Real-time feedback analysis method and system for damming material parameters of earth and rockfill dam with deep covering layer

Abstract

The invention relates to the technical field of damming material parameter analysis, in particular to a real-time feedback analysis method and system for damming material parameters of a deep covering layer earth and rockfill dam, and the method comprises the steps: obtaining the foundation engineering information of the deep covering layer earth and rockfill dam and the initial parameter set value of the damming material; constructing a three-dimensional data acquisition grid system according to the foundation engineering information; based on the constructed three-dimensional data acquisition grid system, respectively acquiring stress strain, pore water pressure and geologic structure data, and integrating the acquired data to obtain a dam body comprehensive monitoring data set; performing multi-scale feature analysis on the dam body comprehensive monitoring data set and the initial parameter set value of the damming material to obtain a key feature sample data set; constructing a damming material parameter dynamic inversion model according to the key feature sample data set; and inputting the dam body comprehensive monitoring data set into the damming material parameter dynamic inversion model, and outputting a parameter adjustment strategy by the damming material parameter dynamic inversion model.

Description

Technical Field

[0001] The present invention relates to the technical field of analyzing parameters of dam building materials, and particularly to a method and system for real-time feedback analysis of parameters of dam building materials for rock-fill dams on deep overburden layers. Background Art

[0002] In the field of water conservancy and hydropower engineering, rock-fill dams, as a common type of dam, are widely used in the construction of water conservancy facilities around the world due to their advantages such as local material utilization, convenient construction, and strong adaptability to foundation deformation. With the advancement of water conservancy project construction into areas with more complex geological conditions, more and more rock-fill dams need to be built on deep overburden foundations. Deep overburden layers are usually composed of loose accumulations with various origins and different properties, having large thickness, complex structures, and non-uniform mechanical properties, which bring many challenges to the design, construction, and operation management of rock-fill dams.

[0003] Accurately mastering the parameters of dam building materials is crucial for ensuring the safety and stability of rock-fill dams on deep overburden layers. The parameters of dam building materials directly affect the stress-strain distribution, seepage characteristics, and overall stability of the dam body. Traditional methods for determining the parameters of dam building materials mainly rely on laboratory tests and empirical values. However, these methods have certain limitations. Laboratory tests are difficult to fully simulate the complex geological conditions and construction processes on site, resulting in deviations between test results and actual situations; empirical values lack pertinence and cannot fully consider the particularities of specific projects. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a method and system for real-time feedback analysis of parameters of dam building materials for rock-fill dams on deep overburden layers, which can improve the safety and stability of the dam body and reduce the errors and uncertainties of human intervention.

[0005] In a first aspect, the present invention provides a method for real-time feedback analysis of parameters of dam building materials for rock-fill dams on deep overburden layers, and the method includes:

[0006] Obtain the basic engineering information of the rock-fill dam on the deep overburden layer and the initial parameter setting values of the dam building materials; construct a three-dimensional data acquisition grid system according to the basic engineering information;

[0007] Based on the constructed three-dimensional data acquisition grid system, collect stress-strain, pore water pressure, and geological structure data respectively, and integrate the collected data to obtain a comprehensive dam body monitoring data set;

[0008] Perform multi-scale feature analysis on the comprehensive dam body monitoring data set and the initial parameter setting values of the dam building materials to obtain a key feature sample data set;

[0009] Construct a dynamic inversion model for the parameters of the dam building materials according to the key feature sample data set;

[0010] Input the comprehensive dam body monitoring data set into the dynamic inversion model of dam building material parameters, and the dynamic inversion model of dam building material parameters outputs the parameter adjustment strategy;

[0011] Preprocess the initial parameter setting values of the dam building materials according to the parameter adjustment strategy to obtain an optimized data set and obtain the real-time parameters of the dam building materials.

[0012] Furthermore, the basic engineering information includes geological section diagrams, soil and rock mechanical properties, groundwater hydrological conditions, dam body design parameters, and construction methods.

[0013] Furthermore, the initial parameter setting values of the dam building materials include physical property parameters and mechanical property parameters.

[0014] Furthermore, in the constructed three-dimensional data acquisition grid system, collect stress and strain, pore water pressure, and geological structure data respectively, and integrate the collected data to obtain a comprehensive dam body monitoring data set, including:

[0015] At each node of the constructed three-dimensional data acquisition grid system, obtain the stress and strain data of each part of the dam body through strain sensors;

[0016] Use pore water pressure gauges to collect the pore water pressure data inside the dam body at different depths and positions;

[0017] Use ground-penetrating radar to collect geological structure data;

[0018] Through the data transmission network, transmit the collected data in real time;

[0019] Integrate the stress and strain data, pore water pressure data, and geological structure data to obtain a comprehensive dam body monitoring data set.

[0020] Furthermore, perform multi-scale feature analysis on the comprehensive dam body monitoring data set and the initial parameter setting values of the dam building materials to obtain a key feature sample data set, including:

[0021] Integrate the comprehensive dam body monitoring data set with the initial parameter setting values of the dam building materials to obtain a comprehensive data set;

[0022] Perform data cleaning and standardization processing on the comprehensive data set;

[0023] Analyze the change trends of stress and strain, pore water pressure, and geological structure parameters of the dam body at different time points, and extract time series features;

[0024] Analyze the spatial features of stress and strain distribution, pore water pressure gradient, and geological structure variation at different positions inside the dam body, and extract spatial distribution rules;

[0025] Perform spectral analysis on the dynamic response of the dam body to vibration and fluctuation, extract the characteristics of different frequency components, and obtain the dynamic characteristics of the dam body;

[0026] Integrate the time series characteristics, spatial distribution rules, and the dynamic characteristics of the dam body to construct a key feature sample data set.

