Intelligent Water Conservancy Inspection Method, Device, Equipment and Storage Medium

Abstract

The present application relates to the field of data processing technology, and discloses an intelligent water conservancy inspection method, device, equipment, and storage medium. The method comprises: dynamically fusion processing of the multi-dimensional water conservancy environmental data of a preset target area to obtain a target digital twin; performing intelligent collaborative analysis on the digital twin to obtain an adaptive inspection strategy; collecting multimodal sensing data of the target area according to the adaptive inspection strategy to obtain multimodal data; performing federated learning processing on the multimodal data to obtain holographic situational awareness data; performing causal reasoning analysis on the holographic situational awareness data to obtain a dynamic risk map; inputting the dynamic risk map into a preset deep Q network for prevention and control strategy analysis to obtain an active prevention and control strategy and an inspection report. The present application improves the efficiency and accuracy of intelligent water conservancy inspections.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to an intelligent water conservancy inspection method, device, equipment and storage medium. Background Art

[0002] Traditional water conservancy inspection methods rely primarily on manual inspections and fixed sensor monitoring. These methods typically include regular on-site inspections, water quality sampling and analysis, and water level monitoring. Technological advancements have introduced automated systems, such as remote sensing and drone inspections, to improve inspection efficiency and coverage. These methods have, to a certain extent, enhanced the supervisory capabilities and emergency response speed of water conservancy facilities.

[0003] However, these traditional methods have significant limitations. First, manual inspections are easily influenced by subjective factors, making comprehensiveness and consistency difficult to ensure. Second, while fixed sensor networks can provide continuous monitoring data, their coverage is often limited, making them incapable of addressing the complex and ever-changing water conservancy environment. Furthermore, these methods are typically reactive and lack the ability to predict potential risks. Most importantly, existing technologies struggle to effectively integrate heterogeneous data from multiple sources and are unable to fully leverage historical data and expert knowledge for intelligent decision-making, resulting in inefficient inspections and difficulty addressing complex water conservancy system risks. Summary of the Invention

[0004] The present application provides an intelligent water conservancy inspection method, device, equipment and storage medium for improving the efficiency and accuracy of intelligent water conservancy inspection.

[0005] In the first aspect, the present application provides an intelligent water conservancy inspection method, which includes: dynamically fusing the acquired multi-dimensional water conservancy environment data of a preset target area to obtain a target digital twin; performing intelligent body collaborative analysis on the digital twin to obtain an adaptive inspection strategy; collecting multimodal sensing data of the target area according to the adaptive inspection strategy to obtain multimodal data; performing federated learning processing on the multimodal data to obtain holographic situational awareness data; performing causal reasoning analysis on the holographic situational awareness data to obtain a dynamic risk map; inputting the dynamic risk map into a preset deep Q network for prevention and control strategy analysis to obtain an active prevention and control strategy and an inspection report.

[0006] In combination with the first aspect, in a first implementation method of the first aspect of the present application, the multi-dimensional water conservancy and environmental data of the preset target area obtained are dynamically fused to obtain a target digital twin, including: performing spatiotemporal alignment processing on the multi-dimensional water conservancy and environmental data to obtain a water conservancy and environmental data set in a unified coordinate system; performing outlier detection and removal on the water conservancy and environmental data set to obtain a cleaned data set; performing multi-scale wavelet transform on the cleaned data set to obtain a time-frequency feature representation; inputting the time-frequency feature representation into an autoencoder for feature extraction to obtain a low-dimensional feature vector; performing principal component analysis on the low-dimensional feature vector to obtain the main feature components; inputting the main feature components into a long short-term memory network for time series modeling to obtain a dynamic feature sequence; performing Kalman filtering on the dynamic feature sequence to obtain a smoothed state estimate; spatially aligning the smoothed state estimate with pre-acquired geographic information system data to obtain geographic correlation features; performing tensor decomposition on the geographic correlation features to obtain multimodal interaction features; and inputting the multimodal interaction features into a graph neural network for dynamic fusion to obtain the target digital twin.

[0007] In combination with the first aspect, in the second implementation method of the first aspect of the present application, the digital twin is subjected to an intelligent collaborative analysis to obtain an adaptive inspection strategy, including: performing multi-agent decomposition on the digital twin to obtain multiple sub-area intelligent agents; performing task assignment on the multiple sub-area intelligent agents to obtain an initial task assignment scheme; inputting the initial task assignment scheme into a reinforcement learning network for training to obtain an intelligent collaborative strategy; performing simulation verification on the intelligent collaborative strategy to obtain simulation result data; inputting the simulation result data into a genetic algorithm for optimization to obtain an optimized collaborative strategy; performing multi-objective evaluation on the optimized collaborative strategy to obtain a strategy evaluation index; inputting the strategy evaluation index into a fuzzy decision tree for analysis to obtain a decision rule set; constructing a knowledge graph for the decision rule set to obtain an inspection knowledge graph; inputting the inspection knowledge graph into a graph attention network for feature extraction to obtain key inspection features; performing dynamic programming calculation on the key inspection features to obtain an adaptive inspection strategy.

[0008] In combination with the first aspect, in a third implementation of the first aspect of the present application, multimodal sensor data collection is performed on the target area according to the adaptive inspection strategy to obtain multimodal data, including: task decomposition of the adaptive inspection strategy to obtain multiple subtasks; inputting the multiple subtasks into a resource allocation algorithm for task allocation to obtain a sensor deployment plan; optimizing and analyzing the sensor deployment plan to obtain a sensor network topology; calibrating sensors according to the sensor network topology to obtain a target sensor network; setting a data collection cycle for the target sensor network to obtain a collection schedule; based on the collection schedule, multimodal sensor data collection is performed on the target area through the target sensor network to obtain raw sensor data; performing noise filtering on the raw sensor data to obtain filtered data; inputting the filtered data into a data fusion algorithm for data fusion to obtain preliminary fused data; performing anomaly detection on the preliminary fused data to obtain anomaly marked data; and performing data fusion on the anomaly marked data and the preliminary fused data to obtain the multimodal data.

[0009] In combination with the first aspect, in a fourth implementation method of the first aspect of the present application, the multimodal data is federated learning processed to obtain holographic situational awareness data, including: sampling and segmenting the multimodal data to obtain multiple local data subsets; inputting the multiple local data subsets into the Laplace differential privacy algorithm for noise addition to obtain privacy-protected data; performing principal component analysis and wavelet transform on the privacy-protected data to obtain local feature vectors; inputting the local feature vectors into the federated averaging algorithm for weighted averaging calculation to obtain a global parameter set; performing trust-based weighted aggregation processing on the global parameter set to obtain preliminary global features; distributing the preliminary global features to preset edge computing nodes for gradient descent calculation to obtain local update results; performing homomorphic encryption and secure multi-party computing on the local update results to obtain global update features; inputting the global update features into the cross-validation algorithm for performance evaluation to obtain performance indicators; performing adaptive threshold judgment and trend analysis on the performance indicators to obtain iterative decisions; performing feature optimization processing on the global features using the Adam optimization algorithm based on the iterative decisions to obtain the holographic situational awareness data.

[0010] In combination with the first aspect, in the fifth implementation method of the first aspect of the present application, the causal reasoning analysis of the holographic situational awareness data is performed to obtain a dynamic risk map, including: feature selection and dimensionality reduction processing of the holographic situational awareness data to obtain a key feature set; inputting the key feature set into a structure learning algorithm to construct a Bayesian network structure to obtain an initial network structure; integrating and correcting the initial network structure with expert knowledge to obtain an optimized network structure; inputting the optimized network structure and the holographic situational awareness data into a parameter learning algorithm to calculate a conditional probability table to obtain a conditional probability table; performing data verification and outlier processing on the conditional probability table to obtain a corrected conditional probability table. rate table; integrating the modified conditional probability table with the optimized network structure to obtain a target Bayesian network; performing sensitivity analysis on the target Bayesian network to obtain a set of key nodes; inputting the set of key nodes into the evidence propagation algorithm to perform probability update calculation to obtain a posterior probability distribution; performing threshold analysis and risk quantification on the posterior probability distribution to obtain a preliminary risk assessment result; inputting the preliminary risk assessment result into the time series analysis algorithm to perform dynamic trend prediction to obtain a risk evolution trend; performing spatial interpolation and visualization on the risk evolution trend to obtain a risk heat map; fusing the risk heat map with the preliminary risk assessment result to obtain a dynamic risk map.

