Monitoring and early warning method for adjusting sampling frequency under adaptive extreme conditions

Abstract

The invention belongs to the technical field of geological disaster monitoring and data processing, and particularly relates to a monitoring and early warning method for adjusting sampling frequency by self-adaption extreme conditions, which automatically adjusts monitoring frequency based on rainfall disaster-causing probability, and constructs a landslide displacement model and a single-ditch debris flow early warning model by using acquired monitoring data. The landslide-debris flow early warning level is determined and published, so that the problem of monitoring resource waste caused by no rainfall in continuous cloudy days and difficult occurrence of chain disasters under the traditional fixed monitoring frequency is solved, and the problem of insufficient monitoring strength in a rainstorm state in which disasters are easy to occur after continuous cloudy days is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of geological disaster monitoring and data processing, and particularly relates to a monitoring and early warning method for adaptively adjusting the sampling frequency under extreme conditions. Background Art

[0002] Rainfall is one of the important factors inducing landslides and debris flows. Geological disasters in the loess area often occur due to landslide-debris flow and other geological disasters triggered by heavy rain after continuous rainy weather. At present, the monitoring means for landslide-debris flow are relatively single. GNSS displacement monitoring is often used to warn of landslides, and rainfall is usually used to warn of debris flows, ignoring the chain relationship between other factors and landslide-debris flow to a certain extent. Some monitoring technical means for loess chain disasters have single monitoring equipment and monitoring schemes based on preset fixed parameters, and cannot achieve targeted real-time and effective monitoring in the event of disasters prone to occur under continuous extreme weather. Therefore, it is necessary to study an intelligent perception method for adaptively adjusting the sampling frequency for the development process of loess chain disasters, realize the encryption of the mutation process of key monitoring indicators in the pre-disaster stage, and provide real-time and reliable data support for successful early warning.

[0003] In the process of monitoring loess chain disasters by the existing geological disaster monitoring system, there are problems such as insufficient working hours of solar panels and insufficient power supply capacity of monitoring equipment in the case of continuous rainy days, and there are fundamental differences in the different content indicators that need to be monitored at different stages of loess chain disasters. In the existing loess chain disaster monitoring system, it is often fixed frequency and fixed equipment. Under conditions such as no rainfall and not prone to geological disasters, high-frequency monitoring consumes electricity and results in ineffective monitoring; while in the case of continuous rainfall and other disaster-prone conditions, due to insufficient power supply caused by the abnormal operation of solar panels, the monitoring intensity is insufficient in the disaster-prone situation. Summary of the Invention

[0004] Aiming at the problem of insufficient monitoring intensity of the existing landslide-debris flow monitoring equipment under extreme weather conditions such as continuous rainy days, this paper proposes a monitoring and early warning method for adaptively adjusting the sampling frequency under extreme conditions. The present invention automatically adjusts the monitoring frequency based on the rainfall-induced disaster probability, constructs a landslide displacement model and a single-gully debris flow early warning model by using the obtained monitoring data, determines and releases the early warning level of landslide-debris flow, and solves the problem of waste of monitoring resources caused by continuous cloudy days without rainfall and not prone to chain disasters under the traditional fixed monitoring frequency, as well as the problem of insufficient monitoring intensity in the heavy rain state prone to disasters after continuous cloudy days.

[0005] Based on the above purposes, the technical solution adopted by the present invention is as follows:

[0006] An adaptive monitoring and early warning method for adjusting the sampling frequency under extreme conditions, comprising the following steps:

[0007] (1) Calculate the monitoring and early warning critical threshold based on historical monitoring data and landslide-debris flow test model parameters, and determine the landslide-debris flow disaster risk area to be monitored according to the monitoring and early warning threshold;

[0008] (2) For the determined landslide-debris flow disaster risk area, calculate the rainfall disaster-causing probability and determine the disaster-causing level based on weather forecast data and real-time monitoring data, automatically adjust the monitoring frequency according to the disaster-causing level, and obtain monitoring data;

[0009] (3) Construct a landslide displacement model and a single-gully debris flow early warning model based on the obtained monitoring data; determine and release the landslide early warning level based on the landslide displacement model;

[0010] (4) Obtain the debris flow early warning value based on the single-gully debris flow early warning model and the landslide influence factor, and determine and release the debris flow early warning level.

