Road slope displacement prediction method

Abstract

The invention discloses a road slope displacement prediction method, and relates to the technical field of road slope displacement monitoring and prediction.The method comprises the steps that a data measurement and collection module is used for collecting the current slope displacement condition in real time, and a unit reflecting the current slope state is used for outputting the current slope displacement value WY1; a visual module is used for observing the change trend of a slope displacement value WYi at the ith moment in the recent period on a linear graph, a slope displacement value WYx at the xth moment is input into a slope stability and displacement change trend evaluation unit, a slope displacement change rate BL is calculated and output, the slope displacement change rate BL is input into a prediction and displacement prediction and adjustment unit, and the slope stability and displacement change trend evaluation unit is used for evaluating the slope stability and displacement change trend. According to the slope displacement prediction method, more reliable data support is provided for slope stability evaluation, a linear graph observation method is introduced, calculation of the change rate can flexibly adapt to slope displacement prediction requirements under different geological conditions and climate conditions, self-adaptive adjustment of a prediction result is achieved, prediction errors are reduced, and the slope displacement prediction accuracy is improved. And the prediction accuracy is improved.

Description

Technical Field

[0001] The invention relates to the technical field of highway slope displacement monitoring and prediction, and in particular to a highway slope displacement prediction method. Background Art

[0002] In the field of highway slope displacement monitoring and prediction, traditional monitoring methods often rely on manual periodic measurements, which is not only time-consuming and labor-intensive, but also difficult to capture subtle changes and real-time trends in slope displacement. With the continuous advancement of sensor technology and computer-aided data analysis methods, formula-based slope displacement prediction methods have emerged.

[0003] At present, the use of sensor technology and computer-aided data analysis methods can achieve the accuracy of highway slope displacement monitoring, and according to the different displacement results monitored and the change rate based on this, the displacement of the highway slope in the future can be further predicted and data can be obtained. Although the existing highway slope displacement prediction method can achieve the purpose of prediction, it is worth noting that there will be errors between the predicted and real-time measured displacement data. If the error value is ignored and iterative prediction is performed, the accuracy of the roadside slope displacement prediction will be reduced, and thus it will not be possible to achieve timely warning of real-time changes and small fluctuations in slope displacement, making the loop feedback system lack an adaptive adjustment mechanism and difficult to cope with sudden changes in slope displacement. In addition, the existing highway slope displacement prediction method is based on the different displacement results monitored and the change rate based on this, and most of the existing methods use the similar last monitoring results and current results to calculate the change rate. However, this method of calculating the change rate will lead to limitations in the calculation of the change rate, thereby further reducing the overall accuracy of the prediction. Summary of the invention

[0004] The purpose of the present invention is to provide a method for predicting highway slope displacement, which solves the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution, including a data measurement and acquisition module, a calculation prediction and adjustment module and a visualization module, wherein the calculation prediction and adjustment module includes a unit for reflecting the current slope state, a unit for evaluating the slope stability and displacement change trend, and a unit for predicting and adjusting displacement;

[0006] The specific implementation steps are as follows:

[0007] Step I: using the data measurement and acquisition module to collect the current slope displacement in real time;

[0008] Step II: using the unit reflecting the current slope state, extracting the initial slope X-axis X0 and the initial slope Y-axis Y0 from the data measurement and acquisition module for input calculation, and outputting the current slope displacement value WY1;

[0009] Step III: Use the visualization module to observe the recent slope displacement value WY at the i-th moment i Change trend on the line graph and calculate and output the slope displacement value WY at the xth moment x ;

[0010] Step III: Set the slope displacement value WY at time x x Input to the slope stability and displacement change trend assessment unit, and combine with the current slope displacement value WY1 to calculate the output slope displacement change rate BL;

[0011] Step IIIII: Input the slope displacement change rate BL into the prediction and prediction adjustment displacement unit, and combine it with the slope displacement value WY at the xth moment x , calculate and output the predicted slope displacement value WY predict ;

[0012] The prediction and prediction adjustment displacement unit calculates the current slope displacement value WY1 and the predicted slope displacement value WY according to the next real-time measurement. predict Compare the differences and make targeted adjustments;

[0013] The equipment used by the data measurement and acquisition module includes sensors, and the sensors include GPS, laser rangefinder, and data recorder;

[0014] The equipment used in the calculation prediction and adjustment module includes a data recorder, a computer, and data analysis software;

[0015] The devices used by the visualization module include data visualization devices.

