Analysis and prediction method and system for factors affecting valley deformation

Abstract

The present invention provides a method and system for analyzing and predicting factors affecting valley deformation, including: obtaining a distribution map of the topography, strata, and geological structural surfaces of the dam site area; establishing a two-dimensional finite element model containing a typical cross-section of the topography, strata, and geological structural surfaces; inverting to determine the boundary water level of the two-dimensional finite element model; calculating the pressure head distribution field after the start of water storage using an unsteady seepage calculation method; establishing a HPST model for analyzing valley deformation using the reservoir water level H, the pressure head P of the deep rock mass, seasonal temperature changes S, and the time-dependent changes T of the rock mass as research components of the two-dimensional finite element model; regressing the valley width measurement line using the HPST model to obtain regression coefficients, and decomposing each component based on the obtained regression coefficients to analyze the dominant factors of valley deformation in different regions; and predicting valley deformation based on the regression coefficients obtained from the regression analysis and the established HPST model. The present invention can accurately predict valley deformation.

Description

Technical Field

[0001] The present invention belongs to the technical field of dam deformation research, and in particular relates to an analysis and prediction method and system for factors affecting valley deformation. Background Art

[0002] After a high dam is filled with water, the mountain near the dam deforms toward the riverbed, a phenomenon known as valley deformation. Valley deformation can compress the dam and, in severe cases, cause cracks in the dam. Because the causes of valley deformation are still unclear, there are currently few statistical or numerical simulation methods that can accurately simulate valley deformation, and the strength of the main influencing factors is difficult to clearly distinguish. Furthermore, during the initial stages of water storage at some projects, due to limited monitoring data and uneven time stamps in the observed time series, conventional machine learning methods fail to consider these influencing factors, making it difficult to accurately predict valley deformation. Summary of the Invention

[0003] The purpose of the present invention is to address the shortcomings of the existing technology and provide a method for analyzing and predicting the factors affecting valley width deformation. This method can overcome the problem that the existing technology is difficult to accurately predict valley width deformation due to the burn-in of monitoring data, can identify the main influencing factors of valley width deformation, and accurately predict valley width deformation based on the results of statistical analysis.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0005] A method for analyzing and predicting factors affecting valley deformation includes the following steps:

[0006] Step 1: Obtain a distribution map of the topography, strata, and geological structure of the dam site, and deploy monitoring devices in the dam area to monitor reservoir water level, groundwater pressure head, and valley deformation data;

[0007] Step 2: Based on the distribution map of the topography, strata, and geological structure of the dam site, a two-dimensional finite element model containing a typical cross-section including topography, strata, and geological structure is established;

[0008] Step 3: Inverse the groundwater level in the dam site area obtained from monitoring in Step 1 to determine the boundary water level of the two-dimensional finite element model;

[0009] Step 4: Using the boundary water level obtained in step 3 as the boundary condition, the unsteady seepage calculation method is used to calculate the pressure head distribution field after the start of water storage in the two-dimensional finite element model;

[0010] Step 5: Using the reservoir water level H, the pressure head P of the deep rock mass, the seasonal temperature change S, and the time-dependent change T of the rock mass as the research components of the valley deformation of the two-dimensional finite element model, a HPST model for analyzing the valley deformation is established;

[0011] Step 6: Based on the valley deformation monitoring data, reservoir water level monitoring data, and groundwater level monitoring data obtained in step 1, the HPST model is used to regress the valley deformation line to obtain the regression coefficient. Based on the obtained regression coefficient, each component is decomposed to analyze the dominant factors of valley deformation in different regions.

[0012] Step 7: Perform prediction analysis of valley deformation based on the regression coefficient obtained from the regression analysis and the established HPST model.

