High slope state early warning system based on intelligent internet-of-things perception

Abstract

The invention relates to the technical field of high slope state monitoring, and discloses a high slope state early warning system based on intelligent internet-of-things perception, which comprises a division module, a plurality of data acquisition modules, a holographic monitoring module and an analysis module, the data acquisition module comprises a surface layer data acquisition unit and a deep part data acquisition unit; dividing the high slope into a plurality of monitoring areas through a dividing module; acquiring real-time surface data and real-time deep data of the surface layer of the high slope through a plurality of data acquisition modules; an overall holographic three-dimensional view of the high slope is established through the holographic monitoring module; according to the method, the real-time condition of the high slope can be remotely known by a worker, the real-time surface data and the real-time deep data are analyzed through an analysis module, the safety risk coefficient of each monitoring area is obtained, and the state of each monitoring area is graded; whether early warning is carried out or not is judged; therefore, the state analysis of each monitoring area is more accurate, and the rating result of each monitoring area is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of high slope status monitoring, and in particular to a high slope status early warning system based on intelligent Internet of Things perception. Background Art

[0002] As a basic industry of transportation, civil aviation plays a fundamental, strategic and leading role in the national economy and social development. With the rapid development of civil aviation and aviation safety, it has become an indispensable logistics method in modern international trade, especially for perishable commodities such as fresh seafood and seasonal fruits. Air transportation ensures the freshness and market value of commodities. Therefore, in order to transport goods more conveniently, it is necessary to establish a large number of airports across the country to facilitate the transportation of commodities from all over the country by civil aviation.

[0003] Among them, some cities have many mountains, hills and gullies, complex geological conditions, and high artificial filling heights. The stability of the fill slope has become one of the important factors affecting the construction and operation safety of the airport. During the filling process of the high slope, the lower soil layer will produce excessive compression deformation under the gravity of the upper soil layer. As the filling height increases, the risk of slope instability also increases significantly. Therefore, it is necessary to monitor the stability of the high slope in real time during construction.

[0004] However, the current monitoring method for the stability of high slopes is mostly manual observation, which has low measurement efficiency and cannot perceive the deformation and displacement of the deep high slopes in real time. In addition, manual analysis is required after manual observation, and the analysis speed is slow. If any abnormality occurs in the high slope, it is impossible to issue an early warning in time. Summary of the invention

[0005] The purpose of the present invention is to provide a high slope status early warning system based on intelligent IoT perception to solve the following technical problems: How to automatically determine whether the overall condition of a high slope poses a safety risk.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A high slope status early warning system based on intelligent IoT perception, the early warning system comprising: A division module is used to divide the high slope into several monitoring areas; Multiple data collection modules, corresponding to the monitoring areas one by one, for collecting monitoring data of the corresponding monitoring areas; The data acquisition module includes a surface data acquisition unit and a deep data acquisition unit; the surface data acquisition unit is used to acquire real-time surface data of the surface of the high slope; the deep data acquisition unit is used to acquire real-time deep data of the deep part of the high slope; Holographic monitoring module, used to analyze the real-time surface data and real-time deep data in each monitoring area, and establish an overall holographic three-dimensional view of the high slope based on the analysis results; The analysis module is used to analyze the real-time surface data and real-time deep data in each monitoring area, obtain the safety risk coefficient of each monitoring area, and rate the status of each monitoring area according to the safety risk coefficient of each monitoring area; and determine whether to issue an early warning based on the rating results.

[0007] As a further solution of the present invention: the monitoring area includes an upper slope, a lower slope and a platform arranged between the upper slope and the lower slope; The upper slope and the lower slope each have a plurality of first monitoring points; the first monitoring points are arranged in an array; The surface data acquisition unit includes a plurality of first monitoring units, each of which corresponds to a first monitoring point one by one, and is used to obtain real-time surface point monitoring data corresponding to the first monitoring point; The real-time surface data includes all real-time surface point monitoring data in the corresponding monitoring area.

[0008] As a further solution of the present invention: a plurality of second monitoring points are arranged on the platform; the second monitoring points are arranged in an array; The deep data acquisition unit includes a plurality of second monitoring units, each of which corresponds to a second monitoring point one by one, and is used to obtain real-time deep point monitoring data corresponding to the second monitoring point; The real-time deep data includes all real-time deep point monitoring data in the corresponding monitoring area.