[0011] The present invention calculates the rainfall disaster-causing probability, determines the disaster-causing level accordingly, then automatically adjusts the monitoring frequency based on different disaster-causing levels to obtain monitoring data, and further uses the obtained monitoring data to construct a landslide displacement model and a single-gully debris flow early warning model, and determines and releases the landslide-debris flow early warning level, solving the problem of insufficient monitoring intensity of existing landslide-debris flow monitoring equipment under extreme weather conditions such as continuous rainy days.

[0012] Preferably, in step (2), the rainfall disaster-causing probability P is calculated by the following formula:

[0013] P = a + bE + cE 2 + dE 3 (1)

[0014] where a, b, c, and d are parameters that vary with the rainfall threshold; E is the effective rainfall;

[0015] The effective rainfall E is calculated by the following formula:

[0016]

[0017] where n represents the total number of days of rainfall start, r k represents the daily rainfall, with the unit of millimeter.

[0018] Preferably, in step (2), the monitoring frequency is automatically adjusted according to the disaster-causing level, specifically as follows:

[0019] When the rainfall disaster probability is 0 - 0.2, determine the disaster level as the first level, and increase the observation period of the GNSS monitoring station by 1 hour during rainfall to monitor landslides;

[0020] When the rainfall disaster probability is 0.2 - 0.4, determine the disaster level as the second level, increase the observation period of the GNSS monitoring station to 2 hours to monitor landslides; wake up the soil moisture meter and the rain gauge to conduct auxiliary monitoring of landslides at a frequency of 30 minutes / time;

[0021] When the rainfall disaster probability is 0.4 - 0.6, determine the disaster level as the third level. On the basis of increasing the observation period, the GNSS monitoring station monitors in real time with the rainfall time, and extends the observation by 1 hour after the rainfall ends; wake up the soil moisture meter and the rain gauge to increase the frequency to 10 minutes / time for auxiliary monitoring of landslides; wake up the radar - type water level gauge to conduct observations at a frequency of 30 minutes / time to monitor and warn of the occurrence of debris flows;

[0022] When the rainfall disaster probability is 0.6 - 0.8, determine the disaster level as the fourth level, modify the GNSS monitoring station to real - time all - weather monitoring; wake up the soil moisture meter and the rain gauge to conduct auxiliary monitoring of landslides at a frequency of 5 minutes / time; the radar - type water level gauge conducts auxiliary monitoring and warning of debris flows at a frequency of 5 minutes / time;

[0023] When the rainfall disaster probability exceeds 0.8, determine the disaster level as the fifth level, the GNSS monitoring station is real - time all - weather monitoring; the soil moisture meter and the water level gauge monitor landslides at a frequency of 5 minutes / time; the radar - type water level gauge switches to the real - time monitoring mode to warn of debris flows; at the same time, start video monitoring for manual monitoring and judgment.

[0024] The present invention adopts the above method of automatically adjusting the monitoring frequency according to the rainfall disaster probability and the disaster level, which can effectively extend the monitoring duration. In the case of insufficient working hours of the solar panels and insufficient power supply capacity of the monitoring equipment caused by continuous rainy weather, it can still effectively monitor landslide - debris flow disasters.

[0025] Preferably, step (3) constructs a landslide displacement model based on the monitoring data, including the following steps:

[0026] I. Align the time stamps of the obtained monitoring data, and use the interpolation formula to complete the missing monitoring data. The monitoring data includes GNSS monitoring data, soil moisture data, rain gauge data, and radar - type water level gauge data;

[0027] II. Generate an observation matrix using the completed monitoring data, obtain the correlation coefficient matrix through standardization processing, and obtain the correlation coefficients of GNSS monitoring data, soil moisture data, rain gauge data, and radar - type water level gauge data;

[0028] III. Calculate the landslide surface displacement based on the monitoring data and the corresponding correlation coefficients.

[0029] Preferably, the interpolation formula in step I is as follows:

[0030]

[0031] When using this interpolation formula to complete GNSS monitoring data, where y represents the vertical displacement obtained by interpolation; x represents the horizontal displacement obtained by interpolation; y0 represents the vertical displacement of the first point; x0 represents the known horizontal displacement of the first point; x1 represents the horizontal displacement of the second point; y1 represents the known vertical displacement of the second point;

[0032] When using the interpolation formula to complete soil moisture data, where y represents the soil moisture content obtained by interpolation; x represents the interpolation point time; y0 represents the first known soil moisture content; x0 represents the time of the first known soil moisture content; y1 represents the second known soil moisture content; x1 represents the time of the second known soil moisture content;

[0033] When using this interpolation formula to complete rain gauge data, where y represents the rainfall obtained by interpolation; x represents the time of the rainfall obtained by interpolation; y0 represents the first known rainfall; x0 represents the time of the first known rainfall; y1 represents the second known rainfall; x1 represents the time of the second known rainfall;

[0034] When using this interpolation formula to complete radar water level gauge data, where y represents the water level value obtained by interpolation; x represents the time of the water level value obtained by interpolation; y0 represents the first known water level value; x0 represents the time of the first known water level value; y1 represents the second known water level value; x1 represents the time of the second known water level value.

