Hydrological digital twinborn model construction method and system based on artificial intelligence technology

Abstract

The invention discloses a hydrological digital twinborn model construction method and system based on an artificial intelligence technology, and relates to the technical field of hydrological monitoring and intelligent early warning, and the method comprises the steps: constructing a hydrological element measuring point monitoring model fusing multi-source sensor data, and introducing a graph convolution network to capture the complex correlation between environmental factors and hydrological elements; constructing a mapping model by using an improved long-short term memory neural network, and designing a multi-target dynamic monitoring loss function to balance data precision and space consistency; a measuring point prediction value and a global simulation value are fused through domain self-adaptive adversarial training, a basic hydrological digital twinborn model is generated, and an interaction model is constructed in combination with an extreme working condition parameter migration strategy; and constructing an inference mechanism based on the hydrological disaster knowledge graph, and outputting an entity engineering disposal scheme. The problems that a traditional hydrological model is weak in generalization ability, insufficient in data fusion, poor in extreme working condition adaptability and the like are solved, and high-precision real-time sensing and intelligent decision making in the hydrological process are achieved.

Description

Technical Field

[0001] This invention relates to the field of hydrological monitoring and intelligent early warning technology, and more specifically to a method and system for constructing a hydrological digital twin model based on artificial intelligence technology. Background Technology

[0002] Hydrological systems are crucial carriers of the water resource cycle, and their operational status directly affects flood control safety, water resource utilization efficiency, and ecological balance. In recent years, influenced by global climate change and human activities, extreme hydrological events have become more frequent, placing higher demands on the real-time performance of hydrological monitoring, forecast accuracy, and emergency response capabilities.

[0003] Currently, hydrological monitoring and forecasting mainly rely on three types of technical means: First, traditional statistical models, such as the unit hydrograph method and hydrological frequency analysis. These models rely on empirical assumptions, making it difficult to capture the complex nonlinear relationships between hydrological elements and have weak generalization ability. Second, numerical simulation methods, such as the finite element method and the finite difference method, can reflect the mechanism of hydrological processes, but they have high computational complexity, poor real-time performance, and are difficult to effectively integrate monitoring data from multiple sources of sensors. Third, shallow machine learning models, such as support vector machines and random forests, can handle some nonlinear problems, but they lack the ability to extract features from high-dimensional hydrological data, and the prediction accuracy drops significantly under extreme conditions.

[0004] The development of deep learning technologies (such as LSTM and GCN) has provided new pathways for hydrological forecasting, but limitations remain: on the one hand, models are mostly designed for single hydrological elements, making it difficult to achieve collaborative forecasting of multiple elements; on the other hand, data scarcity under extreme conditions leads to insufficient model generalization ability. Digital twin technology, as an important means of virtual-real fusion, is still in its early stages of application in the hydrological field, facing technical bottlenecks such as difficulties in digitizing physical models, insufficient fusion of multi-source data, and weak digital-physical interaction feedback. In summary, existing technologies are insufficient to meet the high-precision, real-time, and intelligent monitoring and early warning needs of hydrological systems.

[0005] Therefore, how to propose a method and system for constructing a hydrological digital twin model based on artificial intelligence technology, and overcome the shortcomings of existing technologies, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention provides a method and system for constructing a hydrological digital twin model based on artificial intelligence technology, aiming to solve core technical problems such as the digitization of physical models of hydrological systems, deep fusion of multi-source data, adaptation to extreme operating conditions, and intelligent decision feedback, and to overcome the limitations of traditional methods in terms of real-time performance, accuracy, and generalization ability. To achieve the above objectives, the present invention adopts the following technical solution: A method for constructing a hydrological digital twin model based on artificial intelligence technology includes: Construct a monitoring model for hydrological element monitoring points based on multi-source sensor monitoring data; Using a long short-term memory neural network framework with spatial correlation constraints, we learn the mapping relationship between hydrological parameters of sensor measuring points and corresponding finite element nodes, as well as the information transmission law between sensor measured values ​​and simulated values ​​of surrounding nodes, and construct a mapping model. The predicted values ​​of hydrological elements output by the monitoring point model are input into the mapping model. The model parameters are optimized by domain adaptive adversarial training. The monitoring point model and the mapping model are fused to obtain the basic hydrological digital twin model. Finally, the hydrological digital twin model is constructed through transfer learning. A reasoning dataset is constructed based on a hydrological disaster knowledge graph. The dataset is used to train a hydrological digital twin interactive model. The predicted values ​​of hydrological elements across the entire region output by the final hydrological digital twin model are used as input. The trained hydrological digital twin interactive model outputs the disposal measures for physical hydrological engineering projects.

[0007] Optionally, the construction of the hydrological element monitoring model based on multi-source sensor monitoring data includes: Hydrological environment driving factors are extracted as input variables to construct a hydrological environment feature extractor; Based on the hydrological environment feature extractor, flow feature extractor, water level feature extractor, sediment concentration feature extractor, and pore water pressure feature extractor are constructed respectively to extract the correlation features between environmental and hydrological elements, and splice them to generate multi-element coupled features of hydrology. The coupled features output the predicted values ​​of multiple types of hydrological elements simultaneously through a shared trainable decoder. Supervised learning is performed on the core physical features of the hydrological element feature extractor, and the model is optimized using a multi-teacher knowledge distillation loss function. On the newly added sensor monitoring dataset, the long-trained feature decoder is parameter-sharing and fine-tuned to update the parameters of the measurement point monitoring model and complete the model iteration.

