A shallow loess landslide early warning method and device

Abstract

The present invention relates to the field of landslide early warning technology, and in particular to a shallow loess landslide early warning method and device. The present invention discloses a shallow loess landslide early warning method and device, wherein the method comprises: obtaining topographic data of a landslide body to be detected in a loess region, the topographic data comprising the slope of the landslide body, the area of ​​the landslide body, and the area of ​​the catchment area on the upper side of the landslide body; calculating the topographic factor of the landslide body to be detected according to the topographic data; obtaining real-time rainfall data in the loess region, performing rainfall segmentation on the real-time rainfall data, and obtaining first rainfall data that triggers shallow loess landslides; calculating a landslide early warning value according to the topographic factor and the first rainfall data; outputting an early warning signal according to a comparison result of the landslide early warning value and a preset landslide early warning critical value, thereby improving the accuracy of early warning of shallow loess landslides.

Description

Technical Field

[0001] The invention relates to the technical field of landslide early warning, and in particular to a shallow loess landslide early warning method and device. Background Art

[0002] Shallow loess landslide refers to the phenomenon that the soil in the thick loess high slope area slides down along the weak surface under the action of gravity. According to the thickness of the landslide body, landslides can be divided into four types: shallow landslides, middle-layer landslides, back-layer landslides, and thick-layer landslides. Shallow loess landslides have the characteristics of high frequency of outbreaks and high distribution density, which can easily cause sudden regional geological disasters. Therefore, it is necessary to monitor shallow loess landslides and issue early warnings in time to reduce safety risks.

[0003] The existing landslide early warning technology is to analyze and warn landslides through rainfall data. However, not all rainfall will cause landslide risks, and different terrains have different geographical characteristics. Even under the same rainfall conditions, there may be different impacts. Therefore, rainfall data and terrain conditions will affect the accuracy of landslide early warning. How to propose a shallow loess landslide early warning method to improve the accuracy of shallow loess landslide early warning has become a problem to be solved. Summary of the invention

[0004] The invention provides a shallow loess landslide early warning method and device, which are used to improve the accuracy of shallow loess landslide early warning.

[0005] The present invention provides a shallow loess landslide early warning method, comprising:

[0006] Acquire topographic data of the landslide body to be detected in the loess region, wherein the topographic data includes the slope of the landslide body, the area of ​​the landslide body, and the area of ​​the catchment area above the landslide body;

[0007] Calculate the terrain factor of the landslide body to be measured according to the terrain data;

[0008] Acquire real-time rainfall data in the loess region, perform rainfall segmentation on the real-time rainfall data, and obtain first rainfall data that triggers shallow loess landslide;

[0009] Calculating a landslide warning value according to the terrain factor and the first rainfall data;

[0010] According to the comparison result between the landslide warning value and the preset landslide warning critical value, a warning signal is output.

[0011] Optionally, the acquiring of real-time rainfall data in the loess region, performing rainfall segmentation on the real-time rainfall data, and obtaining first rainfall data that triggers shallow loess landslides comprises:

[0012] S1: Acquire meteorological data of the Loess Plateau region, and determine a rainfall critical value and a rainfall intensity critical value according to the meteorological data;

[0013] S2: acquiring and accumulating the real-time rainfall in the loess region to obtain a second real-time rainfall total value, and accumulating the collection time of the real-time rainfall to obtain a second rainfall duration;

[0014] S3: when the second real-time total rainfall value reaches the rainfall critical value, determine whether the second rainfall duration is greater than a second preset duration, if not, execute S4; if yes, clear the second real-time total rainfall value and the second rainfall duration, and re-execute S2;

[0015] S4: Calculate the second rainfall intensity based on the second rainfall duration and the second real-time rainfall total value. When the second rainfall intensity is less than the rainfall intensity critical value, output the second real-time rainfall total value and the second rainfall duration as the first rainfall data for triggering the shallow loess landslide. Then clear the second real-time rainfall total value and the second rainfall duration, and re-execute S2.

[0016] Optionally, S4 also includes:

[0017] When the second rainfall intensity is not less than the rainfall intensity critical value, continue to update the second real-time rainfall total value and the second rainfall duration according to S2, and update the second rainfall intensity according to the updated second real-time rainfall total value and the updated second rainfall duration until the updated second rainfall intensity is less than the rainfall intensity critical value.

[0018] Optionally, the first rainfall data includes a first real-time total rainfall value and a first rainfall duration, and the calculating of the landslide warning value according to the terrain factor and the first rainfall data includes:

[0019] Calculate a first rainfall intensity according to the first real-time rainfall total value and the first rainfall duration;

[0020] Calculating a landslide warning value according to the terrain factor, the first rainfall intensity and the first rainfall duration;

[0021] The calculation formula of the landslide warning value is:

[0022] Cr=T(I / IM)(D / Dd)0.45

[0023] Cr is the landslide warning value, T is the terrain factor, I is the first rainfall intensity, IM is the rainfall intensity critical value, D is the first rainfall duration, and Dd is the unit time.

[0024] Optionally, the meteorological data includes an annual average rainfall and an average value of the annual maximum and minimum rainfall intensities; the step of determining a rainfall critical value and a rainfall intensity critical value according to the meteorological data includes:

[0025] Calculate the rainfall threshold based on the average annual rainfall;

[0026] The critical value of rainfall intensity is calculated based on the average value of the maximum and minimum rainfall intensities in the year.

[0027] Optionally, the calculation formula for the rainfall critical value is:

[0028] R*=0.1RN

[0029] Among them, R* is the critical value of rainfall, and RN is the average annual rainfall in the Loess Plateau.

[0030] Optionally, the calculation formula for the rainfall intensity critical value is:

[0031] I*=0.02IM

[0032] Among them, I* is the critical value of rainfall intensity, and IM is the average value of the annual maximum and minimum rainfall intensity in the Loess Plateau.

[0033] Optionally, the calculation formula for obtaining the terrain factor of the landslide body to be measured according to the terrain data is:

[0034] T=tana+1.25U=(1+1.25Au / A)tana

[0035] Among them, U is the upward slowing factor; Au is the upper catchment area; a is the slope of the landslide; A is the area of ​​the landslide; and T is the terrain factor.

[0036] Optionally, outputting a warning signal according to a comparison result between the landslide warning value and a preset landslide warning critical value comprises:

[0037] When the landslide warning value is less than the first landslide warning critical value, outputting a first level warning signal;

[0038] When the landslide warning value is greater than or equal to the first landslide warning critical value and less than the second landslide warning critical value, outputting a second level warning signal;

[0039] When the landslide warning value is greater than or equal to the second landslide warning critical value and less than the third landslide warning critical value, outputting a third level warning signal;

[0040] When the landslide warning value is greater than or equal to the third landslide warning critical value, a fourth level warning signal is output.

[0041] Another aspect of the present invention provides a shallow loess landslide early warning device, the device comprising:

[0042] An acquisition module is used to acquire topographic data of a landslide body to be detected in a loess region, wherein the topographic data includes a slope of the landslide body, an area of ​​the landslide body, and an area of ​​a catchment area above the landslide body;

[0043] A first calculation module, used for calculating the terrain factor of the landslide body to be measured according to the terrain data;

[0044] A rainfall segmentation module is used to obtain real-time rainfall data in the loess region, perform rainfall segmentation on the real-time rainfall data, and obtain first rainfall data that triggers shallow loess landslides.

