Quaternary covering layer slope landslide risk real-time evaluation system based on Internet of Things

Abstract

The invention relates to landslide risk assessment, which is used for solving the problem that landslide risk assessment focuses on single data, neglects the comprehensive influence of related factors, and does not consider the influence of vegetation landslide resistance and rock strength, so that the real-time assessment of landslide risks is inaccurate. The fourth system covering layer slope landslide risk real-time evaluation system based on the Internet of Things comprises a slope monitoring module; according to the method, the vegetation coverage is calculated, the height and crown breadth change rate of the vegetation are analyzed, the influence of the growth condition of the vegetation on the slope stability is evaluated, and the effect of the vegetation on the landslide risk is more accurately judged through the comprehensive vegetation data processing mode; after land types are distinguished, risk judgment is carried out on the rock land based on rock stress related indexes, errors caused by unified standard evaluation are avoided through the distinguishing treatment mode, and the evaluation result better conforms to the actual situation; and the rock strength in the rock land is analyzed in detail, so that the stability of the rock land is mastered more accurately.

Description

Technical Field

[0001] The invention relates to landslide risk assessment, and in particular to a real-time assessment system for landslide risk of a Quaternary overburden slope based on the Internet of Things. Background Art

[0002] The Quaternary system is a geological unit, and the strata formed during this period are called Quaternary strata; the Quaternary strata are mainly composed of loose sediments, such as clay, silt, sand, gravel, etc.; the Quaternary cover slope refers to the slope mainly formed by the loose deposits of the Quaternary period; In the existing technology, when conducting real-time assessment of the landslide risk of Quaternary overburden slope, there are still the following deficiencies: Only focusing on geological data or rainfall data to assess slope landslide risk, ignoring the comprehensive impact of factors related to the anti-landslide performance of vegetation, the assessment is simplistic; At the same time, when assessing the risk of slope landslides, the rock strength within the rocky land is not taken into consideration. Most of them simply distinguish between rocky land and soil land, resulting in inaccurate real-time assessment of landslide risks.

[0003] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0004] The purpose of the present invention is to analyze the height and crown change rate of vegetation by calculating vegetation coverage, and then evaluate the impact of vegetation growth on slope stability. Through this comprehensive vegetation data processing method, the role of vegetation on landslide risk can be more accurately judged; after distinguishing the land types, risk judgment is made based on rock stress-related indicators for rocky land. This differentiated treatment avoids the errors caused by unified standard evaluation and makes the evaluation results more in line with actual conditions; and a detailed analysis of the rock strength in the rocky land can more accurately grasp the stability of the rocky land, solve the problem that landslide risk assessment focuses on single data, ignores the comprehensive influence of other related factors, and does not consider the anti-landslide performance of vegetation and the influence of rock strength, resulting in inaccurate real-time landslide risk assessment, and proposes a real-time landslide risk assessment system for Quaternary cover layer slopes based on the Internet of Things.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A real-time assessment system for landslide risk in the Quaternary overburden layer based on the Internet of Things, including a slope monitoring module, a meteorological monitoring module, and a risk assessment module; The slope monitoring module detects the slope data, geological data, land force data and vegetation data of the slope, and processes the detected slope data, geological data, land force data and vegetation data to obtain a landslide risk estimation value; The meteorological monitoring module detects the rainfall data in the area where the slope is located, and processes the detected rainfall data to obtain the second estimated value of the landslide risk; The risk assessment module receives the landslide risk estimation value 1 and the landslide risk estimation value 2 transmitted by the slope monitoring module and the meteorological monitoring module, calculates the comprehensive risk index, divides the risk level according to the comprehensive risk index, and then issues an early warning based on the risk level.