[0008] Optionally, the construction of the hydrological environment feature extractor includes constructing the hydrological environment feature extractor through a graph convolutional network: ; ; ; in, Represents the initial environmental feature matrix. , , , Let these represent the sets of hydraulic load, air temperature, precipitation, and time-dependent driving factors at time t, respectively. , This represents a trainable weight matrix. , They represent the first The feature matrix of the k-th layer and the node of the k-th layer. Represents the normalization degree matrix, Let A denote the adjacency matrix with self-connection, where A represents the original adjacency matrix and I represents the identity matrix.

[0009] Optionally, the expression for the coupling characteristics of the hydrological multi-element features is as follows: ; in, This indicates the characteristics of the environment-traffic relationship. This indicates the characteristics of the environment-water level correlation. This indicates the characteristics of the relationship between environment and sediment content. This indicates the characteristics of the environmental-pore water pressure correlation.

[0010] Optionally, the expression for the mapping model is as follows: ; ; in, This represents the finite element node hydrological parameter values ​​output by the mapping model. This represents the simulated value of the finite element node corresponding to the sensor measurement point. This represents the actual measured value from the sensor. This represents an LSTM model with spatial constraints. This represents element-wise product, where V and c represent trainable parameters. This represents the domain-adaptive loss. Indicates the model's predicted distribution parameters. This represents the standard distribution parameter.

[0011] Optionally, it also includes a multi-objective dynamic monitoring loss function for constructing the mapping model: ; ; ; ; in, This represents the optimal parameter set of the mapping model after domain-adaptive training. This represents the total loss function of the mapping model. This represents the trainable parameters of the mapping model. , These are weighting coefficients used to balance error loss and spatial consistency loss. Represents the error loss function. This represents the spatial consistency loss function, where M represents the total number of nodes in the finite element method. This represents the distance metric function, and k represents the loop variable. , These represent the spatial gradients of the finite element simulation values ​​and the model prediction values, respectively. Indicates the loss allocation coefficient. This represents the prediction error loss at the sensor measurement points. This represents the simulation error loss of the finite element node corresponding to the measurement point. This represents the global error loss of all finite element nodes; the hydrological parameters include at least two of the following: flow rate, water level, sediment concentration, and pore water pressure.

[0012] Optionally, the predicted hydrological elements include predicted flow rate, predicted water level, predicted sediment concentration, or predicted pore water pressure.

[0013] Optionally, the expression for the final hydrological digital twin model is as follows: ; ; in, L represents the parameters after model fine-tuning under extreme conditions. This represents the loss function under extreme operating conditions. This represents the predicted value from the extreme operating condition model. This represents the finite element simulation value under extreme conditions. Representing a digital twin model, This represents the basic model parameters.

[0014] Optionally, the expression of the hydrological digital twin interaction model is as follows: ; ; ; Where Y represents the output of physical engineering disposal measures, This represents the function for identifying hydrological disaster damage patterns. , Indicates the interaction model parameters. , These represent the model parameters before and after the iteration, respectively. Indicates the learning rate. Indicates the interaction model loss. This represents the real-time calculated safety factor for hydrological engineering. This represents the safety factor threshold required by the standard.

[0015] Optionally, a hydrological digital twin model construction system based on artificial intelligence technology includes: a data acquisition module for constructing a monitoring model of hydrological element measuring points based on multi-source sensor monitoring data; Mapping module: Used to learn the mapping relationship between hydrological parameters of sensor measuring points and corresponding finite element nodes, and the information transmission law between sensor measured values ​​and simulated values ​​of surrounding nodes using a long short-term memory neural network framework with spatial correlation constraints, and to build a mapping model; The transfer learning module is used to input the predicted values ​​of hydrological elements output by the monitoring model into the mapping model, optimize the model parameters by using domain adaptive adversarial training, fuse the monitoring model and the mapping model to obtain the basic hydrological digital twin model, and construct the final hydrological digital twin model through transfer learning. Intelligent Reasoning Module: This module is used to construct a reasoning dataset based on a hydrological disaster knowledge graph, train a hydrological digital twin interactive model using the dataset, and take the predicted values ​​of hydrological elements across the entire region output by the final hydrological digital twin model as input. The trained hydrological digital twin interactive model then outputs the disposal measures for the physical hydrological engineering project.

[0016] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method and system for constructing a hydrological digital twin model based on artificial intelligence technology, which has the following beneficial effects: This invention proposes a method for constructing a hydrological digital twin model based on artificial intelligence technology, comprising: constructing a hydrological element monitoring model based on multi-source sensor monitoring data; utilizing a long short-term memory neural network framework with spatial correlation constraints to learn the mapping relationship between hydrological parameters of sensor monitoring points and corresponding finite element nodes, and the information transmission rules between sensor measured values ​​and simulated values ​​of surrounding nodes, and constructing a mapping model; inputting the predicted hydrological element values ​​output by the monitoring model into the mapping model, optimizing the model parameters using domain adaptive adversarial training, fusing the monitoring model and the mapping model to obtain a basic hydrological digital twin model, and constructing a final hydrological digital twin model through transfer learning; constructing an inference dataset based on a hydrological disaster knowledge graph, using the dataset to train a hydrological digital twin interaction model, taking the predicted values ​​of hydrological elements across the entire domain output by the final hydrological digital twin model as input, and outputting the disposal measures for physical hydrological engineering through the trained hydrological digital twin interaction model.

