CN117195766B - Landslide-surge climbing disaster override probability evaluation method, landslide-surge climbing disaster override probability evaluation equipment and storage equipment - Google Patents
Landslide-surge climbing disaster override probability evaluation method, landslide-surge climbing disaster override probability evaluation equipment and storage equipment Download PDFInfo
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Abstract
The application provides a surge climbing disaster override probability evaluation method, which comprises the following steps: obtaining a parameter value range of a target area and an initial sample value of the target area, constructing a numerical simulation, calculating a response value of the initial sample value through the numerical simulation, storing the initial sample value and the response value of the initial sample value as mat files, inputting the mat files into software, setting a limit state function and an iteration error according to the position of a disaster object of the target area, generating a performance function through constructing an RBF meta-model and a iHL-RF algorithm, generating a design point according to the iHL-RF algorithm, calculating the response value of the design point through the numerical simulation, inputting the response value of the design point into MATLAB software, continuously iterating the RBF meta-model, calculating the iterated override probability value and a new design point until the iteration error reaches a preset condition, and outputting the override probability value.
Description
Technical Field
The application relates to the field of geological disaster prevention and control, in particular to a surge climbing disaster override probability evaluation method, equipment and storage equipment.
Background
The construction and operation of the hydropower station are obviously changed to the surrounding original environment, wherein the most prominent expression is that a large number of landslides along the coast are induced or revived, and then the generated surge disasters cause surge climbing to seriously influence the life safety of residents in a storage area. The surge climb prediction model mainly comprises a traditional model and a fluid dynamics (CFD) model. The traditional model generally fits a formula through a model experiment, and cannot consider the influence of actual complex terrain. The CFD model can overcome the limitations of the traditional model to a certain extent, and can carry out fine analysis on important information such as the propagation distance, the climbing height, the propagation speed and the like of the surge.
In the prior art, a deterministic analysis method is adopted for forward analysis of the surge climbing height, a determined value is input into a CFD model for simulation calculation, but in practice, the spatial variability of a landslide body and the difficulty in obtaining parameters are caused, so that the input parameters have larger uncertainty, a predicted result and the actual occurrence have larger access, in addition, the landslide parameters are quantized into landslide-surge prediction, a large number of numerical simulation calculation is needed, and the calculation cost is increased. How to quantify the uncertainty of landslide parameters into landslide-surge prediction by using smaller calculation cost is a problem to be solved urgently.
Disclosure of Invention
The application aims to solve the technical problems that the conventional surge climbing height analysis method is high in calculation cost and difficult to quantify the uncertainty of landslide parameters into landslide-surge prediction, and provides a surge climbing disaster override probability evaluation method, equipment and storage equipment.
The above object of the present application is achieved by the following technical solutions:
s1: acquiring a parameter value range of a target area and an initial sample value of the target area;
S2: constructing a numerical simulation, calculating a response value of the initial sample value through the numerical simulation, storing the initial sample value and the response value of the initial sample value as a mat file, and inputting the mat file into software;
s3: setting a limit state function and an iteration error according to the position of the disaster-affected body of the target area;
s4: generating a performance function by constructing an RBF meta-model and iHL-RF algorithm, wherein the performance function is used for calculating an initial override probability value;
S5: generating a design point according to the iHL-RF algorithm, and calculating a response value of the design point through the numerical simulation;
s6: and inputting the response value of the design point into MATLAB software, continuously iterating the RBF meta-model, calculating an iterated overrun probability value and a new design point until the iterated error reaches a preset condition, and outputting the overrun probability value.
Optionally, step S1 includes:
s11: determining the parameter value range and the section view of the target area according to the investigation information of the target area and the investigation information of the target area;
S12: generating the initial sample value through sampling according to the parameter value range;
S13: and processing the section through CAD software, and storing the section as a file which can be identified by CFD software.
Optionally, step S3 includes:
S31: determining the positions of all disaster-stricken bodies in the cross-section according to the positions of all disaster-stricken bodies in the target area, and calculating the height of each disaster-stricken body relative to the horizontal plane;
S32: setting the limit state function corresponding to each disaster-stricken body as follows:
g(X)＝Hthreshold-H
Wherein g (X) represents a function of disaster recovery height H threshold minus the response value H;
s33: judging whether the response value exceeds the disaster-stricken height or not, and determining a judging result as follows:
when g (X) is less than 0, the surge climbing height is greater than the disaster-stricken height;
when g (X) =0, the surge climbing is equal to the disaster-stricken height;
When g (X) is more than 0, the landslide climbing height is smaller than the disaster-stricken height;
S34: and setting the iteration error according to the judging result and the project actual requirement.