[0035] Preferably, the calculation process of generating an observation matrix using the completed monitoring data, obtaining a correlation coefficient matrix through standardization processing, and obtaining the correlation coefficients of GNSS monitoring data, soil moisture data, rain gauge data, and radar water level gauge in step II is as follows:

[0036] Generate an observation matrix X using the completed monitoring data,

[0037]

[0038] That is, X is the observation value matrix, using Calculate the mean of each column; use Calculate the variance of each column;

[0039] Use Standardize the data; the standardized matrix Z is obtained as follows:

[0040]

[0041] Using Equation Calculate the correlation coefficient matrix R as follows:

[0042]

[0043] Where r ij That is, the sample sequence relationship between the i-th column and the j-th column of the X matrix, and the correlation coefficients for GNSS monitoring data, soil moisture data, rain gauge data, and radar water level gauge can be obtained.

[0044] Preferably, the landslide surface displacement described in step III is calculated by the following formula:

[0045] T = x·r 11 + y·r 12 + z·r 13 + q·r 14 (7)

[0046] Where T is the landslide surface displacement, x is the GNSS monitoring data, r 11 is the correlation coefficient of the GNSS monitoring data; y is the rain gauge data, r 12 is the correlation coefficient of the rain gauge data; z is the soil moisture gauge data, r 13 is the correlation coefficient of the soil moisture data; q is the radar water level gauge data, r 14 is the correlation coefficient of the radar water level gauge data.

[0047] Preferably, the determination and release of the landslide warning level based on the landslide displacement model are specifically as follows:

[0048] Take the ratio of the landslide surface displacement to the corresponding time to obtain the deformation rate v, and at the same time use the tangent angle expressed by the displacement-time curve characteristics to assist in discriminating the warning landslide, so as to specifically release the warning information for the constant velocity deformation, initial acceleration, uniform acceleration, and impending sliding stages. The specific landslide comprehensive warning basis based on the deformation rate threshold and the tangent angle is shown in the following table:

[0049]

[0050] Preferably, the single-gully debris flow warning model described in step (3) is expressed by the following equation:

[0051]

[0052] Where P is the early warning value of single - gully debris flow; R is the rainfall factor; G is the geological factor; T is the terrain factor of the debris - flow formation area;

[0053] The expression of the terrain factor T of the debris - flow formation area is:

[0054]

[0055] Where, T is the terrain factor of the debris - flow formation area; J is the longitudinal gradient of the gully bed in the debris - flow formation area; A is the area of the debris - flow formation area; L is the length of the debris - flow formation gully (km); A0 is the unit area (1 km 2 )

[0056] The expression of the geological factor G is:

[0057] G = F0·C1·C2·C3·C4 (10)

[0058] G is the geological factor, and the average firmness coefficient F0 of the lithology in the debris - flow formation area in the loess area is 0.3, C1 is the structure (fault zone), C2 is the seismic intensity, C3 is the physical weathering correction factor, and C4 is the chemical weathering correction factor;

[0059] The expression of the rainfall factor R is:

[0060]

[0061] Where B is the cumulative rainfall in the early stage of debris - flow outbreak (mm); I is the rainfall in 1 h that triggers debris - flow (mm); R0 is the local annual average precipitation (mm); C r is the local 10 - minute rainfall variation coefficient.

[0062] Preferably, the landslide influence factor in step (4) is calculated by the following formula:

[0063] Q = y·r 12 +t·r 13 +q·r 14 (12)

[0064] Where Q is the landslide influence factor, y is the data of the rain gauge, z is the soil moisture data, and q is the data of the radar - type water level gauge.

[0065] Preferably, the debris - flow early - warning value is obtained based on the single - gully debris - flow early - warning model and the landslide influence factor in step (4), specifically:

[0066]

[0067] Where P is the debris flow warning value; Q is the landslide influencing factor; G is the geological factor; T is the terrain factor in the debris flow formation area; the debris flow warning value is used to divide the debris flow warning levels according to the risk level critical values of 0.28, 0.35, and 0.47.