[0006] As a preferred embodiment of the present invention, the slope monitoring module processes the slope data in the following steps: Step 1: Take the bottom and top of the slope as two fixed points, and the angle between the line connecting the two fixed points and the horizontal plane at the bottom of the slope is the slope angle data , sample the slope and detect the sampling volume data and sampling weight data , the volume data of the slope Calculate the weight of the slope based on the ratio. ; Step 2: Slope force data ; Slope anti-sliding force data , is the cohesion of the slope soil, is the potential sliding surface area, is the internal friction angle of the slope soil; then the safety factor of the slope , safety factor The larger the value, the higher the slope stability and the lower the landslide risk.

[0007] As a preferred embodiment of the present invention, the slope monitoring module processes geological data in the following steps: Step 1: Shear strength of slope soil , is the normal stress data; soil cohesion data and internal friction angle data The larger it is, the harder the geotechnical type data of the slope soil is; Step 2: For the fault area, risk assessment is performed based on the distance and activity of the fault. The influence factor of the distance to the fault is , the closer to the fault, The smaller the value, the higher the landslide risk. For the folded area, when the slope rock layer inclination is consistent with the slope direction, it is easy to cause landslide along the layer. Step 3: The rock layer dip data is , when the sum of the rock layer inclination data and the slope angle data is close to When the total stress data Equal to effective stress data With pore water pressure data The sum of the above factors is comprehensively considered to obtain the slope landslide risk index based on geological data. , , and Weight coefficient.

[0008] As a preferred embodiment of the present invention, the slope monitoring module processes the land stress data in the following steps: Step 1: Divide the slope detection area into grids Square areas of the same size are divided, and several excavation points are randomly selected in the divided square areas to dig shallow pits. The number, shape and size of rocks on the pit wall and bottom are recorded. The ratio of rock to soil in the divided area is calculated. If the ratio of rock to soil is greater than , it is determined to be rocky land, and the number of rocky land is counted and recorded as When determining the landslide risk, the rock stress data is used for judgment; otherwise, it is determined to be soil land, and the amount of soil land is counted and recorded as ,When determining the landslide risk, the soil pressure data is used for judgment; Step 2: Earth pressure data is divided into active earth pressure data and passive earth pressure data. The active earth pressure data at the slope position is obtained by detecting the earth pressure gauge. and passive earth pressure data , then the influence factor of earth pressure is The closer the ratio is to 1, the higher the landslide risk; Step 3: If the rock land ratio , it is judged that the slope area is more stable and the risk of landslide is lower, and the rock strength is judged by rock stress data; the maximum principal stress data of rock and minimum principal stress data , the critical minimum principal stress data when rock failure occurs is , then the influencing factor of rock stress is , The larger the value, the higher the landslide risk; in the ternary coordinate system, three mutually perpendicular stress components are established. , , and the corresponding shear stress , , , then construct the stress tensor and solve the characteristic equation of the stress tensor Calculate the three principal stress data , , , is the stress tensor, is the identity matrix, is the principal stress; Step 4: Define the safety factor of rock by the ratio of rock strength to actual stress. , is the normal stress of the maximum principal stress on the failure surface, is the rock cohesion data; if , the rock is judged to be stable; otherwise, the rock is judged to be less stable; Step 5: If the rock is judged to be in a compressed state and is stable, it indicates that the rock land in the corresponding divided area is stable and is not prone to landslide risk; otherwise, it indicates that the rock land in the corresponding divided area is unstable and has the risk of landslide.

