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

Abstract

The present invention relates to the technical field of slope engineering monitoring, and in particular to a monitoring and early warning method and system for linear displacement of highway slope diseases. The method and system are used to solve the technical problem in the prior art that existing highway slope disease monitoring methods are highly dependent on images, resulting in a large number of configured equipment and system components. The method specifically comprises: selecting a predetermined monitoring slope area for monitoring and measurement, and presenting the monitoring and measurement results as a slope profile in the form of a cloud map; selecting coordinate points with trend directions in the slope profile formed by the cloud map as measuring points; judging the stationarity of horizontal displacement sequences of monitoring points on the monitoring line, and distinguishing them into stationary sequences or trending sequences; calculating an average value sequence prediction value based on the distinguished stationary sequence and trending sequence, and then calculating a deformation displacement prediction value based on the average value sequence prediction value; and calculating a residual value between the deformation displacement prediction values, and outputting an alarm message when the residual value exceeds a predetermined range.

Description

Technical Field

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

[0002] Highway slopes are designed to ensure the stability of the roadbed through a specific slope design. In essence, they are slope structures set up to balance the earth pressure generated by road fill and excavation. However, with the influence of natural environment and geological conditions, highway slopes have various diseases. The main types of slope diseases include (1) slope collapse, which is affected by natural climate and geological factors and is sudden and common in steep excavation sections. When the collapse is large, it can cause road interruption; (2) slope landslide, which is caused by changes in geological or external loads; (3) spalling and relaxation, in which the surface rock and soil gradually fall off due to weathering or rain erosion; (4) staggering, in which different rock and soil bodies slide in blocks along multiple sliding surfaces; these types of diseases are affected by geological factors and climate factors, such as rainfall infiltration, which increases the weight of the soil and reduces the shear strength, that is, they exceed the designed bearing capacity. Obviously, appropriate monitoring settings are extremely necessary, which can reduce safety hazards and property losses to a certain extent.

[0003] The current monitoring and inspection methods for slopes are limited in scope and low in coverage. Furthermore, most inspection and monitoring rely on visual recognition equipment, requiring a lot of equipment and human resources, resulting in high costs. This means that previous inspection and monitoring methods are unable to meet demand. Emerging inspection technologies primarily rely on acquiring three-dimensional deformation data, obtaining deformation data for the slope's x, y, and z axes, thus achieving multi-dimensional monitoring. However, this requires multiple types of monitoring equipment.

[0004] For example, the Chinese invention patent, application number CN202310899505.5, authorization announcement number CN116630899B, patent name "A Highway Slope Disease Monitoring and Early Warning System", belongs to the field of image processing technology. The main implementation method of this patent is to grayscale process the highway slope image to obtain a grayscale image, then extract pixels with texture features based on feature thresholds to obtain a texture feature map, then enhance the texture feature map to enhance the texture and improve the texture resolution, and then identify the enhanced texture map through a disease recognition unit. This patent has a strong dependence on images and has the problem of many types of monitoring configuration equipment and complex configuration;

[0005] Another example is the Chinese invention patent application number CN201510168823.X, authorization announcement number CN104713491B, and the patent name is "Slope monitoring system capable of obtaining three-dimensional slope deformation data and method for obtaining three-dimensional slope deformation data thereof". The core of this technical solution is to use an information processing device to establish an original three-dimensional coordinate system and a converted three-dimensional coordinate system to calculate the three-dimensional data of the deformed slope relative to the slope of the previous day and the three-dimensional data of the slope relative to the initial state. The monitoring system is not affected by weather factors and can monitor slope deformation in real time. It can accurately determine whether the slope is convex forward or backward, or shifted left or right, or up or down. The technology actually used combines long-distance high-precision laser ranging technology with image recognition technology to perform comprehensive calculations and ultimately obtain the three-dimensional deformation of the slope. Its technical advantage is mainly reflected in real-time performance, but it collects a lot of comprehensive data, and monitoring also relies on a large amount of image recognition data.