[0016] Optionally, the calculation formula reflecting the current slope state unit is as follows:

[0017]

[0018] in:

[0019] WY1 is the current slope displacement value;

[0020] X1 is the current slope X axis;

[0021] Y1 is the Y axis of the current slope;

[0022] (X1, Y1) reflects the real-time displacement position of the current slope;

[0023] X0 is the initial slope X axis;

[0024] Y0 is the initial slope Y axis;

[0025] (X0, Y0) reflects the position when the sensor initially measured the same slope as the current one.

[0026] Optional, based on the recent slope displacement value WY at the i-th moment i The analysis options for the line chart trend in the visualization module are as follows:

[0027] If the slope displacement value WY at the recent i-th moment i If the linear graph shows a gentle trend, it means that the displacement rate of the slope is relatively stable. The slope displacement value WY at the xth moment of the previous long time interval T should be selected relative to the current slope displacement value WY1. x ;

[0028] If the slope displacement value WY at the recent i-th moment i If the linear graph shows a fluctuating trend, it indicates that the displacement rate of the slope is unstable. The slope displacement value WY at the xth moment in the previous short time interval T should be selected relative to the current slope displacement value WY1. x , that is, the slope displacement value WY at the xth moment with a large fluctuation difference relative to the current slope displacement value WY1 x .

[0029] Optionally, the slope displacement value WY at the xth moment x The calculation formula is as follows:

[0030]

[0031] in:

[0032] WY x is the slope displacement value at the xth moment;

[0033] X x is the X-axis of the slope at the xth moment;

[0034] Y x is the Y axis of the slope at the xth moment;

[0035] (X x , Y x ) reflects the real-time displacement position of the slope at the xth moment.

[0036] Optionally, the calculation formula for evaluating the slope stability and displacement change trend unit is as follows:

[0037] BL=(WY x -WY1) / T;

[0038] in:

[0039] BL is the slope displacement change rate;

[0040] T is the time interval, T reflects the current slope displacement value WY1 and the slope displacement value WY at the xth moment.x The time interval between the two.

[0041] Optionally, the calculation formulas for the prediction and prediction adjustment displacement units are as follows:

[0042] WY predict =WY x +BL×(T predict -T now );

[0043] in:

[0044] WY predict To predict the slope displacement value;

[0045] T predict is the prediction time, T predict Reflects the need to predict the slope displacement at a certain point in the future;

[0046] T now is the current moment, T now Reflects the real-time time point of the current sensing displacement value.

[0047] Optionally, based on the predicted slope displacement value WY predict , and reaches the predicted time T predict hour

[0048] If the predicted slope displacement value WY predict Equal to the predicted time T predict The current slope displacement value WY1 is then predicted by maintaining the original calculation formula of the prediction and prediction adjustment displacement unit;

[0049] If the predicted slope displacement value WY predict Not equal to the predicted time T predict The current slope displacement value WY1 is adjusted, and the calculation formula of the prediction and prediction adjustment displacement unit is adjusted as follows:

[0050] WY predict =[WY x +BL×(T predict -T now )]×v;

[0051]

[0052] v is the adjustment coefficient;

[0053] n is the total number of historical measurements, reflecting the total number of sensor measurements in the past historical moments;

[0054] WY ni is the i-th historical slope displacement value;

[0055] WY predict，i is the i-th historical predicted displacement value.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] 1. The present invention introduces high-precision sensors and real-time data acquisition technology to achieve continuous monitoring and real-time data transmission of slope displacement. Combined with the current slope state reflection unit, the slope stability and displacement change trend evaluation unit and the displacement prediction adjustment unit, a more accurate current slope displacement value WY1, slope displacement change rate BL and predicted slope displacement value WY1 can be calculated. predict , providing more reliable data support for slope stability assessment.

[0058] Second, the present invention introduces a linear graph observation method based on the current slope state unit, the slope stability and displacement change trend unit and the prediction and adjustment displacement unit, and combines the recent observation of the slope displacement value WY at the i-th moment. i According to the real-time situation of the change trend, the current slope displacement value WY1 and the slope displacement value WY at the xth moment are selected differently. x This method can more flexibly adapt to the needs of slope displacement prediction under different geological and climatic conditions, thereby improving the accuracy and reliability of the prediction.

