Displacement prediction method and device for slope monitoring point, electronic equipment and medium

Abstract

The application provides a displacement prediction method and device for a slope monitoring point, an electronic device and a medium, wherein the method comprises: acquiring a historical displacement set of the slope monitoring point within a historical monitoring time range; acquiring a plurality of monitoring displacement subsets of the historical displacement set, and acquiring a target historical parameter quantity ratio curve of the slope monitoring point within the historical monitoring time range according to respective parameter quantity ratio values of the plurality of monitoring displacement subsets; determining a target curve form of the target historical parameter quantity ratio curve to determine a target prediction function of the slope monitoring point within a prediction time range; and acquiring a target prediction displacement of the slope monitoring point within the prediction time range according to the target prediction function. In the application, the step of manual processing is omitted, the labor cost is saved, and the accuracy and efficiency of displacement prediction of the slope monitoring point are improved.

Description

Technical Field

[0001] This application relates to the field of data processing, and in particular to a method, apparatus, electronic device, and medium for predicting displacement at slope monitoring points. Background Technology

[0002] In practice, slopes in the environment can be monitored. By acquiring the monitoring data of the slopes, the movement of rocks on the slopes can be identified, thereby determining the deformation state of the slopes. In related technologies, it is possible to manually identify whether the deformation state of the slopes determined in this scenario matches the actual situation.

[0003] Correspondingly, based on the determined deformation state of the slope and the obtained historical time series monitoring data, it is also possible to manually predict the possible deformation of the slope within the prediction time range. However, this method is labor-intensive, inefficient, and has low accuracy. Summary of the Invention

[0004] The purpose of this application is to at least partially solve one of the technical problems in the aforementioned technologies.

[0005] The first aspect of this application provides a displacement prediction method for slope monitoring points, comprising: acquiring a historical displacement set of the slope monitoring point within a historical monitoring time range; acquiring multiple monitoring displacement subsets of the historical displacement set, and acquiring a target historical parameter ratio curve of the slope monitoring point within the historical monitoring time range based on the parameter ratio of each of the multiple monitoring displacement subsets; determining the target curve shape of the target historical parameter ratio curve to determine a target prediction function of the slope monitoring point within a prediction time range; and acquiring the target predicted displacement of the slope monitoring point within the prediction time range based on the target prediction function.

[0006] The displacement prediction method for slope monitoring points provided in the first aspect of this application also has the following technical features, including:

[0007] According to an embodiment of this application, determining the target curve shape of the target historical parameter ratio curve to determine the target prediction function of the slope monitoring point within the prediction time range includes: obtaining the target historical parameter ratio curves of the slope monitoring point in multiple monitoring directions; obtaining a preset calibration parameter ratio, and determining the target parameter ratio points in multiple monitoring directions corresponding to the calibration parameter ratio from the target historical parameter ratio curves in multiple monitoring directions; obtaining a first number of target parameter ratio points in multiple monitoring directions, and a second number of extreme points on the target historical parameter ratio curves in multiple monitoring directions; determining the target curve shape of the target historical parameter ratio curves in multiple monitoring directions based on the first number and the second number; and determining the target prediction function of the slope monitoring point in multiple monitoring directions within the prediction time range based on the target curve shape.

[0008] According to an embodiment of this application, obtaining the target historical parameter ratio curves of the slope monitoring points in multiple monitoring directions includes: obtaining the historical displacement component sets of the slope monitoring points in multiple monitoring directions based on the historical displacement set; obtaining the component parameter ratios of multiple monitoring displacement component subsets of the historical displacement component sets in multiple monitoring directions, so as to obtain the target historical parameter ratio curves of the slope monitoring points in multiple monitoring directions within the historical monitoring time range.

[0009] According to an embodiment of this application, determining the target curve shape of the target historical parameter ratio curves for multiple monitoring directions based on the first quantity and the second quantity includes: determining a single-peaked and stable-boundary shape as the target curve shape in response to the first quantity being equal to 1 and the second quantity being equal to 1; determining a multi-peaked and non-converging-boundary shape as the target curve shape in response to the first quantity being equal to 1 and the second quantity being greater than 1; determining the single-peaked and stable-boundary shape as the target curve shape in response to the first quantity being equal to 2 and the second quantity being equal to 1; and determining a trapezoidal and stable-boundary shape, and / or a bimodal shape with slowly deforming boundaries, and / or a multi-peaked shape with rapidly deforming boundaries as the target curve shape in response to the first quantity being greater than or equal to 2 and the second quantity being greater than 1.

[0010] According to one embodiment of this application, determining the target prediction function for each of the slope monitoring points in multiple monitoring directions within the prediction time range based on the target curve shape includes: determining a linear function as the target prediction function in response to the target curve shape being a single-peaked and stable boundary shape; determining an exponential function as the target prediction function in response to the target curve shape being multi-peaked and non-converging boundary shape; determining a linear function as the target prediction function in response to the target curve shape being trapezoidal and stable boundary shape; determining a quadratic function as the target prediction function in response to the target curve shape being bi-peaked and slowly deforming boundary shape; and determining a linear function as the target prediction function in response to the target curve shape being multi-peaked and rapidly deforming boundary shape.

[0011] According to an embodiment of this application, obtaining the target predicted displacement of the slope monitoring point within the prediction time range based on the target prediction function includes: obtaining a reference historical displacement set required for the slope monitoring point to make predictions within the prediction time range based on the historical displacement set within the historical monitoring time range; obtaining the predicted displacement components generated by the slope monitoring point in multiple prediction directions within the prediction time range based on the reference historical displacement set and the target prediction function; and obtaining the target predicted displacement of the slope monitoring point within the prediction time range based on the predicted displacement components in the multiple prediction directions.

[0012] According to an embodiment of this application, the method further includes: obtaining the actual monitored displacement of the slope monitoring point within the predicted time range, and identifying whether there is an error in the target predicted displacement based on the actual monitored displacement; in response to identifying an error in the target predicted displacement, obtaining a new target historical parameter ratio curve of the slope monitoring point within the historical monitoring time range, and determining a new target prediction function based on the new target curve shape corresponding to the new target historical parameter ratio curve, so as to obtain a new target predicted displacement of the slope monitoring point within the predicted time range.

[0013] A second aspect of this application provides a displacement prediction device for a slope monitoring point, comprising: a monitoring module for acquiring a historical displacement set of the slope monitoring point within a historical monitoring time range; an acquisition module for acquiring multiple monitored displacement subsets of the historical displacement set, and acquiring a target historical parameter ratio curve of the slope monitoring point within the historical monitoring time range based on the parameter ratios of the multiple monitored displacement subsets; a determination module for determining the target curve shape of the target historical parameter ratio curve to determine the target prediction function of the slope monitoring point within a prediction time range; and a prediction module for acquiring the target predicted displacement of the slope monitoring point within the prediction time range based on the target prediction function.