[0006] In addition, the use of GNSS technology to track displacement changes in real time is also one of the main methods of slope monitoring at present, but it requires the use of abundant network resources. Based on the above analysis, it is obvious that the characteristics of the existing technology for monitoring highway slopes are that the monitoring relies heavily on actual slope images, and the equipment and auxiliary systems are numerous and complex, and there is also a lack of preventive response mechanisms. Stability prediction and early warning issues are directly related to the safety of highway sections. In order to effectively and predictably deal with slope diseases, it is extremely necessary to strengthen the management and control of safety evaluation and prediction, and to promptly maintain and treat highway slope diseases in the early stages. Summary of the Invention

[0007] The present invention aims to solve the technical problems in the existing technology regarding highway slope disease monitoring, namely, the high reliance on images, resulting in the configuration of multiple equipment and system components, and the lack of a predictive defense mechanism, and provides a monitoring and early warning method and system for the linear displacement of highway slope diseases.

[0008] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0009] The first is a monitoring and early warning method for linear displacement of highway slope diseases, including:

[0010] Step S1: Select a predetermined monitoring slope area for monitoring and measurement, and present the monitoring and measurement results as a slope profile in the form of a cloud map. Step S2: Set a coordinate origin of the slope profile, form a plane coordinate system of the slope profile based on the coordinate origin, determine multiple horizontal coordinates at equal intervals, obtain vertical coordinates corresponding to the multiple horizontal coordinates as coordinate points, and select the coordinate points with trend directions in the slope profile formed by the cloud map as measuring points.

[0011] 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;

[0012] 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;

[0013] Performing a stationary judgment on the horizontal displacement sequence of the monitoring points on the monitoring line to distinguish it as a stationary sequence or a trending sequence;

[0014] Step S4, calculating the average sequence prediction value, includes:

[0015] The average value sequence prediction value is calculated based on the differentiated stationary sequence and the trend sequence, and the deformation displacement prediction value is calculated based on the average value sequence prediction value;

[0016] 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.

[0017] Furthermore, 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;

[0018] The random coordinate points may be selected based on the address environment information or the lithologic similarity measurement points shown in the cloud map.

[0019] Furthermore, the monitoring and measurement process 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 a difference interferometry method;

[0021] 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;

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

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

[0024] Step S101, setting the horizontal displacement value of the monitoring point of the highway slope in the monitoring slope area trend direction to have a random interference term ;

[0025] The random interference term Defined as a stationary series ;

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

[0027] The monitoring slope area constituting the whole of the monitoring line is pre-set The average deformation value sequence of the monitoring points is ;

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

[0029] (1).

[0030] Furthermore, the following process is also included:

[0031] Get the average deformation value sequence The stationary series term and trend items ;

[0032] 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;

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

[0034] (2);

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

[0036] (3);

[0037] Among them, for a time series with stationary variance, the predicted value of the stationary series item is calculated using the least squares formula ;

[0038] Among them, for the time series with non-stationary variance, the original series Perform logarithmic transformation to obtain the trend term The predicted value of .

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

[0040] The mean value series predicts the value for: (4).

[0041] Furthermore, it includes: calculating the deformation prediction value of each monitoring point, specifically according to formula (5), the deformation prediction value of the monitoring point is: : (5);

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

[0043] The predicted value of the stationary series of the mean value.

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

[0045] The radar monitoring and acquisition module is used to scan and measure the highway slopes within the monitoring range, and output the monitoring data in the form of cloud maps and monitoring section coordinate values;

[0046] A monitoring point selection module, configured to receive the coordinate values ​​scanned and measured by the radar monitoring acquisition module, select the coordinates based on the selection rule of the measurement points, and output the selected coordinates as a monitoring line map having the monitoring points;

[0047] A linear prediction judgment module is used to judge the stationarity of the horizontal displacement sequence of the monitoring points on the monitoring line and distinguish it as a stationary sequence or a trending sequence;

[0048] an average value sequence prediction value calculation module, 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;

[0049] The result output module can calculate the residual value between the deformation displacement prediction values ​​based on the selected set threshold value, and when the residual value exceeds a predetermined range, an early warning message is issued through an early warning module.

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

[0051] First, the data of the monitoring points can be obtained based on the existing common monitoring equipment, that is, by obtaining the cloud map of the monitored slope area, without using other equipment and implementing the collection of separate image data and image information, which can reduce the investment in technology and cost;

[0052] Secondly, the average deformation prediction value of the monitoring line in this technical solution serves as an indicator for predicting overall slope deformation. It can comprehensively reflect the deformation trend of the monitoring line and is an advantageous means for predicting the overall deformation of the slope in the area, that is, the slope disease situation. Compared with single-point monitoring analysis, it is more reliable.