[0009] As a further solution of the present invention: the first monitoring unit includes a tilt sensor, and the real-time surface point monitoring data includes a real-time slope attitude angle value of the corresponding monitoring point; The second monitoring unit includes an acceleration sensor, a soil moisture meter and a second temperature sensor arranged deep in the slope, and the real-time surface point monitoring data includes real-time acceleration values, soil moisture content and real-time deep temperature values ​​in three-axis directions of the corresponding monitoring point.

[0010] As a further solution of the present invention: the slope stability risk coefficient of the monitoring area is calculated by the formula: Calculation of slope stability risk factor ; in, is the judgment function, when hour, ;when hour, ; is the number of the first monitoring points, ; is the number of randomly selected time points within the preset time period, ; is the number of the second monitoring points, ; For the The angle variation curve of the slope attitude angle value at the first monitoring point over time; is the preset attitude angle and angular velocity; is the preset attitude angle change; The end time of the preset time period; The preset duration of the preset time period; The first time point; For the The second monitoring point The displacement change corresponding to each time point; is the preset displacement change; For the The displacement change curve of the slope real-time displacement change at the second monitoring point over time; is the first weight coefficient; is the second weight coefficient; is the third weight coefficient; is the fourth weight coefficient; is the first preset constant; is the second preset constant; is the third preset constant; It is the fourth preset constant.

[0011] As a further solution of the present invention: the slope frost heave risk coefficient of the monitoring area is obtained by the formula; Calculation of slope frost heave risk factor ; in, For the The second monitoring point The soil moisture content corresponding to each time point; To preset soil moisture content; For the The second monitoring point The deep soil temperature corresponding to the time point; To preset the deep soil temperature.

[0012] As a further solution of the present invention: the safety risk coefficient is calculated by the formula: Calculate safety risk factor ; in, is the first comprehensive weight coefficient; is the second comprehensive weight coefficient.

[0013] As a further solution of the present invention: the rating process is: The safety risk factor and the first preset value , the second preset value , the third preset value Make a comparison; ; when When , the rating result is excellent; when When , the rating result is good; when When , the rating result is medium; when When , the rating result is ; when , the rating result is poor.

[0014] Beneficial effects of the present invention: The present invention divides the high slope into several monitoring areas through a division module; then collects real-time surface data and real-time deep data of the surface of the high slope through multiple data acquisition modules; then analyzes the real-time surface data and real-time deep data in each monitoring area through a holographic monitoring module, and establishes an overall holographic three-dimensional view of the high slope according to the analysis results; analyzes the real-time surface data and real-time deep data of each monitoring area through the holographic monitoring module, and displays them in a visual form; enables staff to remotely and in real time understand the real-time situation of the high slope, and then analyzes the real-time surface data and real-time deep data in each monitoring area through an analysis module to obtain the safety risk coefficient of each monitoring area, and rates the status of each monitoring area according to the safety risk coefficient of each monitoring area; and determines whether to issue an early warning according to the rating results; by monitoring the surface and deep part of the high slope in each monitoring area, the status analysis of each monitoring area is more accurate, the rating results of each monitoring area are more accurate, and automatic judgment is realized on whether the overall status of the high slope has a safety risk; enables staff to timely and accurately understand the status of each monitoring area of ​​the high slope without going to the site, and perform corresponding processing according to the rating results. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present invention will be further described below in conjunction with the accompanying drawings.

[0016] Figure 1 A system module framework diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not 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.