[0014] The displacement prediction device for slope monitoring points provided in the second aspect of this application also has the following technical features, including:

[0015] According to an embodiment of this application, the determining module is further configured to: obtain the target historical parameter ratio curves of the slope monitoring points in multiple monitoring directions; obtain a preset calibration parameter ratio, and determine the target parameter ratio points in multiple monitoring directions corresponding to the calibration parameter ratios from the target historical parameter ratio curves in multiple monitoring directions; obtain a first number of target parameter ratio points in multiple monitoring directions, and a second number of extreme points on the target historical parameter ratio curves in multiple monitoring directions; determine the target curve shape of the target historical parameter ratio curves in multiple monitoring directions based on the first number and the second number; and determine the target prediction function of the slope monitoring points in multiple monitoring directions within the prediction time range based on the target curve shape.

[0016] According to an embodiment of this application, the determining module is further configured to: obtain the set of historical displacement components of the slope monitoring point in multiple monitoring directions based on the historical displacement set; obtain the component parameter ratio of each of the multiple monitoring displacement component subsets of the historical displacement component set in multiple monitoring directions, so as to obtain the target historical parameter ratio curve of the slope monitoring point in multiple monitoring directions within the historical monitoring time range.

[0017] According to an embodiment of this application, the determining module is further configured to: determine a single-peaked and boundary-stable shape as the target curve shape in response to the first quantity being equal to 1 and the second quantity being equal to 1; determine a multi-peaked and boundary-non-converging shape as the target curve shape in response to the first quantity being equal to 1 and the second quantity being greater than 1; determine the single-peaked and boundary-stable shape as the target curve shape in response to the first quantity being equal to 2 and the second quantity being equal to 1; and determine a trapezoidal and boundary-stable shape, and / or a bimodal shape with slowly deforming boundaries, and / or a multi-peaked shape with rapidly deforming boundaries as the target curve shape in response to the first quantity being greater than or equal to 2 and the second quantity being greater than 1.

[0018] According to one embodiment of this application, the determining module is further configured to: determine a linear function as the target prediction function in response to the target curve being unimodal and having stable boundaries; determine an exponential function as the target prediction function in response to the target curve being multimodal and having non-convergent boundaries; determine a linear function as the target prediction function in response to the target curve being trapezoidal and having stable boundaries; determine a quadratic function as the target prediction function in response to the target curve being bimodal and having slowly deforming boundaries; and determine a linear function as the target prediction function in response to the target curve being multimodal and having rapidly deforming boundaries.

[0019] According to an embodiment of this application, the prediction module is further configured to: obtain a reference historical displacement set required for the slope monitoring point to make a prediction within the prediction time range based on the historical displacement set within the historical monitoring time range; obtain the predicted displacement components generated by the slope monitoring point in multiple prediction directions within the prediction time range based on the reference historical displacement set and the target prediction function; and obtain the target predicted displacement of the slope monitoring point within the prediction time range based on the predicted displacement components in the multiple prediction directions.

[0020] According to one embodiment of this application, the device further includes a correction module, configured to: acquire the actual monitored displacement of the slope monitoring point within the predicted time range, and identify whether there is an error in the target predicted displacement based on the actual monitored displacement; in response to identifying an error in the target predicted displacement, acquire a new target historical parameter ratio curve of the slope monitoring point within the historical monitoring time range, and determine a new target prediction function based on the new target curve shape corresponding to the new target historical parameter ratio curve, so as to acquire a new target predicted displacement of the slope monitoring point within the predicted time range.

[0021] A third aspect of this application provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute the displacement prediction method for slope monitoring points provided in the first aspect of this application.

[0022] A fourth aspect of this application provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the displacement prediction method for slope monitoring points provided in the first aspect of this application.

[0023] The fifth aspect of this application provides a computer program product that, when executed by an instruction processor, performs the displacement prediction method for slope monitoring points provided in the first aspect of this application.

[0024] The displacement prediction method and apparatus for slope monitoring points provided in this application acquire a set of historical displacements of the slope monitoring points within a historical monitoring time range. Based on the parameter ratios of multiple subsets of monitored displacements within the historical displacement set, a target historical parameter ratio curve is obtained within the historical monitoring time range. Further, based on the target curve shape in the target historical parameter ratio curve, a target prediction function for displacement prediction of the slope monitoring points is determined. Then, based on the determined target prediction function, the possible displacements of the slope monitoring points within the prediction time range are predicted to obtain the target predicted displacement. In this application, the target prediction function is determined based on the target curve shape of the target historical parameter ratio curve, thereby achieving the acquisition of the target predicted displacement of the slope monitoring points within the prediction time range. This eliminates the manual processing step, saves labor costs, and improves the accuracy and efficiency of displacement prediction for slope monitoring points.

[0025] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0026] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0027] Figure 1 This is a flowchart illustrating a method for predicting the displacement of slope monitoring points according to an embodiment of this application.

[0028] Figure 2 This is a flowchart illustrating a displacement prediction method for slope monitoring points according to another embodiment of this application.

[0029] Figure 3This is a schematic diagram of the target historical parameter ratio curve according to an embodiment of this application;

[0030] Figure 4 This is a schematic diagram of the target historical parameter ratio curve according to another embodiment of this application;

[0031] Figure 5 This is a schematic diagram of the target historical parameter ratio curve according to another embodiment of this application;

[0032] Figure 6 This is a schematic diagram of the target historical parameter ratio curve according to another embodiment of this application;

[0033] Figure 7 This is a schematic diagram of the target historical parameter ratio curve according to another embodiment of this application;

[0034] Figure 8 This is a schematic diagram of the target historical parameter ratio curve according to another embodiment of this application;

[0035] Figure 9 This is a schematic diagram of the target historical parameter ratio curve according to another embodiment of this application;

[0036] Figure 10 This is a schematic diagram of the structure of a displacement prediction device for a slope monitoring point according to an embodiment of this application;

[0037] Figure 11 This is a block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0038] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0039] The following description, with reference to the accompanying drawings, outlines a method, apparatus, electronic device, and storage medium for predicting displacement at slope monitoring points according to embodiments of this application.

[0040] Figure 1 This is a flowchart illustrating a displacement prediction method for slope monitoring points according to an embodiment of this application, as shown below. Figure 1 As shown, the method includes:

[0041] S101, obtain the historical displacement set of slope monitoring points within the historical monitoring time range.

[0042] In practice, the slope can be monitored within a set time range. Specifically, points on the slope can be monitored to obtain relevant parameters of the slope displacement within the set time range.

[0043] Optionally, the points monitored on the slope can be designated as slope monitoring points.

[0044] In this embodiment of the application, relevant parameters of the displacement generated by the slope monitoring point within a historical set time range can be obtained, thereby predicting the possible displacement of the slope monitoring point within a set prediction time range.

[0045] Optionally, the historical time range can be defined as the historical monitoring time range for monitoring the displacement of slope monitoring points.

