Water conservancy project operation trend prediction method and system

Abstract

The invention relates to the technical field of hydraulic engineering operation prediction, in particular to a hydraulic engineering operation trend prediction method and system. The method comprises the following steps: acquiring multi-source hydraulic engineering data, and generating a hydraulic engineering three-dimensional model through multi-source space fusion processing and three-dimensional model simulation; performing geometric complexity identification and stress path analysis on the three-dimensional model to determine key nodes in the water conservancy project; historical water conservancy data are obtained, time sequence conversion and potential mode mining are carried out, and potential modes related to engineering are obtained; performing stress superposition comparison on the key nodes of the water conservancy project and the potential mode to generate stress superposition comparison data, and iteratively adjusting the key nodes according to a result to obtain adjusted stress nodes; constructing a prediction model based on the adjusted stress node and the potential mode, performing operation trend prediction, and generating an operation trend prediction result of the water conservancy project; the high-precision and high-adaptability water conservancy project operation trend prediction method is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy project operation prediction, and in particular to a water conservancy project operation trend prediction method and system. Background Art

[0002] In the field of water conservancy projects, with the continuous increase in the scale and complexity of projects, accurate prediction of the operation trend of water conservancy projects has become a key task to improve project safety and optimize resource management. Current water conservancy project management methods often rely on traditional monitoring methods and static data analysis, which often seem to be inadequate when faced with complex and changing environmental conditions and project status. Existing water conservancy project models and prediction technologies are usually difficult to effectively integrate information from different data sources, and lack in-depth mining of potential patterns, resulting in insufficient prediction accuracy and limited ability to respond to emergencies. Although many studies have attempted to optimize the design and operation of water conservancy projects through three-dimensional modeling and stress analysis, these methods are often limited to static data and simple analysis models. Although the geometric complexity identification and stress path analysis of three-dimensional models can reveal the stress distribution of key nodes, they still lack sufficient sensitivity and responsiveness to dynamic environmental changes and potential patterns in historical data. Therefore, existing methods have significant deficiencies in dynamic adjustment and real-time prediction, and are difficult to cope with complex operating environments and changing engineering conditions. Summary of the invention

[0003] Based on this, it is necessary to provide a method and system for predicting the operation trend of a water conservancy project to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for predicting the operation trend of a water conservancy project comprises the following steps: Step S1: acquiring multi-source water conservancy project data; performing multi-source spatial fusion processing on the multi-source water conservancy project data, and performing three-dimensional model simulation to obtain a three-dimensional model of the water conservancy project; Step S2: Identify the geometric complexity of the three-dimensional model of the water conservancy project, perform stress path analysis, and generate key nodes of the water conservancy project; Step S3: Obtain historical water conservancy data; perform time series conversion on the historical water conservancy data and perform potential pattern mining to obtain potential patterns of water conservancy projects; Step S4: performing stress superposition comparison on key nodes of the water conservancy project and potential modes of the water conservancy project to obtain stress superposition comparison data; iteratively adjusting the key nodes of the water conservancy project according to the stress superposition comparison data to generate adjusted stress nodes; Step S5: construct a prediction model for the adjustment stress nodes and the potential mode of the water conservancy project, and perform operation trend prediction to generate a predicted water conservancy project operation trend.

[0005] The present invention can fully integrate information from different data sources by acquiring and fusing multi-source water conservancy project data and performing three-dimensional model simulation. It realizes all-round modeling of the water conservancy project environment and effectively improves the accuracy and comprehensiveness of the model. The geometric complexity identification and stress path analysis of the three-dimensional model of the water conservancy project can accurately identify the key nodes in the project. This meticulous analysis helps to timely discover potential structural problems and improve the safety and stability of the project. By performing time series conversion and potential pattern mining on historical water conservancy data, potential operating modes can be extracted from the data. This pattern mining can reveal periodic trends and abnormal behaviors in historical data and provide a rich information basis for the construction of prediction models. The key nodes of the water conservancy project are stress-superimposed and compared with the potential patterns, and iterative adjustments are made according to the results, which can ensure the dynamic optimization of the stress state in the project. This method can make timely adjustments according to the actual stress situation and improve the ability to cope with emergencies. By constructing a prediction model for the adjusted stress nodes and potential patterns, a high-precision water conservancy project operation trend prediction can be generated. This prediction capability helps project managers to foresee problems in advance and take preventive measures, thereby improving the management efficiency and safety of water conservancy projects.

[0006] Preferably, step S1 comprises the following steps: Step S11: Acquire multi-source water conservancy project data; Step S12: calibrating the multi-source water conservancy project data to obtain compatible water conservancy project data; Step S13: performing spatial data fusion on compatible water conservancy project data to generate fused spatial water conservancy data; Step S14: performing point cloud conversion processing on the fused spatial water conservancy data to generate water conservancy point cloud data; Step S15: Perform three-dimensional model simulation on the fused spatial water conservancy data according to the water conservancy point cloud data to obtain a three-dimensional model of the water conservancy project.

[0007] The present invention can ensure that data from different sources have a unified format by calibrating the format consistency of multi-source water conservancy project data. This compatibility improves the availability and processing efficiency of data and avoids problems caused by inconsistent data formats. Spatial data fusion of compatible water conservancy project data can effectively integrate spatial information from different sources. This fusion process ensures the comprehensiveness and consistency of data, provides a more accurate spatial information basis, and facilitates subsequent model construction. By performing point cloud conversion processing on the fused spatial water conservancy data, complex spatial data can be converted into point cloud data. This processing method can describe the spatial structure of the water conservancy project in detail and provide accurate geometric information for the creation of a three-dimensional model. Three-dimensional model simulation based on water conservancy point cloud data can generate a high-precision three-dimensional model of the water conservancy project. This model can truly reflect the spatial characteristics of the project and provide reliable visual and data support for engineering analysis and decision-making. Combining point cloud data with fused spatial data provides detailed engineering information.

[0008] Preferably, step S15 comprises the following steps: Step S151: extract dynamic data from the fused spatial water conservancy data to obtain dynamic water conservancy data; Step S152: performing time series analysis on dynamic water conservancy data to generate a continuous dynamic water flow field; Step S153: performing fluid dynamics calculation on the continuous dynamic water flow field to obtain a water flow simulation parameter set; Step S154: constructing a three-dimensional model of the fused spatial hydraulic data according to the water flow simulation parameter set to obtain an initial three-dimensional hydraulic model; Step S155: performing spatial coordinate standardization processing on the water conservancy point cloud data to obtain point cloud data in a unified coordinate system; Step S156: Perform surface point cloud mapping on the initial three-dimensional water conservancy model based on the unified coordinate system point cloud data to generate a three-dimensional model of the water conservancy project.

[0009] The present invention can obtain dynamic water conservancy data reflecting the current state of a water conservancy project in real time by extracting dynamic data from fused spatial water conservancy data. This real-time performance ensures the timeliness and accuracy of the data, and helps to timely understand the operating status of the water conservancy project. By performing time series analysis on the dynamic water conservancy data, a continuous dynamic water flow field can be generated. This continuous water flow field data provides detailed water flow change information, making the prediction of water flow behavior more accurate and reliable. By performing fluid dynamics calculation on the continuous dynamic water flow field, an accurate water flow simulation parameter set can be obtained. These parameter sets can accurately describe the dynamic characteristics of the water flow, help to build a more realistic three-dimensional model, and improve the simulation accuracy of the model. By constructing a three-dimensional model of the fused spatial water conservancy data according to the water flow simulation parameter set, an initial three-dimensional water conservancy model can be obtained. This model can accurately reflect the spatial characteristics and dynamic behavior of the water conservancy project, and provides a reliable basis for subsequent model optimization and analysis. By performing spatial coordinate standardization processing on the water conservancy point cloud data, point cloud data of a unified coordinate system is obtained. This processing ensures that the point cloud data and the three-dimensional model are aligned in the same coordinate system, thereby improving the accuracy and consistency of the model. Surface point cloud mapping based on unified coordinate system point cloud data can generate a high-precision three-dimensional model of water conservancy projects, enhancing the model's detail expression and authenticity.

