Landslide surge speed time history determination method and device based on multi-class function self-adaption

Abstract

The invention relates to the technical field of water conservancy projects, in particular to a landslide surge speed time history determination method and device based on multi-class function self-adaption, and the method comprises the steps: carrying out a surge physical model test, and obtaining a first measurement point data set and a second measurement point data set through the surge physical model test; calibrating the position parameters of each measuring point based on the first measuring point data set to obtain the position parameters of the measuring points, and generating a wave height-time analysis data three-dimensional array according to the position parameters of the measuring points and the second measuring point data set; segmenting the three-dimensional array according to a time history, and fitting to obtain a segmented wave height multi-class function adaptive formula; derivation is carried out on the segmented wave height multi-class function adaptive formula, and a time history curve of the surge climbing speed of the measuring point is drawn. According to the method, the landslide surge speed time history can be determined with high precision and high efficiency, the problem that the surge speed cannot be obtained when indoor test equipment is limited is solved, surge data characteristics of an indoor surge physical model test are known more clearly, and test surge speed characteristics are summarized efficiently.

Description

Technical Field

[0001] The present application relates to the technical field of water conservancy engineering, and in particular to a method and device for determining a landslide surge velocity time history based on multi-class function adaptation. Background Art

[0002] Landslides entering water may generate surges and threaten the safety of life and property of residents and towns along the route. Since the generation of surges is a relatively complex process, indoor experimental research using proportional physical models is a reliable and credible research method, but it also has the characteristics of large investment in manpower and property, and obvious limitations in equipment monitoring content. It is of great significance to make full use of limited monitoring data, enrich the research content based on monitoring data, fully explore the physical model test data, and summarize the regular characteristics of surges.

[0003] The common surge physical model test can monitor the wave height at different locations and time points in the river by deploying multiple wave height meters along the river, and collect the data of the wave height change over time at the specified location. Fourier series, multi-peak Gaussian function, and sine sum function are all mature and reliable fitting functions that can be used to fit surge monitoring data and are reliable basis functions for solving fitting equations.

[0004] At present, there are few studies on the lack of monitoring of surge climbing speed variables during physical model tests. At the same time, since surge monitoring in the test will be affected by noise and there are clutter, the surge waveform characteristics cannot be well analyzed. In the absence of surge climbing speed monitoring, how to make full use of the monitored wave height data to obtain the climbing speed is of great significance. At the same time, it is necessary to clearly characterize the waveform and wave speed change characteristics during surge propagation. Summary of the invention

[0005] To achieve the above objectives, this application provides the following technical solutions:

[0006] According to a first aspect of the present invention, the present invention claims a method for determining landslide surge velocity time history based on multi-class function adaptation, comprising:

[0007] Implementing a surge physical model test, and obtaining a first measurement point data set and a second measurement point data set through the surge physical model test;

[0008] Calibrate the position parameters of each measuring point based on the first measuring point data set to obtain the measuring point position parameters, and generate a three-dimensional array of wave height-time analysis data based on the measuring point position parameters and the second measuring point data set;

[0009] The three-dimensional array of wave height-time analysis data is segmented according to the time course, and a multi-class function adaptive formula of wave height after segmentation is obtained by fitting;

[0010] The segmented multi-class function adaptive formula of wave height is derived to draw a time history curve of the surge climbing speed at the measuring point.

[0011] Furthermore, the performing of the surge physical model test to obtain the first measurement point data set and the second measurement point data set through the surge physical model test further includes:

[0012] The first measurement point data set at least includes:

[0013] Measuring point elevation data, test water level elevation;

[0014] The second measuring point data set at least includes:

[0015] Measuring point time data and measuring point wave height data.

[0016] Further, calibrating the position parameters of each measuring point based on the first measuring point data set to obtain the measuring point position parameters also includes:

[0017] When the test water level elevation is less than or equal to the measuring point elevation data, the measuring point location parameter is 1;

[0018] When the test water level elevation is greater than the measuring point elevation data, the measuring point location parameter is 0.