[0045] A second calculation module, used for calculating a landslide warning value according to the terrain factor and the first rainfall data;

[0046] The early warning module is used to output an early warning signal according to the comparison result between the landslide early warning value and a preset landslide early warning critical value.

[0047] It can be seen from the above technical solutions that the present invention has the following advantages:

[0048] The present invention provides a shallow loess landslide early warning method. The method obtains the topographic data of a landslide body to be detected in a loess region, calculates the topographic factors of the landslide body to be detected according to the topographic data, takes into account the influence of the topographic conditions of the loess region on the shallow loess landslide, obtains the real-time rainfall data of the loess region, performs rainfall segmentation on the real-time rainfall data, obtains the first rainfall data that triggers the shallow loess landslide, distinguishes the rainfall data that affects the landslide and the rainfall data that does not affect the landslide, avoids the interference of the rainfall data that does not affect the landslide on the shallow loess landslide early warning, improves the accuracy of the shallow loess landslide early warning, calculates the landslide early warning value by using the first rainfall data and the topographic factor, outputs the early warning signal according to the comparison result of the landslide early warning value and the preset landslide early warning critical value, fully takes into account the joint effect of the geological factors and rainfall factors in the loess region on the shallow loess landslide, makes the calculated landslide early warning value more consistent with the internal texture conditions that induce the shallow loess landslide, can accurately predict the landslide risk of the landslide body to be detected in the loess region, and improves the accuracy of the shallow loess landslide early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0050] Figure 1 A schematic diagram of a shallow loess landslide early warning method provided by an embodiment of the present invention;

[0051] Figure 2 A schematic diagram of a shallow loess landslide early warning method provided by another embodiment of the present invention;

[0052] Figure 3 A shallow loess landslide early warning device is provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The invention provides a shallow loess landslide early warning method and device, which are used to improve the accuracy of shallow loess landslide early warning.

[0054] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] It should be noted that the present invention is applied to shallow landslide early warning in loess areas, specifically shallow landslide early warning in loess areas with specific terrain conditions, wherein the specific terrain conditions refer to that the upper catchment area of ​​the landslide body is on the loess plateau, and the catchment area is a gentle terrain with a slope of almost 0 degrees, and there is an open surface at the bottom of the landslide body.

[0056] See also Figure 1 , Figure 1 A schematic diagram of a shallow loess landslide early warning method provided in accordance with an embodiment of the present invention.

[0057] This embodiment provides a shallow loess landslide early warning method, including:

[0058] 101. Obtain topographic data of the landslide body to be measured in the loess region, the topographic data including the slope of the landslide body, the area of ​​the landslide body, and the area of ​​the catchment area above the landslide body.

[0059] It should be noted that the landslide body to be tested refers to a potential landslide body, that is, a mountain body that is more prone to landslides.

[0060] The terrain data can be obtained through on-site investigation and mapping, or by pre-measuring the terrain data of each landslide body and storing it in a database, numbering each landslide body, associating the landslide body number with the corresponding terrain data, and storing the corresponding association relationship. When obtaining, the corresponding terrain data can be queried by the number of the landslide body to be measured.

[0061] Schematic diagram of landslide Figure 3 As shown, Au is the upper catchment area; a is the slope of the landslide; A is the area of ​​the landslide.

[0062] 102. The terrain factors of the landslide body to be tested are calculated based on the terrain data.

[0063] In this embodiment, the terrain factor of the landslide body is calculated based on the slope of the landslide body, the area of ​​the landslide body, and the area of ​​the catchment area above the landslide body. The calculation formula is:

[0064] T=tana+1.25U=(1+1.25Au / A)tana

[0065] Among them, U is the upward slowing factor; Au is the upper catchment area; a is the slope of the landslide; A is the area of ​​the landslide; and T is the terrain factor.

[0066] It is understandable that, for the upper catchment area of ​​the landslide body, which is located on the loess plateau, the catchment area has a gentle terrain with a slope of almost 0 degrees, and the lower part of the landslide body has a free surface. The upper slope is gentle, and the slope of the landslide body is large, which is easy to cause tensile cracks, and rainwater is easy to infiltrate, causing the soil components to be saturated and softened. When the landslide body has a free surface, rainwater continues to infiltrate, and after forming a shear surface along the channel down along the soil body, it is easy to penetrate and seep out at the free surface, causing the slope to slide down and trigger a shallow loess landslide. Therefore, in view of this special terrain feature that is easy to trigger a shallow loess landslide, this embodiment calculates the terrain factor of the landslide body by obtaining the slope of the landslide body to be tested, the area of ​​the landslide body, and the area of ​​the catchment area on the upper side of the landslide body, and makes full use of the terrain factor to perform early warning analysis on the shallow loess landslide, thereby improving the accuracy of the shallow loess landslide early warning.

[0067] 103. Acquire real-time rainfall data in the loess region, perform rainfall segmentation on the real-time rainfall data, and obtain the first rainfall data that triggers shallow loess landslides.

[0068] It should be noted that the present embodiment obtains real-time rainfall data on the plateau in the loess region. The upper catchment area of ​​the landslide is located on the plateau, and the rainfall elevation on the plateau is consistent with the elevation of the catchment area of ​​the landslide, so that the rainfall data on the plateau is basically the same as the rainfall data of the landslide to be tested. Therefore, by obtaining the real-time rainfall data on the plateau as the rainfall data of the landslide to be tested, the error caused by rainfall collection can be effectively reduced, thereby further improving the reliability of rainfall data.

[0069] After obtaining real-time rainfall data, the rainfall data is segmented to distinguish between rainfall that has an impact on shallow loess landslides and rainfall that has no impact, thereby obtaining the first rainfall data that triggers shallow loess landslides, avoiding the interference of rainfall data that has no impact on landslides on landslide warnings, and thus improving the accuracy of landslide warnings.

[0070] It can be understood that the first rainfall data that triggers shallow loess landslide refers to rainfall data that may induce shallow loess landslide. The first rainfall data includes the first real-time rainfall total value and the first rainfall duration.

[0071] 104. Calculate the landslide warning value according to the terrain factors and the first rainfall data.

[0072] It should be noted that the first rainfall data includes the first rainfall duration and the first real-time rainfall total value, and the first rainfall intensity can be determined based on the first real-time rainfall total value and the first rainfall duration. Then, the landslide warning value is calculated based on the first rainfall intensity, the first rainfall duration and the terrain factor.

[0073] The calculation formula of landslide warning value is:

[0074] Cr=T(I / IM)(D / Dd) 0.45

[0075] Cr is the landslide warning value, T is the terrain factor, I is the first rainfall intensity, IM is the rainfall intensity critical value, D is the first rainfall duration, and Dd is the unit time.

[0076] It should be noted that in this embodiment, Dd is preferably 1h.

