Slope landslide monitoring method and system

Abstract

The invention discloses a side slope landslide monitoring method and system, and the method comprises the steps: carrying out the processing, detection and calculation of an echo signal after pulse compression, and obtaining rainfall data; determining landslide confidence data according to the accumulated rainfall; digital beam forming imaging is carried out on the echo signal after pulse compression, and a two-dimensional multiple scattering image is obtained; compensating the two-dimensional complex scattering image according to the rainfall intensity; pS points are extracted and deformation inversion is carried out to obtain deformation data; evaluating a comprehensive landslide risk grade in real time according to the deformation data and the accumulated rainfall so as to determine an acquisition frequency, and determining an initial landslide risk grade according to the deformation data; and correcting the initial landslide risk grade according to the landslide confidence data, and carrying out landslide risk early warning according to the corrected landslide risk grade. According to the invention, deformation monitoring and rainfall monitoring can be realized at the same time by adopting one set of radar equipment, the purchase, installation, operation and maintenance cost of one set of equipment is effectively reduced, the cost is reduced, and the monitoring failure rate is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of radar monitoring, and in particular relates to a slope landslide monitoring method and system. Background Art

[0002] In actual slope monitoring applications, the vast majority of landslide geological disasters are caused by rainfall. Therefore, in the design of a landslide monitoring system, measuring key landslide triggers (such as rainfall) and monitoring key specific manifestations of the landslide process (such as surface deformation) are essential. In the construction of a landslide monitoring system, both deformation monitoring and rainfall monitoring require separate equipment procurement, installation, and maintenance to ensure the fidelity of monitoring data and the accuracy of early warning information, which places certain pressures on system construction and application costs.

[0003] The sources of rainfall information in existing slope areas include:

[0004] (1) Automatic weather stations: mainly refer to small-area rainfall sensors such as tipping bucket rain gauges, piezoelectric rain gauges, and photoelectric rain gauges. The sensing area is very limited, only a few square meters, and it is impossible to obtain real rainfall data over a large area or area. The monitoring method of using points to represent areas and multiple points to represent surfaces is only a data fitting algorithm. The accuracy of the fitted data is highly limited by the density of the monitoring points. The high density of monitoring points brings high cost pressure to the monitoring system. At the same time, actual rainfall often shows non-uniform and nonlinear characteristics over a wide area. The rainfall data in local areas is very likely to be seriously distorted.

[0005] (2) Rainfall radar: It can accurately obtain the location, intensity and speed of rainfall in real time. It is one of the important sources of rainfall information for the meteorological department. However, the construction cost is very high. It is only used in important urban areas and the coverage rate is very low.

[0006] (3) Meteorological satellites: They estimate precipitation by observing cloud cover and water vapor content. They are one of the main sources of rainfall information for the meteorological department. However, they have problems such as inaccurate precipitation data, inaccurate precipitation time data, and inaccurate precipitation area data. They are not suitable for the high-precision and high-real-time requirements of rainfall information in slope monitoring.

[0007] In actual implementation, due to insufficient monitoring budget, some construction parties tend to use rainfall information released by the meteorological department for monitoring and acquisition of rainfall information. Therefore, due to the inaccuracy of some rainfall information, the early warning information in actual slope monitoring is extremely unreliable.

[0008] Slope radar is a radar device used to monitor slope stability. It monitors slope surface deformation and displacement by collecting phase changes on the slope surface. Environmental changes (rainfall), natural movement on the slope surface, and human activities have a strong impact on the imaging of radar echoes. Therefore, there is a certain interference in the measurement of phase interference. The method of relying on its own data for self-correction compensation has certain limitations. At the same time, existing slope monitoring radars cannot directly perceive changes in the external environment and need to use external environment perception data to filter out interference signals.

[0009] In practical slope monitoring scenarios, the vast majority occur outdoors. Power and grid conditions for monitoring equipment are extremely limited, and most require solar power. However, current limitations in solar panel conversion efficiency, effective sunshine duration, and the size and cost of installing solar panels necessitate low-power monitoring equipment. However, radar equipment consumes tens of watts of power, making solar power a poor choice for widespread deployment under typical operating conditions.

[0010] Currently, radar equipment uses an intermittent sleep mode to reduce its average power consumption over time. However, the pace of signal acquisition needs to match the actual landslide risk at the monitoring site. Otherwise, too high a frequency will result in excessive power consumption, while too low a frequency will fail to meet the real-time monitoring data requirements during periods of high landslide risk. Currently, the radar's acquisition frequency is primarily controlled by assessing risk levels based on its own deformation data. However, deformation data is susceptible to interference, resulting in limited accuracy. Fusion of external information with radar deformation data to improve the accuracy of risk assessments increases the system's external dependence and exacerbates the problem of reduced data accuracy due to external interference.

