A dam deformation assessment and early warning method, system, device and storage medium

Abstract

The present invention belongs to the technical field of dam inspection, and in particular relates to a dam deformation assessment and early warning method, system, equipment and storage medium. It aims to solve the technical problems existing in the prior art, such as isolated monitoring data, lack of spatiotemporal correlation analysis, and difficulty in early warning of risks to the overall structure of the dam. It includes: obtaining multi-source historical dam data and preprocessing it to obtain preprocessed data; using a prediction model to extract spatiotemporal correlation features from the preprocessed data to obtain spatiotemporal correlation data, and predicting dam deformation based on the spatiotemporal correlation data to obtain initial prediction data; constructing a simulation model based on a coupled dynamics method, using the simulation model to compare and verify the initial prediction data with multi-source real-time dam data to obtain verification results, and optimizing the parameters of the prediction model based on the verification results to obtain optimized prediction data; performing risk assessment on the optimized prediction results to obtain a risk index, and outputting a risk warning decision based on the risk index.

Description

Technical Field

[0001] The present invention belongs to the technical field of dam body inspection, and in particular relates to a dam body deformation assessment and early warning method, system, equipment and storage medium. Background Art

[0002] Dams are hydraulic structures that intercept rivers and store water to form reservoirs. Their core functions include flood control, power generation, water supply, and shipping. Dams are subject to long-term pressure, temperature, and geological activity, which can cause structural deformation. Dam deformation monitoring primarily tracks parameters such as dam displacement and settlement to assess dam structural safety and prevent dam failures.

[0003] In the existing technology, the main method for monitoring dam deformation is manual regular inspection and reporting. In the later stage of dam deformation monitoring, inclinometers or piezometers are mainly used for single-point monitoring, and dam deformation analysis is performed based on the monitoring data.

[0004] The existing technology has the following technical problems:

[0005] 1. Manual measurement methods are inefficient, easily affected by environmental factors such as rain and fog, and are highly subjective, making it difficult to quantify and assess hidden damage.

[0006] 2. Existing monitoring data is isolated and lacks temporal and spatial correlation analysis, making it difficult to warn of risks to the overall structure of the dam. Summary of the Invention

[0007] The present invention provides a dam deformation assessment and early warning method, system, equipment and storage medium, aiming to solve the technical problems existing in the above-mentioned existing technologies, namely, the low efficiency of manual measurement methods, which are easily disturbed by environmental factors such as rainy and foggy weather, and are highly subjective, making it difficult to quantify and evaluate and discover hidden damage; and the isolation of existing monitoring data, the lack of spatiotemporal correlation analysis, and the difficulty in early warning of risks to the overall structure of the dam.

[0008] The present invention solves the above-mentioned technical problems with the following technical solutions: A dam deformation assessment and early warning method comprising:

[0009] Acquire multi-source historical dam body data, and pre-process the multi-source historical dam body data to obtain pre-processed data;

[0010] Constructing a prediction model, extracting spatiotemporal correlation features from the preprocessed data using the prediction model to obtain spatiotemporal correlation data, and predicting dam deformation based on the spatiotemporal correlation data to obtain initial prediction data;

[0011] Acquire multi-source real-time dam body data, construct a simulation model based on a 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 a stress boundary condition and a seepage boundary condition;

[0012] Perform risk assessment on the optimized prediction results to obtain a risk index, and output a risk warning decision based on the risk index.

[0013] The above-mentioned technical solution achieves the following beneficial effects: It implements closed-loop management of the entire process, from historical data analysis to real-time prediction, simulation verification, model optimization, risk assessment, and early warning output. It also combines the dual advantages of multi-source data-driven and physical modeling, enhancing the model's ability to depict complex dam deformation behavior. Through a model parameter optimization mechanism, the prediction model can continuously adapt to changes in the dam's state, improving the stability and accuracy of long-term predictions.

[0014] Furthermore, the multi-source historical dam body data mentioned above includes displacement data, seepage data, stress data and temperature data.

[0015] Furthermore, the above-mentioned preprocessing of the multi-source historical dam body data to obtain the preprocessed data specifically includes:

[0016] An iterative method is used to perform residual compensation and error correction on the multi-source historical dam body data to obtain corrected data;

[0017] Performing multi-scale feature extraction on the correction data using a feature analysis method to obtain multi-scale feature data;

[0018] The multi-scale feature data are spatially registered using the ICP method to obtain preprocessed data.

