Abstract
A large number of high slopes will be formed in the construction of expressways in many mountainous areas, which is very unfavorable to the construction of local expressways. Effective monitoring methods are essential to solving these problems, and the Internet of Things technology is a good dynamic monitoring method. Therefore, it is necessary to carry out research on the dynamic monitoring system of rock and soil high slope based on the Internet of Things perception. The purpose of this article is to solve the problem of dynamic monitoring of high slopes, taking highway slopes in Chongqing as an example, making full use of the literature method, field investigation method, regression analysis method, time series analysis method, and theoretical analysis method. Through real-time monitoring of the entire process of rock and soil high slope construction in different locations in a certain area in the southwest, and continuously adjusting and verifying the monitoring situation and solving related problems, the high slope instability at this point was accurately monitored And at any time find the weak links in the construction process of high slopes, and further find its potential quality and safety risks, and then establish a comprehensive monitoring system and early warning system. The research results show that this comprehensive monitoring and early warning system improves the accuracy of early warning and monitoring from qualitative judgment to quantitative analysis and from macro to micro monitoring, and can provide a reference for other types of projects.
Introduction
Since the 1980 s, driven by reform and opening-up policies, China’s economy has begun to develop at a high speed. As convenient transportation is increasingly important for economic development, China’s territory is very wide and the terrain is complex. For economic development and the construction of people’s livelihood projects, China has invested heavily in infrastructure projects. At that time, China had become the world’s largest hydropower development and Development Company. State of civil engineering [1]. Later, in different fields, different engineering directions also brought many high-slope projects. However, the terrain and geological conditions in China are very complicated, especially in the southwest and other related areas. Due to the influence of plateau geological activities, the gerontological characteristics of alpine canyons have been formed. A large number of high and steep slopes [2]. In addition, it brought unprecedented high slope instability to the construction of large hydropower stations in the local area. In large-scale engineering construction, rock high slopes are used as the basic environment of engineering buildings. Engineering construction will largely break the equilibrium state of the original natural slopes, causing the slopes to deviate from or even away from the equilibrium state. Control and management Improper will bring the slope deformation and instability, forming a slope geological disaster [3]. Therefore, the stability of high rock slopes involves not only the safety of the project itself, but also the safety of the overall environment; the instability of the high rock slopes will not only directly destroy the project construction itself, but also pass environmental disasters. Impacts and disasters on people’s lives and property safety [4]. The research on the stability of high rock slopes has become a hot and difficult scientific and engineering problem in the field of Technicolor engineering in China in the 21st century [5]. The causes of slope instability are very complex, and the instability of key slopes will generally cause serious consequences.
Although the causes of high rock and soil slope instability are complex, the causes can be found and solved. Generally, the instability and failure of rock slope engineering generally have a development process from gradual change to abrupt change and some kind of precursor before failure [6]. But often because the factors affecting the stability of high slopes are complicated, the mechanical parameters and stable state of the slope rock and soil are not only difficult to determine but also not static [7]. Therefore, it is difficult to find the development process of the slope state by people’s intuition and experience [8]. Therefore, it is necessary to rely on the installation of various monitoring instruments for thorough monitoring of rock and soil. Therefore, safety monitoring has become an important method for determining the stable state of slopes because it can obtain the true state of the slope at different times and is suitable for long-term evaluation [9]. It is undoubtedly important to find a safe, reliable and accurate monitoring method. In recent years, a technology called the Internet of Things has been born and developed rapidly. It originated from the media field and is known as the third revolution of the information technology industry [10]. The Internet of Things connects everything with the network through certain technical means, and objects exchange information and communicate through information transmission media to achieve intelligent identification, positioning, and monitoring functions. Among them, highly sensitive sensors and embedded technology are making monitoring easier and more accurate. Applying the Internet of Things technology to the comprehensive safety monitoring of the construction process of high rock and soil slopes is bound to be more secure and reliable.