[0019] Further, generating a three-dimensional array of wave height-time analysis data according to the measuring point position parameters and the second measuring point data set also includes:

[0020] A three-dimensional array of wave height-time analysis data is constructed through the position parameters of each measuring point, the time point data of the measuring point, and the wave height data of the measuring point. The wave height-time analysis data of the measuring point is a three-dimensional array [x, y, z'] with a dimension of t*3, where t is the number of time measuring points of the measuring point monitoring data.

[0021] Furthermore, the three-dimensional array of wave height-time analysis data is segmented according to the time course, further comprising:

[0022] The three-dimensional array of wave height-time analysis data is segmented according to the time course, and the wave height data time course is segmented by determining the number of surge wave crests, and the number of wave crests in each segment cannot exceed a preset threshold.

[0023] Furthermore, fitting obtains the segmented wave height multi-class function adaptive formula, and also includes:

[0024] Determining different fitting basis functions according to the measuring point position parameters;

[0025] Based on the fitting basis function, fitting calculations are performed at different orders to calculate the determination coefficient of the fitting equation;

[0026] The determination coefficients are compared to determine that the fitting function corresponding to the maximum determination coefficient is the wave height multi-class function adaptive formula.

[0027] Furthermore, determining different fitting basis functions according to the measuring point position parameters also includes:

[0028] When the measuring point position parameter is 1, the first fitting basis function is used;

[0029] When the measuring point position parameter is 0, the first fitting basis function, the second fitting basis function or the third fitting basis function is used.

[0030] Furthermore, the method further comprises:

[0031] The first fitting basis function is:

[0032] Function1:

[0033] Among them, a n ,b n ,c n are all expansion coefficients, m is the upper limit of the fitting order, n is the fitting order, the range of n is an integer of [1, m], and x is time;

[0034] The second fitting basis function is:

[0035] Function2:

[0036] The third fitting basis function is:

[0037] Function3:

[0038] Among them, a0,a n ,b n , w are expansion coefficients, n is the fitting order, and x is time.

[0039] Further, based on the fitting basis function, fitting calculations are performed at different orders to calculate the determination coefficient of the fitting equation, which also includes:

[0040] Perform fitting calculations for different basis functions at different orders and calculate the determination coefficient R of the fitting equation 2 ;

[0041]

[0042] If the measurement point location parameter is 1, let n∈{1,2,3,4,5,6}, and obtain the fitting function f nj (x), calculate j = 1;

[0043] If the measurement point position parameter is 0, let n∈{1,2,3,4,5,6}, and obtain the fitting function f nj (x), calculate j∈{1,2,3};

[0044] The comparison of the determination coefficients and determination of the fitting function corresponding to the maximum determination coefficient as the wave height multi-class function adaptive formula also include:

[0045] Comparison of coefficient of determination Determine the maximum The corresponding fitting function f nj (x) is the final fitting formula f(x), f(x) = f nj (x).

[0046] According to a second aspect of the present invention, the present invention claims a device for determining landslide surge velocity time history based on multi-class function adaptation, comprising:

[0047] one or more processors;

[0048] A memory having one or more programs stored thereon, when the one or more programs are executed by the one or more processors, the one or more processors implement the method for determining the time history of landslide surge velocity based on multi-type function adaptation.

