environment change amount of the mountain to be monitored, deltaQ=Q2-Q1, and the influence coefficient of the mountain environment change amount is 0< beta < 1;
and taking the calculated mountain data change rate X and mountain environment change rate W as the data to be input.
In one embodiment, when the data to be input is input to a landslide prediction model, outputting a landslide prediction value of the mountain to be monitored based on the landslide prediction model includes:
selecting a plurality of historical mountain data change rates and historical mountain environment change rates of the mountain to be monitored from a historical database;
taking the historical mountain data change rate and the historical mountain environment change rate as data sets to be divided;
dividing the data set to be divided to obtain a training data set and a test data set;
Training a memory neural network based on the training data set to obtain the landslide prediction model;
inputting the mountain data change rate X and the mountain environment change rate W into the mountain landslide prediction model to obtain a mountain landslide prediction value of the mountain to be monitored;
R=LⅩexp{1/2πX+2πW}Ⅹ（1+λ）；
wherein R is a landslide prediction value of a mountain to be monitored, L is a landslide prediction function value, exp is an exponential function symbol, and lambda is a loss factor in the landslide prediction process of the mountain to be monitored.
In one embodiment, when judging whether the mountain to be monitored has a mountain landslide risk based on the mountain landslide prediction value, the method includes:
judging whether the mountain landslide risk exists in the mountain to be monitored according to the relation between the landslide predicted value R and the preset landslide predicted value R,
if R is less than R, judging that the mountain landslide risk does not exist in the mountain to be monitored;
and if R is less than or equal to R, judging that the mountain landslide risk exists in the mountain to be monitored.
In one embodiment, when sending out early warning signals of different grades according to the landslide prediction value, the method comprises the following steps:
presetting a first preset landslide prediction value and a second preset landslide prediction value, wherein the second preset landslide prediction value is larger than the preset landslide prediction value;
Different early warning signals are sent out according to the landslide prediction value, the first preset landslide prediction value and the second preset landslide prediction;
when the landslide predicted value is smaller than the first preset landslide predicted value, a first-level alarm signal is sent out;
when the landslide predicted value is larger than or equal to the first preset landslide predicted value and the landslide predicted value is smaller than the second preset landslide predicted value, a secondary alarm signal is sent out;
and when the landslide prediction value is greater than or equal to the second preset landslide prediction value, sending out a three-level alarm signal.
In one embodiment, after judging that the mountain to be monitored does not have a landslide risk, the method further includes:
calculating a landslide prediction difference value R-R between the landslide prediction value R and the preset landslide prediction value R;
and setting the data acquisition initial interval duration of the mountain to be monitored according to the landslide prediction difference value R-R.
In one embodiment, when setting the initial interval duration of data acquisition of the mountain to be monitored according to the landslide prediction difference R-R, the method includes:
Presetting a landslide prediction difference matrix B, and setting B (B1, B2, B3 and B4), wherein B1 is a first preset landslide prediction difference, B2 is a second preset landslide prediction difference, B3 is a third preset landslide prediction difference, B4 is a fourth preset landslide prediction difference, and B1 is more than 2 and less than B3 and less than B4;
presetting an initial interval duration matrix C for data acquisition of a mountain to be monitored, and setting C (C1, C2, C3, C4 and C5), wherein C1 is a first preset interval duration for data acquisition, C2 is a second preset interval duration for data acquisition, C3 is a third preset interval duration for data acquisition, C4 is a fourth preset interval duration for data acquisition, C5 is a fifth preset interval duration for data acquisition, and C1 is more than C2 and less than C3 and less than C4 and less than C5;
setting initial interval duration of data acquisition of the mountain to be monitored according to the relation between the landslide prediction difference R-R and each preset landslide prediction difference:
when R-R is smaller than B1, selecting the first preset data acquisition initial interval duration C1 as the data acquisition initial interval duration of the mountain to be monitored;
when B1 is less than or equal to R-R and less than B2, selecting the second preset data acquisition initial interval duration C2 as the data acquisition initial interval duration of the mountain to be monitored;
When B2 is less than or equal to R-R and less than B3, selecting the third preset data acquisition initial interval duration C3 as the data acquisition initial interval duration of the mountain to be monitored;
when the R-R is smaller than B4 and smaller than B3, selecting the fourth preset data acquisition initial interval duration C4 as the data acquisition initial interval duration of the mountain to be monitored;
and when B4 is less than or equal to R-R, selecting the fifth preset data acquisition initial interval duration C5 as the data acquisition initial interval duration of the mountain to be monitored.
In one embodiment, after setting the data acquisition interval duration of the mountain to be monitored according to the landslide prediction difference R-R, the method further includes:
acquiring the soil saturation S of the mountain to be monitored and the rock stratum rigidity T of the mountain to be monitored;
correcting the initial interval duration of data acquisition of the mountain to be monitored according to the soil saturation S, and taking the corrected initial interval duration of data acquisition of the mountain to be monitored as the corrected interval duration of data acquisition of the mountain to be monitored;
correcting the data acquisition correction interval duration of the mountain to be monitored according to the rock stratum rigidity T of the mountain to be monitored, and taking the corrected data acquisition correction interval duration of the mountain to be monitored as the data acquisition target interval duration of the mountain to be monitored;
And monitoring the mountain to be monitored when the target interval is acquired based on the data of the mountain to be monitored.