[0059] 3. The present invention aims at the deviation problem existing in the loop feedback system and introduces a calculation method of the adjustment coefficient v. By calculating The calculation formula is used to obtain the v value, and the "[WY x +BL×(T predict -T now )]×v” to adjust the displacement value of the next prediction moment. This method can realize adaptive adjustment of the prediction results, reduce prediction errors and improve prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a flow chart of the method for predicting highway slope displacement;

[0061] Figure 2 It is a structural schematic diagram of the calculation prediction and adjustment module of the present invention;

[0062] Figure 3 This is a gentle linear schematic diagram of recent slope displacement in this highway slope displacement prediction method;

[0063] Figure 4 This is a linear schematic diagram of the fluctuation of recent slope displacement in this highway slope displacement prediction method. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] Regarding the highway slope displacement prediction method, it is different from the existing highway slope displacement prediction method. The existing highway slope displacement prediction method not only ignores the errors between the predicted and real-time measured displacement data, but also makes the loop feedback system lack an adaptive adjustment mechanism, making it difficult to cope with the sudden change of slope displacement, and reduces the overall accuracy of the prediction. The algorithm unit provides more reliable data support for slope stability assessment, and introduces a line graph observation method, so that the calculation of the change rate can flexibly adapt to the slope displacement prediction needs under different geological conditions and climatic conditions, realize adaptive adjustment of the prediction results, reduce prediction errors, and improve prediction accuracy.

[0066] For example, see Figures 1 to 4 ,This implementation provides a highway slope displacement prediction method, including a data measurement and acquisition module, a calculation prediction and adjustment module and a visualization module, the calculation prediction and adjustment module includes a unit reflecting the current slope state, a unit evaluating the slope stability and displacement change trend, and a unit predicting and adjusting displacement;

[0067] The specific implementation steps are as follows:

[0068] Step I: Use the data measurement and acquisition module to collect the current slope displacement in real time;

[0069] Step II: using the unit reflecting the current slope state, extracting the initial slope X-axis X0 and the initial slope Y-axis Y0 from the data measurement and acquisition module for input calculation, and outputting the current slope displacement value WY1;

[0070] Step III: Use the visualization module to observe the recent slope displacement value WY at the i-th moment i Change trend on the line graph and calculate and output the slope displacement value WY at the xth moment x ;

[0071] Step III: Set the slope displacement value WY at time x x Input to the slope stability and displacement change trend assessment unit, and combine with the current slope displacement value WY1 to calculate the output slope displacement change rate BL;

[0072] Step IIIII: Input the slope displacement change rate BL into the prediction and prediction adjustment displacement unit, and combine it with the slope displacement value WY at the xth moment x , calculate and output the predicted slope displacement value WY predict ;

[0073] The prediction and prediction adjustment displacement unit calculates the current slope displacement value WY1 and the predicted slope displacement value WY according to the next real-time measurement. predict Compare the differences and make targeted adjustments;

[0074] The equipment used in the data measurement and acquisition module includes sensors, including GPS, laser rangefinder, and data recorder;

[0075] The equipment used in the calculation prediction and adjustment module includes data recorders, computers, and data analysis software;

[0076] The devices used by the visualization module include data visualization devices.

[0077] In this embodiment, the system forms a complete prediction system through the cooperation of three algorithm units, combining WY1, BL and WY predict The three calculation results are used to optimize the prediction effect and reduce the prediction cost. Specifically, WY1 is the current slope displacement value, which provides a reference point for the entire prediction process. By accurately measuring the X-axis and Y-axis coordinates of the slope at the initial moment and the current moment, and using the Pythagorean theorem to calculate, the displacement of the slope during this period of time is obtained. This displacement not only reflects the current state of the slope, but also provides important data support for subsequent monitoring and prediction. BL is the slope displacement change rate. This change rate not only helps to understand the current trend of the slope displacement, but also provides key parameters for subsequent predictions. When the slope displacement change rate BL fluctuates abnormally, it means that the slope is at risk of instability. At this time, corresponding countermeasures need to be taken to ensure the safety and stability of the slope. WY predict To predict the slope displacement value, the predicted value not only provides enough time to formulate countermeasures, but also helps to evaluate the accuracy and reliability of the prediction method, thereby ensuring the safety and stability of the slope. predict The calculation results can also affect the feedback to the next WY1, BL and WY predict Calculation, through the calculation of three algorithms of this system, can improve data accuracy and real-time performance, enhance the adaptability of calculation formula application, optimize the loop feedback system and innovate the slope displacement prediction method.