[0019] The above technical solution has the following beneficial effects: Preprocessing eliminates noise caused by sensor errors, environmental interference, and other factors, ensuring the reliability of model training and prediction results. ICP registration solves the problem of coordinate unification between multi-source heterogeneous data, achieving spatiotemporal consistency modeling.

[0020] Furthermore, the above-mentioned initial prediction data specifically includes:

[0021] Constructing a neural network module including a temporal attention unit and a spatial attention unit, and using the neural network module to perform temporal feature extraction and spatial feature extraction on the preprocessed data to obtain temporal feature data and spatial feature data;

[0022] Using a convolutional layer to fuse the temporal feature data and the spatial feature data to obtain a fused feature map;

[0023] The fusion feature map is processed by time series regularization method to obtain spatiotemporal correlation data;

[0024] Perform dam deformation prediction based on the spatiotemporal correlation data to obtain initial prediction data;

[0025] Among them, the prediction model includes the neural network module, the convolutional layer and the time series regularization method.

[0026] By adopting the above technical solution, the present invention has the following beneficial effects: the introduction of an attention mechanism significantly improves the model's focus on important time segments and key spatial regions, enhancing prediction accuracy and robustness. The joint spatiotemporal modeling capability enables the model to simultaneously capture the temporal evolution and spatial distribution characteristics of dam deformation, more closely resembling real physical processes. By combining convolution with time series regularization, the model effectively processes non-uniform time series, improving its tolerance for irregular or missing data and making it suitable for complex monitoring scenarios in actual engineering projects.

[0027] Furthermore, the formula of the displacement equation is as follows:

[0028]

[0029] Where u represents displacement, t represents time variable, α represents diffusion coefficient, ▽ 2 represents the Laplace operator, β represents the coupling coefficient of the effect of osmotic pressure on displacement, and p represents the osmotic pressure;

[0030] The formula of the osmotic pressure equation is shown below:

[0031]

[0032] Where γ represents the coupling coefficient of stress on displacement, σ represents the stress tensor, ▽ represents the divergence operator, δ represents the coupling coefficient of flow on seepage pressure, and q represents the flow rate;

[0033] The formula of the stress boundary condition is as follows:

[0034] σ nn =Constant

[0035] Among them, σ nn represents the normal stress of the dam body;

[0036] The formula of the infiltration boundary condition is as follows:

[0037] q=-k▽p

[0038] Where k represents the permeability coefficient.

[0039] Furthermore, the above-mentioned discrete element comparative test method and numerical inversion method are used to compare and verify the initial prediction data and the multi-source real-time dam body data to obtain verification results.

[0040] By adopting the above technical solution, the beneficial effects of the present invention are as follows: the present invention realizes the function of inferring model parameters from observation data through the numerical inversion method, so that the model continuously approaches the actual dam body response and improves the prediction accuracy.

[0041] Furthermore, the above-mentioned risk assessment of the optimized prediction results is performed to obtain a risk index, and outputting a risk warning decision based on the risk index specifically includes:

[0042] Using a real-time stream computing platform to perform data integration on the optimization prediction results to obtain integrated data;

[0043] Preset multiple levels of risk threshold ranges, determine which level of risk threshold range the integrated data falls within, and obtain a risk index for the corresponding level;

[0044] Determine whether the risk index is within the normal range:

[0045] If so, a risk warning will be issued through the early warning APP;

[0046] If not, continue to monitor historical dam data from multiple sources.

[0047] By adopting the above technical solution, the beneficial effects of the present invention are as follows: the present invention realizes efficient data integration and low-latency processing based on the real-time stream computing platform, and meets the timeliness requirements of the emergency warning system.