[0027] Furthermore, the construction method of the dynamic inversion model for dam filling material parameters includes:

[0028] Select a machine learning model as the basic framework of the dynamic inversion model for dam filling material parameters; the machine learning model includes random forest, support vector machine, neural network, multiple linear regression, and non-linear regression;

[0029] Divide the key feature sample data set into a training set, a validation set, and a test set;

[0030] Use the training set data to train the model so that the model learns the mapping relationship between the key features and the dam filling material parameters;

[0031] Use the validation set for cross-validation to optimize the model;

[0032] Use the test set data to test the trained model and evaluate the performance of the model; according to the test results, evaluate the performance of the model;

[0033] Deploy the trained model to the real-time feedback analysis system, receive the comprehensive dam body monitoring data set as input, and output the adjustment strategy of the dam filling material parameters.

[0034] Furthermore, the generation method of the parameter adjustment strategy includes:

[0035] Convert the format of the comprehensive dam body monitoring data set;

[0036] Load the trained and validated dynamic inversion model for dam filling material parameters;

[0037] Input the format-converted comprehensive dam body monitoring data set into the trained machine learning model, and the model calculates the corresponding predicted values of the dam filling material parameters;

[0038] Compare the predicted values of the dam filling material parameters with the preset standard values of the dam filling material to determine the direction and amplitude of parameter adjustment;

[0039] Summarize the adjustment directions and amplitudes of each dam filling material parameter to obtain a complete parameter adjustment strategy.

[0040] On the other hand, the present application also provides a real-time feedback analysis system for dam filling material parameters of an earth-rock dam with a deep overburden layer, and the system includes:

[0041] An information acquisition module acquires the basic engineering information of an earth-rock dam with a deep overburden layer and the initial parameter setting values of the dam materials; according to the basic engineering information, a three-dimensional data acquisition grid system is constructed;

[0042] A data acquisition and integration module respectively acquires stress-strain, pore water pressure and geological structure data based on the constructed three-dimensional data acquisition grid system, and integrates the acquired data to obtain a comprehensive dam monitoring data set;

[0043] A feature analysis module performs multi-scale feature analysis on the comprehensive dam monitoring data set and the initial parameter setting values of the dam materials to obtain a key feature sample data set;

[0044] A model construction module constructs a dynamic inversion model for the parameters of the dam materials based on the key feature sample data set;

[0045] A strategy generation module inputs the comprehensive dam monitoring data set into the dynamic inversion model for the parameters of the dam materials, and the dynamic inversion model for the parameters of the dam materials outputs a parameter adjustment strategy;

[0046] A parameter optimization module preprocesses the initial parameter setting values of the dam materials according to the parameter adjustment strategy to obtain an optimized data set and obtain the real-time parameters of the dam materials.

[0047] In a third aspect, the present application provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor. The transceiver, the memory and the processor are connected through the bus, and when the computer program is executed by the processor, the steps in any one of the above methods are implemented.

[0048] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in any one of the above methods are implemented.

[0049] The beneficial effects of the present invention compared with the prior art are as follows: The method first obtains the basic engineering information of the earth-rock dam with deep overburden layer and the initial parameter setting values of the dam building materials, laying a foundation for subsequent analysis; Based on this, the constructed three-dimensional data acquisition grid system can accurately locate each monitoring point of the dam body, collect stress and strain, pore water pressure and geological structure data in all directions, change the deficiency of traditional monitoring that only obtains data on the surface or local area of the dam body, and comprehensively reflect the true state inside the dam body; Integrate the collected data, and use multi-scale feature analysis technology, combined with methods such as wavelet transform and principal component analysis, to deeply analyze the comprehensive monitoring data set of the dam body and the initial parameter setting values of the dam building materials, extract key features, and obtain the key feature sample data set, which can more deeply explore the data value and provide strong support for subsequent model construction; According to the key feature sample data set, appropriate methods such as the finite element method or machine learning algorithm can be flexibly selected to construct a dynamic inversion model of the dam building material parameters, and the accuracy of the model can also be verified through historical data, so that the model can better adapt to the complex conditions of different projects and overcome the lack of pertinence of traditional methods;

[0050] By obtaining the basic engineering information to construct a three-dimensional data acquisition grid system, it can collect stress and strain, pore water pressure and geological structure data in all directions and integrate them, realizing comprehensive and accurate real-time monitoring of the dam body state; Multi-scale feature analysis is based on the comprehensive monitoring data set and the initial parameter setting values, excavates deep features, and obtains the key feature sample data set; Input the comprehensive monitoring data set into the dynamic inversion model to obtain the parameter adjustment strategy, and then preprocess the initial parameter setting values according to this to obtain real-time parameters, forming a dynamic closed loop from data collection, analysis, model operation to parameter optimization; As time goes by and the project progresses, each link continuously interacts with each other, and the parameters can be adjusted in real time according to the actual situation, so that the dam building material parameters always fit the engineering reality, effectively improving the safety and stability of the earth-rock dam in the process of design, construction and operation management, and overcoming the static and lagging shortcomings of traditional methods;

