Slope safety monitoring and early warning method based on intelligent particles

Abstract

The invention provides a slope safety monitoring and early warning method based on intelligent particles. The method comprises the following steps: S1, arranging the intelligent particles and building a monitoring system; s2, intelligent particle accumulated corner abnormal value identification based on Z-score; s3, performing intelligent particle cumulative corner time registration based on Kalman filtering and cubic interpolation; s4, intelligent particle cumulative corner fusion based on an adaptive fusion algorithm; s5, carrying out slope deformation stage division based on an accumulative corner time sequence and a tangent angle method; and S6, slope damage moment estimation based on the cumulative corner time sequence and a cumulative corner speed reciprocal method. According to the invention, the intelligent particle sensor is applied to slope safety monitoring and early warning, the accumulated corner is used as an index for dividing the slope deformation stage and estimating the slope damage moment, the slope stability change can be sensitively reflected, and early warning can be issued in time.

Description

Technical Field

[0001] The invention belongs to the field of civil engineering, and specifically designs a slope safety monitoring and early warning method based on intelligent particles. Background Art

[0002] Slope collapse is a major geological disaster that can cause huge losses of property and life. There is a potential risk of landslide disasters in all regions of the world. At present, advanced technical means for slope monitoring and early warning include global navigation satellite systems, interferometric radar, lidar, fiber optic sensing technology and optical remote sensing. However, these technologies are mainly used to monitor the displacement changes on the slope surface. When the slope is affected by the external environment and its stability decreases, deformation and stress begin to change from the inside. Only when the internal changes of the sliding body are sufficient, will the landslide surface show obvious macroscopic deformation. Only when the macroscopic deformation exceeds a certain degree can the surface monitoring equipment capture these changes. Therefore, surface deformation is not a sufficient condition for landslides to occur, and it is difficult to achieve accurate early warning of landslides. Although technologies such as inclinometers and array displacement meters can be used to monitor deep displacement of slopes, these technologies have not been widely and long-term applied due to their limited measurement range and high cost. Inclinometers installed on auxiliary structures can also reflect the stability of slopes through their inclination changes, but inclinometers only use part of the attitude data and fail to give full play to the attitude change information of the sensor. Therefore, for the attitude sensing technology represented by smart particles, developing a set of monitoring and early warning methods that can be directly embedded in the slope is of great significance for slope health assessment and engineering safety assurance. Summary of the invention

[0003] Purpose of the invention: The purpose of the present invention is to provide a slope safety monitoring and early warning method based on intelligent particles, which uses the quaternion data obtained by intelligent particle monitoring to calculate the cumulative rotation angle, achieve more sensitive slope deformation stage division and slope failure time estimation, and timely release early warning information.

[0004] Technical solution, in order to solve the above technical problems and achieve the purpose of the present invention, the present invention proposes a slope safety monitoring and early warning method based on smart particles, comprising the following steps:

[0005] S1. Drill holes in the slope and bury intelligent particle sensors, configure data transmission hardware equipment, and build an intelligent particle monitoring system;

[0006] S2, identification and elimination of outliers of the accumulated rotation angle of smart particles based on Z-score, calculating the angle distance and accumulated rotation angle time series of each smart particle sensor’s quaternion time series, and detecting and eliminating outliers;

[0007] S3, performing time alignment on the cumulative rotation angle time series of each smart particle sensor after removing abnormal values ​​based on Kalman filtering and cubic interpolation method, so that each smart particle sensor has a cumulative rotation angle value at the monitoring time;

[0008] S4, processing the cumulative rotation angle time series of each intelligent particle sensor after time registration obtained in S3 using an adaptive fusion algorithm to obtain a fused cumulative rotation angle time series;

[0009] S5, the fused cumulative rotation angle time series obtained in S4 is processed using the tangent angle method to obtain the division of slope deformation stages and conduct a qualitative analysis of slope safety and stability;

[0010] S6. The fused cumulative rotation angle time series obtained in S4 is processed using the cumulative rotation angle velocity inverse method to obtain an estimated value of the slope failure moment and to conduct a quantitative analysis of the slope safety and stability.