[0068] Preferably, the specific numerical range of the debris flow warning value P corresponds to the warning levels as shown in the following table:

[0069] Early warning value P range P＜0.28 0.28≤P＜0.35 0.35≤P<0.47 0.47≤P Early warning level General risk Mild risk Moderate risk High risk

[0070] Compared with the prior art, the technical effects of the present invention are as follows:

[0071] The present invention automatically adjusts the monitoring frequency through the rainfall disaster-causing probability and disaster-causing level, can effectively delay the monitoring duration, solves the problem of waste of monitoring resources in the state where chain disasters are not likely to occur under continuous cloudy days without rainfall under the traditional fixed monitoring frequency, and the problem of insufficient monitoring intensity in the heavy rain state where disasters are likely to occur after continuous cloudy days. The present invention can truly achieve no waste of monitoring resources in the state of basically no risk and effectively make up for the problem of insufficient monitoring in the state where disasters are likely to occur in extreme weather. Description of the Drawings

[0072] Figure 1 is the overall flowchart of the monitoring and warning method for adapting to extreme conditions and adjusting the sampling frequency in Embodiment 1;

[0073] Figure 2 is to determine the device wake-up frequency based on rainfall;

[0074] Figure 3 is the flowchart of multi-source device collaborative early warning of landslide-debris flow risks. Detailed Embodiments

[0075] To better illustrate the purpose, technical solution, and advantages of the present invention, the present invention will be further described below in conjunction with specific embodiments. Those skilled in the art should understand that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0076] Embodiment 1

[0077] This embodiment provides a monitoring and warning method for adapting to extreme conditions and adjusting the sampling frequency, as Figures 1 - 3 shown, where the overall process schematic diagram is as Figure 1As shown, the monitoring and early warning critical thresholds are obtained through historical monitoring data and landslide-debris flow experimental model parameters; it is determined whether there is a possibility of disasters in the study area based on the monitoring and early warning critical thresholds ("Understanding and Thinking on Issues Related to Landslide Monitoring and Early Warning", Xu Qiang, Journal of Engineering Geology, 28(2): 360-374). For areas where there is a possibility of landslide-debris flow disasters, the following method is used for the monitoring and early warning method of adaptively adjusting the sampling frequency under extreme conditions, including the following steps:

[0078] (1) Obtain the weather forecast for the next 7 days. When the sunlight conditions cannot meet the working conditions of the solar panels, that is, when the daily effective sunshine duration does not reach 6 hours or the sun intensity is insufficient for a continuous period of 3 days, devices such as multi-source sensors and GNSS receivers enter the low-power semi-sleep state: the multi-source sensors broadcast data at a frequency of 1 hour / time; the GNSS receiver observes at a fixed frequency of 1h / time and 4 times a day.

[0079] (2) The DI signal generated by the rain gauge flipping is input into the remote terminal control system (RTU) for wake-up, and a signal is sent to the remote center. Combining the forecast rainfall level for the day, the rainfall data is preprocessed initially, that is, it is judged whether there is human intervention or false alarm by 3 times the forecast rainfall for the day.

[0080] (3) Wake up the sensor for encrypted incremental broadcasting according to the rainfall-induced disaster probability P, as Figure 2 shown, the rainfall-induced disaster probability P can be expressed by the equation:

[0081] P = a + bE + cE 2 + dE 3 (1)

[0082] where a, b, c, and d are parameters that change with the rainfall threshold, and the specific parameter values are determined according to the region and the rainfall threshold (see the industry standard QX / T 487-2019 Meteorological Risk Warning Level for Geological Disasters Induced by Heavy Rain); E is the effective rainfall. And the rainfall-induced disaster probability P takes values between 0 and 1, and is divided into five grade intervals of 0, 0.2, 0.4, 0.6, and 0.8 respectively to start the corresponding monitoring plan.

[0083] The effective rainfall E can be expressed by the equation:

[0084]

[0085] where n represents the total number of days of starting rainfall, and r k represents the daily rainfall, with the unit of millimeters.

[0086] When the rainfall disaster probability calculated according to step (3) is at the first level of 0 - 0.2, the observation period of the GNSS monitoring station can be increased by 1 hour during rainfall to monitor landslides.

[0087] When the rainfall disaster probability calculated according to step (3) is at the second level of 0.2 - 0.4, the observation period of the GNSS monitoring station is increased to 2 hours to monitor landslides; the soil moisture meter and rain gauge are awakened to assist in monitoring landslides at a frequency of 30 minutes per time.