[0053] Thirdly, in this technical solution, the overall prediction of highway slope deformation is more reliable than the single-point deformation prediction. It can eliminate data anomalies caused by single-point deformation detection, weaken the impact 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 accompanying drawings and specific embodiments.

[0055] Figure 1 A schematic flow chart of the method of the present invention;

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

[0057] Figure 3 This is an example of a scanning cloud map of the monitoring slope area of ​​the present invention;

[0058] Figure 4 Schematic diagram of the selection rule of the measuring point of the present invention;

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

[0060] Figure 6 The duration curves of different monitoring points of a monitoring line for horizontal displacement of the present invention;

[0061] Figure 7 This is a schematic diagram of the system module structure used in the present invention.

[0062] Reference numerals

[0063] 100. Radar monitoring and acquisition module, 200. Monitoring point selection module, 300. Linear prediction judgment module, 400. Average value sequence prediction value calculation module, 500. Result output module, 600. Early warning module. DETAILED DESCRIPTION

[0064] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings 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; it should be noted that in this application, for the convenience of description, the "left side" in the current view is referred to as the "first end", the "right side" is referred to as the "second end", the "upper side" is referred to as the "first end", and the "lower side" is referred to as the "second end". The purpose of such description is to clearly express the technical solution, and it should not be understood as an improper limitation on the technical solution of this application.

[0065] The present invention aims to solve the technical problems in the existing technology of highway slope disease monitoring, which is that the existing methods rely heavily on images, resulting in a large number of equipment and system components, and lack of prediction and defense mechanisms. The existing research direction for highway slope disease monitoring relies on image data and integrates monitoring data. Theoretically, a single-point monitoring method is adopted. The disadvantage of single-point monitoring is that it is difficult to effectively generate information correlation between monitoring points, and auxiliary deduction from image data is required.

[0066] In fact, during the construction phase, the structure and shape of the highway slope, mainly the foundation section of the slope, have all been artificially constructed, and the slope of the highway slope has also been adjusted and maintained, so that the highway slope has initial safety during the construction phase of the highway; after completion, after continuous accumulation of time and environmental changes, disease types gradually appear. Obviously, the emergence of disease types is related to the overall deformation trend and deformation law of the slope, that is, the displacement deformation of the slope measuring point can reflect the occurrence of the disease, and the single point processing in the existing technology cannot effectively and directly utilize the relevant information. Therefore, this technical solution first proposes a monitoring and early warning method for the linear displacement of highway slope diseases, that is, the monitored single point is expressed in the form of a monitoring line, and the correlation between the single point change and the monitoring line change is used to predict the linear deformation, that is, the linear deformation can reflect the changes in the effects of various disease types. The specific method steps are:

[0067] Step S1: Select a predetermined monitoring slope area for monitoring and measurement, and present the monitoring and measurement results as a slope profile in the form of a cloud map. Step S2: Set a coordinate origin of the slope profile, form a plane coordinate system of the slope profile based on the coordinate origin, determine multiple horizontal coordinates at equal intervals, obtain vertical coordinates corresponding to the multiple horizontal coordinates as coordinate points, and select the coordinate points with trend directions in the slope profile formed by the cloud map as measuring points.

[0068] 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;

[0069] As attached Figure 3 As shown in , the measurement points of the trend items are coordinate points Y1, Y2, Y3, Y4, and a random measurement point YS. This technical solution uses four trend measurement points and one random item measurement point to illustrate the specific situation; Appendix Figure 3 In the above method, continuous measuring points of the slope are obtained by measuring the plane coordinate system of the slope profile with cloud map. In order to reflect the prediction of possible slope damage, this technical solution selects measuring points with trend items in a way that the horizontal coordinates are equally spaced. Since the slope is located on the highway section, that is, the part of this type of slope below the coordinate origin O is a construction slope, which is a natural slope in the plane coordinate system. Therefore, this technical solution selects measuring points with trend items after establishing a coordinate system with the cloud map parameters;

[0070] 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;

[0071] Performing a stationary judgment on the horizontal displacement sequence of the monitoring points on the monitoring line to distinguish it as a stationary sequence or a trending sequence;

[0072] Step S4, calculating the average sequence prediction value, includes:

[0073] The average value sequence prediction value is calculated based on the differentiated stationary sequence and the trend sequence, and the deformation displacement prediction value is calculated based on the average value sequence prediction value;

[0074] Step S5: Using the residual value between the actual displacement value and the predicted deformation displacement value as a threshold value for real-time warning.