[0051] In summary, the real-time feedback analysis method for the dam building material parameters of the earth-rock dam with deep overburden layer can improve the safety and stability of the dam body and reduce the errors and uncertainties of human intervention. Brief Description of the Drawings

[0052] Figure 1 is the flowchart of the present invention;

[0053] Figure 2 is the flowchart of the construction method of the dynamic inversion model of the dam building material parameters;

[0054] Figure 3 is the structure diagram of the real-time feedback analysis method and system for the dam building material parameters of the earth-rock dam with deep overburden layer. Detailed Embodiments

[0055] In the description of the present application, those skilled in the art should know that the present application can be implemented as a method, a device, an electronic device, and a computer-readable storage medium. Therefore, the present application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), and a combination of hardware and software. In addition, in some embodiments, the present application can also be implemented in the form of a computer program product in one or more computer-readable storage media, which contains computer program code.

[0056] The above-mentioned computer-readable storage media can adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, compact disc read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In the present application, a computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or component.

[0057] In the technical solution of the present application, the acquisition, storage, use, processing, etc. of data all comply with the relevant provisions of national laws.

[0058] The present application describes the provided method, device, and electronic device through flowcharts and / or block diagrams.

[0059] It should be understood that each block of the flowchart and / or block diagram, as well as the combination of blocks in the flowchart and / or block diagram, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, thereby producing a machine. These computer-readable program instructions are executed by a computer or other programmable data processing devices, resulting in a device that implements the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0060] These computer-readable program instructions can also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product that includes the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0061] Computer-readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other devices, so that a series of operation steps are executed on the computer, other programmable data processing apparatus, or other devices to generate a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing apparatus to provide a process for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0062] The present application will be described below with reference to the accompanying drawings in the present application.

[0063] Embodiment 1: As Figures 1 to 2 shown, the method for real-time feedback analysis of the parameters of the dam building materials of the deep overburden earth-rock dam of the present invention specifically includes the following steps:

[0064] S1. Obtain the basic engineering information of the deep overburden earth-rock dam and the initial parameter setting values of the dam building materials; construct a three-dimensional data acquisition grid system according to the basic engineering information;

[0065] The basic engineering information includes:

[0066] Geological profile: It details the geological structure of the dam site area, including the layering of the deep overburden, the material composition of each layer, the thickness, etc.;

[0067] Soil and rock mechanical properties: Provide mechanical parameters such as the density, compressive strength, shear strength, permeability coefficient, etc. of each layer of soil, as well as the mechanical properties of the rock;

[0068] Groundwater hydrological conditions: Describe the distribution, water level change, flow velocity, flow direction, etc. of groundwater, as well as the impact of groundwater on the stability of the dam body;

[0069] Dam body design parameters: Geometric dimensions such as dam height, top width of the dam, bottom width of the dam, etc.; The selection and proportioning of dam body materials, including the types of dam building materials, particle size distribution, water content, etc.; The structural layout of the dam body, such as drainage systems, anti-seepage systems, etc.;

[0070] Construction methods: The filling method, compaction process, number of rolling passes, etc. of the dam body; The contact treatment measures between the dam body and the foundation, such as foundation reinforcement, anti-seepage treatment, etc.; Quality control standards and detection methods during the construction process;

[0071] The initial parameter setting values of the dam building materials include:

[0072] Physical property parameters: Particle size distribution, which determines the content of particles of different particle sizes through sieve analysis, reflects the fineness and particle composition distribution of the dam building materials, and affects the compactness and permeability of the dam body; Natural density, which refers to the mass per unit volume of the dam building materials in the natural state and is used to calculate the self-weight stress of the dam body; Moisture content, that is, the ratio of the mass of water in the dam building materials to the mass of dry soil, which has a significant impact on the compaction performance and strength of the dam building materials;

[0073] Mechanical property parameters: Shear strength indexes determined through triaxial compression tests, direct shear tests, etc., including the internal friction angle and cohesion, which determine the ability of the dam building material to resist shear failure and are key parameters for the stability analysis of the dam body; Compression modulus, which reflects the compression deformation characteristics of the dam building material under the action of pressure and is used to calculate the settlement of the dam body under its own weight and external loads; Permeability coefficient, which is measured through permeability tests and characterizes the ability of the dam building material to allow water to pass through;

[0074] The construction method of the three-dimensional data acquisition grid system includes:

[0075] According to the structural characteristics, geological conditions and potential risk areas of the dam body, determine the physical quantities to be monitored, such as stress and strain, pore water pressure, displacement, etc.; Determine the requirements for monitoring accuracy, frequency and duration, etc.;

[0076] Inside and around the dam body, according to the monitoring objectives and requirements, design the monitoring point layout plan; The monitoring points should cover the key parts and potential risk areas of the dam body, such as the dam foundation, dam shoulders, inside the dam body, etc.;

[0077] According to the physical quantities and accuracy requirements to be monitored, select monitoring instruments and equipment; Ensure that the monitoring instruments and equipment can work stably for a long time and the data is accurate and reliable;

[0078] Connect the monitoring instruments and equipment to the data acquisition system by wired or wireless means to form a three-dimensional data acquisition grid system; The data acquisition system should have the ability to collect data in real time, continuously and remotely, and be able to automatically transmit the data to the data center for storage and analysis.