[0013] Furthermore, the inversion method of the boundary water level in step 3 includes:

[0014] First, the range of the boundary groundwater level is estimated based on the groundwater level monitoring data arranged in the dam site area. Then, different boundary water levels are set according to the estimated groundwater level range, and the seepage field analysis and calculation are performed for different boundary water levels. After that, the calculation results are compared and analyzed with the pressure head monitoring data, and the groundwater level with the smallest standard deviation is selected as the boundary water level condition of the two-dimensional finite element model.

[0015] Furthermore, the method for calculating the pressure head distribution field in step 4 is:

[0016]

[0017] Where: φ is porosity; S is water storage coefficient; t is time; K x is the permeability coefficient in the x direction; K y is the permeability coefficient in the y direction; h is the hydraulic head; Q is the source term or sink term;

[0018] The initial conditions are: h(x,y,z,0)=h0(x,y,z)

[0019] Where: h(x,y,z,0) is the hydraulic head at position (x,y,z) at time t=0; h0(x,y,z) is the given initial hydraulic head distribution function;

[0020] The boundary conditions are: h(x,y,z,t)=h b (x,y,z,t)

[0021] Where: h b (x, y, z, t) is the known hydraulic head value on the boundary, that is, the boundary water level obtained by inversion in step 3.

[0022] Furthermore, the HPST model established in step 5 is:

[0023] δ=δ H +δ P +δ S +δ T +C

[0024] Where, δ is the total valley deformation; δ H is the reservoir water level component; δ P is the groundwater content; δ S is the time-dependent component; δ T is the temperature component; C is the error term of the model.

[0025] Furthermore, the representation of each component is:

[0026] The representation of reservoir water level components is:

[0027] δ H =a(H i -H0)

[0028] Where: δ H is the reservoir water level component, H0 is the reservoir water level corresponding to the valley start monitoring day, H i is the measured value of the reservoir water level on the ith day based on the valley start monitoring day; a is the regression coefficient of the reservoir water level component;

[0029] Pressure head component δ P The representation form is:

[0030]

[0031] Where: δ P is the pressure head component, P ji is the calculated value of the pressure head at the jth typical measuring point on the ith day based on the valley start monitoring day, obtained by step 4; j0 is the pressure head of the jth typical measuring point corresponding to the valley start monitoring day; b j is the regression coefficient of the pressure head component;

[0032] Seasonal temperature variation component δ S The sine function is used for characterization, and the representation form is:

[0033]

[0034] Where: i is the i-th day based on the valley amplitude starting monitoring day; c and d are the regression coefficients of temperature components;

[0035] Time-dependent deformation component of rock mass δ T The representation form is:

[0036] δ T =e(i+1)+fln(i+1)

[0037] Where: e and f are the regression coefficients of the time-dependent component.

[0038] Furthermore, the analysis method of the dominant factors of valley deformation in each region in step 6 includes:

[0039] Based on the monitoring values ​​of the valley width measurement line, the monitoring values ​​of the reservoir water level, and the calculated and monitored values ​​of the pressure head, the HPST model was used to regress the valley width measurement line to obtain the regression coefficient of each component. Each component was calculated based on the obtained regression coefficient and its representation form. After that, the components were compared. The dominant factors of the valley width deformation in each area were judged according to the principle that the larger the absolute value, the greater the degree of influence on the valley width deformation.

[0040] Another object of the present invention is to provide a system for implementing the above-mentioned method for analyzing and predicting factors affecting valley deformation, comprising:

[0041] The data acquisition module obtains the topography, strata, and geological structure distribution maps of the dam area, as well as the reservoir water level monitoring data, groundwater level monitoring data, and valley width measurement line deformation monitoring data of the dam area;

[0042] A two-dimensional finite element model construction module is used to establish a two-dimensional finite element model containing a typical cross-section of terrain, strata, and geological structure surfaces based on the distribution map of the dam site area;

[0043] The boundary water level inversion module is used to perform inversion based on the acquired groundwater level monitoring data of the dam site area to determine the boundary water level of the two-dimensional finite element model;