[0011] In combination with the first aspect, in the sixth implementation method of the first aspect of the present application, the dynamic risk map is input into a preset deep Q network for prevention and control strategy analysis to obtain an active prevention and control strategy and an inspection report, including: dividing the dynamic risk map into risk levels and prioritizing them to obtain a risk priority list; inputting the risk priority list into a state coding algorithm for state space construction to obtain a discrete state space; defining the action set and designing the reward function for the discrete state space to obtain a reinforcement learning environment; inputting the reinforcement learning environment into a deep Q network for iterative calculation of Q values to obtain an initial Q value table; performing greedy strategy exploration analysis on the initial Q value table to obtain an action sequence; inputting the action sequence into a Monte Carlo tree search algorithm for strategy evaluation to obtain a strategy score; performing multi-objective trade-offs and Pareto optimization on the strategy score to obtain a non-dominated solution set; inputting the non-dominated solution set into a decision tree algorithm for rule extraction to obtain a prevention and control decision rule; performing interpretability analysis and visualization processing on the prevention and control decision rule to obtain an active prevention and control strategy; performing correlation analysis on the active prevention and control strategy with the dynamic risk map to obtain an inspection report.

[0012] In a second aspect, the present application provides an intelligent water conservancy inspection device, the intelligent water conservancy inspection device comprising:

[0013] The acquisition module is used to dynamically fuse the acquired multi-dimensional water conservancy environment data of the preset target area to obtain the target digital twin;

[0014] An analysis module, configured to perform agent collaborative analysis on the digital twin to obtain an adaptive inspection strategy;

[0015] An acquisition module, configured to acquire multimodal sensing data from the target area according to the adaptive inspection strategy to obtain multimodal data;

[0016] a processing module, configured to perform federated learning processing on the multimodal data to obtain holographic situational awareness data;

[0017] A reasoning module, configured to perform causal reasoning analysis on the holographic situational awareness data to obtain a dynamic risk map;

[0018] The input module is used to input the dynamic risk map into a preset deep Q network for prevention and control strategy analysis to obtain active prevention and control strategies and inspection reports.

[0019] The third aspect of the present application provides an intelligent water conservancy inspection device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory to enable the intelligent water conservancy inspection device to execute the above-mentioned intelligent water conservancy inspection method.

[0020] A fourth aspect of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, enable the computer to execute the above-mentioned intelligent water conservancy inspection method.

[0021] In the technical solution provided by this application, a target digital twin is obtained by dynamically fusing multi-dimensional water conservancy environmental data, achieving a high-precision digital representation of complex water conservancy systems. This digital twin can simultaneously consider the interaction of multiple factors such as hydrology, geology, and meteorology, greatly improving the accuracy and predictive ability of the model. Secondly, the digital twin is subjected to intelligent collaborative analysis to obtain an adaptive inspection strategy, which enables the inspection process to be dynamically adjusted according to real-time environmental changes, improving the flexibility and pertinence of the inspection. This enables inspection resources to be allocated more efficiently, reducing unnecessary waste of manpower and material resources. Multimodal sensor data is collected from the target area through an adaptive inspection strategy to obtain multimodal data, greatly enriching the data source and improving the comprehensiveness and reliability of the data. This multimodal data collection method can capture complex problems and potential risks that are difficult to detect with a single sensor. In addition, federated learning processing is performed on the multimodal data to obtain holographic situational awareness data, which not only protects data privacy but also realizes distributed learning, improving the efficiency and security of data processing. This application is suitable for processing water conservancy systems with geographically dispersed locations and huge amounts of data. Holographic situational awareness data is subjected to causal reasoning analysis to generate a dynamic risk map, enabling a comprehensive assessment and dynamic prediction of water conservancy system risks, significantly improving the foresight and accuracy of risk prevention and control. By identifying potential causal relationships, this method can uncover hidden risks that are difficult to detect using traditional methods. Finally, the dynamic risk map is input into a pre-configured deep Q network for prevention and control strategy analysis, generating proactive prevention and control strategies and inspection reports, achieving a closed-loop management system from risk identification to strategy formulation. This deep reinforcement learning-based approach enables continuous learning and optimization of prevention and control strategies, adapting to the complex and ever-changing water conservancy environment. This invention achieves comprehensive intelligent water conservancy inspection through the integrated application of advanced technologies such as digital twins, multi-agent collaboration, multimodal data fusion, federated learning, causal reasoning, and deep reinforcement learning. This not only improves the efficiency and accuracy of inspections, but also enhances the risk prediction and prevention capabilities of water conservancy systems. This application enables the timely identification of potential hazards, enabling the implementation of preventive measures in advance, significantly reducing the likelihood of water conservancy disasters. Furthermore, through adaptive inspection strategies and intelligent data analysis, it significantly reduces human resource investment and inspection costs. More importantly, this application establishes an intelligent system that continuously learns and optimizes, which can continuously accumulate experience over time and improve the accuracy of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 This is a schematic diagram of an embodiment of the intelligent water conservancy inspection method in the embodiment of the present application;

[0024] Figure 2 This is a schematic diagram of an embodiment of an intelligent water conservancy inspection device in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The embodiments of the present application provide an intelligent water conservancy inspection method, device, equipment and storage medium. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices.

[0026] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, an embodiment of the intelligent water conservancy inspection method includes:

[0027] Step S101: Dynamically fuse the acquired multi-dimensional water environment data of a preset target area to obtain a target digital twin;

[0028] Step S102: Performing intelligent collaborative analysis on the digital twin to obtain an adaptive inspection strategy;

[0029] Step S103: collecting multimodal sensing data of the target area according to the adaptive inspection strategy to obtain multimodal data;

[0030] Step S104: performing federated learning processing on the multimodal data to obtain holographic situation awareness data;

[0031] Step S105: Perform causal reasoning analysis on the holographic situation awareness data to obtain a dynamic risk map;

[0032] Step S106: Input the dynamic risk map into the preset deep Q network to perform prevention and control strategy analysis to obtain active prevention and control strategies and inspection reports.

[0033] It is understandable that the execution subject of this application can be an intelligent water conservancy inspection device, or a terminal or a server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0034] Specifically, data from various aspects, including hydrology, geology, and meteorology, are collected. For example, for a large reservoir, data such as water level, water quality, rainfall, and surface deformation may be collected. These data are unified into the same coordinate system through spatiotemporal alignment, and then outlier detection and cleaning are performed. Next, time-frequency features are extracted through multi-scale wavelet transform, and autoencoders are used for feature extraction and dimensionality reduction. After principal component analysis, important features are input into the long-short-term memory network for time series modeling to obtain a dynamic feature sequence. This sequence is then smoothed by Kalman filtering, spatially aligned with geographic information system data, and finally dynamically fused through graph neural networks to form the target digital twin.

