Monitoring and early warning method and system for linear displacement of highway slope diseases

Abstract

The invention relates to the technical field of slope engineering monitoring engineering, in particular to an expressway slope disease linear displacement monitoring and early warning method and system, which are used for solving the technical problem that in the prior art, an existing expressway slope disease monitoring mode has high dependence on images, so that the number of configuration equipment and system components is large. The method specifically comprises the following steps: selecting a predetermined monitoring slope area for monitoring and measurement, and representing a monitoring and measurement result as a slope section in a cloud picture form; selecting a coordinate point with a trend direction in a slope section formed by the cloud picture as a measuring point; performing stability judgment on the horizontal displacement sequence of the monitoring points on the monitoring line, and distinguishing the horizontal displacement sequence into a stable sequence or a trend sequence; calculating to obtain an average value sequence predicted value based on the distinguished stationary sequence and trend direction sequence, and calculating to obtain a deformation displacement predicted value according to the average value sequence predicted value; and calculating a residual value between the deformation displacement predicted values, and when the residual value exceeds a preset range, outputting alarm information.

Description

Technical Field

[0001] The present invention relates to the technical field of slope engineering monitoring, and particularly to a method and system for monitoring and warning linear displacement of slope diseases on expressways. Background Art

[0002] Highway slopes are designed with specific slope surfaces to ensure the stability of the roadbed. Essentially, they are slope structures set up to balance the earth pressure generated by road filling and excavation. However, due to the influence of the natural environment and geological conditions, there are various diseases on highway slopes. The main types of slope diseases include: (1) slope collapse, which is affected by natural climate and geological factors and is highly sudden, commonly found in steep cut sections. When the collapse scale is large, it can cause road interruption; (2) slope landslide, which is triggered by geological or external load changes; (3) exfoliation and relaxation, where the surface rock and soil gradually fall off due to weathering or rain erosion; (4) dislocation, where different rock and soil masses slide in blocks along multiple slip surfaces. These disease types are affected by geological factors and climate factors, such as increased rainfall infiltration increasing the weight of the soil and reducing the shear strength, that is, exceeding the designed bearing capacity. Obviously, appropriate monitoring settings are extremely necessary, which can reduce potential safety hazards and property losses to a certain extent;

[0003] In the current methods, the problems in the monitoring and detection of slopes are limited detection range, low coverage rate, and most detections and monitoring rely on visual recognition equipment, which requires a lot of equipment resources and human resources, resulting in high costs. That is, the previous detection and monitoring methods are difficult to meet the requirements. In emerging detection technologies, it mainly relies on obtaining three-dimensional deformation data to obtain the deformation data of the x, y, and z axes of the slope respectively, that is, to achieve multi-dimensional monitoring, but a variety of monitoring equipment types are involved;

[0004] For example, in a Chinese invention patent with the application number CN202310899505.5 and the authorization announcement number CN116630899B, and the patent name "A Monitoring and Warning System for Highway Slope Diseases", which belongs to the field of image processing technology. The main implementation method of this patent is to perform grayscale processing on the highway slope image to obtain a grayscale image, then based on the feature threshold, extract the pixel points with texture features to obtain a texture feature map, then perform enhancement processing on the texture feature map to enhance the texture and improve the texture resolution, and then identify the enhanced texture map through the disease recognition unit. This patent has a strong dependence on images and has problems such as a large variety of monitoring configuration devices and complex configuration;

[0005] For another example, in the case of a Chinese invention patent with the application number CN201510168823.X and the authorized announcement number CN104713491B, and the patent title "Slope Monitoring System Capable of Obtaining Three-Dimensional Slope Deformation Data and Method for Obtaining Three-Dimensional Slope Deformation Data", the core of this technical solution is that through an information processing device, by establishing an original three-dimensional coordinate system and converting the three-dimensional coordinate system, the three-dimensional data of the deformed slope relative to the slope of the previous day and the three-dimensional data relative to the slope in the initial state are calculated. This monitoring system is not affected by weather factors, can monitor slope deformation in real time, and can accurately determine whether the slope bulges forward or backward or moves left or right or up or down. The technology it actually adopts combines long-distance high-precision laser ranging technology and image recognition technology for comprehensive calculation, and finally obtains the three-dimensional deformation amount of the slope. Its technical advantages are mainly reflected in real-time performance, but the comprehensive data it collects is large, and the monitoring also depends on a large amount of image recognition data;