[0011] Patent publication number CN115993586A discloses a slope radar monitoring method capable of both micro-deformation monitoring and moving target detection. The method includes pulse compression of the radar's echo signal from the target area to obtain a pulse compression output signal. If monitoring micro-deformation in the target area, the two-dimensional image formed by the pulse compression output signal is combined into a complex scattering image pair, and the distance magnitude of the phase compensation after stable PS point fitting is calculated to obtain directional displacement accuracy information of the target area's micro-deformation. If detecting moving targets in the target area, when at least one moving target is detected in the target area, the pulse compression output signal is subjected to two-pulse cancellation and Fourier transform to form a range-velocity spectrum. The motion information of the moving targets in the range-velocity spectrum is calculated using the unit average constant false alarm method, and the continuous motion trajectory corresponding to each moving target is output. While this method improves the accuracy of deformation monitoring and moving target detection, it does not incorporate rainfall data during deformation monitoring, a major contributor to slope landslides. Relying solely on deformation data can result in missed or false alarms. Furthermore, the radar operates in continuous mode, resulting in high power consumption. Summary of the Invention

[0012] The purpose of the present invention is to provide a slope and landslide monitoring method and system to solve at least one of the problems in the prior art, namely, that slope monitoring and rainfall monitoring are two separate types of equipment, resulting in high construction and operation and maintenance costs, that high-accuracy rainfall information is difficult to match with high construction costs, and that deformation monitoring data is inaccurate.

[0013] The present invention solves the above technical problems through the following technical solutions: a slope landslide monitoring method, comprising:

[0014] Acquire an echo signal from a MIMO radar device and perform pulse compression on the echo signal;

[0015] Processing, detecting and calculating the echo signal after pulse compression to obtain rainfall data; wherein the rainfall data includes rainfall intensity and accumulated rainfall;

[0016] determining landslide confidence data based on the accumulated rainfall;

[0017] Perform digital beamforming imaging on the echo signal after pulse compression to obtain a two-dimensional complex scattering image;

[0018] compensating the two-dimensional complex scattering image according to the rainfall intensity;

[0019] Extract PS points from the compensated two-dimensional complex scattering image, and perform deformation inversion based on the PS points to obtain deformation data;

[0020] real-time assessment of the comprehensive landslide risk level based on the deformation data and accumulated rainfall, thereby determining an acquisition frequency, and controlling the operation of the MIMO radar device based on the acquisition frequency;

[0021] determining an initial landslide risk level based on the deformation data;

[0022] The initial landslide risk level is corrected according to the landslide confidence data, and a landslide risk warning is issued according to the corrected landslide risk level.

[0023] Furthermore, the processing, detection and calculation of the echo signal after pulse compression includes:

[0024] Perform clutter suppression on the echo signal after pulse compression;

[0025] Perform FFT operation on the echo signal after clutter suppression to obtain the frequency domain signal and power spectrum;

[0026] Performing constant false alarm detection on the frequency domain signal to obtain a precipitation target;

[0027] Calculating precipitation-related parameters according to the precipitation target;

[0028] smoothing, checking, and screening of precipitation-related parameters;

[0029] The rainfall intensity and the accumulated rainfall are calculated based on the power spectrum and the filtered precipitation-related parameters.

[0030] Furthermore, the calculation formula for the rainfall intensity is: ;

[0031] Where R represents rainfall intensity; a and b represent empirical coefficients; Z represents reflectivity factor; C represents radar constant; r represents the distance from the precipitation target to the radar; Represents the power spectrum.

[0032] Furthermore, determining landslide confidence data according to the accumulated rainfall includes:

[0033] If the accumulated rainfall in time T1 is less than AR1 and the accumulated rainfall in time T2 is less than AR2, the landslide confidence data is 0;

[0034] If the accumulated rainfall within AR1≤T1 is less than AR3, or the accumulated rainfall within AR2≤T2, the landslide confidence data is 0;

[0035] If the accumulated rainfall within AR3≤T1 is less than AR4, or the accumulated rainfall within AR3≤T2, the landslide confidence data is 1;

[0036] If the accumulated rainfall during AR4≤T1 is less than AR5, or the accumulated rainfall during AR4≤T3, the landslide confidence data is 1;

[0037] If the accumulated rainfall within AR5≤T1, or the accumulated rainfall within AR6≤T3, the landslide confidence data is 2;

[0038] Among them, T1, T2 and T3 all represent time periods, AR1, AR2, AR3, AR4, AR5 and AR6 all represent rainfall thresholds, T3<T2<T1, AR1<AR2<AR3, AR4<AR6<AR5.

[0039] Furthermore, compensating the two-dimensional complex scattering image according to the rainfall intensity includes:

[0040] The attenuation coefficient is calculated according to the rainfall intensity. The specific calculation formula is: ;

[0041] in, represents the attenuation coefficient; R represents the rainfall intensity; and All represent empirical coefficients;

[0042] The radar echo path integral attenuation value is calculated according to the attenuation coefficient. The specific calculation formula is: ;

[0043] in, Indicates the radar echo path integral attenuation value; represents the distance variable; Represents the unit distance; r represents the distance from the precipitation target to the radar;

[0044] The radar echo path integral attenuation value is superimposed with the echo intensity of the two-dimensional complex scattering image to obtain a compensated two-dimensional complex scattering image.

