Slope landslide monitoring method, system, device and storage medium

Abstract

The application discloses a kind of side slope landslide monitoring method, system, equipment and storage medium, the monitoring method includes according to the deformation monitoring result in side slope monitoring area The risk level of each sub-region is determined;According to the risk level, the threshold value in CFAR detection algorithm is adaptively adjusted;Wherein, the higher the risk level, the lower the adjusted threshold value;Based on the threshold value after adjustment, moving target detection is carried out on the side slope monitoring area.The present application realizes the simultaneous monitoring of deformation and moving target by using a single radar, and integrates the deformation monitoring result into the moving target detection process.Adjusting the threshold value in CFAR detection algorithm according to the risk level of each sub-region, while improving the capture ability of moving target, effectively suppresses the false alarm caused by pedestrians, animals and other interference.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of slope landslide monitoring, and particularly relates to a slope landslide monitoring method, system, device and storage medium. BACKGROUND

[0002] The deformation monitoring radar technology refers to mainly emitting electromagnetic wave signals to a slope monitoring area and receiving signals reflected back by the slope surface, i.e. echo signals. Radar images are obtained by pulse compression and imaging processing of the received echo signals. The interference graph is obtained by interference processing of the radar images observed at adjacent two times. The interference graph contains phase change information of each point on the slope surface. The phase change information is converted into displacement change of each point. By comparing and analyzing the interference graphs obtained at different times, the deformation trend of each point on the slope surface can be analyzed.

[0003] The rockfall detection radar technology mainly emits electromagnetic wave signals to a slope monitoring area and receives signals reflected back by the slope surface, i.e. echo signals. The distance of rockfall can be accurately measured by pulse compression processing of the received echo signals, and the signal-to-clutter ratio of rockfall can be improved by clutter suppression. The velocity of rockfall can be measured by Doppler processing. The angle information of rockfall can be measured by digital beamforming (DBF) imaging technology. The rockfall target can be accurately detected by constant false alarm rate (CFAR) detection technology. Through the above processing procedures, high-precision measurement of rockfall can be achieved.

[0004] On the one hand, the existing slope rockfall monitoring radar generally has deformation monitoring and moving target detection capabilities. However, in most schemes, deformation monitoring and moving target detection are processed separately and independently, and the two processing results are not fused, which is easy to cause rockfall missed detection and false detection. For example, the patent document with publication number CN115993600A discloses a super-bandwidth slope deformation monitoring radar system and a monitoring method, which includes a deformation monitoring process and a moving target detection process. The system can simultaneously perform deformation monitoring and early collapse and landslide monitoring (moving target) processing, and determine the risk of collapse and landslide and give early warning by comprehensively analyzing the two monitoring results. However, the deformation monitoring and moving target detection of this method are processed separately and independently, and the two processing results are not fused to improve the detection capability of moving targets, which is easy to cause rockfall missed detection and false detection.

[0005] On the other hand, the existing slope rockfall monitoring radar generally relies on cameras or other sensors to filter out false alarms caused by animals, pedestrians and the like when monitoring rockfall. However, this method increases additional costs. SUMMARY

[0006] The present application aims to provide a slope landslide monitoring method, system, device and storage medium, to solve the problem that deformation monitoring and moving target detection are processed separately and independently, which is easy to cause rockfall missed detection and false detection; and the problem that slope rockfall monitoring radar combined with other sensors to filter false alarms will increase additional costs.

[0007] The present application solves the above technical problems by the following technical solutions: a slope landslide monitoring method, comprising:

[0008] According to the deformation monitoring result in the slope monitoring area, the risk level of each sub-area is determined;

[0009] According to the risk level, the threshold value in the CFAR detection algorithm is adaptively adjusted; the higher the risk level, the lower the adjusted threshold value;

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

[0011] Slope landslide usually starts from internal micro-deformation, and then goes through deformation development stage and severe damage stage, and the probability of landslide increases significantly in the deformation development or severe damage stage, and it is more likely to produce moving targets such as rockfall. The traditional CFAR detection uses a fixed threshold value, which cannot distinguish the real-time risk state of different areas of the slope. The present application dynamically correlates the macro risk level of slope deformation monitoring with the micro threshold value of moving target CFAR detection, overcoming the limitations of traditional fixed parameter detection method, specifically:

[0012] In the high-risk stage of deformation aggravation and significant increase of landslide probability, the CFAR detection threshold is automatically reduced, which significantly improves the capture ability of small and initial sliding targets. This enables the system to discover the micro motion signs of landslide precursors earlier and more sensitively in the deformation development stage or severe damage stage, which gains time for early warning and emergency response.