[0077] This embodiment calculates the landslide warning value that induces shallow loess landslide by utilizing the topographic data and rainfall data of the landslide body in the loess region, fully considering the geological characteristics and rainfall characteristics of the loess region, so that the calculated landslide warning value is more in line with the internal texture that induces shallow loess landslide, and can accurately predict the landslide risk of the landslide body to be tested in the loess region, thereby improving the accuracy of the warning for shallow loess landslide.

[0078] 105. Output a warning signal according to the comparison result between the landslide warning value and the preset landslide warning critical value.

[0079] It should be noted that the preset landslide warning critical values ​​include a first landslide warning critical value, a second landslide warning critical value, and a third landslide warning critical value.

[0080] This embodiment compares the landslide warning value and the landslide warning critical value, divides them into multiple risk levels according to the comparison results, and outputs warning signals corresponding to the risk levels to warn of shallow loess landslides. The warning has high precision and improves the practicability of landslide prevention and control.

[0081] The present embodiment provides a shallow loess landslide early warning method, which obtains topographic data of a landslide body to be detected in a loess region, calculates a topographic factor of the landslide body to be detected based on the topographic data, takes into account the influence of the topographic conditions in the loess region on the shallow loess landslide, obtains real-time rainfall data in the loess region, performs rainfall segmentation on the real-time rainfall data, obtains first rainfall data that triggers the shallow loess landslide, distinguishes rainfall data that affects and does not affect the landslide, avoids interference of rainfall data that has no impact on the landslide on the shallow loess landslide early warning, and improves the accuracy of the shallow loess landslide early warning. The first rainfall data and the topographic factor are used to calculate a landslide early warning value, and a warning signal is output based on a comparison result of the landslide early warning value and a preset landslide early warning critical value. The combined effect of the geological factors and rainfall factors in the loess region on the shallow loess landslide is fully considered, so that the calculated landslide early warning value is more consistent with the internal texture that induces the shallow loess landslide, can accurately predict the landslide risk of the landslide body to be detected in the loess region, and improves the accuracy of the shallow loess landslide early warning.

[0082] Embodiment 2:

[0083] See also Figure 2 , Figure 2 A schematic diagram of a shallow loess landslide early warning method provided by an embodiment of the present invention.

[0084] This embodiment further defines step 103 on the basis of including all the contents of embodiment 1. The specific steps are as follows:

[0085] S1: Obtain meteorological data in the Loess Plateau and determine the critical value of rainfall amount and rainfall intensity based on the meteorological data.

[0086] It should be noted that meteorological data are historical data, including the annual average rainfall and the average rainfall intensity of the maximum and minimum hours of the year. Among them, meteorological data can be obtained by referring to the hydrological manual of the loess climate or weather station data.

[0087] After obtaining meteorological data, this embodiment uses the special geographical features of the loess region and combines the meteorological data to determine the rainfall threshold and rainfall intensity threshold that are suitable for the loess region, which are used as a judgment basis for rainfall that affects shallow loess landslides.

[0088] Specifically, the steps for determining the critical value of rainfall amount and rainfall intensity according to meteorological data are as follows:

[0089] S11: Calculate the critical rainfall value according to the annual average rainfall; wherein the calculation formula of the critical rainfall value is:

[0090] R*=0.1RN

[0091] R* is the critical value of rainfall, and RN is the average annual rainfall in the Loess Plateau.

[0092] It should be noted that the permeability coefficient of general homogeneous loess is 10-8m / s to 10-6m / s, that is, through the general infiltration of rainwater, it takes 11.6 days to 1157.4 days to reach a soil depth of 1m, and the thickness of shallow loess landslides is generally 0.5-2m, so general rainfall infiltration cannot directly affect the occurrence of landslides. However, the vertical cracks in loess soil are relatively developed, and the potential landslide body has a steep slope and a gentle slope at the top, so that tensile cracks are formed in the upper catchment area, so that the rainwater collected in the upper part of the landslide body infiltrates to the sliding surface through the vertical cracks-tensile cracks, thereby inducing landslides. However, even with the existence of vertical cracks-tensile cracks, it takes a long time for rainwater to infiltrate in the cracks and infiltrate to the sliding surface along the crack channels, and smaller rainfall will be intercepted by the loess soil layer in the infiltration channel and will not affect the landslide.

[0093] Therefore, according to the geological characteristics of the loess region, this embodiment uses 10% of the local annual average rainfall to calculate the rainfall critical value, and at the same time, the preset duration is determined to be 72 hours according to the infiltration time of rainwater in the loess soil fissure channel in the loess region. Thus, the duration range of 1-72 hours is used as one of the judgment conditions for whether to enter the rainfall calculation stage for stimulating shallow loess landslides, so that the rainfall data for stimulating shallow loess landslides is determined to be more accurate, avoiding the situation where too many rainfall processes are calculated in the rainfall data affecting loess landslides, reducing the possibility of misjudgment of shallow loess landslide warnings, and further improving the accuracy of shallow loess landslide warnings.

[0094] S12: Calculate the critical value of rainfall intensity based on the average values ​​of the maximum and minimum rainfall intensities of the year.

[0095] The calculation formula for the critical value of rainfall intensity is:

[0096] I*=0.02IM

[0097] Among them, I* is the critical value of rainfall intensity, and IM is the average annual maximum and minimum rainfall intensity in the Loess Plateau.

[0098] It should be noted that the loess region is an arid region. The average annual rainfall in arid regions does not exceed 1000 mm, while in the loess region it is generally around 500 mm; the sunshine time in arid regions is generally more than 2000 hours, while in the loess region it is more than 2500 hours. Therefore, the loess region has the characteristics of less rainfall, longer sunshine time, and greater evaporation. In the loess region, less rainfall, accompanied by greater sunshine time and evaporation, makes limited rainfall very easy to evaporate, especially when rainfall does not enter the soil, it is more likely to evaporate, becoming invalid rainfall and unable to affect landslides. In addition, loess landslide bodies all have a nearly straight platform on the upper part, which is the landslide catchment area and the main rainwater collection area for rainfall-induced loess landslides; however, because the slope of the catchment area is basically 0, the rainwater in the catchment area cannot be quickly gathered together, and the evaporation rate is faster. In addition, although there is vegetation in the loess region, the vegetation is sparse, and the vegetation intercepts less rainwater and the root system intercepts less rainwater, resulting in more evaporation in the process of rainwater collection.

[0099] Based on the characteristics of sunshine, topography and vegetation in the above-mentioned loess area, this embodiment calculates the rainfall intensity critical value by using 2% of the local multi-year maximum hourly rainfall average, thereby avoiding the problem of too small rainfall being included in the rainfall process, thereby extending the rainfall duration, causing very small rainfall to be still included in the rainfall process that affects loess landslides, leading to misjudgment of loess landslides, providing effective first rainfall data support for the analysis of loess landslides, and thereby improving the accuracy of shallow loess landslide early warning.

[0100] S2: Obtain and accumulate the real-time rainfall in the Loess Plateau to obtain a second real-time rainfall total value, and accumulate the collection time of the real-time rainfall to obtain a second rainfall duration.