[0006] In addition, the prevention and control technology that uses GNSS technology to track displacement changes in real time is also one of the main methods for slope monitoring at present, but it requires relying on rich network resources; Based on the above analysis, it is obvious that the characteristics of existing technologies for monitoring highway slopes are that the monitoring depends greatly on actual slope images, and the configured equipment and auxiliary systems are numerous and complex, and there is also a lack of corresponding preventive mechanisms; The problem of stability prediction and early warning is directly related to the safety of highway sections. In order to effectively and predictably respond to slope diseases, it is extremely necessary to strengthen the management and control of safety evaluation and prediction, and timely maintain and handle highway slope diseases in the early stage. Summary of the Invention

[0007] The present invention aims to solve the technical problems in the existing technologies for monitoring highway slope diseases, where the existing methods have a great dependence on images, resulting in a large number of configured equipment and system components, and a lack of prediction and defense mechanisms, and provides a method and system for monitoring and early warning of linear displacement of highway slope diseases.

[0008] In order to solve the above technical problems, the technical solution of the present invention is specifically as follows:

[0009] First, for the method for monitoring and early warning of linear displacement of highway slope diseases, it includes:

[0010] Step S1, select a predetermined monitoring slope area for monitoring and measurement, and present the monitoring and measurement results in the form of a cloud map as a slope profile; Step S2, set the coordinate origin of the slope profile, form a plane coordinate system of the slope profile based on the coordinate origin, determine multiple abscissas at equal intervals, obtain the corresponding ordinates of the multiple abscissas as coordinate points, and select the coordinate points with a trend in the slope profile formed by the cloud map as measurement points;

[0011] The selection rule for the measurement points is: select the coordinate points with the ordinates increasing in sequence and multiple random coordinate points as the measurement points;

[0012] Step S3: Mark the measurement points in the monitored slope area as monitoring points, take the linear displacement connection lines between the monitoring points as monitoring lines, and connect multiple said monitoring lines;

[0013] Judge the stationarity of the horizontal displacement sequence of the monitoring points on the monitoring line, and classify them into a stationary sequence or a trending sequence;

[0014] Step S4: Calculate the predicted value of the average value sequence, including:

[0015] Calculate the predicted value of the average value sequence based on the distinguished stationary sequence and trending sequence, and then calculate the predicted value of the deformation displacement according to the predicted value of the average value sequence;

[0016] Step S5: Set a selection threshold, calculate the residual value between the predicted values of the deformation displacement, and output an alarm message when the residual value exceeds the predetermined range.

[0017] Further, in the selection rule of the measurement points, when selecting multiple random coordinate points, the ordinate of the selected random coordinate point is less than the ordinate of an adjacent measurement point;

[0018] The selection of the random coordinate points can be actively selected based on the address environment information shown in the cloud map or the measurement points with lithological similarity.

[0019] Further, the process of the monitoring measurement is as follows:

[0020] The parameters of the cloud map are measured by a slope radar, and the measurement method of the slope radar is the differential interferometry method;

[0021] The slope radar emits radar waves to continuously measure and scan the slope rock mass to obtain the displacement value of a single scan area, and after accumulating the monitoring data, obtain the displacement cloud map of the real-time deformation situation of the slope rock mass;

[0022] The slope radar is connected to a slope radar communication system with a relay station and a terminal.

[0023] The judgment steps of the stationary sequence and the trending sequence include:

[0024] Step S101: Set that the horizontal displacement value of the monitoring points of the highway slope with a trending direction in the monitored slope area has a random interference term ;

[0025] Define the random interference term as a stationary sequence term ;

[0026] The specific process of obtaining the average horizontal displacement sequence of the monitoring line is as follows:

[0027] Preset the average deformation value sequence of the monitoring points that make up the overall monitoring slope area on the monitoring line as ;

[0028] Among them, the deformation sequence of the monitoring point is expressed as , set as the length of the deformation sequence, then the average deformation value sequence is expressed by formula (1):

[0029] (1).