[0045] Furthermore, the comprehensive landslide risk level is assessed in real time based on the deformation data and accumulated rainfall, and the collection frequency is determined, including:

[0046] determining a deformation level and a rainfall level according to the deformation data and the accumulated rainfall respectively;

[0047] The comprehensive landslide risk level is assessed in real time based on the deformation level and rainfall level, and the collection frequency is determined, specifically including:

[0048] If the deformation level is level 1 and / or the rainfall level is level 1, the comprehensive landslide risk level is level 0 and the collection frequency is f1;

[0049] If the deformation level is level 2 and the rainfall level is no greater than level 2, or the deformation level is no greater than level 2 and the rainfall level is level 2, the comprehensive landslide risk level is level 1 and the collection frequency is f2;

[0050] If the deformation level and rainfall level are both level 2, or the deformation level is level 3 and the rainfall level is not greater than level 3, or the deformation level is not greater than level 3 and the rainfall level is level 3, then the comprehensive landslide risk level is level 2 and the collection frequency is f3;

[0051] If the deformation level and rainfall level are both level 3, or the deformation level is level 4 and the rainfall level is not greater than level 4, or the deformation level is not greater than level 4 and the rainfall level is level 4, then the comprehensive landslide risk level is level 3 and the collection frequency is f4;

[0052] If the deformation level and rainfall level are both level 4, the comprehensive landslide risk level is level 4 and the sampling frequency is f5;

[0053] Among them, f1<f2<f3<f4<f5.

[0054] Further, determining the deformation level and the rainfall level according to the deformation data and the accumulated rainfall respectively includes:

[0055] If the cumulative deformation within T4 time is less than AL1, the deformation level is level one;

[0056] If the cumulative deformation within the time AL1≤T4 is less than AL2, the deformation level is level 2;

[0057] If the cumulative deformation within the time AL2≤T4 is less than AL3, the deformation level is level three;

[0058] If the cumulative deformation amount within the time AL3≤T4, the deformation level is level 4;

[0059] If the accumulated rainfall in time T2 is less than AR2, the rainfall level is level 1;

[0060] If the accumulated rainfall during AR2≤T2 is less than AR3, the rainfall level is level 2;

[0061] If the accumulated rainfall during AR3≤T2 is less than AR4, the rainfall level is level 3;

[0062] If the accumulated rainfall during AR4≤T2, the rainfall level is level 4;

[0063] Among them, T4 and T2 represent time periods; AL1, AL2, and AL3 represent deformation thresholds; AR2, AR3, and AR4 represent rainfall thresholds.

[0064] Furthermore, the corrected landslide risk level is equal to the sum of the landslide confidence data and the initial landslide risk level.

[0065] Based on the same concept, the present invention also provides a slope and landslide monitoring system, comprising a MIMO radar device and a host computer, wherein the MIMO radar device communicates with the host computer;

[0066] The MIMO radar device is used to obtain an echo signal of the MIMO radar device and perform pulse compression on the echo signal;

[0067] Processing, detecting and calculating the echo signal after pulse compression to obtain rainfall data; wherein the rainfall data includes rainfall intensity and accumulated rainfall;

[0068] Perform digital beamforming imaging on the echo signal after pulse compression to obtain a two-dimensional complex scattering image;

[0069] compensating the two-dimensional complex scattering image according to the rainfall intensity;

[0070] Extract PS points from the compensated two-dimensional complex scattering image, and perform deformation inversion based on the PS points to obtain deformation data;

[0071] real-time assessment of the comprehensive landslide risk level based on the deformation data and accumulated rainfall, thereby determining an acquisition frequency, and controlling the operation of the MIMO radar device based on the acquisition frequency;

[0072] The host computer is used to determine an initial landslide risk level according to the deformation data; determine landslide confidence data according to the accumulated rainfall; correct the initial landslide risk level according to the landslide confidence data, and issue a landslide risk warning according to the corrected landslide risk level.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] The present invention uses a set of radar equipment to simultaneously realize deformation monitoring and rainfall monitoring, effectively reducing the purchase, installation and operation and maintenance costs of a set of equipment, reducing costs and reducing monitoring failure rates;

[0075] Based on the original functions of radar, the present invention can achieve large-scale, high-frequency, high-resolution and high-precision monitoring coverage of the monitored area without adding hardware equipment. It can obtain more accurate and real-time rainfall data. The rainfall monitoring range is fully matched with the slope monitoring range, which can more accurately correct the landslide risk level and help improve the accuracy of slope landslide monitoring and early warning. The rainfall data that fully matches the slope monitoring range is used to effectively compensate the two-dimensional complex scattering image, thereby improving the accuracy of deformation monitoring.

[0076] The present invention performs comprehensive landslide risk level self-assessment and acquisition frequency adaptation based on the deformation data and rainfall data acquired by the radar itself, and is not affected by communication signal interruption or poor signal. It effectively reduces the communication traffic required for monitoring data in the low-risk period of slope landslide during long-term monitoring, as well as the computing power and storage resources of the platform side (such as the host computer), thereby greatly reducing the monitoring operation and maintenance costs, and reducing the probability of monitoring interruption accidents caused by insufficient computing power and storage resources on the platform side; during the high-risk period of slope landslide, higher-frequency acquisition and analysis are carried out to match the real-time nature of the monitoring data with the urgency of the high-risk period of landslide, ensuring the effectiveness and timeliness of the overall monitoring data, thereby achieving more accurate landslide risk warning.