[0009] As a preferred embodiment of the present invention, the slope monitoring module processes the surface and deep displacement data in the following steps: Step 1: Surface displacement data of the slope and deep displacement data Collect the surface displacement data and deep displacement data, and calculate the corresponding mean values ​​of the surface displacement data and deep displacement data according to the collected surface displacement data and deep displacement data. , and standard deviation , , determine the normal range of the data and , and then check each collected surface displacement data and deep displacement data separately. If the first Data , then the data is judged to be an abnormal value; if the first Data , then the data is determined to be an outlier; remove the outlier, then recalculate the mean and standard deviation, and again determine whether there is an outlier. If there is no outlier, the data is determined to be accurate; Step 2: Sort the surface displacement data and deep displacement data according to the acquisition time sequence, then construct a binary coordinate system with the acquisition time as the x-axis and the surface displacement data and deep displacement data as the y-axis, and draw coordinate points in the constructed binary coordinate system, connect adjacent coordinate points, calculate the slope of each connecting line and record it, the slopes of the surface displacement data and deep displacement data connecting lines are and , set the slope corresponding to the landslide risk threshold ; Step 3: When it appears and In both cases, it is determined that the surface displacement is slowly increasing, the slope is slowly deforming, and the slope has the potential risk of landslide; and In both cases, the slope is judged to have the risk of landslide; the correlation coefficient between the deep displacement data and the surface displacement data is marked as , if the preset correlation threshold data , it is determined that the variability of the deep displacement data is consistent with that of the surface displacement data, and the slope has the risk of landslide.

[0010] As a preferred embodiment of the present invention, the slope monitoring module processes the vegetation data in the following steps: Step 1: Divide the vegetation in the slope area into three categories: trees, shrubs, and herbs, and record the quantity data of the three types of vegetation , , , height data , , and crown width data , , The average height and average crown width of trees, shrubs and herbs were calculated, and the vegetation coverage of the slope was obtained by dividing the sum of the products of the average crown width of trees, shrubs and herbs and the number of the three types of vegetation by the total area of ​​the slope. , if the vegetation coverage threshold is preset , it is judged that the vegetation coverage of the slope is large and the landslide risk is reduced; Step 2: Calculate the three types of vegetation The crown width change rate within the time period; then the three types of vegetation were The height change rate and crown width change rate within the time period are compared with the preset height change rate threshold and crown width change rate threshold of the corresponding vegetation respectively. If the height change rate and crown width change rate of the corresponding vegetation are both less than the corresponding preset thresholds, it is determined that the growth of the corresponding type of vegetation is poor and the anti-landslide ability is poor; Step 3: The impact value of vegetation coverage on plant landslide risk index is marked as The impact value of vegetation growth status on plant landslide resistance risk index is marked as , then the slope anti-landslide risk index based on vegetation data is , and is the weight coefficient; Step 4: Slope angle safety factor data , Slope landslide risk index based on geological data , Slope landslide risk index based on soil stress data , Slope anti-landslide risk index based on vegetation data After normalization, the estimated landslide risk is obtained. , , , and They are the weight coefficients represented by slope data, geological data, land stress data and vegetation data respectively.

[0011] As a preferred embodiment of the present invention, the meteorological monitoring module performs the following steps on the rainfall data: Step 1: Retrieve the recorded data of the rain gauge at the slope location and process the retrieved recorded data to obtain rainfall data , rainfall frequency data and rainfall intensity data , is time; the linear regression model is used to fit the variation trend of cumulative rainfall over time. The variation model of cumulative rainfall over time is marked as The model of the variation of the cumulative average rainfall intensity over time is marked as , the temporal variation model of the average rainfall frequency is marked as ; Step 2: Rainfall trend model , , and The weight coefficients represented by the accumulated rainfall data, average rainfall intensity data and average rainfall frequency data respectively; the landslide risk estimation value II .

