A method and apparatus for determining landslide surge velocity time history based on multi-function adaptive method

Abstract

This application relates to the field of hydraulic engineering technology, and in particular to a method and apparatus for determining the time history of landslide surge velocity based on multi-function adaptive methods. The method involves conducting a surge physical model test to obtain datasets from first and second measurement points. Based on the first measurement point dataset, the location parameters of each measurement point are calibrated to obtain the measurement point location parameters. A three-dimensional array of wave height-time analysis data is generated based on the measurement point location parameters and the second measurement point dataset. The three-dimensional array is segmented according to the time history, and a segmented wave height multi-function adaptive formula is obtained by fitting the segments. The derivative of the segmented wave height multi-function adaptive formula is calculated, and the time history curve of the surge velocity at each measurement point is plotted. This invention can determine the landslide surge velocity time history with high accuracy and efficiency, overcoming the problem of not being able to obtain surge velocity when indoor test equipment is limited. It provides a clearer understanding of the surge data characteristics from indoor surge physical model tests and efficiently summarizes the characteristics of the experimental surge velocity.

Description

Technical Field

[0001] This application relates to the field of water conservancy engineering technology, and in particular to a method and apparatus for determining the time history of landslide surge velocity based on multi-function adaptive methods. Background Technology

[0002] Landslides entering water can generate surges, posing a threat to the lives and property of residents and towns along the affected area. Since surge generation is a complex process, conducting indoor experimental research using scaled physical models is a reliable and credible research method. However, this method also has drawbacks, including significant manpower and financial investment, and limitations in the monitoring capabilities of the equipment. Therefore, making full use of limited monitoring data, enriching research content based on monitoring data, fully exploring the physical model experimental data, and summarizing the characteristics and patterns of surges are of great importance.

[0003] Typical physical model tests of swells monitor wave height at different locations and times along the river by deploying multiple wave meters along the river channel, collecting data on wave height variations over time at specified locations. Fourier series, multi-peaked Gaussian functions, and sine functions are all mature and reliable fitting functions that can be used to fit swell monitoring data and serve as reliable basis functions for solving the fitting equations.

[0004] Current research on monitoring surge rise velocity variables during physical model experiments is limited. Furthermore, surge monitoring in experiments is susceptible to noise and clutter, hindering accurate analysis of surge waveform characteristics. Therefore, in the absence of surge rise velocity monitoring, it is crucial to fully utilize monitored wave height data to obtain the rise velocity, and it is also necessary to clearly characterize the waveform and wave velocity changes during surge propagation. Summary of the Invention

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

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

[0007] A surge physical model test was conducted, and a first measurement point dataset and a second measurement point dataset were obtained through the surge physical model test.

[0008] Based on the first measurement point dataset, the position parameters of each measurement point are calibrated to obtain the measurement point position parameters. Based on the measurement point position parameters and the second measurement point dataset, a three-dimensional array of wave height-time analysis data is generated.

[0009] The three-dimensional array of wave height-time analysis data is segmented according to the time history, and an adaptive formula for multiple wave height functions after segmentation is obtained by fitting.

[0010] Differentiate the adaptive formula of the multi-function wave height after segmentation, and plot the time history curve of the surge rise velocity at the measuring point.

[0011] Furthermore, the implementation of the surge physical model test, and the acquisition of the first measurement point dataset and the second measurement point dataset through the surge physical model test, further includes:

[0012] The first measurement point dataset includes at least:

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

[0014] The second measurement point dataset includes at least:

[0015] Measurement point time data, measurement point wave height data.

[0016] Furthermore, based on the first measurement point dataset, the location parameters of each measurement point are calibrated to obtain the measurement point location parameters, which also includes:

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

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

[0019] Furthermore, generating a three-dimensional array of wave height-time analysis data based on the measured point location parameters and the second measured point dataset also includes:

[0020] A three-dimensional array of wave height-time analysis data is constructed by using the location parameters of each measuring point, the time 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 dimension t*3, where t is the number of measuring points for the time of the measuring point monitoring data.

[0021] Furthermore, segmenting the three-dimensional array of wave height-time analysis data according to time history also includes:

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

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

[0024] Different fitting basis functions are determined based on the measured point location parameters;

[0025] Based on the aforementioned fitting basis functions, fitting calculations are performed at different orders, and the coefficients of determination of the fitting equation are calculated.

[0026] By comparing the coefficients of determination, the fitting function corresponding to the largest coefficient of determination is determined as the adaptive formula for the wave height multi-class function.