[0049] The present application relates to the technical field of water conservancy engineering, and in particular to a method and device for determining the time course of landslide surge velocity based on multi-class function adaptation, implementing a surge physical model test, obtaining a first and a second measuring point data set through the surge physical model test; calibrating the position parameters of each measuring point based on the first measuring point data set to obtain the measuring point position parameters, generating a three-dimensional array of wave height-time analysis data according to the measuring point position parameters and the second measuring point data set; segmenting the three-dimensional array according to the time course, fitting to obtain a multi-class function adaptive formula of the wave height after the segmentation; derivation of the multi-class function adaptive formula of the wave height after the segmentation, and drawing a time course curve of the surge climbing velocity of the measuring point. The present invention can determine the time course of landslide surge velocity with high precision and high efficiency, overcome the problem that the surge velocity cannot be obtained when the indoor test equipment is limited, more clearly understand the surge data characteristics of the indoor surge physical model test, and efficiently summarize the test surge velocity characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A flowchart of a method for determining landslide surge velocity time history based on multi-class function adaptation as claimed in an embodiment of the present application;

[0051] Figure 2A second working flow chart of a method for determining landslide surge velocity time history based on multi-class function adaptation claimed in an embodiment of the present application;

[0052] Figure 3 A schematic diagram of model measurement points of a method for determining landslide surge velocity time history based on multi-class function adaptation as claimed in an embodiment of the present application;

[0053] Figure 4 and 5 A graph showing the variation of experimental wave height over time for a method for determining landslide surge velocity time history based on multi-class function adaptation as claimed in an embodiment of the present application;

[0054] Figure 6 and 7 A schematic diagram of a first derivative v(x) curve of a method for determining landslide surge velocity time history based on multi-type function adaptation as claimed in an embodiment of the present application. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0056] The terms "first", "second" and "third" in this application are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications in the embodiments of the present application (such as up, down, left, right, front, back...) are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0057] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0058] According to the first embodiment of the present invention, the present invention claims a method for determining the time history of landslide surge velocity based on multi-class function adaptation, referring to Figure 1 ,include:

[0059] Implementing a surge physical model test, and obtaining a first measurement point data set and a second measurement point data set through the surge physical model test;

[0060] Calibrate the position parameters of each measuring point based on the first measuring point data set to obtain the measuring point position parameters, and generate a three-dimensional array of wave height-time analysis data based on the measuring point position parameters and the second measuring point data set;

[0061] The three-dimensional array of wave height-time analysis data is segmented according to the time course, and a multi-class function adaptive formula of wave height after segmentation is obtained by fitting;

[0062] The segmented multi-class function adaptive formula of wave height is derived to draw a time history curve of the surge climbing speed at the measuring point.

[0063] Furthermore, the performing of the surge physical model test to obtain the first measurement point data set and the second measurement point data set through the surge physical model test further includes:

[0064] The first measurement point data set at least includes:

[0065] Measuring point elevation data, test water level elevation;

[0066] The second measuring point data set at least includes:

[0067] Measuring point time data and measuring point wave height data.

[0068] In this embodiment, reference is made to Figure 2 , obtain the time history data of surge wave height through the surge physical model test. The data obtained shall include but not be limited to the elevation data of the measuring point h m , test water level elevation h wk , measuring point time data x, measuring point wave height data y.

[0069] Both x and y are t*1 vectors, where t is the number of time measurement points of the monitoring data.

[0070] Further, calibrating the position parameters of each measuring point based on the first measuring point data set to obtain the measuring point position parameters also includes:

[0071] When the test water level elevation is less than or equal to the measuring point elevation data, the measuring point location parameter is 1;

[0072] When the test water level elevation is greater than the measuring point elevation data, the measuring point location parameter is 0.

[0073] In this embodiment, the position parameters of each measuring point are calibrated, and the elevation data h of the measuring point is used to calibrate the position parameters of each measuring point. m , test water level elevation h wk Calibrate the position parameters of each measuring point. If and h wk ≤h m , let the position parameter z' i is 1, otherwise it is 0; update the z in the wave height-time analysis data to the calibrated measurement point position parameter z' i data.

[0074] Further, generating a three-dimensional array of wave height-time analysis data according to the measuring point position parameters and the second measuring point data set also includes:

[0075] A three-dimensional array of wave height-time analysis data is constructed through the position parameters of each measuring point, the time point data of the measuring point, and the wave height data of the measuring point. The wave height-time analysis data of the measuring point is a three-dimensional array [x, y, z'] with a dimension of t*3, where t is the number of time measuring points of the measuring point monitoring data.