[0078] See also Figures 1 to 4 , the calculation formula reflecting the current slope state unit is as follows:

[0079]

[0080] in:

[0081] WY1 is the current slope displacement value;

[0082] X1 is the current slope X axis;

[0083] Y1 is the Y axis of the current slope;

[0084] (X1, Y1) reflects the real-time displacement position of the current slope;

[0085] X0 is the initial slope X axis;

[0086] Y0 is the initial slope Y axis;

[0087] (X0, Y0) reflects the position when the sensor initially measured the same slope as the current one.

[0088] In this embodiment: First, the algorithm unit The calculation formula is to find the straight-line distance difference from the initial moment to the current moment, that is, the current slope displacement value WY1. This value reflects the displacement of the slope during this period of time by calculating the square root of the sum of the squares of the coordinate differences of the slope on the X-axis and Y-axis at two moments. This calculation result reflects the current slope state unit and is also the calculation core. It directly gives the current slope displacement value WY1 and provides the basis for the subsequent calculation of the slope displacement change rate BL and the predicted slope displacement value WY predict Provides basic data;

[0089] In this algorithm, the selection of the initial moment and the current moment is crucial in reflecting the current slope state unit. They determine the accuracy and representativeness of the current slope displacement value WY1. By reasonably selecting these two moments, it can ensure that the current slope displacement value WY1 can truly reflect the displacement of the slope in the initial monitoring period, providing a reliable basis for subsequent calculations and predictions.

[0090] In order to obtain the accurate current slope displacement value WY1, high-precision sensors are needed to measure the X-axis and Y-axis coordinates, which not only improves the accuracy of the current slope displacement value WY1, but also provides more accurate data support for the subsequent calculation and prediction of the change rate.

[0091] See also Figures 1 to 4 , based on the recent slope displacement value WY at the i-th moment i The analysis options for the line chart trend in the visualization module are as follows:

[0092] If the slope displacement value WY at the recent i-th moment iIf the linear graph shows a gentle trend, it means that the displacement rate of the slope is relatively stable. The slope displacement value WY at the xth moment of the previous long time interval T should be selected relative to the current slope displacement value WY1. x ;

[0093] If the slope displacement value WY at the recent i-th moment i If the linear graph shows a fluctuating trend, it indicates that the displacement rate of the slope is unstable. The slope displacement value WY at the xth moment in the previous short time interval T should be selected relative to the current slope displacement value WY1. x , that is, the slope displacement value WY at the xth moment with a large fluctuation difference relative to the current slope displacement value WY1 x ;

[0094] The slope displacement value WY at the xth moment x The calculation formula is as follows:

[0095]

[0096] in:

[0097] WY x is the slope displacement value at the xth moment;

[0098] X x is the X-axis of the slope at the xth moment;

[0099] Y x is the Y axis of the slope at the xth moment;

[0100] (X x , Y x ) reflects the real-time displacement position of the slope at the xth moment.

[0101] In this embodiment, first, when the line graph shows the slope displacement value WY at the recent i-th moment i When the change is gentle, it means that the displacement rate of the slope is relatively stable, without significant acceleration and deceleration. In this case, since the displacement changes slowly, the current slope displacement value WY1 and the slope displacement value WY at the xth moment are selected with a relatively long time interval. x This helps to reduce the number of measurements and costs, while still being able to capture significant changes in slope displacement. In the case of a gentle displacement change, it is more important to focus on the long-term trend of the slope displacement. Therefore, when selecting the current slope displacement value WY1 and the slope displacement value WY at the xth moment, x When we consider whether they can reflect the long-term changes of slope displacement rather than the short-term small fluctuations, we can give priority to whether they can reflect the long-term changes of slope displacement rather than the short-term small fluctuations. Drawing trend lines on the linear graph can help us understand the long-term trend of slope displacement more intuitively. By comparing the trend lines at different time intervals, we can select the current slope displacement value WY1 and the slope displacement value WY at the xth moment that best reflect the long-term trend time interval.x to calculate;