[0044] The pressure head simulation module is used to calculate the pressure head distribution field after water storage in the two-dimensional finite element model using the obtained boundary water level as the boundary condition and the unsteady seepage calculation method;

[0045] The HPST model construction module is used to establish an HPST model for analyzing valley deformation, using the reservoir water level H, the pressure head P of the deep rock mass, the seasonal temperature change S, and the time-dependent change T of the rock mass as the research components of the valley deformation of the two-dimensional finite element model;

[0046] The valley deformation analysis module is used to regress the valley width measurement line using the HPST model based on the obtained valley width measurement line monitoring data, reservoir water level monitoring data, and groundwater level monitoring data to obtain the regression coefficient. The module then decomposes each component based on the obtained regression coefficient to analyze the dominant factors of valley deformation in different regions.

[0047] The valley deformation prediction module is used to perform prediction analysis of valley deformation based on the obtained regression coefficient and the established HPST model.

[0048] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0049] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] By constructing a HPST model, the present invention can more accurately simulate and analyze the effects of reservoir water level, groundwater level, seasonal temperature changes, and rock mass aging changes on valley deformation. Through regression analysis and model decomposition, the dominant factors of valley deformation in different regions can be identified, providing a basis for exploring the mechanism of valley deformation. In addition, based on the obtained regression coefficient and the established HPST model, predictive analysis of valley deformation can be performed to comprehensively analyze the influencing factors of valley deformation and predict the development of valley deformation, thereby achieving prediction of small sample data with high prediction accuracy, providing a basis for engineering decision-making. The systematic data acquisition and processing of the present invention improves the utilization rate and analysis efficiency of monitoring data. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 Flowchart of a method for analyzing and predicting factors affecting valley deformation according to an embodiment of the present invention;

[0053] Figure 2 A distribution map of the topography, strata, and geological structure of the dam site area according to an embodiment of the present invention;

[0054] Figure 3 This is a diagram showing the layout of the dam site monitoring device according to an embodiment of the present invention;

[0055] Figure 4 A two-dimensional finite element model established for an embodiment of the present invention, wherein (a) is an overall schematic diagram, and (b) is a partial enlarged view of the dotted portion in (a);

[0056] Figure 5 A comparison chart of the pressure head monitoring value and the calculated value according to an embodiment of the present invention;

[0057] Figure 6 This is a pressure head distribution diagram for a typical period after water storage begins according to an embodiment of the present invention;

[0058] Figure 7 This is a comparison chart of the regression results and monitoring results of the valley width measurement line according to an embodiment of the present invention;

[0059] Figure 8 This is a decomposition diagram of the influencing factors of the valley width measurement line No. 3 in Example 3 of the present invention;

[0060] Figure 9 This is a verification of the accuracy of HPST predictions by the embodiment of the present invention, wherein the training set is the fitting of the HPST model to the valley deformation monitoring data, and the validation set is the prediction of the valley deformation by the HPST model;

[0061] Figure 10 This is the prediction result of the valley width measurement line No. 3 in Example 3 of the present invention. DETAILED DESCRIPTION

[0062] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0063] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0064] The present invention will be further described below with reference to specific examples, but they are not intended to limit the present invention.

[0065] In view of the problems existing in the prior art, the embodiments of the present invention aim to propose an analysis and identification of factors affecting valley deformation, quantification of the effects of various influencing factors, and a valley deformation prediction method, in order to understand the factors affecting valley deformation and the laws of spatiotemporal deformation, and verify the model based on the Baihetan Hydropower Station in southwest China. The Baihetan Hydropower Station is the second-stage hydropower station in the lower reaches of the Jinsha River. It is located at the junction of Yunnan Province and Sichuan Province. It began to store water in April 2021 and stored water to the normal water level in November 2023. The valley deformation caused by water storage has received widespread attention from scholars. Specifically, if Figure 1 As shown, an embodiment of the present invention discloses a method for analyzing and predicting factors affecting valley deformation, comprising the following steps:

[0066] Step 1: Obtain a distribution map of the topography, strata, and geological structure of the dam site, and deploy monitoring devices in the dam area to monitor reservoir water level, groundwater pressure head, and valley width data;

[0067] Obtain the topography, strata, and distribution maps of the main geological structures in the dam area, such as Figure 2 As shown in the figure, the distribution map of strata and major geological structural surfaces mainly includes the strike, dip and inclination of strata and major geological structural surfaces, as well as geometric dimensions such as thickness and extension length. In order to facilitate the study of the impact of valley deformation, valley displacement measurement lines and monitoring devices were laid out in typical groundwater level holes in the dam area, and reservoir water level monitoring devices were also laid out. The layout of valley measurement lines and groundwater level holes in the dam area is shown in the figure. Figure 3 The pressure head P of a typical water level observation hole in the geological survey of the dam site area, the measured value of valley deformation, the recommended value of permeability coefficient and the reservoir water level are obtained, as shown in Table 1.

[0068] Table 1 Permeability coefficient of Baihetan rock mass

[0069]

[0070]

[0071] Step 2: Based on the distribution map of the topography, strata, and geological structure of the dam site, a two-dimensional finite element model containing a typical cross-section including topography, strata, and geological structure is established;

[0072] Based on the obtained distribution map of the topography, strata and main geological structural surfaces of the dam site area, a two-dimensional CAD drawing containing the topography, strata and structural surfaces is drawn. The two-dimensional CAD drawing is transferred to the RHINO software, and the contour lines are formed into corresponding surfaces, which are then stretched from the normal phase to form solid units. The grid is divided and exported in combination with the Griddle plug-in, and finally a two-dimensional finite element model of a typical section containing topography, strata and main geological structural surfaces is established. For the established two-dimensional finite element model, the side with richer groundwater recharge is selected as the typical section. In this embodiment, the two-dimensional finite element model is 10 times the width of the dam in the transverse direction of the river, that is, the right boundary is set at the riverbed, and the left side extends to the mountain about nine times the width of the dam, and the depth below the dam base is 1 to 2 times the height of the dam. Taking a high arch dam in southwest China as an example, the established two-dimensional finite element model is shown in the attached figure. Figure 4 See Figure 4 After the two-dimensional finite element model was established, it was divided into different zones according to the different permeability coefficients, and the groundwater level monitoring points were marked on the two-dimensional finite element model according to the actual situation (see Figure 4 (b)B, C, D, E, F, G).

[0073] Step 3: Inverse the groundwater level in the dam area obtained from monitoring in Step 1 to determine the boundary water level of the two-dimensional finite element model;

[0074] When the groundwater head for some boundary conditions is uncertain, an inversion analysis of the boundary water level is required. For the two-dimensional finite element model established in step 2, the right boundary is the river channel, and the boundary water level is the river water level. The left boundary is too far from the river channel, and since there are no groundwater level monitoring holes, an inversion analysis of the boundary water level on the left side of the two-dimensional finite element model is required. The general steps of the inversion analysis are as follows: the river water level is known on the right side of the model, and the groundwater level within the mountain on the left side of the model is unknown. Based on the monitoring data of typical groundwater level holes arranged on the left bank of the dam site, the groundwater level on the left boundary can be roughly estimated to be between 1300 and 1350 meters. 11 different boundary water levels are set at intervals of 5 meters, and the pressure head under these 11 boundary water level conditions is calculated using the seepage field calculation method (see step 4 for the seepage field calculation formula). This will not be repeated here. The pressure head calculation results of each boundary water level are compared and analyzed with the pressure head monitoring data of the corresponding groundwater level, and the boundary water level with the smallest root mean square error (RMSE) is selected as the boundary water level condition on the left side of the two-dimensional finite element model:

[0075]

[0076] Where: x i is the pressure head value obtained by calculating the seepage field; y i is the pressure head monitoring value; n is the number of data points.