[0010] Preferably, step S2 comprises the following steps: Step S21: performing grid topology analysis on the three-dimensional model of the water conservancy project to obtain discrete grid data; Step S22: performing independent grid curvature calculation on discrete grid data to generate a geometric feature matrix; Step S23: performing multi-scale shape description processing on the three-dimensional model of the water conservancy project based on the geometric feature matrix to obtain a local shape feature vector; Step S24: performing geometry-stress coupling node identification on the three-dimensional model of the water conservancy project according to the local shape feature vector, and generating key nodes of the water conservancy project.

[0011] The present invention can discretize the three-dimensional model of the water conservancy project efficiently through the grid topology structure analysis. This step not only ensures the geometric integrity of the model, but also lays a solid foundation for the subsequent geometric feature extraction. The accurate generation of discrete grid data helps to improve the analysis accuracy of the entire model. By performing independent grid curvature calculation on the discrete grid data, a geometric feature matrix is ​​generated. The feature matrix can accurately describe the geometric characteristics of the water conservancy project, thereby providing high-quality data support for subsequent shape description and node identification. This step improves the ability to capture details of complex engineering structures and enhances the accuracy of the model. Based on the geometric feature matrix, multi-scale shape description processing is performed to extract the local shape feature vector of the water conservancy project model. This processing method can not only identify small geometric changes, but also capture the features of the model at different scales, ensure a comprehensive description of the model, and improve the accuracy of the prediction. Through geometric-stress coupling node identification, key nodes can be accurately located in the three-dimensional model of the water conservancy project. These nodes are crucial to the stability and safety of the project. The recognition process combines geometric and stress information to ensure the reliability of the identified nodes in the real operating environment, which helps to more effectively predict the operation trend of the project. The generated key nodes provide the core data points of the water conservancy project, which can be used as the focus of monitoring and prediction. Through the continuous monitoring of these nodes, the operation trend of the water conservancy project can be predicted more accurately, potential risks can be avoided, and the foresight and scientific nature of project management can be improved.

[0012] Preferably, step S24 includes the following steps: Cluster the local shape feature vectors to obtain the geometric complexity distribution map; According to the geometric complexity distribution map, the finite element mesh of the three-dimensional model of the water conservancy project is refined to obtain an optimized mesh model; Perform nonlinear finite element analysis on the optimized mesh model to generate an initial stress distribution diagram; A stress tensor field is constructed according to the initial stress distribution diagram to obtain a continuous stress distribution function; Perform minimum path calculation on the continuous stress distribution function to generate the principal stress path; Based on the principal stress path, stress coupling nodes of the three-dimensional model of the water conservancy project are identified to generate key nodes of the water conservancy project.

[0013] The present invention clusters local shape feature vectors and generates a geometric complexity distribution map. The method can effectively identify geometrically complex areas in the three-dimensional model of a water conservancy project. This feature enables targeted optimization according to geometric complexity in the subsequent finite element mesh refinement process, thereby improving the accuracy and efficiency of mesh division. The finite element mesh of the three-dimensional model is refined using the geometric complexity distribution map, and the generated optimized mesh model can more accurately capture the details in the model, especially in the complex structure area. This refinement process improves the calculation accuracy of the model and makes the subsequent nonlinear finite element analysis results more reliable. Nonlinear finite element analysis is performed on the optimized mesh model to generate an initial stress distribution map, so that accurate stress distribution is obtained in the early stage of the model. This lays a solid foundation for the subsequent construction of the stress tensor field and ensures the accuracy of the continuous stress distribution function. The principal stress path is generated by performing the minimum path calculation on the continuous stress distribution function. This process can effectively capture the main path of stress transmission in the model, facilitate the identification of key stress areas in the structure, and provide an important basis for the identification of stress coupling nodes. Identifying stress coupling nodes in the three-dimensional model of a hydraulic engineering project based on the principal stress path helps determine the nodes in the model that play a key role in the safety and stability of the overall structure.

[0014] Preferably, step S3 comprises the following steps: Step S31: Acquire historical water conservancy data; perform equal-interval time series conversion on the historical water conservancy data to obtain an equal-interval time series; Step S32: performing wavelet transform processing on the equally spaced time series to generate multi-scale time-frequency features; Step S33: constructing a continuous barcode for the multi-scale time-frequency features to obtain a topological continuous graph; Step S34: performing popular learning mapping on the topological persistence graph to generate low-dimensional embedded water conservancy data; Step S35: performing manifold folding analysis on the low-dimensional embedded water conservancy data to obtain a water conservancy geometric feature descriptor; Step S36: Perform density peak clustering on the water conservancy geometric feature descriptors to generate potential patterns of water conservancy projects.

[0015] The present invention can eliminate the inhomogeneity in the original data by converting the historical water conservancy data into an equally spaced time series, ensure that the subsequent analysis is carried out on the same time scale, and improve the accuracy and consistency of data processing. This provides a unified time basis for further wavelet transform. Wavelet transform can effectively decompose equally spaced time series data and capture time-frequency features at different scales. By generating multi-scale time-frequency features, the changing laws of water conservancy data in different time periods and frequency ranges can be fully reflected, providing rich information dimensions for subsequent topological analysis. The topological persistence graph is constructed using persistent barcodes, which can identify the topological structure and persistence features in water conservancy data. This method can reveal the complex topological relationships hidden in the data, help to explore the potential operation mode of water conservancy projects, and improve the depth of understanding of the intrinsic structure of the data. The topological persistence graph is subjected to popular learning mapping, which can convert high-dimensional complex data into low-dimensional embedded representation. This low-dimensional representation maintains the important topological features of the data, while simplifying the data structure, which helps to carry out efficient subsequent analysis and processing. Manifold folding analysis can further explore the geometric structure features in the low-dimensional embedded data and generate geometric feature descriptors. These descriptors can effectively characterize the spatial geometric features in water conservancy data, providing a novel method to analyze and understand the operating status of water conservancy projects. By clustering the density peaks of geometric feature descriptors, potential operating modes in water conservancy projects can be identified. This clustering method can effectively distinguish different water conservancy states and help predict operating trends and risks.

[0016] Preferably, step S4 comprises the following steps: Step S41: constructing a three-dimensional stress matrix for key nodes of the water conservancy project to obtain a stress matrix for the key nodes; Step S42: dynamically loading the stress matrix of key nodes according to the potential mode of the water conservancy project to obtain stress node response data; Step S43: decomposing the stress node response data into stress components to generate a decomposed node stress vector set; Step S44: performing stress superposition comparison on the decomposed node stress vector set to obtain stress superposition comparison data; Step S45: iteratively adjust the stress matrix of key nodes based on the stress superposition comparison data to generate adjusted stress nodes.

[0017] The present invention can comprehensively capture the stress distribution of key nodes of water conservancy projects by utilizing three-dimensional stress matrix construction technology, ensuring that the basic data for subsequent analysis has a high degree of accuracy and spatial resolution. The introduced dynamic loading process can update the stress matrix of key nodes in real time according to the potential mode of the water conservancy project, ensuring that the prediction model can reflect the stress changes in actual operation, thereby improving the accuracy and real-time performance of the overall prediction. Through the stress component decomposition technology, the stress data can be refined into a smaller set of stress vectors, enhancing the multi-dimensional analysis capability of stress response, and helping to identify potential structural risk points. Through the stress superposition comparison technology, the abnormal changes in stress between different nodes can be effectively detected, and the early warning capability of potential faults is improved through comparative analysis, ensuring the safe operation of water conservancy projects. Through the iterative adjustment of the stress matrix, the system can automatically optimize the stress distribution model of key nodes, enhance the adaptive ability of the model, and enable it to maintain high prediction accuracy and stability under different working conditions.

[0018] Preferably, step S45 includes the following steps: The principal component analysis of the stress superposition comparison data was performed to obtain the main stress components; The pressure sensitivity matrix of the main stress components is constructed to generate the node pressure sensitivity; Based on the preset adjustment priority data and node pressure sensitivity, the stress matrix of key nodes is adjusted and sorted to obtain a priority adjustment sequence; Perform conjugate gradient calculation on the priority adjustment sequence to generate the adjustment direction; Perform line search processing on the priority adjustment sequence according to the adjustment direction to generate an adjustment step length; Perform gradient descent calculation on the priority adjustment sequence according to the adjustment direction and adjustment step length to obtain the stress update amount; Iteratively adjust the stress matrix of key nodes based on the stress update amount to generate adjusted stress nodes; Perform a convergence check on the adjusted stress node. When the convergence state is a non-convergence state, repeat the above steps; when the convergence state is a convergence state, output the adjusted stress node.