[0088] When the rainfall disaster probability calculated according to step (3) is at the third level of 0.4 - 0.6, on the basis of increasing the observation period, the GNSS monitoring station monitors in real time with the rainfall time, and extends the observation by 1 hour after the rainfall ends; the soil moisture meter and rain gauge are awakened to increase the frequency to 10 minutes per time to assist in monitoring landslides; the radar - type water level gauge is awakened to observe at a frequency of 30 minutes per time to monitor and give early warning of the occurrence of debris flows.

[0089] When the rainfall disaster probability calculated according to step (3) is at the fourth level of 0.6 - 0.8, the GNSS monitoring station is modified to real - time all - weather monitoring; the soil moisture meter and rain gauge are awakened to assist in monitoring landslides at a frequency of 5 minutes per time; the radar - type water level gauge is used to assist in monitoring and giving early warning of debris flows at a frequency of 5 minutes per time.

[0090] When the rainfall disaster probability calculated according to step (3) exceeds 0.8 and is at the fifth level, the GNSS monitoring station is in real - time all - weather monitoring; the soil moisture meter and water level gauge monitor landslides at a frequency of 5 minutes per time; the radar - type water level gauge switches to the real - time monitoring mode to give early warning of debris flows; at the same time, video monitoring is started for manual monitoring and judgment.

[0091] (4) Align the time stamps for the GNSS monitoring data, soil moisture data, rain gauge data, and radar - type water level gauge data collected according to step (3), and use linear interpolation to complete the missing data part, as Figure 3 shown. That is, use the formula as follows:

[0092]

[0093] When using this interpolation formula to complete the GNSS monitoring data, where y represents the vertical displacement obtained by interpolation; x represents the horizontal displacement obtained by interpolation; y0 represents the first vertical displacement; x0 represents the first known horizontal displacement; x1 represents the horizontal displacement of the second point; y1 represents the second known vertical displacement.

[0094] When using the interpolation formula to complete the soil moisture data, where y represents the interpolated soil moisture content; x represents the time of the interpolation point; y0 represents the first known soil moisture content; x0 represents the time of the first known soil moisture content; y1 represents the second known soil moisture content; x1 represents the time of the second known soil moisture content;

[0095] When using the interpolation formula to complete the rain gauge data, where y represents the interpolated rainfall; x represents the time of the interpolated rainfall; y0 represents the first known rainfall; x0 represents the time of the first known rainfall; y1 represents the second known rainfall; x1 represents the time of the second known rainfall;

[0096] When using the interpolation formula to complete the radar water level gauge data, where y represents the interpolated water level value; x represents the time of the interpolated water level value; y0 represents the first known water level value; x0 represents the time of the first known water level value; y1 represents the second known water level value; x1 represents the time of the second known water level value.

[0097] (5) Use the principal component analysis method to conduct monitoring and early warning on the obtained multi-source monitoring data. That is, first use the completed monitoring data to generate an observation matrix, and the detailed calculation process is as follows:

[0098]

[0099] That is, X is the observation value matrix, and use to calculate the mean value of each column; use to calculate the variance of each column; use to standardize the data; obtain the standard standardized matrix Z as follows:

[0100]

[0101] Use Equation to calculate the correlation coefficient matrix R as follows:

[0102]

[0103] In the formula, r ij is the sample sequence relationship between the i-th column and the j-th column of the X matrix, and the correlation coefficients for GNSS monitoring data, soil moisture data, rain gauge data, and radar water level gauge can be obtained.

[0104] (6) Use the correlation coefficients calculated in process (5) to fit the landslide surface displacement, and the following formula can be obtained to predict the stage of landslide deformation:

[0105] T = x·r 11 + y·r12 +z·r 13 +q·r 14 (7)

[0106] where T is the landslide surface displacement, x is the GNSS monitoring data, and r 11 is the correlation coefficient of the GNSS monitoring data; y is the rain gauge data, and r 12 is the correlation coefficient of the rain gauge data; z is the soil moisture meter data, and r 13 is the correlation coefficient of the soil moisture data; q is the radar water level gauge data, and r 14 is the correlation coefficient of the radar water level gauge data.