[0027] Furthermore, determining different fitting basis functions based on the measured point location parameters also includes:

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

[0029] When the measurement 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 also includes:

[0031] The first fitting basis function is:

[0032] Funct i on1:

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

[0034] The second fitting basis function is:

[0035] Funct i on2:

[0036] The third fitting basis function is:

[0037] Funct i on3:

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

[0039] Furthermore, based on the fitted basis functions, fitting calculations are performed at different orders, and the coefficients of determination of the fitted equation are calculated, which also includes:

[0040] Fitting calculations are performed for different basis functions at different orders, and the coefficients of determination R of the fitting equation are calculated. 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 location parameter is 0, let n∈{1,2,3,4,5,6} to obtain the fitting function f. nj (x), calculate j∈{1,2,3};

[0044] The step of comparing the coefficients of determination and determining the fitting function corresponding to the largest coefficient of determination as the adaptive formula for the wave height multi-class function further includes:

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

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

[0047] One or more processors;

[0048] A memory storing one or more programs, which, when executed by one or more processors, enable the one or more processors to implement the aforementioned method for determining the time history of landslide surge velocity based on multi-class function adaptation.

[0049] This application relates to the field of hydraulic engineering technology, and in particular to a method and apparatus for determining the time history of landslide surge velocity based on multi-function adaptive methods. The method involves conducting a surge physical model test to obtain datasets from first and second measurement points. Based on the first measurement point dataset, the location parameters of each measurement point are calibrated to obtain the measurement point location parameters. A three-dimensional array of wave height-time analysis data is generated based on the measurement point location parameters and the second measurement point dataset. The three-dimensional array is segmented according to the time history, and a segmented wave height multi-function adaptive formula is obtained by fitting the segments. The derivative of the segmented wave height multi-function adaptive formula is calculated, and the time history curve of the surge rise velocity at each measurement point is plotted. This invention can determine the landslide surge velocity time history with high accuracy and efficiency, overcoming the problem of not being able to obtain surge velocity when indoor test equipment is limited. It provides a clearer understanding of the surge data characteristics from indoor surge physical model tests and efficiently summarizes the characteristics of the experimental surge velocity. Attached Figure Description

[0050] Figure 1 A flowchart illustrating the process of a landslide surge velocity time history determination method based on multi-class function adaptation, as claimed in this application embodiment;

[0051] Figure 2A second flowchart illustrating a landslide surge velocity time history determination method based on multi-function adaptive method, as claimed in this application embodiment;

[0052] Figure 3 A schematic diagram of the model measuring points for a landslide surge velocity time history determination method based on multi-class function adaptive method claimed in this application embodiment;

[0053] Figure 4 and 5 An experimental wave height versus time curve of a landslide surge velocity time history determination method based on multi-class function adaptive method claimed in this application embodiment;

[0054] Figure 6 and 7 A schematic diagram of the first derivative v(x) curve of a landslide surge velocity time history determination method based on multi-function adaptive method claimed in this application embodiment. Detailed Implementation

[0055] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0056] The terms "first," "second," and "third" in this application 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. Therefore, a feature defined as "first," "second," or "third" 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. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the figures). If the specific orientation changes, the directional indications also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. 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 may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

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

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

[0059] A surge physical model test was conducted, and a first measurement point dataset and a second measurement point dataset were obtained through the surge physical model test.

[0060] Based on the first measurement point dataset, the position parameters of each measurement point are calibrated to obtain the measurement point position parameters. Based on the measurement point position parameters and the second measurement point dataset, a three-dimensional array of wave height-time analysis data is generated.

[0061] The three-dimensional array of wave height-time analysis data is segmented according to the time history, and an adaptive formula for multiple wave height functions after segmentation is obtained by fitting.

[0062] Differentiate the adaptive formula of the multi-function wave height after segmentation, and plot the time history curve of the surge rise velocity at the measuring point.

[0063] Furthermore, the implementation of the surge physical model test, and the acquisition of the first measurement point dataset and the second measurement point dataset through the surge physical model test, further includes:

[0064] The first measurement point dataset includes at least:

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

[0066] The second measurement point dataset includes at least:

[0067] Measurement point time data, measurement point wave height data.

[0068] In this embodiment, reference is made to Figure 2 Time history data of surge wave height were obtained through surge wave physical model tests. The obtained data should include, but is not limited to, the elevation data h of the measuring points. m Test water level elevation h wk , Measurement point time data x, Measurement point wave height data y.

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

[0070] Furthermore, based on the first measurement point dataset, the location parameters of each measurement point are calibrated to obtain the measurement point location parameters, which also includes:

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

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

[0073] In this embodiment, the position parameters of each measuring point are calibrated using the elevation data h of the measuring points. m Test water level elevation h wk The position parameters of each measuring point are calibrated if and h wk ≤h m Let the position parameter z' i If the value is 1, then it is 0; in the updated wave height-time analysis data, z is the calibrated measurement point position parameter z'. i data.