[0019] The present invention can effectively extract the main stress components by performing principal component analysis on the force superposition comparison data, reduce the dimension of the data, reduce the calculation complexity, and thus improve the overall calculation efficiency. In addition, the extracted main stress components can more accurately reflect the key stress changes in the operation of the water conservancy project, and improve the reliability of the prediction results. By constructing a pressure sensitive matrix for the main stress components, the node pressure sensitivity is generated, which provides a scientific basis for subsequent stress adjustment. The pressure sensitive matrix can clarify the response degree of the node to the pressure change, so that the nodes with greater impact on the overall structure are given priority during the adjustment process, ensuring the accuracy and stability of the prediction. The key node stress matrix is ​​adjusted and sorted based on the preset adjustment priority data and node pressure sensitivity, and the nodes that need to be adjusted first during the operation of the water conservancy project can be effectively identified. This adjustment sorting method can ensure that the stress of the key nodes is optimized in the early stage, reduce the potential risks in the overall operation, and improve the accuracy of the prediction of the operation trend of the water conservancy project. Introducing conjugate gradient calculation and execution line search processing in the priority adjustment sequence can optimize the adjustment direction and step size, and ensure the efficiency and accuracy of the adjustment process. This method can avoid the instability caused by blind adjustment and ensure the rapid convergence of the stress adjustment process. By accurately calculating the priority adjustment sequence through the gradient descent method, an accurate stress update amount can be obtained, making the stress adjustment more accurate. This process ensures that the stress distribution is continuously optimized in each iteration and gradually approaches the ideal stress state. Through iterative adjustment and convergence test based on the stress update amount, the stress matrix of the key nodes can be continuously optimized until the ideal convergence state is reached. This process can ensure that the final output of the adjusted stress node is stable and reliable, providing a solid foundation for the accurate prediction of the operation trend of water conservancy projects.

[0020] Preferably, step S5 comprises the following steps: Step S51: performing distributed weight distribution processing on the adjusted stress nodes based on preset weight distribution data to obtain a stress node weight matrix; Step S52: performing multi-dimensional state variable mapping on the potential mode of the water conservancy project according to the stress node weight matrix to obtain a multi-dimensional state variable matrix; Step S53: performing joint modeling processing on the adjustment stress nodes and the potential mode of the water conservancy project according to the multi-dimensional state variable matrix to obtain a water conservancy project prediction model; Step S54: using the water conservancy project prediction model to predict the water conservancy project operation trend and generate a predicted water conservancy project operation trend.

[0021] The present invention uses a distributed weight distribution process to accurately distribute weights to the adjustment stress nodes using preset weight distribution data to obtain a stress node weight matrix. The method effectively improves the accuracy of weight distribution, so that the importance of different stress nodes in water conservancy projects is reasonably reflected, thereby improving the accuracy of the overall prediction model. The stress node weight matrix is ​​used to map the potential mode of the water conservancy project to multidimensional state variables to obtain a multidimensional state variable matrix. This technical feature ensures that the potential mode of the water conservancy project is comprehensively characterized in multiple dimensions, and can capture the complex relationship between different state variables, laying a solid foundation for subsequent joint modeling. According to the multidimensional state variable matrix, the adjustment stress node and the potential mode of the water conservancy project are jointly modeled to obtain a water conservancy project prediction model. The method can generate a more accurate and more adaptable prediction model by integrating the joint modeling of stress node weights and multidimensional state variables, which can better reflect the actual operating status of the water conservancy project. The water conservancy operation trend is predicted using the water conservancy project prediction model, thereby generating a predicted water conservancy project operation trend. This technical feature enables the prediction results to more accurately reflect the future operating trends of water conservancy projects, provide a more reliable reference for decision makers, and help improve the management and scheduling efficiency of water conservancy projects.

[0022] The present invention also provides a water conservancy project operation trend prediction system, which is used to execute the water conservancy project operation trend prediction method as described above. The water conservancy project operation trend prediction system includes: The model simulation module is used to obtain multi-source water conservancy project data; perform multi-source spatial fusion processing on the multi-source water conservancy project data, and perform three-dimensional model simulation to obtain a three-dimensional model of the water conservancy project; Stress analysis module, used to identify the geometric complexity of the three-dimensional model of the water conservancy project, perform stress path analysis, and generate key nodes of the water conservancy project; The pattern mining module is used to obtain historical water conservancy data; perform time series conversion on historical water conservancy data and conduct potential pattern mining to obtain potential patterns of water conservancy projects; The iterative adjustment module is used to perform stress superposition comparison on the key nodes of the water conservancy project and the potential mode of the water conservancy project to obtain stress superposition comparison data; iteratively adjust the key nodes of the water conservancy project according to the stress superposition comparison data to generate adjusted stress nodes; The trend prediction module is used to construct a prediction model for adjusting stress nodes and potential modes of water conservancy projects, and to predict operation trends to generate predicted operation trends of water conservancy projects.

[0023] This system uses the model simulation module to perform multi-source spatial fusion processing on multi-source water conservancy project data, which can comprehensively consider the spatiotemporal characteristics of different data sources and build a more realistic and accurate three-dimensional water conservancy project model. This fusion of multi-source data combined with three-dimensional simulation enhances the comprehensive understanding of the overall situation of the water conservancy project and provides a reliable basis for subsequent analysis. The stress analysis module can effectively locate the key stress nodes in the project by identifying the geometric complexity of the three-dimensional model of the water conservancy project. This stress path analysis based on geometric complexity can accurately identify potential vulnerabilities in the structure of the water conservancy project, which helps to discover potential structural problems in advance and improve the safety and reliability of the project. The pattern mining module can extract potential patterns that have an important impact on the operation of future projects from historical water conservancy data through time series conversion and potential pattern mining. This pattern mining technology provides a key reference for the prediction of the operation trend of water conservancy projects, enabling the prediction model to better capture potential trend changes. The iterative adjustment module realizes the refined adjustment of key nodes through stress superposition comparison. Iterative adjustment of key nodes based on the comparison results helps optimize stress distribution, thereby further enhancing the structural stability of water conservancy projects and providing more accurate data support for trend prediction. The trend prediction module combines the adjusted stress nodes with potential patterns to build a more accurate operation trend prediction model. Through this prediction model, the system can dynamically predict the future operation trend of water conservancy projects and promptly warn of potential risks, thereby improving the scientificity and effectiveness of water conservancy project operation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the steps of a method for predicting the operation trend of a water conservancy project; Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0025] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve this, please refer to Figures 1 to 3 , a method for predicting the operation trend of a water conservancy project comprises the following steps: Step S1: acquiring multi-source water conservancy project data; performing multi-source spatial fusion processing on the multi-source water conservancy project data, and performing three-dimensional model simulation to obtain a three-dimensional model of the water conservancy project; Step S2: Identify the geometric complexity of the three-dimensional model of the water conservancy project, perform stress path analysis, and generate key nodes of the water conservancy project; Step S3: Obtain historical water conservancy data; perform time series conversion on the historical water conservancy data and perform potential pattern mining to obtain potential patterns of water conservancy projects; Step S4: performing stress superposition comparison on key nodes of the water conservancy project and potential modes of the water conservancy project to obtain stress superposition comparison data; iteratively adjusting the key nodes of the water conservancy project according to the stress superposition comparison data to generate adjusted stress nodes; Step S5: construct a prediction model for the adjustment stress nodes and the potential mode of the water conservancy project, and perform operation trend prediction to generate a predicted water conservancy project operation trend.