[0077] The comparison between the measured data of groundwater level in the study area after inversion analysis and the calculated water level value at the corresponding position of the two-dimensional finite element model is shown in Figure 5 , Figure 5 The red line is the calculated value, the black point is the monitoring value, and the blue line is the reservoir water level. Figure 5 It can be seen that the calculated results are consistent with the actual monitoring results, proving that the boundary water level is reasonable.

[0078] Step 4: Using the boundary water level obtained in step 3 as the boundary condition, the unsteady seepage calculation method is used to calculate the pressure head distribution field after the start of water storage in the two-dimensional finite element model;

[0079] This example uses the unsteady seepage calculation method to calculate the pressure head distribution field 10 years after the start of water storage. For the two-dimensional case, the unsteady seepage control equation is:

[0080]

[0081] Where: φ is porosity; S is water storage coefficient; t is time; K x is the permeability coefficient in the x direction; K y is the permeability coefficient in the y direction; h is the total hydraulic head; Q is the source or sink term.

[0082] The initial condition is expressed as: h(x,y,z,0)=h0(x,y,z)

[0083] Where: h(x,y,z,0) is the total hydraulic head at position (x,y,z) at time t=0; h0(x,y,z) is the given initial hydraulic head distribution function;

[0084] The boundary conditions are: h(x,y,z,t)=h b (x,y,z,t);

[0085] Where: h b (x, y, z, t) is the known head value on the boundary, that is, the boundary water level inverted in step 3.

[0086] In the embodiment, the pressure head distribution during a typical period after water storage is shown in the attached Figure 6 (Unit: m). Figure 6 It can be seen that the distribution of groundwater in the mountain is affected to a smaller extent during the initial water storage and long-term reservoir water circulation process, and the changes in groundwater are highly synchronized with the reservoir water.

[0087] Step 5: Using the reservoir water level H, the pressure head P of the deep rock mass, the seasonal temperature change S, and the time-dependent change T of the rock mass as the research components of the valley deformation of the two-dimensional finite element model, a HPST model for analyzing the valley deformation is established;

[0088] In this embodiment, the reservoir water level H, the pressure head P of the deep rock mass, the seasonal temperature change S, and the time-dependent change T of the rock mass are used as the factors affecting the valley deformation. The valley deformation is decomposed according to the effects of each influencing factor and established by the HPST model as follows:

[0089] δ=δ H +δ P +δ S +δ T +C;

[0090] Where: δ is the total valley deformation; δ H is the reservoir water level component; δ P is the groundwater content; δ S is the time-dependent component; δ T is the temperature component; C is the error term of the model;

[0091] The representation of the reservoir water level component is:

[0092] δ H =a(H i -H0)

[0093] Where: δ H is the reservoir water level component, H0 is the reservoir water level corresponding to the valley start monitoring day, Hi is the measured value of the reservoir water level on the ith day based on the first day of valley measurement; a is the regression coefficient of the reservoir water level component;

[0094] Groundwater content δ P The representation form is:

[0095]

[0096] Where: δ P is the pressure head component, P ji P is the calculated value of the pressure head at the jth monitoring point from the i-th day based on the first day of valley measurement, j0 is the pressure head at the jth monitoring point corresponding to the valley start monitoring day; b j is the regression coefficient of groundwater content;

[0097] Seasonal temperature variation component δ S The sine function is used for characterization, and the representation form is:

[0098]

[0099] Where: i is the i-th day based on the first day of valley measurement; c and d are the regression coefficients of temperature components;

[0100] Time-dependent deformation component of rock mass δ T The representation form is:

[0101] δ T =e(i+1)+fln(i+1)

[0102] Where: e and f are the regression coefficients of the time-dependent component.