[0011] Furthermore, in step S1, the smart particle is a sensor, equipped with an accelerometer, a gyroscope, a magnetometer, a thermometer and a pressure gauge, and the external shape of the smart particle is a cube, the edge length is 27-40 mm, and the apparent density is 2.18-2.30 g / cm 3 , the intelligent particle placement and monitoring system is built as follows:

[0012] (2.1) Setting monitoring points, drilling holes on the slope surface and burying smart particle sensors. The drilling depth and the burying depth of the smart particles are shallower than the potential sliding surface, and the lowest burying depth is 1m below the slope surface; the monitoring points are arranged on the cross-section lines parallel to the slope direction and the sliding direction of the landslide or perpendicular to the slope direction and the sliding direction of the landslide, and are arranged 10m outside the sliding influence range; for a single slope, the cross-section lines are arranged in the middle and both sides of the slope or landslide, and there are no less than 3 cross-section lines. For a strip slope group, select cross-section lines greater than the preset number. The spacing between monitoring points is 5m to 40m, and the maximum horizontal spacing is not greater than 40m, and the maximum vertical spacing is not greater than 20m; the preset number is obtained by dividing the length of the strip slope by the horizontal spacing;

[0013] (2.2) Backfilling of monitoring points: using backfill materials to fill holes in layers, and each layer is backfilled before the next layer is filled. The backfill material is plain soil or prepared fly ash soil around the monitoring slope;

[0014] (2.3) Monitoring system hardware configuration: remote roadside signal collectors are set up at preset locations on the monitoring slope. Data is transmitted to the remote roadside signal collectors based on the ZigBee protocol and uploaded to the cloud storage in real time.

[0015] Furthermore, in step (2.1), after the smart particles are started, they enter a periodic sleep and wake-up mode. The smart particles should be monitored once every 7 to 14 days, and each wake-up operation should last for 1 to 2 minutes. During the flood season, rainy season, and construction period of prevention and control projects, monitoring should be intensified, once a day or once every few hours.

[0016] The indicator used for slope safety monitoring and early warning is the cumulative rotation angle of the smart particle sensor, which is calculated through the quaternion attitude data jointly solved by the accelerometer, gyroscope and magnetometer data of the smart particle. The smart particle is at the monitoring time t i After waking up, it enters sleep mode and waits for the next wake-up work. The smart particle records several quaternions according to the working frequency, a total of n. These quaternions are first averaged and then normalized as the quaternion data of the monitoring time t:

[0017]

[0018] In formulas (1) to (3), is the monitoring time t i In the first awakening operation, the intelligent particle recorded the mth original quaternion data, a total of n, i, j, k are imaginary numbers, and their coefficients are is the real part of the mth original quaternion data; the monitoring time starts at t0 and the monitoring ends at t N , that is, the time series has a total of N data points;

[0019] represents the monitoring time t i The average quaternion data of the smart particles, the coefficients of the imaginary parts i, j, k are The real part is represents the monitoring time t i The normalized average quaternion data of the smart particles has a modulus of 1;

[0020] The angular distance is calculated based on the quaternion at adjacent monitoring moments, and the cumulative sum of the angular distance is the cumulative rotation angle of the smart particle over time:

[0021]

[0022] In formula (4)-(5), is the previous monitoring time t i-1 To monitoring time t i angular distance; real is the name of the function that takes the real part of the quaternion; represents the previous monitoring time t i-1 The conjugate quaternion of the normalized mean quaternion; is from the initial monitoring time t0 to the current monitoring time t i The total angle distance traveled by the smart particles is the cumulative turning angle.

[0023] Furthermore, in step S2, for the cumulative turning angle time series from each smart particle, the smart particle cumulative turning angle outliers are identified and eliminated based on the Z-score standard score, and the steps are as follows:

[0024] ①Determine the sliding window size as N window ;

[0025] ②For a certain monitoring time t i , using the data points in the window to calculate the local mean and the local standard deviation The angle distance time series of a certain intelligent particle sensor is: That is, if the time series has N data points, then:

[0026]

[0027] ③For each angle distance value, calculate its Z-score:

[0028]

[0029] ④Set threshold Z threshold , if |Z t |Exceeds the preset threshold Z threshold , then it is believed that If it is an outlier, the data judged as an outlier point will be eliminated, and the angle distance after elimination will be accumulated according to time to obtain the cumulative angle time series after outlier elimination. The outlier is identified and eliminated for the angle distance of each smart particle, and the cumulative angle time series is calculated.