[0107] The deformation rate v is obtained by taking the ratio of the landslide surface displacement to the corresponding time. At the same time, the tangent angle expressed by the displacement-time curve characteristics is used to assist in discriminating and warning of landslides, so as to issue targeted early warning information for the constant velocity deformation, initial acceleration, uniform acceleration, and approaching sliding stages. The specific comprehensive early warning basis of landslides based on the deformation rate threshold and tangent angle is as follows:

[0108]

[0109] (7) The early warning model of single-channel debris flow prone to landslide-debris flow chain disasters in loess areas can be expressed by the equation as follows:

[0110]

[0111] where P is the early warning value; R is the rainfall factor; G is the geological factor; the early warning value p of single-channel debris flow can be divided into early warning levels according to the value of the risk level critical value C t (0.28, 0.35, 0.47); T is the terrain factor in the debris flow formation area.

[0112] The expression of the terrain factor T in the debris flow formation area is:

[0113]

[0114] where T is the terrain factor in the debris flow formation area; J is the longitudinal gradient of the gully bed in the debris flow formation area; A is the area of the debris flow formation area; L is the length of the debris flow formation gully (km); A0 is the unit area (1 km 2 ).

[0115] The expression of the geological factor G is:

[0116] G = F0·C1·C2·C3·C4 (10)

[0117] G is a geological factor, and the average firmness coefficient F0 of the lithology in the debris flow formation area in the loess region is 0.3. C1 is a structure (fault zone), C2 is the seismic intensity, C3 is the physical weathering correction factor, and C4 is the chemical weathering correction factor. Without the correction of other influencing factors, G = F0 = 0.3.

[0118] The expression of the rainfall factor R is:

[0119]

[0120] Where B is the cumulative rainfall (mm) in the early stage before the debris flow outbreak; I is the rainfall (mm) in 1 hour when the debris flow is triggered; R0 is the local annual average precipitation (mm); C r is the local 10-minute rainfall variation coefficient.

[0121] (8) Based on the debris flow warning model calculated in step (7), fit with the correlation coefficients of the rainfall, soil moisture, and channel water level calculated in step (5), that is, the following formula:

[0122] Q = y·r 12 +t·r 13 +q·r 14 (12)

[0123] Where Q is the landslide influencing factor, y is the rain gauge data, z is the soil moisture data, and q is the radar water level gauge data.

[0124] (9) Use the Q landslide influencing factor calculated by formula (12) to replace the rainfall factor in formula (8), and use the warning value P calculated by formula (8) to issue a warning. The specific formula is as follows:

[0125]

[0126] Where P is the warning value; Q is the landslide influencing factor; G is the geological factor; T is the terrain factor in the debris flow formation area; the warning value P is divided into warning levels according to the risk level critical values C t (0.28, 0.35, 0.47).

[0127] The specific numerical range of the warning value P corresponds to the warning levels as follows:

[0128] Early warning value P range P＜0.28 0.28≤P＜0.35 0.35≤P<0.47 0.47≤P Early warning level General risk Mild risk Moderate risk High risk

[0129] Under extreme continuous rainy weather conditions, assuming the effective power of the photovoltaic battery commonly carried in the wild is 40 Ah. Conventional GNSS monitoring uses all-weather observations and can only last for 3 to 4 days under continuous cloudy days when the solar panels are not working, and it cannot achieve effective monitoring in the later stage when landslides are prone to occur under continuous rainy conditions. Under the GNSS observation mode of this solution, the monitoring time can be extended to 10 days under non-rainy conditions, which means that even if heavy rain suddenly occurs after a week of continuous cloudy days, effective GNSS monitoring can still be carried out.

[0130] When 3 soil moisture sensors, 1 radar water level gauge, and 1 rain gauge are carried on the same 40 Ah photovoltaic battery, under the condition of real-time high-frequency monitoring, the multi-source sensors can only support for 2 days; under the sleep-wake-up strategy of this invention, the multi-source sensors can continuously monitor for dozens of days, which means that it can provide effective data support for the monitoring and early warning of landslides and debris flows in extreme weather.

[0131] That is, the purpose of this invention is to achieve intelligent monitoring under extreme conditions. By using an intelligent perception method that adaptively adjusts the sampling frequency, it solves the problem of waste of monitoring resources under the traditional fixed monitoring frequency in the state where chain disasters are not likely to occur due to continuous cloudy days without rainfall, and the problem of insufficient monitoring intensity in the heavy rain state where disasters are prone to occur after continuous cloudy days. This invention can truly achieve no waste of monitoring resources in the state of basically no risk and make up for the lack of monitoring in the state where disasters are prone to occur in extreme weather.