[0012] As a preferred embodiment of the present invention, the risk assessment module processes the data of the landslide risk estimation value 1 and the landslide risk estimation value 2 in the following steps: Step 1: Obtain the comprehensive risk index , and are the weight coefficients of landslide risk estimation value 1 and landslide risk estimation value 2 respectively; Step 2: If the risk level threshold is 1 , it is judged as a low risk level, and a simple monitoring report is sent to the management personnel via SMS or email to inform them that the slope is in a low risk state; if the risk level threshold is two , it is determined to be a high risk level, and the emergency warning mechanism is immediately activated. Warning signs are set up around the slope, and people in the nearby affected areas are evacuated. At the same time, landslide warning information is issued to a wider area through multiple channels such as radio and television; if , it is judged as a medium risk level, the monitoring frequency is increased, and medium risk warning information is displayed via text messages, emails, and on the interface of the monitoring system to remind managers to pay close attention to the status of the slope.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention realizes real-time monitoring of slope-related data based on the Internet of Things technology; the slope monitoring module and the meteorological monitoring module can continuously obtain data, and process and analyze them in a timely manner; at the same time, the risk assessment module divides the risk level according to the comprehensive risk index, and adopts different early warning methods for different levels; 2. The present invention divides the vegetation in the slope area into three categories: trees, shrubs, and herbs, and records their quantity, height, and crown width data; it not only calculates the vegetation coverage, but also analyzes the height and crown width change rate of the three types of vegetation within a certain period of time, so as to evaluate the impact of vegetation growth on slope stability. Through this comprehensive vegetation data processing method, the role of vegetation in landslide risk can be more accurately judged; 3. After distinguishing the land types, risk assessment is conducted based on rock stress-related indicators for rock land, and on soil land through soil pressure and other related indicators. This differentiated approach avoids the errors caused by unified standard assessment and makes the assessment results more in line with the actual situation. In addition, a detailed analysis of the rock strength in the rocky land, with comprehensive consideration from multiple angles such as stress state, principal stress calculation and safety factor definition, can more accurately grasp the stability of the rocky land, thereby improving the accuracy of the real-time assessment of the entire slope landslide risk. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0015] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0016] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example:

[0017] See also Figure 1 As shown, the real-time assessment system for landslide risk of Quaternary overburden slope based on Internet of Things includes slope monitoring module, meteorological monitoring module and risk assessment module; The slope monitoring module detects the slope data, geological data, land force data and vegetation data of the slope, and processes the detected slope data, geological data, land force data and vegetation data to obtain a landslide risk estimation value; The meteorological monitoring module detects the rainfall data in the area where the slope is located, and processes the detected rainfall data to obtain the second estimated value of the landslide risk; The risk assessment module receives the landslide risk estimation value 1 and the landslide risk estimation value 2 transmitted by the slope monitoring module and the meteorological monitoring module, calculates the comprehensive risk index, divides the risk level according to the comprehensive risk index, and then issues an early warning according to the risk level; Slope data includes slope angle data and slope height data. The larger the slope angle data, the higher the landslide risk; the larger the slope height data, the greater the landslide risk and the greater the energy causing damage; The slope monitoring module analyzes the slope data, taking the bottom and top of the slope as two fixed points, and the angle between the line connecting the two fixed points and the horizontal plane at the bottom of the slope is the slope angle data. , sample the slope and detect the sampling volume data and sampling weight data , the volume data of the slope Calculate the weight of the slope based on the ratio. , then the sliding force data of the slope ; Slope anti-sliding force data , is the cohesion of the slope soil, is the potential sliding surface area, is the internal friction angle of the slope soil; then the safety factor of the slope , safety factor The larger the value, the higher the slope stability and the lower the landslide risk; The slope monitoring module analyzes geological data and the shear strength of the slope soil. , is the normal stress data; soil cohesion data and internal friction angle data The larger the value, the harder the geotechnical type data of the slope soil is. For the fault area, the risk assessment is carried out according to the distance and activity of the fault. The influence factor of the distance to the fault is , the closer to the fault, The smaller the value, the higher the landslide risk. For the fold area, when the slope rock layer inclination is consistent with the slope direction, it is easy to cause landslide along the layer. The rock layer inclination data is , when the sum of the rock layer inclination data and the slope angle data is close to When the total stress data Equal to effective stress data With pore water pressure data The sum of the above factors is comprehensively considered to obtain the slope landslide risk index based on geological data. , , and is the weight coefficient; The land stress data include soil pressure data, rock stress data, slope surface displacement data and deep displacement data; the slope monitoring area is divided into grids and divided into Square areas of the same size are divided, and several excavation points are randomly selected in the divided square areas to dig shallow pits. The number, shape and size of rocks on the pit wall and bottom are recorded. The ratio of rock to soil in the divided area is calculated. If the ratio of rock to soil is greater than , it is determined to be rocky land, and the number of rocky land is counted and recorded as When determining the landslide risk, the rock stress data is used for judgment; otherwise, it is determined to be soil land, and the amount of soil land is counted and recorded as ,When determining the landslide risk, the soil pressure data is used for judgment; The earth pressure data is divided into active earth pressure data and passive earth pressure data. The active earth pressure data at the slope position is obtained by detecting the earth pressure gauge. and passive earth pressure data , then the influence factor of earth pressure is The closer the ratio is to 1, the higher the landslide risk; If the rock land ratio , it is determined that the slope area is more stable and the risk of landslide is lower, and the rock strength is judged by rock stress data; Maximum principal stress data of rock and minimum principal stress data , the critical minimum principal stress data when rock failure occurs is , then the influencing factor of rock stress is , The larger the value, the higher the landslide risk; in the ternary coordinate system, three mutually perpendicular stress components are established. , , and the corresponding shear stress , , , then construct the stress tensor , and then solve the characteristic equation of the stress tensor Calculate the three principal stress data , , , is the stress tensor, is the identity matrix, principal stress; if the values ​​of the three principal stress data are all greater than zero, it is determined that the rock is in a compression state. The rock in the compression state has a tighter internal structure and higher strength; if there is a data with a value less than zero among the three principal stress data, it is determined that the rock is in a tension state in the direction where the principal stress data is less than zero. The rock in the tension state has a loose internal structure and lower strength; Based on the Mohr-Coulomb criterion, the safety factor of rock is defined by the ratio of rock strength to actual stress. , is the normal stress of the maximum principal stress on the failure surface, is the rock cohesion data; if , the rock is judged to be stable; otherwise, the rock is judged to be less stable; If the rock is judged to be in a compressed state and is stable, it means that the rock land in the corresponding divided area is stable and is not prone to landslide risk; otherwise, it means that the rock land in the corresponding divided area is unstable and has the risk of landslide; Surface displacement data for slopes and deep displacement data Collect the surface displacement data and deep displacement data, and calculate the corresponding mean values ​​of the surface displacement data and deep displacement data according to the collected surface displacement data and deep displacement data. , and standard deviation , ,according to Principles to determine the normal range of data and , and then check each collected surface displacement data and deep displacement data separately. If the first Data , then the data is judged to be an abnormal value; if the first Data , then the data is determined to be an outlier; remove the outlier, then recalculate the mean and standard deviation, and again determine whether there is an outlier. If there is no outlier, the data is determined to be accurate; The surface displacement data and deep displacement data are sorted according to the acquisition time sequence, and then a binary coordinate system is constructed with the acquisition time as the x-axis and the surface displacement data and deep displacement data as the y-axis. The coordinate points are plotted in the constructed binary coordinate system, and the adjacent coordinate points are connected. The slope of each connecting line is calculated and recorded. The slopes of the connecting lines of the surface displacement data and the deep displacement data are respectively and , set the slope corresponding to the landslide risk threshold , when it appears and In both cases, it is determined that the surface displacement is slowly increasing, the slope is slowly deforming, and the slope has the potential risk of landslide; and In both cases, the slope is judged to have the risk of landslide; the correlation coefficient between deep displacement data and surface displacement data ; and Respectively Surface displacement data and deep displacement data of data points, is the number of data points; and Greater than zero and below the threshold When the correlation between the deep displacement data and the surface displacement data is determined, if the preset correlation threshold data , it is determined that the variability of deep displacement data is consistent with that of surface displacement data, and the slope has the risk of landslide; Data on factors affecting earth pressure , rock land ratio data , rock safety factor , Correlation coefficient between deep displacement data and surface displacement data Normalization is performed to obtain the slope landslide risk index based on land stress data. , , , and is the weight coefficient; The slope monitoring module analyzes vegetation data, divides the vegetation in the slope area into three categories: trees, shrubs, and herbs, and records the quantity data of the three types of vegetation. , , , height data , , and crown width data , , The average height and average crown width of trees, shrubs and herbs were calculated, and the vegetation coverage of the slope was obtained by dividing the sum of the products of the average crown width of trees, shrubs and herbs and the number of the three types of vegetation by the total area of ​​the slope. , if the vegetation coverage threshold is preset , it is judged that the vegetation coverage of the slope is large and the landslide risk is reduced; The three types of vegetation The height change data within the time period is divided by the average height data of the corresponding vegetation and The product of the three types of vegetation is obtained. The height change rate within the time period is calculated based on the average crown width data and average crown width data of the three types of vegetation. The crown width change rate within the time period; then the three types of vegetation were The height change rate and crown width change rate within the time period are compared with the preset height change rate threshold and crown width change rate threshold of the corresponding vegetation respectively. If the height change rate and crown width change rate of the corresponding vegetation are both less than the corresponding preset thresholds, it is determined that the growth of the corresponding type of vegetation is poor and the anti-landslide ability is poor; The impact value of vegetation coverage on plant landslide resistance risk index is = The impact of vegetation growth status on plant landslide risk index is , when it is determined that all vegetation is growing well, When it is determined that the growth condition of vegetation is poor, , then the slope anti-landslide risk index based on vegetation data is , and is the weight coefficient; Safety factor data for slope angle , Slope landslide risk index based on geological data , Slope landslide risk index based on soil stress data , Slope anti-landslide risk index based on vegetation data After normalization, the estimated landslide risk is obtained. , , , and They are the weight coefficients represented by slope data, geological data, land stress data and vegetation data respectively.