[0018] See also Figure 1 As shown, in one embodiment, a high slope state warning system based on intelligent IoT perception is provided, including: A division module is used to divide the high slope into several monitoring areas; Multiple data collection modules, corresponding to the monitoring areas one by one, for collecting monitoring data of the corresponding monitoring areas; The data acquisition module includes a surface data acquisition unit and a deep data acquisition unit; the surface data acquisition unit is used to acquire real-time surface data of the surface of the high slope; the deep data acquisition unit is used to acquire real-time deep data of the deep part of the high slope; Holographic monitoring module, used to analyze the real-time surface data and real-time deep data in each monitoring area, and establish an overall holographic three-dimensional view of the high slope based on the analysis results; The analysis module is used to analyze the real-time surface data and real-time deep data in each monitoring area, obtain the safety risk coefficient of each monitoring area, and rate the status of each monitoring area according to the safety risk coefficient of each monitoring area; and determine whether to issue an early warning based on the rating results; Through the above technical solution, this embodiment divides the high slope into several monitoring areas through the division module; then collects the real-time surface data and real-time deep data of the high slope surface through multiple data acquisition modules; then analyzes the real-time surface data and real-time deep data in each monitoring area through the holographic monitoring module, and establishes an overall holographic three-dimensional view of the high slope according to the analysis results; analyzes the real-time surface data and real-time deep data of each monitoring area through the holographic monitoring module, and displays them in a visual form; so that the staff can remotely understand the real-time situation of the high slope in real time, and then analyzes each monitoring area through the analysis module. Analyze the real-time surface data and real-time deep data in the monitoring area to obtain the safety risk coefficient of each monitoring area, and rate the status of each monitoring area according to the safety risk coefficient of each monitoring area; and determine whether to issue an early warning based on the rating results; by monitoring the surface and deep parts of the high slopes in each monitoring area, the status analysis of each monitoring area is more accurate, the rating results of each monitoring area are more accurate, and it is possible to automatically determine whether the overall status of the high slope has safety risks; so that staff can timely and accurately understand the status of each monitoring area of ​​the high slope without going to the site, and take corresponding measures based on the rating results; It should be noted that the method of obtaining the holographic three-dimensional view is a prior art and will not be described in detail here.

[0019] As an implementation mode of the present invention, the safety risk coefficient includes a slope stability risk coefficient and a slope frost heave risk coefficient; Through the above technical scheme, this embodiment obtains the slope stability risk coefficient and the slope frost heave risk coefficient respectively through analysis of real-time surface data and deep data, evaluates the risk from two aspects of slope stability and frost heave, and improves the accuracy of slope status assessment.

[0020] As an embodiment of the present invention, the monitoring area includes an upper slope, a lower slope and a platform arranged between the upper slope and the lower slope; The upper slope and the lower slope each have a plurality of first monitoring points; the first monitoring points are arranged in an array; As an embodiment of the present invention, the surface data acquisition unit includes a plurality of first monitoring units, each of which corresponds to a first monitoring point one by one, and is used to obtain real-time surface point monitoring data corresponding to the first monitoring point; The real-time surface data includes all real-time surface point monitoring data in the corresponding monitoring area; Through the above technical scheme, this embodiment sets a number of first monitoring points on the upper slope and the lower slope respectively, and places first monitoring units in the first monitoring points respectively, and obtains real-time surface point monitoring data corresponding to the first monitoring points through the first monitoring units; by real-time monitoring of the status of the upper slope and the lower slope, more real-time surface point monitoring data can be obtained in the monitoring area, thereby improving the evaluation results of the surface status of the high slope.

[0021] As an embodiment of the present invention, a plurality of second monitoring points are arranged on the platform; the deep data acquisition unit includes a plurality of second monitoring units, and the second monitoring points are arranged in an array; The second monitoring unit corresponds to the second monitoring point one by one, and is used to obtain real-time deep point monitoring data corresponding to the second monitoring point; The real-time deep data includes all real-time deep point monitoring data in the corresponding monitoring area; Through the above technical solution, this embodiment sets up a number of second monitoring points on the platform, and places second monitoring units in the second monitoring points respectively, and obtains real-time deep point monitoring data corresponding to the second monitoring points through the second monitoring units; the real-time status of the deep part of the platform is monitored, and more real-time deep point monitoring data in the monitoring area can be obtained, thereby improving the evaluation results of the deep status of the high slope.

[0022] As an embodiment of the present invention, the first monitoring unit includes a tilt sensor, and the real-time surface point monitoring data includes a real-time slope attitude angle value of the corresponding monitoring point; The second monitoring unit includes an acceleration sensor, a soil moisture meter and a second temperature sensor arranged in the deep part of the slope, and the real-time surface point monitoring data includes the real-time acceleration value, soil moisture content and real-time deep temperature value in the three-axis direction of the corresponding monitoring point; Through the above technical scheme, this embodiment sets inclination sensors at the first monitoring points of the upper slope and the lower slope to monitor the real-time slope attitude angle values ​​of the first monitoring points; obtains the change in the inclination of the upper slope and the lower slope, so as to judge the structural stability of the upper slope and the lower slope; sets an acceleration sensor deep in the second monitoring point on the platform to obtain the change in the displacement of the internal structure deep in the second monitoring point, sets a soil moisture meter and a second temperature sensor deep in the second monitoring point on the platform to obtain the frost heave risk value deep in the second monitoring point, thereby realizing the stability risk value and frost heave risk value from the surface and deep of the high slope.