[0102] When the line graph shows the slope displacement value WY at the recent i-th moment i When the change fluctuates greatly, it means that the displacement rate of the slope is unstable, and there is acceleration and deceleration. In this case, due to the large fluctuation of displacement change, it is necessary to select the current slope displacement value WY1 and the slope displacement value WY at the xth moment with a shorter time interval. x This helps to capture the significant changes in slope displacement in time, so as to take timely measures for early warning and intervention. In the case of large fluctuations in displacement changes, it is more important to pay attention to the short-term changes in slope displacement. Therefore, when selecting the current slope displacement value WY1 and the slope displacement value WY at the xth moment, x When the slope displacement is short-term, it can give priority to whether they can reflect the short-term fluctuation of the slope displacement so as to respond in time. On the line graph, the abnormal detection algorithm can be used to automatically identify the abnormal changes of the slope displacement. By comparing the abnormal detection results at different time intervals, the current slope displacement value WY1 and the slope displacement value WY at the xth moment that can best reflect the short-term fluctuation time interval can be selected. x to calculate;

[0103] In this embodiment, by reasonably selecting the detection moment and the time interval for calculating the displacement, the number of measurements and costs can be reduced, the monitoring efficiency can be improved, and the trend and change rate of the slope displacement can be more accurately evaluated by combining line graph analysis and formula calculation, thereby improving the accuracy and reliability of the prediction. Specifically, in the case of large fluctuations in displacement changes, by shortening the detection time interval and using anomaly detection algorithms, abnormal changes in slope displacement can be discovered in time, providing strong support for early warning and intervention.

[0104] See also Figures 1 to 4 , the calculation formula for evaluating the slope stability and displacement change trend unit is as follows:

[0105] BL=(WY x -WY1) / T;

[0106] in:

[0107] BL is the slope displacement change rate;

[0108] T is the time interval, T reflects the current slope displacement value WY1 and the slope displacement value WY at the xth moment. x The time interval between the two.

[0109] In this embodiment, the algorithm unit first "WY x -WY1" is used to calculate the displacement change of the slope between two different moments.x The displacement values ​​at two moments can be used to determine the displacement change of the slope during this period. This calculation result is the key to calculating the slope displacement change rate BL. By dividing the displacement change by the time interval T, the slope displacement change rate can be obtained. This change rate reflects the change trend of the slope displacement over time.

[0110] This algorithm can calculate the slope displacement change rate BL in real time by evaluating the slope stability and displacement change trend unit, and thus can promptly detect abnormal changes in slope displacement. At the same time, through high-precision measurement and calculation, the accuracy of the slope displacement change rate BL is guaranteed, providing reliable data support for subsequent predictions.

[0111] The slope displacement change rate BL is an important basis for the prediction and prediction adjustment displacement unit to predict displacement. Its accuracy directly affects the reliability of the prediction results. Therefore, when calculating the slope displacement change rate BL, it is necessary to ensure the accuracy of the measurement data and the rationality of the calculation method.

[0112] In addition, by analyzing the changing trend of the slope displacement change rate BL, the stability of the slope can be evaluated. When the slope displacement change rate BL fluctuates abnormally, it means that the slope is at risk of instability. At this time, corresponding response measures need to be formulated according to the evaluation results to ensure the safety and stability of the slope.

[0113] See also Figures 1 to 4 , the calculation formulas for the predicted and predicted adjusted displacement units are as follows:

[0114] WY predict =WY x +BL×(T predict -T now );

[0115] in:

[0116] WY predict To predict the slope displacement value;

[0117] T predict is the prediction time, T predict Reflects the need to predict the slope displacement at a certain point in the future;

[0118] T now is the current moment, T now Reflects the real-time time point of the current sensing displacement value.

[0119] In this embodiment, the algorithm unit "BL×(T predict -T now )” This calculation part is to find out the future prediction time T predict With the current time Tnow The predicted displacement change value between the two periods can be obtained by multiplying the slope displacement change rate BL to obtain the predicted change in slope displacement during this period. The calculation result is the predicted slope displacement value WY predict The key part is to add the predicted change to the newly calculated slope displacement value WY at the xth moment. x On the basis of the above, we can get the value of T in the future prediction time predict The predicted slope displacement value WY predict , the predicted slope displacement value WY predict It is of great significance for assessing slope stability and formulating early warning measures;

[0120] The prediction and prediction adjustment displacement unit can predict the slope at the future prediction time T in advance. predict The predicted slope displacement value WY predict , thus providing enough time to formulate response measures. This early warning mechanism helps to avoid safety hazards caused by slope instability and ensure the safety and stability of highway slopes;

[0121] By comparing the predicted slope displacement value WY predict Compared with the actual measured displacement values, the accuracy of the prediction method can be evaluated. When there is a difference between the predicted result and the actual measured value, the strategy of slope displacement monitoring and prediction can be optimized according to the difference to improve the accuracy and reliability of the prediction;

[0122] Accurate displacement prediction can reduce unnecessary monitoring and maintenance work. When the prediction results show that the slope displacement is within the normal range, the monitoring frequency and maintenance workload can be appropriately reduced, thereby reducing the cost of slope maintenance.