[0017] This invention proposes a method for constructing a hydrological digital twin model based on artificial intelligence technology. It builds a monitoring model for hydrological element measurement points that integrates multi-source sensor data, introduces a graph convolutional network to capture the complex correlation between environmental factors and hydrological elements, utilizes an improved Long Short-Term Memory (LSTM) neural network to construct a mapping model, and designs a multi-objective dynamic monitoring loss function to balance data accuracy and spatial consistency. Through domain-adaptive adversarial training, it fuses the predicted values ​​of measurement points with the simulated values ​​of the entire domain to generate a basic hydrological digital twin model, and constructs an interactive model by combining extreme condition parameter migration strategies. Based on a hydrological disaster knowledge graph, it constructs a reasoning mechanism to output physical engineering disposal solutions. This invention solves the problems of weak generalization ability, insufficient data fusion, and poor adaptability to extreme conditions in traditional hydrological models, achieving high-precision real-time perception and intelligent decision-making of hydrological processes, and providing technical support for flood control and disaster reduction, and water resource allocation. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 A schematic diagram of the process for constructing a hydrological digital twin model based on artificial intelligence technology provided by the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] This invention discloses a method for constructing a hydrological digital twin model based on artificial intelligence technology, such as... Figure 1 As shown, it includes: Construct a monitoring model for hydrological element monitoring points based on multi-source sensor monitoring data; Using a long short-term memory neural network framework with spatial correlation constraints, we learn the mapping relationship between hydrological parameters of sensor measuring points and corresponding finite element nodes, as well as the information transmission law between sensor measured values ​​and simulated values ​​of surrounding nodes, and construct a mapping model. The predicted values ​​of hydrological elements output by the monitoring point model are input into the mapping model. The model parameters are optimized by domain adaptive adversarial training. The monitoring point model and the mapping model are fused to obtain the basic hydrological digital twin model. Finally, the hydrological digital twin model is constructed through transfer learning. A reasoning dataset is constructed based on a hydrological disaster knowledge graph. The dataset is used to train a hydrological digital twin interactive model. The predicted values ​​of hydrological elements across the entire region output by the final hydrological digital twin model are used as input. The trained hydrological digital twin interactive model outputs the disposal measures for physical hydrological engineering projects.

[0022] By employing multi-teacher knowledge distillation and data transfer learning strategies, the scarcity of data under extreme conditions is addressed, improving the model's prediction accuracy in scenarios such as super-standard floods and extreme precipitation, thus meeting the monitoring needs of complex scenarios. An intelligent reasoning mechanism is constructed based on a hydrological disaster knowledge graph, and the output response measures conform to industry standards and engineering experience, reducing decision-making response time to the minute level, providing efficient support for hydrological disaster emergency response.

[0023] Furthermore, the construction of the hydrological element monitoring model based on multi-source sensor monitoring data includes: Hydrological environment driving factors are extracted as input variables, and a feature extractor-multi-task decoder architecture using a graph convolutional network (GCN) is employed to construct a hydrological environment feature extractor; the hydrological environment driving factors include hydraulic load factors. Temperature factor Precipitation factors Time factor Topographic factors At least three of them; Based on the hydrological environment feature extractor, flow rate feature extractor, water level feature extractor, sediment concentration feature extractor, and pore water pressure feature extractor are constructed respectively to extract the correlation features between environmental and hydrological elements, and splice them to generate multi-element coupled hydrological features. The coupling features simultaneously output predicted values ​​for multiple hydrological elements through a shared trainable decoder: ; in, Let V denote a non-linear activation function, V denote a trainable weight matrix, and c denote a bias term. Supervised learning is performed on the core physical features of the hydrological feature extractor, and the model is optimized using a multi-teacher knowledge distillation loss function, the expression of which is as follows: ; ; ; ; in, This represents the total loss from knowledge distillation. This indicates the students' online prediction of loss. This indicates the student's network output. This represents the measured value. This represents the loss in teacher-student network feature matching. This represents the softening probability function. Indicates student network characteristics, , This indicates different network characteristics of teachers. Indicates temperature parameter, Indicates KL divergence; On the newly added sensor monitoring dataset, the long-trained feature decoder is parameter-sharing and fine-tuned to update the parameters of the measurement point monitoring model and complete the model iteration.

[0024] By integrating the coupling features of multiple hydrological elements and designing a multi-task decoder, the synchronous prediction of flow rate, water level, sediment concentration, and pore water pressure is achieved, comprehensively reflecting the actual characteristics of multi-process coupling in the hydrological system and avoiding the limitations of single-element models.