[0023] As an implementation mode of the present invention, the slope stability risk coefficient of the monitoring area is calculated by the formula: Calculation of slope stability risk factor ; in, is the judgment function, when hour, ;when hour, ; is the number of the first monitoring points, ; is the number of randomly selected time points within the preset time period, ; is the number of the second monitoring points, ; For the The angle variation curve of the slope attitude angle value at the first monitoring point over time; is the preset attitude angle and angular velocity; is the preset attitude angle change; The end time of the preset time period; The preset duration of the preset time period; The first time point; For the The second monitoring point The displacement change corresponding to each time point; is the preset displacement change; For the The displacement change curve of the slope real-time displacement change at the second monitoring point over time; is the first weight coefficient; is the second weight coefficient; is the third weight coefficient; is the fourth weight coefficient; is the first preset constant; is the second preset constant; is the third preset constant; is the fourth preset constant; Through the above technical solution, this embodiment For the The first monitoring point The angular velocity of the slope attitude angle change at a time point; For the The first monitoring point The difference between the angular velocity of the slope attitude angle change at a time point and the angular velocity of the preset attitude angle; , explain The first monitoring point The angular velocity of the slope attitude angle change at each time point does not exceed the preset angular velocity of the attitude angle; the attitude angle change speed is within a reasonable range, The smaller the risk of the first monitoring point, the smaller the risk of the The smaller the slope surface stability risk is, the greater the slope stability risk coefficient is. The smaller; , explain The first monitoring point The angular velocity of the slope attitude angle change at a time point is greater than the preset angular velocity of the attitude angle; the attitude angle changes too fast, The greater the risk of the first monitoring point, the greater the risk of the The greater the slope surface stability risk at the first monitoring point, the greater the slope stability risk coefficient The bigger; is the absolute value of the attitude angle difference in the preset time period; is the difference between the absolute value of the attitude angle difference in the preset time period and the preset attitude angle change; , explain The absolute value of the attitude angle difference of the first monitoring point in the preset time period does not exceed the difference of the preset attitude angle change; the attitude angle change in the preset time period is within a reasonable range; The smaller the risk of the first monitoring point, the smaller the risk of the The smaller the slope surface stability risk is, the greater the slope stability risk coefficient is. The smaller; when , explain The absolute value of the attitude angle difference in the preset time period of the first monitoring point is greater than the difference in the preset attitude angle change; the greater the attitude angle change in the preset time period; The greater the risk of the first monitoring point, the greater the risk of the The greater the risk of the slope surface at the first monitoring point, the greater the slope stability risk coefficient. The bigger; through right A comprehensive analysis is performed on the slope surface at the first monitoring point to obtain the stability coefficient of the slope surface; the smaller the stability coefficient of the slope surface, the higher the slope stability risk coefficient. The smaller the slope surface stability coefficient is, the greater the slope stability risk coefficient is. The bigger; For the The difference between the displacement change corresponding to a time point and the preset displacement change; , explain The second monitoring point The displacement change corresponding to the time point is within the range of the preset displacement change; The risk of the second monitoring point is smaller, so the The second monitoring point has a smaller risk of deep slope stability; the slope stability risk coefficient The smaller; when , explain The second monitoring point The displacement change corresponding to the time point exceeds the preset displacement change range; The greater the risk of the second monitoring point, the greater the risk of the The greater the slope stability risk at the second monitoring point, the greater the slope stability risk coefficient The bigger; For the The cumulative value of the displacement change of the second monitoring point within the preset time period; For the The difference between the accumulated displacement change value of the second monitoring point within the preset time period and the preset displacement change amount; , explain The cumulative displacement change value of the second monitoring point within the preset time period is within the range of the preset displacement change amount; The risk of the second monitoring point is smaller, so the The lower the slope stability risk is, the lower the slope stability risk coefficient is. The smaller; when , explain The cumulative displacement change value of the second monitoring point within the preset time period exceeds the range of the preset displacement change amount; The greater the risk of the second monitoring point, the greater the risk of the The greater the slope stability risk at the second monitoring point, the greater the slope stability risk coefficient The bigger; through right The deep stability of the slope at the second monitoring point is comprehensively analyzed to obtain the stability coefficient of the deep slope; the smaller the stability coefficient of the deep slope, the higher the slope stability risk coefficient. The smaller the slope is, the greater the stability coefficient of the slope depth is; the greater the slope stability risk coefficient is The bigger; It should be noted that the preset attitude angle and angular velocity , the end time of the preset time period , preset duration of preset time period , preset displacement change , preset attitude angle change , the first weight coefficient , the second weight coefficient , the third weight coefficient , the fourth weight coefficient , the first preset constant , the second preset constant , the third preset constant and the fourth preset constant is a preset value, obtained based on experience. For the The displacement change curve of the real-time displacement change of the slope at the second monitoring point over time is obtained through real-time detection of the acceleration sensor; obtaining the displacement change through the acceleration sensor is a prior art and will not be described in detail here.