[0132] The above only discloses several specific embodiments of the present invention. Those skilled in the art can make various changes and modifications to the embodiments of the present invention without departing from the spirit and scope of the present invention. However, the embodiments of the present invention are not limited thereto, and any changes that can be conceived by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A monitoring and early warning method for adaptively adjusting sampling frequency under extreme conditions, characterized in that: The steps include: (1) Based on historical monitoring data and landslide-mudslide test model parameters, the critical threshold for monitoring and early warning is calculated, and the landslide-mudslide disaster risk areas that need to be monitored are determined based on the monitoring and early warning threshold; (2) For the identified landslide-mudslide disaster risk areas, the probability of rainfall causing disasters is calculated and the disaster level is determined based on weather forecast data and real-time monitoring data. The monitoring frequency is automatically adjusted based on the disaster level to obtain monitoring data; (3) Construct a landslide displacement model and a single-channel debris flow warning model based on the acquired monitoring data; determine and issue landslide warning levels based on the landslide displacement model; (4) Based on the single-channel debris flow warning model and landslide influencing factors, the debris flow warning value is obtained, and the debris flow warning level is determined and issued.

2. A monitoring and early warning method for adaptively adjusting sampling frequency under extreme conditions as claimed in claim 1, characterized in that: The probability P of rainfall causing disaster in step (2) is calculated by the following formula: P=a+bE+cE 2 +dE 3 (1) Among them, a, b, c, and d are parameters that change with the rainfall threshold; E is the effective rainfall; The effective rainfall E is calculated by the following formula: Where n represents the total number of days since the beginning of rainfall, r k Expressed as daily rainfall in millimeters.

3. The monitoring and early warning method for adaptively adjusting sampling frequency according to extreme conditions as claimed in claim 1, characterized in that: In step (2), the monitoring frequency is automatically adjusted according to the disaster level, as follows: When the probability of rainfall causing disaster is 0-0.2, the disaster level is determined to be the first level, and the GNSS monitoring station observation period is increased by 1 hour during rainfall to monitor landslides; When the probability of rainfall causing disaster is 0.2-0.4, the disaster level is determined to be the second level, and the observation period of the GNSS monitoring station is increased to 2 hours to monitor the landslide; the soil moisture meter and rain gauge are awakened at a frequency of 30 minutes / time to assist in monitoring the landslide; When the probability of rainfall causing disaster is 0.4-0.6, the disaster level is determined to be the third level. The GNSS monitoring station increases the observation period and conducts real-time monitoring as the rainfall time increases, and extends the observation for 1 hour after the rainfall ends. The soil moisture meter and rain gauge are awakened to increase the frequency to 10 minutes / time to assist in monitoring landslides. The radar water level meter is awakened to observe at a frequency of 30 minutes / time to monitor and warn of debris flow occurrences. When the probability of rainfall causing disaster is 0.6-0.8, the disaster level is determined to be the fourth level, and the GNSS monitoring station is modified to real-time all-weather monitoring; the soil moisture meter and rain gauge are awakened to assist in monitoring landslides at a frequency of 5 minutes / time; the radar water level meter is awakened to assist in monitoring and warning of debris flows at a frequency of 5 minutes / time; When the probability of disaster caused by rainfall exceeds 0.8, the disaster level is determined to be the fifth level, and the GNSS monitoring station conducts real-time and all-weather monitoring; soil moisture meters and water level meters monitor landslides at a frequency of 5 minutes per time; radar water level meters switch to real-time monitoring mode to warn of mudslides; and video monitoring is started at the same time for manual monitoring and judgment.

4. The monitoring and early warning method for adaptively adjusting sampling frequency according to extreme conditions as claimed in claim 1, characterized in that: Step (3) constructing a landslide displacement model based on monitoring data includes the following steps: I. aligning the timestamps of the acquired monitoring data, and using an interpolation formula to complete the missing data, wherein the monitoring data includes GNSS monitoring data, soil moisture data, rain gauge data, and radar water level gauge data; II. Generate an observation matrix using the completed monitoring data, obtain a correlation coefficient matrix after standardization, and obtain the correlation coefficients of GNSS monitoring data, soil moisture data, rain gauge data, and radar water level gauge; III. The landslide surface displacement is calculated based on the monitoring data and the corresponding correlation coefficient.