In one embodiment, when correcting the initial interval duration of data acquisition of the mountain to be monitored according to the soil saturation S, the method includes:
presetting a soil saturation matrix G of a mountain to be monitored, and setting G (G1, G2, G3 and G4), wherein G1 is a first preset soil saturation, G2 is a second preset soil saturation, G3 is a third preset soil saturation, G4 is a fourth preset soil saturation, and G1 is more than G2 and less than G3 and less than G4;
presetting an initial interval duration correction coefficient matrix h for data acquisition of a mountain to be monitored, and setting h (h 1, h2, h3, h4 and h 5), wherein h1 is a first preset data acquisition initial interval duration correction coefficient, h2 is a second preset data acquisition initial interval duration correction coefficient, h3 is a third preset data acquisition initial interval duration correction coefficient, h4 is a fourth preset data acquisition initial interval duration correction coefficient, h5 is a fifth preset data acquisition initial interval duration correction coefficient, and h1 is more than 0.8 and less than h2 and less than h3 and less than h4 and less than h5 and less than 1.2;
when the initial interval duration of data acquisition of the mountain to be monitored is set to be the i-th preset data acquisition initial interval duration Ci, i=1, 2,3,4,5, and the initial interval duration of data acquisition of the mountain to be monitored is corrected according to the relation between the soil saturation S of the mountain to be monitored and each preset soil saturation:
When S is smaller than G1, the first preset data acquisition initial interval duration correction coefficient h1 is selected to correct the ith preset data acquisition initial interval duration Ci, and the corrected data acquisition initial interval duration of the mountain to be monitored is Ci x h1;
when G1 is less than or equal to S < G2, selecting the second preset data acquisition initial interval duration correction coefficient h2 to correct the ith preset data acquisition initial interval duration Ci, wherein the corrected data acquisition initial interval duration of the mountain to be monitored is Ci x h2;
when G2 is less than or equal to S and less than G3, selecting the third preset data acquisition initial interval duration correction coefficient h3 to correct the ith preset data acquisition initial interval duration Ci, wherein the corrected data acquisition initial interval duration of the mountain to be monitored is Ci x h3;
when G3 is less than or equal to S < G4, the fourth preset data acquisition initial interval duration correction coefficient h4 is selected to correct the ith preset data acquisition initial interval duration Ci, and the corrected data acquisition initial interval duration of the mountain to be monitored is Ci x h4;
when G4 is less than or equal to S, the correction coefficient h5 of the fifth preset data acquisition initial interval duration is selected to correct the i-th preset data acquisition initial interval duration Ci, and the corrected data acquisition initial interval duration of the mountain to be monitored is Ci x h5.
In one embodiment, when correcting the data acquisition correction interval duration of the mountain to be monitored according to the rock stratum rigidity T of the mountain to be monitored, the method includes:
presetting a rock stratum rigidity matrix U of a mountain to be monitored, and setting U (U1, U2, U3 and U4), wherein U1 is a first preset rock stratum rigidity, U2 is a second preset rock stratum rigidity, U3 is a third preset rock stratum rigidity, U4 is a fourth preset rock stratum rigidity, and U1 is more than U2 and less than U3 and less than U4;
presetting a data acquisition correction interval duration correction coefficient matrix d of a mountain to be monitored, and setting d (d 1, d2, d3, d4 and d 5), wherein d1 is a first preset data acquisition correction interval duration correction coefficient, d2 is a second preset data acquisition correction interval duration correction coefficient, d3 is a third preset data acquisition correction interval duration correction coefficient, d4 is a fourth preset data acquisition correction interval duration correction coefficient, d5 is a fifth preset data acquisition correction interval duration correction coefficient, and d1 is more than 0.8 and less than d2 and less than d3 and less than d4 and less than d5 and less than 1.2;
when the data acquisition correction interval duration of the mountain to be monitored is set to ci×hz, i=1, 2,3,4,5, z=1, 2,3,4,5, and correcting the data acquisition correction interval duration of the mountain to be monitored according to the relationship between the formation stiffness T of the mountain to be monitored and each preset formation stiffness:
When T is smaller than U1, the first preset data acquisition correction interval length correction coefficient d1 is selected to correct the data acquisition correction interval length Ci of the mountain to be monitored, and the corrected data acquisition correction interval length of the mountain to be monitored is Ci hz d1;
when U1 is less than or equal to T and less than U2, selecting the second preset data acquisition correction interval length correction coefficient d2 to correct the data acquisition correction interval length Ci of the mountain to be monitored, wherein the corrected data acquisition correction interval length of the mountain to be monitored is Ci x hz d2;
when U2 is less than or equal to T and less than U3, selecting the third preset data acquisition correction interval length correction coefficient d3 to correct the data acquisition correction interval length Ci of the mountain to be monitored, wherein the corrected data acquisition correction interval length of the mountain to be monitored is Ci x hz d3;
when U3 is less than or equal to T < U4, selecting the fourth preset data acquisition correction interval length correction coefficient d4 to correct the data acquisition correction interval length Ci hz of the mountain to be monitored, wherein the corrected data acquisition correction interval length Ci hz d4 of the mountain to be monitored;
when U4 is less than or equal to T, selecting a fifth preset data acquisition correction interval time length correction coefficient d5 to correct the data acquisition correction interval time length Ci of the mountain to be monitored, wherein the corrected data acquisition correction interval time length of the mountain to be monitored is Ci x hz d5.