[0025] Furthermore, the construction of the hydrological environment feature extractor includes constructing the hydrological environment feature extractor through a graph convolutional network: ; ; ; in, Represents the initial environmental feature matrix. , , , Let these represent the sets of hydraulic load, air temperature, precipitation, and time-dependent driving factors at time t, respectively. , This represents a trainable weight matrix. , They represent the first The feature matrix of the k-th layer and the node of the k-th layer. Represents the normalization degree matrix, Let A denote the adjacency matrix with self-connection, where A represents the original adjacency matrix and I represents the identity matrix.

[0026] Furthermore, the expression for the coupling characteristics of the hydrological multi-element features is as follows: ; in, This indicates the characteristics of the environment-traffic relationship. This indicates the characteristics of the environment-water level correlation. This indicates the characteristics of the relationship between environment and sediment content. This indicates the characteristics of the environmental-pore water pressure correlation.

[0027] Furthermore, the expression for the mapping model is as follows: ; ; in, This represents the finite element node hydrological parameter values ​​output by the mapping model. This represents the simulated value of the finite element node corresponding to the sensor measurement point. This represents the actual measured value from the sensor. This represents an LSTM model with spatial constraints. This represents element-wise product, where V and c represent trainable parameters. This represents the domain-adaptive loss. Indicates the model's predicted distribution parameters. This represents the standard distribution parameter.

[0028] Furthermore, it also includes constructing a multi-objective dynamic monitoring loss function for the mapping model: ; ; ; ; in, This represents the optimal parameter set of the mapping model after domain-adaptive training. This represents the total loss function of the mapping model. This represents the trainable parameters of the mapping model. , These are weighting coefficients used to balance error loss and spatial consistency loss. Represents the error loss function. This represents the spatial consistency loss function, where M represents the total number of nodes in the finite element method. This represents the distance metric function, and k represents the loop variable. , These represent the spatial gradients of the finite element simulation values ​​and the model prediction values, respectively. Indicates the loss allocation coefficient. This represents the prediction error loss at the sensor measurement points. This represents the simulation error loss of the finite element node corresponding to the measurement point. This represents the global error loss of all finite element nodes; the hydrological parameters include at least two of the following: flow rate, water level, sediment concentration, and pore water pressure.

[0029] The design of a multi-objective dynamic monitoring loss function balances error loss and spatial consistency loss, achieving deep integration of monitoring data and numerical simulation data, improving the spatial continuity of hydrological element prediction across the entire region, and better reflecting the actual distribution characteristics of the hydrological system.

[0030] Furthermore, the predicted hydrological elements include predicted flow rates. Water level prediction Predicted values ​​of sediment content Or predicted pore water pressure At least two of them.

[0031] Furthermore, the expression for the final hydrological digital twin model is as follows: ; ; in, L represents the parameters after model fine-tuning under extreme conditions. This represents the loss function under extreme operating conditions. This represents the predicted value from the extreme operating condition model. This represents the finite element simulation value under extreme conditions. Representing a digital twin model, This represents the basic model parameters.

[0032] Furthermore, the expression for the hydrological digital twin interaction model is as follows: ; ; ; Where Y represents the output of physical engineering disposal measures, This represents the function for identifying hydrological disaster damage patterns. , Indicates the interaction model parameters. , These represent the model parameters before and after the iteration, respectively. Indicates the learning rate. Indicates the interaction model loss. This represents the real-time calculated safety factor for hydrological engineering. This represents the safety factor threshold required by the standard.

[0033] In a specific implementation, a hydrological digital twin model construction system based on artificial intelligence technology includes: a data acquisition module for constructing a monitoring model of hydrological element measurement points based on multi-source sensor monitoring data; Mapping module: Used to learn the mapping relationship between hydrological parameters of sensor measuring points and corresponding finite element nodes, and the information transmission law between sensor measured values ​​and simulated values ​​of surrounding nodes using a long short-term memory neural network framework with spatial correlation constraints, and to build a mapping model; The transfer learning module is used to input the predicted values ​​of hydrological elements output by the monitoring model into the mapping model, optimize the model parameters by using domain adaptive adversarial training, fuse the monitoring model and the mapping model to obtain the basic hydrological digital twin model, and construct the final hydrological digital twin model through transfer learning. Intelligent Reasoning Module: This module is used to construct a reasoning dataset based on a hydrological disaster knowledge graph, train a hydrological digital twin interactive model using the dataset, and take the predicted values ​​of hydrological elements across the entire region output by the final hydrological digital twin model as input. The trained hydrological digital twin interactive model then outputs the disposal measures for the physical hydrological engineering project.

[0034] Furthermore, the data acquisition module also includes a data preprocessing unit, which performs outlier removal, missing value completion, and standardization on the acquired raw data to output a high-quality dataset.

[0035] Furthermore, the intelligent reasoning module also includes a knowledge graph update unit, which is used to dynamically update the knowledge graph nodes and relationships based on new hydrological disaster cases and handling experience, thereby improving the accuracy of reasoning.

[0036] Furthermore, it also includes: a model optimization module: fine-tuning the parameters of each module model based on newly added monitoring data to ensure the model's time-varying adaptability; Furthermore, it also includes: an early warning module: triggering multi-level hydrological disaster early warnings based on the prediction results of the hydrological digital twin interactive model and the safety factor threshold.