[0103] Step 6: Based on the valley width measurement line monitoring data, reservoir water level monitoring data, and groundwater level monitoring data obtained in step 1, the HPST model is used to regress the valley width measurement line to obtain the regression coefficient. Based on the obtained regression coefficient, each component is decomposed to analyze the dominant factors of valley width deformation in different regions;

[0104] According to the monitoring values ​​of the valley width measurement line obtained in step 1, the monitoring values ​​of the reservoir water level and the monitoring values ​​of the groundwater level are substituted into the HPST model to regress the valley width measurement line and decompose each component. Among them, the reservoir water level adopts the daily measured water level during the study period, and the deep pressure head adopts the calculated value and monitoring value of the monitoring point in step 4. The regression coefficients a, b, c, d, e, and f are obtained through regression analysis. In order to verify the accuracy of the HPST model in fitting the valley width deformation, the regression results of each monitoring point are compared with the monitoring values. The comparison results are shown in the figure below. Figure 7 As shown. Figure 7It can be seen that the HPST model has a good effect in the regression analysis of the valley deformation in Baihetan, and the root mean square error (RMSE) of the valley width measurement line is small.

[0105] After obtaining the regression coefficients a, b, c, d, e, and f, we insert them into the representation of each component to decompose each component and calculate δ H , δ P , δ S and δ T , which correspond to the response degree of reservoir water level, pressure head, temperature and time, and then compare the components. The larger the absolute value, the higher the degree of influence on valley deformation. Based on this, the dominant factors of valley deformation in different regions are analyzed. Taking the Baihetan high arch dam as an example, the decomposition results of each component of the No. 3 survey line are as follows: Figure 8 As shown in Figure 2, during the initial impoundment period, the reservoir water level has a greater impact on valley deformation. Over time, the influence of groundwater on valley deformation gradually increases. After impoundment, valley deformation is significantly affected by changes in reservoir water level and groundwater within the reservoir bank, while seasonal temperature fluctuations and time-dependent effects are less significant.

[0106] Step 7: Based on the regression coefficient obtained from the regression analysis in step 6 and the established HPST model, perform prediction analysis on the valley deformation;

[0107] In this step, in order to improve the accuracy of the prediction of the HPST model established in this embodiment, 80% of the valley deformation monitoring data is used for training (i.e., fitting) the HPST model, and 20% of the valley deformation monitoring data is used for accurate verification of the HPST model. In the training set and the validation set, the valley deformation values ​​calculated by the HPST model have a high degree of match with the monitoring values. The accuracy of the HPST model in predicting subsequent valley deformation is verified as follows: Figure 9 As shown, from Figure 9 It can be seen that the monitoring values ​​and predicted values ​​of the monitoring points have a high degree of match, indicating that the prediction effect of the HPST model is good. After verification, the obtained regression coefficient and the subsequent reservoir water level values, pressure head, temperature and time-dependent deformation values ​​are used. Among them, the reservoir water level is determined by the fluctuation law of reservoir water in typical years, the pressure head is determined by the analysis results and monitoring values ​​of the unsteady seepage analysis in step 4, and the temperature and rock mass time-dependent deformation are obtained by the equation in step 5. The components δ of the subsequent i-th day are obtained. Hi , δ Pi , δ Si and δ Ti By adding up the various components, we can get the predicted value of the valley deformation on the i-th day. Repeat the above steps to predict and analyze the subsequent valley deformation. The prediction results of the typical survey line are as follows: Figure 10 As shown. Figure 10It can be seen that the valley deformation of measuring line No. 3 gradually tends to converge, and there is a slight fluctuation with the rise and fall of reservoir water level, but the change is small.