[0079] In this step, by collecting in detail basic engineering information such as geological profiles, soil and rock mechanical properties, groundwater hydrological conditions, dam body design parameters and construction methods, as well as the physical and mechanical property parameters of the dam building material, it provides a solid foundation for the subsequent construction of the three-dimensional data acquisition grid system and the stability analysis of the dam body; Helps to more accurately evaluate the safety and stability of the dam body and ensure the quality of earth-rock dam construction; According to the structural characteristics, geological conditions and potential risk areas of the dam body, design a reasonable monitoring point layout plan and select appropriate monitoring instruments and equipment; Can monitor key physical quantities such as stress and strain, pore water pressure, displacement of the dam body in real time, and provide reliable data support for the safety monitoring and early warning of the dam body; Connect the monitoring instruments and equipment to the data acquisition system by wired or wireless means, realizing the ability to collect data in real time, continuously and remotely; Not only improves the monitoring efficiency, but also ensures the accuracy and reliability of the data; Through the constructed three-dimensional data acquisition grid system, the changes of various physical quantities of the dam body can be monitored in real time, and potential risks and hidden dangers can be discovered and processed in time; Helps to avoid the occurrence of safety accidents such as dam body instability and ensure the safe operation of earth-rock dams.

[0080] S2. Based on the constructed three-dimensional data acquisition grid system, collect stress and strain, pore water pressure, and geological structure data respectively, and integrate the collected data to obtain the dam comprehensive monitoring data set;

[0081] Stress and strain data acquisition: Install high-precision vibrating wire strain gauges or fiber Bragg grating strain sensors at each node and key parts of the constructed three-dimensional data acquisition grid system; The vibrating wire strain gauge determines the strain value by measuring the change in the vibration frequency of the steel wire, featuring high precision and good stability, and is suitable for long-term monitoring; The fiber Bragg grating strain sensor utilizes the optical transmission characteristics of the optical fiber, is sensitive to strain perception, and has strong anti-electromagnetic interference ability; The sensors are arranged in a certain direction and angle to comprehensively capture the stress and strain conditions of the dam under different stress states, thereby obtaining the stress and strain data of each part of the dam under the actions of self-weight, water pressure, construction load, etc.;

[0082] Pore water pressure data acquisition: Use pore water pressure gauges for data acquisition; Install pore water pressure gauges at different depths and positions of the dam, especially in areas where abnormal seepage may occur; Real-time monitor the change of pore water pressure inside the dam;

[0083] Geological structure data acquisition: Use equipment such as ground-penetrating radar and acoustic detectors to collect geological structure data; The ground-penetrating radar emits high-frequency electromagnetic waves and detects the geological structure inside the dam according to the reflection characteristics of electromagnetic waves in different media; The acoustic detector analyzes the density, uniformity of the dam material, and the integrity of the geological structure by using the propagation speed and attenuation characteristics of sound waves in different media;

[0084] During the data acquisition process, establish a unified data transmission network to transmit the data collected by various sensors to the data processing center in real time; Adopt a data fusion algorithm to integrate the collected stress and strain, pore water pressure, and geological structure data; The weighted average fusion assigns corresponding weights according to the reliability and importance of different data, and then performs weighted average calculation to obtain comprehensive data;

[0085] Integrate the stress and strain data, pore water pressure data, and geological structure data to obtain the dam comprehensive monitoring data set; Store the integrated monitoring data set in a dedicated database and conduct effective management.

[0086] In this step, by installing high-precision strain sensors at each node and key parts of the dam body, the stress and strain conditions of the dam body under different stress states can be comprehensively captured. At the same time, equipment such as piezometers and acoustic detectors are used to monitor the changes in pore water pressure inside the dam body and the geological structure characteristics in real time, ensuring the comprehensiveness and accuracy of the data, which helps to understand the working state and potential risks of the dam body more deeply. A unified data transmission network is established to transmit the data collected by various sensors to the data processing center in real time, realizing real-time update and dynamic monitoring of the data, which helps to detect abnormal conditions of the dam body in time and take corresponding measures for treatment, thus improving the safety and stability of the dam body. The data fusion algorithm is used to integrate the collected data. By methods such as weighted average fusion, corresponding weights are assigned according to the reliability and importance of different data, and then weighted average calculation is carried out to obtain comprehensive data, which improves the efficiency and accuracy of data processing and helps to realize intelligent analysis and early warning of the data. This step provides a strong technical guarantee for the safety monitoring and early warning of the dam body by comprehensively, accurately and real-time collecting and integrating the stress and strain, pore water pressure and geological structure data of the dam body, and improves the safety and stability of the dam body.

[0087] S3. Perform multi-scale feature analysis on the comprehensive dam body monitoring data set and the initial parameter setting values of the dam building materials to obtain a key feature sample data set.

[0088] Integrate the comprehensive dam body monitoring data set with the initial parameter setting values of the dam building materials to form a comprehensive data set containing multiple types and dimensions of data.

[0089] Check whether there are outliers, missing values or inconsistencies in the data set, and perform data cleaning through methods such as data interpolation, smoothing processing or outlier removal to ensure the accuracy and integrity of the data. In order to eliminate the dimensional differences between different data, the data is standardized so that each characteristic value is at the same order of magnitude, which is convenient for subsequent analysis.