[0037] In a specific implementation, a method for constructing a hydrological digital twin model based on artificial intelligence technology includes the following steps: (I) Constructing a monitoring model for hydrological element measurement points (1) The graph convolutional feature extractor is designed to extract hydrological environmental driving factors such as hydraulic load, temperature, precipitation, time, and topography. A graph convolutional network (GCN) is used to construct a feature extractor-multi-task decoder architecture. The adjacency matrix is ​​used to represent the correlation between each driving factor, and multiple graph convolutional layers are used to capture the complex nonlinear effects between factors to generate a high-dimensional environmental feature matrix. This process can effectively explore the comprehensive impact of environmental factors on hydrological elements and lay the foundation for subsequent multi-factor prediction.

[0038] (2) The fusion of hydrological multi-element features is based on a graph convolution feature extractor. Specialized feature extractors for flow rate, water level, sediment concentration, and pore water pressure are constructed to extract the correlation features between the environment and each hydrological element. The specialized features are concatenated by dimension to generate hydrological multi-element coupled features. The predicted values ​​of multiple hydrological elements are output simultaneously through a shared trainable decoder. This design realizes multi-element collaborative prediction, avoids the limitations of single-element models, and is more in line with the actual characteristics of multi-process coupling in hydrological systems.

[0039] (3) Physical Constraint Enhancement for Extreme Conditions: To address the scarcity of data for extreme conditions, a multi-teacher knowledge distillation strategy is adopted: physical mechanism models (such as HEC-HMS) and high-precision numerical simulation models are used as teacher networks, and deep learning models are used as student networks. The physical knowledge of the teacher network is transferred to the student network through the distillation loss function, which strengthens the model's understanding of hydrological processes under extreme conditions and improves the model's prediction accuracy in scenarios such as super-standard floods and extreme precipitation.

[0040] (4) Time-varying model update mechanism: Considering the time-varying characteristics of hydrological system parameters (such as channel roughness and soil permeability coefficient), a parameter sharing-fine-tuning mechanism is designed: the feature decoder parameters trained on long-term monitoring data are used as the basis, and some parameters of the decoder are fine-tuned on newly added monitoring data to realize incremental update of the model. This mechanism avoids model retraining, reduces computational costs, and ensures the model's adaptability to the time-varying characteristics of the hydrological system.

[0041] (II) Constructing a mapping model and a multi-objective dynamic monitoring loss function

[0042] (1) The LSTM mapping model with spatial correlation constraints utilizes a Long Short-Term Memory (LSTM) neural network to construct the mapping model and introduces spatial correlation constraints: the spatial location information of sensor measuring points and corresponding finite element nodes is encoded into feature vectors and integrated into the gating mechanism of LSTM, so that the model can learn the data mapping relationship while taking into account the consistency of spatial location. This design realizes the expansion from discrete measuring point data to continuous data across the entire field, providing support for the full-field monitoring of hydrological systems.

[0043] (2) Design of Multi-Objective Dynamic Monitoring Loss Function: The multi-objective loss function integrates error loss and spatial consistency loss. The error loss part considers the errors of sensor measurement points, corresponding finite element nodes, and global nodes to ensure data accuracy. The spatial consistency loss part calculates the spatial gradient difference between simulated and predicted values ​​to ensure the spatial continuity of the data across the entire domain. By balancing the two types of losses through weighting coefficients, the deep integration of monitoring data and numerical simulation data is achieved, thereby improving the overall reliability of the model.

[0044] (III) Constructing basic and final hydrological digital twin models

[0045] (1) Construction of the basic hydrological digital twin model: The multi-element predicted values ​​output by the monitoring point model are input into the mapping model, and domain adaptive adversarial training is adopted: a discriminator is constructed to distinguish the distribution differences between the model's predicted values ​​and the finite element simulation values. Through adversarial learning between the generator and the discriminator, the parameters of the mapping model are optimized so that the distribution of predicted values ​​and the distribution of simulated values ​​tend to be consistent. The optimized mapping model and the monitoring point model are integrated to generate the basic hydrological digital twin model, realizing the virtual-real mapping of the hydrological system.

[0046] (2) Construction of Hydrological Digital Twin Interactive Model: For extreme conditions (such as floods exceeding standard levels and extreme precipitation), a data transfer learning strategy is adopted: using the basic model as the initial framework, the model parameters are fine-tuned using limited extreme condition simulation data (such as design flood processes); a condition identification module is introduced to determine in real time whether the current hydrological scenario is an extreme condition and automatically switch model parameters. This design solves the problem of scarce extreme condition data and realizes the adaptive adjustment of the model to complex scenarios.

[0047] (iv) S4: Construct an intelligent interaction model and output response measures

[0048] (1) Construction of Hydrological Disaster Knowledge Graph: Historical hydrological disaster cases, engineering treatment experience, and industry standards are collected to construct a hydrological disaster knowledge graph. The graph is structured with disaster type, impact range, safety hazards, and treatment measures as core nodes, establishing relationships between nodes to form a structured knowledge system. This graph provides knowledge support for subsequent intelligent reasoning, ensuring the scientific and compliant nature of decision-making.