[0018] The meteorological monitoring module processes the rainfall data, retrieves the recorded data of the rain gauge at the slope, and processes the retrieved recorded data to obtain the rainfall data. , rainfall frequency data and rainfall intensity data , is time; the linear regression model is used to fit the variation trend of cumulative rainfall over time. The variation model of cumulative rainfall over time is: The variation model of cumulative average rainfall intensity over time is: , the temporal variation model of average rainfall frequency is: ; Then, the rainfall trend model , , and The weight coefficients represented by the accumulated rainfall data, average rainfall intensity data and average rainfall frequency data respectively; the landslide risk estimation value II ; If the rainfall preset value is , it is judged as light rain and will not cause landslide risk on the slope; if the rainfall preset value is two , it is judged as heavy rain and the slope has landslide risk; if , it is judged as moderate rain, and the slope has a potential risk of landslide; according to the rainfall trend model, after obtaining the weather forecast information within the set time period after the current time point, the cumulative rainfall data, average rainfall intensity data and average rainfall frequency data when the rainfall is light rain, moderate rain and heavy rain are obtained; For a set of time and the corresponding cumulative rainfall , and To make the predicted value With actual value The number with the smallest sum of squared errors, ; Error sum of squares , in order to obtain and The specific value of and Taking partial derivatives, we get , and then Substitution The partial derivative formula is value, and according to The value of The value of and , and The value of .

[0019] The risk assessment module processes the landslide risk estimation value 1 and the landslide risk estimation value 2 to obtain a comprehensive risk index. , and are the weight coefficients of landslide risk estimation value 1 and landslide risk estimation value 2 respectively; if the risk level threshold is 1 , it is judged as a low risk level, and a simple monitoring report is sent to the management personnel via SMS or email to inform them that the slope is in a low risk state; if the risk level threshold is two , it is determined to be a high risk level, and the emergency warning mechanism is immediately activated. Warning signs are set up around the slope, and people in the nearby affected areas are evacuated. At the same time, landslide warning information is issued to a wider area through multiple channels such as radio and television; if , it is judged as a medium risk level, the monitoring frequency is increased, and medium risk warning information is displayed via text messages, emails, and on the interface of the monitoring system to remind relevant personnel to pay close attention to the status of the slope.