[0046] Furthermore, displacement monitoring can be performed on slope monitoring points within the historical monitoring time range to obtain multiple displacement parameters of the slope monitoring points within the historical monitoring time range, thereby obtaining the historical displacement set of the slope monitoring points within the historical monitoring time range.

[0047] Optionally, the slope monitoring points can be monitored for displacement using the Global Navigation Satellite System (GNSS) to obtain the historical displacement set of the slope monitoring points within the historical monitoring time range.

[0048] S102, obtain multiple subsets of monitored displacements from the historical displacement set, and obtain the target historical parameter ratio curve of the slope monitoring point within the historical monitoring time range based on the parameter ratio of each of the multiple monitored displacement subsets.

[0049] In this embodiment of the application, the historical displacement set of the obtained slope monitoring points within the historical monitoring time range can be analyzed, and based on the analysis results, the possible displacement of the slope monitoring points within the predicted time range can be predicted.

[0050] Optionally, the historical displacement set may include multiple historical displacements generated by the slope monitoring points within the historical monitoring time range. The historical displacement set may be divided to obtain multiple subsets of the historical displacement set, which can be used as multiple monitoring displacement subsets.

[0051] This involves sorting multiple historical displacements included in the historical displacement set, and then dividing the sorted historical displacements into multiple subsets of monitored displacements in the historical displacement set.

[0052] Furthermore, the number of parameters of historical monitoring displacements included in each subset of multiple monitoring displacement subsets is obtained, and these parameters are determined as the parameter quantity of the monitoring displacement subset.

[0053] Optionally, the total number of all historical displacements included in the historical displacement set is obtained, and the ratio of the number of parameters of the monitored displacement subset to the total number of historical displacements included in the historical displacement set is obtained. Further, this ratio can be determined as the ratio of the number of parameters of the monitored displacement subset.

[0054] In this embodiment of the application, a target historical parameter ratio curve corresponding to the historical displacement set can be generated based on the parameter ratios of the multiple monitored displacement subsets included in the historical displacement set.

[0055] Specifically, the X-axis can be determined based on the parameter ratio, and the Y-axis can be determined based on all historical displacements included in the historical displacement set. Then, in the coordinate system composed of the X-axis and Y-axis, the target historical parameter ratio curve corresponding to the historical displacement set can be generated.

[0056] S103, determine the target curve shape of the target historical parameter ratio curve, so as to determine the target prediction function of the slope monitoring point within the prediction time range.

[0057] In this embodiment of the application, the displacement that may occur at the slope monitoring point within the prediction time range can be predicted by the relevant displacement acquisition function.

[0058] Optionally, the corresponding function for calculating and predicting the possible displacement of the slope monitoring point within the predicted time range can be determined based on the shape of the curve corresponding to the target parameter ratio curve.

[0059] Specifically, the shape of the curve corresponding to the target parameter ratio curve can be determined as the target curve shape of the target parameter ratio curve. Through the target curve shape of the target parameter ratio curve, the relevant appearance features of the target parameter ratio curve can be described.

[0060] Accordingly, the function used to calculate and predict the possible displacement of the slope monitoring point within the prediction time range, which is determined by the shape of the target curve based on the ratio curve of the target parameters, can be defined as the target prediction function for the slope monitoring point within the prediction time range.

[0061] S104. Based on the target prediction function, obtain the target predicted displacement of the slope monitoring point within the prediction time range.

[0062] In this embodiment of the application, historical information related to the slope monitoring point can be obtained, and based on this historical information and the determined target prediction function, the possible displacement of the slope monitoring point within the prediction time range can be calculated, thereby obtaining the target predicted displacement of the slope monitoring point within the prediction time range.

[0063] Optionally, the relevant parameters in the historical displacement set of the slope monitoring point within the historical monitoring time range can be processed by the algorithm corresponding to the target prediction function in the relevant technology, and the target predicted displacement of the slope monitoring point within the prediction time range can be obtained based on the result of the algorithm processing.

[0064] It should be noted that the predicted displacement of the slope monitoring point at multiple timestamps within the prediction time range can be obtained based on the target prediction function. Then, based on the predicted displacement at each of the total timestamps, the total target predicted displacement of the slope monitoring point within the prediction time range can be obtained.

[0065] The displacement prediction method for slope monitoring points proposed in this application obtains a set of historical displacements of the slope monitoring points within a historical monitoring time range. Based on the parameter ratios of multiple subsets of monitored displacements within the historical displacement set, a target historical parameter ratio curve is obtained within the historical monitoring time range. Further, based on the target curve shape in the target historical parameter ratio curve, a target prediction function for displacement prediction of the slope monitoring points is determined. Then, based on the determined target prediction function, the possible displacements of the slope monitoring points within the prediction time range are predicted, resulting in the target predicted displacement. In this application, the target prediction function is determined based on the target curve shape of the target historical parameter ratio curve, thereby achieving the acquisition of the target predicted displacement of the slope monitoring points within the prediction time range. This eliminates the manual processing step, saves labor costs, and improves the accuracy and efficiency of displacement prediction for slope monitoring points.

[0066] In the above embodiments, the acquisition of the target prediction function can be combined with... Figure 2 understand, Figure 2 This is a flowchart illustrating a displacement prediction method for slope monitoring points according to another embodiment of this application, as shown below. Figure 2 As shown, the method includes:

[0067] S201, obtain the target historical parameter ratio curves of slope monitoring points in multiple monitoring directions.

[0068] In this embodiment of the application, the slope monitoring point may be displaced in multiple directions. The monitoring direction of the slope monitoring point can be preset to obtain multiple monitoring directions for monitoring the displacement of the slope monitoring point within the historical monitoring time range.

[0069] In this scenario, the displacement components of the slope monitoring points can be monitored in each of the multiple monitoring directions, thereby enabling the prediction of the possible displacement components of the slope monitoring points in multiple directions within the prediction time range.

[0070] Optionally, based on the historical displacement set, the historical displacement component sets of the slope monitoring points in multiple monitoring directions can be obtained.

[0071] In this embodiment of the application, the parameters included in the historical displacement set can be classified based on the monitoring direction, thereby obtaining the historical displacement component set of each slope monitoring point in multiple monitoring directions after classification.

[0072] Optionally, a total of three monitoring directions can be set, including a first direction composed of due north and due south, a second direction composed of due east and due west, and a third direction composed of an upward direction and a downward direction perpendicular to the horizontal plane.

[0073] Specifically, for the slope monitoring points, the displacement in the due north direction (first direction) is positive, and the displacement in the due south direction is negative. Similarly, the displacement in the due east direction (second direction) is positive, and the displacement in the due west direction is negative. Furthermore, the displacement in the upward direction perpendicular to the horizontal plane (third direction) is positive, and the displacement in the downward direction perpendicular to the horizontal plane is negative.