[0049] (2) The hydrological digital twin interactive model training is based on the knowledge graph to construct an inference dataset. The predicted values ​​of hydrological elements (such as water level, flow velocity, and pore water pressure) output by the final hydrological digital twin model are used as input, and the corresponding engineering measures (such as flood discharge scheduling, dam reinforcement, and drainage optimization) are used as output to train the interactive model. The model parameters are optimized through the loss function so that the model has the ability to output targeted measures according to the hydrological status.

[0050] (3) Output of physical engineering disposal measures: Real-time monitoring data is input into the model, and the state of hydrological elements in the whole area is predicted by the final hydrological digital twin model. Combined with the real-time calculated engineering safety factor (such as dam slope stability coefficient and river flow capacity), specific physical engineering disposal measures are output through the hydrological digital twin interactive model to realize the intelligent guidance of the digital twin model for physical engineering.

[0051] In a specific implementation, a hydrological digital twin model construction system based on artificial intelligence technology specifically includes: (a) Data Acquisition Module Hardware components include: ultrasonic flow sensor (measurement range 0.1-10m³ / s, accuracy ±0.5%), radar water level sensor (measurement range 0-30m, accuracy ±2mm), laser sand content sensor (measurement range 0-100kg / m³, accuracy ±1%), pore water pressure sensor (measurement range 0-1MPa, accuracy ±0.2%), and automatic weather station (monitoring air temperature, precipitation, and wind speed), etc.

[0052] Data preprocessing unit: Performs outlier removal (based on 3σ criterion), missing value completion (based on interpolation) and standardization (maps the data to the [0,1] interval) on the collected raw data, and outputs a high-quality dataset to provide data support for subsequent model training.

[0053] Measurement point monitoring unit: Based on the dataset output by the data acquisition module, it constructs a graph convolutional feature extractor and a multi-factor fusion model to achieve collaborative prediction of flow rate, water level, sediment concentration, and pore water pressure. It supports online parameter fine-tuning and updates the model in real time based on new data, ensuring the long-term stability of prediction accuracy.

[0054] (ii) Mapping Module

[0055] Core functions: Construct a spatially constrained LSTM mapping model, design a multi-objective dynamic monitoring loss function, complete the mapping transformation from measurement point data to global data, and generate the global hydrological element distribution.

[0056] Performance evaluation: Built-in model evaluation metrics (such as MAE, RMSE, NSE) monitor model performance in real time and automatically trigger parameter optimization when the metrics exceed the threshold.

[0057] (III) Transfer Learning Module

[0058] Basic model construction: Integrating the monitoring model of measuring points and the mapping model, a basic hydrological digital twin model is generated through domain adaptive adversarial training to realize the virtual-real mapping of the hydrological system.

[0059] Extreme Condition Adaptation: The built-in extreme condition identification and parameter migration module automatically switches model parameters according to the hydrological scenario to ensure prediction accuracy under extreme conditions.

[0060] (iv) Intelligent Reasoning Module

[0061] Knowledge Graph Management: Provides visual editing and updating functions for knowledge graphs, supports the entry of new cases and standards, and dynamically optimizes the knowledge system.

[0062] Interactive Model Training: Train intelligent interactive models based on knowledge graph data, supporting incremental training and performance evaluation of the models.

[0063] Output of response measures: Based on the model prediction results and safety factor, output structured response measure recommendations, including measure type, implementation steps, precautions, etc.

[0064] (v) Model Optimization Module

[0065] Time-varying update: Based on newly added monitoring data, the parameters of the monitoring point monitoring model, mapping model, and interaction model are fine-tuned to avoid model drift and ensure long-term adaptability.

[0066] Performance monitoring: Real-time monitoring of the prediction accuracy and computational efficiency of each model; automatic triggering of optimization processes or sending early warning information to administrators when performance degrades.

[0067] (vi) Early warning module

[0068] Multi-level early warning mechanism: Based on the final hydrological digital twin model prediction results and safety factor thresholds, four levels of early warning are set: blue (attention), yellow (early warning), orange (alert), and red (emergency).

[0069] Warning output: Warning information is output through SMS, platform push, audible and visual alarms, etc., along with the cause of the warning, the scope of impact, and preliminary handling suggestions to support emergency response.

[0070] In a specific embodiment, to verify the effectiveness of the present invention, a large reservoir (total capacity of 1.2 billion m³, dam type is concrete gravity dam) is taken as the research object, and a hydrological digital twin model is constructed. The specific implementation steps are as follows: (a) Data preparation stage Data acquisition was conducted through a data acquisition module to collect monitoring data from 2018 to 2023, including: flow rate (5-minute interval), water level (1-minute interval), sediment concentration (1-hour interval), pore water pressure (1-hour interval), air temperature (10-minute interval), and precipitation (5-minute interval). Simultaneously, a three-dimensional finite element model of the reservoir was constructed based on the finite element software ANSYS to simulate the hydrological parameters of the entire area under different operating conditions.

[0071] Data preprocessing employed the 3σ criterion to remove outliers in flow rate and water level data (approximately 0.8% of the total data), and linear interpolation was used to fill in missing values ​​in sediment concentration and pore water pressure data (missing value rate approximately 1.2%). All data were standardized and divided into training, validation, and test sets in a 7:1:2 ratio.

[0072] (II) Model Training Phase

[0073] (1) Training of the monitoring model for measuring points

[0074] Graph Convolutional Feature Extractor: Input hydraulic load, temperature, precipitation, and time-dependent driving factors; set 2 graph convolutional layers, 64 hidden layer neurons, and ReLU activation function.