The invention provides a landslide monitoring and early warning method, which has the following beneficial effects compared with the prior art:
the invention discloses a landslide monitoring and early warning method, which comprises the steps of obtaining first mountain data and first environment data of a mountain to be monitored, and obtaining second mountain data and second environment data of the mountain to be monitored again within preset time; performing data processing on the first mountain data, the first environment data, the second mountain data and the second environment data to obtain data to be input; inputting data to be input into a landslide prediction model, outputting a landslide prediction value, and judging whether a landslide risk exists in a mountain to be monitored based on the landslide prediction value; when the mountain landslide risk to be monitored is judged, the intelligent monitoring of the mountain to be monitored can be realized by sending out early warning signals of different grades according to the landslide predicted value, the monitoring accuracy and the real-time performance are ensured, and by sending out early warning of different grades, targeted preventive measures can be further taken, so that the loss caused by the landslide is avoided or reduced.
Drawings
Fig. 1 shows a flow diagram of a landslide monitoring and early warning method in an embodiment of the invention;
Fig. 2 is a schematic flow chart of sending out early warning signals of different grades according to landslide prediction values in the embodiment of the application.
Detailed Description
The following describes in further detail the embodiments of the present application with reference to the drawings and examples. The following examples are illustrative of the application and are not intended to limit the scope of the application.
In the description of the present application, it should be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present application and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present application.
The terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.
In the description of the present application, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
The following is a description of preferred embodiments of the application, taken in conjunction with the accompanying drawings.
As shown in fig. 1, the embodiment of the application discloses a landslide monitoring and early warning method, which comprises the following steps:
s110: acquiring first mountain data of a mountain to be monitored and first environment data of the mountain to be monitored, and acquiring second mountain data of the mountain to be monitored and second environment data of the mountain to be monitored again within preset time;
in this embodiment, the preset time may be set according to the actual situation, for example, 2 hours, or 4 hours.
S120: performing data processing on the first mountain data, the first environment data, the second mountain data and the second environment data to obtain data to be input;
In some embodiments of the present application, when performing data processing on the first mountain data, the first environment data, the second mountain data, and the second environment data, obtaining data to be input includes:
calculating the mountain data variation of the mountain to be monitored according to the first mountain data and the second mountain data, wherein the first mountain data comprises a first rock stratum offset M1 and a first rock stratum offset angle N1 of the mountain to be monitored, and the second mountain data comprises a second rock stratum offset M2 and a second rock stratum offset angle N2 of the mountain to be monitored;
calculating the mountain data change rate of the mountain to be monitored according to the mountain data change quantity;
；
wherein X is a mountain data change rate of a mountain to be monitored, Δn is a mountain data change amount of the mountain to be monitored, Δn= { (M2-M1) a1+ (N2-N1) a2}, a1 is a formation offset conversion coefficient, a2 is a formation offset angle conversion coefficient, K is an influence coefficient of the mountain data change amount, hy is a formation offset direction parameter, y=1, 2,3, N, Δw is a formation offset direction change amount, p is a formation offset direction influence coefficient, 0<K is equal to or less than 1,0 is equal to or less than or equal to hy1, 0 is equal to or less than or equal to p is equal to 1;
Calculating the mountain environment variation of the mountain to be monitored according to the first environment data and the second environment data, wherein the first environment data is the water content Q1 of the mountain to be monitored, and the second environment data is the water content Q2 of the mountain to be monitored;
calculating the mountain environment change rate of the mountain to be monitored according to the mountain environment change quantity;
W=ΔQⅩβ;
wherein, W is the mountain environment change rate of the mountain to be monitored, deltaQ is the mountain environment change amount of the mountain to be monitored, deltaQ=Q2-Q1, and the influence coefficient of the mountain environment change amount is 0< beta < 1;
and taking the calculated mountain data change rate X and mountain environment change rate W as the data to be input.
In this embodiment, the formation offset angle refers to a sliding angle of the formation on the earth surface, and the formation offset amount refers to an offset distance generated when the offset occurs.
In this embodiment, the mountain data change rate is an important basis for measuring whether a mountain landslide is generated in a mountain to be monitored.
In this embodiment, the formation offset direction may be plural, such as eastward offset, westward offset, etc., which are not shown here.
In this embodiment, the water content of the mountain to be monitored is another important basis for measuring whether the mountain to be monitored has landslide, and when the water content of the mountain to be monitored is too high, the mountain landslide is more likely to occur.
The beneficial effects of the technical scheme are as follows: according to the application, the mountain data change rate X and the mountain environment change rate W are obtained through calculation, so that a reliable data basis can be laid for subsequently judging whether the mountain to be monitored has landslide or not.
S130: inputting the data to be input into a landslide prediction model, outputting a landslide prediction value of the mountain to be monitored based on the landslide prediction model, and judging whether the mountain to be monitored has a landslide risk based on the landslide prediction value;
in some embodiments of the present application, when inputting the data to be input to a mountain landslide prediction model, outputting a mountain landslide prediction value of the mountain to be monitored based on the mountain landslide prediction model includes:
selecting a plurality of historical mountain data change rates and historical mountain environment change rates of the mountain to be monitored from a historical database;
taking the historical mountain data change rate and the historical mountain environment change rate as data sets to be divided;
dividing the data set to be divided to obtain a training data set and a test data set;
training a memory neural network based on the training data set to obtain the landslide prediction model;
Inputting the mountain data change rate X and the mountain environment change rate W into the mountain landslide prediction model to obtain a mountain landslide prediction value of the mountain to be monitored;
R=LⅩexp{1/2πX+2πW}Ⅹ（1+λ）；
wherein R is a landslide prediction value of a mountain to be monitored, L is a landslide prediction function value, exp is an exponential function symbol, and lambda is a loss factor in the landslide prediction process of the mountain to be monitored.