[0030] Furthermore, it also includes the following process:

[0031] Obtain the stationary sequence terms and trend terms of the average deformation value sequence;

[0032] When the deformation sequence of the highway slope has an upward trend, it is divided into a time series with stationary variance and a time series with non-stationary variance;

[0033] Set the variance as , and the unified calculation formula (2) is:

[0034] (2);

[0035] In formula (2), is the average value of the average deformation value sequence , which can be expressed by formula (3):

[0036] (3);

[0037] Among them, for the time series with stationary variance, the predicted value of the stationary sequence term is calculated by the least squares formula;

[0038] Among them, for the time series with non-stationary variance, the original sequence is logarithmically transformed to obtain the predicted value of the trend term .

[0039] Furthermore, the calculation of the predicted value of the average value sequence is expressed as calculation formula (4):

[0040] The predicted value of the average value sequence is: (4).

[0041] Further, it includes: calculating the deformation prediction value of each monitoring point, specifically as formula (5), and the deformation prediction value of the monitoring point is: : (5);

[0042] Formula (5), is the predicted value of the trend term of the i-th monitoring point;

[0043] is the predicted value of the stationary term of the average value sequence.

[0044] In addition, according to the monitoring and early warning method for the linear displacement of highway slope diseases described above, a monitoring and early warning system for the linear displacement of highway slope diseases is adopted. This system includes:

[0045] A radar monitoring and acquisition module, which is used to scan and measure the highway slope in the monitoring range, and output the monitoring data as the cloud map output graph and the monitoring profile coordinate values;

[0046] A monitoring point selection module, which is used to receive the coordinate values scanned and measured by the radar monitoring and acquisition module, select based on the selection rules of the measuring points, and output a monitoring line graph with monitoring points;

[0047] A linear prediction and judgment module, which judges the stationarity of the horizontal displacement sequence of the monitoring points on the monitoring line, and classifies them into a stationary sequence or a trend sequence;

[0048] An average value sequence predicted value calculation module, which calculates the average value sequence predicted value based on the stationary sequence and the trend sequence, and then calculates the deformation displacement predicted value according to the average value sequence predicted value;

[0049] A result output module, which can calculate the residual value between the deformation displacement predicted values based on the selected set threshold, and when the residual value exceeds the predetermined range, send out a warning message through a warning module.

[0050] The present invention has the following beneficial effects:

[0051] First, based on the current common monitoring equipment, that is, by obtaining the cloud map of the monitored slope area to obtain the data of the monitoring points, without using other equipment and implementations to collect separate image data and image information, it can reduce the investment in technology and cost;

[0052] In the second aspect, the average deformation prediction value of the monitoring line in this technical solution is used as an overall slope deformation prediction index, which can comprehensively reflect the deformation trend of the monitoring line and is a favorable means for predicting the overall deformation of the slopes in this area, that is, the slope disease situation. It has higher reliability compared to single-point monitoring analysis;

[0053] In the third aspect, in this technical solution, the overall prediction of highway slope deformation has higher reliability than single-point deformation prediction. It can exclude data anomalies in single-point detection deformation, weaken the influence of single-point deformation on the overall prediction, and improve the accuracy of monitoring and prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0055] Figure 1 It is a schematic flow chart of the method implementation of the present invention;

[0056] Figure 2 It is a schematic configuration diagram of the radar monitoring and acquisition module of the present invention;

[0057] Figure 3 It is an example of a scanned cloud map of the monitored slope area of the present invention;

[0058] Figure 4 It is a schematic diagram of the selection rule of the measuring points of the present invention;

[0059] Figure 5 It is the prediction result of the average value sequence of the present invention;

[0060] Figure 6 It is a time history curve of different monitoring points on a monitoring line of the horizontal displacement of the present invention;

[0061] Figure 7 It is a schematic diagram of the system module construction adopted by the present invention.

[0062] REFERENCE NUMERALS 100. Radar monitoring and acquisition module, 200. Monitoring point selection module, 300. Linear prediction and judgment module, 400. Average value sequence prediction value calculation module, 500. Result output module, 600. Early warning module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. It should be noted that for the convenience of description in this application, the "left side" in the current view is taken as the "first end", the "right side" as the "second end", the "upper side" as the "first end", and the "lower side" as the "second end". The purpose of such description is to clearly express the technical solution and should not be construed as an improper limitation of the technical solution of this application.