[0074] Furthermore, based on multiple preset monitoring directions, the displacement generated by the slope monitoring points is monitored to obtain the displacement components generated by the slope monitoring points in each of the multiple monitoring directions within the historical monitoring time range. Then, based on the monitored displacement components, the set of historical displacement components of the slope monitoring points in each of the multiple monitoring directions within the historical monitoring time range is obtained.

[0075] Optionally, the component parameter ratios of multiple subsets of historical displacement components in multiple monitoring directions are obtained to obtain the target historical parameter ratio curves of slope monitoring points in multiple monitoring directions within the historical monitoring time range.

[0076] In this embodiment of the application, a set of historical displacement components in one monitoring direction can be obtained from the set of historical displacement components in each of the multiple monitoring directions, and the set of historical displacement components can be divided to obtain multiple subsets of monitoring displacement components in the set of historical displacement components.

[0077] Furthermore, the component parameter ratios of multiple subsets of historical displacement components in the monitoring direction are obtained, thereby obtaining the target historical parameter ratio curve of the slope monitoring point in the monitoring direction within the historical monitoring time range.

[0078] Optionally, based on the above example, the maximum and minimum values ​​in the set of historical displacement components in the first direction can be obtained respectively, the minimum value can be used as the first parameter value included in the first monitored displacement component subset, and the maximum value can be used as the last parameter value included in the last monitored displacement component subset, thereby dividing the set of historical displacement components in the first direction into m1 monitored displacement component subsets.

[0079] Accordingly, based on the method proposed above, the historical displacement component sets in the second direction and the third direction are divided respectively, so as to obtain m2 subsets of monitored displacement components of the slope monitoring point in the second direction and m3 subsets of monitored displacement components of the slope monitoring point in the third direction.

[0080] In this scenario, the component parameter quantity of each subset in the m1 monitored displacement component subsets in the first direction can be obtained, and the ratio of the component parameter quantity to the total component parameter quantity in the m1 monitored displacement component subsets can be obtained, thereby obtaining the component parameter quantity ratio corresponding to each subset in the m1 monitored displacement component subsets.

[0081] For example, if we define the total number of component parameters included in the m1 subsets of monitored displacement as n, and the number of component parameters in each of the m1 subsets of monitored displacement as {k1,k2,k3,……,km1}, then the ratio of the number of component parameters in the subset with component parameter k1 is b1=k1 / n.

[0082] By analogy, the ratio of the component parameter quantity {k1,k2,k3,……,km1} to n is obtained in each of the m1 monitored displacement component subsets, and then the ratio of the component parameter quantity {b1,b2,b3,……,bm1} in each of the m1 monitored displacement component subsets is obtained.

[0083] Accordingly, based on the method proposed above, the component parameter ratio of each subset in the m2 monitored displacement component subsets in the second direction and the component parameter ratio of each subset in the m3 monitored displacement component subsets in the third direction are obtained respectively.

[0084] Furthermore, based on the component parameter ratios corresponding to each subset of the m1 monitored displacement components in the first direction, the target historical parameter ratio curve of the slope monitoring point in the first direction is obtained.

[0085] Specifically, the X-axis can be determined based on the component parameter ratio, and the Y-axis can be determined based on the values ​​of the displacement components included in the m1 monitored displacement component subsets. Thus, a target historical parameter ratio curve in the first direction can be generated based on the component parameter ratios {b1,b2,b3,……,bm1} of each of the m1 monitored displacement component subsets.

[0086] Accordingly, based on the component parameter ratios of each subset of the m2 monitored displacement components in the second direction, the target historical parameter ratio curve of the slope monitoring point in the second direction is obtained. And, based on the component parameter ratios of each subset of the m3 monitored displacement components in the third direction, the target historical parameter ratio curve of the slope monitoring point in the third direction is obtained.

[0087] S202, obtain the preset calibration parameter ratio, and determine the target parameter ratio points corresponding to the calibration parameter ratio in each of the multiple monitoring directions from the target historical parameter ratio curves in each of the multiple monitoring directions.

[0088] In this embodiment of the application, the calibration parameter ratio can be preset based on the component parameter ratio of each subset of multiple monitoring displacement components in each monitoring direction.

[0089] Furthermore, based on the preset calibration parameter ratio, the target parameter ratio point corresponding to the calibration parameter ratio is determined from the target historical parameter ratio curve composed of the component parameter ratios of multiple monitored displacement component subsets.

[0090] Optionally, a target historical parameter ratio curve can be set as follows: Figure 3 As shown, by setting the calibration parameter ratio to 0.05, it can be obtained from... Figure 3 The point corresponding to the calibration parameter ratio of 0.05 is determined on the X-axis as shown, and the following lines are drawn from that point: Figure 3 The dashed line shown is connected to... Figure 3 The intersection point between the target historical parameter ratio curves shown is determined as Figure 3 The target parameter ratio points corresponding to the target historical parameter ratios are marked on the target parameter ratio curve shown.

[0091] Therefore, based on the above-mentioned method for determining the target parameter ratio points, the target parameter ratio points corresponding to the calibrated parameter ratios can be obtained on the target historical parameter ratio curves for multiple monitoring directions.

[0092] S203, obtain the first number of target parameter ratio points in multiple monitoring directions, and the second number of extreme points on the historical target parameter ratio curves in multiple monitoring directions.

[0093] In this embodiment of the application, the target parameter ratio curves of each of the multiple monitoring directions can be analyzed to obtain a first number of target parameter ratio points of each of the multiple monitoring directions, and a second number of extreme points on the target historical parameter ratio curves of each of the multiple monitoring directions.

[0094] For example, still as Figure 3 As shown, Figure 3 The dashed line corresponding to the calibration parameter ratio of 0.05 shown is... Figure 3 The intersection point between the target historical parameter ratio curves shown is the target parameter ratio point. Therefore, as can be seen from the graph, there is one target parameter ratio point on this curve. This number can then be determined as... Figure 3 The first number of target parameter ratio points shown.

[0095] Optionally, based on the method for determining extreme points in related technologies, the ratio curves of the target historical parameter quantities in each of the multiple monitoring directions can be analyzed, and then the extreme points on the ratio curves of the target historical parameter quantities in each of the multiple monitoring directions can be determined according to the analysis results, thereby obtaining a second number of extreme points on the ratio curves of the target historical parameter quantities in each of the multiple monitoring directions.

[0096] S204, based on the first quantity and the second quantity, determine the target curve shape of the target historical parameter quantity ratio curve for each of the multiple monitoring directions.

[0097] In this embodiment of the application, the first quantity and the second quantity may have different values. The target curve shape of the target historical parameter ratio curve in multiple monitoring directions can be determined based on the respective values ​​of the first quantity and the second quantity.

[0098] Optionally, in response to the first quantity being equal to 1 and the second quantity being equal to 1, a unimodal and boundary-stable shape is determined as the target curve shape.