[0101] It should be noted that in this embodiment, sensors are set on the plateau of the loess region to collect rainfall in the loess region. The real-time rainfall refers to the rainfall per hour on the plateau of the loess region, and is accumulated once per hour.

[0102] When obtaining a real-time rainfall, the real-time rainfall obtained this time is superimposed on the total value of the second real-time rainfall accumulated last time to obtain the total value of the second real-time rainfall accumulated this time, and 1 is added to the second rainfall duration accumulated last time to obtain the second rainfall duration accumulated this time. For example, when the 12th acquisition, the accumulated second rainfall duration is 12 hours, and the accumulated second real-time rainfall total value is 20 mm, then when the 13th acquisition, the rainfall obtained is 0.5 mm, then the accumulated second real-time rainfall total value is 20.5 mm, and the accumulated second rainfall duration is 12+1=13 hours.

[0103] It is understandable that when the real-time rainfall is collected for the first time, the accumulated second rainfall duration is one hour. When the real-time rainfall value is collected for the first time, the accumulated second real-time rainfall total value is the real-time rainfall itself. For example, if the real-time rainfall collected for the first time is A, then the accumulated second real-time rainfall total value is A.

[0104] S3: When the second real-time total rainfall value reaches the rainfall critical value, determine whether the second rainfall duration is greater than the second preset duration. If not, execute S4; if so, clear the second real-time total rainfall value and the second rainfall duration, and re-execute S2.

[0105] It should be noted that in steps S2-S3, the second real-time rainfall total value and the second rainfall duration are accumulated once every hour. After the accumulation, it is determined whether the second real-time rainfall total value reaches the rainfall critical value until it is determined that the second real-time rainfall total value reaches the rainfall critical value.

[0106] When the second real-time rainfall total value is less than the rainfall critical value, and the second rainfall duration is not greater than the preset duration, it indicates that the current stage is rainfall accumulation, and further accumulation is required to determine whether rainfall segmentation is to be performed. Therefore, continue to execute S2 to obtain the next real-time rainfall, and on the basis of the second real-time rainfall total value of this time, accumulate the next real-time rainfall to obtain the next second real-time rainfall total value. And, add 1 to the second rainfall duration of this time to accumulate the next second rainfall duration. Afterwards, execute S3 to determine whether the next rainfall total value reaches the rainfall critical value. If it is still less than, continue to execute S2 until the second real-time rainfall total value reaches the rainfall critical value, or the second rainfall duration exceeds the preset duration.

[0107] When the second real-time rainfall total value is less than the rainfall critical value, and the second rainfall duration is greater than the preset duration, it means that the rainfall within the preset duration has never exceeded the rainfall critical value, indicating that the rainfall during this period has no effect on the loess landslide, and therefore it is not included in the rainfall data affecting the landslide, that is, this round of rainfall segmentation is completed, and the rainfall calculation stage that triggers shallow loess landslide is not entered. Therefore, the accumulated second real-time rainfall total value and the second rainfall duration of this round are cleared, and S2 is re-executed to enter the next round of rainfall segmentation.

[0108] When the second real-time rainfall total value reaches the rainfall critical value and the second rainfall duration exceeds the preset duration, although the second real-time rainfall total value exceeds the rainfall critical value, the second rainfall duration has exceeded the preset duration. Therefore, this round of rainfall accumulation stage ends, and the rainfall calculation stage for stimulating shallow loess landslides is not entered. Instead, the next round of rainfall accumulation stage is restarted. Therefore, the moment of judgment end is taken as the current moment, the current accumulation is cleared to obtain the second rainfall duration and the second real-time rainfall total value, and S2 is re-executed to enter the next round of rainfall accumulation to calculate the next rainfall data for stimulating shallow loess landslides.

[0109] S4: Calculate the second rainfall intensity based on the second rainfall duration and the second real-time rainfall total value. When the second rainfall intensity is less than the rainfall intensity critical value, output the second real-time rainfall total value and the second rainfall duration as the first rainfall data for this shallow loess landslide excitation. Then clear the second real-time rainfall total value and the second rainfall duration, and re-execute S2. When the second rainfall intensity is not less than the rainfall intensity critical value, continue to update the second real-time rainfall total value and the second rainfall duration according to S2, and update the second rainfall intensity according to the updated second real-time rainfall total value and the updated second rainfall duration, until the updated second rainfall intensity is less than the rainfall intensity critical value.

[0110] It should be noted that when the second real-time rainfall total value reaches the rainfall critical value, and the second rainfall duration is not greater than the preset duration, it means that the rainfall that affects the loess landslide has begun, and the rainfall calculation stage for stimulating shallow loess landslide has begun. In the rainfall calculation stage, the second real-time rainfall total value at this time is used as the initial value of the rainfall in the rainfall calculation stage, that is, as the preliminary rainfall that stimulates the shallow loess landslide. The second rainfall duration at this time is used as the initial value of the second rainfall duration in the rainfall calculation stage. Based on this, the preliminary rainfall that stimulates the shallow loess landslide and the corresponding second rainfall duration are determined, so that the rainfall that is not related to the stimulation of the shallow loess landslide can be preliminarily distinguished.

[0111] After entering the rainfall calculation stage that triggers shallow loess landslides, the current rainfall intensity is calculated based on the second real-time rainfall total value and the second rainfall duration, and it is continuously judged whether the rainfall intensity is less than the rainfall intensity critical value. According to the judgment result of the rainfall intensity, it can be divided into two situations.

[0112] (1) When it is determined that the rainfall intensity is less than the critical value of rainfall intensity, it means that the rainfall at this time will no longer have an impact on the landslide. Therefore, the rainfall that stimulates the shallow loess landslide ends. The second rainfall duration currently obtained is used as the second rainfall duration that stimulates the shallow loess landslide, and the current second real-time rainfall total value is used as the rainfall total value that stimulates the shallow loess landslide. The second rainfall duration and the rainfall total value are output as the first rainfall data that stimulates the shallow loess landslide. In this way, the rainfall that affects the loess landslide and the rainfall that has no effect on the loess landslide are separated, and the rainfall data that stimulates the shallow loess landslide is obtained, which provides effective rainfall data for the shallow loess landslide early warning and improves the accuracy of the shallow loess landslide early warning.

[0113] It can be understood that the output second rainfall duration is the first rainfall duration in the first rainfall data, and the output total rainfall value is the first real-time total rainfall value in the first rainfall data.

[0114] (2) When the rainfall intensity is not less than the critical value of rainfall intensity, it means that the current rainfall intensity will continue to cause landslides in the loess region, that is, the rainfall that triggered the shallow loess landslide has not stopped and is still continuing. Therefore, based on the current accumulated total value of the second real-time rainfall, continue to accumulate the next real-time rainfall to obtain the next accumulated total value of the second real-time rainfall in the rainfall calculation stage, and add 1 to the current accumulated second rainfall duration to obtain the next accumulated second rainfall duration. After that, calculate the next rainfall intensity based on the next second real-time rainfall total value and the second rainfall duration, and determine whether the next rainfall intensity is less than the critical value of rainfall intensity to determine whether the rainfall that triggered the shallow loess landslide has ended.