[0064] The present invention aims to solve the technical problems in the prior art that the existing methods for monitoring highway slope diseases rely heavily on images, resulting in a large number of configured devices and system components, and lacking a prediction and prevention mechanism. The existing research direction relies on image data for the disease monitoring of highway slopes and comprehensively monitors the data. Theoretically, the single-point monitoring method is adopted. The defect of single-point monitoring is that it is difficult to effectively establish the correlation of information generated by the monitoring points, and auxiliary derivation needs to be carried out from the image data.

[0065] In fact, during the construction stage of highway slopes, their structural shapes are uniform, mainly the basic sections of the slopes, which have all undergone manual construction, and the slopes of highway slopes have also been adjusted and maintained, so that highway slopes have initial safety at the completion stage of the highway. After completion, through continuous time accumulation and environmental change accumulation, disease types gradually occur. Obviously, the occurrence of disease types is related to the overall deformation trend and deformation law of the slope, that is, the displacement deformation of slope measurement points can reflect the occurrence of diseases. However, the single-point processing in the prior art cannot effectively and directly utilize relevant information. Therefore, this technical solution first proposes a monitoring and warning method for the linear displacement of highway slope diseases, that is, presenting the monitored single points in the form of a monitoring line, and using the correlation between the single-point change and the monitoring line change to predict linear deformation, that is, through linear deformation, the changes caused by various disease types can be reflected. The specific method steps are as follows:

[0066] Step S1, select a predetermined monitoring slope area for monitoring and measurement, and present the monitoring and measurement results in the form of a cloud map as a slope profile. Step S2, set the coordinate origin of the slope profile, form a plane coordinate system of the slope profile based on the coordinate origin, determine multiple abscissas at equal intervals, obtain the corresponding ordinates of the multiple abscissas as coordinate points, and select the coordinate points with a trend in the slope profile formed by the cloud map as measurement points.

[0067] The selection rule for the measurement points is: select the coordinate points with sequentially increasing ordinates and multiple random coordinate points as measurement points.

[0068] As shown in the attached Figure 3 As shown in the figure, the measuring points of the obtained trend items are the coordinate points Y1, Y2, Y3, Y4, and the random measuring point YS. This technical solution uses four trend measuring points and one random item measuring point to illustrate the specific situation; in the attached Figure 3 In the figure, through the plane coordinate system of the slope section, the continuous measuring points of the slope are measured by a nephogram. In order to reflect the prediction of possible diseases of the slope, this technical solution selects the measuring points with trend items in the way of equal spacing of the abscissa. Since this slope is in the section of the highway, that is, the part of this type of slope below the coordinate origin O is the construction slope, and this is the natural slope in this plane coordinate system. Therefore, after establishing the coordinate system with the parameters of the nephogram, this technical solution makes a selection with a trend.

[0069] Step S3: Mark the measuring points in the monitored slope area as monitoring points, take the linear displacement connection line between the monitoring points as the monitoring line, and connect multiple monitoring lines;

[0070] Judge the stationarity of the horizontal displacement sequence of the monitoring points on the monitoring line, and distinguish them into a stationary sequence or a trend sequence;

[0071] Step S4: Calculate the predicted value of the average value sequence, including:

[0072] Based on the distinguished stationary sequence and trend sequence, calculate the predicted value of the average value sequence, and then calculate the predicted value of the deformation displacement according to the predicted value of the average value sequence;

[0073] Step S5: Use the residual value between the actual displacement value and the predicted value of the deformation displacement as the selection threshold and conduct real-time early warning.