[0030] Further, in step S3, the intelligent particle cumulative turning angle time registration based on Kalman filtering and cubic interpolation is performed as follows:

[0031] ① Perform Kalman filtering on the cumulative rotation angle time series of each smart particle sensor to remove noise and smooth the data. The data points on the rotation angle time series of a smart particle patent after Kalman filtering are:

[0032]

[0033] ② Unified time axis, which is composed of the wake-up working time of all smart particle sensors;

[0034] ③ Perform cubic interpolation. For the cumulative rotation angle time series of a certain smart particle, the time t to be interpolated is iAs a benchmark, determine the two most recent cumulative rotation angle data in time, respectively, and construct a cubic polynomial F(x) = A i (t i -x) 3 +B i (t i -x) 2 +C i (t i -x)+D i , the independent variable of the function is x, which represents the monitoring time, and the dependent variable of the function is the accumulated turning angle;

[0035] ④ Solve the coefficient A of the cubic polynomial i ,B i ,C i ,D i , interpolate to obtain F(x), so that the cubic polynomial satisfies: the function values ​​at the interpolation points are equal, The first-order derivative is continuous at the interpolation point. The second-order derivative is continuous at the interpolation point. The accumulated rotation angle time series of all smart particles are time-aligned so that the accumulated rotation angle value of each smart particle exists at all monitoring moments.

[0036] Furthermore, in step S4, the accumulated turning angles of all intelligent particles are fused based on the adaptive fusion algorithm, and the accumulated turning angle time series of all intelligent particles are fused into one accumulated turning angle time series for early warning. At the same monitoring moment, the accumulated turning angles from different intelligent particles are fused according to the weight coefficient, and the size of the weight coefficient depends on the difference from the average value of the accumulated turning angle:

[0037]

[0038] In formulas (10) and (11), SAD ti,l is the monitoring time t i The original cumulative turning angle from the lth smart particle, there are L smart particles in total, is the monitoring time t i The variance of the average cumulative turning angle of the lth smart particle; is the monitoring time t i The weight coefficient of the cumulative turning angle of the lth smart particle; is the monitoring time t i Cumulative rotation angle of fused intelligent particles after adaptive weighted fusion.

[0039] Furthermore, in step S5, based on the cumulative rotation angle time series and the slope deformation stage division of the tangent angle method, the slope safety and stability is qualitatively analyzed as follows:

[0040] The fused intelligent particle cumulative rotation angle time series is divided by the fused intelligent particle cumulative rotation angle rate in the uniform deformation stage of the slope to obtain an improved fused cumulative rotation angle time series; the value of the fused intelligent particle cumulative rotation angle rate from the beginning of monitoring to the present monitoring time is taken and continuously updated. When the slope is judged to have entered the initial acceleration stage according to the tangent angle method of the cumulative rotation angle time series, the updating of the fused intelligent particle cumulative rotation angle rate in the uniform deformation stage of the slope is stopped;

[0041] According to the improved tangent angle of the fused cumulative rotation angle time series, the slope deformation stages are divided as follows:

[0042] Select the adjacent monitoring data of the improved fused cumulative turning angle time series, calculate the slope between the two points, and convert it into an angle, i.e., the tangent angle;

[0043] When the tangent angle of the improved fused cumulative rotation angle time series = 45°, the slope is in the uniform deformation stage, and the warning level is blue, a caution warning;

[0044] When the tangent angle of the improved fused cumulative rotation angle time series is in the range of 45° to 80°, the slope is in the initial accelerated deformation stage, and the warning level is yellow and warning level warning;

[0045] When the tangent angle of the improved fused cumulative rotation angle time series is in the range of 80° to 85°, the slope is in the medium accelerated deformation stage, and the warning level is orange and alert level warning;

[0046] When the tangent angle of the improved fused cumulative rotation angle time series is in the range of 85° to 90°, the slope is in the stage of accelerated deformation and imminent sliding, and the warning level is red, an alarm-level warning.