[0029] The present invention can fully integrate information from different data sources by acquiring and fusing multi-source water conservancy project data and performing three-dimensional model simulation. It realizes all-round modeling of the water conservancy project environment and effectively improves the accuracy and comprehensiveness of the model. The geometric complexity identification and stress path analysis of the three-dimensional model of the water conservancy project can accurately identify the key nodes in the project. This meticulous analysis helps to timely discover potential structural problems and improve the safety and stability of the project. By performing time series conversion and potential pattern mining on historical water conservancy data, potential operating modes can be extracted from the data. This pattern mining can reveal periodic trends and abnormal behaviors in historical data and provide a rich information basis for the construction of prediction models. The key nodes of the water conservancy project are stress-superimposed and compared with the potential patterns, and iterative adjustments are made according to the results, which can ensure the dynamic optimization of the stress state in the project. This method can make timely adjustments according to the actual stress situation and improve the ability to cope with emergencies. By constructing a prediction model for the adjusted stress nodes and potential patterns, a high-precision water conservancy project operation trend prediction can be generated. This prediction capability helps project managers to foresee problems in advance and take preventive measures, thereby improving the management efficiency and safety of water conservancy projects.

[0030] In the embodiment of the present invention, refer to Figure 1 , is a schematic diagram of the steps of the method for predicting the operation trend of a water conservancy project of the present invention. In this example, the method for predicting the operation trend of a water conservancy project includes the following steps: Step S1: acquiring multi-source water conservancy project data; performing multi-source spatial fusion processing on the multi-source water conservancy project data, and performing three-dimensional model simulation to obtain a three-dimensional model of the water conservancy project; In this embodiment, water conservancy project related data are obtained from a variety of data sources, including geographic information system (GIS) data, satellite remote sensing images, field sensor data (such as water level, flow sensors), and engineering design drawings. Using data acquisition equipment and systems, this information is obtained from different data sources and stored in a unified database. Next, multi-source spatial fusion processing is performed on these data. A spatial data fusion algorithm is used to align data from different sources to solve the problems of coordinate system differences and inconsistent resolution of the data. For example, using geospatial alignment technology, satellite images and field sensor data are aligned to ensure that they are in the same coordinate system. After completing the data fusion, the fused data is input into the model generation module using 3D modeling software (such as AutoCAD Civil 3D or Bentley MicroStation) for 3D model simulation.

[0031] Step S2: Identify the geometric complexity of the three-dimensional model of the water conservancy project, perform stress path analysis, and generate key nodes of the water conservancy project; In this embodiment, the geometric complexity of the generated three-dimensional model of the water conservancy project is identified. A detailed geometric analysis of the model is performed using three-dimensional modeling and analysis software (such as ANSYS or SolidWorks). The analysis includes calculating the geometric features of the model, such as complex surfaces, intersections, and thickness changes of the structure. The results of the complexity identification will mark the stress concentration areas, which are usually parts of the structure with complex geometric features or significant changes in material properties. Subsequently, a stress path analysis is performed to simulate the distribution of stress in the water conservancy project under different working conditions (such as maximum water flow, extreme weather conditions). Stress path analysis uses finite element analysis (FEA) technology to calculate the stress distribution of the structure under various loads and generate a stress path diagram to help identify key nodes. Key nodes are usually parts with stress concentration, insufficient bearing capacity, or higher risk.

[0032] Step S3: Obtain historical water conservancy data; perform time series conversion on the historical water conservancy data and perform potential pattern mining to obtain potential patterns of water conservancy projects; In this embodiment, historical water conservancy data is obtained, including past water level records, flow records, rainfall, and temperature. These historical data are organized into time series data. During the time series conversion process, the data is first arranged in chronological order to ensure that the time labels of the data are consistent, and missing values ​​and outliers are processed. Then, time series analysis techniques (such as seasonal decomposition and trend analysis) are applied to process and model the data. Statistical methods (such as autoregressive moving average model ARIMA) and machine learning algorithms (such as long short-term memory network LSTM) are used for potential pattern mining. These techniques can identify periodic changes, trends, and abnormal patterns in data. For example, the LSTM network identifies that a specific rainfall pattern can cause a significant change in water level.

[0033] Step S4: performing stress superposition comparison on key nodes of the water conservancy project and potential modes of the water conservancy project to obtain stress superposition comparison data; iteratively adjusting the key nodes of the water conservancy project according to the stress superposition comparison data to generate adjusted stress nodes; In this embodiment, stress superposition comparison is performed between stress data of key nodes and potential patterns. First, stress superposition analysis technology is used to comprehensively analyze stress data of key nodes and potential pattern data. Using the superimposed stress calculation formula, the potential patterns in historical data are superimposed on the stress of key nodes to identify the trend of stress changes and potential risk points. Based on the stress superposition comparison data, iterative adjustments are made to key nodes of water conservancy projects. The adjustment process includes optimizing the stress of key nodes to reduce potential stress concentration and structural risks. Specific adjustment operations include modifying structural parameters, material properties or design schemes, and generating optimized adjusted stress nodes through iterative calculations of finite element models.

[0034] Step S5: construct a prediction model for the adjustment stress nodes and the potential mode of the water conservancy project, and perform operation trend prediction to generate a predicted water conservancy project operation trend.

[0035] In this embodiment, the prediction model is constructed by adjusting the stress nodes and the potential patterns mined. First, a prediction model is constructed using a modeling tool (such as the Scikit-learn library in Python or MATLAB) based on the potential patterns in the adjusted stress node data and historical data. During the model construction process, regression analysis, time series prediction models, or deep learning methods (such as convolutional neural networks CNN or long short-term memory networks LSTM) are applied to simulate future operating trends. During the training process of the model, the adjusted stress node data and its corresponding environmental conditions need to be input to optimize the model parameters and improve the prediction accuracy. After the model is built, the operation trend is predicted, that is, the future working conditions and environmental data are input into the prediction model to generate the operation trend prediction results of the water conservancy project. The prediction results can provide a detailed analysis of the future operation status of the project, including risk points and performance change trends, and provide a scientific basis for project management and maintenance decisions.

[0036] Preferably, step S1 comprises the following steps: Step S11: Acquire multi-source water conservancy project data; Step S12: calibrating the multi-source water conservancy project data to obtain compatible water conservancy project data; Step S13: performing spatial data fusion on compatible water conservancy project data to generate fused spatial water conservancy data; Step S14: performing point cloud conversion processing on the fused spatial water conservancy data to generate water conservancy point cloud data; Step S15: Perform three-dimensional model simulation on the fused spatial water conservancy data according to the water conservancy point cloud data to obtain a three-dimensional model of the water conservancy project.

[0037] In this embodiment, water conservancy project related data are obtained from multiple data sources. These data sources include remote sensing satellite images, geographic information system (GIS) data, field sensor records, and engineering design drawings. The specific operations are as follows: First, the spatial data of the surface and water bodies are obtained through satellite images and aerial photography, and the terrain and structural features of the water conservancy project are extracted using remote sensing image processing technology. Secondly, real-time water level, flow, rainfall and other data are collected through sensors installed on site. These data are usually stored in CSV, JSON or XML format. Finally, structural parameters and design data are extracted from engineering design drawings. The data formats from different sources are converted into a unified standard format, such as converting CSV files and JSON files into a unified database table structure. In this process, all data are standardized to the same coordinate system and unit using data conversion tools or writing data conversion scripts (such as Python's pandas library) to ensure data consistency. Next, data cleaning techniques are used to process missing values ​​and outliers in the data. Spatial data registration technology is used to align data from different sources (such as remote sensing images and sensor data) into the same geographic coordinate system. The terrain data, structural data and hydrological data are combined using spatial data fusion tools (such as ArcGIS's spatial analysis tools). In this process, registration algorithms (such as affine transformation and projection transformation) are applied to align the data to resolve the coordinate and scale differences of different data sources. Next, the terrain data is combined with the sensor data to generate fused spatial water conservancy data. Finally, through the data fusion process, a comprehensive dataset containing terrain, structural and hydrological features is generated. The fused spatial data is processed using point cloud generation algorithms (such as LiDAR scanning or stereo vision technology) and converted into point cloud data. In the process of generating point cloud data, the coordinate values ​​of each point in the spatial data are extracted and recorded using 3D point cloud acquisition technology. For different data sources, such as terrain data and structural data, point cloud processing software (such as CloudCompare) is used to integrate the data to generate 3D point cloud data containing various parts of the water conservancy project. The generated point cloud data is input into 3D modeling software (such as AutoCAD Civil 3D or Bentley MicroStation) and the point cloud data is used to reconstruct the 3D model. Through the spatial distribution and feature information of point cloud data, a 3D geometric model of the water conservancy project is established, including structures such as dams, canals, and drainage systems. Specific operations include noise filtering of point cloud data, gridding of point cloud data, and texture mapping of the 3D model. Finally, the processed and optimized 3D model is output to form a detailed 3D model of the water conservancy project.