[0074] The advantages of this technical solution are as follows:

[0075] First, it can be based on the current common monitoring equipment, that is, obtain the data of the monitoring points by obtaining the nephogram of the monitored slope area, and no longer use other equipment and implementations to collect separate image data and image information, which can reduce the input of technology and cost;

[0076] Second, in this technical solution, the average deformation predicted value of the monitoring line is used as the overall deformation prediction index of the slope, which can comprehensively reflect the deformation trend of the monitoring line and is a favorable means for predicting the overall deformation of the slope in this area, that is, the slope disease situation, and has higher reliability compared with single-point monitoring analysis;

[0077] Thirdly, in this technical solution, the overall prediction of the deformation of the highway slope is more reliable than the single-point deformation prediction. It can eliminate the abnormal data in the single-point detection deformation and weaken the influence of the single-point deformation on the overall prediction, thus improving the accuracy of the monitoring and prediction.

[0078] In a specific implementation process, when selecting the measuring points with a trend in the cloud map, in the selection rule of the measuring points, when multiple random coordinate points are selected, the ordinate of the random coordinate point is less than the ordinate of an adjacent measuring point.

[0079] The selection of random coordinate points can be actively selected based on the address environment information shown in the cloud map or the measuring points with lithological similarity.

[0080] In a specific implementation process, please refer to the appendix Figure 2 、 3 As shown, the process of presenting the monitoring and measurement results in the form of a cloud map for the slope profile is as follows: The parameters of the cloud map are measured by the slope radar, and the measurement method of the slope radar is the differential interferometry method.

[0081] The slope radar emits radar waves to continuously measure and scan the slope rock mass to obtain the displacement value of a single scan area. After accumulating the monitoring data, a cloud map of the displacement of the real-time deformation of the slope rock mass is obtained.

[0082] The slope radar is connected to a slope radar communication system with a relay station and a terminal.

[0083] In order to conduct a predictive study on the linear deformation of the monitoring line formed by the monitoring points, in this technical solution, the stationarity judgment and analysis are carried out through the horizontal displacement sequence. The judgment steps for the stationary sequence and the trend sequence include:

[0084] Set that the horizontal displacement value of the monitoring points of the highway slope with a trend in the monitored slope area has a random interference term ; [[ID=E28]]

[0085] Define the random interference term as the stationary sequence term ;

[0086] The specific process of obtaining the average value sequence of the horizontal displacement of the monitoring line is as follows:

[0087] Preset the average deformation value sequences of the monitoring points that make up the overall monitored slope area on the monitoring line as ;

[0088] Among them, the deformation sequence of the monitoring point is expressed as , set as the length of the deformation sequence, then the average deformation value sequence formula (1) is expressed as:

[0089] (1).

[0090] In the further implementation process, the following process is also included:

[0091] Obtain the average deformation value sequence of the stationary sequence terms and the trend terms ;

[0092] When the deformation sequence of the highway slope has an upward trend, it is divided into a time series with stationary variance and a time series with non-stationary variance;

[0093] Set the variance to , and the unified calculation formula (2) is:

[0094] (2);

[0095] In formula (2), is the average value of the average deformation value sequence , which can be expressed as formula (3):

[0096] (3);

[0097] Among them, for the time series with stationary variance, the predicted value of the stationary sequence terms is calculated by the least squares formula;

[0098] Among them, for the time series with non-stationary variance, the original sequence is logarithmically transformed to obtain the predicted value of the trend terms .

[0099] The calculation of the predicted value of the average value sequence is expressed as calculation formula (4):

[0100] The predicted value of the average value sequence is: (4);

[0101] Among them, the stationary sequence terms can also calculate the predicted value of the stationary sequence terms based on the ARMA dynamic model;

[0102] Among them, the trend terms can also calculate the predicted value of the trend terms based on the linear trend term model.

[0103] The calculation of the deformation predicted value of each monitoring point is specifically formula (5), and the deformation predicted value of the monitoring point is :

[0104] (5);

[0105] Formula (5), is the predicted value of the trend item at the i-th monitoring point;

[0106] is the predicted value of the stationary item of the average value sequence.