[0047] Further, in step S6, based on the prediction of slope failure time using the cumulative rotation angle time series and the cumulative rotation angle velocity inverse method, a quantitative analysis of slope safety and stability is performed, the steps are as follows:

[0048] ① Based on the fused cumulative rotation angle time series, the cumulative rotation angle velocity time series is obtained by difference;

[0049] ② Take the inverse of the cumulative angular velocity time series;

[0050] ③ Perform linear fitting on the cumulative inverse time series of angular velocity, and the intersection of the fitting line and the horizontal axis, i.e., the time axis, represents the moment of slope failure;

[0051] When the slope is in the uniform deformation stage, no estimation of the slope failure moment is performed;

[0052] When the slope is in the initial accelerated deformation stage, the estimated failure time using the cumulative angular velocity and the inverse method of the cumulative angular velocity in this stage is the yellow and warning level failure time tcaution ;

[0053] When the slope is in the medium accelerated deformation stage, the estimated failure time using the angular velocity and cumulative angular velocity inverse method in this stage is the orange and warning level failure time t warning ;

[0054] When the slope is in the stage of accelerated deformation and imminent sliding, the estimated destruction time using the cumulative angular velocity and the inverse method of the cumulative angular velocity in this stage is the red and alarm level destruction time t danger .

[0055] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0056] (1) A slope monitoring and early warning system was built based on intelligent particle sensors, roadside signal collectors and cloud servers. It can sensitively analyze slope stability and issue early warnings, providing support for construction projects and operation and maintenance.

[0057] (2) Based on Z-score, Kalman filtering, cubic interpolation and adaptive fusion algorithm, the intelligent particle monitoring data was processed by outlier identification and elimination, smoothing and fusion, which improved the reliability of monitoring and early warning.

[0058] (3) In view of the uncertain relationship between sensor monitoring data and slope status, the accumulated rotation angle of smart particles is associated with the slope deformation stage based on the improved tangent angle method, which can be used as a reference for relevant departments to take corresponding treatment measures.

[0059] (4) Estimate the time of slope failure in stages to provide time reference for relevant departments to evacuate the people. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments will be briefly introduced below. Obviously, the drawings in the following description only relate to some embodiments of the present invention, but are not intended to limit the present invention.

[0061] Figure 1 A technical roadmap for a slope safety monitoring and early warning method based on smart particles provided for this application;

[0062] Figure 2 It is a smart particle layout diagram of a single slope in this application example;

[0063] Figure 3 It is a schematic diagram of the slope deformation stage division based on the fusion of cumulative rotation angle time series and improved tangent angle method in the example of this application;

[0064] Figure 4This is a schematic diagram of slope failure moment estimation based on the fusion of cumulative rotation angle time series and cumulative rotation angle velocity inverse method in this application example. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Unless otherwise defined, the technical terms or scientific terms used herein should be the common meanings understood by people with ordinary skills in the field to which the present invention belongs.

[0066] Example 1

[0067] like Figure 1-4 As shown, the present invention proposes a slope safety monitoring and early warning method based on intelligent particles. The method takes the intelligent particle sensor as the core, and monitors and warns the slope through intelligent particle deployment, system construction, monitoring data preprocessing and calculation. Specifically, the method includes the following steps:

[0068] Step 1: Intelligent particle placement and monitoring system construction.

[0069] like Figure 2 As shown in the figure, a single slope has a slope height of 20m and a slope angle of 60°. Five section lines parallel to the slope direction are set, with a horizontal spacing of 20m between adjacent section lines. Section lines 1 and 5 are outside the potential landslide range. An intelligent particle sensor is buried at the top and foot of the slope, with a burial depth of 5m and 2m respectively. Section lines 2, 3 and 4 are located within the potential landslide range. An intelligent particle sensor is buried at the top, waist and foot of the slope, with a burial depth of 5m, 10m and 2m respectively. The horizontal spacing of the drilling points on the slope surface is 5m, and the vertical spacing is 8m.

[0070] Use a slope drilling machine to drill holes to the specified depth at the designated monitoring point, bury the intelligent particle sensor, and use clay and fly ash mixed with half soil for layered backfilling. The minimum thickness of a layer of layered backfilling is 2m, and the maximum thickness is 4m. After one layer is filled, it should be compacted before filling the next layer.

[0071] A remote roadside signal collector is set up in a geologically stable area near the monitoring slope. Data is transmitted to the remote roadside signal collector according to the ZigBee protocol and uploaded to the cloud storage in real time. The roadside signal collector GLOBAL01 is selected, equipped with batteries and solar power panels, which is a terminal that can realize outdoor reception, calculation processing, remote monitoring, and cloud big data interface.