[0075] The advantages of this technical solution are:

[0076] First, the data of the monitoring points can be obtained based on the existing common monitoring equipment, that is, by obtaining the cloud map of the monitored slope area, without using other equipment and implementing the collection of separate image data and image information, which can reduce the investment in technology and cost;

[0077] Secondly, the average deformation prediction value of the monitoring line in this technical solution serves as an indicator for predicting overall slope deformation. It can comprehensively reflect the deformation trend of the monitoring line and is an advantageous means for predicting the overall deformation of the slope in the area, that is, the slope disease situation. Compared with single-point monitoring analysis, it is more reliable.

[0078] Thirdly, in this technical solution, the overall prediction of highway slope deformation is more reliable than the single-point deformation prediction. It can eliminate data anomalies caused by single-point deformation detection, weaken the impact of single-point deformation on the overall prediction, and improve the accuracy of monitoring and prediction.

[0079] In a specific implementation process, when selecting a measuring point with a trend direction in the cloud map, in the selection rule of the measuring point, when multiple random coordinate points are selected, the vertical coordinate of the random coordinate point is smaller than the vertical coordinate of an adjacent measuring point;

[0080] The selection of random coordinate points can be actively selected based on the address environment information shown in the cloud map or the measurement points of lithological similarity.

[0081] In a specific implementation process, please refer to the attached Figure 2 、 3 As shown in the figure, the process of expressing the monitoring measurement results as a slope profile in the form of a cloud map 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 difference interferometry method;

[0082] The slope radar transmits radar waves to continuously measure and scan the slope rock mass to obtain the displacement value of the single scanning area, and after accumulating the monitoring data, obtains the displacement cloud map of the real-time deformation of the slope rock mass;

[0083] The slope radar is connected to a slope radar communication system having a relay station and a terminal.

[0084] In order to reflect the predictive research of the linear deformation of the monitoring line formed by the monitoring points, this technical solution uses the horizontal displacement sequence to perform stationary judgment analysis. The judgment steps of the stationary sequence and the trend sequence include:

[0085] The horizontal displacement value of the monitoring point of the highway slope in the monitoring slope area trend direction has a random interference term ;

[0086] random interference Defined as a stationary series ;

[0087] The specific process of obtaining the average value sequence of horizontal displacement of the monitoring line is as follows:

[0088] Pre-set monitoring line to form the entire monitoring slope area The average deformation value sequence of the monitoring points is ;

[0089] Among them, the deformation sequence of the monitoring point is expressed as ,set up is the length of the deformation sequence, then the average deformation value sequence formula (1) is expressed as:

[0090] (1).

[0091] The further implementation process also includes the following steps:

[0092] Get the average deformation value sequence The stationary series term and trend items ;

[0093] 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;

[0094] Set the variance to , the unified calculation formula (2) is:

[0095] (2);

[0096] In formula (2), is the average deformation value sequence The average value can be expressed as formula (3):

[0097] (3);

[0098] Among them, for the time series with stable variance, the stable series term is calculated using the least squares formula The predicted value of ;

[0099] Among them, for the time series with non-stationary variance, the original series Perform logarithmic transformation to obtain trend term The predicted value of .

[0100] The calculation of the average value series forecast value is expressed as formula (4):

[0101] Mean Series Forecast for: (4);

[0102] Among them, the stationary series term It can also calculate the forecast value of the stationary series based on the ARMA dynamic model ;

[0103] Among them, the trend item It is also possible to calculate the trend item forecast value based on the linear trend item model .

[0104] The deformation prediction value of each monitoring point is calculated as follows: :

[0105] (5);

[0106] Formula (5), is the predicted value of the trend item at the i-th monitoring point;

[0107] The predicted value of the stationary series of the mean value.

