Slope landslide monitoring method, system and equipment and storage medium

Abstract

The invention discloses a side slope landslide monitoring method, system and device and a storage medium. The monitoring method comprises the steps that the risk level of each sub-region is determined according to a deformation monitoring result in a side slope monitoring region; according to the risk level, adaptively adjusting a threshold value in a CFAR detection algorithm; wherein the higher the risk level is, the lower the adjusted threshold value is; and carrying out moving target detection on the slope monitoring area based on the adjusted threshold value. According to the method, the deformation and the moving target are monitored at the same time through the single radar, the deformation monitoring result is fused into the moving target detection process, the threshold value in the CFAR detection algorithm is adjusted in a self-adaptive mode according to the risk level of each sub-region, the capturing capacity of the moving target is improved, and meanwhile false alarms caused by interference of pedestrians, animals and the like are effectively restrained.

Description

Technical Field

[0001] This invention belongs to the field of slope landslide monitoring technology, and particularly relates to a slope landslide monitoring method, system, equipment and storage medium. Background Technology

[0002] Deformation monitoring radar technology primarily involves transmitting electromagnetic wave signals into the slope monitoring area and receiving the signals reflected back from the slope surface, i.e., echo signals. Radar images are obtained by pulse compression and imaging processing of the received echo signals. Interferometry is then performed on two adjacent radar images to produce an interferogram. This interferogram contains phase change information at various points on the slope surface, which is converted into displacement changes at each point. By comparing and analyzing interferograms acquired at different times, the deformation trend at various points on the slope surface can be analyzed.

[0003] Rockfall detection radar technology primarily works by emitting electromagnetic wave signals into the slope monitoring area and receiving the echo signals reflected back from the slope surface. By pulse compression processing of the received echo signals, the distance to the falling rocks can be accurately measured. Clutter suppression improves the signal-to-clutter ratio, Doppler processing measures the velocity of the falling rocks, digital beamforming (DBF) imaging technology measures the angle of the falling rocks, and constant false alarm rate (CFAR) detection technology accurately detects the falling rock target. Through these processing steps, high-precision measurement of falling rocks can be achieved.

[0004] On the one hand, existing slope rockfall monitoring radars generally possess deformation monitoring and moving target detection capabilities. However, in most solutions, deformation monitoring and moving target detection are processed separately and independently, without fusing the results, which easily leads to missed or false rockfall detections. For example, patent document CN115993600A discloses an ultra-wideband slope deformation monitoring radar system and monitoring method, including a deformation monitoring process and a moving target detection process. It can simultaneously perform deformation monitoring and early landslide monitoring (moving target) processing, and combine the two monitoring results to determine the risk of landslides and provide early warnings. However, this method processes deformation monitoring and moving target detection separately and independently, without fusing the results to improve the detection capability of moving targets, which easily leads to missed or false rockfall detections.

[0005] On the other hand, existing slope rockfall monitoring radars generally rely on cameras or other sensors to filter out false alarms caused by animals, pedestrians, etc. when monitoring rockfalls, but this method will increase additional costs. Summary of the Invention

[0006] The purpose of this invention is to provide a method, system, device, and storage medium for slope landslide monitoring, in order to solve the problems of separating deformation monitoring and moving target detection, which easily leads to missed and false detections of falling rocks; and the problem that combining slope falling rock monitoring radar with other sensors to filter out false alarms increases additional costs.

[0007] This invention solves the above-mentioned technical problems through the following technical solution: a method for monitoring slope landslides, comprising:

[0008] The risk level of each sub-area is determined based on the deformation monitoring results within the slope monitoring area;

[0009] Based on the risk level, the threshold value in the CFAR detection algorithm is adaptively adjusted; where the higher the risk level, the lower the adjusted threshold value.

[0010] Based on the adjusted threshold value, moving target detection is performed on the slope monitoring area.