[0048] In a second aspect, in order to solve the above technical problems, the present invention further provides a dam deformation assessment and early warning system, comprising:

[0049] A preprocessing module is used to obtain multi-source historical dam body data and preprocess the multi-source historical dam body data to obtain preprocessed data;

[0050] A prediction module is used to construct a prediction model, use the prediction model to extract spatiotemporal correlation features from the preprocessed data to obtain spatiotemporal correlation data, and predict dam deformation based on the spatiotemporal correlation data to obtain initial prediction data;

[0051] an optimization module, configured to obtain multi-source real-time dam body data, construct a simulation model based on a coupled dynamics method, compare and verify the initial prediction data and the multi-source real-time dam body data using the simulation model to obtain a verification result, and optimize parameters of the prediction model based on the verification result to obtain optimized prediction data;

[0052] The early warning module is used to perform risk assessment on the optimized prediction results, obtain a risk index, and output a risk early warning decision based on the risk index.

[0053] In the third aspect, in order to solve the above-mentioned technical problems, the present invention also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the dam deformation assessment and early warning method of the present application is implemented.

[0054] In a fourth aspect, in order to solve the above-mentioned technical problems, the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the dam deformation assessment and early warning method of the present application is implemented.

[0055] Compared with the prior art, the present invention has the following advantages:

[0056] 1. This invention significantly enhances the proactive prevention and control capabilities of water conservancy project safety monitoring by integrating multi-source data fusion, spatiotemporal correlation modeling, and multi-field coupling analysis. By building a dynamic risk assessment system, it achieves a shift from single-parameter monitoring to collaborative multi-physics field sensing, effectively capturing early signs of dam deformation and providing real-time early warnings with millimeter-level accuracy for project safety.

[0057] 2. This invention advances the risk identification window by several weeks, enabling users to formulate targeted prevention and control strategies before disasters occur, 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.

[0058] 3. At the engineering operation and maintenance level, this invention significantly optimizes resource allocation efficiency by establishing a real-time closed loop from data restoration to risk assessment. This reduces repetitive monitoring efforts, lowers the frequency of manual inspections, and reduces equipment maintenance costs. This not only extends the safe service life of the dam structure but also provides a scientific basis for engineering renovation and reinforcement through precise health status assessments, thus achieving an organic unity of economic and safety benefits throughout the entire life cycle.

[0059] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be 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

[0060] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[0061] 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;

[0062] Figure 2 A schematic diagram of a process for obtaining initial prediction data according to an embodiment of the present invention is shown;

[0063] 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;

[0064] Figure 4 A schematic structural diagram of an electronic device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0065] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0066] Figure 1 FIG. 1 shows a flow chart of a dam deformation assessment and early warning method according to an embodiment of the present invention. Figure 1 As shown, a dam deformation assessment and early warning method according to an embodiment of the present invention includes:

[0067] Acquire multi-source historical dam body data, and pre-process the multi-source historical dam body data to obtain pre-processed data;

[0068] Constructing a prediction model, extracting spatiotemporal correlation features from the preprocessed data using the prediction model to obtain spatiotemporal correlation data, and predicting dam deformation based on the spatiotemporal correlation data to obtain initial prediction data;

[0069] Acquire multi-source real-time dam body data, construct a simulation model based on a 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 a stress boundary condition and a seepage boundary condition;

[0070] Perform risk assessment on the optimized prediction results to obtain a risk index, and output a risk warning decision based on the risk index.

[0071] In summary, this invention achieves closed-loop management of the entire process, from historical data analysis to real-time prediction, simulation verification, model optimization, risk assessment, and early warning output. It also combines the advantages of multi-source data-driven and physical modeling, enhancing the model's ability to depict complex dam deformation behavior. Through a model parameter optimization mechanism, the prediction model can continuously adapt to changes in the dam's state, improving the stability and accuracy of long-term predictions.

[0072] Optionally, the multi-source historical dam body data includes displacement data, seepage data, stress data and temperature data.

[0073] In this embodiment, displacement data, seepage data, stress data and temperature data are collected through sensors, and the collected data are transmitted based on the SL / T 308-2022 standard interface protocol to form multi-source historical dam data. In specific implementation, more than five years of historical data are collected, and the average daily sampling frequency is ≥10 times.

[0074] Optionally, preprocessing the multi-source historical dam body data to obtain preprocessed data specifically includes:

[0075] An iterative method is used to perform residual compensation and error correction on the multi-source historical dam body data to obtain corrected data;

[0076] Performing multi-scale feature extraction on the correction data using a feature analysis method to obtain multi-scale feature data;

[0077] The multi-scale feature data are spatially registered using the ICP method to obtain preprocessed data.