[0074] Furthermore, generating a three-dimensional array of wave height-time analysis data based on the measured point location parameters and the second measured point dataset also includes:

[0075] A three-dimensional array of wave height-time analysis data is constructed by using the location parameters of each measuring point, the time 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 dimension t*3, where t is the number of measuring points for the time of the measuring point monitoring data.

[0076] Furthermore, segmenting the three-dimensional array of wave height-time analysis data according to time history also includes:

[0077] The three-dimensional array of wave height-time analysis data is segmented according to the time history. The wave height data time history is segmented by determining the number of wave peaks. The number of wave peaks 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, the fitting of the adaptive formula for the segmented wave height multi-class function also includes:

[0080] Different fitting basis functions are determined based on the measured point location parameters;

[0081] Based on the aforementioned fitting basis functions, fitting calculations are performed at different orders, and the coefficients of determination of the fitting equation are calculated.

[0082] By comparing the coefficients of determination, the fitting function corresponding to the largest coefficient of determination is determined as the adaptive formula for the wave height multi-class function.

[0083] Furthermore, determining different fitting basis functions based on the measured point location parameters also includes:

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

[0085] When the measurement 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 also includes:

[0087] The first fitting basis function is:

[0088] Funct i on1:

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

[0090] The second fitting basis function is:

[0091] Funct i on2:

[0092] The third fitting basis function is:

[0093] Funct i on3:

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

[0095] Furthermore, based on the fitted basis functions, fitting calculations are performed at different orders, and the coefficients of determination of the fitted equation are calculated, which also includes:

[0096] Fitting calculations are performed for different basis functions at different orders, and the coefficients of determination R of the fitting equation are calculated. 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 location parameter is 0, let n∈{1,2,3,4,5,6} to obtain the fitting function f.nj (x), calculate j∈{1,2,3};

[0100] The step of comparing the coefficients of determination and determining the fitting function corresponding to the largest coefficient of determination as the adaptive formula for the wave height multi-class function further includes:

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

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

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

[0104] j is the label of the basis function. For example, when the position parameter is 0, the value 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 differentiating the obtained segmented wave height multi-class function adaptive fitting formula.

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

[0107] By plotting the curve corresponding to the first derivative v(x), the time history curve of the surge rise velocity at each measuring point can be obtained, and the trend analysis of the surge rise velocity can be carried out.

[0108] In this embodiment, the landslide surge wave normal physical model data established at a scale of 1:150 for a typical landslide in the upstream reservoir area of ​​a hydropower station in Southwest China is used. The model measurement points are shown in Figure 3. The method for determining the landslide surge wave velocity time history based on multi-function adaptive methods proposed in this invention is further illustrated, including the following steps:

[0109] Time history data of surge height were obtained through surge physical model experiments.

[0110] Wave height time history data were obtained through physical model experiments, including 500 sets of wave height-time analysis data at measuring point elevations of 810m, test water levels of 825m and 800m. The wave height-time analysis data is a three-dimensional array [x,y,z] with dimensions of 500*3, where x, y, and z are all 500*1 vectors, x represents the time point data, y represents the wave height data, and z represents the test water level elevation.

[0111] Calibrate the position parameters of each measuring point.

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

[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 history.

[0114] The monitoring data at a water level of 825m is divided into four segments;

[0115] The monitoring data at a water level of 800m is divided into two segments.

[0116] Fitting the piecewise wave height multi-class adaptive formula, determining the expression for wave height changing with time, and plotting the curve as shown below. Figure 4 , Figure 5 As shown.

[0117] The formula containing only exp represents the first fitting basis function, only sin represents the second fitting basis function, and formulas containing both sin and cos represent the third fitting basis function. For example, [0, 172) is f1, and [172, 224) is f2. First, this is the 825 water level, with all position parameters set to 0. For [0, 172), R0 is calculated for all three basis functions in various cases from order 1 to 6. 2 After comparison, R was ultimately chosen. 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] Differentiate the adaptive fitting formula for the segmented wave height multi-class function and plot the time history curve of the surge rise velocity at the measuring point.

[0145] The curve corresponding to the first derivative v(x) is shown below. Figure 6 , Figure 7 As shown in the time history curve of the surge rise velocity, trend analysis reveals that at 825m, the rise velocity at the measuring point reaches its maximum of 1.60 m / s at 110s, then begins to decay. For the first 250s, the velocity exhibits a fluctuating decay characteristic, followed by a fluctuating and stable change characteristic after 250s, with relatively small values. At 800m, the velocity change at the measuring point exhibits a fluctuating multi-peak characteristic, with a relatively small wave velocity. The rise velocity range at the measuring point is -1.07 m / s to 0.74 m / s. At 825m, the wave velocity initially reaches its maximum and then decays, exhibiting a fluctuating decay pattern for the first 250s, followed by a fluctuating and stable change characteristic after 250s, with relatively small values. At 800m, the wave velocity exhibits a fluctuating multi-peak characteristic, and is relatively smaller than that at 8825m.

