Dam body deformation evaluation and early warning method, system and equipment and storage medium

Abstract

The invention belongs to the technical field of dam body inspection, and particularly relates to a dam body deformation assessment and early warning method, system and device and a storage medium. The technical problems that in the prior art, monitoring data are isolated, space-time correlation analysis is lacked, and the risk of the overall structure of a dam body is difficult to early warn are solved. Comprising the steps of obtaining multi-source historical dam body data and performing preprocessing to obtain preprocessed data; performing space-time correlation feature extraction on the preprocessed data by using the prediction model to obtain space-time correlation data, and performing dam body deformation prediction based on the space-time correlation data to obtain initial prediction data; constructing a simulation model based on a coupling dynamics method, performing comparison verification on the initial prediction data and the multi-source real-time dam body data by using the simulation model to obtain a verification result, and performing parameter optimization on the prediction model based on the verification result to obtain optimized prediction data; and performing risk assessment on the optimization prediction result to obtain a risk index, and outputting a risk early warning decision based on the risk index.

Description

Technical Field

[0001] The present invention belongs to the technical field of dam inspection, and particularly relates to a method, system, device and storage medium for evaluating and warning dam deformation. Background Art

[0002] A dam is a hydraulic structure that intercepts a river and stores water to form a reservoir. Its core functions include flood control, power generation, water supply, shipping, etc. Due to the long-term influence of water pressure, temperature, and geological activities on the dam, structural deformation may occur. Dam deformation monitoring mainly tracks parameters such as dam displacement and settlement to evaluate the structural safety of the dam and prevent the occurrence of dam break accidents.

[0003] In the prior art, the methods for dam deformation monitoring mainly rely on manual regular inspections and reports. In the later stage of the development of dam deformation monitoring, inclinometers or piezometers are mainly used for single-point monitoring, and dam deformation analysis is carried out based on the monitoring data.

[0004] The prior art has the following technical problems: 1. The method of manual measurement is inefficient, easily affected by environmental interferences such as rain and fog, and is highly subjective, making it difficult to quantitatively evaluate and detect hidden damages.

[0005] 2. The existing monitoring data is isolated, lacking spatio-temporal correlation analysis, and it is difficult to warn of the risks of the overall dam structure. Summary of the Invention

[0006] The present invention provides a method, system, device and storage medium for evaluating and warning dam deformation, aiming to solve the technical problems in the prior art that the method of manual measurement is inefficient, easily affected by environmental interferences such as rain and fog, and is highly subjective, making it difficult to quantitatively evaluate and detect hidden damages; and the existing monitoring data is isolated, lacking spatio-temporal correlation analysis, and it is difficult to warn of the risks of the overall dam structure.

[0007] The technical solution of the present invention to solve the above technical problems is as follows: A method for evaluating and warning dam deformation includes: Obtaining multi-source historical dam data, preprocessing the multi-source historical dam data to obtain preprocessed data; Constructing a prediction model, using the prediction model to extract spatio-temporal correlation features from the preprocessed data to obtain spatio-temporal correlation data, and predicting dam deformation based on the spatio-temporal correlation data to obtain initial prediction data; Obtain multi-source real-time dam body data, construct a simulation model based on the coupled dynamics method, use the simulation model to compare and verify the initial prediction data and the multi-source real-time dam body data to obtain a verification result, and optimize the parameters of the prediction model based on the verification result to obtain optimized prediction data; wherein, the simulation model includes a displacement equation and a seepage pressure equation, as well as stress boundary conditions and seepage boundary conditions; Conduct a risk assessment on the optimized prediction result to obtain a risk index, and output a risk warning decision based on the risk index.