[0099] like Figure 4 As shown, Figure 4 In the target parameter ratio curve shown, the first number of target parameter ratio points is equal to 1 and the second number of extreme points is equal to 1. Figure 4 It can be seen that the curve of the ratio of the target parameter is unimodal and has stable boundaries. Therefore, the unimodal and stable boundary shape can be defined as... Figure 4 The target curve shape is shown as the ratio curve of the target parameter quantity with the first quantity equal to 1 and the second quantity equal to 1.

[0100] Optionally, in response to a first quantity equal to 1 and a second quantity greater than 1, the shape of a multi-peaked curve with non-convergent boundaries is determined as the target curve shape.

[0101] like Figure 5 As shown, Figure 5 In the target parameter ratio curve shown, the first number of target parameter ratio points is equal to 1 and the second number of extreme points is greater than 1. Figure 5 It can be seen that the curve of the ratio of the target parameter is multi-peaked and the boundaries do not converge. Therefore, the multi-peaked and non-convergent shape can be defined as... Figure 5 The target curve shape is shown as the ratio curve of the target parameter quantity with the first quantity equal to 1 and the second quantity greater than 1.

[0102] Optionally, in response to a first quantity equal to 2 and a second quantity equal to 1, a unimodal and boundary-stable shape is determined as the target curve shape.

[0103] like Figure 6 As shown, Figure 6 In the target parameter ratio curve shown, the first number of target parameter ratio points is equal to 2 and the second number of extreme points is equal to 1. Figure 6 It can be seen that the curve of the ratio of the target parameter is unimodal and has stable boundaries. Therefore, the unimodal and stable boundary shape can be defined as... Figure 6 The target curve shape is shown as the ratio curve of the target parameter quantity with the first quantity equal to 2 and the second quantity equal to 1.

[0104] Optionally, in response to a first quantity greater than or equal to 2 and a second quantity greater than 1, a trapezoidal and boundary-stable shape, and / or a bimodal shape with slow boundary deformation, and / or a multimodal shape with rapid boundary deformation is determined as the target curve shape.

[0105] In some implementations, the target parameter ratio curve corresponding to a first number of target parameter ratio points greater than or equal to 2 and a second number of extreme points greater than 1 can be as follows: Figure 7 As shown, by Figure 7 It can be seen that the curve of the ratio of the target parameter is trapezoidal in shape and has stable boundaries. Therefore, the trapezoidal shape with stable boundaries can be defined as... Figure 7 The target curve shape is shown as the ratio curve of the target parameter quantity with the first quantity being greater than or equal to 2 and the second quantity being greater than 1.

[0106] In other implementations, the target parameter ratio curve corresponding to a first number of target parameter ratio points greater than or equal to 2 and a second number of extreme points greater than 1 can be as follows: Figure 8 As shown, by Figure 8 It can be seen that the target parameter ratio curve has a bimodal shape with slow boundary deformation. Therefore, the bimodal shape with slow boundary deformation can be defined as... Figure 8 The target curve shape is shown as the ratio curve of the target parameter quantity with the first quantity being greater than or equal to 2 and the second quantity being greater than 1.

[0107] In other implementations, the target parameter ratio curve corresponding to a first number of target parameter ratio points greater than or equal to 2 and a second number of extreme points greater than 1 can be as follows: Figure 9 As shown, by Figure 9 It can be seen that the target parameter ratio curve has a multi-peak shape and the boundary deforms rapidly. Therefore, the multi-peak shape with rapidly deforming boundaries can be defined as... Figure 9 The target curve shape is shown as the ratio curve of the target parameter quantity with the first quantity being greater than or equal to 2 and the second quantity being greater than 1.

[0108] S205, based on the shape of the target curve, determine the target prediction function for each slope monitoring point in multiple monitoring directions within the prediction time range.

[0109] In this embodiment of the application, the correlation between the curve shape and the prediction function that may be generated by the slope monitoring point in multiple monitoring directions within the prediction time range can be pre-constructed. After determining the target curve shape of the target parameter ratio curve, the prediction function associated with the target curve shape can be determined from all the prediction functions based on the determined target curve shape and the pre-constructed correlation, and used as the target prediction function in the monitoring direction corresponding to the target parameter ratio curve to which the target curve shape belongs.

[0110] Optionally, in response to the target curve having a unimodal shape and stable boundaries, a linear function is determined as the target prediction function.

[0111] In the embodiments of this application, such as Figure 4 and / or Figure 6 As shown, Figure 4 and / or Figure 6 The target curve shape of the target parameter ratio curve shown in the figure is a single-peaked and boundary-stable shape. In this scenario, the linear function can be determined as the target prediction function corresponding to the target curve shape of the single-peaked and boundary-stable shape.

[0112] Optionally, in response to the target curve having a multi-peaked shape and non-convergent boundaries, an exponential function is determined as the target prediction function.

[0113] In the embodiments of this application, such as Figure 5 As shown, Figure 5 The target curve shape of the target parameter ratio curve shown in the figure is a multi-peaked shape with non-convergent boundaries. In this scenario, the exponential function can be determined as the target prediction function corresponding to the target curve shape with a multi-peaked shape and non-convergent boundaries.

[0114] Optionally, in response to the target curve being trapezoidal in shape and having stable boundaries, a linear function is determined as the target prediction function.

[0115] In the embodiments of this application, such as Figure 7 As shown, Figure 7 The target curve shape of the target parameter ratio curve shown is trapezoidal and has a stable boundary. In this scenario, the linear function can be determined as the target prediction function corresponding to the trapezoidal and stable boundary shape of the target curve.

[0116] Optionally, in response to the target curve having a bimodal shape and slow boundary deformation, a quadratic function is determined as the target prediction function.

[0117] In the embodiments of this application, such as Figure 8 As shown, Figure 8 The target curve shape shown in the figure is a bimodal shape with slow boundary deformation. In this scenario, the quadratic function can be determined as the target prediction function corresponding to the target curve shape with bimodal shape and slow boundary deformation.

[0118] Optionally, in response to the target curve having a multi-peaked shape and rapidly deforming boundaries, a linear function is determined as the target prediction function.

[0119] In the embodiments of this application, such as Figure 9 As shown, Figure 9 The target curve shape shown in the figure is a multi-peaked shape with rapidly deforming boundaries. In this scenario, a linear function can be determined as the target prediction function corresponding to the target curve shape with a multi-peaked shape and rapidly deforming boundaries.

[0120] S206, Based on the target prediction function, obtain the target predicted displacement of the slope monitoring point within the prediction time range.

[0121] Optionally, based on the target prediction function and combined with the historical displacement set of the slope monitoring point within the historical monitoring time range, the possible displacement of the slope monitoring point within the prediction time range can be predicted, thereby obtaining the target predicted displacement of the slope monitoring point within the prediction time range.

[0122] Specifically, the reference historical displacement set required for slope monitoring points to make predictions within the prediction time range can be obtained based on the historical displacement set within the historical monitoring time range.

[0123] In this embodiment of the application, multiple historical displacement data required for predicting the displacement of slope monitoring points within the prediction time range can be obtained from the historical displacement set and used as a reference historical displacement set.