[0011] Slope landslides typically begin with internal micro-deformation, followed by a deformation development stage and a severe failure stage. The probability of a landslide increases significantly during the deformation development or severe failure stages, making it more prone to generating moving targets such as falling rocks. Traditional CFAR (Cross-Fault Anomaly Detection and Ranging) detection uses fixed threshold values, which cannot distinguish the real-time risk status of different areas of the slope. This invention overcomes the limitations of traditional fixed-parameter detection methods by dynamically correlating the macroscopic risk level of slope deformation monitoring with the microscopic threshold values ​​of moving target CFAR detection. Specifically:

[0012] During high-risk phases of intensified deformation and significantly increased landslide probability, the system's ability to detect small, early-stage sliding targets is significantly enhanced by automatically lowering the CFAR detection threshold. This allows the system to detect microscopic movement signs of landslide precursors earlier and more sensitively during the deformation development or severe damage phases, buying time for early warning and emergency response.

[0013] In slope safety monitoring, a high detection probability and a low false alarm rate are contradictory. This invention cleverly resolves this contradiction by setting CFAR detection thresholds differently based on risk level. In low-risk areas, a higher threshold is used to effectively suppress false alarms caused by irrelevant interference such as vegetation movement, ensuring system stability. In high-risk areas, priority is given to capturing early signals, allowing for a slightly higher false alarm rate. This differentiated detection strategy effectively captures real moving targets while significantly suppressing environmental false alarms in stable areas, thus optimizing the overall system performance.

[0014] This invention, when using a single radar to simultaneously achieve deformation and moving target detection, no longer views deformation monitoring and moving target detection in isolation, but reveals the inherent connection between the two in the landslide evolution process: deformation monitoring locates the risk zone, and moving target detection finds moving targets within the risk zone, making the monitoring process have cognitive and decision-making capabilities, which is more in line with the disaster formation mechanism of landslide disasters.

[0015] Furthermore, the deformation monitoring process of the slope monitoring area is as follows:

[0016] The preprocessed radar echo signal is subjected to pulse accumulation and imaging processing to obtain a complex scattering map of the slope monitoring area.

[0017] Interference processing is performed on the complex scattering patterns at adjacent time points to obtain the interference phase;

[0018] The deformation at adjacent moments is calculated based on the interference phase;

[0019] The deformation variables at different adjacent time points are accumulated to obtain the accumulated deformation variables, and then the deformation curve is obtained.

[0020] The deformation rate and deformation acceleration at different times are calculated based on the deformation curve.

[0021] Furthermore, the deformation monitoring results include cumulative deformation, deformation rate, and deformation acceleration; the risk level of each sub-region is determined based on the deformation monitoring results within the slope monitoring area, including:

[0022] If the cumulative deformation, deformation rate, and deformation acceleration of a sub-region are all less than the corresponding first threshold, then the risk level of the sub-region is low risk.

[0023] If at least one of the cumulative deformation, deformation rate, and deformation acceleration of a sub-region is greater than or equal to the corresponding first threshold and less than or equal to the corresponding second threshold, then the risk level of the sub-region is medium risk.

[0024] If at least one of the cumulative deformation, deformation rate, and deformation acceleration of a sub-region is greater than the corresponding second threshold, then the risk level of the sub-region is high risk.

[0025] Furthermore, the risk levels include low risk, medium risk, and high risk; based on the risk levels, the threshold values ​​in the CFAR detection algorithm are adaptively adjusted, including:

[0026] If the risk level is low, then increase the threshold value in the CFAR detection algorithm;

[0027] If the risk level is medium risk, then the threshold value in the CFAR detection algorithm remains unchanged;

[0028] If the risk level is high, then the threshold value in the CFAR detection algorithm is reduced.

[0029] Furthermore, based on the adjusted threshold value, moving target detection is performed on the slope monitoring area, including:

[0030] Clutter suppression processing is applied to the preprocessed radar echo signal;

[0031] Multibeam synthesis is performed on the radar echo signal after clutter suppression to obtain multiple range-Doppler images.

[0032] Based on the adjusted threshold value, CFAR detection is performed on each distance-Doppler image to obtain the corresponding binary image;

[0033] Cluster each binary image to obtain the moving targets in the corresponding binary image;

[0034] Calculate the distance, velocity, and angle of each moving target in each binary image.

[0035] Furthermore, the monitoring method also includes risk warning, specifically including:

[0036] The sub-region where the moving target is located is determined based on its position within the slope monitoring area;

[0037] Risk warnings are issued based on the risk level of the sub-region where the moving target is located.

[0038] Based on the same concept, the present invention also provides a slope landslide monitoring system, including a radar, said radar being configured to:

[0039] The risk level of each sub-area is determined based on the deformation monitoring results within the slope monitoring area;

[0040] Based on the risk level, the threshold value in the CFAR detection algorithm is adaptively adjusted; where the higher the risk level, the lower the adjusted threshold value.