[0078] In this example, the iterative method uses a modified EM algorithm to handle missing data values. By reasonably estimating missing values, it can avoid bias caused by incomplete data, thereby improving the accuracy of model training and prediction. The feature analysis method uses wavelet packet decomposition to extract multi-scale features, providing strong support for subsequent spatiotemporal correlation feature extraction and dam deformation prediction.

[0079] In summary, this paper eliminates noise caused by factors such as sensor errors and environmental interference through preprocessing, ensuring the reliability of model training and prediction results. The ICP method is used to unify coordinates between multi-source heterogeneous data, achieving spatiotemporal consistency modeling.

[0080] Optionally, obtaining initial prediction data specifically includes:

[0081] Constructing a neural network module including a temporal attention unit and a spatial attention unit, and using the neural network module to perform temporal feature extraction and spatial feature extraction on the preprocessed data to obtain temporal feature data and spatial feature data;

[0082] Using a convolutional layer to fuse the temporal feature data and the spatial feature data to obtain a fused feature map;

[0083] The fusion feature map is processed by time series regularization method to obtain spatiotemporal correlation data;

[0084] Perform dam deformation prediction based on the spatiotemporal correlation data to obtain initial prediction data;

[0085] Among them, the prediction model includes the neural network module, the convolutional layer and the time series regularization method.

[0086] In this embodiment, Figure 2 As shown in the figure, the preprocessed data is processed using a spatiotemporal attention network model to generate temporal and spatial feature data. A 3×3 convolution kernel is used to convolve the temporal and spatial feature data, extracting local features and enabling fusion of different data sources. Through multi-layer convolution and pooling operations, data from different sources is deeply fused in time and space to form a fused feature map. Finally, a dynamic time warping algorithm is used to perform time series processing on the fused feature map, addressing issues such as inconsistent time series lengths and sampling rate differences.

[0087] In this embodiment, the indicators of the output initial prediction data are: displacement prediction MAPE ≤ 3%; seepage flow interpretation R² ≥ 0.85.

[0088] In summary, the introduction of the attention mechanism in this paper significantly improves the model's focus on important time segments and key spatial regions, enhancing prediction accuracy and robustness. The joint spatiotemporal modeling capability enables the model to simultaneously capture the temporal evolution and spatial distribution characteristics of dam deformation, more closely resembling real physical processes. By combining convolution with time regularization, the model effectively processes non-uniform time series, improving its tolerance for irregular or missing data and making it suitable for complex monitoring scenarios in actual engineering projects.

[0089] Optionally, the simulation model includes a displacement equation and a permeation pressure equation; the displacement equation is as follows:

[0090]

[0091] Where u represents displacement, t represents time variable, α represents diffusion coefficient, ▽ 2 represents the Laplace operator, β represents the coupling coefficient of the effect of osmotic pressure on displacement, and p represents the osmotic pressure;

[0092] The formula of the osmotic pressure equation is shown below:

[0093]

[0094] Where γ represents the coupling coefficient of stress on displacement, σ represents the stress tensor, ▽ represents the divergence operator, δ represents the coupling coefficient of flow on seepage pressure, and q represents the flow rate;

[0095] The simulation model also includes a stress boundary condition and a permeability boundary condition. The formula of the stress boundary condition is shown below:

[0096] σ nn =Constant

[0097] Among them, σ nn represents the normal stress of the dam body;

[0098] The formula of the infiltration boundary condition is as follows:

[0099] q=-k▽p

[0100] Where k represents the permeability coefficient.

[0101] Optionally, a discrete element comparative 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.

[0102] In this example, the scattered element comparison test method primarily focuses on verifying the accuracy of the prediction model using real-time multi-source dam data; while the numerical inversion method focuses on using existing data to improve the prediction model parameters, ensuring that the prediction model reflects reality as closely as possible. The two methods complement each other and jointly ensure that the prediction model can play an important role in a wide range of engineering applications.

[0103] In summary, the present invention realizes the function of inferring model parameters from observation data through the numerical inversion method, so that the model continuously approaches the actual dam body response and improves the prediction accuracy.

[0104] 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:

[0105] Using a real-time stream computing platform to perform data integration on the optimization prediction results to obtain integrated data;

[0106] Preset multiple levels of risk threshold ranges, determine which level of risk threshold range the integrated data falls within, and obtain a risk index for the corresponding level;

[0107] Determine whether the risk index is within the normal range:

[0108] If so, a risk warning will be issued through the early warning APP;

[0109] If not, continue to monitor historical dam data from multiple sources.