[0107] During the further implementation process, it can be obtained from Appendix Figure 4 , Appendix Figure 5 that the information that can be obtained is that due to the high correlation of the monitoring points on the same monitoring line, the stationary item of the average value sequence , integrating the characteristics of each monitoring point on this monitoring line, is the representative of the stationary sequences of each monitoring point; it is equivalent to passing through multiple monitoring lines, and by applying this sequence, a stationary sequence prediction model with each monitoring point is formed for the monitored slope area. The predicted value of the average stationary sequence calculated is also the predicted value of the stationary sequence of each monitoring point; afterwards, the residual can be calculated through the actual monitoring value, and then by setting a threshold for alarm, the disease alarm information for the linear monitoring of the highway slope can be completed;

[0108] That is, the predicted value of the average deformation of the monitoring line in this technical solution is used as the prediction index for the overall deformation of the slope, which can comprehensively reflect the deformation trend of the monitoring line and is a favorable means for predicting the overall deformation of the slope in this area, that is, the slope disease situation, and has higher reliability compared to the single-point monitoring analysis;

[0109] It can exclude the data anomalies in the single-point detection deformation, weaken the influence of the single-point deformation on the overall prediction, and improve the accuracy of the monitoring prediction.

[0110] Regarding the setting of the threshold and the calculation of the residual, refer to Appendix Figure 4 , Appendix Figure 5 as shown in. The period in Appendix Figure 4 , 5 is set to a period of 8 intervals, with each interval being a continuous measurement of 30 days. The obtained change data is as shown in Appendix Figure 4 . The set range refers to the allowable range of slope displacement, usually ±10 - 20 mm, and 15 mm is selected as the allowable range in this technical solution;

[0111] After obtaining the predicted values of multiple coordinate points, in an implementable example, the average value of the period prediction in Appendix Figure 4 and the set range are used as the set threshold, and the difference between the set threshold and the average value of the predicted value can be used as the residual value;

[0112] The residual value refers to the difference between the slope monitoring data and the predicted value of the theoretical model, which reflects the dispersion degree of the monitoring data during the slope deformation process and is used to verify the reliability of the monitoring system and identify abnormal deformation trends.

[0113] In a specific embodiment, please refer to the attached Figure 2 As shown, the above monitoring and early warning method for the linear displacement of highway slope diseases adopts a monitoring and early warning system for the linear displacement of highway slope diseases. The system includes:

[0114] A radar monitoring and acquisition module 100, which is used to scan and measure the highway slope within the monitoring range and output the monitoring data as a cloud map, a graphic, and the coordinate values of the monitoring profile;

[0115] A monitoring point selection module 200, which is used to receive the coordinate values scanned and measured by the radar monitoring and acquisition module, select based on the selection rules of the measurement points, and output a monitoring line map with monitoring points; a linear prediction and judgment module 300, which judges the stationarity of the horizontal displacement sequence of the monitoring points on the monitoring line and classifies them into a stationary sequence or a trend sequence; an average value sequence prediction value calculation module 400, which calculates the average value sequence prediction value based on the stationary sequence and the trend sequence, and then calculates the deformation displacement prediction value according to the average value sequence prediction value;

[0116] A result output module 500, which can calculate the residual value between the deformation displacement prediction values based on the selected set threshold. When the residual value exceeds the predetermined range, a warning message is sent through a warning module 600. Obviously, the above embodiments are only examples for clear illustration and not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. And the obvious changes or modifications derived therefrom are still within the protection scope of the present invention.

Claims

1. A monitoring and early warning method for linear displacement of highway slope diseases, characterized by: include: Step S1, selecting a predetermined monitoring slope area for monitoring measurement, and displaying the monitoring measurement results as a slope profile in the form of a cloud map; Step S2: setting the coordinate origin of the slope profile, forming a plane coordinate system of the slope profile based on the coordinate origin, determining a plurality of horizontal coordinates at equal intervals, obtaining the vertical coordinates corresponding to the plurality of horizontal coordinates as coordinate points, and selecting the coordinate points with trend directions in the slope profile formed by the cloud map as measuring points; The selection rule of the measuring points is: selecting the coordinate points with increasing vertical coordinates and a plurality of random coordinate points as measuring points; Step S3, marking the measuring points in the monitoring slope area as monitoring points, using the linear displacement lines between the monitoring points as monitoring lines, and connecting multiple monitoring lines; Performing a stationary judgment on the horizontal displacement sequence of the monitoring points on the monitoring line, and distinguishing whether the scan is a stationary sequence or a trending sequence; Step S4, calculating the average value sequence prediction value, including: calculating the average value sequence prediction value based on the differentiated stationary sequence and the trend sequence, and then calculating the deformation displacement prediction value based on the average value sequence prediction value; Step S5: setting a selection threshold, calculating a residual value between the deformation displacement prediction values, and outputting an alarm message when the residual value exceeds a predetermined range.