[0013] In slope safety monitoring, high detection probability and low false alarm rate are contradictory. The present application solves this contradiction by differentiating the CFAR detection threshold according to the risk level. In the low-risk area, a higher threshold is used to effectively suppress the false alarm caused by irrelevant interference such as grass and tree swaying, ensuring the stability of the system; in the high-risk area, the capture ability of early signals is prioritized, and the false alarm rate is allowed to be appropriately increased. This differentiated detection strategy effectively captures real moving targets while significantly suppressing environmental false alarms in stable areas, optimizing the overall performance of the system.

[0014] The application discloses a method for simultaneously realizing deformation monitoring and moving target detection by using a single radar.

[0015] Further, the deformation monitoring process of the slope monitoring area comprises:

[0016] Pulse accumulation and imaging processing are performed on the preprocessed radar echo signal to obtain a complex scattering diagram of the slope monitoring area;

[0017] Interference processing is performed on the complex scattering diagrams of adjacent time points to obtain an interference phase;

[0018] The deformation amount of adjacent time points is calculated according to the interference phase;

[0019] The deformation amounts of different adjacent time points are accumulated to obtain an accumulated deformation amount, and then a deformation curve is obtained;

[0020] The deformation velocity and the deformation acceleration of different time points are calculated according to the deformation curve.

[0021] Further, the deformation monitoring result comprises the accumulated deformation amount, the deformation velocity and the deformation acceleration; the risk level of each sub-area is determined according to the deformation monitoring result in the slope monitoring area, and the risk level comprises:

[0022] If the accumulated deformation amount, the deformation velocity and the deformation acceleration of the sub-area are all less than corresponding first threshold values, the risk level of the sub-area is a low risk level;

[0023] If at least one of the accumulated deformation, the deformation velocity and the deformation acceleration of the sub-area is greater than or equal to a corresponding first threshold value and less than or equal to a corresponding second threshold value, the risk level of the sub-area is a medium risk level;

[0024] If at least one of the accumulated deformation amount, the deformation velocity and the deformation acceleration of the sub-area is greater than a corresponding second threshold value, the risk level of the sub-area is a high risk level.

[0025] Further, the risk level comprises a low risk level, a medium risk level and a high risk level; according to the risk level, the threshold value in the CFAR detection algorithm is adaptively adjusted, and the threshold value comprises:

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

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

[0028] if the risk level is a high risk level, then a threshold value in a CFAR detection algorithm is lowered.

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

[0030] Clutter suppression processing is performed on the pre-processed radar echo signal;

[0031] Multi-beam synthesis processing is performed on the radar echo signal after the clutter suppression processing, to obtain a plurality of range-Doppler maps;

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

[0033] Each binary image is clustered to obtain moving targets in the corresponding binary image;

[0034] The distance, speed and angle of each moving target in each binary image are calculated.

[0035] Further, the monitoring method further includes risk warning, specifically including:

[0036] According to the position of the moving target in the slope monitoring area, the sub-area where the moving target is located is determined;

[0037] According to the risk level of the sub-area where the moving target is located, risk warning is performed.

[0038] Based on the same concept, the present application also provides a slope landslide monitoring system, comprising a radar, wherein the radar is configured to:

[0039] According to the deformation monitoring result in the slope monitoring area, the risk level of each sub-area is determined;

[0040] According to the risk level, the threshold value in the CFAR detection algorithm is adaptively adjusted; wherein 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 application also provides an electronic device, comprising a memory, a processor and a computer program or instructions stored in the memory, wherein the processor executes the computer program or instructions to realize the slope landslide monitoring method as described above.

[0043] Based on the same concept, the present application also provides a computer readable storage medium, which stores a computer program or instructions, wherein the computer program or instructions are executed by a processor to realize the slope landslide monitoring method as described above.

[0044] Compared with the prior art, the present application has the beneficial effects that:

[0045] The present application realizes the simultaneous monitoring of deformation and moving targets by using a single radar, and fuses the deformation monitoring results into the moving target detection process, and adaptively adjusts the threshold value in the CFAR detection algorithm according to the risk level of each sub-region, thereby improving the capture ability of moving targets and effectively suppressing false alarms caused by pedestrians, animals and other interference; The present application improves the accuracy of slope landslide moving target detection without using external sensors, reduces the missed detection rate and false detection rate, and does not need to increase additional costs.