In order to better monitor the structural weakness of high slopes, a dynamic monitoring and early warning system is established in this paper. Among them, LI Bing quan introduced the environmental characteristics of high slope in detail and pointed out the importance and feasibility of the monitoring system [11]. Xin Li put forward the application of the Internet of Things in monitoring environmental changes in his article, and explained the establishment of a dynamic monitoring system based on the Internet of Things and the main technical problems, and made a solution to this [12]. He H and Yan Z Elaborated in this article the feasibility of establishing a dynamic monitoring system for high-slope Technicolor slopes based on the Internet of Things perception, and independently set up a monitoring system and applied it to actual engineering. A large amount of monitoring data [13]. You Z Put forward some common problems in the Internet of Things technology and put forward ideas for problems that may be encountered in building a corresponding monitoring system [14]. Wang W proposed the general concept of the common technology IoT when building a monitoring system based on the Internet of Things [15]. DuHan proposed the application of the monitoring system in practical engineering and the solution of common problems [16]. Yury proposed the specific construction process of the monitoring system, which is very useful for the establishment of the target system [17]. Ahmad analyzed the common stability problems of high Technicolor slopes, especially the causes and precursors of slope instability [18]. D W. Wang proposed various pressures for high slopes of rock and soil and provided the objective equations and constraints for the construction of the monitoring system model [19]. Zhou X R and others put forward the technical implementation problems of the early warning system part of the monitoring system, laying a theoretical foundation for building an early warning system [20].
In short, this article focuses on the establishment of a dynamic monitoring system for high-slope rock and soil based on the Internet of Things perception, which is to understand and analyze the causes and precursors of high-slope instability, and then converts them into specific data based on these characteristics. Then set up specific measures for its dynamic monitoring system based on the Internet of Things. Specifically, the main research content of this article is roughly divided into five parts: the first part is the introduction part, which aims to systematically summarize the main research content of this article from the research background, research purpose, research ideas and methods; the second part is The theoretical basis, a detailed and systematic summary of the current theory and methods of security monitoring and the current research status of security monitoring in slope engineering, and the current application of the Internet of Things technology in monitoring is also introduced. The third part is related research. The monitoring system is established from the aspects of overall and function through specific survey data and monitoring results. The fourth part is to improve the design and inspection of the design results. The integrated dynamic monitoring system and early warning system based on the Internet of Things perception of high-slope rock and soil are actually applied to the construction process of high-slope fields. The deficiencies are further improved and amended; the fifth part is the summary and recommendations of this article, a summary of the results of the article, and an outlook for the dynamic monitoring system of technical high slopes based on the Internet of Things perception.
Monitoring and early warning system design
Research method
(1) Literature method
According to the specific research content and research purpose of this article, I consulted more than 100 domestic and foreign literates through relevant libraries and other relevant places in the school library, China Hollowness, Wan fang Data Platform, and China Academic Journals Full-text Database, including the Internet of Things, Related environmental knowledge such as perceptual environment, high slope, monitoring system, etc., and then according to the specific research content of this article, practically solve the corresponding problems in combination with the actual situation.
(2) Field inspection method
By participating in a project carried out in a certain area in the southwest, and taking the rock slope of the project as an example, the corresponding research content, research objects, and research methods were formulated, and a large amount of relevant data was collected to summarize the reading literature. The theoretical knowledge achievements have been checked and improved.
(3) Regression analysis method
In order to establish an early warning system, in the development of slope deformation prediction methods, because displacement is one of the important information fed back during the change of rock and soil structure, it can intuitively and accurately reflect high slopes and rock masses. Deformed state and easy to measure. Therefore, the information obtained by measuring the displacement is transformed into data through regression analysis, and the internal relationships are found and integrated into a functional relationship as a basis for establishing an early warning system.
(4) Timing analysis method
Because the development and evolution of high-slope Technicolor engineering systems are affected and interfered with by many unknown factors, and their appearance is somewhat random, time series analysis can be used to study the problems in this project.
(5) Theoretical analysis method
Based on a large amount of data information and established equations, a dynamic monitoring system and early warning system for rock and soil high and low slopes based on the Internet of Things perception environment are established. Before being applied in practice, it is subjected to a large number of theoretical calculations and numerical simulations to the entire system. Detailed theoretical analysis of reliability, safety, etc.
Monitoring and early warning system overall design and functional design
Based on the characteristics of the construction slope, the monitoring system has determined a monitoring policy of “economical and reasonable, convenient and efficient, wireless transmission, unattended, real-time evaluation, and automatic warning”. Slope deformation and rainfall are used as the main monitoring variables. Based on GPRS Network to realize remote real-time monitoring of high slope stability. The overall design scheme of the monitoring system is rough as follows: the solar energy supply detection unit and data collector and GPRS module and other functional devices are required. First, the monitoring unit is identified and determined, and then a large number of relevant sensors are used to collect a large amount of relevant data. The wireless devices use this information to transfer to wireless base stations, etc., and then feed it back to the monitoring center. The monitoring center feed-backs and solves customer requirements and problems.