[0035] An agent-based collaborative analysis of the digital twin generates an adaptive inspection strategy. This process first decomposes the digital twin into multiple sub-region agents, such as the reservoir's dam, spillway, and water intake. These agents are then assigned preliminary tasks and trained using a reinforcement learning network to develop a collaborative strategy. This strategy is validated through simulation and optimized using a genetic algorithm. A set of decision rules is then generated through fuzzy decision tree analysis. These rules are constructed into an inspection knowledge graph. A graph attention network extracts key features, and the final adaptive inspection strategy is calculated using dynamic programming. Based on the adaptive inspection strategy, the system collects multimodal sensor data from the target area. This step first decomposes the inspection strategy into specific subtasks, such as inspecting dam leaks or monitoring water quality changes. A resource allocation algorithm is then used to develop a sensor deployment plan and optimize the sensor network topology. Based on this topology, the system calibrates the sensors, sets the data collection cycle, and begins collecting multimodal data. This raw data undergoes noise filtering, data fusion, and anomaly detection to form a high-quality multimodal dataset. Federated learning is then used to process this multimodal data to generate holographic situational awareness data. First, the data is segmented into multiple local data subsets and distributed across different edge computing nodes. Each subset is processed using the Laplace differential privacy algorithm to protect privacy, followed by feature extraction. The local feature vectors of each node are weighted averaged using the federated averaging algorithm to obtain a global parameter set. This parameter set is then weightedly aggregated based on trust to form a preliminary global feature. Each node uses this feature to perform local gradient descent calculations, and the results are homomorphically encrypted and secure multi-party computation to obtain a global updated feature. The system then cross-validates and evaluates the performance of this feature, deciding whether further iteration is necessary based on the evaluation results. Finally, the global feature is optimized using the Adam optimization algorithm to obtain holographic situational awareness data.

[0036] The holographic situational awareness data is then subjected to causal reasoning analysis to generate a dynamic risk map. This process begins with feature selection and dimensionality reduction of the data to extract key features. A structural learning algorithm is then used to construct an initial Bayesian network structure, optimized in conjunction with expert knowledge. This optimized network structure, along with the holographic situational awareness data, is then input into a parameter learning algorithm to calculate a conditional probability table. This probability table, after verification and outlier processing, is then integrated with the network structure to form a complete Bayesian network. The system then performs sensitivity analysis on this network, identifies key nodes, and uses an evidence propagation algorithm to update the probability distribution. Preliminary risk assessment results are obtained through threshold analysis and risk quantification. These results are then subjected to time series analysis to predict risk evolution trends. Finally, spatial interpolation and visualization are used to generate a dynamic risk map.

[0037] Finally, the dynamic risk map is input into a pre-configured deep Q-network for prevention and control strategy analysis, resulting in a proactive prevention and control strategy and inspection report. The risk map is first hierarchically classified and prioritized, then a state encoding algorithm is used to construct a discrete state space. Within this space, an action set and reward function are defined, forming a reinforcement learning environment. The deep Q-network iteratively calculates Q-values within this environment to generate an initial Q-value table. The system then performs a greedy strategy exploration on this table, generating action sequences. These sequences are evaluated using a Monte Carlo tree search to generate a strategy score. The scoring results are then subjected to multi-objective trade-offs and Pareto optimization to form a non-dominated solution set. A decision tree algorithm is used to extract prevention and control decision rules from this solution set. After interpretability analysis and visualization, a proactive prevention and control strategy is ultimately formed. This strategy is then correlated with the dynamic risk map to generate the final inspection report.

[0038] In the embodiment of the present application, a target digital twin is obtained by dynamically fusion processing multi-dimensional water conservancy environmental data, achieving a high-precision digital expression of a complex water conservancy system. This digital twin can simultaneously consider the interaction of multiple factors such as hydrology, geology, and meteorology, greatly improving the accuracy and predictive ability of the model. Secondly, the digital twin is subjected to intelligent collaborative analysis to obtain an adaptive inspection strategy, which enables the inspection process to be dynamically adjusted according to real-time environmental changes, improving the flexibility and pertinence of the inspection. This enables inspection resources to be allocated more efficiently, reducing unnecessary waste of manpower and material resources. Multimodal sensor data is collected from the target area through an adaptive inspection strategy to obtain multimodal data, greatly enriching the data source and improving the comprehensiveness and reliability of the data. This multimodal data collection method can capture complex problems and potential risks that are difficult to detect with a single sensor. In addition, federated learning processing is performed on the multimodal data to obtain holographic situational awareness data, which not only protects data privacy but also realizes distributed learning and improves the efficiency and security of data processing. The present application is suitable for processing water conservancy systems with geographically dispersed locations and huge amounts of data. Holographic situational awareness data is subjected to causal reasoning analysis to generate a dynamic risk map, enabling a comprehensive assessment and dynamic prediction of water conservancy system risks, significantly improving the foresight and accuracy of risk prevention and control. By identifying potential causal relationships, this method can uncover hidden risks that are difficult to detect using traditional methods. Finally, the dynamic risk map is input into a pre-configured deep Q network for prevention and control strategy analysis, generating proactive prevention and control strategies and inspection reports, achieving a closed-loop management system from risk identification to strategy formulation. This deep reinforcement learning-based approach enables continuous learning and optimization of prevention and control strategies, adapting to the complex and ever-changing water conservancy environment. This invention achieves comprehensive intelligent water conservancy inspection through the integrated application of advanced technologies such as digital twins, multi-agent collaboration, multimodal data fusion, federated learning, causal reasoning, and deep reinforcement learning. This not only improves the efficiency and accuracy of inspections, but also enhances the risk prediction and prevention capabilities of water conservancy systems. This application enables the timely identification of potential hazards, enabling the implementation of preventive measures in advance, significantly reducing the likelihood of water conservancy disasters. Furthermore, through adaptive inspection strategies and intelligent data analysis, it significantly reduces human resource investment and inspection costs. More importantly, this application establishes an intelligent system that continuously learns and optimizes, which can continuously accumulate experience over time and improve the accuracy of decision-making.

[0039] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0040] (1) Perform spatiotemporal alignment processing on multi-dimensional water conservancy and environmental data to obtain a water conservancy and environmental dataset in a unified coordinate system;

[0041] (2) Detect and remove outliers in the water conservancy and environment dataset to obtain a cleaned dataset;

[0042] (3) Perform multi-scale wavelet transform on the cleaned data set to obtain time-frequency feature representation;

[0043] (4) Input the time-frequency feature representation into the autoencoder for feature extraction to obtain a low-dimensional feature vector;

[0044] (5) Perform principal component analysis on the low-dimensional feature vector to obtain the main feature components;

[0045] (6) Input the main feature components into the long short-term memory network for time series modeling to obtain a dynamic feature sequence;

[0046] (7) Perform Kalman filtering on the dynamic feature sequence to obtain a smooth state estimate;

[0047] (8) spatially registering the smoothed state estimate with pre-acquired geographic information system data to obtain geographic correlation features;

[0048] (9) Perform tensor decomposition on geographic correlation features to obtain multimodal interaction features;

[0049] (10) The multimodal interaction features are input into the graph neural network for dynamic fusion to obtain the target digital twin.

[0050] Specifically, multi-dimensional water conservancy and environmental data are temporally and spatially aligned. For example, for a large reservoir, data such as water level, water quality, rainfall, and surface deformation may be collected simultaneously. These data may come from different sensors with different temporal resolutions and spatial reference systems. Temporal and spatial alignment will unify all data into a common time scale (such as one data point per hour) and spatial coordinate system (such as the WGS84 coordinate system), thereby obtaining a water conservancy and environmental dataset in a unified coordinate system. Outliers are detected and removed from this unified dataset. Statistical methods such as the Z-score or machine learning methods such as the isolation forest algorithm may be used to identify outliers. For example, if the water level data at a certain point in time is suddenly 10 times higher than the normal value, this may be an outlier caused by a sensor failure and needs to be removed. After this step, a clean cleaning dataset is obtained.

[0051] A multi-scale wavelet transform is performed on the cleaned dataset to obtain a time-frequency feature representation. The wavelet transform can analyze signals at different time scales and is particularly well-suited for processing non-stationary time series such as hydrological data. For example, it can simultaneously capture both diurnal and seasonal variations in water levels. This step converts the raw data into a series of time-frequency coefficients. These time-frequency feature representations are then input into an autoencoder for feature extraction. An autoencoder is an unsupervised learning algorithm that learns a compact representation of data by attempting to reconstruct the input. In this process, the autoencoder may learn underlying relationships, such as between water level and rainfall, resulting in a low-dimensional feature vector.