[0038] Preferably, step S15 comprises the following steps: Step S151: extract dynamic data from the fused spatial water conservancy data to obtain dynamic water conservancy data; Step S152: performing time series analysis on dynamic water conservancy data to generate a continuous dynamic water flow field; Step S153: performing fluid dynamics calculation on the continuous dynamic water flow field to obtain a water flow simulation parameter set; Step S154: constructing a three-dimensional model of the fused spatial hydraulic data according to the water flow simulation parameter set to obtain an initial three-dimensional hydraulic model; Step S155: performing spatial coordinate standardization processing on the water conservancy point cloud data to obtain point cloud data in a unified coordinate system; Step S156: Perform surface point cloud mapping on the initial three-dimensional water conservancy model based on the unified coordinate system point cloud data to generate a three-dimensional model of the water conservancy project.

[0039] In this embodiment, a data extraction tool is used to extract time-related dynamic data from the fused spatial data set, such as real-time water flow, rainfall, and water level changes. In order to ensure the dynamic characteristics of the data, a data stream processing platform (such as Apache Kafka) can be used to stream real-time data, extract and store data from a data warehouse or a real-time sensor system. Through a data processing script (such as the Pandas library in Python), a data set at each time point is extracted from the fused data to generate dynamic water conservancy data. A time series analysis tool (such as the "forecast" package in R language or the "statsmodels" library in Python) is used to process dynamic water conservancy data. The trend, seasonality, and cyclical factors in dynamic water conservancy data are separated by time series decomposition technology. The process of time series analysis includes building a time series model (such as an ARIMA model) to model and predict dynamic data such as water flow, rainfall, and water level. The results of the analysis will generate a continuous dynamic water flow field, describing the changes in water flow at different time points and spatial positions. A numerical model containing a water flow field is established, and simulation is performed by setting boundary conditions and initial conditions. Fluid dynamics calculations include solving parameters such as velocity field, pressure field and eddy field of water flow. In the calculation process, numerical methods (such as finite volume method) are used to discretize fluid dynamics equations (such as Navier-Stokes equations) to obtain water flow simulation parameter sets. The water flow simulation parameter set is applied to the fused spatial water conservancy data using 3D modeling software (such as AutoCAD Civil 3D or Bentley MicroStation). The behavior of water flow in 3D space is modeled by setting boundary conditions and physical properties in the water flow model. The process of 3D model construction includes accurate modeling of water flow path, velocity distribution and water flow dynamics. According to the water flow simulation parameters, an initial 3D water conservancy model is constructed to simulate the actual behavior of water flow in water conservancy projects, including the water flow dynamics of structures such as dams, channels and pumping stations. Point cloud processing tools (such as CloudCompare or Meshlab) are used to transform the coordinates of point cloud data. By setting a unified coordinate system, the coordinates of each point in the point cloud data are transformed into a standard coordinate system. Coordinate standardization processing includes aligning the coordinate system of the point cloud data with the coordinate system of the fused spatial water conservancy data. Use spatial registration algorithms (such as ICP algorithm, iterative closest point algorithm) to register and adjust the point cloud data to ensure that all point cloud data are unified in the same coordinate system. Input the point cloud data in the unified coordinate system into the 3D modeling software (such as 3ds Max or Blender), and perform surface point cloud mapping on the initial 3D water conservancy model. Use the surface fitting technology of point cloud data to map the point cloud data to the surface of the initial 3D water conservancy model. During the mapping process, apply the point cloud to surface mapping algorithm (such as point cloud reconstruction algorithm) to align the point cloud data with the surface of the 3D model.Ultimately, the resulting 3D model of the hydraulic project will accurately reflect the actual measurements of the point cloud data, including detailed surface features of water flow, structure, and terrain.

[0040] Preferably, step S2 comprises the following steps: Step S21: performing grid topology analysis on the three-dimensional model of the water conservancy project to obtain discrete grid data; Step S22: performing independent grid curvature calculation on discrete grid data to generate a geometric feature matrix; Step S23: performing multi-scale shape description processing on the three-dimensional model of the water conservancy project based on the geometric feature matrix to obtain a local shape feature vector; Step S24: performing geometry-stress coupling node identification on the three-dimensional model of the water conservancy project according to the local shape feature vector, and generating key nodes of the water conservancy project.

[0041] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: performing grid topology analysis on the three-dimensional model of the water conservancy project to obtain discrete grid data; In this embodiment, a grid topology structure analysis is performed on the three-dimensional model of the water conservancy project. The three-dimensional model is discretized using a grid generation tool (such as GMSH or TetGen) to decompose the model into multiple small grid units (such as tetrahedrons, hexahedrons, etc.). The core task of grid topology structure analysis is to identify the various units in the grid and their connection relationships, including the relationship between nodes, edges and faces. Through topological analysis, discrete grid data is obtained, including the node coordinates, boundary information and connection relationships of each grid unit.

[0042] Step S22: performing independent grid curvature calculation on discrete grid data to generate a geometric feature matrix; In this embodiment, independent grid curvature calculation is performed on discrete grid data. Each grid unit is processed using a curvature calculation method (e.g., based on the Laplacian smoothing algorithm or Gaussian curvature calculation). Specifically, a mathematical tool (such as the Curve Fitting Toolbox in MATLAB or the SciPy library in Python) is used to fit the local area of ​​the grid to calculate the curvature value of each grid unit. The calculation results generate a geometric feature matrix, which contains the curvature information and geometric features (such as concave-convex degree, surface smoothness, etc.) of each grid unit.

[0043] Step S23: performing multi-scale shape description processing on the three-dimensional model of the water conservancy project based on the geometric feature matrix to obtain a local shape feature vector; In this embodiment, a multi-scale shape description process is performed on the three-dimensional model of the water conservancy project based on the geometric feature matrix. The geometric feature matrix is ​​processed using a multi-scale analysis technique (such as fractal analysis or scale space method) to extract local shape features. The specific method includes encoding the geometric features of each grid unit using a shape descriptor (such as feature extraction based on principal component analysis). By analyzing the geometric features at different scales, local shape feature vectors are generated, which describe the shape information of the water conservancy project model at different scales.

[0044] Step S24: performing geometry-stress coupling node identification on the three-dimensional model of the water conservancy project according to the local shape feature vector, and generating key nodes of the water conservancy project.

[0045] In this embodiment, the local shape feature vector is processed using a geometry-stress coupling analysis method (such as a coupling model in finite element analysis). By combining shape features and stress distribution information, nodes with key stress responses in the model are identified. The specific method includes using a stress-shape coupling algorithm (such as stress concentration analysis) to analyze the nodes and determine their importance in the operation of the project. Finally, a list of key nodes of the water conservancy project is generated.

[0046] Preferably, step S24 comprises the following steps: Cluster the local shape feature vectors to obtain the geometric complexity distribution map; According to the geometric complexity distribution map, the finite element mesh of the three-dimensional model of the water conservancy project is refined to obtain an optimized mesh model; Perform nonlinear finite element analysis on the optimized mesh model to generate an initial stress distribution diagram; A stress tensor field is constructed according to the initial stress distribution diagram to obtain a continuous stress distribution function; Perform minimum path calculation on the continuous stress distribution function to generate the principal stress path; Based on the principal stress path, stress coupling nodes of the three-dimensional model of the water conservancy project are identified to generate key nodes of the water conservancy project.