[0008] Adopting the above technical solution, the beneficial effects of the present invention are as follows: The present invention realizes the full-process closed-loop management from historical data analysis to real-time prediction, simulation verification, model optimization, and then to risk assessment and warning output. Moreover, it integrates the dual advantages of multi-source data-driven and physical modeling, enhancing the model's ability to depict complex dam body deformation behaviors. Through the model parameter optimization mechanism, the prediction model can continuously adapt to the changes in the dam body state, improving the stability and accuracy of long-term prediction.

[0009] Further, the above-mentioned multi-source historical dam body data includes displacement data, seepage data, stress data, and temperature data.

[0010] Further, the specific steps of preprocessing the above-mentioned multi-source historical dam body data to obtain preprocessed data are as follows: Adopt an iterative method to perform residual compensation and error correction on the multi-source historical dam body data to obtain corrected data; Use a feature analysis method to perform multi-scale feature extraction on the corrected data to obtain multi-scale feature data; Use the ICP method to perform spatial registration on the multi-scale feature data to obtain preprocessed data.

[0011] Adopting the above technical solution, the beneficial effects of the present invention are as follows: The present invention eliminates the noise brought by factors such as sensor errors and environmental interference through preprocessing, ensuring the reliability of model training and prediction results. The coordinate unification problem between multi-source heterogeneous data is solved through ICP method registration, realizing spatio-temporal consistency modeling.

[0012] Further, the specific steps of obtaining the initial prediction data are as follows: Construct a neural network module including a time attention unit and a spatial attention unit, and use the neural network module to perform time feature extraction and spatial feature extraction on the preprocessed data to obtain time feature data and spatial feature data; Use a convolutional layer to perform data fusion on the time feature data and the spatial feature data to obtain a fused feature map; Use a time series regularization method to perform time series regularization processing on the fused feature map to obtain spatio-temporal correlation data; Based on the spatio-temporal correlation data, dam deformation prediction is carried out to obtain initial prediction data; Among them, the prediction model includes the neural network module, the convolutional layer and the time series regularization method.

[0013] Adopting the above technical solution, the beneficial effects of the present invention are as follows: The present invention introduces an attention mechanism, which significantly improves the attention of the model to important time segments and key spatial regions, and enhances the prediction accuracy and robustness. Through the spatio-temporal joint modeling ability, the model can simultaneously capture the time evolution law and spatial distribution characteristics of dam deformation, which is closer to the real physical process. By combining convolution and time series regularization, non-uniform time series can be effectively processed, and the tolerance of the model to irregular or missing data is improved, which is applicable to complex monitoring scenarios in actual engineering.

[0014] Furthermore, the formula of the above displacement equation is shown as follows:

[0015] Among them, u represents displacement, t represents the time variable, α represents the diffusion coefficient, ▽ 2 represents the Laplace operator, β represents the coupling coefficient of the influence of seepage pressure on displacement, and p represents seepage pressure; The formula of the seepage pressure equation is shown as follows:

[0016] Among them, γ represents the coupling coefficient of the influence of stress on displacement, σ represents the stress tensor, ▽ represents the divergence operator, δ represents the coupling coefficient of the influence of flow rate on seepage pressure, and q represents the flow rate; The formula of the stress boundary condition is shown as follows: σ nn = constant Among them, σ nn represents the normal stress of the dam body; The formula of the seepage boundary condition is shown as follows: q = -k▽p Among them, k represents the permeability coefficient.

[0017] Furthermore, the discrete element comparison test method and the numerical inversion method are used to compare and verify the initial prediction data and the multi-source real-time dam body data to obtain the verification result.

[0018] Adopting the above technical solution, the beneficial effects of the present invention are as follows: The present invention realizes the function of inversely deducing model parameters from observation data through the numerical inversion method, making the model continuously approach the real dam body response and improving the prediction accuracy.

[0019] Further, the risk assessment of the optimized prediction result to obtain a risk index and the output of a risk warning decision based on the risk index specifically include: Use a real-time stream computing platform to integrate the data of the optimized prediction result to obtain integrated data; Preset multi-level risk threshold ranges, determine which level of risk threshold range the integrated data is located in, and obtain the corresponding level of risk index; Judge whether the risk index belongs to the normal range: If so, conduct a risk warning through the warning APP; If not, continuously monitor the multi-source historical dam body data.