[0124] Optionally, all historical displacement data included in the historical displacement set can be divided by hour to obtain 24 sets of historical displacement data. Further, a set number of historical displacement data can be obtained from each of the 24 sets of historical displacement data as reference historical displacement data for each of the 24 sets of historical displacement data.

[0125] Furthermore, the 24 sets of historical reference displacement data obtained are integrated to obtain the set of historical reference displacements required for slope monitoring points to make predictions within the prediction time range.

[0126] This can be based on a preset strategy for acquiring historical displacement data, referencing the number of historical displacement data included in the historical displacement set.

[0127] For example, the number of all historical displacement data included in the historical displacement set in each of multiple monitoring directions can be obtained, multiplied by a preset calibration parameter ratio, and the resulting product is determined as the number of historical displacement data included in the reference historical displacement set in each of the historical displacement sets in each monitoring direction.

[0128] As can be seen from the above example, if the calibration parameter ratio is 0.05, then the total number of historical displacement data included in the historical displacement set in each of the multiple monitoring directions can be multiplied by 0.05, and the resulting product is determined as the number of historical displacement data included in the reference historical displacement set in each of the historical displacement sets in each monitoring direction.

[0129] Optionally, based on the reference historical displacement set and the target prediction function, the predicted displacement components generated by the slope monitoring point in multiple prediction directions within the prediction time range are obtained, and the target predicted displacement of the slope monitoring point within the prediction time range is obtained based on the predicted displacement components in multiple prediction directions.

[0130] In this embodiment of the application, the predicted direction of the slope monitoring point can be determined according to the monitoring direction of the slope monitoring point. In this case, the directions that are the same as multiple monitoring directions of the slope monitoring point can be determined as multiple predicted directions of the slope monitoring point within the prediction time range.

[0131] For example, in a scenario where the first direction formed by due north and due south is the monitoring direction, the first direction formed by due north and due south can also be used as the prediction direction in that scenario.

[0132] For example, in a scenario where the second direction formed by the due east and due west is the monitoring direction, the second direction formed by the due east and due west can also be used as the prediction direction in that scenario.

[0133] In this scenario, the target prediction function in each of the multiple monitoring directions can be determined as the target prediction function in the corresponding prediction direction among the multiple prediction directions.

[0134] Optionally, the reference historical displacement set in the corresponding monitoring direction in the multiple monitoring directions can be processed by the target prediction function in each of the multiple prediction directions to obtain the predicted displacement components in each of the multiple prediction directions.

[0135] Furthermore, the predicted displacement components in each of the multiple predicted directions are integrated to obtain the target predicted displacement of the slope monitoring point within the predicted time range.

[0136] For example, the predicted displacement components of the slope monitoring point in the first direction, the second direction, and the third direction can be obtained separately within the prediction time range. The predicted displacement components in each of these three directions can be integrated to obtain the target predicted displacement of the slope monitoring point within the prediction time range.

[0137] It should be noted that the target predicted displacement obtained within the prediction time range may differ from the actual monitored displacement within that prediction time range. In this scenario, the prediction algorithm for the target predicted displacement can be corrected based on the actual monitored displacement.

[0138] Optionally, the actual monitored displacement of the slope monitoring point within the predicted time range can be obtained, and the error of the predicted target displacement can be identified based on the actual monitored displacement.

[0139] Among these methods, the actual monitored displacement and the target predicted displacement can be compared using error identification methods in related technologies to identify whether there is an error in the target predicted displacement.

[0140] Optionally, in response to the identification of an error in the target predicted displacement, a new target historical parameter ratio curve for the slope monitoring point within the historical monitoring time range is obtained, and a new target prediction function is determined based on the new target curve shape corresponding to the new target historical parameter ratio curve, so as to obtain the new target predicted displacement for the slope monitoring point within the prediction time range.

[0141] In this embodiment of the application, when it is found that there is an error in the target predicted displacement based on the actual monitored displacement, the new target historical parameter ratio curves in each of the multiple monitoring directions can be obtained again based on the historical displacement set of the slope monitoring point within the historical monitoring time range.

[0142] Furthermore, based on the new target curve shape of the new target historical parameter ratio curve in each of the multiple monitoring directions, the new target prediction function in each of the multiple monitoring directions is determined, thereby obtaining the new target predicted displacement of the slope monitoring point within the prediction time range.

[0143] In some implementations, the new target curve shape may differ from the original target curve shape. In this scenario, a new target prediction function can be determined based on the new target curve shape, thereby obtaining a new target prediction displacement.

[0144] In some implementations, the new target curve shape may be the same as the original target curve shape. In this scenario, the target prediction function corresponding to the target curve shape can be adjusted to obtain a new target prediction function, and a new target prediction displacement can be obtained based on the new target prediction function.

[0145] The displacement prediction method for slope monitoring points proposed in this application obtains the target historical parameter ratio curves of the slope monitoring points in multiple monitoring directions. This yields a first number of target parameter ratio points and a second number of extreme points on the target historical parameter ratio curves in each of the multiple monitoring directions. Based on the first and second numbers, the target curve shape of the target historical parameter ratio curves in each of the multiple monitoring directions is determined, thereby determining the target prediction function for the slope monitoring points in each direction. This results in the target predicted displacement of the slope monitoring points within the prediction time range. In this application, the target prediction function is determined based on the target curve shape of the target historical parameter ratio curves, thus enabling the acquisition of the target predicted displacement of the slope monitoring points within the prediction time range. This eliminates the need for manual processing, saves labor costs, and improves the accuracy and efficiency of displacement prediction for slope monitoring points.

[0146] Corresponding to the displacement prediction methods for slope monitoring points proposed in the above embodiments, an embodiment of this application also proposes a displacement prediction device for slope monitoring points. Since the displacement prediction device for slope monitoring points proposed in this application corresponds to the displacement prediction methods for slope monitoring points proposed in the above embodiments, the implementation methods of the above displacement prediction methods for slope monitoring points are also applicable to the displacement prediction device for slope monitoring points proposed in this application, and will not be described in detail in the following embodiments.

[0147] Figure 10 This is a schematic diagram of the structure of a displacement prediction device for a slope monitoring point according to an embodiment of this application, as shown below. Figure 10 As shown, the displacement prediction device 100 for slope monitoring points includes a monitoring module 11, an acquisition module 12, a determination module 13, and a prediction module 14, wherein:

[0148] Monitoring module 11 is used to acquire the historical displacement set of slope monitoring points within the historical monitoring time range;

[0149] The acquisition module 12 is used to acquire multiple subsets of monitored displacements from the historical displacement set, and to acquire the target historical parameter ratio curve of the slope monitoring point within the historical monitoring time range based on the parameter ratio of each of the multiple monitored displacement subsets.