[0090] Analyze the change trends of parameters such as stress and strain and pore water pressure of the dam body at different time points, and extract time series features to reflect the characteristics of the dam body changing with time.

[0091] Use methods such as spatial interpolation and geostatistics to analyze the spatial characteristics such as the stress and strain distribution, pore water pressure gradient and geological structure variation at different positions inside the dam body, and extract the spatial distribution law.

[0092] Perform spectrum analysis on the dynamic responses such as vibration and fluctuation of the dam body, and extract the characteristics of different frequency components to reflect the dynamic characteristics of the dam body.

[0093] Using methods such as correlation analysis and principal component analysis (PCA), evaluate the correlation between each feature and the dam material parameters, and screen out the features that are sensitive to changes in the dam material parameters and have a high correlation;

[0094] For a dataset with a high feature dimension, adopt feature dimensionality reduction techniques to reduce the number of features and improve the calculation efficiency;

[0095] Using the feature importance evaluation method, evaluate the contribution of each feature to the prediction of the dam material parameters, and further optimize the feature set;

[0096] Based on the features extracted and optimized in the above steps, construct a key feature sample data set; the key feature sample data set contains key features that can comprehensively reflect the stress and strain of the dam body, seepage characteristics, geological structure, and changes in the dam material parameters, providing basic data for the subsequent construction of a dynamic inversion model of the dam material parameters.

[0097] In this step, by integrating the dam body comprehensive monitoring data set and the initial parameter setting values of the dam materials, a comprehensive data set containing multi-type and multi-dimensional data is formed, providing a comprehensive data basis for subsequent analysis; at the same time, the data cleaning process ensures the accuracy and integrity of the data, eliminating the potential impact of outliers, missing values, or inconsistencies on the analysis results; in the multi-scale feature analysis process, not only time series features are extracted to reflect the characteristics of the dam body changing over time, but also spatial distribution laws are extracted using methods such as spatial interpolation and geostatistics, and the dynamic characteristics of the dam body are obtained through spectral analysis; it helps to understand the behavioral characteristics of the dam body more comprehensively; through methods such as correlation analysis and principal component analysis (PCA), the correlation between each feature and the dam material parameters is evaluated, and the features that are sensitive to changes in the dam material parameters and have a high correlation are screened out; in addition, for a dataset with a high feature dimension, a feature dimensionality reduction technique is adopted, effectively reducing the number of features and improving the calculation efficiency; it helps to build a more concise and efficient model; using the feature importance evaluation method in machine learning algorithms, the contribution of each feature to the prediction of the dam material parameters is scientifically evaluated; it helps to further optimize the feature set and improve the prediction accuracy of the model; based on the features extracted and optimized in the above steps, a key feature sample data set is constructed; it contains key features that can comprehensively reflect the stress and strain of the dam body, seepage characteristics, geological structure, and changes in the dam material parameters, providing high-quality basic data for the subsequent construction of a dynamic inversion model of the dam material parameters; it helps to improve the prediction performance of the model.

[0098] S4. Based on the key feature sample data set, construct a dynamic inversion model of the dam material parameters;

[0099] The construction method of the dynamic inversion model of the dam material parameters includes:

[0100] According to the characteristics of earth-rock dams with deep overburden layers and the characteristics of the key feature sample data set, a machine learning model is selected as the basic architecture of the dynamic inversion model for dam construction material parameters; the machine learning model includes random forest, support vector machine, neural network, multiple linear regression, and non-linear regression; according to the selected model type, the specific structure and parameters of the model are designed.

[0101] The key feature sample data set is divided into a training set, a validation set, and a test set; the training set is used for the training and learning of the model; the validation set is used to adjust the parameters of the model and select the optimal model; the test set is used to evaluate the performance and generalization ability of the model.

[0102] Use the training set data to train the model so that the model can learn the mapping relationship between the key features and the dam construction material parameters.

[0103] Optimize the model by adjusting the parameters and optimization algorithms of the model, and using methods such as cross-validation and grid search with the validation set to improve the prediction accuracy and generalization ability of the model.

[0104] Use the test set data to test the trained model and evaluate the performance of the model; according to the test results, evaluate the performance of the model; if the model performance is not good, it is necessary to readjust the model structure or parameters, or even change the model type.

[0105] Deploy the trained model to the real-time feedback analysis system so that in actual applications, it can receive the comprehensive dam monitoring data set as input and output the adjustment strategy for the dam construction material parameters.

[0106] In this step, by selecting a suitable machine learning model as the basic architecture of the dynamic inversion model for dam construction material parameters and designing the model structure and parameters according to the characteristics of earth-rock dams with deep overburden layers and the characteristics of the key feature sample data set, the accuracy and reliability of the model can be significantly improved; by dividing the key feature sample data set into a training set, a validation set, and a test set, and using methods such as cross-validation and grid search to optimize the model, it can be ensured that the model will not overfit or underfit during the training process, while improving the prediction accuracy and generalization ability of the model, reducing errors and uncertainties; deploying the trained model to the real-time feedback analysis system can realize the functions of online monitoring and dynamic adjustment of dam construction material parameters; when the comprehensive dam monitoring data set is used as input, the system can quickly output the adjustment strategy for the dam construction material parameters, so as to respond to the changes of the dam in a timely manner and ensure the safety and stability of the dam; by constructing a dynamic inversion model for dam construction material parameters, the rapid prediction and evaluation of the performance of dam construction materials can be realized, so as to guide engineering design and construction; it can not only improve engineering efficiency, reduce trial-and-error costs, but also timely discover potential safety hazards, take corresponding measures for prevention and treatment, and ensure the safety of earth-rock dam projects.