[0115] For example: when the total value of the second real-time rainfall accumulated in the rainfall accumulation stage is greater than the rainfall critical value R*, and the corresponding second rainfall duration does not exceed 72 hours, the rainfall calculation stage is entered, and the total value of the second real-time rainfall accumulated in the rainfall accumulation stage is used as the initial value R0 (i.e., previous rainfall) of the rainfall in the rainfall calculation stage; the second rainfall duration accumulated in the rainfall accumulation stage is used as the initial value D0 of the second rainfall duration in the rainfall calculation stage.

[0116] In the rainfall calculation stage, the initial value of rainfall R0 is divided by the initial value of the second rainfall duration D0 to obtain the average rainfall intensity I0. When the average rainfall intensity I0 is less than the critical value of rainfall intensity, it means that the rainfall that stimulates the shallow loess landslide has ended, and the total rainfall value that stimulates the shallow loess landslide is R0, and the total second rainfall duration is D0. After that, the current accumulation is cleared to obtain the second real-time rainfall total value and the second rainfall duration, and the next round of rainfall accumulation stage and rainfall calculation stage are entered, thereby completing the next round of rainfall segmentation.

[0117] When the average rainfall intensity I0 is greater than or equal to the critical value of rainfall intensity, the rainfall that stimulates the shallow loess landslide is still continuing. Therefore, the real-time rainfall R1 collected in the next hour is superimposed on R0, and the second real-time rainfall total value R is R0+R1. And add 1 to D0, and the second rainfall duration corresponding to the next hour is D=D0+1. After that, the rainfall intensity I1=R / D=(R0+R1) / (D0+1) is calculated. After that, it is determined whether the rainfall intensity I1 is less than the critical rainfall intensity. If so, it means that the rainfall that stimulates the shallow loess landslide has ended, and the total rainfall value that stimulates the shallow loess landslide is R0+R1, and the second rainfall duration is (D0+1). If not, it means that the rainfall that stimulates the shallow loess landslide is still continuing. On the basis of R0+R1, continue to superimpose the real-time rainfall R2 collected in the next hour, and obtain the second real-time rainfall total value R of R0+R1+R2. On the basis of D0+1, continue to superimpose for one hour, and the second rainfall duration obtained is D=D0+1+1=D0+2. Calculate the current rainfall intensity I2=R / D=(R0+R1+R2) / (D0+2), and judge whether the current rainfall intensity I2 is less than the rainfall intensity critical value. If so, it means that the rainfall that stimulates the shallow loess landslide is over. If not, continue to accumulate the second rainfall duration and the second real-time rainfall total value until it is determined that the rainfall intensity is less than the rainfall intensity critical value. End the rainfall calculation that stimulates the shallow loess landslide, and obtain the total rainfall value and the second rainfall duration. The total rainfall value R = R0 + R1 + R2 + ... + Rn, the corresponding second rainfall duration D = D0 + n is calculated, and the total average rainfall intensity I is calculated by I = R / D. n is the nth hour. Rn is the real-time rainfall collected in the nth hour during the rainfall calculation stage.

[0118] In this embodiment, whether the rainfall calculation stage for stimulating shallow loess landslide has begun is determined by judging the second real-time rainfall total value and the second rainfall duration, thereby determining the initial value of the rainfall for stimulating shallow loess landslide and the rainfall start time, and determining whether the rainfall for stimulating shallow loess landslide has ended and the rainfall end time for stimulating shallow loess landslide are determined by judging the rainfall intensity, thereby avoiding the problem of including rainfall data irrelevant to loess landslide in the rainfall data for stimulating shallow loess landslide and reducing the accuracy of shallow loess landslide early warning, thereby distinguishing between rainfall that has an impact on loess landslide and rainfall that has no impact, and obtaining effective rainfall data for stimulating shallow loess landslide, thereby improving the accuracy of shallow loess landslide early warning.

[0119] The rainfall data for stimulating shallow loess landslides calculated in this embodiment is not limited by time and is only related to the rainfall process. There is no need to artificially control the attenuation coefficient of the previous rainfall for stimulating shallow loess landslides. Therefore, the calculated rainfall data affecting shallow landslides is more in line with the actual situation, and can effectively separate the rainfall for stimulating shallow loess landslides from the rainfall that is not related to the landslides, thereby being able to well reflect the impact of rainfall on the infiltration of loess tensile cracks, improve the reference of the initial rainfall in the rainfall calculation stage for stimulating shallow loess landslides, and thus improve the accuracy of landslide warning.

[0120] In another specific embodiment, the real-time rainfall is collected by sensors arranged on the plateau.

[0121] This embodiment arranges sensors for collecting real-time rainfall on the loess plateau, so that the altitude of rainfall monitoring is the same as the altitude of the catchment area of ​​the landslide body. Therefore, the real-time rainfall collected by the sensors is more accurate and more in line with the rainfall characteristics of the loess region, which greatly improves the data reference and accuracy, and is more conducive to improving the accuracy of landslide warning.

[0122] In a specific embodiment, there are multiple sensors.

[0123] It should be noted that in this embodiment, multiple sensors for collecting real-time rainfall are arranged on the plateau of the loess region to improve the accuracy of data collection. After obtaining the real-time rainfall collected by multiple sensors, the real-time rainfall collected by the multiple sensors is averaged to obtain the real-time rainfall average value, and the real-time rainfall average value is used as the real-time rainfall in the loess region to be measured, which further improves the accuracy of the real-time rainfall, thereby being more conducive to improving the accuracy of landslide warning.

[0124] In another specific embodiment, when arranging sensors, the plateau in the loess region can be divided into equal grid areas according to the area of ​​the plateau, and a sensor is set at the center of each grid area, so as to further improve the accuracy of the real-time rainfall collected.

[0125] Embodiment three:

[0126] This embodiment provides a shallow loess landslide early warning method, which includes the first embodiment or the second embodiment, and defines step 105. The specific steps are as follows:

[0127] When the landslide warning value is less than the first landslide warning critical value, a first level warning signal is output;

[0128] When the landslide warning value is greater than or equal to the first landslide warning critical value and less than the second landslide warning critical value, a second level warning signal is output;

[0129] When the landslide warning value is greater than or equal to the second landslide warning critical value and less than the third landslide warning critical value, a third level warning signal is output;

[0130] When the landslide warning value is greater than or equal to the third landslide warning critical value, a fourth level warning signal is output.

[0131] It should be noted that the first landslide warning critical value is 0.69. When the warning critical value Cr of the shallow loess landslide is less than 0.69, it means that the possibility of landslide is small, which is the first level, and the first level warning signal is output.

[0132] The second landslide warning critical value is 0.88. When the warning critical value Cr of the shallow loess landslide satisfies: 0.69≤Cr<0.88, it means that the possibility of landslide is medium, which is the second level, and the second level warning signal is output.

[0133] The third landslide warning critical value is 1.15. When the warning critical value Cr of the shallow loess landslide satisfies: 0.88≤Cr<1.15, it means that the possibility of landslide is high, which is the third level, and the third level warning signal is issued.