[0047] In this embodiment, the geometric complexity distribution map of the three-dimensional model of the water conservancy project is determined by clustering analysis of the local shape feature vectors. The local shape feature vectors are processed using the K-means clustering algorithm or DBSCAN (Density-Based Spatial Clustering of Applications with Noise). First, the local shape feature vectors are standardized using a feature extraction tool (such as Python's Scikit-learn library) to eliminate the effects of different scales. Subsequently, these vectors are grouped by a clustering algorithm to obtain different geometric complexity categories. The geometric complexity of each category is visualized on the three-dimensional model to generate a geometric complexity distribution map. According to the geometric complexity distribution map, the finite element mesh of the three-dimensional model of the water conservancy project is refined to obtain an optimized mesh model. The three-dimensional model is processed using a finite element mesh generation tool (such as Abaqus or ANSYS Meshing). First, the high complexity areas of the geometric complexity distribution map are set as refinement areas to increase the mesh density of these areas. For low complexity areas, the roughness of the mesh can be maintained. By setting mesh refinement parameters (such as mesh size and refinement ratio), smaller mesh elements are generated in complex areas, while larger mesh elements are maintained in simple areas. Select nonlinear finite element analysis tools (such as ABAQUS or COMSOL Multiphysics) to analyze the optimized mesh model. Set analysis parameters, including material nonlinearity, boundary conditions, and load conditions. By solving nonlinear equations, the stress distribution of the model under different loads is obtained. Finally, an initial stress distribution diagram is generated, which shows the stress intensity and distribution in different areas. During the analysis, different stress indicators (such as principal stress and shear stress) are used to obtain comprehensive stress information. The initial stress distribution diagram is processed using tensor field construction tools (such as MATLAB's Partial Differential Equation Toolbox or Python's NumPy library). First, the discrete initial stress data is converted into a continuous stress tensor field by interpolation methods (such as spline interpolation). Then, a continuous stress distribution function is constructed using numerical calculation methods (such as finite element method or finite difference method) to describe the stress distribution of the model at each point. The minimum path calculation is performed on the continuous stress distribution function to generate the principal stress path. The stress tensor field is processed using the shortest path algorithm (such as the Dijkstra algorithm or the A* algorithm). First, the stress tensor field is discretized into a grid and the stress gradient is set as the path weight. Then, the shortest path algorithm is applied starting from the boundary or critical area of ​​the model to calculate the stress variation along the minimum stress path and finally generate the principal stress path diagram. The stress path analysis results (such as the principal stress path and stress distribution diagram) are used to screen the nodes in the three-dimensional model.Apply stress coupling algorithms (such as stress concentration analysis or fatigue analysis) to identify nodes near the main stress path, which play a key role in the stress transfer process. By calculating the stress response of these nodes, their importance in the structure is determined. The generated list of key nodes provides high-risk areas in the hydraulic engineering model, guiding subsequent structural optimization and operation trend prediction. For example, nodes located in the main stress concentration area are marked as key nodes for further analysis and design improvement.

[0048] Preferably, step S3 comprises the following steps: Step S31: Acquire historical water conservancy data; perform equal-interval time series conversion on the historical water conservancy data to obtain an equal-interval time series; Step S32: performing wavelet transform processing on the equally spaced time series to generate multi-scale time-frequency features; Step S33: constructing a continuous barcode for the multi-scale time-frequency features to obtain a topological continuous graph; Step S34: performing popular learning mapping on the topological persistence graph to generate low-dimensional embedded water conservancy data; Step S35: performing manifold folding analysis on the low-dimensional embedded water conservancy data to obtain a water conservancy geometric feature descriptor; Step S36: Perform density peak clustering on the water conservancy geometric feature descriptors to generate potential patterns of water conservancy projects.

[0049] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: Acquire historical water conservancy data; perform equal-interval time series conversion on the historical water conservancy data to obtain an equal-interval time series; In this embodiment, historical water conservancy data is obtained from historical record documents and converted into equally spaced time series, where the data includes indicators such as water level, flow, and precipitation. In order to ensure the consistency and comparability of the time series, the data needs to be converted into an equally spaced time series. Linear interpolation or spline interpolation technology is used to smoothly map data points with irregular time intervals to uniform time intervals. For example, if the time intervals of the original data are inconsistent (such as every hour or every half hour), the missing values ​​are filled in by linear interpolation method, and all data are adjusted to hourly time intervals. Step S32: performing wavelet transform processing on the equally spaced time series to generate multi-scale time-frequency features; In this embodiment, wavelet transform is performed on the equally spaced time series to extract multi-scale time-frequency features. First, a suitable wavelet function (such as Daubechies wavelet or Haar wavelet) is selected for discrete wavelet transform. The processed time series is decomposed into subsequences of different frequencies to capture the changing characteristics of the data at different scales. Each subsequence represents a different frequency component of the data, which can help identify periodic patterns and short-term fluctuations. By analyzing the wavelet coefficients, the time-frequency characteristics of the time series at multiple scales are obtained, for example, periodic changes in water flow or sudden flood events.

[0050] Step S33: constructing a continuous barcode for the multi-scale time-frequency features to obtain a topological continuous graph; In this embodiment, a topological data analysis tool is used to construct a persistent barcode for multi-scale time-frequency features and generate a topological persistence map. The Ripser library is used to calculate the persistence barcode. First, a point cloud consisting of time-frequency feature data is created, and then its persistence homology is calculated. The ripser function in Ripser is used to input the time-frequency feature data and calculate the persistence of each feature. A persistent barcode is generated to record the appearance and disappearance of the feature at different scales. The barcode is visualized through the Matplotlib library to obtain a topological persistence map.

[0051] Step S34: performing popular learning mapping on the topological persistence graph to generate low-dimensional embedded water conservancy data; In this embodiment, the topological persistence graph is subjected to popular learning mapping to generate low-dimensional embedded water conservancy data. The UMAP (Uniform Manifold Approximation and Projection) algorithm is used to reduce the dimension of the topological persistence graph data to reduce the dimension of the data for subsequent analysis. Select the UMAP function in the UMAP library, set the target dimension of dimensionality reduction (for example, two-dimensional or three-dimensional), and apply the topological persistence graph data for mapping. Generate low-dimensional embedded data by adjusting the hyperparameters of the UMAP algorithm (such as n_neighbors and min_dist).

[0052] Step S35: performing manifold folding analysis on the low-dimensional embedded water conservancy data to obtain a water conservancy geometric feature descriptor; In this embodiment, manifold folding analysis is performed on low-dimensional embedded water conservancy data to extract water conservancy geometric feature descriptors. Manifold learning tools (such as Isomap or LLE algorithm in Scikit-learn) are applied to analyze low-dimensional embedded data to identify the manifold structure in the data. The hyperparameters of the manifold learning algorithm, such as neighborhood size and minimum distance, are set to process the data to reveal its intrinsic geometric features. Through manifold folding analysis, the geometric feature descriptors of each data point are obtained, and these descriptors represent the geometric structure characteristics of the water conservancy data.

[0053] Step S36: Perform density peak clustering on the water conservancy geometric feature descriptors to generate potential patterns of water conservancy projects.

[0054] In this embodiment, the water conservancy geometric feature descriptors are clustered by density peaks to identify the potential patterns of water conservancy projects. The MeanShift or DBSCAN algorithm in the Scikit-learn library is used for density clustering analysis, and appropriate parameters (such as bandwidth or distance threshold) are set to find density peaks in the data. The clustering algorithm is applied to process the geometric feature descriptors to identify different cluster centers. The potential pattern of water conservancy projects is generated to represent the main structural patterns and features in the given data.

[0055] Preferably, step S41: constructing a three-dimensional stress matrix for key nodes of a water conservancy project to obtain a stress matrix for key nodes; Step S42: dynamically loading the stress matrix of key nodes according to the potential mode of the water conservancy project to obtain stress node response data; Step S43: decomposing the stress node response data into stress components to generate a decomposed node stress vector set; Step S44: performing stress superposition comparison on the decomposed node stress vector set to obtain stress superposition comparison data; Step S45: iteratively adjust the stress matrix of key nodes based on the stress superposition comparison data to generate adjusted stress nodes.