[0041] Based on the adjusted threshold value, moving target detection is performed on the slope monitoring area.

[0042] Based on the same concept, the present invention also provides an electronic device, including a memory, a processor, and a computer program or instructions stored in the memory, wherein the processor executes the computer program or instructions to implement the slope landslide monitoring method as described above.

[0043] Based on the same concept, the present invention also provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implements the slope landslide monitoring method as described above.

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0045] This invention utilizes a single radar to simultaneously monitor deformation and moving targets, and integrates the deformation monitoring results into the moving target detection process. It adaptively adjusts the threshold value in the CFAR detection algorithm according to the risk level of each sub-region, thereby improving the ability to capture moving targets while effectively suppressing false alarms caused by interference from pedestrians, animals, etc. This invention improves the accuracy of moving target detection on slopes and landslides without using other external sensors, and reduces the missed detection rate and false detection rate without incurring additional costs.

[0046] This invention utilizes a single radar to simultaneously monitor deformation and moving targets. Deformation monitoring enables the monitoring of minute displacements in the early stages of landslides, while moving target detection allows for the detection and tracking of moving targets after a landslide, thus achieving monitoring and early warning of the entire landslide process.

[0047] This invention provides early warnings based on the risk level determined by deformation monitoring results, which improves the accuracy of early warnings and avoids unnecessary emergency response costs caused by false alarms. Attached Figure Description

[0048] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only one embodiment of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart of the slope landslide monitoring method in an embodiment of the present invention. Detailed Implementation

[0050] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0052] Example 1

[0053] Figure 1 A flowchart of the slope landslide monitoring method provided by this invention is shown. Figure 1As shown, the slope landslide monitoring method includes the following steps:

[0054] Step 1: Preprocess the received radar echo signal.

[0055] The radar emits electromagnetic wave signals towards the slope monitoring area. These signals are reflected back after passing through the monitoring area, and the radar receives the echo signals. In a specific embodiment of the invention, preprocessing includes data rearrangement, pulse compression, and channel correction.

[0056] Since radar echo signals are time-domain data and there are phase differences between receiving channels, data rearrangement is required to arrange the radar echo signals received by each receiving channel into a format suitable for batch processing, so as to facilitate parallel processing. Range dimension information can be obtained through pulse compression, and then the phase and amplitude differences between channels are corrected to ensure the accuracy of subsequent angle measurement steps.

[0057] Step 2: Perform deformation monitoring on the preprocessed radar echo signal to obtain the deformation monitoring results.

[0058] In a specific embodiment of the present invention, the specific process for deformation monitoring of the preprocessed radar echo signal is as follows:

[0059] Step 2.1: Perform pulse accumulation and imaging processing on the preprocessed radar echo signal to obtain a complex scattering map.

[0060] Step 2.2: Perform interferometry on the complex scattering diagrams at adjacent time points to obtain the interferometric phase.

[0061] For adjacent time points, the complex scattering image of the previous time point is used as the master image, and the complex scattering image of the current time point is used as the slave image. The master image and the slave image form an interference pair. By performing interference processing on the interference pair, the interference phase can be obtained.

[0062] Step 2.3: Calculate the deformation at adjacent time points based on the interference phase. The specific calculation formula is as follows:

[0063] (1)

[0064] in, Represents the deformation at adjacent time points. Indicates the interference phase. This indicates the wavelength of the radar electromagnetic wave signal.

[0065] Step 2.4: Accumulate the deformation at different adjacent times to obtain the accumulated deformation, and then obtain the deformation curve.

[0066] Repeat steps 2.2 and 2.3 to obtain the deformation at different adjacent time points. Then, accumulate the deformation at different adjacent time points to obtain the accumulated deformation, which in turn forms the deformation curve.

[0067] Step 2.5: Calculate the deformation velocity and deformation acceleration at different times based on the deformation curve. The specific calculation formula is as follows:

[0068] (2)

[0069] (3)

[0070] in, These represent the deformation rates at time t and time t-1, respectively. These represent the cumulative deformations at time t and time t-1, respectively. Indicates a time interval; This represents the deformation acceleration at time t.