[0134] When the warning critical value Cr of shallow loess landslide is ≥1.15, it indicates that the possibility of landslide is very high, which is the fourth level and the fourth warning signal is issued.

[0135] Among them, the color of the first warning signal is green, the color of the second warning signal is yellow, the color of the third warning signal is orange, and the color of the fourth warning signal is red.

[0136] This embodiment classifies landslides into different grades based on the comparison result between the landslide warning value and the preset landslide warning critical value, thereby improving the precision and intuitiveness of the warning.

[0137] The following will further illustrate the early warning method for shallow loess landslide provided by the present invention in conjunction with specific application examples.

[0138] From July 7 to 13, 2013, continuous heavy rainfall in Baota District, Yanchuan County and Yanchang County of Yan'an City caused large-scale landslides, resulting in damage to many cave dwellings and many casualties. The landslides occurred at 9:00 on July 13, 2013. The local annual average rainfall RN is: 572.3mm in Baota District, Yan'an City, 561.9mm in Yanchuan County, and 514.8mm in Yanchang County. The multi-year maximum hourly rainfall intensity average IM is: 29.5mm / h in Baota District, Yan'an City, 29.9mm / h in Yanchuan County, and 29.3mm / h in Yanchang County. The terrain factor T of 71 landslide bodies was measured on site. According to the location of the rainfall station, hourly rainfall and the location of the landslide point, the rainfall intensity I and rainfall duration D of each landslide point were interpolated point by point and hourly. The terrain factor, first rainfall intensity, first rainfall duration and landslide warning critical value of each landslide point are calculated as shown in Table 1.

[0139] Table 1 Landslide parameters, rainfall parameters and warning table in Yan'an City on July 13, 2013

[0140]

[0141]

[0142]

[0143]

[0144] The comparison results of landslide grades are shown in Table 2.

[0145] Table 2 Correspondence table of landslide grade judgment

[0146]

[0147] It can be seen from Table 2 that the landslide risk level of all landslide bodies is above the second level; the number of landslides in the third and fourth levels is 80.3%, that is, 80.3% of the landslide bodies have a high probability of landslide, and the number of landslide bodies in the fourth level is 32.4%, that is, nearly 1 / 3 (32.4%) of the landslide bodies have a high probability of landslide. After verification of the above historical data, the shallow loess landslide early warning method of this embodiment can accurately predict the landslide situation, greatly reduce the missed judgment rate, and ensure the reliability of landslide early warning.

[0148] In summary, the rainfall segmentation method for shallow loess landslide provided by the embodiment of the present invention has the following beneficial effects:

[0149] 1. In the embodiment of the present invention, the terrain data of the landslide body to be tested in the loess region are obtained, and the terrain factors of the landslide body to be tested are calculated according to the terrain data, the influence of the terrain conditions in the loess region on the shallow loess landslide is considered, and the real-time rainfall data in the loess region is obtained, and the real-time rainfall data is segmented to obtain the first rainfall data that triggers the shallow loess landslide, and the rainfall data that affects the landslide and the rainfall data that does not affect the landslide are distinguished, so as to avoid the interference of the rainfall data that does not affect the landslide on the shallow loess landslide warning, and improve the accuracy of the shallow loess landslide warning, and the landslide warning value is calculated by using the first rainfall data and the terrain factor, and the warning signal is output according to the comparison result of the landslide warning value and the preset landslide warning critical value, and the combined effect of the geological factors and rainfall factors in the loess region on the shallow loess landslide is fully considered, so that the calculated landslide warning value is more consistent with the internal texture that induces the shallow loess landslide, and the landslide risk of the landslide body to be tested in the loess region can be accurately predicted, and the accuracy of the shallow loess landslide warning is improved.

[0150] 2. In the embodiment of the present invention, when the acquired real-time rainfall data is subjected to rainfall segmentation to obtain the first rainfall data that stimulates the shallow loess landslide, the meteorological data of the loess area is obtained, and the rainfall critical value and the rainfall intensity critical value are determined based on the meteorological data. Then, the real-time rainfall in the loess area is obtained to accumulate the rainfall duration and the total real-time rainfall value, and it is determined whether the total real-time rainfall reaches or exceeds the rainfall critical value within a preset time period. If so, it is counted as the initial rainfall value that stimulates the shallow loess landslide. According to the calculated rainfall intensity, the corresponding rainfall data that stimulates the shallow loess landslide is accumulated to obtain the first rainfall data that stimulates the shallow loess landslide.

[0151] In the embodiment of the present invention, the calculated rainfall data for stimulating shallow loess landslide is not limited by time and is only related to the rainfall process. There is no need to artificially control the attenuation coefficient of the previous rainfall that stimulates the shallow loess landslide. Therefore, the calculated rainfall data affecting the landslide is more in line with the actual situation in the loess area, and can effectively separate the rainfall that stimulates the shallow loess landslide from the rainfall that is not related to the landslide, so as to well reflect the infiltration effect of rainfall on the loess tensile cracks, improve the reference of the initial rainfall in the rainfall calculation stage for stimulating shallow loess landslide, and provide more accurate and effective data support for the subsequent calculation of the landslide warning value, thereby improving the accuracy of landslide warning and reducing the heavy losses and casualties caused by landslides.

[0152] 2. In the embodiment of the present invention, a plurality of sensors are arranged on the plateau in the loess region so that the elevation of rainfall monitoring is consistent with the elevation of the landslide catchment area. By adopting this specific arrangement, the real-time monitoring of rainfall by the sensors is more accurate, which greatly improves the reference value of the real-time rainfall data and is conducive to improving the accuracy of landslide warning.

[0153] 3. In the embodiment of the present invention, the second rainfall intensity of each time period is calculated to determine whether to recalculate the previous rainfall that triggers the shallow loess landslide. When the second rainfall intensity I is less than the critical value I*, the second real-time rainfall total value is cleared and the previous rainfall is recalculated. Therefore, the calculated second real-time rainfall total value that triggers the shallow loess landslide is obtained through the analysis of the rainfall process and the possibility of landslide, and has a wider range of applications.

[0154] Fourth, in the embodiment of the present invention, the rainfall segmentation method is not based on the situation that a large number of collapses and landslides occur in the strong earthquake area. Therefore, it is applicable to loess areas without earthquake impact or with less earthquake impact and in the long period of time after the earthquake, and has wide applicability for the calculation of the early rainfall that induces landslides. In addition, according to the monitored landslide points, this embodiment arranges multiple sensors on the plateau, monitors the rainfall in real time through the sensors, and obtains the rainfall within 1-72 hours under hourly conditions, and obtains the judgment result of the early rainfall that triggers shallow loess landslides by comparing the rainfall critical value, which greatly reduces the misjudgment of landslides caused by low rainfall and greatly improves the accuracy of early warning.

[0155] 5. In the embodiment of the present invention, the preset time is determined to be 72 hours according to the special geology of the loess region, and the rainfall critical value is calculated using 10% of the local annual average rainfall, and the rainfall critical value is used as a judgment basis for whether to include the second real-time rainfall total value into the rainfall data that stimulates shallow loess landslides. The calculated rainfall data that stimulates shallow loess landslides includes the influence of short-term heavy rainfall, and will not miss the rainfall process of medium rainfall but longer time, and is not limited to the rainfall process of only 72 hours, thereby avoiding the interference of invalid rainfall duration on landslide warning, greatly improving the accuracy of warning, and reducing the occurrence of false warnings.