[0056] In this embodiment, the key nodes marked in the three-dimensional model of the water conservancy project are used to construct a three-dimensional stress matrix based on the geometric and physical properties of these nodes. During the construction of the stress matrix, a suitable finite element analysis tool, such as ABAQUS or ANSYS, is selected to perform stress analysis. A small area is defined around each key node, and the area is divided into multiple finite element units. Then, the preset boundary conditions and load conditions are applied to calculate the stress distribution of each unit. Finally, these calculation results are integrated into the stress matrix of each key node to form a three-dimensional stress matrix, which contains stress component data in all directions. The dynamic load conditions defined in the potential mode of the water conservancy project are extracted, such as different flow fluctuations or water pressure changes. Then, these dynamic load conditions are applied to the constructed three-dimensional stress matrix. Using a dynamic loading analysis tool, such as the dynamic analysis module of ABAQUS, the load conditions at different time points are imported into the model for analysis. During the analysis process, the response data of each key node under different dynamic load conditions are recorded, including information such as displacement and stress. The stress node response data finally generated reflects the actual stress changes of each key node under dynamic load. The principal stress decomposition method is used to decompose the comprehensive stress in the stress node response data into principal stress components (maximum principal stress, minimum principal stress and intermediate principal stress). The stress components of each node in different directions are calculated by linear algebra methods, and the complex stress state is converted into a simplified principal stress vector set. Stress component decomposition can generate a stress component data set containing all key nodes by using tools such as matrix operation functions in MATLAB. Select a comparison method, such as stress superposition analysis, to compare the decomposed node stress vector set with the expected stress state. Use the superposition analysis tool to calculate the superposition effect of stress vectors at each key node. For example, compare the stress changes recorded in the historical data with the current stress vector to evaluate the impact of stress superposition on the node. Analyze the stress concentration area or abnormal node found in the stress superposition comparison data. Then, adjust the stress value in the three-dimensional stress matrix to optimize the stress distribution and reduce the stress concentration phenomenon. Through the iterative calculation tool, gradually adjust the parameters in the stress matrix, and re-perform dynamic loading processing to verify the adjustment effect. After each adjustment, the stress node response data must be regenerated and a new stress superposition comparison must be performed. Finally, based on the results of repeated adjustments and verifications, the optimized adjusted stress nodes are obtained to form the final stress matrix.

[0057] Preferably, step S45 includes the following steps: The principal component analysis of the stress superposition comparison data was performed to obtain the main stress components; The pressure sensitivity matrix of the main stress components is constructed to generate the node pressure sensitivity; Based on the preset adjustment priority data and node pressure sensitivity, the stress matrix of key nodes is adjusted and sorted to obtain a priority adjustment sequence; Perform conjugate gradient calculation on the priority adjustment sequence to generate the adjustment direction; Perform line search processing on the priority adjustment sequence according to the adjustment direction to generate an adjustment step length; Perform gradient descent calculation on the priority adjustment sequence according to the adjustment direction and adjustment step length to obtain the stress update amount; Iteratively adjust the stress matrix of key nodes based on the stress update amount to generate adjusted stress nodes; Perform a convergence check on the adjusted stress node. When the convergence state is a non-convergence state, repeat the above steps; when the convergence state is a convergence state, output the adjusted stress node.

[0058] In this embodiment, principal component analysis is performed on the stress superposition comparison data. Principal component analysis (PCA) converts multidimensional stress data into several main stress components by calculating the eigenvalues ​​and eigenvectors of the covariance matrix of the stress superposition comparison data. This process uses linear algebra tools to calculate the covariance matrix of the data and obtain the principal components by eigenvalue decomposition. The tool for principal component analysis can be the pca function in MATLAB or the sklearn.decomposition.PCA module in Python. The pressure sensitive matrix is ​​constructed for the main stress components. When constructing the pressure sensitive matrix, the sensitivity of each node to stress changes is calculated based on the main stress components. This process obtains the sensitivity index of each node to stress changes by performing matrix operations on the changes in the main stress components. Mathematical tools such as the matrix operation function of MATLAB or the NumPy library of Python can be used to process matrix calculations, and finally generate a node pressure sensitivity matrix. According to the preset adjustment priority data, the nodes that need to be adjusted first are determined, and then these nodes are sorted according to the node pressure sensitivity. The basis for sorting is the size of the node pressure sensitivity, and the nodes with higher sensitivity are adjusted first. The sorting process uses a sorting algorithm, such as quick sort or merge sort, to generate a prioritized adjustment sequence. A conjugate gradient calculation is performed on the prioritized adjustment sequence. The conjugate gradient method is used to solve optimization problems. The adjustment direction is determined by iterative calculation. The conjugate gradient direction is calculated using an optimization tool, usually through iterative updates, using a calculation tool such as MATLAB's conjugateGradient function or Python's scipy.optimize module. The obtained adjustment direction will be used to determine how to modify the stress matrix. A line search process is performed on the prioritized adjustment sequence based on the adjustment direction. The line search process determines the best step size in the conjugate gradient direction to minimize the objective function. When performing a line search, an initial range of step sizes is first defined, and then the objective function value is calculated by gradually adjusting the step size to determine the best step size. This can be done by a line search algorithm, such as the golden section method or Newton's method, to obtain a suitable adjustment step size. A gradient descent calculation is performed on the prioritized adjustment sequence based on the adjustment direction and the adjustment step size. During the gradient descent calculation, the stress matrix value is updated using the adjustment direction and step size. The update process includes calculating the gradient of the stress and adjusting the step size along the adjustment direction. Gradient descent calculations can use numerical optimization tools such as MATLAB's fminunc function or Python's scipy.optimize module to generate stress updates. Based on the stress updates, the stress matrix of key nodes is iteratively adjusted to generate adjusted stress nodes. The adjustment process includes applying the stress update to the stress matrix and calculating the new stress node values. During the iterative adjustment process, the above steps need to be repeated until the convergence conditions are met. Convergence testing can be done by setting a convergence threshold, calculating the change in the stress matrix update, and determining whether it is below the threshold.If convergence has not occurred, repeat the above steps; if convergence has occurred, output the adjusted stress nodes.

[0059] Preferably, step S5 comprises the following steps: Step S51: performing distributed weight distribution processing on the adjusted stress nodes based on preset weight distribution data to obtain a stress node weight matrix; Step S52: performing multi-dimensional state variable mapping on the potential mode of the water conservancy project according to the stress node weight matrix to obtain a multi-dimensional state variable matrix; Step S53: performing joint modeling processing on the adjustment stress nodes and the potential mode of the water conservancy project according to the multi-dimensional state variable matrix to obtain a water conservancy project prediction model; Step S54: using the water conservancy project prediction model to predict the water conservancy project operation trend and generate a predicted water conservancy project operation trend.

[0060] In this embodiment, the weight allocation rule of each stress adjustment node is determined according to the preset weight allocation data. The weighted average method is used to apply the weight data to each node to generate a stress node weight matrix. The specific operation includes: assigning corresponding weight values ​​to each node, and arranging these values ​​in matrix form to form a stress node weight matrix. During the processing, matrix operation tools such as the matrix multiplication function in MATLAB or the NumPy library in Python can be used to perform matrix calculations. According to the data relationship between the stress node weight matrix and the potential mode of the water conservancy project, the potential mode data is converted into a multidimensional state variable matrix through a multidimensional mapping algorithm. This process includes: defining a mapping rule for a multidimensional state variable, and converting the potential mode data into a state variable through a mapping function. For example, the merge function in the pandas library of Python can be used to combine the potential mode data with the weight matrix data to generate a multidimensional state variable matrix. According to the multidimensional state variable matrix, the stress adjustment node and the potential mode of the water conservancy project are jointly modeled to obtain a water conservancy project prediction model. The joint modeling process includes: combining the multidimensional state variable matrix with the stress adjustment node data, and generating a water conservancy project prediction model through regression analysis or a machine learning algorithm. The specific operation can use machine learning tools such as LinearRegression or RandomForestRegressor in Scikit-learn, take the state variable matrix as input features, adjust the stress node as the target variable, and perform model training and verification. Use the water conservancy project prediction model to predict the water conservancy operation trend and generate the predicted water conservancy project operation trend. The prediction process includes: inputting the actual water conservancy project data into the generated prediction model, and obtaining the prediction results of the water conservancy operation through model calculation. The specific operation includes: using the predict function of the model, inputting new data into the model, and generating prediction results.

[0061] The present invention also provides a water conservancy project operation trend prediction system, which is used to execute the water conservancy project operation trend prediction method as described above. The water conservancy project operation trend prediction system includes: The model simulation module is used to obtain multi-source water conservancy project data; perform multi-source spatial fusion processing on the multi-source water conservancy project data, and perform three-dimensional model simulation to obtain a three-dimensional model of the water conservancy project; Stress analysis module, used to identify the geometric complexity of the three-dimensional model of the water conservancy project, perform stress path analysis, and generate key nodes of the water conservancy project; The pattern mining module is used to obtain historical water conservancy data; perform time series conversion on historical water conservancy data and conduct potential pattern mining to obtain potential patterns of water conservancy projects; The iterative adjustment module is used to perform stress superposition comparison on the key nodes of the water conservancy project and the potential mode of the water conservancy project to obtain stress superposition comparison data; iteratively adjust the key nodes of the water conservancy project according to the stress superposition comparison data to generate adjusted stress nodes; The trend prediction module is used to construct a prediction model for adjusting stress nodes and potential modes of water conservancy projects, and to predict operation trends to generate predicted operation trends of water conservancy projects.