[0072] The intelligent particles are set to monitor once every 7 days, with each wake-up operation lasting 1 minute; during the flood season, rainy season and construction period of prevention and control projects, monitoring is intensified, once a day.

[0073] Step 2: Perform Z-score-based intelligent particle cumulative angle outlier identification.

[0074] Calculate the cumulative turning angle. For a certain monitoring time t i , using the data points in the window to calculate the local mean and the local standard deviation The angle distance time series of a certain intelligent particle sensor is: but:

[0075]

[0076] For each angular distance value, calculate its Z-score:

[0077]

[0078] Set the threshold Z threshold =3, and identify outliers. Exceeding the preset threshold Z threshold , then it is believed that If it is an outlier, the data determined as an outlier point will be removed. The angle distance after removal is accumulated according to time to obtain the cumulative angle time series after outlier removal.

[0079] Step 3: Intelligent particle cumulative rotation time registration based on Kalman filtering and cubic interpolation.

[0080] The accumulated rotation angle time series of each smart particle sensor is processed by Kalman filtering to remove noise and smooth the data. The rotation angle time series of a smart particle patent after Kalman filtering is: Unified time axis, which is composed of the awakening working time of all intelligent particle sensors. That is, in general, the interval of monitoring data points is one week. During the flood season, rainy season and construction period of prevention and control projects, the monitoring is intensified once a day, and the data interval is one day. i If a smart particle is excluded due to abnormal monitoring value, while other smart particles are normal and recorded and uploaded, the cumulative rotation angle of the smart particle at time t i Completion. The time t to be interpolated i As a benchmark, determine the two most recent cumulative rotation angle data in time, forward and backward, respectively, and construct a cubic polynomial F(x) = a i (t i -x) 3 +B i (ti -x) 2 +C i (t i -x)+D i ,, the independent variable of the function is x, which represents the monitoring time, and the dependent variable of the function is the accumulated rotation angle. Solve the coefficient A of the cubic polynomial i ,B i ,C i ,D i , so that the cubic polynomial satisfies: the function values ​​at the interpolation points are equal, The first-order derivative is continuous at the interpolation point. The second-order derivative is continuous at the interpolation point. Substituting the coefficient and monitoring time, we can get the completed cumulative turning angle data

[0081] Step 4: Intelligent particle cumulative corner fusion based on adaptive fusion algorithm.

[0082] The cumulative turning angle time series of all smart particles are fused into one cumulative turning angle time series for early warning. At the same monitoring moment, the cumulative turning angles from different smart particles are fused according to the weight coefficient, and the size of the weight coefficient depends on the difference from the average value of the cumulative turning angle:

[0083]

[0084] In formulas (4)-(6), is the monitoring time t i The original cumulative turning angle from the lth smart particle, there are L smart particles in total, is the monitoring time t i The variance of the average cumulative turning angle of the lth smart particle; is the monitoring time t i The weight coefficient of the cumulative turning angle of the lth smart particle; is the monitoring time t i Cumulative rotation angle of fused intelligent particles after adaptive weighted fusion.

[0085] Step 5: Slope deformation stage division based on the cumulative rotation angle time series and the improved tangent angle method.

[0086] like Figure 3As shown in the figure, the fused intelligent particle cumulative rotation angle time series is divided by the fused intelligent particle cumulative rotation angle rate in the uniform deformation stage of the slope to obtain the improved fused cumulative rotation angle time series. The value of the fused intelligent particle cumulative rotation angle rate from the beginning of monitoring to the present monitoring time is taken and continuously updated. When the slope is judged to have entered the initial acceleration stage according to the improved tangent angle method of the cumulative rotation angle time series, the fused intelligent particle cumulative rotation angle rate in the uniform deformation stage of the slope is stopped from being updated.