[0052] Principal component analysis (PCA) is performed on this low-dimensional feature vector to further reduce its dimensionality and extract the main characteristic components. PCA can identify the main trends in the data. For example, it may reveal that reservoir water level fluctuations are primarily influenced by rainfall and upstream water inflow. The resulting main characteristic components are then fed into a long short-term memory (LSTM) network for time series modeling. LSTM networks excel at capturing long-term dependencies and are well-suited for processing time series with long-term memory effects, such as hydrological data. For example, they can learn how upstream rainfall affects downstream water levels over time. Through the LSTM network, a dynamic feature sequence is generated.

[0053] This dynamic feature sequence is subjected to a Kalman filter to obtain a smoothed state estimate. Kalman filtering is a recursive algorithm that can optimally estimate the system state in the presence of measurement noise. In water conservancy monitoring, it can smooth out short-term random fluctuations and obtain a more stable trend estimate. The smoothed state estimate is spatially aligned with pre-acquired geographic information system (GIS) data. GIS data includes a three-dimensional terrain model of the reservoir and surrounding geological information. Spatial alignment links dynamic monitoring data with static geographic information. For example, water level data can be accurately mapped to the specific location of the reservoir. This step produces geographic correlation features. These geographic correlation features are subjected to tensor decomposition to obtain multimodal interaction features. Tensor decomposition can capture high-order correlations between different modal data. For example, it may reveal complex interactions between water level, rainfall, and geological conditions, which may be related to the risk of reservoir leakage.

[0054] Finally, the multimodal interaction features are fed into a graph neural network for dynamic fusion, yielding the target digital twin. Graph neural networks are particularly well-suited for processing data with spatial relationships. In water conservancy systems, spatial correlations exist between different monitoring points, and graph neural networks can effectively model these relationships. For example, they can learn how upstream water level changes propagate downstream, or how geological conditions in different regions influence overall hydrological characteristics.

[0055] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0056] (1) Decompose the digital twin into multiple agents to obtain multiple sub-region agents;

[0057] (2) Assign tasks to multiple sub-region agents and obtain an initial task assignment plan;

[0058] (3) Input the initial task allocation plan into the reinforcement learning network for training to obtain the agent collaboration strategy;

[0059] (4) Conduct simulation verification on the agent collaboration strategy and obtain simulation result data;

[0060] (5) Input the simulation result data into the genetic algorithm for optimization to obtain the optimized collaborative strategy;

[0061] (6) Conduct multi-objective evaluation on the optimization collaborative strategy to obtain strategy evaluation indicators;

[0062] (7) Inputting the strategy evaluation index into the fuzzy decision tree for analysis to obtain a decision rule set;

[0063] (8) Construct a knowledge graph for the decision rule set to obtain an inspection knowledge graph;

[0064] (9) Input the inspection knowledge graph into the graph attention network for feature extraction to obtain key inspection features;

[0065] (10) Dynamic programming calculation is performed on key inspection features to obtain an adaptive inspection strategy.

[0066] Specifically, the digital twin is decomposed into multiple sub-agent agents to obtain multiple sub-area agents. For example, a large reservoir might be decomposed into multiple sub-areas, such as the dam, spillway, inlet, and reservoir area. Each sub-area is considered an agent, with its own state, behavior, and goals. For example, the dam agent might focus on structural integrity and leakage, while the reservoir agent might focus on water quality and the ecological environment. Tasks are assigned to these sub-area agents to obtain an initial task allocation plan. This task allocation takes into account the characteristics and current state of each agent. For example, if there has been heavy rainfall recently, more resources might be allocated to the dam and spillway agents for inspection. If water quality anomalies occur in a certain area of the reservoir, the inspection frequency of that area might be increased. The initial task allocation plan includes inspection routes, frequency, key focus areas, and more.

[0067] This initial task allocation is fed into a reinforcement learning network for training, yielding a collaborative strategy for the agents. The reinforcement learning network learns the optimal strategy by repeatedly trying different inspection strategies and evaluating their effectiveness. For example, the network might learn to increase the frequency of dam inspections during the flood season while prioritizing water quality monitoring during the dry season. Collaborative strategies consider not only the tasks of individual agents but also the collaboration between agents, such as the coordinated efforts of dam and spillway agents to address flood risks.

[0068] The resulting agent collaboration strategy is simulated and validated to produce simulation results. During the simulation, various extreme scenarios, such as sudden heavy rainfall and earthquakes, may be simulated to test the effectiveness and robustness of the collaboration strategy. The simulation results include various performance indicators, such as risk detection rate, resource utilization efficiency, and response time.

[0069] The simulation results are then fed into a genetic algorithm for optimization, yielding an optimized collaborative strategy. Genetic algorithms mimic the biological evolution process, continuously selecting, crossover, and mutating to generate better strategies. For example, by combining the characteristics of multiple well-performing strategies, they can generate a new strategy that performs well in a variety of situations.

[0070] The optimized collaborative strategy is evaluated across multiple objectives to generate strategy evaluation metrics. These metrics include safety (such as risk detection rate), efficiency (such as inspection coverage), and economy (such as resource consumption). This step requires balancing multiple, potentially conflicting objectives; for example, improving safety may increase economic costs.

[0071] These policy evaluation indicators are fed into a fuzzy decision tree for analysis, generating a set of decision rules. Fuzzy decision trees can handle fuzzy relationships and uncertainty between indicators. For example, a rule might be generated such as "If rainfall is heavy and the water level approaches the warning line, significantly increase the frequency of dam inspections."

[0072] A knowledge graph is constructed for the decision rule set to generate an inspection knowledge graph. A knowledge graph represents various rules, entities, and relationships in a graphical form. For example, it might include nodes such as "rainfall," "water level," and "dam inspection," along with the relationships between them. This representation helps capture complex causal and dependency relationships.

[0073] The constructed inspection knowledge graph is fed into a graph attention network for feature extraction, yielding key inspection features. The graph attention network automatically learns the importance of each node in the graph and extracts the most critical features. For example, it might discover that, in certain circumstances, the rate of water level change is more important than the absolute water level, or that dam leakage detection warrants special attention under certain conditions.

[0074] Finally, dynamic programming is performed on the extracted key inspection features to generate an adaptive inspection strategy. Dynamic programming considers decision optimization over time series and can determine the optimal strategy based on the current state and expected future states. For example, a strategy might be generated that dynamically adjusts inspection focus and frequency based on weather forecasts, upstream water flow conditions, and other factors.

[0075] This adaptive inspection strategy, the final output of the entire process, can flexibly adjust inspection plans based on real-time conditions. For example, if an abnormal leak is detected in a certain part of the dam, the strategy might immediately increase the inspection frequency in that area while adjusting inspection plans for other areas to ensure efficient overall resource utilization. Alternatively, if heavy rainfall is expected, the strategy might preemptively increase inspections of the drainage system and slope stability.

[0076] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0077] (1) Decompose the adaptive inspection strategy into multiple subtasks;

[0078] (2) Input multiple subtasks into the resource allocation algorithm for task allocation and obtain the sensor deployment plan;

[0079] (3) Optimize and analyze the sensor deployment plan to obtain the sensor network topology;

[0080] (4) Perform sensor calibration according to the sensor network topology to obtain the target sensor network;

[0081] (5) Setting the data collection cycle for the target sensor network to obtain a collection schedule;

[0082] (6) Based on the collection schedule, multimodal sensor data is collected from the target area through the target sensor network to obtain raw sensor data;

[0083] (7) performing noise filtering on the original sensor data to obtain filtered data;

[0084] (8) Inputting the filtered data into the data fusion algorithm for data fusion to obtain preliminary fused data;

[0085] (9) Perform anomaly detection on the preliminary fusion data to obtain abnormal labeled data;

[0086] (10) The abnormal labeled data is fused with the preliminary fusion data to obtain multimodal data.

[0087] Specifically, the adaptive inspection strategy is broken down into multiple subtasks. For example, for a large reservoir, the adaptive inspection strategy includes monitoring dam structure, water quality, and ecological and environmental assessments. These tasks are broken down into more specific subtasks, such as dam leakage detection, water eutrophication monitoring, and fish population surveys. Each subtask has its own specific monitoring requirements and importance weighting.