[0046] The present application realizes the simultaneous monitoring of deformation and moving targets by using a single radar, and realizes the monitoring of the early deformation of the slope landslide by deformation monitoring, and realizes the detection and tracking of the moving target after the landslide by moving target detection, thereby realizing the monitoring and early warning of the whole process of the slope landslide.

[0047] The present application realizes the simultaneous monitoring of deformation and moving targets by using a single radar, and realizes the monitoring of the early deformation of the slope landslide by deformation monitoring, and realizes the detection and tracking of the moving target after the landslide by moving target detection, thereby realizing the monitoring and early warning of the whole process of the slope landslide. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only one embodiment of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0049] Figure 1 It is a flow chart of the slope landslide monitoring method in the embodiment of the present application. DETAILED DESCRIPTION

[0050] The technical solutions in the present application will be described clearly and completely in combination with the drawings in the embodiment of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0051] The technical solutions of the present application will be described in detail in combination with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in some embodiments.

[0052] Embodiment one

[0053] Figure 1 The present application provides a flow chart of the slope landslide monitoring method. As shown in Figure 1As shown, the slope landslide monitoring method comprises the following steps:

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

[0055] The radar transmits an electromagnetic wave signal to the slope monitoring area, the electromagnetic wave signal is reflected back after passing through the slope monitoring area, and the radar receives the echo signal. In the specific embodiment of the present application, the preprocessing includes data rearrangement, pulse compression and channel correction.

[0056] Since the radar echo signal is time domain data, and there is a phase difference between the receiving channels, it is necessary to rearrange the data so that the radar echo signals received by each receiving channel are arranged in a format suitable for batch processing to facilitate parallel processing; after pulse compression, the distance dimension information can be obtained, and then the phase and amplitude differences between the channels are corrected to ensure the accuracy of the subsequent angle measurement step.

[0057] Step 2: Deformation monitoring of the preprocessed radar echo signal to obtain a deformation monitoring result.

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

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

[0060] Step 2.2: Interference processing of the complex scattering maps of adjacent time points to obtain an interference phase.

[0061] For adjacent time points, the complex scattering map of the previous time point is the main image, and the complex scattering map of the current time point is the slave image, the main image and the slave image form an interference pair, and the interference pair is interfered to obtain the interference phase.

[0062] Step 2.3: Calculating the deformation of adjacent time points according to the interference phase, and the specific calculation formula is:

[0063] (1)

[0064] wherein, represents the deformation of adjacent time points, represents the interference phase, represents the wavelength of the radar electromagnetic wave signal.

[0065] Step 2.4: Accumulating the deformation of different adjacent time points to obtain an accumulated deformation, and further obtaining a 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. Indicates 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, assuming that the detection probability is 0.7 and the signal-to-noise ratio is 12.8 dB. For the high-risk area, reducing the threshold value by 40% can increase the detection probability to 0.8795, thereby improving the moving target detection capability of the area; for the low-risk area, increasing the threshold value by 40% can reduce the detection probability to 0.497, thereby suppressing the false alarm in the area.

[0089] Step 5: Based on the adjusted threshold value, moving target detection is performed on the slope monitoring area.

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

[0091] Step 5.1: The preprocessed radar echo signal is subjected to clutter suppression processing.

[0092] Through clutter suppression processing, the clutter in the scene is suppressed, and the signal-to-clutter ratio of the moving target is improved.

[0093] Step 5.2: The radar echo signal after clutter suppression processing is subjected to multi-beam synthesis processing to obtain multiple range-Doppler maps.

[0094] Through multi-beam synthesis processing, the radar can obtain the motion information of all areas in the entire scene in real time, providing the most basic data for subsequent target detection, classification and tracking. The number of range-Doppler maps is the same as the number of beams, and each range-Doppler map 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 speed of the target in the radar radial direction.

[0095] Step 5.3: Based on the adjusted threshold value, CFAR detection is performed on each range-Doppler map to obtain the corresponding binary map.

[0096] When performing CFAR detection on each range-Doppler map, the threshold value of the sub-area corresponding to the range-Doppler map is adopted. When the point in the range-Doppler map is greater than or equal to the corresponding threshold value, 1 is taken; when the point in the range-Doppler map is less than the corresponding threshold value, 0 is taken, thereby forming a binary map corresponding to the range-Doppler map.