[0123] For example 2, please refer to Figures 1 to 4 , based on the predicted slope displacement value WY predict , and reaches the predicted time T predict hour

[0124] If the predicted slope displacement value WY predict Equal to the predicted time T predict The current slope displacement value WY1 is then predicted by maintaining the original calculation formula of the prediction and prediction adjustment displacement unit;

[0125] If the predicted slope displacement value WY predict Not equal to the predicted time T predict The current slope displacement value WY1 is adjusted, and the calculation formula of the prediction and prediction adjustment displacement unit is adjusted as follows:

[0126] WY predict =[WY x +BL×(T predict -Tnow )]×v;

[0127]

[0128] v is the adjustment coefficient;

[0129] n is the total number of historical measurements, reflecting the total number of sensor measurements in the past historical moments;

[0130] WY ni is the i-th historical slope displacement value;

[0131] WY predict，i is the i-th historical predicted displacement value.

[0132] In this embodiment, when the predicted slope displacement value WY predict Reach the predicted time T predict And with the prediction time T predict When there is a deviation in the current slope displacement value WY1, it can be used Calculate the adjustment factor v and use "[WY x +BL×(T predict -T now )]×v” adjusts the displacement value at the next prediction moment. This dynamic adjustment mechanism can correct and optimize the prediction results according to the actual measurement situation, thereby improving the accuracy and reliability of the prediction.

[0133] The loop feedback system can adaptively adjust the parameters in the prediction model to cope with the sudden change of slope displacement. This adaptability makes the prediction method more flexible and reliable, and can better adapt to the needs of slope displacement monitoring under different geological and climatic conditions. Through continuous loop feedback and adjustment, the prediction method can be gradually optimized and improved. This potential for continuous improvement enables the prediction method to continuously improve the accuracy and stability of the prediction, thereby providing more accurate and reliable prediction results.

[0134] Combining line graph analysis and formula calculation can more accurately evaluate the trend and change rate of slope displacement, thereby improving the accuracy and reliability of prediction. Specifically, line graph is a tool that intuitively displays the trend of data changes over time. In slope displacement monitoring, line graphs can be used to display the changes of slope displacement over time, and by observing line graphs, the change law of slope displacement can be found, including periodic changes and accelerated changes. At the same time, formula calculation can be used to quantify the change rate and trend of slope displacement, and these calculated quantitative data can provide strong support for the prediction model, thereby improving the accuracy and reliability of the prediction;

[0135] In addition, in slope displacement monitoring, timely warning and intervention are the key to preventing slope instability and disasters. By shortening the detection time interval and using anomaly detection algorithms, abnormal changes in slope displacement can be discovered in a timely manner, providing strong support for warning and intervention. Once abnormal changes are detected, the system can immediately issue a warning signal and initiate the corresponding emergency plan.

[0136] In summary, by reasonably selecting the detection time and the time interval T for calculating the displacement, combining line graph analysis and formula calculation, and timely early warning and intervention measures, slope displacement monitoring and early warning can be carried out more effectively. These measures can not only improve monitoring efficiency and enhance prediction accuracy, but also enable timely early warning and intervention, providing strong support for slope stability assessment and disaster prevention and control.