Optionally, step S4 includes:
S41: the method comprises the steps that an input design variable X and a target variable Y are standardized through Matlab software, so that a data mean value is zero, and a variance is 1;
S42: searching an optimal radial basis function parameter c through a mode of minimizing a mean square error and an optimization algorithm, and constructing the RBF meta-model;
s43: and calculating the coefficient of the RBF model by adopting a least square method, and perfecting the RBF model.
Optionally, step S5 includes:
s51: inputting the response value of the design point into CFD simulation for calculation, and outputting a calculation result;
s52: and generating a response value corresponding to the design point according to the calculation result.
The storage equipment stores instructions and data for realizing a surge climbing disaster override probability evaluation method.
A surge climbing disaster override probability evaluation device comprising: a processor and a storage device; the processor loads and executes instructions and data in the storage device to realize a surge climbing disaster override probability evaluation method.
The technical scheme provided by the application has the beneficial effects that:
Acquiring a parameter value range and an initial sample value of a target area, and calculating a response value corresponding to the initial sample through numerical simulation; the method comprises the steps that after the data are processed into an mat file with initial sample values and corresponding response value information, the mat file is imported into software; setting a limit state function and an iteration error according to the position of the disaster-affected body of the target area; calculating an initial surmounting probability value of the initial surge height exceeding the disaster-stricken height by constructing an RBF meta-model and iHL-RF algorithm; calculating a response value corresponding to the design point through numerical simulation according to the design point provided by the iHL-RF algorithm, inputting the design point and the response value thereof into Matlab software, iteratively updating the RBF meta-model again, generating a limit state function again with the iHL-RF algorithm, and calculating an overrun probability value after iterative updating and a new design point so as to continuously update iteratively; and finally stopping iteration after the iteration error meets the set precision requirement, and outputting an overrun probability value of the landslide-surge climbing overrun disaster-stricken altitude. The uncertainty of landslide parameters is quantized into landslide-surge climbing disaster prediction, so that the calculation cost is reduced, the function of rapidly evaluating the landslide-surge climbing disaster override probability is realized, and a foundation is provided for further researching the early warning and forecasting, disaster management and prevention stability evaluation of landslide-surge disasters.
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The application will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a step diagram of a surge climbing disaster override probability evaluation method in an embodiment of the application;
FIG. 2 is a Latin sample of a surge climbing disaster override probability evaluation method in an embodiment of the present application;
FIG. 3 is a response value diagram of a surge climbing disaster override probability evaluation method in an embodiment of the application;
FIG. 4 is a limit state function diagram of a surge climbing disaster override probability evaluation method in an embodiment of the application;
FIG. 5 is a schematic diagram of the operation of a hardware device in an embodiment of the application.
Detailed Description
For a clearer understanding of technical features, objects and effects of the present application, a detailed description of embodiments of the present application will be made with reference to the accompanying drawings.
The embodiment of the application provides a surge climbing disaster override probability evaluation method, equipment and storage equipment.
Referring to fig. 1, fig. 1 is a step diagram of a method for evaluating the surging and climbing disasters exceeding probability in an embodiment of the application, which specifically includes the following steps:
s1: acquiring a parameter value range of a target area and an initial sample value of the target area;
S2: constructing a numerical simulation, calculating a response value of the initial sample value through the numerical simulation, storing the initial sample value and the response value of the initial sample value as a mat file, and inputting the mat file into software;
s3: setting a limit state function and an iteration error according to the position of the disaster-affected body of the target area;
s4: generating a performance function by constructing an RBF meta-model and iHL-RF algorithm, wherein the performance function is used for calculating an initial override probability value;
S5: generating a design point according to the iHL-RF algorithm, and calculating a response value of the design point through the numerical simulation;
s6: and inputting the response value of the design point into MATLAB software, continuously iterating the RBF meta-model, calculating an iterated overrun probability value and a new design point until the iterated error reaches a preset condition, and outputting the overrun probability value.