[0071] Deformation monitoring results include cumulative deformation, deformation rate, and deformation acceleration.

[0072] Step 3: Determine the risk level of each sub-region based on the deformation monitoring results.

[0073] In a specific embodiment of the present invention, determining the risk level of each sub-region based on deformation monitoring results includes:

[0074] If the cumulative deformation, deformation rate, and deformation acceleration of a sub-region are all less than the corresponding first threshold, then the risk level of the sub-region is low risk.

[0075] If at least one of the cumulative deformation, deformation rate, and deformation acceleration of a sub-region is greater than or equal to the corresponding first threshold and less than or equal to the corresponding second threshold, then the risk level of the sub-region is medium risk.

[0076] If at least one of the cumulative deformation, deformation rate, and deformation acceleration of a sub-region is greater than the corresponding second threshold, then the risk level of the sub-region is high risk.

[0077] In this embodiment, the first and second thresholds corresponding to the cumulative deformation are 10mm and 50mm, respectively; the first and second thresholds corresponding to the deformation rate are 2mm / month and 10mm / month, respectively; and the first and second thresholds corresponding to the deformation acceleration are 0.5mm / month, respectively. 2 and 3mm / month 2 Then we have:

[0078] If the cumulative deformation of the sub-region is <10mm, the deformation rate is <2mm / month, and the deformation acceleration is <0.5mm / month 2If so, the risk level of this sub-region is low risk.

[0079] If 10mm ≤ cumulative deformation of the sub-region ≤ 50mm, 2mm / month ≤ deformation rate ≤ 10mm / month, or 0.5mm / month 2 ≤Deformation acceleration≤3mm / month 2 If so, the risk level of this sub-region is medium risk.

[0080] If 50mm < the cumulative deformation of the sub-region, 10mm / month < the deformation rate, or 3mm / month 2 If the deformation acceleration is less than a certain value, then the risk level of this sub-region is high risk.

[0081] When the deformation acceleration is large (e.g., greater than 3 mm / month) 2 When the deformation rate is in a rapid increase phase, the cumulative deformation also keeps increasing. That is, as long as one of them is large, the other two will also increase. Therefore, when only one value is large, it is judged as a high-risk level.

[0082] Step 4: Adaptively adjust the threshold value in the CFAR detection algorithm according to the risk level.

[0083] In a specific embodiment of the present invention, the threshold value in the CFAR detection algorithm is adaptively adjusted according to the risk level, including:

[0084] If the risk level is low, then increase the threshold value in the CFAR detection algorithm;

[0085] If the risk level is medium risk, the threshold value in the CFAR detection algorithm remains unchanged;

[0086] If the risk level is high, then lower the threshold value in the CFAR detection algorithm.

[0087] The detection probability is adjusted by adaptively changing the threshold value in the CFAR detection algorithm to improve the ability to detect moving targets while effectively suppressing false alarms. The relationship between the detection probability and the false alarm probability in CFAR detection is as follows: , Indicates the detection probability. This represents the probability of a false alarm. The linear value representing the signal-to-noise ratio; the relationship between the false alarm probability and the threshold value is: ,in, Indicates the threshold value. Indicates noise power. When the adjusted threshold value is At that time, the adjusted false alarm probability is: The adjusted detection probability is: .

[0088] For example, suppose the detection probability is 0.7 and the signal-to-noise ratio is 12.8 dB. For high-risk areas, reducing the threshold by 40% can increase the detection probability to 0.8795, thereby improving the moving target detection capability in that area; for low-risk areas, increasing the threshold by 40% can reduce the detection probability to 0.497, thereby suppressing false alarms in that area.

[0089] Step 5: Based on the adjusted threshold values, perform moving target detection in the slope monitoring area.

[0090] In a specific embodiment of the present invention, moving target detection is performed on the slope monitoring area based on the adjusted threshold value, including:

[0091] Step 5.1: Perform clutter suppression processing on the preprocessed radar echo signal.

[0092] Clutter suppression processing reduces clutter in the scene, improving the signal-to-clutter ratio of moving targets.

[0093] Step 5.2: Perform multi-beam synthesis processing on the radar echo signal after clutter suppression to obtain multiple range-Doppler images.