5. A monitoring and early warning method for adaptively adjusting sampling frequency according to extreme conditions as claimed in claim 4, characterized in that: The interpolation formula in step I is as follows: When the interpolation formula is used to complete GNSS monitoring data, y represents the vertical displacement obtained by interpolation; x represents the horizontal displacement obtained by interpolation; y0 represents the vertical displacement of the first point; x0 represents the known horizontal displacement of the first point; x1 represents the horizontal displacement of the second point; y1 represents the second known vertical displacement; When the interpolation formula is used to complete soil moisture data, y represents the soil moisture content obtained by interpolation; x represents the interpolation point time; y0 represents the first known soil moisture content; x0 represents the first known soil moisture content time; y1 represents the second known soil moisture content; x1 represents the second known soil moisture content time; When the interpolation formula is used to complete the rain gauge data, y represents the rainfall obtained by interpolation; x represents the rainfall time obtained by interpolation; y0 represents the first known rainfall; x0 represents the first known rainfall time; y1 represents the second known rainfall; x1 represents the second known rainfall time; When the interpolation formula is used to complete the radar water level gauge data, y represents the water level value obtained by interpolation; x represents the time of the water level value obtained by interpolation; y0 represents the first known water level value; x0 represents the time of the first known water level value; y1 represents the second known water level value; x1 represents the time of the second known water level value.

6. A monitoring and early warning method for adaptively adjusting sampling frequency according to extreme conditions as claimed in claim 4, characterized in that: In step II, the observation matrix is ​​generated by using the completed monitoring data, and the correlation coefficient matrix is ​​obtained by standardization. The calculation process of the correlation coefficient of the GNSS monitoring data, soil moisture data, rain gauge data and radar water level gauge is as follows: Generate the observation matrix X using the completed monitoring data, That is, X is the observation matrix, using Calculate the mean of each column; use Calculate the variance of each column; use The data is standardized; the standard standardized matrix Z is obtained as follows: Utilization The calculated correlation coefficient matrix R is as follows: Where r ij That is, the sample sequence relationship between the i-th column and the j-th column of the X matrix can obtain the correlation coefficient for GNSS monitoring data, soil moisture data, rain gauge data and radar water level gauge.

7. The monitoring and early warning method for adaptively adjusting sampling frequency according to extreme conditions as claimed in claim 4, characterized in that: The landslide surface displacement in step III is calculated by the following formula: T=x·r 11 +y·r 12 +z·r 13 +q·r 14 (7) Where T is the displacement of the landslide surface, x is the GNSS monitoring data, r 11 is the correlation coefficient of GNSS monitoring data; y is the rain gauge data, r 12 is the correlation coefficient of the rain gauge data; z is the soil moisture meter data, r 13 is the correlation coefficient of soil moisture data; q is the radar water level gauge data, r 14 is the correlation coefficient of radar water level gauge data.

8. The monitoring and early warning method for adaptively adjusting sampling frequency according to extreme conditions as claimed in claim 1, characterized in that: The single-channel debris flow warning model in step (3) is expressed by the following equation: Where P is the single-hook debris flow warning value; R is the rainfall factor; G is the geological factor; T is the terrain factor of the debris flow formation area; The expression of terrain factor T in debris flow formation area is: Among them, T is the terrain factor of debris flow formation area; J is the longitudinal gradient of the gully bed in the debris flow formation area; A is the area of ​​debris flow formation area; L is the length of the debris flow formation channel (km); A0 is the unit area (1km 2 ); The expression of geological factor G is: G=F0·C1·C2·C3·C4 (10) G is the geological factor, and the average rock strength coefficient of the debris flow formation area in the Loess Plateau is F0 = 0.3, C1 is the structure (fault zone), C2 is the earthquake intensity, C3 is the physical differentiation correction factor, and C4 is the chemical weathering correction factor; The expression of rainfall factor R is: Where B is the cumulative rainfall before the debris flow (mm); I is the rainfall in 1 hour after the debris flow (mm); R0 is the local annual average precipitation (mm); C r is the local 10-min rainfall variation coefficient.

9. The monitoring and early warning method for adaptively adjusting sampling frequency according to extreme conditions as claimed in claim 1, characterized in that: The landslide impact factor in step (4) is calculated by the following formula: Q=y·r 12 +t·r 13 +q·r 14 (12) Among them, Q is the factor affecting landslide, y is the rain gauge data, z is the soil moisture data, and q is the radar water level gauge data.

10. A monitoring and early warning method for adaptively adjusting sampling frequency according to extreme conditions as claimed in claim 9, characterized in that: In step (4), the debris flow warning value is obtained based on the single-channel debris flow warning model and the landslide impact factor, specifically: Among them, P is the debris flow warning value; Q is the factor affecting landslides; G is the geological factor; T is the terrain factor of the debris flow formation area; the debris flow warning value is divided into debris flow warning levels according to the risk level critical values ​​of 0.28, 0.35, and 0.47.

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