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

[0147] one or more processors;

[0148] A memory storing one or more programs, which, when executed by one or more processors, enable the one or more processors to implement the aforementioned method for determining the time history of landslide surge velocity based on multi-class function adaptation.

[0149] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0150] Furthermore, the functional units in the various embodiments of this 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 integrated units described above can be implemented in hardware or as software functional units. The above are merely embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

[0151] The specific embodiments of the invention have been described in detail above, but they are only examples, and this application is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions to the invention are also within the scope of this application. Therefore, all equivalent changes, modifications, and improvements made without departing from the spirit and principles of this application should be covered within the scope of this application.

Claims

1. A method for determining the time history of landslide surge velocity based on multi-class function adaptation, characterized in that, include: A surge physical model test was conducted, and a first measurement point dataset and a second measurement point dataset were obtained through the surge physical model test. Based on the first measurement point dataset, the position parameters of each measurement point are calibrated to obtain the measurement point position parameters. Based on the measurement point position parameters and the second measurement point dataset, a three-dimensional array of wave height-time analysis data is generated. The three-dimensional array of wave height-time analysis data is segmented according to the time history, and an adaptive formula for multiple wave height functions after segmentation is obtained by fitting. Differentiate the adaptive formula of the multi-function wave height after segmentation, and plot the time history curve of the surge rise velocity at the measuring point; The fitting process for obtaining the adaptive formula for the segmented wave height multi-class function also includes: Different fitting basis functions are determined based on the measured point location parameters; Based on the aforementioned fitting basis functions, fitting calculations are performed at different orders, and the coefficients of determination of the fitting equation are calculated. By comparing the coefficients of determination, the fitting function corresponding to the largest coefficient of determination is determined as the adaptive formula for the wave height multi-class function.

2. The method for determining the time history of landslide surge velocity based on multi-function adaptive method according to claim 1, characterized in that, The implementation of the surge physical model test, and the acquisition of the first measurement point dataset and the second measurement point dataset through the surge physical model test, further includes: The first measurement point dataset includes at least: Measurement point elevation data, test water level elevation; The second measurement point dataset includes at least: Measurement point time data, measurement point wave height data.

3. The method for determining the landslide surge velocity time history based on multi-function adaptive method according to claim 2, characterized in that, Based on the first measurement point dataset, the location parameters of each measurement point are calibrated to obtain the measurement point location parameters, and the method further includes: When the test water level elevation is less than or equal to the measuring point elevation data, the measuring point position parameter is 1; When the test water level is greater than the elevation data of the measuring point, the position parameter of the measuring point is 0.

4. The method for determining the landslide surge velocity time history based on multi-function adaptive method according to claim 3, characterized in that, Based on the measured point location parameters and the second measured point dataset, a three-dimensional array of wave height-time analysis data is generated, which also includes: A three-dimensional array of wave height-time analysis data is constructed by using the location parameters of each measuring point, the time data of each measuring point, and the wave height data of each measuring point. This wave height-time analysis data has a dimension of [missing information]. t *3 three-dimensional array , t The number of monitoring points for the monitoring data over time.

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

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

7. The method for determining the time history of landslide surge velocity based on multi-function adaptive method according to claim 6, characterized in that, Also includes: The first fitting basis function is: Function1: ; in, All are expansion coefficients, and m is the upper limit of the fitting order. The fitting order is n, which is an integer in the range [1, m]. For time; The second fitting basis function is: Function2: ; The third fitting basis function is: Function3: ; in, All are expansion coefficients. The fitting order is... For time.

8. The method for determining the time history of landslide surge velocity based on multi-function adaptive method according to claim 6, characterized in that, Based on the aforementioned fitting basis functions, fitting calculations are performed at different orders, and the coefficients of determination of the fitting equation are calculated. The process also includes: Fitting calculations are performed for different basis functions at different orders, and the coefficients of determination of the fitting equation are calculated. ; If the measuring point position parameter is 1, let Obtain the fitting function ,calculate , ; If the position parameter of the measuring point is 0, let Obtain the fitting function ,calculate , ; The step of comparing the coefficients of determination and determining the fitting function corresponding to the largest coefficient of determination as the adaptive formula for the wave height multi-class function further includes: Comparison of coefficients of determination Determine the maximum Corresponding fitting function For the final fitting formula , .

9. A device for determining the time history of landslide surge velocity based on multi-class function adaptation, characterized in that, include: One or more processors; A memory having stored one or more programs that, when executed by one or more processors, cause the one or more processors to implement a method for determining landslide surge velocity time history based on multi-class function adaptation as described in any one of claims 1 to 8.

Images