[0020] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A real-time assessment system for landslide risk of Quaternary overburden slope based on the Internet of Things, characterized by: Including slope monitoring module, meteorological monitoring module and risk assessment module; The slope monitoring module detects the slope data, geological data, land force data and vegetation data of the slope, and processes the detected slope data, geological data, land force data and vegetation data to obtain a landslide risk estimation value; The meteorological monitoring module detects the rainfall data in the area where the slope is located, and processes the detected rainfall data to obtain the second estimated value of the landslide risk; The risk assessment module receives the landslide risk estimation value 1 and the landslide risk estimation value 2 transmitted by the slope monitoring module and the meteorological monitoring module, calculates the comprehensive risk index, divides the risk level according to the comprehensive risk index, and issues early warning according to the risk level after the division; The meteorological monitoring module processes rainfall data in the following steps: Step 1: Retrieve the recorded data of the rain gauge at the slope location and extract the rainfall data from the recorded data , rainfall frequency data and rainfall intensity data , For time; The linear regression model is used to fit the variation trend of cumulative rainfall over time. The variation model of cumulative rainfall over time is marked as The model of cumulative average rainfall intensity changing over time is marked as , the temporal variation model of the average rainfall frequency is marked as ; Step 2: Rainfall trend model , , and The weight coefficients represented by the accumulated rainfall data, average rainfall intensity data and average rainfall frequency data respectively; the landslide risk estimation value II .

2. The real-time assessment system for Quaternary overburden slope landslide risk based on the Internet of Things according to claim 1 is characterized in that: The data processing steps of the slope monitoring module for slope data are as follows: Step 1: Take the bottom and top of the slope as two fixed points, and the angle between the line connecting the two fixed points and the horizontal plane at the bottom of the slope is the slope angle data , sample the slope and detect the sampling volume data and sampling weight data , the volume data of the slope Calculate the weight of the slope based on the ratio. ; Step 2: Slope force data ; Slope resistance data , is the cohesion of the slope soil, is the potential sliding surface area, is the internal friction angle of the slope soil; then the safety factor of the slope .

3. The real-time assessment system for Quaternary overburden slope landslide risk based on the Internet of Things according to claim 2 is characterized in that: The data processing steps of the slope monitoring module for geological data are as follows: Step 1: Shear strength of slope soil , is the normal stress data; for the fault area, the influence factor of the distance to the fault is preset as ; Step 2: Preset the rock formation dip data as , total stress data Equal to effective stress data With pore water pressure data The slope landslide risk index based on geological data is calculated by the formula , , and is the weight coefficient.

4. The real-time assessment system for Quaternary overburden slope landslide risk based on the Internet of Things according to claim 3 is characterized in that: The data processing steps of the slope monitoring module for land force data are as follows: Step 1: Divide the slope detection area into grids Divide the square area into square areas of the same size, randomly select excavation points in the divided square area to dig shallow pits, record the number, shape and size of rocks on the pit wall and bottom, and then calculate the ratio of rock to soil in the divided area. If the ratio of rock to soil is greater than , it is determined to be rocky land, and the number of rocky land is counted and recorded as When determining the landslide risk, the rock stress data is used for judgment; otherwise, it is determined to be soil land, and the amount of soil land is counted and recorded as ; Step 2: Earth pressure data is divided into active earth pressure data and passive earth pressure data. The active earth pressure data at the slope position is obtained by detecting the earth pressure gauge. and passive earth pressure data , then the influence factor of earth pressure is ; Step 3: If the rock land ratio , the rock strength is judged by the rock stress data; the influencing factors of rock stress are preset as ; In the ternary coordinate system, establish three stress components in mutually perpendicular directions , , and the corresponding shear stress , , , then construct the stress tensor and solve the characteristic equation of the stress tensor Three principal stress data are calculated , , , is the stress tensor, is the identity matrix, is the principal stress; Step 4: Define the safety factor of rock by the ratio of rock strength to actual stress. , is the normal stress of the maximum principal stress on the failure surface, is the rock cohesion data; Step 5: Data on factors affecting earth pressure , rock land ratio data , rock safety factor , Correlation coefficient between deep displacement data and surface displacement data Normalization is performed to obtain the slope landslide risk index based on land stress data. , , , and is the weight coefficient.