[0097] Step 5.4: Clustering is performed on each binary map to obtain the moving target in the corresponding binary map.

[0098] Through the clustering algorithm, spatially adjacent pixel points in the binary map are classified into the same target, and an area threshold is used for filtering to eliminate targets with a small number of pixel points (e.g. less than 3) or a large number of pixel points (e.g. greater than 100), thereby obtaining the moving target in the binary map.

[0099] Step 5.5: Calculate the distance, speed 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 the centroid position, the horizontal coordinate of the centroid corresponds to the distance dimension index, and the vertical coordinate of the centroid corresponds to the speed dimension index, and then the distance and speed of the corresponding moving target are calculated:

[0101] (4)

[0102] (5)

[0103] wherein, represents the distance of the moving target; represents the distance dimension index of the centroid of the moving target; represents the unit distance value; represents the minimum distance of detection; represents the speed of the moving target; represents the speed dimension index of the centroid of the moving target; represents the unit speed value; represents the minimum speed of detection.

[0104] The angle information of the moving target can be obtained by processing the binary image by the goniometric method. Based on the detected moving target information (i.e. distance, speed and angle), tracking processing is performed to obtain stable moving target information.

[0105] Step 6: Risk warning according to the risk level of the sub-region where the moving target is located.

[0106] According to the position of the moving target in the slope monitoring region, the sub-region where the moving target is located is determined, and then risk warning is performed according to the risk level of the sub-region where the moving target is located.

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

[0108] In the present application, the deformation risk level is introduced in the risk warning, and in the low risk area, even if the target is detected, it is determined as a non-dangerous event (such as animal activity or pedestrian) and no alarm is given, so as to filter out most of the daily interference and false alarm; only in the unstable area itself (medium and high risk area), the moving target is determined as a dangerous signal, which greatly improves the credibility of the warning.

[0109] Example Two

[0110] The embodiment of the present application also provides a slope landslide monitoring system, which comprises a radar configured to: pre-process a received radar echo signal; perform deformation monitoring on the pre-processed radar echo signal to obtain a deformation monitoring result; determine a risk level of each sub-region according to the deformation monitoring result; adaptively adjust a threshold value in a CFAR detection algorithm according to the risk level; and perform moving target detection on a slope monitoring region based on the adjusted threshold value.

[0111] In the specific embodiments of the present application, the monitoring system further comprises a warning unit configured to perform risk warning according to the risk level of the sub-region where the moving target is located.

[0112] In some specific embodiments of the present application, the slope landslide monitoring system can combine the features of the slope landslide monitoring method in the first embodiment of the present application, and vice versa, which will not be described here again.

[0113] Embodiment three

[0114] The embodiment of the present application also provides an electronic device, which comprises a memory, a processor and a computer program or instructions stored in the memory, and the processor executes the computer program or instructions to implement the slope landslide monitoring method in the embodiment of the present application.

[0115] Although not shown, the electronic device comprises a processor, which can execute various appropriate operations and processes according to programs and / or data stored in a read-only memory (ROM) or loaded from a storage part into a random access memory (RAM). The processor can be a multi-core processor or can comprise a plurality of processors. In some embodiments, the processor can comprise a general-purpose main processor and one or more special-purpose coprocessors, such as a central processing unit, a graphics processing unit (GPU), a neural network processing unit (NPU), a digital signal processor (DSP), etc. In the RAM, various programs and data required for device operation are also stored. The processor, the ROM and the RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

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

[0117] Although not shown, the embodiment of the present application also provides a computer readable storage medium having a computer program or instructions stored thereon, and the computer program or instructions are executed by a processor to implement the slope landslide monitoring method in the embodiment of the present application.

[0118] Read only memory (ROM), electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technologies, compact disc read only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store information which can be accessed by a computing device. According to the definition used herein, computer readable medium does not include transitory media, such as modulated data signals and carrier waves.