The overall design of the early warning system is roughly the same as that of the monitoring system. It also uses solar energy to supply forecasting data collectors and GPRS modules, which require functional equipment. First, the monitoring unit is identified and determined, and then a large number of relevant sensors are used to collect a large amount of relevant data. The wireless device of the device uses the information to transfer to the wireless base station, etc., and then feeds it back to the monitoring center. The monitoring center feedbacks and solves customer requirements and problems.
According to the needs analysis, the monitoring system can be divided into three major functional modules: a data acquisition module, a monitoring and early warning analysis module, and a disaster assessment release module. The data acquisition module includes sensors, data collection, data transmission and power supply, lightning protection and other maintenance measures. The monitoring analysis and early warning module include real-time monitoring, implementation evaluation and safety early warning. The disaster release module includes disaster assessment and information release.
The function of the early warning system mainly reports the severe situation of disasters in three levels. The high-slope early warning level is divided into three levels, two levels, and one level according to the different deformation stages of the slope, which correspond to the constant velocity deformation stage, accelerated deformation stage, and near the sliding stage of slope deformation, respectively. The system finally developed in this paper is web-based software designed based on the characteristics of the Shijiazhuang highway construction slope, which can realize functions such as dynamic data management and intelligent analysis. After logging in to the system, first, carry out the measurement point layout; after entering the on-site monitoring interface, add setting sensors and sequentially number them; after entering the predictive analysis interface, it is displayed as the time curve diagram from the start of project monitoring to the current time, view historical data, and analyze the scene at Changes over time.
Selection of evaluation indicators for monitoring and early warning systems
The evaluation index can reflect the reliability and feasibility of a system. Different evaluation indicators represent the different contents of the system. Whether the evaluation indicators are selected properly is directly related to whether the final evaluation results are true and credible. In selecting evaluation indicators, the principles of typicality, independence, hierarchy, reliability, and quantification should be followed. Typicality: The evaluation index should be clear in concept, capable of measuring and reflecting the characteristics of a certain aspect of the system. Independence: The slope characteristic information reflected by different evaluation indicators should be different. If the evaluation indicators are compatible with each other, consideration should be given to combining the indicators with the same meaning. Hierarchy: The slope stability evaluation is divided into several levels for consideration, and the lower-level evaluation indicators are reasonably determined according to specific diagnostic indicators to form a structural system. Reliability: The evaluation index must truly reflect the condition of the slope. The slope status determined by each evaluation index should be consistent with the actual situation. Quantifiable: To objectively analyze and evaluate the target, you must use quantitative methods. Therefore, the evaluation index should be selected from those that can be quantified according to a certain principle. It should be converted into a quantifiable indicator. “Slope stability is a complex system problem. There are many factors affecting slope stability. However, it is impossible to consider all aspects of the factors when selecting evaluation indicators. Choosing the indicators that can best explain the problem “The project is most concerned about the evolution of the state of the slope, but due to the limitations of understanding, this process is not well understood at present, that is, the evolution of the state of the slope is in one, so the understanding is limited. In the case of a comprehensive evaluation of slope stability, it should be based on the information that most directly reflects the change of slope stability. “Obviously, the on-site monitoring data is the information that most directly reflects the slope status, so from the typical, reliability and from a quantifiable perspective, slope engineering that has undergone long-term safety monitoring should mainly use observation information for synthesis Evaluation.
Experiment preparation and model construction
Related processing of experimental data
The object of this experiment is to experimentally monitor the high slope of a project in a certain area in the southwest to verify the feasibility of the monitoring and early warning system. During the experiment, there is a large amount of monitoring data to be processed, and there must be errors in these data. It is also very important to handle the errors appropriately. Therefore, before using the safety monitoring data for forward and reverse analysis, the error processing and analysis of the original measured data should be performed first. Generally, the errors of safety monitoring data can be classified into system errors, random errors and gross errors. Among them, random errors are often caused by random factors, and their signs and absolute values are irregular. However, as the number of monitoring times increases, random errors are generally considered to be normally distributed. The gross error mainly refers to that during observation, due to the carelessness of the observer, or sudden changes in environmental conditions, unstable instruments, etc., the observation error does not conform to a certain statistical distribution rule, which usually belongs to measurement error. System error is the error caused by the measurement instrument, the change of the measurement reference and the influence of external conditions. At present, the test of systematic errors of observations generally composes corresponding statistics based on the statistical characteristics of observations, and then makes test hypotheses based on its probability distribution characteristics, and makes judgments by comparing actual calculated values with quantifier values. Common test methods are: U test, variance test, t-test and so on. In the measurement process, the gross error should be eliminated, and the system error should be eliminated or weakened, so that the observation value contains only the random error I.