[0076] Furthermore, the three-dimensional array of wave height-time analysis data is segmented according to the time course, further comprising:

[0077] The three-dimensional array of wave height-time analysis data is segmented according to the time course, and the wave height data time course is segmented by determining the number of surge wave crests, and the number of wave crests in each segment cannot exceed a preset threshold.

[0078] In this embodiment, the number of peaks in each segment cannot exceed the recommended value of 6.

[0079] Furthermore, fitting obtains the segmented wave height multi-class function adaptive formula, and also includes:

[0080] Determining different fitting basis functions according to the measuring point position parameters;

[0081] Based on the fitting basis function, fitting calculations are performed at different orders to calculate the determination coefficient of the fitting equation;

[0082] The determination coefficients are compared to determine that the fitting function corresponding to the maximum determination coefficient is the wave height multi-class function adaptive formula.

[0083] Furthermore, determining different fitting basis functions according to the measuring point position parameters also includes:

[0084] When the measuring point position parameter is 1, the first fitting basis function is used;

[0085] When the measuring point position parameter is 0, the first fitting basis function, the second fitting basis function or the third fitting basis function is used.

[0086] Furthermore, the method further comprises:

[0087] The first fitting basis function is:

[0088] Function1:

[0089] Among them, a n ,b n ,c n are all expansion coefficients, m is the upper limit of the fitting order, n is the fitting order, the range of n is an integer of [1, m], and x is time;

[0090] The second fitting basis function is:

[0091] Function2:

[0092] The third fitting basis function is:

[0093] Function3:

[0094] Among them, a0,a n ,b n , w are expansion coefficients, n is the fitting order, and x is time.

[0095] Further, based on the fitting basis function, fitting calculations are performed at different orders to calculate the determination coefficient of the fitting equation, which also includes:

[0096] Perform fitting calculations for different basis functions at different orders and calculate the determination coefficient R of the fitting equation 2 ;

[0097]

[0098] If the measurement point location parameter is 1, let n∈{1,2,3,4,5,6}, and obtain the fitting function f nj (x), calculate j = 1;

[0099] If the measurement point position parameter is 0, let n∈{1,2,3,4,5,6}, and obtain the fitting function fnj (x), calculate j∈{1,2,3};

[0100] The comparison of the determination coefficients and determination of the fitting function corresponding to the maximum determination coefficient as the wave height multi-class function adaptive formula also include:

[0101] Comparison of coefficient of determination Determine the maximum The corresponding fitting function f nj (x) is the final fitting formula f(x), f(x) = f nj (x).

[0102] The curve shapes when the position parameter is 0 and 1 are different. The curves when the position parameter is 0 are all greater than or equal to 0, and the curves when the position parameter is 1 can be complex. All corresponding basis functions must conform to the basic shape of the curve itself.

[0103] When using the rule, calculate the coefficient of determination of all different basis functions at different orders, and then select the n-order basis function corresponding to the largest r square as the final function.

[0104] j is the label of the basis function. For example, when the position parameter is 0, the value range of j is {1, 2, 3}, and when the position parameter is 1, the value is {1}.

[0105] In this embodiment, the time history expression of the surge velocity is determined by taking derivatives based on the obtained adaptive fitting formula of the multi-class function of the segmented wave height.

[0106] The surge velocity time history expression is: v(x) = f'(x) = d(f(x)) / dx

[0107] By drawing the curve corresponding to the first derivative v(x), we can obtain the time history curve of the surge rising speed at each measuring point, and carry out the trend analysis of the surge rising speed.