[0108] The embodiment of the present invention further provides a system for implementing the above-mentioned method for analyzing and predicting factors affecting valley deformation, comprising:

[0109] The data acquisition module obtains the topography, strata, and geological structure distribution maps of the dam area, as well as the reservoir water level monitoring data, groundwater level monitoring data, and valley width measurement line deformation monitoring data of the dam area;

[0110] A two-dimensional finite element model construction module is used to establish a two-dimensional finite element model containing a typical cross-section of terrain, strata, and geological structure surfaces based on the distribution map of the dam site area;

[0111] The boundary water level inversion module is used to perform inversion based on the acquired groundwater level monitoring data of the dam site area to determine the boundary water level of the two-dimensional finite element model;

[0112] The pressure head simulation module is used to calculate the pressure head distribution field after water storage in the two-dimensional finite element model using the obtained boundary water level as the boundary condition and the unsteady seepage calculation method;

[0113] The HPST model construction module is used to establish an HPST model for analyzing valley deformation, using the reservoir water level H, the pressure head P of the deep rock mass, the seasonal temperature change S, and the time-dependent change T of the rock mass as the research components of the valley deformation of the two-dimensional finite element model;

[0114] The valley deformation analysis module is used to regress the valley width measurement line using the HPST model based on the obtained valley width measurement line monitoring data, reservoir water level monitoring data, and groundwater level monitoring data to obtain the regression coefficient. The module then decomposes each component based on the obtained regression coefficient to analyze the dominant factors of valley deformation in different regions.

[0115] The valley deformation prediction module is used to perform prediction analysis of valley deformation based on the obtained regression coefficient and the established HPST model.

[0116] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0117] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0118] The above are only preferred embodiments of the present invention and do not limit the implementation mode and protection scope of the present invention. For those skilled in the art, it should be aware that all solutions obtained by equivalent substitutions and obvious changes made using the contents of the present invention specification should be included in the protection scope of the present invention.

Claims

1. A method for analyzing and predicting factors affecting valley deformation, characterized in that: The following steps are involved: Step 1: Obtain a distribution map of the topography, strata, and geological structure of the dam site, and deploy monitoring devices in the dam area to monitor reservoir water level, groundwater pressure head, and valley deformation data; Step 2: Based on the distribution map of the topography, strata, and geological structure of the dam site, a two-dimensional finite element model containing a typical cross-section including topography, strata, and geological structure is established; Step 3: Inverse the groundwater level in the dam area obtained from monitoring in Step 1 to determine the boundary water level of the two-dimensional finite element model; Step 4: Using the boundary water level obtained in step 3 as the boundary condition, the unsteady seepage calculation method is used to calculate the pressure head distribution field after the two-dimensional finite element model is filled with water; Step 5: Using the reservoir water level H, the pressure head P of the deep rock mass, the seasonal temperature change S, and the time-dependent change T of the rock mass as the research components of the valley deformation of the two-dimensional finite element model, a HPST model for analyzing the valley deformation is established; Step 6: Based on the valley deformation monitoring data, reservoir water level monitoring data, and groundwater level monitoring data obtained in step 1, the HPST model is used to regress the valley deformation line to obtain the regression coefficient. Based on the obtained regression coefficient, each component is decomposed to analyze the dominant factors of valley deformation in different regions. Step 7: Perform prediction analysis of valley deformation based on the regression coefficient obtained from the regression analysis and the established HPST model.

2. The method for analyzing and predicting factors affecting valley deformation according to claim 1, characterized in that: The inversion method of the boundary water level in step 3 includes: First, the range of the boundary groundwater level is estimated based on the groundwater level monitoring data arranged in the dam site area. Then, different boundary water levels are set according to the estimated groundwater level range, and the seepage field analysis and calculation are performed for different boundary water levels. After that, the calculation results are compared and analyzed with the pressure head monitoring data, and the groundwater level with the smallest standard deviation is selected as the boundary water level condition of the two-dimensional finite element model.

3. The method for analyzing and predicting factors affecting valley deformation according to claim 1, characterized in that: The method for calculating the pressure head distribution field in step 4 is: Where: φ is porosity; S is water storage coefficient; t is time; K x is the permeability coefficient in the x direction; K y is the permeability coefficient in the y direction; h is the hydraulic head; Q is the source term or sink term; The initial conditions are: h(x,y,z,0)=h0(x,y,z) Where: h(x,y,z,0) is the hydraulic head at position (x,y,z) at time t=0; h0(x,y,z) is the given initial hydraulic head distribution function; The boundary conditions are: h(x,y,z,t)=h b (x,y,z,t) Where: h b (x, y, z, t) is the known hydraulic head value on the boundary, that is, the boundary water level obtained by inversion in step 3.