[0087] According to the improved tangent angle of the fused cumulative rotation angle time series, the slope deformation stages are divided, and the division criteria are:

[0088] Select the adjacent monitoring data of the improved fused cumulative turning angle time series, calculate the slope between the two points, and convert it into an angle, i.e., the tangent angle;

[0089] When the tangent angle of the improved fused cumulative rotation angle time series is ≈45°, the slope is in the uniform deformation stage, and the warning level is blue, a caution warning;

[0090] When the tangent angle of the improved fused cumulative rotation angle time series is in the range of 45° to 80°, the slope is in the initial accelerated deformation stage, and the warning level is yellow and warning level warning;

[0091] When the tangent angle of the improved fused cumulative rotation angle time series is in the range of 80° to 85°, the slope is in the medium accelerated deformation stage, and the warning level is orange and alert level warning;

[0092] When the tangent angle of the improved fused cumulative rotation angle time series is in the range of 85° to 90°, the slope is in the stage of accelerated deformation and imminent sliding, and the warning level is red, an alarm-level warning.

[0093] Step 6: Prediction of slope failure time based on the cumulative rotation angle time series and the inverse method of cumulative rotation angle velocity.

[0094] like Figure 4 As shown, based on the fused cumulative rotation angle time series, the cumulative rotation angle velocity time series is obtained by difference; the inverse of the cumulative rotation angle velocity time series is taken; a linear fit is performed on the inverse of the cumulative rotation angle velocity time series, and the intersection of the fitting straight line and the horizontal axis, that is, the time axis, represents the moment of slope failure.

[0095] When the slope is in the uniform deformation stage, no estimation of the slope failure moment is performed;

[0096] When the slope is in the initial accelerated deformation stage, the estimated failure time using the cumulative angular velocity and the inverse method of the cumulative angular velocity in this stage is the yellow and warning level failure time t caution ;

[0097] When the slope is in the medium accelerated deformation stage, the estimated failure time using the angular velocity and cumulative angular velocity inverse method in this stage is the orange and warning level failure time t warning ;

[0098] When the slope is in the stage of accelerated deformation and imminent sliding, the estimated destruction time using the cumulative angular velocity and the inverse method of the cumulative angular velocity in this stage is the red and alarm level destruction time t danger .

[0099] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A slope safety monitoring and early warning method based on intelligent particles, characterized in that: The method comprises the following steps: S1. Drill holes in the slope and bury intelligent particle sensors, configure data transmission hardware equipment, and build an intelligent particle monitoring system; S2, identification and elimination of outliers of the accumulated rotation angle of smart particles based on Z-score, the quaternion time series of each smart particle sensor is used to calculate the angle distance and the accumulated rotation angle time series to detect and eliminate outliers; S3, performing time alignment on the cumulative rotation angle time series of each smart particle sensor after removing abnormal values ​​based on Kalman filtering and cubic interpolation method, so that each smart particle sensor has a cumulative rotation angle value at the monitoring time; S4, processing the cumulative rotation angle time series of each intelligent particle sensor after time registration obtained in S3 using an adaptive fusion algorithm to obtain a fused cumulative rotation angle time series; S5, the fused cumulative rotation angle time series obtained in S4 is processed using the tangent angle method to obtain the division of slope deformation stages and conduct a qualitative analysis of slope safety and stability; S6. The fused cumulative rotation angle time series obtained in S4 is processed using the cumulative rotation angle velocity inverse method to obtain an estimated value of the slope failure moment and to conduct a quantitative analysis of the slope safety and stability.

2. A slope safety monitoring and early warning method based on intelligent particles according to claim 1, characterized in that: In step S1, the smart particle is a sensor, which is equipped with an accelerometer, a gyroscope, a magnetometer, a thermometer and a pressure gauge. The external shape of the smart particle is a cube, the edge length is 27-40 mm, and the apparent density is 2.18-2.30 g / cm 3 , the intelligent particle placement and monitoring system is built as follows: (2.1) Set up monitoring points, drill holes on the slope surface and bury smart particle sensors. The drilling depth and smart particle burial depth are shallower than the potential sliding surface, and the lowest burial depth is 1m below the slope surface; the monitoring points are arranged on the cross-section lines parallel to the slope direction and the sliding direction of the landslide, or perpendicular to the slope direction and the sliding direction of the landslide, and are arranged 10m outside the sliding influence range; for single slopes, the cross-section lines are arranged in the middle and both sides of the slope or landslide, and there are no less than 3 cross-section lines; for strip slope groups, select cross-section lines greater than the preset number, and the spacing between monitoring points is 5m to 40m, with the maximum horizontal spacing not greater than 40m and the maximum vertical spacing not greater than 20m; The preset number is obtained by dividing the length of the strip slope by the horizontal spacing; (2.2) Backfilling of monitoring points: using backfill materials to fill holes in layers, and each layer is backfilled before the next layer is filled. The backfill material is plain soil or prepared fly ash soil around the monitoring slope; (2.3) Monitoring system hardware configuration: remote roadside signal collectors are set up at preset locations on the monitoring slope. Data is transmitted to the remote roadside signal collectors based on the ZigBee protocol and uploaded to the cloud storage in real time.