[0077] Compared with the prior art (i.e., patent document with publication number CN115993586A), the present invention combines deformation data with rainfall data to eliminate interference from non-rainfall factors, triggering an early warning when rainfall and deformation occur simultaneously, thereby reducing false alarms; and providing an early warning when the deformation does not reach the threshold but the rainfall exceeds the standard, thereby reducing missed alarms; in addition, the present invention also determines the radar equipment's collection frequency based on the deformation data and rainfall data, so that the radar equipment is not always in continuous working mode, thereby reducing power consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only one embodiment of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0079] Figure 1 4 is a flow chart of a slope landslide monitoring method in an embodiment of the present invention. DETAILED DESCRIPTION

[0080] The following is a clear and complete description of the technical solutions of the present invention in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts are within the scope of protection of the present invention.

[0081] The following specific embodiments are used to describe the technical solution of the present invention in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0082] Example 1

[0083] like Figure 1As shown, the slope landslide monitoring method provided by the embodiment of the present invention includes the following steps:

[0084] S1: Acquire the echo signal of the MIMO radar device and perform pulse compression on the echo signal.

[0085] MIMO (Multiple-Input Multiple-Output) radar equipment uses multiple transmitting antennas and multiple receiving antennas. On the transmitting end, different transmitting antennas emit mutually orthogonal signals, which propagate through space and illuminate the target. On the receiving end, multiple receiving antennas simultaneously receive the signals reflected from the target (i.e., echo signals). By processing and analyzing these echo signals, the correlation and differences between the signals are used to obtain target information.

[0086] The MIMO radar device controls the transmission of signals based on the acquisition frequency, receives echo signals, and then performs pulse compression on the echo signals, simultaneously processing the two signals for deformation monitoring and rainfall monitoring. Pulse compression achieves high range resolution while retaining the high energy of long pulses, ensuring long-range detection capabilities. This helps to distinguish minute displacements in deformation monitoring and different precipitation layers in rainfall monitoring, thereby improving the accuracy of both deformation and rainfall monitoring.

[0087] S2: Process, detect and calculate the echo signal after pulse compression to obtain rainfall data.

[0088] In a specific embodiment of the present invention, processing, detecting and calculating the echo signal after pulse compression includes:

[0089] S2.1: Perform clutter suppression on the echo signal after pulse compression.

[0090] The echo signal after pulse compression is subjected to clutter suppression through filtering and gain adjustment, thereby improving the signal-to-noise ratio and signal-to-clutter ratio of the echo signal and retaining the effective signal; avoiding the nonlinear distortion of the amplifier caused by strong clutter, balancing the signal strength at different distances, and ensuring the monitoring accuracy of rainfall data.

[0091] S2.2: Perform FFT (Fast Fourier Transform) on the echo signal after clutter suppression to obtain the frequency domain signal and power spectrum.

[0092] By performing an FFT operation on the echo signal after clutter suppression, the time domain echo signal is converted to the frequency domain, that is, the frequency domain signal is obtained. The power spectrum of the echo is obtained by performing an FFT operation on multiple echo signals after clutter suppression.

[0093] S2.3: Perform constant false alarm detection on the frequency domain signal to obtain a precipitation target.

[0094] Based on the statistical characteristics of noise and clutter in the local area, the detection threshold is adaptively set. Constant false alarm detection (CFAR) is used to detect targets in the frequency domain, enabling the detection of true precipitation targets in noisy and cluttered environments. Specifically, the detection threshold is obtained by first estimating the average intensity of the neighboring cells surrounding the target to be detected and then multiplying this average by a fixed threshold.

[0095] S2.4: Calculate precipitation-related parameters based on precipitation targets.

[0096] In this embodiment, the parameters related to precipitation include echo intensity, velocity, spectrum width, reflectivity factor, and the distance from the target to the radar.

[0097] S2.5: Smooth, check, and screen precipitation-related parameters.

[0098] In order to reduce data fluctuations and noise, the parameters related to precipitation are smoothed by using methods such as mean filtering or median filtering, and then checked and screened: for example, whether the echo intensity is within a reasonable range, whether the speed conforms to the physical laws, whether the spectral width is abnormal, etc. The echo intensity that exceeds the reasonable range, the speed that does not conform to the physical laws, and the abnormal spectral width are marked or eliminated, and repaired or supplemented as needed.

[0099] S2.6: Calculate rainfall intensity and accumulated rainfall based on the power spectrum and the selected precipitation-related parameters.

[0100] In this embodiment, the calculation formula for rainfall intensity is: (1) (2)

[0101] Where R represents rainfall intensity; a and b are both empirical coefficients that vary with precipitation type (rain, snow, hail) and regional climate differences; Z represents the reflectivity factor; C represents the radar constant, which is related to radar parameters; r represents the distance from the precipitation target to the radar, in km; In this embodiment, if the layered shape is rain, a is set to 100 and b is set to 1.6.

[0102] The rainfall is obtained based on parameters related to precipitation, and then accumulated over time to obtain the accumulated rainfall, such as the accumulated rainfall within 24 hours, the accumulated rainfall within 6 hours, and the accumulated rainfall within 3 hours.

[0103] S3: Determine landslide confidence data based on accumulated rainfall.