[0088] These subtasks are then fed into a resource allocation algorithm for task allocation, resulting in a sensor deployment plan. The resource allocation algorithm considers the importance of each subtask, the difficulty of monitoring, and the type and number of available sensors. For example, for dam leakage detection, high-precision pressure sensors and fiber optic sensors might be assigned; for water quality monitoring, multi-parameter water quality sensors and automatic samplers might be deployed. The algorithm balances the monitoring needs of each area to maximize the overall monitoring effect. The sensor deployment plan is optimized and analyzed to obtain the sensor network topology. The optimization process takes into account factors such as communication requirements between sensors, energy consumption, and data transmission efficiency. For example, temperature sensors might be set up at different depths in a reservoir to form a vertical profile monitoring network, or a series of water level sensors might be deployed along a river to construct a flood warning network. The optimized network topology ensures comprehensive data collection while also ensuring network reliability and efficiency.

[0089] Sensor calibration is performed based on the determined sensor network topology to create the target sensor network. This calibration process ensures that each sensor provides accurate data at its specific location. For example, a water level sensor needs to be calibrated based on its installation elevation; a water quality sensor requires regular calibration using a standard solution. This step is crucial to ensuring data reliability.

[0090] Set the data collection cycle for the target sensor network to create a collection schedule. Different sensor types and monitoring objectives may require different collection frequencies. For example, water level data may need to be collected hourly, while water quality data may be collected several times a day. During flood season, the collection frequency of certain key parameters may increase. Designing a collection schedule requires balancing data accuracy and system resource consumption.

[0091] Based on a defined collection schedule, the target sensor network collects multimodal sensor data from the target area, generating raw sensor data. This data includes water level, flow, water quality parameters (such as pH, dissolved oxygen, and turbidity), meteorological data (such as rainfall and temperature), and special sensor data (such as dam deformation monitoring data).

[0092] The collected raw sensor data is subjected to noise filtering to produce filtered data. Different types of sensor data may require different filtering methods. For example, for water level data, a moving average filter might be used to remove short-term fluctuations; for water quality data, a median filter might be used to remove outliers. The filtering process requires striking a balance between removing noise and retaining valid information.

[0093] The filtered data is fed into a data fusion algorithm for fusion, generating preliminary fused data. The data fusion process comprehensively considers data from different sources and types to generate more comprehensive and reliable information. For example, water level data, rainfall data, and upstream water inflow data might be combined to produce more accurate flood forecasts; or water quality data might be combined with ecological survey data to assess the health of a water body.

[0094] Anomaly detection is performed on the initially fused data to generate anomaly-labeled data. Anomaly detection algorithms may be based on statistical methods, machine learning models, or expert rules. For example, if the water level at a certain location suddenly rises abnormally while surrounding rainfall and upstream water inflow have not increased significantly, this may be flagged as an anomaly and require further investigation.

[0095] Finally, the anomaly-labeled data is fused with the preliminary fused data to produce the final multimodal data. This step integrates the anomaly information into the overall data, providing richer context for subsequent analysis. For example, if a water quality anomaly is detected, the associated hydrological, meteorological, and ecological data are also tagged, allowing for a comprehensive analysis of the cause of the anomaly.

[0096] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0097] (1) Sampling and segmenting multimodal data to obtain multiple local data subsets;

[0098] (2) Multiple local data subsets are input into the Laplace differential privacy algorithm to add noise and obtain privacy-preserving data;

[0099] (3) Perform principal component analysis and wavelet transform on the privacy-preserving data to obtain local feature vectors;

[0100] (4) Input the local feature vector into the federated averaging algorithm for weighted averaging to obtain the global parameter set;

[0101] (5) Perform weighted aggregation processing on the global parameter set based on trust to obtain preliminary global features;

[0102] (6) Distribute the preliminary global features to the preset edge computing nodes for gradient descent calculation to obtain local update results;

[0103] (7) Perform homomorphic encryption and secure multi-party computation on the local update results to obtain the global update features;

[0104] (8) Inputting the global updated features into the cross-validation algorithm for performance evaluation to obtain performance indicators;

[0105] (9) Adaptive threshold judgment and trend analysis of performance indicators to obtain iterative decisions;

[0106] (10) Based on the iterative decision, the Adam optimization algorithm is used to optimize the global features to obtain holographic situation awareness data.

[0107] Specifically, multimodal data is sampled and segmented to obtain multiple local data subsets. Taking a large reservoir group as an example, multimodal data includes various types of data, such as water level, flow, water quality, and meteorological data. The sampling and segmentation process may be based on geographic location or data type, for example, dividing upstream, midstream, and downstream reservoir data into different subsets, or classifying hydrological, water quality, and meteorological data separately. This segmentation not only ensures data representativeness but also lays the foundation for subsequent distributed processing. These local data subsets are input into the Laplace differential privacy algorithm for noise addition to obtain privacy-protected data. Differential privacy is an important privacy protection technology that protects individual privacy by adding noise to the original data. In water conservancy inspection scenarios, it can protect sensitive water conservancy facility data. For example, for dam stress data, adding noise can retain overall trend information without revealing specific values, which is crucial for protecting the security of critical infrastructure.

[0108] Principal component analysis and wavelet transform are performed on the privacy-protected data to obtain local feature vectors. Principal component analysis can extract the main features of the data and reduce the data dimension, while wavelet transform can capture the characteristics of the data at different time scales. For example, for water level data, principal component analysis may find that water level changes are mainly affected by upstream water and rainfall, while wavelet transform can capture both short-term fluctuations and long-term seasonal changes. The obtained local feature vectors are input into the federated averaging algorithm for weighted averaging to obtain the global parameter set. Federated averaging is one of the core algorithms of federated learning, which allows model training without sharing the original data. In water conservancy inspections, this means that each reservoir or monitoring station can jointly build a global model while protecting the privacy of its own data. For example, a water level prediction model can be jointly trained based on the historical data of multiple reservoirs.

[0109] The global parameter set is weightedly aggregated based on trust to obtain preliminary global features. This step takes into account the reliability and importance of different data sources. For example, a reservoir equipped with more advanced sensors may receive a higher weight, while a monitoring station known to have measurement errors may be given a lower weight. The preliminary global features are distributed to the preset edge computing nodes for gradient descent calculation to obtain local update results. Edge computing nodes may be distributed in various reservoirs or important monitoring points, and they use local data to fine-tune the global model. For example, upstream reservoirs may pay more attention to inflow forecasts, while downstream reservoirs may pay more attention to flood control scheduling, so they will locally optimize the model according to their respective needs.

[0110] The local update results are homomorphically encrypted and subjected to secure multi-party computation to obtain global update features. These technologies ensure that no sensitive information is leaked when aggregating the update results of each node. For example, even if the specific water level data of a reservoir is confidential, its contribution to the global model can still be securely integrated. The global update features are input into the cross-validation algorithm for performance evaluation to obtain performance indicators. Cross-validation can evaluate the performance of the model on different data subsets to ensure the generalization ability of the model. In water conservancy inspections, this may mean that the model needs to accurately predict water conditions in different seasons and under different hydrological conditions.

[0111] Adaptive threshold judgments and trend analysis are performed on performance indicators to generate iterative decisions. This step determines whether further model optimization is necessary. For example, if the model performs poorly when predicting water level changes under extreme weather conditions, more iterative training may be necessary. Finally, based on the iterative decision, the global features are optimized using the Adam optimization algorithm to generate holographic situational awareness data. The Adam optimization algorithm is an efficient variant of gradient descent that quickly converges to the optimal solution. During this step, the model is continuously adjusted to improve its predictive accuracy and generalization capabilities.