4. The method for analyzing and predicting factors affecting valley deformation according to claim 1, characterized in that: The HPST model established in step 5 is: d=d H +d P +d S +d T +C Where δ is the total valley deformation; δ H is the reservoir water level component; δ P is the groundwater content; δ S For the time-sensitive component; δ T is the temperature component; C is the error term of the model.

5. The method for analyzing and predicting factors affecting valley deformation according to claim 4, characterized in that: The representation of each component is: The representation of reservoir water level components is: d H =a(H i -H0) Where: δ H is the reservoir water level component, H0 is the reservoir water level corresponding to the valley start monitoring day, H i is the measured value of the reservoir water level on the ith day based on the valley start monitoring day; a is the regression coefficient of the reservoir water level component; Pressure head component δ P The representation form is: Where: δ P is the pressure head component, P ji is the calculated value of the pressure head on the ith day based on the valley amplitude starting monitoring day of the jth typical measuring point, obtained by step 4; j0 is the pressure head of the jth typical measuring point corresponding to the valley start monitoring day; b j is the regression coefficient of the pressure head component; Seasonal temperature variation component δ S The sine function is used for characterization, and the representation form is: Where: i is the i-th day based on the valley amplitude starting monitoring day; c and d are the regression coefficients of temperature components; Time-dependent deformation component of rock mass δ T The representation form is: δ T =e(i+1)+fln(i+1) Where: e and f are the regression coefficients of the time-dependent component.

6. The method for analyzing and predicting factors affecting valley deformation according to claim 5, characterized in that: The analysis methods for the dominant factors of valley deformation in each region in step 6 include: Based on the monitoring values ​​of the valley width measurement line, the monitoring values ​​of the reservoir water level, and the calculated and monitored values ​​of the pressure head, the HPST model was used to regress the valley width measurement line to obtain the regression coefficient of each component. Each component was calculated based on the obtained regression coefficient and its representation form. After that, the components were compared. The dominant factors of the valley width deformation in each area were judged according to the principle that the larger the absolute value, the greater the degree of influence on the valley width deformation.

7. A system for implementing the method for analyzing and predicting factors affecting valley deformation according to any one of claims 1 to 6, characterized in that: include: The data acquisition module obtains the topography, strata, and geological structure distribution maps of the dam area, as well as the reservoir water level monitoring data, groundwater level monitoring data, and valley width measurement line deformation monitoring data of the dam area; A two-dimensional finite element model construction module is used to establish a two-dimensional finite element model containing a typical cross-section of terrain, strata, and geological structure surfaces based on the distribution map of the dam site area; The boundary water level inversion module is used to perform inversion based on the acquired groundwater level monitoring data of the dam site area to determine the boundary water level of the two-dimensional finite element model; The pressure head simulation module is used to calculate the pressure head distribution field after water storage in the two-dimensional finite element model using the obtained boundary water level as the boundary condition and the unsteady seepage calculation method; The HPST model construction module is used to establish an HPST model for analyzing valley deformation, using the reservoir water level H, the pressure head P of the deep rock mass, the seasonal temperature change S, and the time-dependent change T of the rock mass as the research components of the valley deformation of the two-dimensional finite element model; The valley deformation analysis module is used to regress the valley width measurement line using the HPST model based on the obtained valley width measurement line monitoring data, reservoir water level monitoring data, and groundwater level monitoring data to obtain the regression coefficient. The module then decomposes each component based on the obtained regression coefficient to analyze the dominant factors of valley deformation in different regions. The valley deformation prediction module is used to perform prediction analysis of valley deformation based on the obtained regression coefficient and the established HPST model.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images