3. A slope safety monitoring and early warning method based on intelligent particles according to claim 2, characterized in that: In step (2.1), the smart particle enters a periodic sleep and wake-up mode after startup. The smart particle should be monitored once every 7 to 14 days, and each wake-up operation lasts for 1 to 2 minutes; The indicator used for slope safety monitoring and early warning is the cumulative rotation angle of the smart particle sensor, which is calculated through the quaternion attitude data jointly solved by the accelerometer, gyroscope and magnetometer data of the smart particle. The smart particle is at the monitoring time t i After waking up, it enters sleep mode and waits for the next wake-up. The smart particle records multiple quaternions according to the working frequency, a total of n. These quaternions are first averaged and then normalized as the monitoring time t i Quaternion data: In formulas (1) to (3), is the monitoring time t i In the first awakening operation, the intelligent particle recorded the mth original quaternion data, a total of n, i, j, k are imaginary numbers, and their coefficients are is the real part of the mth original quaternion data; the monitoring time starts at t0 and the monitoring ends at t N , that is, the time series has a total of N data points; represents the monitoring time t i The average quaternion data of the smart particles, the coefficients of the imaginary parts i, j, k are The real part is represents the monitoring time t i The normalized average quaternion data of the smart particles has a modulus of 1; The angular distance is calculated based on the quaternion at adjacent monitoring moments, and the cumulative sum of the angular distance is the cumulative rotation angle of the smart particle over time: In formula (4)-(5), is the previous monitoring time t i-1 To monitoring time t i angular distance; real is the name of the function that takes the real part of the quaternion; represents the previous monitoring time t i-1 The conjugate quaternion of the normalized mean quaternion; is from the initial monitoring time t0 to the current monitoring time t i The total angle distance traveled by the smart particles is the cumulative turning angle.

4. A slope safety monitoring and early warning method based on intelligent particles according to claim 3, characterized in that: In step S2, for the cumulative turning angle time series from each smart particle, the smart particle cumulative turning angle outliers are identified and eliminated based on the Z-score standard score, and the steps are as follows: ①Determine the sliding window size as N window ; ②For a certain monitoring time t i , using the data points in the window to calculate the local mean and the local standard deviation The angle distance time series of a certain intelligent particle sensor is: That is, if the time series has N data points, then: ③For each angle distance value, calculate its Z-score: ④Set threshold Z threshold , if |Z t |Exceeds the preset threshold Z threshold , then it is believed that If it is an outlier, the data judged as an outlier point will be eliminated, and the angle distance after elimination will be accumulated according to time to obtain the cumulative angle time series after outlier elimination. The outlier is identified and eliminated for the angle distance of each smart particle, and the cumulative angle time series is calculated.

5. A slope safety monitoring and early warning method based on intelligent particles according to claim 4, characterized in that: In step S3, the intelligent particle cumulative rotation time registration based on Kalman filtering and cubic interpolation is as follows: ① Perform Kalman filtering on the cumulative rotation angle time series of each smart particle sensor to remove noise and smooth the data. The data points on the rotation angle time series of a smart particle patent after Kalman filtering are: ② Unified time axis, which is composed of the wake-up working time of all smart particle sensors; ③ Perform cubic interpolation. For the cumulative rotation angle time series of a certain smart particle, the time t to be interpolated is i As a benchmark, determine the two most recent cumulative rotation angle data in time, respectively, and construct a cubic polynomial F(x) = A i (t i -x) 3 +B i (t i -x) 2 +C i (t i -x)+D i , the independent variable of the function is x, which represents the monitoring time, and the dependent variable of the function is the accumulated turning angle; ④ Solve the coefficient A of the cubic polynomial i ,B i ,C i ,D i , interpolate to obtain F(x), so that the cubic polynomial satisfies: the function values ​​at the interpolation points are equal, The first-order derivative is continuous at the interpolation point. The second-order derivative is continuous at the interpolation point. The accumulated rotation angle time series of all smart particles are time-aligned so that the accumulated rotation angle value of each smart particle exists at all monitoring moments.