[0024] As an implementation mode of the present invention, the slope frost heave risk coefficient of the monitoring area is obtained by the formula; Calculation of slope frost heave risk factor in, For the The second monitoring point The soil moisture content corresponding to each time point; To preset soil moisture content; For the The second monitoring point The deep soil temperature corresponding to the time point; To preset the deep soil temperature; Through the above technical solution, this embodiment for The average soil moisture content of the second monitoring point; for The difference between the average soil moisture content of the second monitoring point and the preset soil moisture content; When , it means that the average soil moisture content does not exceed the preset soil moisture content; therefore, there is no risk of frost heave; when When , it means that the average soil moisture content exceeds the preset soil moisture content; therefore, there is a risk of frost heave; the higher the average soil moisture content, the greater the risk of frost heave; for The average deep soil temperature at the second monitoring point; To preset the deep soil temperature and The difference between the average deep soil temperature of the second monitoring point; When , it means that the average deep soil temperature exceeds the preset deep soil temperature; therefore, there is no risk of frost heave; when When the average soil deep temperature exceeds the preset soil deep temperature, there is a risk of frost heaving. The lower the average soil deep temperature, the greater the risk of frost heaving. It should be noted that the preset soil moisture content and preset deep soil temperature It is a preset value obtained based on experience and will not be described in detail here.

[0025] As an implementation mode of the present invention, the safety risk coefficient is calculated by the formula: Calculate safety risk factor ; in, is the first comprehensive weight coefficient; is the second comprehensive weight coefficient; Through the above technical solution, the slope stability risk coefficient of this embodiment is The larger the value, the lower the stability of the high slope, so the safety risk factor The larger the slope stability risk factor is, the greater the slope stability risk factor is. The smaller it is, the higher the stability of the high slope is, so the safety risk factor The smaller the slope frost heave risk coefficient The larger the value, the lower the risk of frost heave on the high slope. The larger the slope frost heave risk factor is, the greater the slope frost heave risk is. The smaller it is, the lower the risk of frost heave on the high slope is, so the safety risk factor The smaller; It should be noted that the first comprehensive weight coefficient and the second comprehensive weight coefficient It is a preset value obtained based on experience and will not be described in detail here.

[0026] As an implementation mode of the present invention, the rating process is as follows: The safety risk factor and the first preset value , the second preset value , the third preset value Make a comparison; ; when When , the rating result is excellent; when When , the rating result is good; when When , the rating result is medium; when When , the rating result is ; when When , the rating result is poor; Through the above technical solution, this embodiment increases the safety risk factor by and the first preset value , the second preset value , the third preset value To compare; when When , it indicates that the high slope is highly stable and has no risk of frost heave, so the rating result is excellent; when When , the safety risk of the high slope is small, so the rating result is good; when When the safety risk of the high slope is small, the rating result is medium; when When the safety risk of the high slope is high, the rating result is secondary; when When the safety risk of high slope is extremely high, the rating result is poor; When the rating result is poor, an early warning is issued; It should be noted that the first preset value , the first preset value and the first preset value It is a preset value obtained based on experience and will not be described in detail here.