[0020] Adopting the above technical solution, the beneficial effect of the present invention is that the present invention realizes efficient data integration and low-latency processing based on a real-time stream computing platform, meeting the timeliness requirements of the emergency warning system.

[0021] In a second aspect, the present invention also provides a dam deformation evaluation and warning system to solve the above technical problems, including: A preprocessing module for obtaining multi-source historical dam body data and preprocessing the multi-source historical dam body data to obtain preprocessed data; A prediction module for constructing a prediction model, using the prediction model to extract spatio-temporal correlation features from the preprocessed data to obtain spatio-temporal correlation data, and performing dam deformation prediction based on the spatio-temporal correlation data to obtain initial prediction data; An optimization module for obtaining multi-source real-time dam body data, constructing a simulation model based on the coupled dynamics method, using the simulation model to compare and verify the initial prediction data and the multi-source real-time dam body data to obtain a verification result, and optimizing the parameters of the prediction model based on the verification result to obtain optimized prediction data; A warning module for performing risk assessment on the optimized prediction result to obtain a risk index and outputting a risk warning decision based on the risk index.

[0022] In a third aspect, the present invention also provides an electronic device to solve the above technical problems. The electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the dam deformation evaluation and warning method of the present application is implemented.

[0023] In a fourth aspect, the present invention also provides a computer-readable storage medium to solve the above technical problems. A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the dam deformation evaluation and warning method of the present application is implemented.

[0024] Compared with the prior art, the present invention has the following advantages: 1. This invention significantly improves the active prevention and control capabilities of water conservancy project safety monitoring by integrating multi-source data fusion, spatiotemporal correlation modeling and multi-field coupling analysis technology. By building a dynamic risk assessment system, it realizes the transition from single parameter monitoring to multi-physical field collaborative perception, effectively captures the early characteristics of dam deformation, and provides millimeter-level precision real-time warning for engineering safety.

[0025] 2. The present invention advances the risk identification window by several weeks, allowing users to formulate targeted prevention and control strategies before a disaster occurs, significantly reducing the probability of sudden accidents under extreme working conditions, and at the same time providing data-driven scientific decision-making support for the health management of the entire life cycle of the project.

[0026] 3. At the engineering operation and maintenance level, the present invention greatly optimizes the efficiency of resource allocation by opening up a real-time closed loop from data repair to risk assessment. It reduces the input of repeated monitoring, the frequency of manual inspections and the cost of equipment maintenance. It not only extends the safe service life of the dam structure, but also provides a scientific basis for engineering transformation and reinforcement through precise health status assessment, thereby achieving the organic unity of economic benefits and safety benefits throughout the life cycle.

[0027] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 A schematic flow chart of a dam deformation assessment and early warning method according to an embodiment of the present invention is shown; Figure 2 A schematic diagram of a process for obtaining initial prediction data according to an embodiment of the present invention is shown; Figure 3 It shows a structural schematic diagram of a dam deformation assessment and early warning system according to an embodiment of the present invention; Figure 4 A schematic structural diagram of an electronic device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0031] Figure 1 Fig. shows a schematic flowchart of a dam deformation assessment and early warning method according to an embodiment of the present invention. As Figure 1 shown, a dam deformation assessment and early warning method according to an embodiment of the present invention includes: Obtain multi-source historical dam data, preprocess the multi-source historical dam data to obtain preprocessed data; Construct a prediction model, use the prediction model to extract spatio-temporal correlation features from the preprocessed data to obtain spatio-temporal correlation data, and perform dam deformation prediction based on the spatio-temporal correlation data to obtain initial prediction data; Obtain multi-source real-time dam data, construct a simulation model based on the coupled dynamics method, use the simulation model to compare and verify the initial prediction data and the multi-source real-time dam data to obtain a verification result, and optimize the parameters of the prediction model based on the verification result to obtain optimized prediction data; wherein, the simulation model includes a displacement equation, a seepage pressure equation, as well as stress boundary conditions and seepage boundary conditions; Perform risk assessment on the optimized prediction result to obtain a risk index, and output a risk early warning decision based on the risk index.