[0112] Through this complex series of data processing and model training steps, the intelligent water conservancy inspection method effectively utilizes large-scale, multi-source, and heterogeneous data while protecting data privacy and security. The resulting holographic situational awareness data provides comprehensive, accurate, and real-time information support for water conservancy management. For example, this data can be used to predict future water conditions, optimize water resource scheduling, and promptly identify potential risks. In practical applications, this method can help water conservancy management departments better cope with complex water conservancy situations, such as accurately predicting flood risks during the flood season and rationally allocating water resources during the dry season, greatly improving the scientific nature and effectiveness of water conservancy management.

[0113] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0114] (1) Perform feature selection and dimensionality reduction on holographic situational awareness data to obtain a key feature set;

[0115] (2) Input the key feature set into the structure learning algorithm to construct the Bayesian network structure and obtain the initial network structure;

[0116] (3) Integrate expert knowledge and modify the initial network structure to obtain an optimized network structure;

[0117] (4) Calculating the conditional probability table by inputting the optimized network structure and the holographic situational awareness data into the parameter learning algorithm to obtain the conditional probability table;

[0118] (5) Perform data verification and outlier processing on the conditional probability table to obtain a modified conditional probability table;

[0119] (6) Integrate the modified conditional probability table with the optimized network structure to obtain the target Bayesian network;

[0120] (7) Perform sensitivity analysis on the target Bayesian network to obtain a set of key nodes;

[0121] (8) Input the key node set into the evidence propagation algorithm to perform probability update calculation and obtain the posterior probability distribution;

[0122] (9) Perform threshold analysis and risk quantification on the posterior probability distribution to obtain preliminary risk assessment results;

[0123] (10) Input the preliminary risk assessment results into the time series analysis algorithm for dynamic trend prediction to obtain the risk evolution trend;

[0124] (11) Perform spatial interpolation and visualization of risk evolution trends to obtain a risk heat map;

[0125] (12) The risk heat map is integrated with the preliminary risk assessment results to obtain a dynamic risk map.

[0126] Specifically, feature selection and dimensionality reduction are performed on holographic situational awareness data to obtain a set of key features. This allows the extraction of the most representative and predictive features from massive amounts of water conservancy data. For example, for a large reservoir system, holographic situational awareness data may include dozens or even hundreds of features, such as water level, flow, rainfall, evaporation, upstream inflow, and downstream water demand. By using principal component analysis (PCA) or correlation-based feature selection methods, it may be possible to identify water level changes as primarily influenced by upstream inflow, rainfall, and evaporation, thus identifying these as key features. This set of key features is then input into a structured learning algorithm to construct a Bayesian network structure, yielding an initial network structure. A Bayesian network is a probabilistic graphical model that effectively represents causal relationships between variables. For example, the algorithm may automatically learn a causal chain where upstream inflow affects reservoir water level, which in turn affects downstream flow. In a water conservancy system, this network structure can reflect the complex interactions between hydrological elements.

[0127] The initial network structure is subjected to expert knowledge fusion and correction to obtain an optimized network structure. Water conservancy experts may adjust the network structure generated by the algorithm based on their experience. For example, experts may add the relationship of the impact of geological conditions on dam safety, because this relationship may be difficult to learn directly from ordinary data. Then, the optimized network structure and holographic situational awareness data are input into the parameter learning algorithm to calculate the conditional probability table to obtain a conditional probability table. This step quantifies the probabilistic relationship between each node in the network. For example, the probability of the reservoir water level exceeding the warning line under different upstream water inflow and rainfall conditions may be calculated. The obtained conditional probability table is subjected to data verification and outlier processing to obtain a revised conditional probability table. This step ensures the rationality of the probability estimate. For example, if the probability estimate in certain extreme cases is obviously unreasonable (such as a high probability of flooding predicted in the dry season), it needs to be corrected.

[0128] The modified conditional probability table is integrated with the optimized network structure to obtain the target Bayesian network. This network now incorporates both data-driven statistical relationships and expert knowledge, fully representing the dynamic characteristics of the water conservancy system. A sensitivity analysis is performed on the target Bayesian network to obtain a set of key nodes. This step identifies the factors that most influence the system state. For example, it may be found that during the flood season, upstream water inflow has the most significant impact on reservoir water levels, while during the dry season, evaporation may become the key factor.

[0129] These key node sets are fed into the evidence propagation algorithm for probability update calculations, resulting in a posterior probability distribution. When new observational data is obtained, such as real-time rainfall data, the algorithm updates the probability distribution of the entire network. This allows the model to adjust its predictions based on the latest situation. Threshold analysis and risk quantification are performed on the posterior probability distribution to obtain preliminary risk assessment results. For example, if the probability of the water level exceeding the warning line exceeds a certain threshold, the system will issue an alarm. Different levels of risk (such as low, medium, and high) may correspond to different probability intervals.

[0130] The preliminary risk assessment results are fed into a time series analysis algorithm for dynamic trend prediction, resulting in a risk evolution trend. This step takes into account the temporal dynamics of risk. For example, the changing flood risk trend may be predicted for the next 24, 48, and 72 hours, providing time margin for flood prevention decisions. Spatial interpolation and visualization of the risk evolution trend are performed to produce a risk heat map. This maps risk information onto geographic space. For example, a heat map showing the future flood risk of a reservoir and its surrounding areas may be generated, with high-risk areas marked in red and low-risk areas in green.

[0131] Finally, the risk heat map is integrated with the preliminary risk assessment results to generate a dynamic risk map. This map not only displays the current risk status but also captures the temporal and spatial evolution of risk. For example, it can show how the risk level at a specific location changes over time, while also illustrating differences in risk across different geographic locations. This enables a comprehensive and dynamic assessment of water conservancy system risk. This method not only reflects the current risk status in real time but also predicts possible future risk evolution. For example, in practical applications, water conservancy authorities can use this dynamic risk map to conduct precise flood control operations: preemptively depleting reservoir capacity when high risk is predicted and appropriately storing water during low-risk periods to prepare for possible droughts. Furthermore, this method provides a scientific basis for long-term planning of water conservancy projects. For example, long-term risk trends can be used to determine whether to reinforce certain levees or construct new water conservancy facilities. Overall, this Bayesian network-based dynamic risk assessment method significantly improves the scientific and forward-looking nature of water conservancy management and provides strong technical support for water conservancy safety.

[0132] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0133] (1) Classify and prioritize the dynamic risk map to obtain a risk priority list;

[0134] (2) Input the risk priority list into the state coding algorithm to construct the state space and obtain a discrete state space;

[0135] (3) Define the action set and design the reward function in the discrete state space to obtain the reinforcement learning environment;

[0136] (4) Input the reinforcement learning environment into the deep Q network to iteratively calculate the Q value and obtain the initial Q value table;

[0137] (5) Perform greedy strategy exploration analysis on the initial Q-value table to obtain the action sequence;

[0138] (6) Input the action sequence into the Monte Carlo tree search algorithm for strategy evaluation to obtain the strategy score;

[0139] (7) Perform multi-objective trade-offs and Pareto optimization on the strategy scores to obtain a non-dominated solution set;

[0140] (8) Input the non-dominated solution set into the decision tree algorithm for rule extraction to obtain the prevention and control decision rules;

[0141] (9) Conduct interpretable analysis and visualization of prevention and control decision rules to obtain active prevention and control strategies;

[0142] (10) Conduct correlation analysis between active prevention and control strategies and dynamic risk maps to obtain inspection reports.

[0143] Specifically, the dynamic risk map is classified and prioritized to produce a risk priority list. For example, for a large reservoir system, risk levels might be categorized into four levels: low, medium, high, and very high, and ranked based on the urgency and potential impact of the risk. For example, a dam leak might be classified as a very high risk with the highest priority, while a minor water quality abnormality might be classified as a medium risk with a lower priority. The risk priority list is then input into a state encoding algorithm to construct a state space, resulting in a discrete state space. This step transforms the complex water conservancy system state into a machine-understandable form. For example, a multidimensional vector can be used to represent the system state, including discrete values of key parameters such as water level, flow rate, and water quality indicators. For dam leak risk, integers ranging from 0 to 3 might be used to represent different states, ranging from "no leak" to "severe leak." Within the discrete state space, action sets are defined and reward functions are designed, resulting in a reinforcement learning environment. The action set might include actions such as "increase inspection frequency," "activate emergency response plan," and "adjust reservoir water level." The reward function reflects the effectiveness of different actions; for example, successfully preventing a flood may receive a high reward, while an unnecessary alarm may receive a negative reward.