[0075] Multi-element fusion model: splices features of flow rate, water level, sediment concentration, and pore water pressure, sets up a decoder with 1 hidden layer and 32 neurons, and outputs predicted values ​​of 4 types of hydrological elements.

[0076] Training parameters: learning rate 0.001, batch size 64, number of iterations 500, Adam optimizer used, and multi-task MSE loss function used. After training, the model achieved a flow prediction NSE coefficient of 0.92 and a water level prediction MAE of 0.03m on the test set, meeting the accuracy requirements.

[0077] (2) Mapping model training

[0078] LSTM model with spatial constraints: Input the predicted values ​​of the measurement points and the corresponding simulated values ​​of the finite element nodes, set 2 LSTM layers, 128 hidden layer neurons, and spatial constraint weight coefficient of 0.3.

[0079] Multi-objective loss function: set error loss weight μ1=0.7, spatial consistency loss weight μ2=0.3, and loss balance coefficient β=0.6.

[0080] Training parameters: learning rate 0.0005, batch size 32, number of iterations 300, and Adam optimizer. After training, the spatial gradient difference between the model's output of the global data and the finite element simulation data was reduced by 40%.

[0081] (3) Training of the final hydrological digital twin model

[0082] Basic model optimization: Domain-adaptive adversarial training is adopted, the discriminator is set with 2 fully connected layers, the activation function is Sigmoid, and the number of adversarial training iterations is 200.

[0083] Extreme Condition Fine-tuning: Using simulation data of a design flood (once-in-a-century), the parameters of the basic model were fine-tuned with a fine-tuning rate of 0.1 and 50 iterations. After fine-tuning, the water level prediction error under extreme conditions was reduced by 28%.

[0084] (4) Training of intelligent interaction model

[0085] Knowledge graph construction: Collect 500+ historical flood cases and establish the relationship between "flood type - water level exceeding warning value - dam body hidden dangers - flood discharge plan".

[0086] Interactive model training: Inputting predicted global water level and pore water pressure values, the model outputs flood discharge flow and reinforcement measure suggestions. Three fully connected layers are used, with Softmax as the activation function, and 150 training iterations are performed. After training, the model achieves an 85% accuracy rate in matching treatment measures.

[0087] (III) Model Application Stage

[0088] The real-time monitoring and forecasting system inputs real-time monitoring data (water level, flow rate, and precipitation) from the 2024 flood season into the model. The basic digital twin model outputs the water level and flow velocity distribution across the entire reservoir area, predicts the water level change trend for the next 24 hours, and has a prediction error (MAE) of 0.04m, meeting the requirements for real-time dispatch.

[0089] When encountering rainfall exceeding the standard (daily precipitation of 200 mm), the operating condition identification module determines it to be an extreme scenario and automatically switches to the final hydrological digital twin model, predicting that the water level will exceed the warning level by 1.2 m and the pore water pressure will exceed the safety threshold within the next 12 hours. The early warning module triggers an orange alert and pushes the warning information to management personnel.

[0090] Based on the prediction results, the intelligent interactive model output the following measures: 1) Open spillway tunnels #3 and #4, controlling the flood discharge flow at 1500 m³ / s; 2) Clean the downstream drainage holes of the dam to improve drainage efficiency; 3) Strengthen dam displacement monitoring, increasing the monitoring frequency to 15 minutes per instance. After the measures were implemented, the water level was effectively controlled, and no safety accidents occurred.

[0091] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0092] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily 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 invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for constructing a hydrological digital twin model based on artificial intelligence technology, characterized in that, include: Construct a monitoring model for hydrological element monitoring points based on multi-source sensor monitoring data; Using a long short-term memory neural network framework with spatial correlation constraints, we learn the mapping relationship between hydrological parameters of sensor measuring points and corresponding finite element nodes, as well as the information transmission law between sensor measured values ​​and simulated values ​​of surrounding nodes, and construct a mapping model. The predicted values ​​of hydrological elements output by the monitoring point model are input into the mapping model. The model parameters are optimized by domain adaptive adversarial training. The monitoring point model and the mapping model are fused to obtain the basic hydrological digital twin model. Finally, the hydrological digital twin model is constructed through transfer learning. A reasoning dataset is constructed based on a hydrological disaster knowledge graph. The dataset is used to train a hydrological digital twin interactive model. The predicted values ​​of hydrological elements across the entire region output by the final hydrological digital twin model are used as input. The trained hydrological digital twin interactive model outputs the disposal measures for physical hydrological engineering projects.

2. The method for constructing a hydrological digital twin model based on artificial intelligence technology according to claim 1, characterized in that, The construction of the hydrological element monitoring model based on multi-source sensor monitoring data includes: Hydrological environment driving factors are extracted as input variables to construct a hydrological environment feature extractor; Based on the hydrological environment feature extractor, flow feature extractor, water level feature extractor, sediment concentration feature extractor, and pore water pressure feature extractor are constructed respectively to extract the correlation features between environmental and hydrological elements, and splice them to generate multi-element coupled features of hydrology. The coupled features output the predicted values ​​of multiple types of hydrological elements simultaneously through a shared trainable decoder. Supervised learning is performed on the core physical features of the hydrological element feature extractor, and the model is optimized using a multi-teacher knowledge distillation loss function. On the newly added sensor monitoring dataset, the long-trained feature decoder is parameter-sharing and fine-tuned to update the parameters of the measurement point monitoring model and complete the model iteration.