6. A slope safety monitoring and early warning method based on intelligent particles according to claim 5, characterized in that: In step S4, the accumulated turning angles of smart particles are fused based on the adaptive fusion algorithm, and all the accumulated turning angle time series of smart particles are fused into one accumulated turning angle time series for early warning. At the same monitoring time, the accumulated turning angles from different smart particles are fused according to the weight coefficient, and the size of the weight coefficient depends on the difference from the average value of the accumulated turning angle: In formulas (10)-(11), is the monitoring time t i The original cumulative turning angle from the lth smart particle, there are L smart particles in total, is the monitoring time t i The variance of the average cumulative turning angle of the lth smart particle; is the monitoring time t i The weight coefficient of the cumulative turning angle of the lth smart particle; is the monitoring time t i Cumulative rotation angle of fused intelligent particles after adaptive weighted fusion.

7. A slope safety monitoring and early warning method based on intelligent particles according to claim 6, characterized in that: In step S5, based on the cumulative rotation angle time series and the slope deformation stage division of the tangent angle method, the slope safety and stability are qualitatively analyzed as follows: The fused intelligent particle cumulative rotation angle time series is divided by the fused intelligent particle cumulative rotation angle rate in the uniform deformation stage of the slope to obtain an improved fused cumulative rotation angle time series; the value of the fused intelligent particle cumulative rotation angle rate from the beginning of monitoring to the present monitoring time is taken and continuously updated. When the slope is judged to have entered the initial acceleration stage according to the tangent angle method of the cumulative rotation angle time series, the updating of the fused intelligent particle cumulative rotation angle rate in the uniform deformation stage of the slope is stopped; According to the improved tangent angle of the fused cumulative rotation angle time series, the slope deformation stages are divided as follows: Select the adjacent monitoring data of the improved fusion cumulative turning angle time series, calculate the slope between the two points, and convert it into an angle, i.e., the tangent angle; When the tangent angle of the improved fused cumulative rotation angle time series = 45°, the slope is in the uniform deformation stage, and the warning level is blue, a caution warning; When the tangent angle of the improved fused cumulative rotation angle time series is in the range of 45° to 80°, the slope is in the initial accelerated deformation stage, and the warning level is yellow and warning level warning; When the tangent angle of the improved fused cumulative rotation angle time series is in the range of 80° to 85°, the slope is in the medium accelerated deformation stage, and the warning level is orange and alert level warning; When the tangent angle of the improved fused cumulative rotation angle time series is in the range of 85° to 90°, the slope is in the stage of accelerated deformation and imminent sliding, and the warning level is red, an alarm-level warning.

8. A slope safety monitoring and early warning method based on intelligent particles according to claim 7, characterized in that: In step S6, based on the prediction of slope failure time using the cumulative rotation angle time series and the cumulative rotation angle velocity inverse method, a quantitative analysis of slope safety and stability is performed, and the steps are as follows: ① Based on the fused cumulative rotation angle time series, the cumulative rotation angle velocity time series is obtained by difference; ② Take the inverse of the cumulative angular velocity time series; ③ Perform linear fitting on the cumulative inverse time series of angular velocity, and the intersection of the fitting line and the horizontal axis, i.e., the time axis, represents the moment of slope failure; When the slope is in the uniform deformation stage, no estimation of the slope failure moment is performed; When the slope is in the initial accelerated deformation stage, the estimated failure time using the cumulative angular velocity and the inverse method of the cumulative angular velocity in this stage is the yellow and warning level failure time t caution ; When the slope is in the medium accelerated deformation stage, the estimated failure time using the angular velocity and cumulative angular velocity inverse method in this stage is the orange and warning level failure time t warning ; When the slope is in the stage of accelerated deformation and imminent sliding, the estimated destruction time using the cumulative angular velocity and the inverse method of the cumulative angular velocity in this stage is the red and alarm level destruction time t danger .

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