Specifically, an initial sample value is obtained by a latin sampling method, as shown in fig. 2; and calculating a response value of the initial sample value through the numerical simulation, as shown in fig. 3. And setting a limit state function and an iteration error according to the position of the disaster-affected body of the target area, as shown in fig. 4.
Specifically, a landslide-surge numerical simulation is constructed through fluid dynamic software (CFD), each sample value in landslide parameters is a single working condition, each group of working conditions generates a corresponding response value (landslide-surge climbing value), and each group of initial sample values and response values thereof are counted and stored as mat files.
The step S1 comprises the following steps:
s11: determining the parameter value range and the section view of the target area according to the investigation information of the target area and the investigation information of the target area;
S12: generating the initial sample value through sampling according to the parameter value range;
S13: and processing the section through CAD software, and storing the section as a file which can be identified by CFD software.
The step S3 comprises the following steps:
S31: determining the positions of all disaster-stricken bodies in the cross-section according to the positions of all disaster-stricken bodies in the target area, and calculating the height of each disaster-stricken body relative to the horizontal plane;
S32: setting the limit state function corresponding to each disaster-stricken body as follows:
g(X)＝Hthreshold-H
Wherein g (X) represents a function of disaster recovery height H threshold minus the response value H;
s33: judging whether the response value exceeds the disaster-stricken height or not, and determining a judging result as follows:
when g (X) is less than 0, the surge climbing height is greater than the disaster-stricken height;
when g (X) =0, the surge climbing is equal to the disaster-stricken height;
When g (X) is more than 0, the landslide climbing height is smaller than the disaster-stricken height;
S34: and setting the iteration error according to the judging result and the project actual requirement.
The step S4 includes:
S41: the method comprises the steps that an input design variable X and a target variable Y are standardized through Matlab software, so that a data mean value is zero, and a variance is 1;
S42: searching an optimal radial basis function parameter c through a mode of minimizing a mean square error and an optimization algorithm, and constructing the RBF meta-model;
s43: and calculating the coefficient of the RBF model by adopting a least square method, and perfecting the RBF model.
Specifically, the specific process of implementing iHL-RF algorithm: assigning the correlation parameters and results to an "RBF_model" structure, including original design variables and target variables, normalized correlation parameters, optimal radial basis function parameters c, and coefficients "lambda" of the RBF model;
a cross-validation function "ks_mse" is defined and the performance of the RBF model is evaluated by a K-fold cross-validation method. In the cross-validation process, randomly dividing the data set into K subsets, using one subset as a test set each time, using other subsets as training sets, and calculating test errors;
The auxiliary functions E_dis, RB_fun and predt _dis are used for calculating the Euclidean distance matrix and the radial basis function value; programming by Matlab software, firstly initializing a reliability index Ui into an all-zero vector in iHL-RF algorithm, and setting an iteration counter k to be 1; calculating the partial derivative of the objective function at the current point Ui to each dimension through a differential format, so as to obtain a gradient vector; calculating a penalty parameter according to the objective function, and calculating the descending direction of the objective function; searching a one-dimensional interval to find the most suitable descending step length so that the objective function can be well descended at the Ui; updating the reliability index Ui, adding the reliability index Ui into the record list U, and continuously repeating the steps until the set error is met, namely, the termination condition.
The step S5 comprises the following steps:
s51: inputting the response value of the design point into CFD simulation for calculation, and outputting a calculation result;
s52: and generating a response value corresponding to the design point according to the calculation result.
Referring to fig. 5, fig. 5 is a schematic working diagram of a hardware device according to an embodiment of the present application, where the hardware device specifically includes: a surge climbing disaster override probability evaluation device 401, a processor 402 and a storage device 403.
Surge climbing disaster override probability evaluation device 401: the surge climbing disaster override probability evaluation device 401 realizes a surge climbing disaster override probability evaluation method.
Processor 402: the processor 402 loads and executes instructions and data in the memory device 403 for implementing a surge crawling disaster override probability evaluation method.
Storage device 403: storage device 403 stores instructions and data; the storage device 403 is used for implementing a surge climbing disaster override probability evaluation method.
Table 1 is an override probability calculation result table of the surge climbing disaster override probability evaluation method in the embodiment of the application.
TABLE 1
The foregoing is only illustrative of the present application and is not to be construed as limiting thereof, but rather as various modifications, equivalent arrangements, improvements, etc., within the spirit and principles of the present application.