3. The method for constructing a hydrological digital twin model based on artificial intelligence technology according to claim 2, characterized in that, The construction of the hydrological environment feature extractor includes building the hydrological environment feature extractor through a graph convolutional network: ； ； ； in, Represents the initial environmental feature matrix. , , , Let these represent the sets of hydraulic load, air temperature, precipitation, and time-dependent driving factors at time t, respectively. , This represents a trainable weight matrix. , They represent the first The feature matrix of the k-th layer and the node of the k-th layer. Represents the normalization degree matrix, Let A represent the adjacency matrix with self-joins, where A represents the original adjacency matrix and I represents the identity matrix.

4. The method for constructing a hydrological digital twin model based on artificial intelligence technology according to claim 2, characterized in that, The expression for the coupling characteristics of the multiple hydrological elements is as follows: ； in, This indicates the characteristics of the environment-traffic relationship. This indicates the characteristics of the environment-water level correlation. This indicates the characteristics of the relationship between environment and sediment content. This indicates the characteristics of the environmental-pore water pressure correlation.

5. The method for constructing a hydrological digital twin model based on artificial intelligence technology according to claim 1, characterized in that, The expression for the mapping model is as follows: ； ； in, This represents the finite element node hydrological parameter values ​​output by the mapping model. This represents the simulated value of the finite element node corresponding to the sensor measurement point. This represents the actual measured value from the sensor. This represents an LSTM model with spatial constraints. This represents element-wise product, where V and c represent trainable parameters. Represents the domain-adaptive loss. Indicates the model's predicted distribution parameters. This represents the standard distribution parameter.

6. The method for constructing a hydrological digital twin model based on artificial intelligence technology according to claim 5, characterized in that, It also includes a multi-objective dynamic monitoring loss function for constructing the mapping model: ； ； ； ； in, This represents the optimal parameter set of the mapping model after domain-adaptive training. This represents the total loss function of the mapping model. This represents the trainable parameters of the mapping model. , These are weighting coefficients used to balance error loss and spatial consistency loss. Represents the error loss function. This represents the spatial consistency loss function, where M represents the total number of nodes in the finite element method. This represents the distance metric function, and k represents the loop variable. , These represent the spatial gradients of the finite element simulation values ​​and the model prediction values, respectively. Indicates the loss allocation coefficient. This represents the prediction error loss at the sensor measurement points. This represents the simulation error loss of the finite element node corresponding to the measurement point. This represents the global error loss of all finite element nodes; the hydrological parameters include at least two of the following: flow rate, water level, sediment concentration, and pore water pressure.

7. The method for constructing a hydrological digital twin model based on artificial intelligence technology according to claim 1, characterized in that, The predicted values ​​of the hydrological elements include predicted flow rate, predicted water level, predicted sediment concentration, or predicted pore water pressure.

8. The method for constructing a hydrological digital twin model based on artificial intelligence technology according to claim 1, characterized in that, The expression for the final hydrological digital twin model is as follows: ； ； in, L represents the parameters after model fine-tuning under extreme conditions. Represents the loss function under extreme operating conditions. This represents the predicted value from the extreme operating condition model. This represents the finite element simulation value under extreme conditions. Representing a digital twin model, This represents the basic model parameters.

9. The method for constructing a hydrological digital twin model based on artificial intelligence technology according to claim 1, characterized in that, The expression of the hydrological digital twin interaction model is as follows: ； ； ； Where Y represents the output of physical engineering disposal measures, This represents the function for identifying hydrological disaster damage patterns. , Indicates the interaction model parameters. , These represent the model parameters before and after the iteration, respectively. Indicates the learning rate. Indicates the interaction model loss. This represents the real-time calculated safety factor for hydrological engineering. This represents the safety factor threshold required by the standard.

10. A hydrological digital twin model construction system based on artificial intelligence technology, characterized in that, Includes: Acquisition module: used to build a monitoring model of hydrological element monitoring points based on multi-source sensor monitoring data; Mapping module: Used to learn the mapping relationship between hydrological parameters of sensor measuring points and corresponding finite element nodes, and the information transmission law between sensor measured values ​​and simulated values ​​of surrounding nodes using a long short-term memory neural network framework with spatial correlation constraints, and to build a mapping model; The transfer learning module is used to input the predicted values ​​of hydrological elements output by the monitoring model into the mapping model, optimize the model parameters by using domain adaptive adversarial training, fuse the monitoring model and the mapping model to obtain the basic hydrological digital twin model, and construct the final hydrological digital twin model through transfer learning. Intelligent Reasoning Module: This module is used to construct a reasoning dataset based on a hydrological disaster knowledge graph, train a hydrological digital twin interactive model using the dataset, and take the predicted values ​​of hydrological elements across the entire region output by the final hydrological digital twin model as input. The trained hydrological digital twin interactive model then outputs the disposal measures for the physical hydrological engineering project.

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