At home and abroad, the least square method is mainly used to process the monitoring data. The least-square method assumes that the observations contain only occasional errors, which is actually impossible. For this reason, a new theory for systematic errors and gross errors has been developed. At present, the more effective method for processing system errors is the additional parameter method; there are two methods for processing gross errors. One is the data detection method that still belongs to the category of least squares, and the second is the method of robustness estimation that is different from the least squares method or robust estimation. In addition, in the actual monitoring project, the measured object is constantly changing, and the measurement system is also in motion. Therefore, it can be considered that safety monitoring is a dynamic measurement, and the errors contained in the monitoring data must be dynamic and time-varying. The gross errors in the monitoring data often make it difficult to determine the statistical distribution or even exceed the general statistical law. Moreover, the monitoring data at different stages have large differences, and it is difficult to meet the requirements for processing data with traditional methods. Modern error theory believes that the measured true value cannot be determined, and the existence of the quantum effect excludes the existence of the unique true value, so the error cannot be accurately obtained. The error used in the measurement in the past is actually a kind of deviation; the measured measurement error is actually the degree of uncertainty in the measurement, that is, the uncertainty. Uncertainty indicates the degree of uncertainty of the measured value due to the existence of measurement errors and is an evaluation of the range of values that characterize the true value of the measurement. Considering the characteristics of slope deformation, the credibility of the measured value can be considered according to the continuity, progressiveness, and rationality of the measured value, that is, the degree to which the measured value can be affirmed. A high degree of confidence indicates that the uncertainty of the measured value is small; a low degree of confidence indicates that the uncertainty of the measured value is high. To describe the credibility of the data, the theory in ascertains mathematics can be used. The following uses the method in ascertainment mathematics to analyze the raw data and handle the error.
Experimental model establishment
After obtaining the on-site monitoring information of the slope, the important work is to analyze the safety status of the slope from the monitoring information and control the safety and stability of the slope during construction and operation. In actual engineering, some slopes with large deformation amount are very safe, and some slopes with small deformation amount are damaged, so it is a very complicated problem to reasonably evaluate the slope safety and stability based on the monitoring results. The relationship between monitoring information and security status is the security monitoring model. Deformation control is the key to controlling the safety and stability of slopes. Therefore, in the research of monitoring models, deformation monitoring models are mainly established. The purpose of establishing a deformation monitoring model is to establish a functional relationship between the amount of cause that affects displacement and the corresponding amount of displacement, that is, to use the information obtained from monitoring to determine the influence of various factors on displacement and establish a function model that reflects the law of displacement change. After obtaining the fitted function of the displacement, the evolution trend of the slope deformation can be predicted and compared with the measured value. The slope state is evaluated based on the difference between the calculated value and the measured value.
Commonly used methods for establishing statistical models include stepwise regression, multiple regression, and weighted regression. There are many factors that affect the deformation of rock slopes, including load factors and environmental factors. However, it is difficult to measure these factors during the monitoring process, and the deformation of rock slopes is mainly caused by time-dependent deformation. Therefore, the influence of time on deformation is mainly considered in the establishment of statistical models. The creep properties of rock materials are the basis of rock mass deformation such as slopes, so the statistical model can be established by referring to the existing rock creep formulas. There are mainly three types of empirical creep formulas for rocks obtained through experiments. These include power functions, logarithmic, and exponential. In addition, the time series of the monitoring model is established to determine the relevant monitoring indicators. After the statistical model is established, the monitoring indicators can be determined. From the knowledge of mathematical statistics, it can be known that when the statistical model established based on the least square method satisfies the conditions of the Gaussian assumption and the normal distribution of residuals, the obtained statistical model is the best-unbiased estimation. This model can be used for overall estimation and prediction. Under normal circumstances, the residual sequence obtained after fitting the observations will not be abnormal; if an abnormal value appears, it may indicate a precursor of instability. The upper and lower limits for judging whether the measured value is abnormal are called monitoring indicators. There are two common methods for establishing monitoring indicators based on statistical models: the confidence interval method and the small probability method.