[0108] Among them, in this embodiment, combined with a typical landslide in the upstream reservoir area of ​​a hydropower station in the southwest, a landslide surge normal physical model data is established at a scale of 1:150, and the model measurement points are shown in FIG. 3. The method for determining the velocity time history of a landslide surge based on multi-class function adaptation proposed by the present invention is further described, including the following steps:

[0109] Obtaining surge wave height time history data through surge physical model tests

[0110] The surge wave height time history data was obtained through the surge physical model test, with a total of 500 sets of measurement point elevation 810m, test water level elevation 825m and 800m, and measurement point wave height-time analysis data. The point wave height-time analysis data is a three-dimensional array [x, y, z] with a dimension of 500*3, where x, y, and z are all 500*1 vectors, x is the time point data, y is the wave height data, and z is the test water level elevation.

[0111] Calibrate the position parameters of each measuring point

[0112] The measuring point elevation data is 810m, and the test water level data is 825m and 800m. The position parameter of the test water level data of 825m is calibrated to 0, and the position parameter of 800m is calibrated to 1. The z in the updated wave height-time analysis data is the calibrated measuring point position parameter data.

[0113] Construct a three-dimensional array of wave height-time analysis data, and segment the three-dimensional array of wave height-time analysis data according to the time course

[0114] The monitoring data of the water level of 825m is divided into 4 sections;

[0115] The monitoring data of the water level of 800m is divided into two sections.

[0116] Fit the adaptive formula of multi-class function of segmented wave height, determine the expression of wave height changing with time, and draw the curve as follows Figure 4 , Figure 5 shown.

[0117] The formula with only exp is the first fitting basis function, the formula with only sin is the second fitting basis function, and the formula with sin and cos is the third fitting basis function, such as [0,172) is f1, and [172,224) is f2. First, this is the 825 water level, all position parameters are 0, and for [0,172), the R of the three basis functions in various cases with orders from 1 to 6 is calculated 2 After comparison, I finally chose R 2 The largest basis function.

[0118] Water level 825m, when x∈[0,172):

[0119] f(x)=30.7858*exp(-((x-116.1594) / 9.0428) 2 )+37.8995*exp(-((x-134.0030) / 13.2551) 2 )

[0120] +5.0593*exp(-((x-89.9099) / 8.9655) 2) - 41.4605 * exp(-((x - 127.1090) / 20.2518) 2 )

[0121] + 3.5641 * exp(-((x - 163.8110) / 5.0883) 2 )

[0122] When x ∈ [172, 224):

[0123] f(x) = 5.6918 * sin(0.0909 * x + 7.7197) + 0.5914 * sin(0.4001 * x - 9.5118)

[0124] + 1.5276 * sin(0.3096 * x - 11.4231) + 5.7031 * sin(0.0779 * x - 5.5797)

[0125] When x ∈ [224, 301):

[0126] f(x) = 0.2287 + 0.2374 * cos(x * 0.0426) + 0.2936 * sin(x * 0.0426) + 0.5566 * cos(2 * x * 0.0426)

[0127] + 0.2983 * sin(2 * x * 0.0426) + 0.9144 * cos(3 * x * 0.0426) + 0.2560 * sin(3 * x * 0.0426)

[0128] - 0.9626 * cos(4 * x * 0.0426) - 0.3764 * sin(4 * x * 0.0426) + 0.3849 * cos(5 * x * 0.0426)

[0129] - 0.5471 * sin(5 * x * 0.0426) - 0.3263 * cos(6 * x * 0.0426) - 0.3541 * sin(6 * x * 0.0426)

[0130] + 0.3833 * cos(7 * x * 0.0426) + 0.2451 * sin(7 * x * 0.0426)

[0131] When x ∈ [301, 500]:

[0132] f(x) = 0.2192 + 0.2512 * cos(x * 0.0776) + 0.4730 * sin(x * 0.0776) - 0.3926 * cos(2 * x * 0.0776)

[0133] -0.1992*sin(2*x*0.0776)+0.0445*cos(3*x*0.0776)+0.5162*sin(3*x*0.0776)

[0134] -0.0192*cos(4*x*0.0776)+0.4070*sin(4*x*0.0776)+0.0276*cos(5*x*0.0776)