2. The method for monitoring and early warning of linear displacement of highway slope damage according to claim 1 is characterized in that: In the selection rule of the measuring point, when a plurality of the random coordinate points are selected, the vertical coordinate of the selected random coordinate point is smaller than the vertical coordinate of an adjacent measuring point; The random coordinate points may be selected based on the address environment information or the lithologic similarity measurement points shown in the cloud map.

3. The method for monitoring and early warning of linear displacement of highway slope damage according to claim 2 is characterized in that: The monitoring and measurement process is as follows: The parameters of the cloud map are measured by a slope radar, and the measurement method of the slope radar is a difference interferometry method; The slope radar transmits radar waves to continuously measure and scan the slope rock mass to obtain the displacement value of a single scanning area, and obtains a displacement cloud map of the real-time deformation of the slope rock mass after accumulating the monitoring data; The slope radar is connected to a slope radar communication system having a relay station and a terminal.

4. The method for monitoring and early warning of linear displacement of highway slope damage according to claim 3 is characterized in that: The steps of judging the stationary sequence and the trending sequence include: The horizontal displacement value of the monitoring point of the highway slope in the monitoring slope area trend direction is set to have a random interference term ; The random interference term Defined as a stationary series ; The specific process of obtaining the average value sequence of horizontal displacement of the monitoring line is as follows: Preset the average deformation value sequence of the monitoring points that make up the overall monitored slope area on the monitoring line as ; Among them, the deformation sequence of the monitoring point is expressed as , set as the length of the deformation sequence, and is expressed by the average deformation value sequence formula (1) as: （1）。 5. The monitoring and early warning method for linear displacement of highway slope diseases according to claim 4, characterized in that It also includes the following processes: Obtain the sequence of the average deformation values of the stationary sequence terms and the trend terms ; When the highway slope deformation series has an upward trend, it is divided into a time series with stable variance and a time series with non-stationary variance; Set the variance to be , and the unified calculation formula (2) is as follows: （2）； In Formula (2), is the average value of the average deformation value sequence and can be expressed by Formula (3): （3）； Among them, for a time series with stationary variance, the predicted value of the stationary sequence term is calculated using the least squares formula ; Among them, for a time series with non-stationary variance, the original series is logarithmically transformed to obtain the predicted value of the trend term .

6. The method for monitoring and early warning of linear displacement of highway slope damage according to claim 5 is characterized in that: The calculation of the average value sequence prediction value is expressed as formula (4): The predicted value of the average value sequence is as follows: （4）。 7. The method for monitoring and early warning of linear displacement of highway slope damage according to claim 6, characterized in that: include: The deformation prediction value of each monitoring point is calculated as follows: : （5）； Formula (5), is the predicted value of the trend term of the i-th monitoring point; The predicted value of the stationary series of the mean value.

8. The monitoring and early warning method for linear displacement of highway slope diseases according to claim 7, characterized in that, A monitoring and early warning system for linear displacement of highway slope diseases is used, which includes: A radar monitoring and acquisition module (100) is used to scan and measure the highway slope within the monitoring range, and output the monitoring data in the form of a cloud map and monitoring profile coordinate values; A monitoring point selection module (200) is used to receive the coordinate values scanned and measured by the radar monitoring acquisition module, select based on the selection rule of the measurement points, and output as a monitoring line map with monitoring points; A linear prediction judgment module (300) performs a stationary judgment on the horizontal displacement sequence of the monitoring points on the monitoring line, and distinguishes it as a stationary sequence or a trending sequence; A mean value sequence prediction value calculation module (400) calculates the mean value sequence prediction value based on the stationary sequence and the trend sequence, and then calculates the deformation displacement prediction value based on the mean value sequence prediction value; The result output module (500) is capable of calculating a residual value between deformation displacement prediction values based on a selected set threshold value, and when the residual value exceeds a predetermined range, an early warning message is issued through an early warning module (600).

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