[0094] Multi-beam synthesis enables radar to acquire motion information across all areas of the entire scene in real time, providing the most fundamental data for subsequent target detection, classification, and tracking. The number of range-Doppler images is the same as the number of beams. Each range-Doppler image represents a "range-velocity" profile in a specific direction. The range dimension represents the radial distance of radar wave propagation, and the Doppler dimension represents the target's velocity in the radar's radial direction.

[0095] Step 5.3: Based on the adjusted threshold value, perform CFAR detection on each distance-Doppler image to obtain the corresponding binary image.

[0096] When performing CFAR detection on each distance-Doppler image, a threshold value corresponding to the sub-region of the distance-Doppler image is used. When a point in the distance-Doppler image is greater than or equal to the corresponding threshold value, the threshold value is set to 1; when a point in the distance-Doppler image is less than the corresponding threshold value, the threshold value is set to 0, thus forming a binary image corresponding to the distance-Doppler image.

[0097] Step 5.4: Cluster each binary image to obtain the moving targets in the corresponding binary image.

[0098] Clustering algorithms are used to classify spatially adjacent pixels in a binary image into the same target, and area thresholds are used for filtering to remove targets with a small number of pixels (e.g., less than 3) or a large number of pixels (e.g., greater than 100), thus obtaining the moving targets in the binary image.

[0099] Step 5.5: Calculate the distance, velocity, and angle of each moving target in each binary image.

[0100] For each moving target region in the binary image, the weighted center method is used to extract its centroid position. The horizontal coordinate of the centroid corresponds to the distance dimension index, and the vertical coordinate of the centroid corresponds to the velocity dimension index. Then, the distance and velocity of the corresponding moving target are calculated.

[0101] (4)

[0102] (5)

[0103] in, Indicates the distance to a moving target; The distance dimension index representing the centroid of a moving target; Indicates the cell distance value; Indicates the minimum detection distance; Indicates the velocity of a moving target; The velocity dimension index representing the center of mass of a moving target; Indicates the unit velocity value; This indicates the minimum speed required for detection.

[0104] By processing the binary image using angle measurement methods, the angle information of the moving target can be obtained. Based on the detected moving target information (i.e., distance, velocity, and angle), tracking processing is performed to obtain stable moving target information.

[0105] Step 6: Issue a risk warning based on the risk level of the sub-region where the moving target is located.

[0106] The sub-region where the moving target is located is determined based on its location in the slope monitoring area, and then risk warnings are issued based on the risk level of the sub-region where the moving target is located.

[0107] In a specific embodiment of the present invention, if the risk level of the sub-region where the moving target is located is high risk, a high risk warning is issued; if the risk level of the sub-region where the moving target is located is medium risk, a low risk warning is issued; if the risk level of the sub-region where the moving target is located is low risk, no risk warning is issued.

[0108] This invention introduces a deformation risk level during risk warning. In low-risk areas, even if a target is detected, it is determined to be a non-dangerous event (such as animal activity or pedestrians) and no alarm is triggered, thereby filtering out most daily interference and false alarms. Only in areas that are already unstable (medium or high-risk areas) will moving targets be identified as dangerous signals, which greatly improves the reliability of the warning.

[0109] Example 2

[0110] This invention also provides a slope landslide monitoring system, which includes a radar configured to: preprocess received radar echo signals; perform deformation monitoring on the preprocessed radar echo signals to obtain deformation monitoring results; determine the risk level of each sub-region based on the deformation monitoring results; adaptively adjust the threshold value in the CFAR detection algorithm based on the risk level; and perform moving target detection on the slope monitoring area based on the adjusted threshold value.

[0111] In a specific embodiment of the present invention, the monitoring system further includes an early warning unit, which is used to provide risk warnings based on the risk level of the sub-area where the moving target is located.

[0112] In some specific embodiments of the present invention, the slope landslide monitoring system may combine the features of the slope landslide monitoring method in Embodiment 1 of the present invention, and vice versa, which will not be repeated here.

[0113] Example 3

[0114] This invention also provides an electronic device, which includes a memory, a processor, and a computer program or instructions stored in the memory. The processor executes the computer program or instructions to implement the slope landslide monitoring method of this invention.