In this embodiment, a large amount of historical data about the mountain to be monitored is stored in the historical database, including but not limited to the historical mountain data change rate and the historical mountain environment change rate of the mountain to be monitored.
In this embodiment, a plurality of historical mountain data change rates and historical mountain environment change rates are selected as the data sets to be divided according to actual demands.
In this embodiment, a mountain landslide prediction model is trained according to the divided training data set and the test data set, and the mountain landslide prediction model is a specific mathematical calculation model.
In this embodiment, the mountain landslide prediction value of the mountain to be monitored can be obtained by inputting the mountain data change rate X and the mountain environment change rate W into the mountain landslide prediction model.
The beneficial effects of the technical scheme are as follows: according to the mountain landslide prediction method, the mountain data change rate X and the mountain environment change rate W are input into the mountain landslide prediction model, so that the mountain landslide prediction value of the mountain to be monitored is obtained, the monitoring accuracy and the real-time performance of the mountain to be monitored can be ensured, complex logic operation is avoided, the monitoring efficiency is improved, and the phenomenon that the landslide is not monitored timely is avoided.
In some embodiments of the present application, when judging whether the mountain to be monitored is at risk of landslide based on the landslide prediction value, the method includes:
judging whether the mountain landslide risk exists in the mountain to be monitored according to the relation between the landslide predicted value R and the preset landslide predicted value R,
if R is less than R, judging that the mountain landslide risk does not exist in the mountain to be monitored;
and if R is less than or equal to R, judging that the mountain landslide risk exists in the mountain to be monitored.
S140: and when judging that the mountain landslide risk exists in the mountain to be monitored, sending out early warning signals of different grades according to the landslide predicted value.
As shown in fig. 2, in some embodiments of the present application, when sending out early warning signals of different levels according to the landslide prediction value, the method includes:
s141: presetting a first preset landslide prediction value and a second preset landslide prediction value, wherein the second preset landslide prediction value is larger than the first preset landslide prediction value;
s142: different early warning signals are sent out according to the landslide prediction value, the first preset landslide prediction value and the second preset landslide prediction;
S143: when the landslide predicted value is smaller than the first preset landslide predicted value, a first-level alarm signal is sent out;
s144: when the landslide predicted value is larger than or equal to the first preset landslide predicted value and the landslide predicted value is smaller than the second preset landslide predicted value, a secondary alarm signal is sent out;
s145: and when the landslide prediction value is greater than or equal to the second preset landslide prediction value, sending out a three-level alarm signal.
In this embodiment, the first preset landslide prediction value and the second preset landslide prediction value may be set according to actual situations, and it is only required to satisfy that the second preset landslide prediction value is greater than the first preset landslide prediction value.
The beneficial effects of the technical scheme are as follows: according to the relation among the first preset landslide prediction value, the second preset landslide prediction value and the landslide prediction value, different early warning signals can be sent out to the mountain to be monitored, so that different preventive measures can be adopted by staff, manpower and material resources can be effectively saved, and meanwhile loss caused by landslide can be avoided.
In some embodiments of the present application, after determining that the mountain to be monitored does not have a landslide risk, the method further includes:
calculating a landslide prediction difference value R-R between the landslide prediction value R and the preset landslide prediction value R;
and setting the data acquisition initial interval duration of the mountain to be monitored according to the landslide prediction difference value R-R.
Specifically, a landslide prediction difference matrix B is preset, B (B1, B2, B3 and B4) is set, wherein B1 is a first preset landslide prediction difference, B2 is a second preset landslide prediction difference, B3 is a third preset landslide prediction difference, B4 is a fourth preset landslide prediction difference, and B1 is more than B2 is less than B3 and less than B4;
presetting an initial interval duration matrix C for data acquisition of a mountain to be monitored, and setting C (C1, C2, C3, C4 and C5), wherein C1 is a first preset interval duration for data acquisition, C2 is a second preset interval duration for data acquisition, C3 is a third preset interval duration for data acquisition, C4 is a fourth preset interval duration for data acquisition, C5 is a fifth preset interval duration for data acquisition, and C1 is more than C2 and less than C3 and less than C4 and less than C5;
setting initial interval duration of data acquisition of the mountain to be monitored according to the relation between the landslide prediction difference R-R and each preset landslide prediction difference:
When R-R is smaller than B1, selecting the first preset data acquisition initial interval duration C1 as the data acquisition initial interval duration of the mountain to be monitored;
when B1 is less than or equal to R-R and less than B2, selecting the second preset data acquisition initial interval duration C2 as the data acquisition initial interval duration of the mountain to be monitored;
when B2 is less than or equal to R-R and less than B3, selecting the third preset data acquisition initial interval duration C3 as the data acquisition initial interval duration of the mountain to be monitored;
when the R-R is smaller than B4 and smaller than B3, selecting the fourth preset data acquisition initial interval duration C4 as the data acquisition initial interval duration of the mountain to be monitored;
and when B4 is less than or equal to R-R, selecting the fifth preset data acquisition initial interval duration C5 as the data acquisition initial interval duration of the mountain to be monitored.
In this embodiment, the preset landslide prediction value r may be set according to the actual situation of the mountain to be monitored.