Claims (5)
1. A surge climbing disaster override probability evaluation method is characterized by comprising the following steps:
S1: the method for acquiring the parameter value range of the target area and the initial sample value of the target area specifically comprises the following steps:
s11: determining the parameter value range and the section view of the target area according to the investigation information of the target area and the investigation information of the target area;
S12: generating the initial sample value through sampling according to the parameter value range;
s13: processing the section through CAD software and storing the section as a file identifiable by CFD software;
S2: constructing a numerical simulation, calculating a response value of the initial sample value through the numerical simulation, storing the initial sample value and the response value of the initial sample value as a mat file, and inputting the mat file into software;
S3: setting a limit state function and an iteration error according to the position of the disaster-affected body of the target area, wherein the method specifically comprises the following steps:
S31: determining the positions of all disaster-stricken bodies in the cross-section according to the positions of all disaster-stricken bodies in the target area, and calculating the height of each disaster-stricken body relative to the horizontal plane;
S32: setting the limit state function corresponding to each disaster-stricken body as follows:
g(X)＝Hthreshold-H
Wherein g (X) represents a function of disaster recovery height H threshold minus the response value H;
s33: judging whether the response value exceeds the disaster-stricken height or not, and determining a judging result as follows:
when g (X) is less than 0, the surge climbing height is greater than the disaster-stricken height;
when g (X) =0, the surge climbing is equal to the disaster-stricken height;
when g (X) is greater than 0, the surge climbing height is smaller than the disaster-stricken height;
s34: setting the iteration error according to the judging result and the project actual requirement;
s4: generating a performance function by constructing an RBF meta-model and iHL-RF algorithm, wherein the performance function is used for calculating an initial override probability value;
S5: generating a design point according to the iHL-RF algorithm, and calculating a response value of the design point through the numerical simulation;
s6: and inputting the response value of the design point into MATLAB software, continuously iterating the RBF meta-model, calculating an iterated overrun probability value and a new design point until the iterated error reaches a preset condition, and outputting the overrun probability value.
2. The method for evaluating the surging and climbing disaster override probability according to claim 1, wherein the step S4 comprises:
S41: the method comprises the steps that an input design variable X and a target variable Y are standardized through Matlab software, so that a data mean value is zero, and a variance is 1;
S42: searching an optimal radial basis function parameter c through a mode of minimizing a mean square error and an optimization algorithm, and constructing the RBF meta-model;
s43: and calculating the coefficient of the RBF model by adopting a least square method, and perfecting the RBF model.
3. The method for evaluating the surging and climbing disaster override probability according to claim 1, wherein the step S5 comprises:
s51: inputting the response value of the design point into CFD simulation for calculation, and outputting a calculation result;
s52: and generating a response value corresponding to the design point according to the calculation result.
4. A memory device, characterized by: the storage device stores instructions and data for implementing the surge climbing disaster override probability evaluation method of any one of claims 1 to 3.
5. The utility model provides a surge climbing calamity surpasses probability evaluation equipment which characterized in that: comprising the following steps: a processor and a storage device; the processor loads and executes the instructions and data in the storage device for implementing the surge climbing disaster override probability evaluation method according to any one of claims 1 to 3.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106844859A (en) * | 2016-12-21 | 2017-06-13 | 河海大学 | A kind of Simulations of Water Waves Due To Landslides computational methods |
| CN108073767A (en) * | 2017-12-14 | 2018-05-25 | 华能澜沧江水电股份有限公司 | The simulation method and device of Simulations of Water Waves Due To Landslides disaster |
| CN115906256A (en) * | 2022-12-05 | 2023-04-04 | 中国电建集团成都勘测设计研究院有限公司 | Reservoir landslide surge numerical simulation method and system |
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| US11403554B2 (en) * | 2018-01-31 | 2022-08-02 | The Johns Hopkins University | Method and apparatus for providing efficient testing of systems by using artificial intelligence tools |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106844859A (en) * | 2016-12-21 | 2017-06-13 | 河海大学 | A kind of Simulations of Water Waves Due To Landslides computational methods |
| CN108073767A (en) * | 2017-12-14 | 2018-05-25 | 华能澜沧江水电股份有限公司 | The simulation method and device of Simulations of Water Waves Due To Landslides disaster |
| CN115906256A (en) * | 2022-12-05 | 2023-04-04 | 中国电建集团成都勘测设计研究院有限公司 | Reservoir landslide surge numerical simulation method and system |
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