[0104] In order to calibrate the initial landslide risk level according to rainfall data and improve the accuracy of landslide risk level assessment, the landslide confidence data is first determined based on the accumulated rainfall, as shown in Table 1:

[0105] In Table 1, T1, T2, and T3 represent time periods, and AR1, AR2, AR3, AR4, AR5, and AR6 represent rainfall thresholds. T3 < T2 < T1, AR1 < AR2 < AR3, and AR4 < AR6 < AR5. In this embodiment, T1 is set to 24 hours, T2 is set to 6 hours, and T3 is set to 3 hours; AR1 is set to 20 mm, AR2 is set to 30 mm, AR3 is set to 50 mm, AR4 is set to 100 mm, AR5 is set to 200 mm, and AR6 is set to 150 mm.

[0106] The landslide confidence data is determined through rainfall data, and the initial landslide risk levels of each local area generated by the corresponding thresholds of indicators such as deformation displacement, velocity, acceleration, inverse velocity, and tangent angle of the displacement curve are corrected. Finally, landslide risk warnings are pushed according to the corrected landslide risk levels to improve the accuracy of the warnings.

[0107] S4: Perform digital beam forming (DBF) imaging on the echo signal after pulse compression to obtain a two-dimensional complex scattering image.

[0108] DBF imaging can achieve two-dimensional high-resolution imaging (range + azimuth), which can simultaneously achieve high precision, high resolution and strong anti-interference capability, and is conducive to accurately improving the phase change of the deformation area.

[0109] S5: Compensate the two-dimensional complex scattering image according to the rainfall intensity.

[0110] In order to improve the deformation monitoring accuracy, the two-dimensional complex scattering image is compensated according to the rainfall intensity. The specific compensation steps are as follows:

[0111] S5.1: Calculate the attenuation coefficient based on rainfall intensity. The specific calculation formula is: (3)

[0112] in, represents the attenuation coefficient; R represents the rainfall intensity; and Both represent empirical coefficients and are related to the radar operating frequency. Taking the X-band as an example, .

[0113] S5.2: Calculate the radar echo path integral attenuation value based on the attenuation coefficient. The specific calculation formula is: (4)

[0114] in, Indicates the radar echo path integral attenuation value; represents the distance variable; represents the unit distance; r represents the distance from the precipitation target to the radar.

[0115] S5.3: Integral attenuation value for radar echo path The compensated two-dimensional complex scattering image is obtained by superimposing the echo intensity of the two-dimensional complex scattering image.

[0116] The echo intensity after compensation is equal to the sum of the radar echo path integral attenuation value and the echo intensity before compensation.

[0117] The echo signals of slope monitoring radars are affected by rainfall, and existing slope monitoring radars lack effective solutions for eliminating and compensating for rainfall effects. Therefore, this invention uses rainfall data, which closely matches the resolution and position information of deformation monitoring radar image data, to effectively compensate for the two-dimensional complex scattering image. This improves the intensity and phase stability of the monitoring points, thereby enhancing the accuracy of deformation monitoring.

[0118] S6: Extract PS points (Permanent Scatterer) from the compensated two-dimensional complex scattering image, and perform deformation inversion based on the PS points to obtain deformation data.

[0119] Points with high quality, coherence coefficient greater than the coefficient threshold, and meeting the amplitude deviation requirement are extracted from the compensated two-dimensional complex scattering image as PS points.

[0120] A point in a two-dimensional complex scattering image refers to a single resolution unit, the size of which is determined by the radar's range and angular resolution. The two-dimensional complex scattering image contains information such as the distance, angle, echo intensity, and phase of each imaged target. In a compensated two-dimensional complex scattering image, a high-quality point is one whose echo intensity reaches an intensity threshold (e.g., a signal-to-noise ratio greater than 100dB) and whose echo intensity and phase are stable over a certain period of time. Phase stability means that the phase fluctuations of the same target in multiple scans over a certain period of time remain within a very small range (i.e., within the phase threshold range). The coefficient threshold is typically adjusted based on environmental differences. In this embodiment, the coefficient threshold is set between 0.7 and 0.9. The more stable the phase, the higher the coherence coefficient.

[0121] Amplitude deviation refers to the difference between the echo intensity of the current frame and the average value (or other reference value) of the echo intensity of multiple frames within a certain period of time. It is mainly used to judge the stability of the target echo intensity.

[0122] Differential interference is performed on the phase of the PS point to obtain the phase difference; the atmospheric phase compensation of the compensated two-dimensional complex scattering image is performed using the phase difference of the PS point with high echo intensity (i.e., the echo intensity reaches the intensity threshold) and stable phase at different distance segments.

[0123] Atmospheric phase compensation and deformation inversion are both existing technologies. The phase difference between points with high echo intensity and stable phase within each distance segment is used as the phase fluctuation caused by atmospheric interference, and this phase difference is used as the phase compensation value for all points within the current distance segment. For distance segments where a phase compensation value cannot be selected, the phase difference between the previous and next points is combined with the distance difference, and a linear function is applied to determine the atmospheric phase compensation value for that distance segment.

[0124] Based on the two-dimensional complex scattering image after atmospheric phase compensation, the deformation displacement is calculated using the inversion formula: (5)

[0125] in, represents deformation displacement; represents the radar wavelength; Indicates the phase difference.

[0126] S7: Assess the comprehensive landslide risk level in real time based on deformation data and accumulated rainfall, and then determine the acquisition frequency. Control the operation of the MIMO radar equipment based on the acquisition frequency.