[0135] -0.2369*sin(5*x*0.0776)

[0136] Water level 800m, when x ∈ [1, 196):

[0137] f(x) = 1.795*exp(-((x - 104.5) / 2.346) 2 ) + 4.557*exp(-((x - 100.6) / 8.9) 2 )

[0138] + 2.29*exp(-((x - 123.1) / 7.223) 2 ) + 2.018*exp(-((x - 152.4) / 9.46) 2 )

[0139] When x ∈ [196, 440]:

[0140] f(x) = 2.279*exp(-((x - 220.9) / 2.901) 2 ) + 1.676*exp(-((x - 262.6) / 2.995) 2 )

[0141] + 1.668*exp(-((x - 234) / 12.17) 2 ) + 2.04*exp(-((x - 269.5) / 7.801) 2 )

[0142] + 1.656*exp(-((x - 325.9) / 8.232) 2 ) + 1.88*exp(-((x - 205.6) / 3.804) 2 )

[0143] + 1.722*exp(-((x - 351.7) / 13.12) 2 ) + 1.104*exp(-((x - 387.4) / 21.89) 2 )

[0144] Derived the adaptive fitting formula of multi-class function of segmented wave height, and drew the time history curve of surge climbing speed at the measuring point

[0145] Draw the curve corresponding to the first derivative v(x) as follows Figure 6 , Figure 7 As shown in the figure, that is, the surge climbing speed time history curve. The surge climbing speed trend analysis shows that under the 825m working condition, the climbing speed of the measuring point reaches the maximum at 110s, which is 1.60m / s, and then begins to decay. In the first 250s, the speed has a fluctuating decay characteristic, and after 250s, it shows a fluctuating stable change characteristic, and the value is relatively small. Under the 800m working condition, the measuring point rate change has a fluctuating multi-peak characteristic, and the wave speed is relatively small. The measuring point climbing speed range is -1.07m / s to 0.74m / s. 825m shows the characteristics of reaching the maximum wave speed in the early stage and then decaying, and the first 250s show a fluctuating decay law, and after 250s, it shows a fluctuating stable change law, and the value is relatively small. The 800m working condition has a fluctuating multi-peak characteristic, and the wave speed is smaller than the 8825m working condition.

[0146] According to a second embodiment of the present invention, the present invention claims a device for determining landslide surge velocity time history based on multi-class function adaptation, comprising:

[0147] one or more processors;

[0148] A memory having one or more programs stored thereon, when the one or more programs are executed by the one or more processors, the one or more processors implement the method for determining the time history of landslide surge velocity based on multi-type function adaptation.

[0149] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0150] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. The above is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the specification and drawings of this application, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present application.

[0151] The specific implementation methods of the invention are described in detail above, but they are only examples, and the present application is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the present application, and therefore, the equalization, modification, and improvement made without departing from the spirit and principle of the present application should be included in the scope of the present application.

Claims

1. A method for determining landslide surge velocity time history based on multi-class function adaptation, It is characterized in that include: Implementing a surge physical model test, and obtaining a first measurement point data set and a second measurement point data set through the surge physical model test; Calibrate the position parameters of each measuring point based on the first measuring point data set to obtain the measuring point position parameters, and generate a three-dimensional array of wave height-time analysis data based on the measuring point position parameters and the second measuring point data set; The three-dimensional array of wave height-time analysis data is segmented according to the time course, and a multi-class function adaptive formula of wave height after segmentation is obtained by fitting; The segmented multi-class function adaptive formula of wave height is derived to draw a time history curve of the surge climbing speed at the measuring point.

2. According to claim 1, a method for determining landslide surge velocity time history based on multi-class function adaptation, It is characterized in that The performing of the surge physical model test and obtaining the first measurement point data set and the second measurement point data set through the surge physical model test further includes: The first measurement point data set at least includes: Measuring point elevation data, test water level elevation; The second measuring point data set at least includes: Measuring point time data and measuring point wave height data.