[0107] S5. Input the comprehensive dam body monitoring data set into the dynamic inversion model of dam filling material parameters, and the dynamic inversion model of dam filling material parameters outputs a parameter adjustment strategy.

[0108] The method for generating the parameter adjustment strategy includes:

[0109] Convert the format of the comprehensive dam body monitoring data set; for the machine learning inversion model, normalize or standardize the data to make it meet the numerical range requirements of the model input. In addition, check the integrity and accuracy of the data, process possible outliers or missing values, use interpolation methods to supplement missing values, and use statistical methods to identify and correct outliers.

[0110] Load the trained and verified dynamic inversion model of dam filling material parameters; for the machine learning model, ensure that the programming language environment required for running the model is running properly, and the files such as the weights and parameters of the model are loaded correctly.

[0111] Input the preprocessed comprehensive dam body monitoring data set into the trained machine learning model; the model calculates the predicted values of the corresponding dam filling material parameters through the forward propagation algorithm according to the input data and the learned weight and threshold relationships.

[0112] According to the relevant standards and specifications of the earth-rock dam project, combined with engineering practical experience, analyze and judge the results of the dam filling material parameters output by the model; if the permeability coefficient value output by the model exceeds the safety range specified by the specifications, it is necessary to consider adjusting the relevant parameters of the dam filling material to ensure the seepage stability of the dam body.

[0113] Determine the direction and amplitude of parameter adjustment based on the difference between the model output result and the engineering standard; if the shear strength of a certain area of the dam body calculated by the model is lower than the design requirement, consider appropriately increasing the internal friction angle or cohesion parameter of the dam filling material; the determination of the adjustment amplitude can refer to engineering experience, sensitivity analysis results and the error situation of the model.

[0114] Summarize the adjustment directions and amplitudes of each dam filling material parameter to form a complete parameter adjustment strategy; the strategy should clearly indicate the parameter names to be adjusted, the numerical values or change ranges before and after adjustment, and the adjustment sequence and other information.

[0115] In this step, by performing format conversion, normalization or standardization on the comprehensive dam body monitoring data set, as well as checking the data integrity and accuracy, the quality of the input data is ensured; it helps to improve the prediction accuracy of the dynamic inversion model of dam filling material parameters, because high-quality data is the basis for the model to effectively learn and accurately predict; loading the already trained and verified model and ensuring its normal operating environment avoids prediction errors caused by model or environmental problems; ensuring the reliability and practicality of the parameter adjustment strategy enables engineering personnel to make decisions based on the model output; the model calculates the predicted values of the dam filling material parameters through the forward propagation algorithm and conducts analysis and judgment in combination with the relevant standards and specifications of the earth-rock dam project and engineering actual experience, ensuring the scientificity and rationality of the parameter adjustment strategy; according to the difference between the model output result and the engineering standard, the direction and amplitude of parameter adjustment are determined; it helps engineering personnel quickly and accurately identify the parameters that need to be adjusted and reasonably determine the adjustment amplitude, thus ensuring the safety and stability of the dam body; the formed parameter adjustment strategy clarifies information such as the name of the parameter to be adjusted, the values or change ranges before and after adjustment, and the adjustment sequence; this makes the strategy highly complete and operable, and engineering personnel can directly operate according to the strategy without additional analysis and judgment.

[0116] S6. Preprocess the initial parameter setting values of the dam filling material according to the parameter adjustment strategy to obtain an optimized data set and obtain the real-time parameters of the dam filling material;

[0117] According to the parameter adjustment strategy, preliminarily adjust the initial parameter setting values of the dam filling material; including adjusting the physical properties, mechanical properties and other related parameters of the dam filling material;

[0118] Use a three-dimensional data acquisition grid system to continuously monitor key indicators such as the stress-strain and pore water pressure of the adjusted dam body;

[0119] Compare the data before and after adjustment to evaluate whether the adjustment effect meets the expectations and whether it has a positive impact on the overall stability of the dam body;

[0120] According to the evaluation results, finely adjust the dam filling material parameters to further optimize the performance of the dam body; repeat the process of monitoring, evaluation and adjustment until the best parameter configuration is achieved;

[0121] During the parameter adjustment process, continuously use the three-dimensional data acquisition grid system to collect data on the stress-strain, pore water pressure and geological structure of the dam body; ensure the timeliness and accuracy of the data to timely reflect the actual state of the dam body;

[0122] According to the real-time monitoring data, using the dynamic inversion model of the dam-fill parameters, calculate the real-time parameters of the dam-fill in real time; compare the calculated real-time parameters with the initial parameter setting values, update the dam-fill parameter database, and ensure the timeliness and accuracy of the parameters;

[0123] Feed back the real-time parameters to the project management personnel so that they can understand the actual state of the dam body in a timely manner; according to the feedback results of the real-time parameters, further adjust and optimize the dam-fill parameters;