[0127] In a specific embodiment of the present invention, the comprehensive landslide risk level is assessed in real time based on deformation data and accumulated rainfall, and the collection frequency is determined, including:

[0128] S7.1: Determine the deformation level and rainfall level based on the deformation data and accumulated rainfall, respectively, as shown in Table 2:

[0129] In Table 2, T4 and T2 represent time periods; AL1, AL2, and AL3 represent deformation thresholds; and AR2, AR3, and AR4 represent rainfall thresholds. In this embodiment, T4 is set to 12 hours, T2 is set to 6 hours, AL1 is set to 5 mm, AL2 is set to 10 mm, AL3 is set to 50 mm, AR2 is set to 30 mm, AR3 is set to 50 mm, and AR4 is set to 100 mm.

[0130] S7.2: Assess the comprehensive landslide risk level in real time based on the deformation level and rainfall level, and then determine the data collection frequency, including:

[0131] If the deformation level is level 1 and / or the rainfall level is level 1, the comprehensive landslide risk level is level 0 and the collection frequency is f1;

[0132] If the deformation level is level 2 and the rainfall level is no greater than level 2, or the deformation level is no greater than level 2 and the rainfall level is level 2, the comprehensive landslide risk level is level 1 and the collection frequency is f2;

[0133] If the deformation level and rainfall level are both level 2, or the deformation level is level 3 and the rainfall level is not greater than level 3, or the deformation level is not greater than level 3 and the rainfall level is level 3, then the comprehensive landslide risk level is level 2 and the collection frequency is f3;

[0134] If the deformation level and rainfall level are both level 3, or the deformation level is level 4 and the rainfall level is not greater than level 4, or the deformation level is not greater than level 4 and the rainfall level is level 4, then the comprehensive landslide risk level is level 3 and the collection frequency is f4;

[0135] If the deformation level and rainfall level are both level four, the comprehensive landslide risk level is level four and the collection frequency is f5.

[0136] Wherein, f1 < f2 < f3 < f4 < f5. In this embodiment, f1 is set to 1 hour / time, f2 is set to 15 minutes / time, f3 is set to 5 minutes / time, f4 is set to 1 minute / time, and f5 is set to 10 seconds / time.

[0137] Long-term deformation data locates high-risk slopes, while short-term rainfall data filters triggering opportunities, reducing waste of monitoring resources. A comprehensive landslide risk self-assessment based on deformation and rainfall levels can comprehensively reflect the inherent mechanisms and external causes of landslides, shifting landslide risk assessment from passive response to active prediction, providing a more reliable basis for decision-making in disaster prevention and mitigation. Multi-source data fusion significantly improves accuracy, timeliness, and reliability. The radar signal acquisition frequency is determined by simultaneously acquiring deformation and rainfall data within the radar device. Based on this acquisition frequency, the signal generation, transmission, reception, and processing of the MIMO radar device are controlled, ensuring that the frequency of signal acquisition matches the actual landslide risk status at the monitoring site. This reduces power consumption while meeting the real-time requirements for monitoring data during periods of high landslide risk.

[0138] S8: Determine the initial landslide risk level based on deformation data.

[0139] The deformation data includes deformation displacement, velocity, acceleration, inverse velocity and tangent angle of the displacement curve. The host computer performs an initial assessment of the landslide risk level of each local area based on the long-term deformation data according to the threshold. The specific assessment process is based on existing technology.

[0140] S9: Correct the initial landslide risk level according to the landslide confidence data, and issue a landslide risk warning based on the corrected landslide risk level.

[0141] The corrected landslide risk level is equal to the sum of the landslide confidence data and the initial landslide risk level. For example, if the initial landslide risk level is level 3 (corresponding to a value of 3) and the landslide confidence data is 1, the corrected landslide risk level is equal to 4, and a red alert is issued according to Table 3.

[0142] Landslide risk warning is carried out according to the corrected landslide risk level, as shown in Table 3:

[0143] Because existing slope monitoring radar and rainfall monitoring equipment are two separate devices, and slope surface deformation monitoring and rainfall information monitoring are two indispensable monitoring components of a slope and landslide monitoring system, these two types of equipment must be purchased separately in actual slope and landslide monitoring systems. This results in a large number of system applications, high procurement costs, high installation costs, high operation and maintenance costs, a high probability of monitoring failures, and low rainfall information accuracy. The present invention simultaneously processes both deformation and rainfall monitoring signals using the echo signal of a single MIMO radar device after pulse compression, effectively reducing the procurement, installation, and maintenance costs of a single set of equipment and lowering the probability of monitoring interruptions caused by hardware failures.

[0144] Since rainfall in reality often exhibits non-uniform and nonlinear characteristics over a wide area, existing local rainfall information collection technologies that use points to represent surfaces have the problem of insufficient data accuracy. At the same time, high-accuracy rain radars cannot be fully popularized in the coverage area due to their high cost. Meteorological satellite data also has problems with inaccurate precipitation data, inaccurate precipitation time data, and inaccurate precipitation area data. In order to solve the above technical problems, the present invention, based on the original functions of the slope monitoring radar, can achieve large-scale, high-frequency, high-resolution and high-precision monitoring coverage of the monitored area without adding hardware equipment, and can obtain more accurate and real-time rainfall data. The rainfall monitoring range is fully matched with the slope monitoring range, and the landslide risk level can be more accurately corrected, which helps to improve the accuracy of slope landslide monitoring and early warning.