In this embodiment, the data acquisition initial interval duration refers to acquiring the mountain data and the environmental data of the mountain to be monitored again after a certain time, where the data acquisition initial interval duration is a specific time value, such as 1 hour, 2 hours, and the like.
The beneficial effects of the technical scheme are as follows: according to the method, the initial interval duration of data acquisition of the mountain to be monitored is set according to the relation between the landslide prediction difference R-R and each preset landslide prediction difference, and the technical problem that monitoring period adjustment cannot be carried out according to actual conditions of the landslide in the prior art can be solved by setting the initial interval duration of data acquisition of the mountain to be monitored, so that the problem that workload is increased when a large amount of data are acquired is avoided.
In some embodiments of the present application, after setting the data acquisition interval duration of the mountain to be monitored according to the landslide prediction difference R-R, the method further includes:
acquiring the soil saturation S of the mountain to be monitored and the rock stratum rigidity T of the mountain to be monitored;
correcting the initial interval duration of data acquisition of the mountain to be monitored according to the soil saturation S, and taking the corrected initial interval duration of data acquisition of the mountain to be monitored as the corrected interval duration of data acquisition of the mountain to be monitored;
correcting the data acquisition correction interval duration of the mountain to be monitored according to the rock stratum rigidity T of the mountain to be monitored, and taking the corrected data acquisition correction interval duration of the mountain to be monitored as the data acquisition target interval duration of the mountain to be monitored;
And monitoring the mountain to be monitored when the target interval is acquired based on the data of the mountain to be monitored.
In some embodiments of the present application, when correcting the initial interval duration of data acquisition of the mountain to be monitored according to the soil saturation S, the method includes:
presetting a soil saturation matrix G of a mountain to be monitored, and setting G (G1, G2, G3 and G4), wherein G1 is a first preset soil saturation, G2 is a second preset soil saturation, G3 is a third preset soil saturation, G4 is a fourth preset soil saturation, and G1 is more than G2 and less than G3 and less than G4;
presetting an initial interval duration correction coefficient matrix h for data acquisition of a mountain to be monitored, and setting h (h 1, h2, h3, h4 and h 5), wherein h1 is a first preset data acquisition initial interval duration correction coefficient, h2 is a second preset data acquisition initial interval duration correction coefficient, h3 is a third preset data acquisition initial interval duration correction coefficient, h4 is a fourth preset data acquisition initial interval duration correction coefficient, h5 is a fifth preset data acquisition initial interval duration correction coefficient, and h1 is more than 0.8 and less than h2 and less than h3 and less than h4 and less than h5 and less than 1.2;
when the initial interval duration of data acquisition of the mountain to be monitored is set to be the i-th preset data acquisition initial interval duration Ci, i=1, 2,3,4,5, and the initial interval duration of data acquisition of the mountain to be monitored is corrected according to the relation between the soil saturation S of the mountain to be monitored and each preset soil saturation:
When S is smaller than G1, the first preset data acquisition initial interval duration correction coefficient h1 is selected to correct the ith preset data acquisition initial interval duration Ci, and the corrected data acquisition initial interval duration of the mountain to be monitored is Ci x h1;
when G1 is less than or equal to S < G2, selecting the second preset data acquisition initial interval duration correction coefficient h2 to correct the ith preset data acquisition initial interval duration Ci, wherein the corrected data acquisition initial interval duration of the mountain to be monitored is Ci x h2;
when G2 is less than or equal to S and less than G3, selecting the third preset data acquisition initial interval duration correction coefficient h3 to correct the ith preset data acquisition initial interval duration Ci, wherein the corrected data acquisition initial interval duration of the mountain to be monitored is Ci x h3;
when G3 is less than or equal to S < G4, the fourth preset data acquisition initial interval duration correction coefficient h4 is selected to correct the ith preset data acquisition initial interval duration Ci, and the corrected data acquisition initial interval duration of the mountain to be monitored is Ci x h4;
when G4 is less than or equal to S, the correction coefficient h5 of the fifth preset data acquisition initial interval duration is selected to correct the i-th preset data acquisition initial interval duration Ci, and the corrected data acquisition initial interval duration of the mountain to be monitored is Ci x h5.
In this embodiment, the soil saturation of the mountain to be monitored is an important factor affecting landslide.
The beneficial effects of the technical scheme are as follows: when the initial interval duration of data acquisition of the mountain to be monitored is set as the i preset data acquisition initial interval duration Ci, i=1, 2,3,4,5, and the initial interval duration of data acquisition of the mountain to be monitored is corrected according to the relation between the soil saturation S of the mountain to be monitored and each preset soil saturation.