[0124] In this step, by continuously using the three-dimensional data acquisition grid system, key indicators such as the stress and strain, pore water pressure of the dam body are monitored in real time, ensuring the timeliness and accuracy of the data; enabling project management personnel to understand the actual state of the dam body in a timely manner and providing reliable data support for subsequent parameter adjustment; after initially adjusting the initial parameter setting values of the dam-fill according to the parameter adjustment strategy, through real-time monitoring and evaluation, fine-tune the parameters to further optimize the performance of the dam body; ensuring the accuracy and rationality of the dam-fill parameters, thereby improving the overall stability and safety of the dam body; during the parameter adjustment process, use the dynamic inversion model of the dam-fill parameters to calculate the real-time parameters of the dam-fill in real time, compare them with the initial parameter setting values, and update the dam-fill parameter database in a timely manner; making the parameter adjustment process more flexible and efficient, and enabling continuous improvement according to the actual situation of the dam body; the acquisition and update of real-time parameters provide important decision-making support for project management personnel; according to the change trend and abnormal conditions of the real-time parameters, measures can be taken in a timely manner for risk management and response, thus avoiding potential safety hazards and accidents; by precisely controlling the dam-fill parameters, the design and construction of the dam body can be optimized, and unnecessary material waste and project costs can be reduced.

[0125] Embodiment 2: As Figure 3 shown, the real-time feedback analysis system for the dam-fill parameters of the earth-rock dam with deep overburden layer of the present invention specifically includes the following modules;

[0126] An information acquisition module, which acquires the basic engineering information of the earth-rock dam with deep overburden layer and the initial parameter setting values of the dam-fill; constructs a three-dimensional data acquisition grid system according to the basic engineering information;

[0127] A data acquisition and integration module, based on the constructed three-dimensional data acquisition grid system, respectively acquires stress and strain, pore water pressure and geological structure data, and integrates the acquired data to obtain a comprehensive dam body monitoring data set;

[0128] A feature analysis module, which performs multi-scale feature analysis on the comprehensive dam body monitoring data set and the initial parameter setting values of the dam-fill to obtain a key feature sample data set;

[0129] A model construction module constructs a dynamic inversion model for the parameters of dam building materials based on a key feature sample data set;

[0130] A strategy generation module inputs the comprehensive dam monitoring data set into the dynamic inversion model for the parameters of dam building materials, and the dynamic inversion model for the parameters of dam building materials outputs a parameter adjustment strategy;

[0131] A parameter optimization module preprocesses the initial parameter setting values of the dam building materials according to the parameter adjustment strategy to obtain an optimized data set and obtains the real-time parameters of the dam building materials.

[0132] By collecting the stress and strain, pore water pressure and geological structure data of the dam in real time, the system can quickly reflect the actual working state of the dam; combined with multi-scale feature analysis and dynamic inversion models, the system can more accurately evaluate the performance of the dam building materials and output real-time parameters, providing a strong guarantee for the safety and stability of the dam;

[0133] The system covers the whole process from information acquisition, data collection and integration, feature analysis, model construction to strategy generation and parameter optimization, forming a complete and systematic solution; enabling the system to comprehensively consider various factors, including geological conditions, construction environment and dam design parameters, etc., so as to more comprehensively evaluate the performance of the dam building materials;

[0134] The three-dimensional data acquisition grid system constructed by the system can be flexibly adjusted according to different basic engineering information to meet the requirements of different geological conditions and dam types; the dynamic inversion model can also be updated and optimized in real time according to the real-time monitoring data, so as to ensure that the system can always accurately reflect the actual working state of the dam;

[0135] Through automated and intelligent technical means, the system realizes the real-time feedback and adjustment of the parameters of the dam building materials, greatly improving the work efficiency; at the same time, the system can also automatically preprocess the initial parameters of the dam building materials according to the parameter adjustment strategy, thus avoiding the cumbersome and uncertainty of manual intervention;

[0136] The system adopts advanced technical means and data analysis methods to ensure the accuracy and reliability of the data; through real-time monitoring and dynamic adjustment, the system can timely discover and handle potential safety hazards, thus ensuring the stability and safety of the dam;

[0137] In summary, the real-time feedback analysis system for the parameters of dam building materials in deep overburden earth-rock dams has the advantages of real-time, accurate, comprehensive, systematic, adaptable, flexible, efficient, intelligent, reliable and stable when solving problems in the field of water conservancy and hydropower engineering; enabling the system to better meet the construction requirements of earth-rock dams under complex geological conditions and providing a strong guarantee for the safety and stability of the dam.

[0138] In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected through the bus. When the computer program is executed by the processor, it implements each process of the method embodiment for controlling the output data and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0139] The foregoing is only a preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A real-time feedback analysis method for the parameters of the dam materials of an earth-rock dam with a deep overburden layer, characterized in that, The method includes: Obtaining the basic engineering information of the earth-rock dam with deep overburden and the initial parameter setting values of the dam materials; constructing a three-dimensional data acquisition grid system according to the basic engineering information; Based on the constructed three-dimensional data acquisition grid system, collecting stress and strain, pore water pressure, and geological structure data respectively, and integrating the collected data to obtain a comprehensive dam monitoring data set; Performing multi-scale feature analysis on the comprehensive dam monitoring data set and the initial parameter setting values of the dam materials to obtain a set of key feature sample data; Constructing a dynamic inversion model for the dam material parameters based on the set of key feature sample data; Inputting the comprehensive dam monitoring data set into the dynamic inversion model for the dam material parameters, and the dynamic inversion model for the dam material parameters outputs a parameter adjustment strategy; Preprocessing the initial parameter setting values of the dam materials according to the parameter adjustment strategy to obtain an optimized data set and obtain the real-time parameters of the dam materials.