[0032] In summary, the present invention realizes the full-process closed-loop management from historical data analysis to real-time prediction, simulation verification, model optimization, and then to risk assessment and early warning output. And it integrates the dual advantages of multi-source data-driven and physical modeling, improving the ability of the model to depict complex dam deformation behaviors. Through the model parameter optimization mechanism, the prediction model can continuously adapt to the changes in the dam state, improving the stability and accuracy of long-term prediction.

[0033] Optionally, the multi-source historical dam data includes displacement data, seepage data, stress data, and temperature data.

[0034] In this embodiment, the displacement data, seepage data, stress data, and temperature data are collected by sensors, and the collected data is transmitted based on the SL / T 308-2022 standard interface protocol to form multi-source historical dam data. Specifically, historical data over five years is collected during implementation, and the daily sampling frequency ≥ 10 times.

[0035] Optionally, preprocessing the multi-source historical dam body data to obtain preprocessed data specifically includes: Using an iterative method to perform residual compensation and error correction on the multi-source historical dam body data to obtain corrected data; Using a feature analysis method to perform multi-scale feature extraction on the corrected data to obtain multi-scale feature data; Using the ICP method to perform spatial registration on the multi-scale feature data to obtain preprocessed data.

[0036] In this embodiment, the iterative method uses an improved EM algorithm to handle missing data values. By reasonably estimating the missing values, the deviation caused by incomplete data can be avoided, thereby improving the accuracy of model training and prediction. The feature analysis method uses wavelet packet decomposition to achieve the extraction of multi-scale features, providing strong support for subsequent spatio-temporal correlation feature extraction and dam body deformation prediction.

[0037] In summary, the present invention eliminates the noise brought by factors such as sensor errors and environmental interference through preprocessing, ensuring the reliability of model training and prediction results. The coordinate unification problem between multi-source heterogeneous data is solved through ICP method registration, realizing spatio-temporal consistency modeling.

[0038] Optionally, obtaining the initial prediction data specifically includes: Constructing a neural network module including a time attention unit and a space attention unit, and using the neural network module to perform time feature extraction and space feature extraction on the preprocessed data to obtain time feature data and space feature data; Using a convolutional layer to perform data fusion on the time feature data and the space feature data to obtain a fused feature map; Using a time series regularization method to perform time series regularization processing on the fused feature map to obtain spatio-temporal correlation data; Performing dam body deformation prediction based on the spatio-temporal correlation data to obtain initial prediction data; Wherein, the prediction model includes the neural network module, the convolutional layer, and the time series regularization method.

[0039] In this embodiment, as Figure 2 shown, a spatio-temporal attention mechanism network model is used to process the preprocessed data to obtain time feature data and space feature data. A 3×3 convolutional kernel is used to perform convolutional operations on the time feature data and the space feature data to extract local features and achieve the fusion between different data sources. Through multi-layer convolutional and pooling operations, the data from different sources are deeply fused in time and space to form a fused feature map. Finally, the dynamic time warping algorithm is used to perform time series processing on the fused feature map to solve problems such as inconsistent time series lengths and sampling rate differences.

[0040] In this embodiment, the metrics of the output initial prediction data are: displacement prediction MAPE ≤ 3%; seepage flow interpretability R² ≥ 0.85.