In some embodiments of the present application, when correcting the data acquisition correction interval duration of the mountain to be monitored according to the formation stiffness T of the mountain to be monitored, the method includes:
presetting a rock stratum rigidity matrix U of a mountain to be monitored, and setting U (U1, U2, U3 and U4), wherein U1 is a first preset rock stratum rigidity, U2 is a second preset rock stratum rigidity, U3 is a third preset rock stratum rigidity, U4 is a fourth preset rock stratum rigidity, and U1 is more than U2 and less than U3 and less than U4;
Presetting a data acquisition correction interval duration correction coefficient matrix d of a mountain to be monitored, and setting d (d 1, d2, d3, d4 and d 5), wherein d1 is a first preset data acquisition correction interval duration correction coefficient, d2 is a second preset data acquisition correction interval duration correction coefficient, d3 is a third preset data acquisition correction interval duration correction coefficient, d4 is a fourth preset data acquisition correction interval duration correction coefficient, d5 is a fifth preset data acquisition correction interval duration correction coefficient, and d1 is more than 0.8 and less than d2 and less than d3 and less than d4 and less than d5 and less than 1.2;
when the data acquisition correction interval duration of the mountain to be monitored is set to ci×hz, i=1, 2,3,4,5, z=1, 2,3,4,5, and correcting the data acquisition correction interval duration of the mountain to be monitored according to the relationship between the formation stiffness T of the mountain to be monitored and each preset formation stiffness:
when T is smaller than U1, the first preset data acquisition correction interval length correction coefficient d1 is selected to correct the data acquisition correction interval length Ci of the mountain to be monitored, and the corrected data acquisition correction interval length of the mountain to be monitored is Ci hz d1;
when U1 is less than or equal to T and less than U2, selecting the second preset data acquisition correction interval length correction coefficient d2 to correct the data acquisition correction interval length Ci of the mountain to be monitored, wherein the corrected data acquisition correction interval length of the mountain to be monitored is Ci x hz d2;
When U2 is less than or equal to T and less than U3, selecting the third preset data acquisition correction interval length correction coefficient d3 to correct the data acquisition correction interval length Ci of the mountain to be monitored, wherein the corrected data acquisition correction interval length of the mountain to be monitored is Ci x hz d3;
when U3 is less than or equal to T < U4, selecting the fourth preset data acquisition correction interval length correction coefficient d4 to correct the data acquisition correction interval length Ci hz of the mountain to be monitored, wherein the corrected data acquisition correction interval length Ci hz d4 of the mountain to be monitored;
when U4 is less than or equal to T, selecting a fifth preset data acquisition correction interval time length correction coefficient d5 to correct the data acquisition correction interval time length Ci of the mountain to be monitored, wherein the corrected data acquisition correction interval time length of the mountain to be monitored is Ci x hz d5.
In this embodiment, the formation stiffness T of the mountain to be monitored is also an important factor affecting landslide, and when the formation stiffness of the mountain to be monitored is greater, the landslide is less likely to occur, whereas the landslide is more likely to occur.
The beneficial effects of the technical scheme are as follows: when the data acquisition correction interval duration of the mountain to be monitored is set to Ci, i=1, 2,3,4,5, z=1, 2,3,4,5, and correcting the data acquisition correction interval duration of the mountain to be monitored according to the relation between the rock stratum rigidity T of the mountain to be monitored and the preset rock stratum rigidity.
In the description of the above embodiments, particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
Although the invention has been described hereinabove with reference to embodiments, various modifications thereof may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the features of the disclosed embodiments may be combined with each other in any manner as long as there is no structural conflict, and the entire description of these combinations is not made in the present specification merely for the sake of omitting the descriptions and saving resources. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Those of ordinary skill in the art will appreciate that: the above is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that the present invention is described in detail with reference to the foregoing embodiments, and modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The landslide monitoring and early warning method is characterized by comprising the following steps of:
acquiring first mountain data of a mountain to be monitored and first environment data of the mountain to be monitored, and acquiring second mountain data of the mountain to be monitored and second environment data of the mountain to be monitored again within preset time;
performing data processing on the first mountain data, the first environment data, the second mountain data and the second environment data to obtain data to be input;
inputting the data to be input into a landslide prediction model, outputting a landslide prediction value of the mountain to be monitored based on the landslide prediction model, and judging whether the mountain to be monitored has a landslide risk based on the landslide prediction value;
and when judging that the mountain landslide risk exists in the mountain to be monitored, sending out early warning signals of different grades according to the landslide predicted value.
2. The mountain landslide monitoring and early warning method of claim 1, wherein when data processing is performed on the first mountain data, the first environment data, the second mountain data and the second environment data, data to be input is obtained, the method comprises:
Calculating the mountain data variation of the mountain to be monitored according to the first mountain data and the second mountain data, wherein the first mountain data comprises a first rock stratum offset M1 and a first rock stratum offset angle N1 of the mountain to be monitored, and the second mountain data comprises a second rock stratum offset M2 and a second rock stratum offset angle N2 of the mountain to be monitored;
calculating the mountain data change rate of the mountain to be monitored according to the mountain data change quantity;
；
wherein X is a mountain data change rate of a mountain to be monitored, Δn is a mountain data change amount of the mountain to be monitored, Δn= { (M2-M1) a1+ (N2-N1) a2}, a1 is a formation offset conversion coefficient, a2 is a formation offset angle conversion coefficient, K is an influence coefficient of the mountain data change amount, hy is a formation offset direction parameter, y=1, 2,3, N, Δw is a formation offset direction change amount, p is a formation offset direction influence coefficient, 0<K is equal to or less than 1,0 is equal to or less than or equal to hy1, 0 is equal to or less than or equal to p is equal to 1;
calculating the mountain environment variation of the mountain to be monitored according to the first environment data and the second environment data, wherein the first environment data is the water content Q1 of the mountain to be monitored, and the second environment data is the water content Q2 of the mountain to be monitored;
Calculating the mountain environment change rate of the mountain to be monitored according to the mountain environment change quantity;
W=ΔQⅩβ;
wherein, W is the mountain environment change rate of the mountain to be monitored, deltaQ is the mountain environment change amount of the mountain to be monitored, deltaQ=Q2-Q1, and the influence coefficient of the mountain environment change amount is 0< beta < 1;
and taking the calculated mountain data change rate X and mountain environment change rate W as the data to be input.