[0156] Sixth, in the embodiment of the present invention, when performing rainfall segmentation, the rainfall that triggers the shallow loess landslide is divided into two processes: early rainfall (rainfall accumulation stage) + late rainfall (rainfall calculation stage). The early rainfall is at least 10% of the local annual average rainfall, and in the late rainfall, the rainfall intensity critical value is used as the end point judgment benchmark of the late rainfall, so that in the rainfall data that triggers the shallow loess landslide, the early rainfall belongs to a large rainfall process, and the subsequent rainfall still needs to maintain a large rainfall to be attributed to the rainfall data that triggers the loess landslide, thereby avoiding the situation where the rainfall data when the rainfall is not large causes the misjudgment of the landslide warning, reducing the occurrence of misjudgment of the landslide warning, and greatly improving the accuracy of the rainfall warning landslide.

[0157] 7. In the embodiment of the present invention, a critical rainfall value is calculated based on the annual average rainfall, and the critical rainfall value is used as a criterion for determining the starting point of rainfall that triggers shallow loess landslides, thereby eliminating interference caused by evaporation of rainfall through the upper catchment area and absorption by loess soil. The first rainfall data obtained only includes rainfall that infiltrates into deeper soil along tensional fissure channels, which may continue to affect landslides. The calculated landslide warning value is more consistent with the geological conditions and rainfall conditions of the landslide body to be tested, thereby improving the accuracy of the warning.

[0158] 8. In the embodiment of the present invention, a critical value of rainfall intensity is calculated based on the average value of the annual maximum and minimum rainfall intensities, and the critical value of rainfall intensity is used as a criterion for judging the end point of rainfall that triggers shallow loess landslides. Only when the rainfall intensity is greater than or equal to the critical value of rainfall intensity will it continue to be included in the rainfall data that triggers shallow loess landslides. This eliminates the situation where, under a smaller rainfall intensity, rainwater in the upper catchment area of ​​the landslide will not continue to infiltrate in the tensile cracks and continue to affect the landslide due to evaporation or absorption by the loess soil. This improves the credibility of the first rainfall data, thereby reducing the misjudgment rate of landslide warnings.

[0159] Embodiment 4:

[0160] See also Figure 3 , Figure 3 A shallow loess landslide early warning device is provided in an embodiment of the present invention. The device comprises:

[0161] An acquisition module 301 is used to acquire topographic data of a landslide body to be detected in a loess region, wherein the topographic data includes a slope of the landslide body, an area of ​​the landslide body, and an area of ​​a catchment area above the landslide body;

[0162] The first calculation module 302 is used to calculate the terrain factor of the landslide body to be measured according to the terrain data;

[0163] The rainfall segmentation module 303 is used to obtain real-time rainfall data in the loess region, perform rainfall segmentation on the real-time rainfall data, and obtain first rainfall data that triggers shallow loess landslides.

[0164] A second calculation module 304 is used to calculate a landslide warning value according to the terrain factor and the first rainfall data;

[0165] The warning module 305 is used to output a warning signal according to the comparison result between the landslide warning value and the preset landslide warning critical value.

[0166] In a specific embodiment, the rainfall segmentation module 303 includes:

[0167] The first acquisition submodule is used to obtain meteorological data in the Loess Plateau region and determine the critical value of rainfall and the critical value of rainfall intensity according to the meteorological data;

[0168] The second acquisition submodule is used to acquire and accumulate the real-time rainfall in the loess region to obtain a second real-time rainfall total value, and accumulate the collection time of the real-time rainfall to obtain a second rainfall duration;

[0169] The first judgment submodule is used to judge whether the second rainfall duration is greater than the second preset duration when the second real-time rainfall total value reaches the rainfall critical value, and if not, trigger the second judgment submodule; if so, clear the second real-time rainfall total value and the second rainfall duration, and re-trigger the second acquisition submodule;

[0170] The second judgment submodule is used to calculate the second rainfall intensity according to the second rainfall duration and the second real-time rainfall total value. When the second rainfall intensity is less than the rainfall intensity critical value, the second real-time rainfall total value and the second rainfall duration are output as the first rainfall data for this shallow loess landslide, and then the second real-time rainfall total value and the second rainfall duration are cleared to re-trigger the second acquisition submodule.

[0171] In a specific embodiment, the second judgment submodule is also used to calculate the second rainfall intensity based on the second rainfall duration and the second real-time rainfall total value. When the second rainfall intensity is less than the rainfall intensity critical value, the second real-time rainfall total value and the second rainfall duration are output as the first rainfall data for this time triggering the shallow loess landslide, and then the second real-time rainfall total value and the second rainfall duration are cleared to re-trigger the second acquisition submodule.

[0172] In a specific embodiment, the first calculation module 302 includes:

[0173] A first calculation submodule, configured to calculate a first rainfall intensity according to a first real-time rainfall total value and a first rainfall duration;

[0174] The second calculation submodule is used to calculate the landslide warning value according to the terrain factor, the first rainfall intensity and the first rainfall duration.

[0175] In a specific embodiment, the first acquisition submodule further includes:

[0176] The third calculation submodule is used to calculate the rainfall critical value according to the annual average rainfall;

[0177] The fourth calculation submodule is used to calculate the rainfall intensity critical value according to the average value of the maximum and minimum rainfall intensities in the year.

[0178] In a specific embodiment, the early warning module 305 includes:

[0179] The first level warning module 305 is used to output a first level warning signal when the landslide warning value is less than the first landslide warning critical value;

[0180] The second level warning module 305 is used to output a second level warning signal when the landslide warning value is greater than or equal to the first landslide warning critical value and less than the second landslide warning critical value;

[0181] The third level warning module 305 is used to output a third level warning signal when the landslide warning value is greater than or equal to the second landslide warning critical value and less than the third landslide warning critical value;

[0182] The fourth level warning module 305 is used to output a fourth level warning signal when the landslide warning value is greater than or equal to the third landslide warning critical value.

[0183] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0184] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0185] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0186] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each functional unit may be physically separate, or two or more functional units may be integrated into one processing unit. The above integrated units may be implemented in the form of hardware or software functional units.