[0041] In summary, the present invention introduces an attention mechanism, which significantly improves the model's attention to important time segments and key spatial regions, enhances the prediction accuracy and robustness. Through the spatio-temporal joint modeling ability, the model can simultaneously capture the time evolution law and spatial distribution characteristics of dam deformation, which is closer to the real physical process. By combining convolution with temporal regularization, non-uniform time series are effectively processed, and the model's tolerance to irregular or missing data is improved, making it suitable for complex monitoring scenarios in practical engineering.

[0042] Optionally, the simulation model includes a displacement equation and an osmotic pressure equation; the formula of the displacement equation is as follows:

[0043] where u represents displacement, t represents the time variable, α represents the diffusion coefficient, ▽ 2 represents the Laplace operator, β represents the coupling coefficient of the influence of osmotic pressure on displacement, and p represents osmotic pressure; The formula of the osmotic pressure equation is as follows:

[0044] where γ represents the coupling coefficient of the influence of stress on displacement, σ represents the stress tensor, ▽ represents the divergence operator, δ represents the coupling coefficient of the influence of flow rate on osmotic pressure, and q represents the flow rate; The simulation model also includes stress boundary conditions and osmotic boundary conditions, and the formula of the stress boundary conditions is as follows: σ nn = constant where σ nn represents the normal stress of the dam body; The formula of the osmotic boundary conditions is as follows: q = -k▽p where k represents the permeability coefficient.

[0045] Optionally, a discrete element comparison test method and a numerical inversion method are used to compare and verify the initial prediction data and the multi-source real-time dam body data to obtain a verification result.

[0046] In this embodiment, the discrete element comparison test method mainly focuses on verifying the correctness of the prediction model through multi-source real-time dam body data; while the numerical inversion method focuses on using existing data to improve the parameters of the prediction model, so that the prediction model can reflect the real situation as much as possible. The two complement each other and jointly ensure that the prediction model can play an important role in a wide range of engineering applications.

[0047] In summary, the present invention realizes the function of inversely deducing model parameters from observed data through the numerical inversion method, enabling the model to continuously approach the true dam body response and improving the prediction accuracy.

[0048] Optionally, performing a risk assessment on the optimized prediction result to obtain a risk index, and outputting a risk warning decision based on the risk index specifically includes: Using a real-time stream computing platform to integrate the data of the optimized prediction result to obtain integrated data; Presetting a multi-level risk threshold range, determining which level of risk threshold range the integrated data is located in, and obtaining the corresponding level of risk index; Determining whether the risk index belongs to the normal range: If so, performing a risk warning through the warning APP; If not, continuously monitoring the multi-source historical dam body data.

[0049] In summary, the present invention realizes efficient data integration and low-latency processing based on a real-time stream computing platform, meeting the timeliness requirements of the emergency warning system.

[0050] Based on the same principle as the method shown in Figure 1 The embodiment of the present invention also provides a dam deformation evaluation and warning system, as shown in Figure 3 shown, including: A preprocessing module for obtaining multi-source historical dam body data and preprocessing the multi-source historical dam body data to obtain preprocessed data; A prediction module for constructing a prediction model, using the prediction model to extract spatio-temporal correlation features from the preprocessed data to obtain spatio-temporal correlation data, and performing dam deformation prediction based on the spatio-temporal correlation data to obtain initial prediction data; An optimization module for obtaining multi-source real-time dam body data, constructing a simulation model based on the coupled dynamics method, using the simulation model to compare and verify the initial prediction data and the multi-source real-time dam body data to obtain a verification result, and optimizing the parameters of the prediction model based on the verification result to obtain optimized prediction data; A warning module for performing a risk assessment on the optimized prediction result to obtain a risk index, and outputting a risk warning decision based on the risk index.

[0051] The dam body deformation evaluation and early warning system according to the embodiments of the present invention can execute the dam body deformation evaluation and early warning method provided by the embodiments of the present invention, and their implementation principles are similar. The actions performed by each module and unit in the dam body deformation evaluation and early warning system in each embodiment of the present invention correspond to the steps in the dam body deformation evaluation and early warning method in each embodiment of the present invention. For the detailed function descriptions of each module of the dam body deformation evaluation and early warning system, reference can specifically be made to the descriptions in the corresponding dam body deformation evaluation and early warning method shown above, and details will not be repeated here.