[0110] In summary, the present invention realizes efficient data integration and low-latency processing based on the real-time stream computing platform, meeting the timeliness requirements of the emergency warning system.

[0111] Based on Figure 1 Based on the same principle as the method shown in , the embodiment of the present invention also provides a dam deformation assessment and early warning system, such as Figure 3 As shown in , including:

[0112] A preprocessing module is used to obtain multi-source historical dam body data and preprocess the multi-source historical dam body data to obtain preprocessed data;

[0113] A prediction module is used to construct a prediction model, use the prediction model to extract spatiotemporal correlation features from the preprocessed data to obtain spatiotemporal correlation data, and predict dam deformation based on the spatiotemporal correlation data to obtain initial prediction data;

[0114] an optimization module, configured to obtain multi-source real-time dam body data, construct a simulation model based on a coupled dynamics method, compare and verify the initial prediction data and the multi-source real-time dam body data using the simulation model to obtain a verification result, and optimize parameters of the prediction model based on the verification result to obtain optimized prediction data;

[0115] The early warning module is used to perform risk assessment on the optimized prediction results, obtain a risk index, and output a risk early warning decision based on the risk index.

[0116] The dam deformation assessment and early warning system of the embodiment of the present invention can execute the dam deformation assessment and early warning method provided by the embodiment of the present invention. The implementation principle is similar. The actions performed by each module and unit in the dam deformation assessment and early warning system in each embodiment of the present invention correspond to the steps in the dam deformation assessment and early warning method in each embodiment of the present invention. For the detailed functional description of each module of the dam deformation assessment and early warning system, please refer to the description in the corresponding dam deformation assessment and early warning method shown in the previous text, which will not be repeated here.

[0117] Among them, the above-mentioned dam deformation assessment and early warning system can be a computer program (including program code) running in a computer device, for example, the dam deformation assessment and early warning system is an application software; the application software can be used to execute the corresponding steps in the method provided in the embodiment of the present invention.

[0118] In some embodiments, the dam deformation assessment and early warning system provided by the embodiments of the present invention can be implemented by a combination of software and hardware. As an example, the dam deformation assessment 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 deformation assessment and early warning method provided by the embodiments of the present invention. For example, the processor in the form of a hardware decoding processor can adopt 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.

[0119] The modules involved in the embodiments of the present invention may be implemented in software or hardware, wherein the name of a module does not necessarily limit the module itself.

[0120] 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, which may include but is not limited to: a processor and a memory; the memory is used to store computer programs; the processor is used to execute the method shown in any embodiment of the present invention by calling the computer program.

[0121] In an alternative embodiment, an electronic device is provided, such as Figure 4 As shown, Figure 4The electronic device shown includes a processor and a memory. The processor and the memory are connected, for example, via a bus. Optionally, the electronic device may further include a transceiver, which can be used for data exchange between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, there is not limited to one transceiver, and the structure of the electronic device does not constitute a limitation on the embodiments of the present invention.

[0122] The processor may 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 may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the present disclosure. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0123] A bus may include a path for transmitting information between the above components. The bus may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0124] The memory may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0125] The memory is used to store application code (computer program) for executing the solution of the present invention, and the processor controls the execution of the application code. The processor is used to execute the application code stored in the memory to implement the content shown in the above method embodiment.

[0126] Among them, the electronic device can also be a terminal device, Figure 4 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0127] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding contents of the aforementioned method embodiment.

[0128] According to another aspect of the present invention, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various implementations described above.

[0129] Computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving 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).

[0130] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code 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 box can also occur in a different order than the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0131] 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, device or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with 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 thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or device.

[0132] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0133] The above description is merely a preferred embodiment of the present invention and an illustration of the technical principles employed. 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-mentioned technical features, but also includes other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in the present invention.