[0115] Although not shown, the electronic device includes a processor that can perform various appropriate operations and processes based on programs and / or data stored in read-only memory (ROM) or loaded from a storage portion into random access memory (RAM). The processor can be a multi-core processor or may contain multiple processors. In some embodiments, the processor may include a general-purpose main processor and one or more specialized coprocessors, such as a central processing unit, graphics processing unit (GPU), neural network processor (NPU), digital signal processor (DSP), etc. Various programs and data required for device operation are also stored in RAM. The processor, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0116] The processor and memory described above are used together to execute programs / instructions stored in the memory. When the program / instructions are executed by the computer, they can implement the methods, steps, or functions described in the above embodiments.

[0117] Although not shown, embodiments of the present invention also provide a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implements the slope landslide monitoring method of the present invention.

[0118] Readable storage media include both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0119] The above description only discloses specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or modifications that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for monitoring slope landslides, characterized in that, The monitoring method includes: The risk level of each sub-area is determined based on the deformation monitoring results within the slope monitoring area; Based on the risk level, the threshold value in the CFAR detection algorithm is adaptively adjusted; where the higher the risk level, the lower the adjusted threshold value. Based on the adjusted threshold value, moving target detection is performed on the slope monitoring area.

2. The slope landslide monitoring method according to claim 1, characterized in that, The deformation monitoring process for the slope monitoring area is as follows: The preprocessed radar echo signal is subjected to pulse accumulation and imaging processing to obtain a complex scattering map of the slope monitoring area. Interference processing is performed on the complex scattering patterns at adjacent time points to obtain the interference phase; The deformation at adjacent moments is calculated based on the interference phase; The deformation variables at different adjacent time points are accumulated to obtain the accumulated deformation variables, and then the deformation curve is obtained. The deformation rate and deformation acceleration at different times are calculated based on the deformation curve.

3. The slope landslide monitoring method according to claim 1, characterized in that, The deformation monitoring results include the cumulative deformation, deformation rate, and deformation acceleration. The risk level of each sub-area is determined based on the deformation monitoring results within the slope monitoring area, including: If the cumulative deformation, deformation rate, and deformation acceleration of a sub-region are all less than the corresponding first threshold, then the risk level of the sub-region is low risk. If at least one of the cumulative deformation, deformation rate, and deformation acceleration of a sub-region is greater than or equal to the corresponding first threshold and less than or equal to the corresponding second threshold, then the risk level of the sub-region is medium risk. If at least one of the cumulative deformation, deformation rate, and deformation acceleration of a sub-region is greater than the corresponding second threshold, then the risk level of the sub-region is high risk.

4. The slope landslide monitoring method according to claim 1, characterized in that, The risk levels include low risk, medium risk, and high risk. Based on the aforementioned risk level, the threshold value in the CFAR detection algorithm is adaptively adjusted, including: If the risk level is low, then increase the threshold value in the CFAR detection algorithm; If the risk level is medium risk, then the threshold value in the CFAR detection algorithm remains unchanged; If the risk level is high, then the threshold value in the CFAR detection algorithm is reduced.

5. The slope landslide monitoring method according to claim 1, characterized in that, Based on the adjusted threshold value, moving target detection is performed on the slope monitoring area, including: Clutter suppression processing is applied to the preprocessed radar echo signal; Multibeam synthesis is performed on the radar echo signal after clutter suppression to obtain multiple range-Doppler images. Based on the adjusted threshold value, CFAR detection is performed on each distance-Doppler image to obtain the corresponding binary image; Cluster each binary image to obtain the moving targets in the corresponding binary image; Calculate the distance, velocity, and angle of each moving target in each binary image.

6. The slope landslide monitoring method according to any one of claims 1 to 5, characterized in that, The monitoring method also includes risk warning, specifically including: The sub-region where the moving target is located is determined based on its position within the slope monitoring area; Risk warnings are issued based on the risk level of the sub-region where the moving target is located.

7. A slope landslide monitoring system, comprising radar, characterized in that, The radar is configured as follows: The risk level of each sub-area is determined based on the deformation monitoring results within the slope monitoring area; Based on the risk level, the threshold value in the CFAR detection algorithm is adaptively adjusted; where the higher the risk level, the lower the adjusted threshold value. Based on the adjusted threshold value, moving target detection is performed on the slope monitoring area.

8. An electronic device comprising a memory, a processor, and a computer program or instructions stored in the memory, characterized in that, The processor executes the computer program or instructions to implement the slope landslide monitoring method as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by the processor, they implement the slope landslide monitoring method as described in any one of claims 1 to 6.

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