[0052] Among them, the above-mentioned dam body deformation evaluation and early warning system can be a computer program (including program code) running in a computer device. For example, the dam body deformation evaluation and early warning system is an application software; this application software can be used to execute the corresponding steps in the method provided by the embodiments of the present invention.

[0053] In some embodiments, the dam body deformation evaluation and early warning system provided by the embodiments of the present invention can be implemented in a combination of software and hardware. As an example, the dam body deformation evaluation and early warning system provided by the embodiments of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the dam body deformation evaluation and early warning method provided by the embodiments of the present invention. For example, a processor in the form of a hardware decoding processor can employ one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0054] The modules involved in the embodiments of the present invention can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation to the module itself in some cases.

[0055] Based on the same principle as the method shown in the embodiments of the present invention, an electronic device is also provided in the embodiments of the present invention. The electronic device can include, but is not limited to: a processor and a memory; the memory is used to store a computer program; the processor is used to execute the method shown in any embodiment of the present invention by calling the computer program.

[0056] In an alternative embodiment, an electronic device is provided, as Figure 4 shown, Figure 4The electronic device shown includes: a processor and a memory. Among them, the processor and the memory are connected, such as through a bus. Optionally, the electronic device may further include a transceiver, which can be used for data interaction between the electronic device and other electronic devices, such as data sending and / or data receiving, etc. It should be noted that in practical applications, the transceiver is not limited to one, and the structure of the electronic device does not constitute a limitation to the embodiments of the present invention.

[0057] The processor can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in connection with the disclosure of the present invention. The processor can also be a combination that realizes computing functions, such as a combination including one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0058] The bus may include a path for transmitting information between the above components. The bus can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 4 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0059] The memory can be a ROM (Read Only Memory), or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory), or other types of dynamic storage devices that can store information and instructions. It can also be an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0060] The memory is used to store the application program code (computer program) for implementing the solution of the present invention and is controlled by the processor for execution. The processor is used to execute the application program code stored in the memory to implement the content shown in the foregoing method embodiments.

[0061] Among them, the electronic device can also be a terminal device. Figure 4 The electronic device shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0062] The embodiments of the present invention provide a computer-readable storage medium, on which a computer program is stored. When it runs on a computer, it enables the computer to execute the corresponding content in the foregoing method embodiments.

[0063] According to another aspect of the present invention, there is also provided a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the above various implementation manners.

[0064] Computer program code for performing the operations of the present invention may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0065] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0066] The computer-readable storage medium provided by the embodiments of the present invention may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0067] The above computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to execute the method shown in the above embodiments.

[0068] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles applied. Those skilled in the art should understand that the scope of disclosure involved in the present invention is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosure concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions disclosed in the present invention.

Claims

1. A method for evaluating and warning dam deformation, characterized in that, The method includes: Obtaining multi-source historical dam data, preprocessing the multi-source historical dam data to obtain preprocessed data; Constructing a prediction model, using the prediction model to extract spatio-temporal correlation features from the preprocessed data to obtain spatio-temporal correlation data, and performing dam deformation prediction based on the spatio-temporal correlation data to obtain initial prediction data; Obtaining multi-source real-time dam data, constructing a simulation model based on the coupled dynamics method, using the simulation model to compare and verify the initial prediction data and the multi-source real-time dam data to obtain a verification result, and optimizing the parameters of the prediction model based on the verification result to obtain optimized prediction data; wherein, the simulation model includes a displacement equation and a seepage pressure equation, as well as stress boundary conditions and seepage boundary conditions; Performing risk assessment on the optimized prediction result to obtain a risk index, and outputting a risk warning decision based on the risk index.