[0119] The above disclosure is merely a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or modifications within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A method of monitoring a slope landslide, characterized by, The monitoring method comprises: determining the risk level of each sub-region according to the deformation monitoring result in the slope monitoring region; according to the risk level, adaptively adjusting the threshold value in the CFAR detection algorithm; wherein the higher the risk level is, the lower the adjusted threshold value is; based on the adjusted threshold value, performing moving target detection on the slope monitoring region; wherein the deformation monitoring result comprises accumulated deformation, deformation velocity and deformation acceleration; determining the risk level of each sub-region according to the deformation monitoring result in the slope monitoring region comprises: if the accumulated deformation, deformation velocity and deformation acceleration of a sub-region are all less than the corresponding first threshold value, the risk level of the sub-region is a low risk level; if at least one of the accumulated deformation, deformation velocity and deformation acceleration of a sub-region is greater than or equal to the corresponding first threshold value and less than or equal to the corresponding second threshold value, the risk level of the sub-region is a medium risk level; if at least one of the accumulated deformation, deformation velocity and deformation acceleration of a sub-region is greater than the corresponding second threshold value, the risk level of the sub-region is a high risk level; the risk level comprises a low risk level, a medium risk level and a high risk level; according to the risk level, adaptively adjusting the threshold value in the CFAR detection algorithm comprises: if the risk level is a low risk level, increasing the threshold value in the CFAR detection algorithm; if the risk level is a medium risk level, keeping the threshold value in the CFAR detection algorithm unchanged; if the risk level is a high risk level, decreasing the threshold value in the CFAR detection algorithm.

2. The method of claim 1, wherein, The deformation monitoring process of the slope monitoring region comprises: performing pulse accumulation and imaging processing on the preprocessed radar echo signal to obtain a complex scattering map of the slope monitoring region; performing interference processing on the complex scattering maps of adjacent time instants to obtain interference phases; calculating the deformation of adjacent time instants according to the interference phases; accumulating the deformations of different adjacent time instants to obtain accumulated deformations, and further obtaining a deformation curve; calculating the deformation velocity and deformation acceleration at different time instants according to the deformation curve.

3. The method of claim 1, wherein, Based on the adjusted threshold value, performing moving target detection on the slope monitoring region comprises: performing clutter suppression processing on the preprocessed radar echo signal; performing multi-beam synthesis processing on the radar echo signal after the clutter suppression processing to obtain multiple range-Doppler maps; performing CFAR detection on each range-Doppler map based on the adjusted threshold value to obtain the corresponding binary map; performing clustering on each binary map to obtain the moving targets in the corresponding binary map; calculating the distance, velocity and angle of each moving target in each binary map.

4. The method of claim 1-3, wherein, The monitoring method further comprises risk warning, specifically comprising: determining the sub-region where the moving target is located according to the position of the moving target in the slope monitoring region; performing risk warning according to the risk level of the sub-region where the moving target is located.

5. A slope landslide monitoring system comprising a radar, characterized in that, The radar is configured to: determine the risk level of each sub-region according to the deformation monitoring result in the slope monitoring region; according to the risk level, adaptively adjust the threshold value in the CFAR detection algorithm; wherein the higher the risk level is, the lower the adjusted threshold value is; Based on the adjusted threshold value, a moving target detection is performed on the slope monitoring area; The deformation monitoring result includes an accumulated deformation variable, a deformation velocity, and a deformation acceleration; a risk level of each sub-area is determined according to the deformation monitoring result in the slope monitoring area, including: If the accumulated deformation variable, the deformation velocity, and the deformation acceleration of the sub-area are all less than the corresponding first threshold value, respectively, the risk level of the sub-area is a low risk level; If at least one of the accumulated deformation variable, the deformation velocity, and the deformation acceleration of the sub-area is greater than or equal to the corresponding first threshold value and less than or equal to the corresponding second threshold value, the risk level of the sub-area is a medium risk level; If at least one of the accumulated deformation variable, the deformation velocity, and the deformation acceleration of the sub-area is greater than the corresponding second threshold value, the risk level of the sub-area is a high risk level; The risk level includes a low risk level, a medium risk level, and a high risk level; according to the risk level, a threshold value in the CFAR detection algorithm is adaptively adjusted, including: If the risk level is a low risk level, the threshold value in the CFAR detection algorithm is increased; If the risk level is a medium risk level, the threshold value in the CFAR detection algorithm is kept unchanged; If the risk level is a high risk level, the threshold value in the CFAR detection algorithm is decreased.

6. An electronic device comprising a memory, a processor, and a computer program or instructions stored on the memory, wherein the computer program or instructions, when executed by the processor, cause the electronic device to perform the method of any one of claims 1-5. The processor executes the computer program or instruction to implement the slope landslide monitoring method in any one of claims 1-4.

7. A computer readable storage medium having stored thereon a computer program or instructions, characterized in that, The computer program or instruction is executed by the processor to implement the slope landslide monitoring method in any one of claims 1-4.

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