Experimental conditions and equipment
The monitoring system established in this article mainly uses the Internet of Things perception and identification technology, Internet of Things communication and application layer technology. These technologies and the equipment needed are the main experimental conditions and equipment for this experiment. The so-called Internet of Things perception and identification technology refers to the Internet of Anything collection of information through perception and identification and is the main data source of the Internet of Things. Commonly used technologies are two-dimensional code technology, radio frequency identification RFID technology, infrared sensing technology, GPS satellite positioning technology, audio and visual identification technology, biometric identification technology, etc. Sensing technology mainly embeds sensors around or on an object, collects data of the object or surrounding environment, and senses various physical or chemical changes. Commonly used technologies include sensor technology, radio frequency identification technology, and so on. The sensor (English name: transducer/sensor) is the main source of information for the application of the Internet of Things. It is by sensing the status information of the measured object and converting the perceived information into electrical signals or other forms of information, and then output, satisfying the requirements of information transmission, storage, processing, recording, display and control finally realize the functions of automatic detection and automatic control. The national standard GB7665-87 defines the sensor as A device or device that can sense a specified measured object and convert it into a usable signal according to a certain rule, usually consisting of sensitive elements and conversion elements.
The so-called Internet of Things communication and application layer technology refers to the fact that information technology can be divided into two categories according to the transmission medium: wired communication technology and wireless communication technology. In recent years, with the widespread use of mobile communication equipment (such as mobile phones, tablets, etc.), wireless communication has become the fastest-growing and most widely used communication method. It transmits information from one place in the atmospheric space through electromagnetic wave signals. To another place, so as to realize the wireless transmission of data, the main technologies include radio communication, infrared communication, microwave communication and optical communication. A wireless communication network is a communication network composed of wireless communication devices connected to each other on the basis of communication standards and protocols. In the network, the communication terminal communicates by accessing the network and relying on the network. According to the way of accessing the network, it can be divided into two types: self-organizing network and centralized network with a central control point.
Simulation analysis
Feasibility analysis of high slope deformation pattern recognition
The deformation characteristics of rock slopes during the excavation process are relatively complicated. When encountering unfavorable loads or other factors, the possible deformation and failure modes of the slopes include global sliding, local sliding, shallow sliding, and deep sliding. If the deformation is large and the displacement increases rapidly, the slope may be unstable. On the contrary, the slope as a whole is stable. If the deformation trend of different parts of the slope is greatly different during excavation, and some parts are significantly larger than other parts, it means that the possibility of local failure of the slope is greater. The overall deformation information arranged according to the collected monitoring data, as shown in Table 1:
Analysis of the total deformation of a high slope joint
Analysis of the total deformation of a high slope joint
The data in Table 1 reflects the deformation of a high and low slope collected by the monitoring system. It can be seen that the deformation of the high slope is very large, and it gradually changes from 5-6 mm in 2017 to 20–27 mm in 2019. The problem is very serious, and effective monitoring methods for these parts are necessary. In addition, when slopes are excavated in stages, displacement observation points are usually arranged on slopes or platforms at different levels as the excavation progresses, and the displacement observation data at different elevations can be compared to determine the deformation characteristics of the slope.
For the identification of deformation patterns of high slopes, the stability and reliability of monitoring data need to be determined. That is, after the basic deformation stage of the slope is judged, further analysis and research on the observed value of the slope in the deformation stage are needed to identify the abnormal deformation site. In testing whether the rock mass of the slope has undergone aging deformation, the stability test can be performed by constituting statistics. One of the ideal stability testing methods is the mean squared difference test, which is a method to test whether the mean of the population gradually moves during the observation process. Through this method, the paper uses the coefficient of stability and coefficient of variation to analyze the displacement observation data obtained by the previous monitoring system in detail, which is mainly divided into two time periods. The specific analysis results are shown in Fig. 1:
Monitoring displacement stability coefficient ratio.
From the data in Fig. 1, it can be seen that the stability coefficient of each displacement meter is the largest during the period from March 2017 to June 2018, and the coefficient of variation is basically the smallest, indicating that the horizontal deformation of the slope during this period is compared. Stable. During the period from March 2018 to June 2019, the coefficient of stability was small and the coefficient of variation also increased slightly. From the table above, with the exception of individual measurement points, the slope stability of the slope after 2018 has decreased compared to the second half of 2017.