[0027] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A high slope status warning system based on intelligent IoT perception, characterized in that: The early warning system includes: A division module is used to divide the high slope into several monitoring areas; Multiple data collection modules, corresponding to the monitoring areas one by one, for collecting monitoring data of the corresponding monitoring areas; The data acquisition module includes a surface data acquisition unit and a deep data acquisition unit; the surface data acquisition unit is used to acquire real-time surface data of the surface of the high slope; the deep data acquisition unit is used to acquire real-time deep data of the deep part of the high slope; Holographic monitoring module, used to analyze the real-time surface data and real-time deep data in each monitoring area, and establish an overall holographic three-dimensional view of the high slope based on the analysis results; The analysis module is used to analyze the real-time surface data and real-time deep data in each monitoring area, obtain the safety risk coefficient of each monitoring area, and rate the status of each monitoring area according to the safety risk coefficient of each monitoring area; and determine whether to issue an early warning based on the rating results.

2. According to claim 1, a high slope status early warning system based on intelligent IoT perception is characterized in that: The monitoring area includes an upper slope, a lower slope, and a platform arranged between the upper slope and the lower slope; The upper slope and the lower slope each have a plurality of first monitoring points; the first monitoring points are arranged in an array; The surface data acquisition unit includes a plurality of first monitoring units, each of which corresponds to a first monitoring point one by one, and is used to obtain real-time surface point monitoring data corresponding to the first monitoring point; The real-time surface data includes all real-time surface point monitoring data in the corresponding monitoring area.

3. According to claim 2, a high slope status early warning system based on intelligent IoT perception is characterized in that: A plurality of second monitoring points are arranged on the platform; the second monitoring points are arranged in an array; The deep data acquisition unit includes a plurality of second monitoring units, each of which corresponds to a second monitoring point one by one, and is used to obtain real-time deep point monitoring data corresponding to the second monitoring point; The real-time deep data includes all real-time deep point monitoring data in the corresponding monitoring area.

4. According to claim 3, a high slope status early warning system based on intelligent IoT perception is characterized in that: The first monitoring unit includes a tilt sensor, and the real-time surface point monitoring data includes the real-time slope attitude angle value of the corresponding monitoring point; The second monitoring unit includes an acceleration sensor, a soil moisture meter and a second temperature sensor arranged deep in the slope, and the real-time surface point monitoring data includes real-time acceleration values, soil moisture content and real-time deep temperature values ​​in three-axis directions of the corresponding monitoring point.

5. According to claim 4, a high slope status early warning system based on intelligent IoT perception is characterized in that: The slope stability risk factor of the monitoring area is given by the formula: Calculation of slope stability risk factor ; in, is the judgment function, when hour, ;when hour, ; is the number of the first monitoring points, ; is the number of randomly selected time points within the preset time period, ; is the number of the second monitoring points, ; For the The angle variation curve of the slope attitude angle value at the first monitoring point over time; is the preset attitude angle and angular velocity; is the preset attitude angle change; The end time of the preset time period; The preset duration of the preset time period; The first time point; For the The second monitoring point The displacement change corresponding to each time point; is the preset displacement change; For the The displacement change curve of the slope real-time displacement change at the second monitoring point over time; is the first weight coefficient; is the second weight coefficient; is the third weight coefficient; is the fourth weight coefficient; is the first preset constant; is the second preset constant; is the third preset constant; It is the fourth preset constant.

6. According to claim 5, a high slope status early warning system based on intelligent IoT perception is characterized in that: The slope frost heave risk coefficient of the monitoring area is calculated by the formula: Calculation of slope frost heave risk factor ; in, For the The second monitoring point The soil moisture content corresponding to each time point; To preset soil moisture content; For the The second monitoring point The deep soil temperature corresponding to the time point; To preset the deep soil temperature.

7. The high slope status warning system based on intelligent IoT perception according to claim 6 is characterized in that: The safety risk factor is given by the formula: Calculate safety risk factor ; in, is the first comprehensive weight coefficient; is the second comprehensive weight coefficient.

8. The high slope status warning system based on intelligent IoT perception according to claim 7 is characterized in that: The rating process is: The safety risk factor and the first preset value , the second preset value , the third preset value Make a comparison; ; when When , the rating result is excellent; when When , the rating result is good; when When , the rating result is medium; when When , the rating result is ; when , the rating result is poor.

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