3. The mountain landslide monitoring and early warning method of claim 2, wherein when inputting the data to be input to a mountain landslide prediction model, outputting a mountain landslide prediction value of the mountain to be monitored based on the mountain landslide prediction model, comprises:
selecting a plurality of historical mountain data change rates and historical mountain environment change rates of the mountain to be monitored from a historical database;
taking the historical mountain data change rate and the historical mountain environment change rate as data sets to be divided;
dividing the data set to be divided to obtain a training data set and a test data set;
training a memory neural network based on the training data set to obtain the landslide prediction model;
inputting the mountain data change rate X and the mountain environment change rate W into the mountain landslide prediction model to obtain a mountain landslide prediction value of the mountain to be monitored;
R=LⅩexp{1/2πX+2πW}Ⅹ（1+λ）；
Wherein R is a landslide prediction value of a mountain to be monitored, L is a landslide prediction function value, exp is an exponential function symbol, and lambda is a loss factor in the landslide prediction process of the mountain to be monitored.
4. A mountain landslide monitoring and warning method as recited in claim 3, wherein when judging whether the mountain to be monitored is at risk of landslide based on the landslide prediction value, comprising:
judging whether the mountain landslide risk exists in the mountain to be monitored according to the relation between the landslide predicted value R and the preset landslide predicted value R,
if R is less than R, judging that the mountain landslide risk does not exist in the mountain to be monitored;
and if R is less than or equal to R, judging that the mountain landslide risk exists in the mountain to be monitored.
5. The mountain landslide monitoring and early warning method of claim 4, comprising, when sending out early warning signals of different grades according to the landslide prediction value:
presetting a first preset landslide prediction value and a second preset landslide prediction value, wherein the second preset landslide prediction value is larger than the preset landslide prediction value;
different early warning signals are sent out according to the landslide prediction value, the first preset landslide prediction value and the second preset landslide prediction;
When the landslide predicted value is smaller than the first preset landslide predicted value, a first-level alarm signal is sent out;
when the landslide predicted value is larger than or equal to the first preset landslide predicted value and the landslide predicted value is smaller than the second preset landslide predicted value, a secondary alarm signal is sent out;
and when the landslide prediction value is greater than or equal to the second preset landslide prediction value, sending out a three-level alarm signal.
6. The mountain landslide monitoring and warning method of claim 4, further comprising, after determining that the mountain to be monitored is not at risk of landslide:
calculating a landslide prediction difference value R-R between the landslide prediction value R and the preset landslide prediction value R;
and setting the data acquisition initial interval duration of the mountain to be monitored according to the landslide prediction difference value R-R.
7. The mountain landslide monitoring and early warning method of claim 6, wherein when setting the data acquisition initial interval duration of the mountain to be monitored based on the landslide prediction difference R-R, comprising:
presetting a landslide prediction difference matrix B, and setting B (B1, B2, B3 and B4), wherein B1 is a first preset landslide prediction difference, B2 is a second preset landslide prediction difference, B3 is a third preset landslide prediction difference, B4 is a fourth preset landslide prediction difference, and B1 is more than 2 and less than B3 and less than B4;
Presetting an initial interval duration matrix C for data acquisition of a mountain to be monitored, and setting C (C1, C2, C3, C4 and C5), wherein C1 is a first preset interval duration for data acquisition, C2 is a second preset interval duration for data acquisition, C3 is a third preset interval duration for data acquisition, C4 is a fourth preset interval duration for data acquisition, C5 is a fifth preset interval duration for data acquisition, and C1 is more than C2 and less than C3 and less than C4 and less than C5;
setting initial interval duration of data acquisition of the mountain to be monitored according to the relation between the landslide prediction difference R-R and each preset landslide prediction difference:
when R-R is smaller than B1, selecting the first preset data acquisition initial interval duration C1 as the data acquisition initial interval duration of the mountain to be monitored;
when B1 is less than or equal to R-R and less than B2, selecting the second preset data acquisition initial interval duration C2 as the data acquisition initial interval duration of the mountain to be monitored;
when B2 is less than or equal to R-R and less than B3, selecting the third preset data acquisition initial interval duration C3 as the data acquisition initial interval duration of the mountain to be monitored;
when the R-R is smaller than B4 and smaller than B3, selecting the fourth preset data acquisition initial interval duration C4 as the data acquisition initial interval duration of the mountain to be monitored;
And when B4 is less than or equal to R-R, selecting the fifth preset data acquisition initial interval duration C5 as the data acquisition initial interval duration of the mountain to be monitored.
8. The mountain landslide monitoring and early warning method of claim 7, further comprising, after setting the data acquisition interval duration of the mountain to be monitored based on the landslide prediction difference R-R:
acquiring the soil saturation S of the mountain to be monitored and the rock stratum rigidity T of the mountain to be monitored;
correcting the initial interval duration of data acquisition of the mountain to be monitored according to the soil saturation S, and taking the corrected initial interval duration of data acquisition of the mountain to be monitored as the corrected interval duration of data acquisition of the mountain to be monitored;
correcting the data acquisition correction interval duration of the mountain to be monitored according to the rock stratum rigidity T of the mountain to be monitored, and taking the corrected data acquisition correction interval duration of the mountain to be monitored as the data acquisition target interval duration of the mountain to be monitored;
and monitoring the mountain to be monitored when the target interval is acquired based on the data of the mountain to be monitored.