[0062] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0063] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for predicting the operation trend of a water conservancy project, characterized in that: The following steps are involved: Step S1: acquiring multi-source water conservancy project data; performing multi-source spatial fusion processing on the multi-source water conservancy project data, and performing three-dimensional model simulation to obtain a three-dimensional model of the water conservancy project; Step S2: Identify the geometric complexity of the three-dimensional model of the water conservancy project, perform stress path analysis, and generate key nodes of the water conservancy project; Step S3: Obtain historical water conservancy data; perform time series conversion on the historical water conservancy data and perform potential pattern mining to obtain potential patterns of water conservancy projects; Step S4: performing stress superposition comparison on key nodes of the water conservancy project and potential modes of the water conservancy project to obtain stress superposition comparison data; iteratively adjusting the key nodes of the water conservancy project according to the stress superposition comparison data to generate adjusted stress nodes; Step S5: construct a prediction model for the adjustment stress nodes and the potential mode of the water conservancy project, and perform operation trend prediction to generate a predicted water conservancy project operation trend.

2. The method for predicting the operation trend of a water conservancy project according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire multi-source water conservancy project data; Step S12: calibrating the multi-source water conservancy project data to obtain compatible water conservancy project data; Step S13: performing spatial data fusion on compatible water conservancy project data to generate fused spatial water conservancy data; Step S14: performing point cloud conversion processing on the fused spatial water conservancy data to generate water conservancy point cloud data; Step S15: Perform three-dimensional model simulation on the fused spatial water conservancy data according to the water conservancy point cloud data to obtain a three-dimensional model of the water conservancy project.

3. The method for predicting the operation trend of a water conservancy project according to claim 2, characterized in that: Step S15 includes the following steps: Step S151: extract dynamic data from the fused spatial water conservancy data to obtain dynamic water conservancy data; Step S152: performing time series analysis on dynamic water conservancy data to generate a continuous dynamic water flow field; Step S153: performing fluid dynamics calculation on the continuous dynamic water flow field to obtain a water flow simulation parameter set; Step S154: constructing a three-dimensional model of the fused spatial hydraulic data according to the water flow simulation parameter set to obtain an initial three-dimensional hydraulic model; Step S155: performing spatial coordinate standardization processing on the water conservancy point cloud data to obtain point cloud data in a unified coordinate system; Step S156: Perform surface point cloud mapping on the initial three-dimensional water conservancy model based on the unified coordinate system point cloud data to generate a three-dimensional model of the water conservancy project.

4. The method for predicting the operation trend of a water conservancy project according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing grid topology analysis on the three-dimensional model of the water conservancy project to obtain discrete grid data; Step S22: performing independent grid curvature calculation on discrete grid data to generate a geometric feature matrix; Step S23: performing multi-scale shape description processing on the three-dimensional model of the water conservancy project based on the geometric feature matrix to obtain a local shape feature vector; Step S24: performing geometry-stress coupling node identification on the three-dimensional model of the water conservancy project according to the local shape feature vector, and generating key nodes of the water conservancy project.

5. The method for predicting the operation trend of a water conservancy project according to claim 4, characterized in that: Step S24 includes the following steps: Cluster the local shape feature vectors to obtain the geometric complexity distribution map; According to the geometric complexity distribution map, the finite element mesh of the three-dimensional model of the water conservancy project is refined to obtain an optimized mesh model; Perform nonlinear finite element analysis on the optimized mesh model to generate an initial stress distribution diagram; A stress tensor field is constructed according to the initial stress distribution diagram to obtain a continuous stress distribution function; Perform minimum path calculation on the continuous stress distribution function to generate the principal stress path; Based on the principal stress path, stress coupling nodes of the three-dimensional model of the water conservancy project are identified to generate key nodes of the water conservancy project.

6. The method for predicting the operation trend of a water conservancy project according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Acquire historical water conservancy data; perform equal-interval time series conversion on the historical water conservancy data to obtain an equal-interval time series; Step S32: performing wavelet transform processing on the equally spaced time series to generate multi-scale time-frequency features; Step S33: constructing a continuous barcode for the multi-scale time-frequency features to obtain a topological continuous graph; Step S34: performing popular learning mapping on the topological persistence graph to generate low-dimensional embedded water conservancy data; Step S35: performing manifold folding analysis on the low-dimensional embedded water conservancy data to obtain a water conservancy geometric feature descriptor; Step S36: Perform density peak clustering on the water conservancy geometric feature descriptors to generate potential patterns of water conservancy projects.

7. The method for predicting the operation trend of a water conservancy project according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: constructing a three-dimensional stress matrix for key nodes of the water conservancy project to obtain a stress matrix for the key nodes; Step S42: dynamically loading the stress matrix of key nodes according to the potential mode of the water conservancy project to obtain stress node response data; Step S43: decomposing the stress node response data into stress components to generate a decomposed node stress vector set; Step S44: performing stress superposition comparison on the decomposed node stress vector set to obtain stress superposition comparison data; Step S45: iteratively adjust the stress matrix of key nodes based on the stress superposition comparison data to generate adjusted stress nodes.

8. The method for predicting the operation trend of a water conservancy project according to claim 7, characterized in that: Step S45 includes the following steps: The principal component analysis of the stress superposition comparison data was performed to obtain the main stress components; The pressure sensitivity matrix of the main stress components is constructed to generate the node pressure sensitivity; Based on the preset adjustment priority data and node pressure sensitivity, the stress matrix of key nodes is adjusted and sorted to obtain a priority adjustment sequence; Perform conjugate gradient calculation on the priority adjustment sequence to generate the adjustment direction; Perform line search processing on the priority adjustment sequence according to the adjustment direction to generate an adjustment step length; Perform gradient descent calculation on the priority adjustment sequence according to the adjustment direction and adjustment step length to obtain the stress update amount; Iteratively adjust the stress matrix of key nodes based on the stress update amount to generate adjusted stress nodes; Perform a convergence check on the adjusted stress node. When the convergence state is a non-convergence state, repeat the above steps; when the convergence state is a convergence state, output the adjusted stress node.

9. The method for predicting the operation trend of a water conservancy project according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: performing distributed weight distribution processing on the adjusted stress nodes based on preset weight distribution data to obtain a stress node weight matrix; Step S52: performing multi-dimensional state variable mapping on the potential mode of the water conservancy project according to the stress node weight matrix to obtain a multi-dimensional state variable matrix; Step S53: performing joint modeling processing on the adjustment stress nodes and the potential mode of the water conservancy project according to the multi-dimensional state variable matrix to obtain a water conservancy project prediction model; Step S54: using the water conservancy project prediction model to predict the water conservancy project operation trend and generate a predicted water conservancy project operation trend.

10. A water conservancy project operation trend prediction system, characterized in that: Used to execute the water conservancy project operation trend prediction method as claimed in claim 1, the water conservancy project operation trend prediction system comprises: The model simulation module is used to obtain multi-source water conservancy project data; perform multi-source spatial fusion processing on the multi-source water conservancy project data, and perform three-dimensional model simulation to obtain a three-dimensional model of the water conservancy project; Stress analysis module, used to identify the geometric complexity of the three-dimensional model of the water conservancy project, perform stress path analysis, and generate key nodes of the water conservancy project; The pattern mining module is used to obtain historical water conservancy data; perform time series conversion on historical water conservancy data and conduct potential pattern mining to obtain potential patterns of water conservancy projects; The iterative adjustment module is used to perform stress superposition comparison on the key nodes of the water conservancy project and the potential mode of the water conservancy project to obtain stress superposition comparison data; iteratively adjust the key nodes of the water conservancy project according to the stress superposition comparison data to generate adjusted stress nodes; The trend prediction module is used to construct a prediction model for adjusting stress nodes and potential modes of water conservancy projects, and to predict operation trends to generate predicted operation trends of water conservancy projects.

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