[0150] Module 13 is used to determine the target curve shape of the target historical parameter ratio curve, so as to determine the target prediction function of the slope monitoring point within the prediction time range;

[0151] Prediction module 14 is used to obtain the target predicted displacement of the slope monitoring point within the prediction time range according to the target prediction function.

[0152] In this embodiment of the application, the determining module 13 is further configured to: obtain the target historical parameter ratio curves of the slope monitoring points in multiple monitoring directions; obtain a preset calibration parameter ratio, and determine the target parameter ratio points in multiple monitoring directions corresponding to the calibration parameter ratios from the target historical parameter ratio curves in multiple monitoring directions; obtain a first number of target parameter ratio points in multiple monitoring directions, and a second number of extreme points on the target historical parameter ratio curves in multiple monitoring directions; determine the target curve shape of the target historical parameter ratio curves in multiple monitoring directions based on the first number and the second number; and determine the target prediction function of the slope monitoring points in multiple monitoring directions within the prediction time range based on the target curve shape.

[0153] In this embodiment of the application, the determining module 13 is further configured to: obtain the set of historical displacement components of the slope monitoring point in multiple monitoring directions based on the historical displacement set; obtain the component parameter ratio of each subset of the historical displacement component set in multiple monitoring directions, so as to obtain the target historical parameter ratio curve of the slope monitoring point in multiple monitoring directions within the historical monitoring time range.

[0154] In this embodiment of the application, the determining module 13 is further configured to: determine a single-peaked and boundary-stable shape as the target curve shape in response to a first quantity equal to 1 and a second quantity equal to 1; determine a multi-peaked and boundary-non-converging shape as the target curve shape in response to a first quantity equal to 1 and a second quantity greater than 1; determine a single-peaked and boundary-stable shape as the target curve shape in response to a first quantity equal to 2 and a second quantity equal to 1; and determine a trapezoidal and boundary-stable shape, and / or a bimodal shape with slow boundary deformation, and / or a multi-peaked shape with rapid boundary deformation as the target curve shape in response to a first quantity greater than or equal to 2 and a second quantity greater than 1.

[0155] In this embodiment of the application, the determining module 13 is further configured to: determine a linear function as the target prediction function in response to the target curve being unimodal and having stable boundaries; determine an exponential function as the target prediction function in response to the target curve being multimodal and having non-convergent boundaries; determine a linear function as the target prediction function in response to the target curve being trapezoidal and having stable boundaries; determine a quadratic function as the target prediction function in response to the target curve being bimodal and having slowly deforming boundaries; and determine a linear function as the target prediction function in response to the target curve being multimodal and having rapidly deforming boundaries.

[0156] In this embodiment of the application, the prediction module 14 is further configured to: obtain a reference historical displacement set required for predicting the slope monitoring point within the prediction time range based on the historical displacement set within the historical monitoring time range; obtain the predicted displacement components generated by the slope monitoring point in multiple prediction directions within the prediction time range based on the reference historical displacement set and the target prediction function; and obtain the target predicted displacement of the slope monitoring point within the prediction time range based on the predicted displacement components in multiple prediction directions.

[0157] In this embodiment of the application, the device further includes a correction module, used to: acquire the actual monitored displacement of the slope monitoring point within the predicted time range, and identify whether there is an error in the target predicted displacement based on the actual monitored displacement; in response to the identification that there is an error in the target predicted displacement, acquire a new target historical parameter ratio curve of the slope monitoring point within the historical monitoring time range, and determine a new target prediction function based on the new target curve shape corresponding to the new target historical parameter ratio curve, so as to acquire a new target predicted displacement of the slope monitoring point within the predicted time range.

[0158] The displacement prediction device for slope monitoring points proposed in this application acquires a set of historical displacements of the slope monitoring point within a historical monitoring time range. Based on the parameter ratios of multiple subsets of monitored displacements within the historical displacement set, a target historical parameter ratio curve is obtained within the historical monitoring time range. Further, based on the target curve shape in the target historical parameter ratio curve, a target prediction function for displacement prediction of the slope monitoring point is determined. Then, based on the determined target prediction function, the possible displacements of the slope monitoring point within the prediction time range are predicted, resulting in the target predicted displacement. In this application, the target prediction function is determined based on the target curve shape of the target historical parameter ratio curve, thereby achieving the acquisition of the target predicted displacement of the slope monitoring point within the prediction time range. This eliminates the manual processing step, saves labor costs, and improves the accuracy and efficiency of displacement prediction for slope monitoring points.

[0159] To achieve the above embodiments, this application also provides an electronic device, a computer-readable storage medium, and a computer program product.

[0160] Figure 11 This is a block diagram of an electronic device according to an embodiment of this application, such as... Figure 11 As shown, device 1100 includes a memory 111, a processor 112, and a computer program stored in the memory 111 and executable on the processor 112. When the processor 112 executes program instructions, it performs... Figures 1 to 9 The embodiment of the displacement prediction method for slope monitoring points.

[0161] To implement the above embodiments, this application also provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute... Figures 1 to 9 The embodiment of the displacement prediction method for slope monitoring points.

[0162] To implement the above embodiments, this application also provides a computer program product that, when executed by an instruction processor, performs... Figures 1 to 9 The embodiment of the displacement prediction method for slope monitoring points.

[0163] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0164] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0165] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0166] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0167] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0168] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0169] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0170] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for predicting displacement of a slope monitoring point, characterized by, The method comprises: obtaining a historical displacement set of a slope monitoring point in a historical monitoring time range; sorting the historical displacements in the historical displacement set in chronological order, and dividing the sorted historical displacements to obtain a plurality of monitoring displacement subsets; based on each monitoring displacement subset, calculating a parameter quantity ratio value thereof, wherein the parameter quantity ratio value is a ratio of the number of displacements in the monitoring displacement subset to the total number of displacements in the historical displacement set; generating a target historical parameter quantity ratio curve of the slope monitoring point in the historical monitoring time range according to the parameter quantity ratio values of the plurality of monitoring displacement subsets and the corresponding historical displacement values; obtaining the target historical parameter quantity ratio curve of the slope monitoring point in each of a plurality of monitoring directions; obtaining a preset calibration parameter quantity ratio value, and determining a plurality of target parameter quantity ratio points of each of the plurality of monitoring directions corresponding to the calibration parameter quantity ratio value from the target historical parameter quantity ratio curve of each of the plurality of monitoring directions; obtaining a first number of the target parameter quantity ratio points of each of the plurality of monitoring directions, and a second number of maximum points on the target historical parameter quantity ratio curve of each of the plurality of monitoring directions; determining a target curve shape of the target historical parameter quantity ratio curve of each of the plurality of monitoring directions according to the first number and the second number; determining a target prediction function of each of the plurality of monitoring directions of the slope monitoring point in a prediction time range according to the target curve shape; obtaining a target prediction displacement of the slope monitoring point in the prediction time range according to the target prediction function.