[0145] Since the existing landslide risk level assessment is based on the composite judgment of long-term, multi-source data, and a single sensor device does not have the ability to collect multi-source data, the existing landslide risk level assessment is basically carried out by the platform side (such as the host computer), that is, the platform side sends data collection and uploads the collection frequency to the monitoring terminal according to the determined landslide risk level. However, since many slope monitoring scenarios are in remote field environments, the problems of intermittent data interruption and poor signal cannot be completely solved, resulting in the platform side being unable to send the corresponding data collection strategy to the monitoring terminal in a timely manner, unable to achieve the adaptation of the landslide risk level and the collection frequency, and thus unable to accurately perceive the key deformation data of the high landslide risk level, which ultimately leads to inaccurate monitoring and early warning. In order to solve the above problems, the present invention performs comprehensive landslide risk level self-assessment and collection frequency adaptation based on the deformation data and rainfall data inside a single MIMO radar device, which is not affected by interruption or poor communication signals, and effectively reduces the communication traffic required for monitoring data in the low-risk period of slope landslide during long-term monitoring, as well as the computing power resources and storage resources on the platform side, thereby greatly reducing the monitoring operation and maintenance costs, and reducing the probability of monitoring interruption accidents caused by insufficient computing power resources and storage resources on the platform side; during the high-risk period of slope landslide, higher-frequency collection and analysis are carried out to match the real-time nature of the monitoring data with the urgency of the high-risk period of landslide, ensuring the effectiveness and timeliness of the overall monitoring data, thereby achieving more accurate landslide risk warnings.

[0146] Example 2

[0147] The slope and landslide monitoring system provided by the embodiment of the present invention includes a MIMO radar device and a host computer, and the MIMO radar device communicates with the host computer.

[0148] MIMO radar equipment is used for:

[0149] Acquire the echo signal of the MIMO radar device and perform pulse compression on the echo signal;

[0150] Processing, detecting and calculating the echo signal after pulse compression to obtain rainfall data; wherein the rainfall data includes rainfall intensity and accumulated rainfall;

[0151] Perform digital beamforming imaging on the echo signal after pulse compression to obtain a two-dimensional complex scattering image;

[0152] Compensate the two-dimensional complex scattering image according to rainfall intensity;

[0153] Extract PS points from the compensated two-dimensional complex scattering image, and perform deformation inversion based on the PS points to obtain deformation data;

[0154] The comprehensive landslide risk level is assessed in real time based on deformation data and accumulated rainfall, and the acquisition frequency is determined, and the operation of the MIMO radar equipment is controlled based on the acquisition frequency.

[0155] The host computer is used to: determine the initial landslide risk level based on deformation data; determine landslide confidence data based on accumulated rainfall; correct the initial landslide risk level based on the landslide confidence data, and issue a landslide risk warning based on the corrected landslide risk level.

[0156] In some specific embodiments of the present invention, the landslide monitoring system may be combined with the features of the landslide monitoring method in the first embodiment of the present invention, and vice versa.

[0157] The above disclosure is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or modifications within the technical scope disclosed in the present invention, and they should all be covered by the scope of protection of the present invention.

Claims

1. A slope landslide monitoring method, characterized in that: The monitoring method comprises: Acquire an echo signal from a MIMO radar device and perform pulse compression on the echo signal; Processing, detecting and calculating the echo signal after pulse compression to obtain rainfall data; wherein the rainfall data includes rainfall intensity and accumulated rainfall; determining landslide confidence data based on the accumulated rainfall; Perform digital beamforming imaging on the echo signal after pulse compression to obtain a two-dimensional complex scattering image; compensating the two-dimensional complex scattering image according to the rainfall intensity; Extract PS points from the compensated two-dimensional complex scattering image, and perform deformation inversion based on the PS points to obtain deformation data; real-time assessment of the comprehensive landslide risk level based on the deformation data and accumulated rainfall, thereby determining an acquisition frequency, and controlling the operation of the MIMO radar device based on the acquisition frequency; determining an initial landslide risk level based on the deformation data; The initial landslide risk level is corrected according to the landslide confidence data, and a landslide risk warning is issued according to the corrected landslide risk level.

2. The slope landslide monitoring method according to claim 1, characterized in that: The processing, detection and calculation of the echo signal after pulse compression include: Perform clutter suppression on the echo signal after pulse compression; Perform FFT operation on the echo signal after clutter suppression to obtain the frequency domain signal and power spectrum; Performing constant false alarm detection on the frequency domain signal to obtain a precipitation target; Calculating precipitation-related parameters according to the precipitation target; smoothing, checking, and screening of precipitation-related parameters; The rainfall intensity and the accumulated rainfall are calculated based on the power spectrum and the filtered precipitation-related parameters.

3. The slope landslide monitoring method according to claim 2, characterized in that: The calculation formula for the rainfall intensity is: ； Where R represents rainfall intensity; a and b represent empirical coefficients; Z represents reflectivity factor; C represents radar constant; r represents the distance from the precipitation target to the radar; Represents the power spectrum.