5. The real-time assessment system for Quaternary overburden slope landslide risk based on the Internet of Things according to claim 4 is characterized in that: The data processing steps of the slope monitoring module for surface and deep displacement data are as follows: Step 1: Surface displacement data of the slope and deep displacement data Collect the surface displacement data and deep displacement data, and calculate the corresponding mean values ​​of the surface displacement data and deep displacement data according to the collected surface displacement data and deep displacement data. , and standard deviation , , determine the normal range of the data and , and then check each collected surface displacement data and deep displacement data separately. If the first Data , then the data is judged to be an outlier; If the deep displacement data Data , then the data is judged to be an outlier; Remove outliers and recalculate the mean and standard deviation; Step 2: Sort the surface displacement data and deep displacement data according to the acquisition time sequence, then construct a binary coordinate system with the acquisition time as the x-axis and the surface displacement data and deep displacement data as the y-axis, and draw coordinate points in the constructed binary coordinate system, connect adjacent coordinate points, calculate the slope of each connecting line and record it, the slopes of the surface displacement data and deep displacement data connecting lines are and , set the slope corresponding to the landslide risk threshold .

6. The real-time assessment system for Quaternary overburden slope landslide risk based on the Internet of Things according to claim 5 is characterized in that: The data processing steps of the slope monitoring module for vegetation data are as follows: Step 1: Divide the vegetation in the slope area into three categories: trees, shrubs, and herbs, and record the quantity data of the three types of vegetation , , , height data , , and crown width data , , The average height and average crown width of trees, shrubs and herbs were calculated, and the vegetation coverage of the slope was obtained by dividing the sum of the products of the average crown width of trees, shrubs and herbs and the number of the three types of vegetation by the total area of ​​the slope. ; Step 2: Calculate the three types of vegetation The crown width change rate within the time period; then the three types of vegetation were The height change rate and crown width change rate within the time period are compared with the preset height change rate threshold and crown width change rate threshold of the corresponding vegetation respectively. If the height change rate and crown width change rate of the corresponding vegetation are both less than the corresponding preset thresholds, it is determined that the growth of the corresponding type of vegetation is poor and the anti-landslide ability is poor; Step 3: Mark the impact value of vegetation coverage on plant landslide risk index as The impact value of vegetation growth status on plant landslide resistance risk index is marked as , then the slope anti-landslide risk index based on vegetation data is , and is the weight coefficient; Step 4: Safety factor for slope angle , Slope landslide risk index based on geological data , Slope landslide risk index based on soil stress data , Slope anti-landslide risk index based on vegetation data After normalization, the estimated landslide risk is obtained. , , , and They are the weight coefficients represented by slope data, geological data, land stress data and vegetation data respectively.

7. The real-time assessment system for Quaternary overburden slope landslide risk based on the Internet of Things according to claim 6 is characterized in that: The data processing steps of the risk assessment module for landslide risk estimation value 1 and landslide risk estimation value 2 are as follows: Step 1: Obtain comprehensive risk index , and are the weight coefficients of landslide risk estimation value 1 and landslide risk estimation value 2 respectively; Step 2: If the risk level threshold is 1 , it is judged as a low risk level, and a monitoring report is sent to the management personnel via SMS or email to inform them that the slope is in a low risk state; If the risk level threshold is 2 , it is determined to be a high risk level, and the emergency warning mechanism is immediately activated. Warning signs are set up around the slope, people in the nearby affected areas are evacuated, and landslide warning information is issued to the corresponding areas through radio and television; if , it is judged as a medium risk level, the monitoring frequency is increased, and medium risk warning information is displayed via text messages, emails, and on the interface of the monitoring system to remind managers to pay close attention to the status of the slope.

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