[0137] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A highway slope displacement prediction method, characterized in that: It includes a data measurement and acquisition module, a calculation prediction and adjustment module and a visualization module. The calculation prediction and adjustment module includes a unit reflecting the current slope state, a unit evaluating the slope stability and displacement change trend, and a unit predicting and adjusting displacement; The specific implementation steps are as follows: Step I: using the data measurement and acquisition module to collect the current slope displacement in real time; Step II: using the unit reflecting the current slope state, extracting the initial slope X-axis X0 and the initial slope Y-axis Y0 from the data measurement and acquisition module for input calculation, and outputting the current slope displacement value WY1; Step III: Use the visualization module to observe the recent slope displacement value WY at the i-th moment i Change trend on the line graph and calculate and output the slope displacement value WY at the xth moment x ; Step III: Set the slope displacement value WY at time x x Input to the slope stability and displacement change trend assessment unit, and combine with the current slope displacement value WY1 to calculate the output slope displacement change rate BL; Step IIIII: Input the slope displacement change rate BL into the prediction and prediction adjustment displacement unit, and combine it with the slope displacement value WY at the xth moment x , calculate and output the predicted slope displacement value WY predict ; The prediction and prediction adjustment displacement unit calculates the current slope displacement value WY1 and the predicted slope displacement value WY according to the next real-time measurement. predict Compare the differences and make targeted adjustments.

2. A highway slope displacement prediction method according to claim 1, characterized in that: The equipment used by the data measurement and acquisition module includes sensors, and the sensors include GPS, laser rangefinder, and data recorder; The equipment used in the calculation prediction and adjustment module includes a data recorder, a computer, and data analysis software; The devices used by the visualization module include data visualization devices.

3. A highway slope displacement prediction method according to claim 2, characterized in that: The calculation formula reflecting the current slope state unit is as follows: in: WY1 is the current slope displacement value; X1 is the current slope X axis; Y1 is the Y axis of the current slope; (X1, Y1) reflects the real-time displacement position of the current slope; X0 is the initial slope X axis; Y0 is the initial slope Y axis; (X0, Y0) reflects the position when the sensor initially measured the same slope as the current one.

4. A highway slope displacement prediction method according to claim 3, characterized in that: Based on the recent slope displacement value WY at the i-th moment i The analysis options for the line chart trend in the visualization module are as follows: If the slope displacement value WY at the recent i-th moment i If the linear graph shows a gentle trend, it means that the displacement rate of the slope is relatively stable. The slope displacement value WY at the xth moment of the previous long time interval T should be selected relative to the current slope displacement value WY1. x ; If the slope displacement value WY at the recent i-th moment i If the linear graph shows a fluctuating trend, it indicates that the displacement rate of the slope is unstable. The slope displacement value WY at the xth moment in the previous short time interval T should be selected relative to the current slope displacement value WY1. x , that is, the slope displacement value WY at the xth moment with a large fluctuation difference relative to the current slope displacement value WY1 x .

5. A highway slope displacement prediction method according to claim 4, characterized in that: The slope displacement value WY at the xth moment x The calculation formula is as follows: in: WY x is the slope displacement value at the xth moment; X x is the X-axis of the slope at the xth moment; Y x is the Y axis of the slope at the xth moment; (X x , Y x ) reflects the real-time displacement position of the slope at the xth moment.

6. A highway slope displacement prediction method according to claim 5, characterized in that: The calculation formula for evaluating the slope stability and displacement change trend unit is as follows: BL=(WY x -WY1) / T; in: BL is the slope displacement change rate; T is the time interval, T reflects the current slope displacement value WY1 and the slope displacement value WY at the xth moment. x The time interval between the two.

7. A highway slope displacement prediction method according to claim 6, characterized in that: The calculation formulas for the predicted and predicted adjustment displacement units are as follows: WY predict =WY x +BL×(T predict -T now ); in: WY predict To predict the slope displacement value; T predict is the prediction time, T predict Reflects the need to predict the slope displacement at a certain point in the future; T now is the current moment, T now Reflects the real-time time point of the current sensing displacement value.

8. A highway slope displacement prediction method according to claim 7, characterized in that: Based on the predicted slope displacement value WY predict , and reaches the predicted time T predict hour If the predicted slope displacement value WY predict Equal to the predicted time T predict The current slope displacement value WY1 is then predicted by maintaining the original calculation formula of the prediction and prediction adjustment displacement unit; If the predicted slope displacement value WY predict Not equal to the predicted time T predict The current slope displacement value WY1 is adjusted, and the calculation formula of the prediction and prediction adjustment displacement unit is adjusted as follows: WY predict =[WY x +BL×(T predict -T now )]×v; v is the adjustment coefficient; n is the total number of historical measurements, reflecting the total number of sensor measurements in the past historical moments; WY ni is the i-th historical slope displacement value; WY predict，i is the i-th historical predicted displacement value.

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