4. The slope landslide monitoring method according to claim 1, characterized in that: Determining landslide confidence data based on the accumulated rainfall includes: If the accumulated rainfall in time T1 is less than AR1 and the accumulated rainfall in time T2 is less than AR2, the landslide confidence data is 0; If the accumulated rainfall within AR1≤T1 is less than AR3, or the accumulated rainfall within AR2≤T2, the landslide confidence data is 0; If the accumulated rainfall within AR3≤T1 is less than AR4, or the accumulated rainfall within AR3≤T2, the landslide confidence data is 1; If the accumulated rainfall during AR4≤T1 is less than AR5, or the accumulated rainfall during AR4≤T3, the landslide confidence data is 1; If the accumulated rainfall within AR5≤T1, or the accumulated rainfall within AR6≤T3, the landslide confidence data is 2; Among them, T1, T2 and T3 all represent time periods, AR1, AR2, AR3, AR4, AR5 and AR6 all represent rainfall thresholds, T3<T2<T1, AR1<AR2<AR3, AR4<AR6<AR5.

5. The slope landslide monitoring method according to claim 1, characterized in that: Compensating the two-dimensional complex scattering image according to the rainfall intensity includes: The attenuation coefficient is calculated according to the rainfall intensity. The specific calculation formula is: ； in, represents the attenuation coefficient; R represents the rainfall intensity; and All represent empirical coefficients; The radar echo path integral attenuation value is calculated according to the attenuation coefficient. The specific calculation formula is: ； in, Indicates the radar echo path integral attenuation value; represents the distance variable; Represents the unit distance; r represents the distance from the precipitation target to the radar; The radar echo path integral attenuation value is superimposed with the echo intensity of the two-dimensional complex scattering image to obtain a compensated two-dimensional complex scattering image.

6. The slope landslide monitoring method according to any one of claims 1 to 5, characterized in that: The comprehensive landslide risk level is assessed in real time based on the deformation data and accumulated rainfall, and the collection frequency is determined, including: determining a deformation level and a rainfall level according to the deformation data and the accumulated rainfall respectively; The comprehensive landslide risk level is assessed in real time based on the deformation level and rainfall level, and the collection frequency is determined, specifically including: If the deformation level is level 1 and / or the rainfall level is level 1, the comprehensive landslide risk level is level 0 and the collection frequency is f1; If the deformation level is level 2 and the rainfall level is no greater than level 2, or the deformation level is no greater than level 2 and the rainfall level is level 2, the comprehensive landslide risk level is level 1 and the collection frequency is f2; If the deformation level and rainfall level are both level 2, or the deformation level is level 3 and the rainfall level is not greater than level 3, or the deformation level is not greater than level 3 and the rainfall level is level 3, then the comprehensive landslide risk level is level 2 and the collection frequency is f3; If the deformation level and rainfall level are both level 3, or the deformation level is level 4 and the rainfall level is not greater than level 4, or the deformation level is not greater than level 4 and the rainfall level is level 4, then the comprehensive landslide risk level is level 3 and the collection frequency is f4; If the deformation level and rainfall level are both level 4, the comprehensive landslide risk level is level 4 and the sampling frequency is f5; Among them, f1<f2<f3<f4<f5.

7. The slope landslide monitoring method according to claim 6, characterized in that: Determining a deformation level and a rainfall level according to the deformation data and the accumulated rainfall, respectively, includes: If the cumulative deformation within T4 time is less than AL1, the deformation level is level one; If the cumulative deformation within the time AL1≤T4 is less than AL2, the deformation level is level 2; If the cumulative deformation within the time AL2≤T4 is less than AL3, the deformation level is level three; If the cumulative deformation amount within the time AL3≤T4, the deformation level is level 4; If the accumulated rainfall in time T2 is less than AR2, the rainfall level is level 1; If the accumulated rainfall during AR2≤T2 is less than AR3, the rainfall level is level 2; If the accumulated rainfall during AR3≤T2 is less than AR4, the rainfall level is level 3; If the accumulated rainfall during AR4≤T2, the rainfall level is level 4; Among them, T4 and T2 represent time periods; AL1, AL2, and AL3 represent deformation thresholds; AR2, AR3, and AR4 represent rainfall thresholds.

8. The slope landslide monitoring method according to claim 1, characterized in that: The corrected landslide risk level is equal to the sum of the landslide confidence data and the initial landslide risk level.

9. A slope landslide monitoring system, characterized in that: The monitoring system includes a MIMO radar device and a host computer, and the MIMO radar device communicates with the host computer; The MIMO radar device is used to obtain an echo signal of the MIMO radar device and perform pulse compression on the echo signal; Processing, detecting and calculating the echo signal after pulse compression to obtain rainfall data; wherein the rainfall data includes rainfall intensity and accumulated rainfall; Perform digital beamforming imaging on the echo signal after pulse compression to obtain a two-dimensional complex scattering image; compensating the two-dimensional complex scattering image according to the rainfall intensity; Extract PS points from the compensated two-dimensional complex scattering image, and perform deformation inversion based on the PS points to obtain deformation data; real-time assessment of the comprehensive landslide risk level based on the deformation data and accumulated rainfall, thereby determining an acquisition frequency, and controlling the operation of the MIMO radar device based on the acquisition frequency; The host computer is used to determine an initial landslide risk level according to the deformation data; determine landslide confidence data according to the accumulated rainfall; correct the initial landslide risk level according to the landslide confidence data, and issue a landslide risk warning according to the corrected landslide risk level.

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