[0144] The constructed reinforcement learning environment is fed into a deep Q-network for iterative Q-value calculations, generating an initial Q-value table. The Q-value represents the long-term expected benefit of taking a particular action in a given state. For example, in the state "water levels approach the warning line," the action "increase outflow" may have a high Q-value because it effectively reduces flood risk. A greedy exploration strategy is then applied to the initial Q-value table to generate an action sequence. A greedy strategy tends to select the action with the highest Q-value, but it also randomly selects other actions with a certain probability to explore new possibilities. This may result in a series of decisions, such as "increase outflow from the upstream reservoir" → "strengthen downstream levee inspections" → "prepare flood control supplies." These action sequences are then fed into a Monte Carlo tree search algorithm for policy evaluation, generating a policy score. Monte Carlo tree search evaluates policy effectiveness by simulating a large number of possible scenarios. For example, it might simulate the effects of various flood control strategies under different rainfall conditions to determine a comprehensive score for each strategy.

[0145] The strategy scores are then subjected to multi-objective trade-offs and Pareto optimization to obtain a set of non-dominated solutions. Water management often requires balancing multiple objectives, such as safety, economy, and ecology. Pareto optimization identifies a set of solutions that cannot be improved without sacrificing any of the objectives. For example, a set of strategies may be obtained that strike different balances between flood control safety and ecological protection.

[0146] The non-dominated solution set is fed into a decision tree algorithm for rule extraction, resulting in prevention and control decision rules. Decision trees can simplify complex decision-making processes into a series of if-then rules. For example, a rule might be something like, "If the water level exceeds the warning line and the rainfall forecast is greater than 100 mm, initiate emergency flood discharge."

[0147] Perform interpretable analysis and visualization of prevention and control decision rules to derive proactive prevention and control strategies. This step transforms the machine learning results into a form that is understandable and actionable by humans. This may generate a decision flowchart that clearly illustrates the actions to be taken in different situations.

[0148] Finally, the proactive prevention and control strategies are correlated with the dynamic risk map to generate an inspection report. This report integrates risk assessment results and response strategies, providing comprehensive decision-making support for water conservancy managers. For example, the report might state: "Given the forecast of heavy rainfall within the next 48 hours, there is a high risk of leakage in Dam Area A. It is recommended to immediately increase the frequency of inspections in this area and prepare to initiate an emergency flood discharge plan."

[0149] Through this series of complex calculations and analysis steps, the intelligent water conservancy inspection method achieves full intelligence throughout the entire process, from risk identification to strategy formulation. This approach not only promptly identifies potential risks but also provides targeted response strategies. In practical applications, this can significantly improve the safety and management efficiency of water conservancy systems. For example, before the flood season arrives, the system may automatically generate an optimized flood control scheduling plan based on historical data and current conditions, including when to adjust reservoir water levels and how to coordinate the joint operation of upstream and downstream reservoir groups. Furthermore, the system can continuously adjust its strategies based on real-time monitoring data. For example, if an anomaly is detected in a particular dike, it can immediately adjust the inspection focus and frequency. This dynamic and intelligent management approach greatly enhances the water conservancy system's ability to cope with various complex situations.

[0150] The above describes the intelligent water conservancy inspection method in the embodiment of the present application. The following describes the intelligent water conservancy inspection device in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of the intelligent water conservancy inspection device includes:

[0151] The acquisition module 201 is used to dynamically fuse the acquired multi-dimensional water environment data of the preset target area to obtain a target digital twin;

[0152] An analysis module 202 is configured to perform an intelligent agent collaborative analysis on the digital twin to obtain an adaptive inspection strategy;

[0153] The acquisition module 203 is configured to acquire multimodal sensing data from the target area according to the adaptive inspection strategy to obtain multimodal data;

[0154] A processing module 204 is configured to perform federated learning processing on the multimodal data to obtain holographic situational awareness data;

[0155] The reasoning module 205 is used to perform causal reasoning analysis on the holographic situation awareness data to obtain a dynamic risk map;

[0156] The input module 206 is used to input the dynamic risk map into a preset deep Q network to perform prevention and control strategy analysis to obtain an active prevention and control strategy and an inspection report.

[0157] Through the collaborative cooperation of the above components, the target digital twin is obtained by dynamically fusing and processing multi-dimensional water conservancy environmental data, achieving a high-precision digital representation of complex water conservancy systems. This digital twin can simultaneously consider the interaction of multiple factors such as hydrology, geology, and meteorology, greatly improving the accuracy and predictive ability of the model. Secondly, the digital twin is subjected to intelligent collaborative analysis to obtain an adaptive inspection strategy, which enables the inspection process to be dynamically adjusted according to real-time environmental changes, improving the flexibility and pertinence of the inspection. This enables inspection resources to be allocated more efficiently and reduces unnecessary waste of manpower and material resources. Multimodal sensor data is collected from the target area through the adaptive inspection strategy, resulting in multimodal data, which greatly enriches the data source and improves the comprehensiveness and reliability of the data. This multimodal data collection method can capture complex problems and potential risks that are difficult to detect with a single sensor. In addition, federated learning processing of the multimodal data produces holographic situational awareness data, which not only protects data privacy but also implements distributed learning, improving the efficiency and security of data processing. This application is suitable for processing water conservancy systems with geographically dispersed locations and huge amounts of data. Holographic situational awareness data is subjected to causal reasoning analysis to generate a dynamic risk map, enabling a comprehensive assessment and dynamic prediction of water conservancy system risks, significantly improving the foresight and accuracy of risk prevention and control. By identifying potential causal relationships, this method can uncover hidden risks that are difficult to detect using traditional methods. Finally, the dynamic risk map is input into a pre-configured deep Q network for prevention and control strategy analysis, generating proactive prevention and control strategies and inspection reports, achieving a closed-loop management system from risk identification to strategy formulation. This deep reinforcement learning-based approach enables continuous learning and optimization of prevention and control strategies, adapting to the complex and ever-changing water conservancy environment. This invention achieves comprehensive intelligent water conservancy inspection through the integrated application of advanced technologies such as digital twins, multi-agent collaboration, multimodal data fusion, federated learning, causal reasoning, and deep reinforcement learning. This not only improves the efficiency and accuracy of inspections, but also enhances the risk prediction and prevention capabilities of water conservancy systems. This application enables the timely identification of potential hazards, enabling the implementation of preventive measures in advance, significantly reducing the likelihood of water conservancy disasters. Furthermore, through adaptive inspection strategies and intelligent data analysis, it significantly reduces human resource investment and inspection costs. More importantly, this application establishes an intelligent system that continuously learns and optimizes, which can continuously accumulate experience over time and improve the accuracy of decision-making.

[0158] The present application also provides an intelligent water conservancy inspection device, which includes a memory and a processor. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor executes the steps of the intelligent water conservancy inspection method in the above embodiments.

[0159] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the intelligent water conservancy inspection method.

[0160] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0161] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0162] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent water conservancy inspection method, characterized in that: The intelligent water conservancy inspection method includes: Dynamically fuse the acquired multi-dimensional water conservancy and environmental data of the preset target area to obtain the target digital twin; Performing intelligent agent collaborative analysis on the digital twin to obtain an adaptive inspection strategy; Performing multimodal sensing data collection on the target area according to the adaptive inspection strategy to obtain multimodal data; Performing federated learning processing on the multimodal data to obtain holographic situational awareness data; Performing causal reasoning analysis on the holographic situational awareness data to obtain a dynamic risk map; The dynamic risk map is input into a preset deep Q network for prevention and control strategy analysis to obtain active prevention and control strategies and inspection reports.