[0108] In the further implementation process, Figure 4 , Attachment Figure 5 The information that can be obtained is that due to the high correlation of the monitoring points on the same monitoring line, the average value series is stationary. , which integrates the characteristics of each monitoring point on the monitoring line and is the representative of the stationary sequence of each monitoring point; it is equivalent to forming a stationary sequence prediction model with each monitoring point in the monitoring slope area by applying this sequence through multiple monitoring lines. The calculated average stationary sequence prediction value is also the stationary sequence prediction value of each monitoring point; after that, the residual can be calculated based on the actual monitoring value, and then the threshold alarm can be set to complete the linear monitoring disease alarm information of the highway slope;

[0109] That is, the average deformation prediction value of the monitoring line in this technical solution is used as an indicator for predicting the overall deformation of the slope. It can fully reflect the deformation trend of the monitoring line and is an advantageous means for predicting the overall deformation of the slope in the area, that is, the slope disease situation. Compared with single-point monitoring analysis, it is more reliable.

[0110] It can eliminate data anomalies caused by single-point deformation detection, weaken the impact of single-point deformation on the overall prediction, and improve the accuracy of monitoring and prediction.

[0111] For more information on threshold setting and residual calculation, see Appendix Figure 4 , Attachment Figure 5 As shown in the Figure 4 、 5 The cycle is set to 8 intervals, each interval is 30 days of continuous measurement, and the obtained change data is shown in the attached Figure 4 As shown in , the set range refers to the allowable range of slope displacement, which is usually ±10 to 20 mm. In this technical solution, 15 mm is selected as the allowable range;

[0112] After obtaining the predicted values ​​of multiple coordinate points, in an achievable strength, Figure 4 The average value of the periodic prediction 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;

[0113] The residual value refers to the difference between the slope monitoring data and the theoretical model prediction value, reflecting the degree of discreteness of the monitoring data during the slope deformation process. It is used to verify the reliability of the monitoring system and identify abnormal deformation trends.

[0114] In a specific embodiment, please refer to the attached Figure 2 As shown, the above-mentioned monitoring and early warning method for linear displacement of highway slope diseases adopts a monitoring and early warning system for linear displacement of highway slope diseases, which includes:

[0115] The radar monitoring and acquisition module 100 is used to scan and measure the highway slopes within the monitoring range and output the monitoring data in the form of cloud maps and monitoring profile coordinate values;

[0116] The monitoring point selection module 200 receives the coordinate values ​​scanned and measured by the radar monitoring acquisition module, selects them based on the measurement point selection rules, and outputs them as a monitoring line diagram with monitoring points. The linear prediction judgment module 300 judges the stationarity of the horizontal displacement sequence of the monitoring points on the monitoring line, distinguishing them as stationary sequences or trending sequences. The average value sequence prediction value calculation module 400 calculates the average value sequence prediction value based on the stationary sequence and the trending sequence, and then calculates the deformation displacement prediction value based on the average value sequence prediction value.

[0117] The result output module 500 can calculate the residual value between the deformation displacement prediction values ​​based on the selected set threshold value. When the residual value exceeds the predetermined range, an early warning message is issued through an early warning module 600. Obviously, the above embodiment is only an example for clear explanation and does not limit the implementation method. For those skilled 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 list all the implementation methods here. However, the obvious changes or modifications derived from this are still within the scope of protection of the 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: The monitoring slope area constituting the whole of the monitoring line is pre-set The average deformation value sequence of the monitoring points is ; Among them, the deformation sequence of the monitoring point is expressed as ,set up is the length of the deformation sequence, then the average deformation value sequence formula (1) is expressed as: （1）。 5. The method for monitoring and early warning of linear displacement of highway slope damage according to claim 4 is characterized in that: It also includes the following processes: Get the average deformation value sequence The stationary series term and trend items ; 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 , the unified calculation formula (2) is: （2）； In formula (2), is the average deformation value sequence The average value can be expressed as formula (3): （3）； Among them, for a time series with stationary variance, the predicted value of the stationary series item is calculated using the least squares formula ; Among them, for the time series with non-stationary variance, the original series Perform logarithmic transformation to obtain the trend term The predicted value of .

6. The method for monitoring and early warning of linear displacement of highway slope damage according to claim 5, characterized in that: The calculation of the average value sequence prediction value is expressed as formula (4): The mean value series predicts the value for: （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 item of the i-th monitoring point; The predicted value of the stationary series of the mean value.

8. The method for monitoring and early warning of linear displacement of highway slope damage 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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