9. The mountain landslide monitoring and early warning method of claim 8, wherein when correcting the data acquisition initial interval duration of the mountain to be monitored according to the soil saturation S, comprising:
Presetting a soil saturation matrix G of a mountain to be monitored, and setting G (G1, G2, G3 and G4), wherein G1 is a first preset soil saturation, G2 is a second preset soil saturation, G3 is a third preset soil saturation, G4 is a fourth preset soil saturation, and G1 is more than G2 and less than G3 and less than G4;
presetting an initial interval duration correction coefficient matrix h for data acquisition of a mountain to be monitored, and setting h (h 1, h2, h3, h4 and h 5), wherein h1 is a first preset data acquisition initial interval duration correction coefficient, h2 is a second preset data acquisition initial interval duration correction coefficient, h3 is a third preset data acquisition initial interval duration correction coefficient, h4 is a fourth preset data acquisition initial interval duration correction coefficient, h5 is a fifth preset data acquisition initial interval duration correction coefficient, and h1 is more than 0.8 and less than h2 and less than h3 and less than h4 and less than h5 and less than 1.2;
when the initial interval duration of data acquisition of the mountain to be monitored is set to be the i-th preset data acquisition initial interval duration Ci, i=1, 2,3,4,5, and the initial interval duration of data acquisition of the mountain to be monitored is corrected according to the relation between the soil saturation S of the mountain to be monitored and each preset soil saturation:
When S is smaller than G1, the first preset data acquisition initial interval duration correction coefficient h1 is selected to correct the ith preset data acquisition initial interval duration Ci, and the corrected data acquisition initial interval duration of the mountain to be monitored is Ci x h1;
when G1 is less than or equal to S < G2, selecting the second preset data acquisition initial interval duration correction coefficient h2 to correct the ith preset data acquisition initial interval duration Ci, wherein the corrected data acquisition initial interval duration of the mountain to be monitored is Ci x h2;
when G2 is less than or equal to S and less than G3, selecting the third preset data acquisition initial interval duration correction coefficient h3 to correct the ith preset data acquisition initial interval duration Ci, wherein the corrected data acquisition initial interval duration of the mountain to be monitored is Ci x h3;
when G3 is less than or equal to S < G4, the fourth preset data acquisition initial interval duration correction coefficient h4 is selected to correct the ith preset data acquisition initial interval duration Ci, and the corrected data acquisition initial interval duration of the mountain to be monitored is Ci x h4;
when G4 is less than or equal to S, the correction coefficient h5 of the fifth preset data acquisition initial interval duration is selected to correct the i-th preset data acquisition initial interval duration Ci, and the corrected data acquisition initial interval duration of the mountain to be monitored is Ci x h5.
10. The mountain landslide monitoring and early warning method of claim 9, wherein when correcting the data acquisition correction interval duration of the mountain to be monitored according to the formation stiffness T of the mountain to be monitored, comprising:
presetting a rock stratum rigidity matrix U of a mountain to be monitored, and setting U (U1, U2, U3 and U4), wherein U1 is a first preset rock stratum rigidity, U2 is a second preset rock stratum rigidity, U3 is a third preset rock stratum rigidity, U4 is a fourth preset rock stratum rigidity, and U1 is more than U2 and less than U3 and less than U4;
presetting a data acquisition correction interval duration correction coefficient matrix d of a mountain to be monitored, and setting d (d 1, d2, d3, d4 and d 5), wherein d1 is a first preset data acquisition correction interval duration correction coefficient, d2 is a second preset data acquisition correction interval duration correction coefficient, d3 is a third preset data acquisition correction interval duration correction coefficient, d4 is a fourth preset data acquisition correction interval duration correction coefficient, d5 is a fifth preset data acquisition correction interval duration correction coefficient, and d1 is more than 0.8 and less than d2 and less than d3 and less than d4 and less than d5 and less than 1.2;
when the data acquisition correction interval duration of the mountain to be monitored is set to ci×hz, i=1, 2,3,4,5, z=1, 2,3,4,5, and correcting the data acquisition correction interval duration of the mountain to be monitored according to the relationship between the formation stiffness T of the mountain to be monitored and each preset formation stiffness:
When T is smaller than U1, the first preset data acquisition correction interval length correction coefficient d1 is selected to correct the data acquisition correction interval length Ci of the mountain to be monitored, and the corrected data acquisition correction interval length of the mountain to be monitored is Ci hz d1;
when U1 is less than or equal to T and less than U2, selecting the second preset data acquisition correction interval length correction coefficient d2 to correct the data acquisition correction interval length Ci of the mountain to be monitored, wherein the corrected data acquisition correction interval length of the mountain to be monitored is Ci x hz d2;
when U2 is less than or equal to T and less than U3, selecting the third preset data acquisition correction interval length correction coefficient d3 to correct the data acquisition correction interval length Ci of the mountain to be monitored, wherein the corrected data acquisition correction interval length of the mountain to be monitored is Ci x hz d3;
when U3 is less than or equal to T < U4, selecting the fourth preset data acquisition correction interval length correction coefficient d4 to correct the data acquisition correction interval length Ci hz of the mountain to be monitored, wherein the corrected data acquisition correction interval length Ci hz d4 of the mountain to be monitored;
when U4 is less than or equal to T, selecting a fifth preset data acquisition correction interval time length correction coefficient d5 to correct the data acquisition correction interval time length Ci of the mountain to be monitored, wherein the corrected data acquisition correction interval time length of the mountain to be monitored is Ci x hz d5.
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