2. The intelligent water conservancy inspection method according to claim 1, characterized in that: The dynamically fusion processing of the acquired multi-dimensional water conservancy environment data of the preset target area to obtain the target digital twin includes: Performing spatiotemporal alignment processing on the multi-dimensional water conservancy and environmental data to obtain a water conservancy and environmental data set in a unified coordinate system; Performing outlier detection and removal on the water conservancy environment dataset to obtain a cleaned dataset; Performing a multi-scale wavelet transform on the cleaned data set to obtain a time-frequency feature representation; Inputting the time-frequency feature representation into the autoencoder for feature extraction to obtain a low-dimensional feature vector; Performing principal component analysis on the low-dimensional feature vector to obtain main feature components; Inputting the main characteristic components into a long short-term memory network for time series modeling to obtain a dynamic feature sequence; Performing Kalman filtering on the dynamic feature sequence to obtain a smoothed state estimate; spatially registering the smoothed state estimate with pre-acquired geographic information system data to obtain geographic correlation features; Performing tensor decomposition on the geographic correlation features to obtain multimodal interaction features; The multimodal interaction features are input into a graph neural network for dynamic fusion to obtain the target digital twin.

3. The intelligent water conservancy inspection method according to claim 1, characterized in that: The intelligent agent collaborative analysis of the digital twin is performed to obtain an adaptive inspection strategy, including: Performing multi-agent decomposition on the digital twin to obtain multiple sub-region agents; Performing task allocation on the multiple sub-region intelligent agents to obtain an initial task allocation plan; Inputting the initial task allocation scheme into a reinforcement learning network for training to obtain an agent collaboration strategy; Performing simulation verification on the agent collaboration strategy to obtain simulation result data; Inputting the simulation result data into a genetic algorithm for optimization to obtain an optimized collaborative strategy; Performing a multi-objective evaluation on the optimization collaborative strategy to obtain a strategy evaluation index; Inputting the strategy evaluation index into a fuzzy decision tree for analysis to obtain a decision rule set; Constructing a knowledge graph for the decision rule set to obtain an inspection knowledge graph; Input the inspection knowledge graph into the graph attention network for feature extraction to obtain key inspection features; Dynamic programming calculation is performed on the key inspection features to obtain an adaptive inspection strategy.

4. The intelligent water conservancy inspection method according to claim 1, characterized in that: The multimodal sensing data collection of the target area according to the adaptive inspection strategy to obtain the multimodal data includes: Decomposing the adaptive inspection strategy into multiple subtasks; Inputting the multiple subtasks into a resource allocation algorithm for task allocation to obtain a sensor deployment plan; Optimizing and analyzing the sensor deployment scheme to obtain a sensor network topology; Perform sensor calibration according to the sensor network topology to obtain a target sensor network; Setting a data collection period for the target sensor network to obtain a collection schedule; Based on the collection schedule, multimodal sensor data collection is performed on the target area through the target sensor network to obtain raw sensor data; Performing noise filtering on the raw sensor data to obtain filtered data; Inputting the filtered data into a data fusion algorithm for data fusion to obtain preliminary fused data; Performing anomaly detection on the preliminary fused data to obtain anomaly labeled data; The abnormal labeled data is fused with the preliminary fused data to obtain the multimodal data.

5. The intelligent water conservancy inspection method according to claim 1, characterized in that: The performing of federated learning processing on the multimodal data to obtain holographic situational awareness data includes: Sampling and segmenting the multimodal data to obtain multiple local data subsets; Inputting the multiple local data subsets into the Laplace differential privacy algorithm to add noise to obtain privacy-preserving data; Performing principal component analysis and wavelet transform on the privacy-preserving data to obtain a local feature vector; Inputting the local feature vector into the federated averaging algorithm for weighted averaging calculation to obtain a global parameter set; Performing a weighted aggregation process based on trust on the global parameter set to obtain preliminary global features; Distribute the preliminary global features to each preset edge computing node for gradient descent calculation to obtain a local update result; Performing homomorphic encryption and secure multi-party computation on the local update results to obtain global update features; Inputting the global updated features into a cross-validation algorithm for performance evaluation to obtain a performance index; Performing adaptive threshold judgment and trend analysis on the performance indicators to obtain iterative decisions; According to the iterative decision, the Adam optimization algorithm is used to optimize the global features to obtain the holographic situation awareness data.

6. The intelligent water conservancy inspection method according to claim 1, characterized in that: The causal reasoning analysis of the holographic situational awareness data to obtain a dynamic risk map includes: Performing feature selection and dimensionality reduction processing on the holographic situational awareness data to obtain a key feature set; Inputting the key feature set into a structure learning algorithm to construct a Bayesian network structure to obtain an initial network structure; Performing expert knowledge fusion and correction on the initial network structure to obtain an optimized network structure; Perform conditional probability table calculation on the optimized network structure and the holographic situation awareness data input parameter learning algorithm to obtain a conditional probability table; Performing data verification and outlier processing on the conditional probability table to obtain a modified conditional probability table; Integrating the modified conditional probability table with the optimized network structure to obtain a target Bayesian network; Performing sensitivity analysis on the target Bayesian network to obtain a set of key nodes; Inputting the key node set into the evidence propagation algorithm to perform probability update calculation to obtain the posterior probability distribution; Performing threshold analysis and risk quantification on the posterior probability distribution to obtain a preliminary risk assessment result; Inputting the preliminary risk assessment results into a time series analysis algorithm for dynamic trend prediction to obtain a risk evolution trend; Performing spatial interpolation and visualization processing on the risk evolution trend to obtain a risk heat map; The risk heat map is integrated with the preliminary risk assessment results to obtain a dynamic risk map.

7. The intelligent water conservancy inspection method according to claim 1, characterized in that: The dynamic risk map is input into a preset deep Q network for prevention and control strategy analysis to obtain active prevention and control strategies and inspection reports, including: Performing risk level classification and priority sorting on the dynamic risk map to obtain a risk priority list; Inputting the risk priority list into a state coding algorithm to construct a state space to obtain a discrete state space; Defining an action set and designing a reward function for the discrete state space to obtain a reinforcement learning environment; Inputting the reinforcement learning environment into a deep Q network to perform iterative calculation of Q values to obtain an initial Q value table; Performing greedy strategy exploration analysis on the initial Q value table to obtain an action sequence; Inputting the action sequence into a Monte Carlo tree search algorithm for strategy evaluation to obtain a strategy score; Performing multi-objective trade-offs and Pareto optimization on the strategy scores to obtain a non-dominated solution set; Inputting the non-dominated solution set into a decision tree algorithm to extract rules and obtain prevention and control decision rules; Perform interpretable analysis and visualization on the prevention and control decision rules to obtain active prevention and control strategies; The active prevention and control strategy is correlated with the dynamic risk map to obtain an inspection report.

8. An intelligent water conservancy inspection device, used to implement the intelligent water conservancy inspection method according to any one of claims 1 to 7, characterized in that: The intelligent water conservancy inspection device includes: The acquisition module is used to dynamically fuse the acquired multi-dimensional water conservancy environment data of the preset target area to obtain the target digital twin; An analysis module, configured to perform agent collaborative analysis on the digital twin to obtain an adaptive inspection strategy; An acquisition module, configured to acquire multimodal sensing data from the target area according to the adaptive inspection strategy to obtain multimodal data; a processing module, configured to perform federated learning processing on the multimodal data to obtain holographic situational awareness data; A reasoning module, configured to perform causal reasoning analysis on the holographic situational awareness data to obtain a dynamic risk map; The input module is used to input the dynamic risk map into the preset deep Q network for prevention and control strategy analysis to obtain active prevention and control strategies and inspection reports.

9. An intelligent water conservancy inspection device, characterized in that: The intelligent water conservancy inspection device includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory to enable the intelligent water conservancy inspection device to execute the intelligent water conservancy inspection method according to any one of claims 1 to 7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the intelligent water conservancy inspection method according to any one of claims 1 to 7 is implemented.

Images