[0187] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or partly contributed to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0188] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0189] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A shallow loess landslide early warning method, characterized in that: The method comprises: Acquire topographic data of the landslide body to be detected in the loess region, wherein the topographic data includes the slope of the landslide body, the area of ​​the landslide body, and the area of ​​the catchment area above the landslide body; Calculate the terrain factor of the landslide body to be measured according to the terrain data; Acquire real-time rainfall data in the loess region, perform rainfall segmentation on the real-time rainfall data, and obtain first rainfall data that triggers shallow loess landslide; Calculating a landslide warning value according to the terrain factor and the first rainfall data; Outputting a warning signal according to a comparison result between the landslide warning value and a preset landslide warning critical value; the first rainfall data includes a first real-time rainfall total value and a first rainfall duration, and calculating the landslide warning value according to the terrain factor and the first rainfall data includes: Calculate a first rainfall intensity according to the first real-time rainfall total value and the first rainfall duration; Calculating a landslide warning value according to the terrain factor, the first rainfall intensity and the first rainfall duration; The calculation formula of the landslide warning value is: Cr=T(I / IM)(D / Dd) 0.45 Cr is the landslide warning value, T is the terrain factor, I is the first rainfall intensity, IM is the critical value of rainfall intensity, D is the first rainfall duration, and Dd is the unit time; The calculation formula for obtaining the terrain factor of the landslide body to be measured based on the terrain data is: T=tana+1.25U=(1+1.25Au / A)tana Among them, U is the upward slowing factor; Au is the upper catchment area; a is the slope of the landslide; A is the area of ​​the landslide; T is the terrain factor; The acquiring of the real-time rainfall data in the loess region and performing rainfall segmentation on the real-time rainfall data to obtain the first rainfall data that triggers the shallow loess landslide comprises: S1: Acquire meteorological data of the Loess Plateau region, and determine a rainfall critical value and a rainfall intensity critical value according to the meteorological data; The meteorological data includes the annual average rainfall and the annual maximum and minimum rainfall intensity averages; the step of determining the rainfall critical value and the rainfall intensity critical value according to the meteorological data includes: Calculate the rainfall threshold based on the average annual rainfall; Calculate the critical value of rainfall intensity based on the average of the maximum and minimum rainfall intensities in the year; The calculation formula of the rainfall critical value is: R*=0.1RN Among them, R* is the critical value of rainfall, and RN is the average annual rainfall in the Loess Plateau; The calculation formula of the critical value of rainfall intensity is: I*=0.02IM Among them, I* is the critical value of rainfall intensity, IM is the average value of the annual maximum and minimum rainfall intensity in the Loess Plateau; S2: acquiring and accumulating the real-time rainfall in the loess region to obtain a second real-time rainfall total value, and accumulating the collection time of the real-time rainfall to obtain a second rainfall duration; S3: When the second real-time total rainfall value reaches the rainfall critical value, determine whether the second rainfall duration is greater than the second preset duration, if not, execute S4; if yes, clear the second real-time total rainfall value and the second rainfall duration, and re-execute S2; wherein the second preset duration is 72 hours; S4: Calculate the second rainfall intensity according to the second rainfall duration and the second real-time rainfall total value, and when the second rainfall intensity is less than the rainfall intensity critical value, output the second real-time rainfall total value and the second rainfall duration as the first rainfall data for this shallow loess landslide excitation, then clear the second real-time rainfall total value and the second rainfall duration, and re-execute S2; When the second rainfall intensity is not less than the rainfall intensity critical value, continue to update the second real-time rainfall total value and the second rainfall duration according to S2, and update the second rainfall intensity according to the updated second real-time rainfall total value and the updated second rainfall duration until the updated second rainfall intensity is less than the rainfall intensity critical value.

2. The method according to claim 1, characterized in that Outputting a warning signal according to a comparison result between the landslide warning value and a preset landslide warning critical value comprises: When the landslide warning value is less than the first landslide warning critical value, outputting a first level warning signal; When the landslide warning value is greater than or equal to the first landslide warning critical value and less than the second landslide warning critical value, outputting a second level warning signal; When the landslide warning value is greater than or equal to the second landslide warning critical value and less than the third landslide warning critical value, outputting a third level warning signal; When the landslide warning value is greater than or equal to the third landslide warning critical value, a fourth level warning signal is output.

3. A shallow loess landslide early warning device, characterized in that: The device comprises: An acquisition module is used to acquire topographic data of a landslide body to be detected in a loess region, wherein the topographic data includes a slope of the landslide body, an area of ​​the landslide body, and an area of ​​a catchment area above the landslide body; The first calculation module is used to calculate the terrain factor of the landslide body to be measured according to the terrain data; wherein the calculation formula of the terrain factor is: T=tana+1.25U=(1+1.25Au / A)tana Among them, U is the upward slowing factor; Au is the upper catchment area; a is the slope of the landslide; A is the area of ​​the landslide; T is the terrain factor; A rainfall segmentation module is used to obtain real-time rainfall data in the loess region, perform rainfall segmentation on the real-time rainfall data, and obtain first rainfall data that triggers shallow loess landslides. A second calculation module is used to calculate a landslide warning value according to the terrain factor and the first rainfall data; the first rainfall data includes a first real-time rainfall total value and a first rainfall duration; An early warning module, configured to output an early warning signal according to a comparison result between the landslide early warning value and a preset landslide early warning critical value; The second calculation module is specifically used to calculate a first rainfall intensity according to the first real-time rainfall total value and the first rainfall duration; and calculate a landslide warning value according to the terrain factor, the first rainfall intensity and the first rainfall duration; The calculation formula of the landslide warning value is: Cr=T(I / IM)(D / Dd) 0.45 Cr is the landslide warning value, T is the terrain factor, I is the first rainfall intensity, IM is the critical value of rainfall intensity, D is the first rainfall duration, and Dd is the unit time; The rainfall segmentation module is specifically used to perform the following steps: S1: Acquire meteorological data of the Loess Plateau region, and determine a rainfall critical value and a rainfall intensity critical value according to the meteorological data; The meteorological data includes the annual average rainfall and the annual maximum and minimum rainfall intensity averages; the step of determining the rainfall critical value and the rainfall intensity critical value according to the meteorological data includes: Calculate the rainfall threshold based on the average annual rainfall; Calculate the critical value of rainfall intensity based on the average of the maximum and minimum rainfall intensities in the year; The calculation formula of the rainfall critical value is: R*=0.1RN Among them, R* is the critical value of rainfall, and RN is the average annual rainfall in the Loess Plateau; The calculation formula of the critical value of rainfall intensity is: I*=0.02IM Among them, I* is the critical value of rainfall intensity, IM is the average value of the annual maximum and minimum rainfall intensity in the Loess Plateau; S2: acquiring and accumulating the real-time rainfall in the loess region to obtain a second real-time rainfall total value, and accumulating the collection time of the real-time rainfall to obtain a second rainfall duration; S3: When the second real-time total rainfall value reaches the rainfall critical value, determine whether the second rainfall duration is greater than the second preset duration, if not, execute S4; if yes, clear the second real-time total rainfall value and the second rainfall duration, and re-execute S2; wherein the second preset duration is 72 hours; S4: Calculate the second rainfall intensity according to the second rainfall duration and the second real-time rainfall total value, and when the second rainfall intensity is less than the rainfall intensity critical value, output the second real-time rainfall total value and the second rainfall duration as the first rainfall data for this shallow loess landslide excitation, then clear the second real-time rainfall total value and the second rainfall duration, and re-execute S2; When the second rainfall intensity is not less than the rainfall intensity critical value, continue to update the second real-time rainfall total value and the second rainfall duration according to S2, and update the second rainfall intensity according to the updated second real-time rainfall total value and the updated second rainfall duration until the updated second rainfall intensity is less than the rainfall intensity critical value.

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