Claims

1. A dam deformation assessment and early warning method, characterized in that: The method comprises: Acquire multi-source historical dam body data, and pre-process the multi-source historical dam body data to obtain pre-processed data; Constructing a prediction model, extracting spatiotemporal correlation features from the preprocessed data using the prediction model to obtain spatiotemporal correlation data, and predicting dam deformation based on the spatiotemporal correlation data to obtain initial prediction data; Acquire multi-source real-time dam body data, construct a simulation model based on a 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 a stress boundary condition and a seepage boundary condition; The displacement equation is as follows: Where u represents displacement, t represents time variable, α represents diffusion coefficient, ▽ 2 represents the Laplace operator, β represents the coupling coefficient of the effect of osmotic pressure on displacement, and p represents the osmotic pressure; The formula of the osmotic pressure equation is shown below: Where γ represents the coupling coefficient of stress on displacement, σ represents the stress tensor, ▽ represents the divergence operator, δ represents the coupling coefficient of flow 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 infiltration boundary condition is as follows: q=-k▽p Wherein, k represents the permeability coefficient; a risk assessment is performed on the optimization prediction result to obtain a risk index, and a risk warning decision is output based on the risk index.

2. A dam deformation assessment and early warning method according to claim 1, characterized in that: The multi-source historical dam body data includes displacement data, seepage data, stress data and temperature data.

3. A dam deformation assessment and early warning method according to claim 1, characterized in that: Preprocessing the multi-source historical dam body data to obtain preprocessed data specifically includes: An iterative method is used to perform residual compensation and error correction on the multi-source historical dam body data to obtain corrected data; Performing multi-scale feature extraction on the correction data using a feature analysis method to obtain multi-scale feature data; The multi-scale feature data are spatially registered using the ICP method to obtain preprocessed data.

4. A dam deformation assessment and early warning method according to claim 1, characterized in that: The initial forecast data includes: Constructing a neural network module including a temporal attention unit and a spatial attention unit, and using the neural network module to perform temporal feature extraction and spatial feature extraction on the preprocessed data to obtain temporal feature data and spatial feature data; Using a convolutional layer to fuse the temporal feature data and the spatial feature data to obtain a fused feature map; The fusion feature map is processed by time series regularization method to obtain spatiotemporal correlation data; Perform dam deformation prediction based on the spatiotemporal correlation data to obtain initial prediction data; Among them, the prediction model includes the neural network module, the convolutional layer and the time series regularization method.

5. The dam deformation assessment and early warning method according to claim 1 is characterized in that: The initial prediction data and the multi-source real-time dam body data are compared and verified using a discrete element comparative test method and a numerical inversion method to obtain verification results.

6. A dam deformation assessment and early warning method according to claim 1, characterized in that: Performing risk assessment on the optimized prediction results 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 perform data integration on the optimization prediction results to obtain integrated data; Preset multiple levels of risk threshold ranges, determine which level of risk threshold range the integrated data falls within, and obtain a risk index for the corresponding level; Determine whether the risk index is within the normal range: If so, a risk warning will be issued through the early warning APP; If not, continue to monitor historical dam data from multiple sources.

7. A dam deformation assessment and early warning system, characterized in that: include: A preprocessing module is used to obtain multi-source historical dam body data and preprocess the multi-source historical dam body data to obtain preprocessed data; A prediction module is used to construct a prediction model, use the prediction model to extract spatiotemporal correlation features from the preprocessed data to obtain spatiotemporal correlation data, and predict dam deformation based on the spatiotemporal correlation data to obtain initial prediction data; an optimization module, configured to obtain multi-source real-time dam body data, construct a simulation model based on a coupled dynamics method, compare and verify the initial prediction data with the multi-source real-time dam body data using the simulation model to obtain a verification result, and optimize 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; The displacement equation is as follows: Where u represents displacement, t represents time variable, α represents diffusion coefficient, ▽ 2 represents the Laplace operator, β represents the coupling coefficient of the effect of osmotic pressure on displacement, and p represents the osmotic pressure; The formula of the osmotic pressure equation is shown below: Where γ represents the coupling coefficient of stress on displacement, σ represents the stress tensor, ▽ represents the divergence operator, δ represents the coupling coefficient of flow 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 infiltration boundary condition is as follows: q=-k▽p Where k represents the permeability coefficient; The early warning module is used to perform risk assessment on the optimized prediction results, obtain a risk index, and output a risk early warning decision based on the risk index.

8. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 6 when executing the computer program.

9. A computer storage medium, characterized in that The computer storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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