2. The dam body deformation evaluation and early warning method according to claim 1, wherein, The multi-source historical dam data includes displacement data, seepage data, stress data and temperature data.

3. A method for evaluating and warning the deformation of a dam body according to claim 1, characterized in that, Preprocessing the multi-source historical dam data to obtain preprocessed data specifically includes: Using an iterative method to perform residual compensation and error correction on the multi-source historical dam data to obtain corrected data; Using a feature analysis method to perform multi-scale feature extraction on the corrected data to obtain multi-scale feature data; Using the ICP method to perform spatial registration on the multi-scale feature data to obtain preprocessed data.

4. A method for evaluating and warning the deformation of a dam body according to claim 1, characterized in that, Obtaining initial prediction data specifically includes: Constructing a neural network module including a time attention unit and a spatial attention unit, using the neural network module to extract time features and spatial features from the preprocessed data to obtain time feature data and spatial feature data; Using a convolutional layer to perform data fusion on the time feature data and the spatial feature data to obtain a fused feature map; Using a time series regularization method to perform time series regularization processing on the fused feature map to obtain spatio-temporal correlation data; Performing dam deformation prediction based on the spatio-temporal correlation data to obtain initial prediction data; Wherein, the prediction model includes the neural network module, the convolutional layer and the time series regularization method.

5. A method for evaluating and warning of dam body deformation according to claim 1, characterized in that, The formula of the displacement equation is as follows: where u represents displacement, t represents the time variable, α represents the diffusion coefficient, and ▽ 2 represents the Laplace operator, β represents the coupling coefficient of the influence of osmotic pressure on displacement, and p represents the osmotic pressure; The formula of the seepage pressure equation is as follows: Wherein, γ represents the coupling coefficient of the influence of stress on displacement, σ represents the stress tensor, ▽ represents the divergence operator, δ represents the coupling coefficient of the influence of flow rate on seepage pressure, and q represents the flow rate; The formula of the stress boundary condition is as follows: σ nn = constant Among them, σ nn represents the normal stress of the dam body; The formula of the seepage boundary condition is as follows: q = -k▽p Wherein, k represents the permeability coefficient.

6. A method for evaluating and warning the deformation of a dam body according to claim 1, characterized in that, Using the discrete element comparison test method and the numerical inversion method to compare and verify the initial prediction data and the multi-source real-time dam data to obtain a verification result.

7. The dam body deformation evaluation and early warning method according to claim 1, wherein Performing risk assessment on the optimized prediction result to obtain a risk index, and outputting a risk warning decision based on the risk index specifically includes: Using a real-time stream computing platform to integrate the optimized prediction result to obtain integrated data; Presetting multi-level risk threshold ranges, determining which level of risk threshold range the integrated data is located in to obtain the corresponding level of risk index; Determine whether the risk index belongs to the normal range: If so, conduct risk early warning through the early warning APP; If not, continuously monitor the multi-source historical dam body data.

8. A dam deformation evaluation and early warning system, characterized in that It includes: A preprocessing module for obtaining multi-source historical dam body data, preprocessing the multi-source historical dam body data to obtain preprocessed data; A prediction module for constructing a prediction model, using the prediction model to extract spatio-temporal correlation features from the preprocessed data to obtain spatio-temporal correlation data, and predicting the dam body deformation based on the spatio-temporal correlation data to obtain initial prediction data; An optimization module for obtaining multi-source real-time dam body data, constructing a simulation model based on the coupled dynamics method, using the simulation model to compare and verify the initial prediction data and the multi-source real-time dam body data to obtain a verification result, and optimizing the parameters of the prediction model based on the verification result to obtain optimized prediction data; An early warning module for conducting risk assessment on the optimized prediction result to obtain a risk index, and outputting a risk early warning decision based on the risk index.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the method according to any one of claims 1-7.

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

Images