3. According to claim 2, a method for determining landslide surge velocity time history based on multi-class function adaptation, It is characterized in that Calibrate the position parameters of each measuring point based on the first measuring point data set to obtain the measuring point position parameters, and further include: When the test water level elevation is less than or equal to the measuring point elevation data, the measuring point location parameter is 1; When the test water level elevation is greater than the measuring point elevation data, the measuring point location parameter is 0.

4. The method for determining the time history of landslide surge velocity based on multi-class function adaptation according to claim 3, It is characterized in that Generating a three-dimensional array of wave height-time analysis data according to the measuring point location parameters and the second measuring point data set also includes: A three-dimensional array of wave height-time analysis data is constructed through the position parameters of each measuring point, the time point data of the measuring point, and the wave height data of the measuring point. The wave height-time analysis data of the measuring point is a three-dimensional array [x, y, z'] with a dimension of t*3, where t is the number of time measuring points of the measuring point monitoring data.

5. The method for determining the time history of landslide surge velocity based on multi-class function adaptation according to claim 3, It is characterized in that The three-dimensional array of wave height-time analysis data is segmented according to the time course, further comprising: The three-dimensional array of wave height-time analysis data is segmented according to the time course, and the wave height data time course is segmented by determining the number of surge wave crests, and the number of wave crests in each segment cannot exceed a preset threshold.

6. The method for determining the time history of landslide surge velocity based on multi-class function adaptation according to claim 3, It is characterized in that The adaptive formula of multi-class function of wave height after segmentation is obtained by fitting, and also includes: Determining different fitting basis functions according to the measuring point position parameters; Based on the fitting basis function, fitting calculations are performed at different orders to calculate the determination coefficient of the fitting equation; The determination coefficients are compared to determine that the fitting function corresponding to the maximum determination coefficient is the wave height multi-class function adaptive formula.

7. The method for determining the time history of landslide surge velocity based on multi-class function adaptation according to claim 6, It is characterized in that Determining different fitting basis functions according to the measuring point position parameters also includes: When the measuring point position parameter is 1, the first fitting basis function is used; When the measuring point position parameter is 0, the first fitting basis function, the second fitting basis function or the third fitting basis function is used.

8. The method for determining the time history of landslide surge velocity based on multi-class function adaptation according to claim 7, It is characterized in that Also includes: The first fitting basis function is: Function1: Among them, a n ,b n ,c n are all expansion coefficients, m is the upper limit of the fitting order, n is the fitting order, the range of n is an integer of [1, m], and x is time; The second fitting basis function is: Function2: The third fitting basis function is: Function3: Among them, a 0 ,a n ,b n , w are expansion coefficients, n is the fitting order, and x is time.

9. The method for determining the time history of landslide surge velocity based on multi-class function adaptation according to claim 7, It is characterized in that Based on the fitting basis function, fitting calculations are performed at different orders to calculate the determination coefficient of the fitting equation, which also includes: Perform fitting calculations for different basis functions at different orders and calculate the determination coefficient R of the fitting equation 2 ; If the measurement point location parameter is 1, let n∈{1,2,3,4,5,6}, and obtain the fitting function f nj (x), calculate j = 1; If the measurement point position parameter is 0, let n∈{1,2,3,4,5,6}, and obtain the fitting function f nj (x), calculate j∈{1,2,3}; The comparison of the determination coefficients and determination of the fitting function corresponding to the maximum determination coefficient as the wave height multi-class function adaptive formula also include: Comparison of coefficient of determination Determine the maximum The corresponding fitting function f nj (x) is the final fitting formula f(x), f(x) = f nj (x).

10. A device for determining landslide surge velocity time history based on multi-class function adaptation, It is characterized in that include: one or more processors; A memory having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement a method for determining a landslide surge velocity time history based on multi-class function adaptation according to any one of claims 1 to 9.

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