2. The method of claim 1, wherein, The method comprises: obtaining a historical displacement set of a slope monitoring point in a historical monitoring time range; sorting the historical displacements in the historical displacement set in chronological order, and dividing the sorted historical displacements to obtain a plurality of monitoring displacement subsets; 3. The method of claim 1, wherein, based on each monitoring displacement subset, calculating a parameter quantity ratio value thereof, wherein the parameter quantity ratio value is a ratio of the number of displacements in the monitoring displacement subset to the total number of displacements in the historical displacement set; generating a target historical parameter quantity ratio curve of the slope monitoring point in the historical monitoring time range according to the parameter quantity ratio values of the plurality of monitoring displacement subsets and the corresponding historical displacement values; obtaining the target historical parameter quantity ratio curve of the slope monitoring point in each of a plurality of monitoring directions; obtaining a preset calibration parameter quantity ratio value, and determining a plurality of target parameter quantity ratio points of each of the plurality of monitoring directions corresponding to the calibration parameter quantity ratio value from the target historical parameter quantity ratio curve of each of the plurality of monitoring directions; obtaining a first number of the target parameter quantity ratio points of each of the plurality of monitoring directions, and a second number of maximum points on the target historical parameter quantity ratio curve of each of the plurality of monitoring directions; determining a target curve shape of the target historical parameter quantity ratio curve of each of the plurality of monitoring directions according to the first number and the second number; determining a target prediction function of each of the plurality of monitoring directions of the slope monitoring point in a prediction time range according to the target curve shape; obtaining a target prediction displacement of the slope monitoring point in the prediction time range according to the target prediction function. The method comprises: obtaining a historical displacement set of a slope monitoring point in a historical monitoring time range; sorting the historical displacements in the historical displacement set in chronological order, and dividing the sorted historical displacements to obtain a plurality of monitoring displacement subsets; based on each monitoring displacement subset, calculating a parameter quantity ratio value thereof, wherein the parameter quantity ratio value is a ratio of the number of displacements in the monitoring displacement subset to the total number of displacements in the historical displacement set; generating a target historical parameter quantity ratio curve of the slope monitoring point in the historical monitoring time range according to the parameter quantity ratio values of the plurality of monitoring displacement subsets and the corresponding historical displacement values; obtaining the target historical parameter quantity ratio curve of the slope monitoring point in each of a plurality of monitoring directions; obtaining a preset calibration parameter quantity ratio value, and determining a plurality of target parameter quantity ratio points of each of the plurality of monitoring directions corresponding to the calibration parameter quantity ratio value from the target historical parameter quantity ratio curve of each of the plurality of monitoring directions; obtaining a first number of the target parameter quantity ratio points of each of the plurality of monitoring directions, and a second number of maximum points on the target historical parameter quantity ratio curve of each of the plurality of monitoring directions; determining a target curve shape of the target historical parameter quantity ratio curve of each of the plurality of monitoring directions according to the first number and the second number; determining a target prediction function of each of the plurality of monitoring directions of the slope monitoring point in a prediction time range according to the target curve shape; obtaining a target prediction displacement of the slope monitoring point in the prediction time range according to the target prediction function. In response to the first quantity being greater than or equal to 2 and the second quantity being greater than 1, the target historical parameter quantity ratio curve has a bimodal shape and a boundary slow deformation, and it is determined that the bimodal and boundary slow deformation is the target curve shape; In response to the first quantity being greater than or equal to 2 and the second quantity being greater than 1, the target historical parameter quantity ratio curve has a multi-peak shape and a boundary fast deformation, and it is determined that the multi-peak and boundary fast deformation is the target curve shape.

4. The method of claim 3, wherein, The method further comprises: In response to the target curve shape being a unimodal and boundary stable shape, a first linear function is determined as the target prediction function; In response to the target curve shape being a multi-peak and boundary non-convergent shape, an exponential function is determined as the target prediction function; In response to the target curve shape being a trapezoidal and boundary stable shape, a second linear function is determined as the target prediction function; In response to the target curve shape being a bimodal and boundary slow deformation shape, a quadratic function is determined as the target prediction function; In response to the target curve shape being a multi-peak and boundary fast deformation shape, a third linear function is determined as the target prediction function.

5. The method according to any one of claims 1 to 4, characterized in that, The method further comprises: According to the historical displacement set in the historical monitoring time range, a reference historical displacement set required for the slope monitoring point to make a prediction in the prediction time range is obtained; According to the reference historical displacement set and the target prediction function, a prediction displacement component generated by the slope monitoring point in a plurality of prediction directions in the prediction time range is obtained, and the target prediction displacement of the slope monitoring point in the prediction time range is obtained according to the prediction displacement components in the plurality of prediction directions.

6. The method of claim 5, wherein, The method further comprises: An actual monitoring displacement of the slope monitoring point in the prediction time range is obtained, and it is identified whether the target prediction displacement has an error according to the actual monitoring displacement; In response to identifying that the target prediction displacement has an error, a new target historical parameter quantity ratio curve of the slope monitoring point in the historical monitoring time range is obtained, and a new target prediction function is determined according to a new target curve shape corresponding to the new target historical parameter quantity ratio curve, so as to obtain a new target prediction displacement of the slope monitoring point in the prediction time range.

7. A displacement prediction device for a slope monitoring point, characterized by, The device comprises: A monitoring module is configured to obtain a historical displacement set of a slope monitoring point in a historical monitoring time range; The acquisition module is configured to sort the historical displacements in the historical displacement set in chronological order, divide the sorted historical displacements, and obtain a plurality of monitoring displacement subsets; calculate a parameter quantity ratio of each monitoring displacement subset based on the monitoring displacement subset, wherein the parameter quantity ratio is a ratio of a number of displacements in the monitoring displacement subset to a total number of displacements in the historical displacement set; and generate a target historical parameter quantity ratio curve of the slope monitoring point in the historical monitoring time range according to the parameter quantity ratios of the plurality of monitoring displacement subsets and corresponding historical displacement values. The determination module is configured to obtain respective target historical parameter quantity ratio curves of the slope monitoring point in a plurality of monitoring directions; obtain a preset calibration parameter quantity ratio, and determine respective target parameter quantity ratio points of the plurality of monitoring directions corresponding to the calibration parameter quantity ratio from the respective target historical parameter quantity ratio curves of the plurality of monitoring directions; obtain a first number of the respective target parameter quantity ratio points of the plurality of monitoring directions and a second number of maximum points on the respective target historical parameter quantity ratio curves of the plurality of monitoring directions; determine a target curve shape of the respective target historical parameter quantity ratio curves of the plurality of monitoring directions according to the first number and the second number; and determine respective target prediction functions of the slope monitoring point in the plurality of monitoring directions in a prediction time range according to the target curve shape. The prediction module is configured to obtain target predicted displacements of the slope monitoring point in the prediction time range according to the target prediction functions.

8. An electronic device, comprising: The apparatus comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

9. A non-transitory computer-readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to enable the computer to perform the method of any one of claims 1-6.

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