2. The real-time feedback analysis method for the parameters of the dam building materials of the deep overburden rockfill dam according to claim 1, characterized in that The basic engineering information includes geological profiles, soil and rock mechanical properties, groundwater hydrological conditions, dam design parameters, and construction methods.

3. The real-time feedback analysis method for the parameters of the dam building materials of the deep overburden rock-fill dam according to claim 1, characterized in that The initial parameter setting values of the dam materials include physical property parameters and mechanical property parameters.

4. The real-time feedback analysis method for the parameters of the dam materials of the earth-rock dam with deep overburden layer as described in claim 1, characterized in that Based on the constructed three-dimensional data acquisition grid system, collecting stress and strain, pore water pressure, and geological structure data respectively, and integrating the collected data to obtain a comprehensive dam monitoring data set, including: At each node of the constructed three-dimensional data acquisition grid system, obtaining the stress and strain data of each part of the dam through strain sensors; Using pore water pressure gauges to collect the pore water pressure data inside the dam at different depths and positions; Using ground penetrating radar to collect geological structure data; Through a data transmission network, transmitting the collected data in real time; Integrating the stress and strain data, pore water pressure data, and geological structure data to obtain a comprehensive dam monitoring data set.

5. The real-time feedback analysis method for the parameters of the dam materials of the earth-rock dam with deep overburden layer as claimed in claim 1, characterized in that Performing multi-scale feature analysis on the comprehensive dam monitoring data set and the initial parameter setting values of the dam materials to obtain a set of key feature sample data, including: Integrating the comprehensive dam monitoring data set with the initial parameter setting values of the dam materials to obtain a comprehensive data set; Performing data cleaning and standardization processing on the comprehensive data set; Analyzing the change trends of the stress and strain, pore water pressure, and geological structure parameters of the dam at different time points, and extracting time series features; Analyzing the spatial features of the stress and strain distribution, pore water pressure gradient, and geological structure variation at different positions inside the dam, and extracting spatial distribution laws; Performing spectral analysis on the dynamic response of the vibration and fluctuation of the dam to extract the features of different frequency components and obtain the dynamic characteristics of the dam; Integrating the time series features, spatial distribution laws, and dynamic characteristics of the dam to construct a set of key feature sample data.

6. The real-time feedback analysis method for the parameters of the dam materials of the earth-rock dam with deep overburden layer as claimed in claim 1, characterized in that The construction method of the dynamic inversion model for the dam material parameters includes: Selecting a machine learning model as the basic framework of the dynamic inversion model for the dam material parameters; the machine learning model includes random forest, support vector machine, neural network, multiple linear regression, and non-linear regression; Dividing the set of key feature sample data into a training set, a validation set, and a test set; Using the training set data to train the model so that the model learns the mapping relationship between the key features and the dam material parameters; Use the validation set for cross-validation to optimize the model; Use the test set data to test the trained model and evaluate the performance of the model; According to the test results, evaluate the performance of the model; Deploy the trained model to the real-time feedback analysis system, receive the comprehensive dam body monitoring data set as input, and output the adjustment strategy of the dam building material parameters.

7. The real-time feedback analysis method for the parameters of the dam materials of the earth-rock dam with deep overburden layer as described in claim 1, characterized in that The method for generating the parameter adjustment strategy includes: Convert the format of the comprehensive dam body monitoring data set; Load the dynamically inverted model of the dam building material parameters that has been trained and verified; Input the format-converted comprehensive dam body monitoring data set into the trained machine learning model, and the model calculates the corresponding predicted values of the dam building material parameters; Compare the predicted values of the dam building material parameters with the preset standard values of the dam building materials to determine the direction and amplitude of parameter adjustment; Summarize the adjustment directions and amplitudes of each dam building material parameter to obtain a complete parameter adjustment strategy.

8. A real-time feedback analysis system for parameters of dam materials of an earth-rock dam with deep overburden layer, characterized in that, The system includes: An information acquisition module that acquires the basic engineering information of the earth-rock dam with deep overburden and the initial parameter setting values of the dam building materials; According to the basic engineering information, construct a three-dimensional data acquisition grid system; A data acquisition and integration module that respectively acquires stress and strain, pore water pressure, and geological structure data based on the constructed three-dimensional data acquisition grid system, and integrates the acquired data to obtain a comprehensive dam body monitoring data set; A feature analysis module that performs multi-scale feature analysis on the comprehensive dam body monitoring data set and the initial parameter setting values of the dam building materials to obtain a key feature sample data set; A model construction module that constructs a dynamically inverted model of the dam building material parameters based on the key feature sample data set; A strategy generation module that inputs the comprehensive dam body monitoring data set into the dynamically inverted model of the dam building material parameters, and the dynamically inverted model of the dam building material parameters outputs a parameter adjustment strategy; A parameter optimization module that preprocesses the initial parameter setting values of the dam building materials according to the parameter adjustment strategy to obtain an optimized data set and obtain the real-time parameters of the dam building materials.

9. An electronic device for real-time feedback analysis of parameters of dam building materials for earth-rock dams with deep overburden layers, including a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected through the bus, and is characterized in that, When the computer program is executed by the processor, it implements the steps in the method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps in the method according to any one of claims 1-7.

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