Based on the deformation of the displacement gauge, the anchor cable dynamo-meters MS1, MS2, MS3, and MS4 on the slope were analyzed in detail to verify the reliability and accuracy of the monitoring system. The arrangement time of the anchor dynamiter is June 2017, so the analysis object is the load observation data after June 2017. The observed data is shown in Fig. 2.
Anchor line dynamo meter displacement data.
It can be seen from Fig. 2 that the coefficient of variation of the anchor cable dynamiter is relatively small, indicating that the measured values are not highly dispersed. In the period from March 2017 to June 2018, except for the small stability coefficient value of MS3, the values of other anchor cable dynamometers are similar. After entering 2018, the values of MS1, MS3 and MS4 have improved significantly. But in general, the data monitored by the monitoring system designed in this paper is not much different, which is in line with the expected results.
The slope monitoring indicators discussed earlier are all based on the idea of deformation control and must be based on a large amount of measured data. Due to the limitation of conditions in actual engineering, it is sometimes difficult to immediately monitor the slope. And generally, before the slope design, the stability of the slope needs to be roughly evaluated in advance to understand the safety of the slope under adverse conditions. Therefore, based on the deformation monitoring of the slope, it is necessary to study the stability of the slope in the limit state. The wreckage and damage of any structure can be attributed to the damage of strength and stability. The strength conditions are tensile and comprehensive strength, and the stability condition is anti-sliding. For strength conditions, tensile and comprehensive stresses, and for sliding conditions, stable conditions. If you want to find a suitable safety factor, you must know the relevant physical and mechanical parameters of high-slope rock and soil materials. The specific parameters are shown in Table 2:
Physical and mechanical parameters of Technicolor materials for slope
Physical and mechanical parameters of Technicolor materials for slope
It can be known from Table 2 that the physical parameters of high-slope rock and soil materials are very different, which poses no small challenge for the corresponding safety and reliability safety factor. Solving the safety factor requires more efficient and reliable methods to achieve. Among them, the strength reduction method is very helpful for the calculation of the safety factor. The proposal of the strength reduction method makes it possible to calculate the safety factor using numerical analysis methods such as finite elements. However, there is generally a long process for the slope to develop from progressive deformation to failure. When the rock and soil at steady state have undergone plastic failure or plastic flow, the amount of deformation is usually large, so problems such as instability in mathematical solutions often occur when using finite element analysis. In addition, there are many other factors that can affect the determination of the safety factor. Among them, the influence of cohesion and friction coefficient on the safety factor is common, as shown in Fig. 3:
Influence of cohesion and friction coefficient on the safety coefficient.
It can be seen from Fig. 3 that the safety factor of the slope decreases significantly with the reduction of cohesion and friction coefficient, and the safety factor decreases faster with the reduction of friction coefficient. This shows that the influence of the friction coefficient on the safety factor of the slope is greater than the cohesive force, that is, the safety factor is more sensitive to the friction coefficient of the material. However, when the reduction factor reaches a certain level, the effects of cohesion and friction coefficient are not much different. Comparing the effects of cohesion and friction coefficient on slope displacement, it can be found that the friction coefficient affects the slope deformation and instability they should be greater than the cohesion, but the degree is different. Therefore, when using numerical calculation methods to evaluate the safety and stability of slopes, we must pay attention to the accuracy of the material strength parameters; especially the determination of the friction coefficient should be as accurate as possible.
The index of safety factor should have a corresponding comprehensive evaluation structure system, which can further improve the reliability and accuracy of the safety factor. According to the relevant standards, the established safety factor index standards are shown in Fig. 4:
For safety factor reliability judgment index.
As can be seen from Fig. 4, the reliability of the judgment safety index is divided into four indexes, of which the normal index accounts for 22% and these indexes are very effective for judging the reliability of the safety factor. Reliable safety factors dominate the implementation of the designed monitoring and early warning system, which is essential for the systematic monitoring of changes in the high-slope environment.
This article analyzes the common problems existing in high slopes of rock and soil, discusses these problems without solving them, and proposes corresponding solutions. Introduced the Internet of Things technology, combined the Internet of Things technology with dynamic monitoring of the high-slope environment and established a monitoring and early warning system including a monitoring unit, a data acquisition unit, and a remote data transmission unit. The research on the identification of deformation patterns of high slopes is carried out, and the corresponding technical and theoretical guidance is proposed to solve the problem of the slope system deformation pattern identification of the monitoring system. The problem of the safety factor in high slope is discussed, and the evaluation standard of safety factor reliability and the specific solution method of safety factors are established.