Intelligent water conservancy inspection method, device and equipment and storage medium

Abstract

The invention relates to the technical field of data processing, and discloses an intelligent water conservancy inspection method, device and equipment and a storage medium. The method comprises the following steps: performing dynamic fusion processing on acquired multi-dimensional water conservancy environment data of a preset target area to obtain a target digital twinborn body; performing agent collaborative analysis on the digital twinborn body to obtain a self-adaptive inspection strategy; performing multi-modal sensing data acquisition on the target area according to the self-adaptive inspection strategy to obtain multi-modal data; performing federated learning processing on the multi-modal data to obtain holographic situation awareness data; performing causal reasoning analysis on the holographic situation awareness data to obtain a dynamic risk map; and 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. According to the invention, the efficiency and accuracy of intelligent water conservancy inspection are improved.

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 mainly rely on manual inspections and fixed sensor monitoring. These methods usually include regular on-site inspections, water quality sampling and analysis, water level monitoring, etc. With the development of technology, some automated systems have been introduced, such as remote sensing technology and drone inspections, to improve the efficiency and coverage of inspections. These methods have improved the supervision capabilities and emergency response speed of water conservancy facilities to a certain extent.

[0003] However, these traditional methods have obvious limitations. First, manual inspections are easily affected by subjective factors and it is difficult to ensure comprehensiveness and consistency. Second, although fixed sensor networks can provide continuous monitoring data, they often have limited coverage and are difficult to cope with complex and changing water conservancy environments. In addition, these methods are usually passive and lack the ability to predict potential risks. Most importantly, existing technologies have difficulty in effectively integrating multi-source heterogeneous data, and cannot fully utilize historical data and expert knowledge for intelligent decision-making, resulting in low inspection efficiency and difficulty in coping with 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 fusion processing of 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 sensor 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 situation awareness data; performing causal reasoning analysis on the holographic situation 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 a 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 a second implementation method of the first aspect of the present application, the digital twin is subjected to an intelligent agent collaborative analysis to obtain an adaptive inspection strategy, including: performing a multi-agent decomposition on the digital twin to obtain multiple sub-area agents; performing task assignments on the multiple sub-area agents to obtain an initial task assignment plan; inputting the initial task assignment plan into a reinforcement learning network for training to obtain an intelligent agent collaborative strategy; performing simulation verification on the intelligent agent 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 a 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 calculations 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, the multimodal sensor data collection of the target area according to the adaptive inspection strategy to obtain multimodal data includes: decomposing 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 the sensor 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, collecting multimodal sensor data of 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 to add noise 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 each preset edge computing node 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 a 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 according to the iterative decisions to obtain the holographic situational awareness data.

[0010] In combination with the first aspect, in a fifth implementation method of the first aspect of the present application, the causal reasoning analysis of the holographic situation awareness data is performed to obtain a dynamic risk map, including: performing feature selection and dimensionality reduction processing on the holographic situation 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; fusing and correcting the initial network structure with expert knowledge to obtain an optimized network structure; inputting the optimized network structure and the holographic situation 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 the 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 for 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 for 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 a 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 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 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 between the active prevention and control strategy and 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] An acquisition module is used to dynamically fuse the acquired multi-dimensional water conservancy environment data of a preset target area to obtain a target digital twin;

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

[0015] A collection module, used for collecting multimodal sensing data of the target area according to the adaptive inspection strategy to obtain multimodal data;

[0016] A processing module, used for performing federated learning processing on the multimodal data to obtain holographic situation awareness data;

[0017] A reasoning module, used for performing causal reasoning analysis on the holographic situation 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 so that the intelligent water conservancy inspection device executes the above-mentioned intelligent water conservancy inspection method.

[0020] A fourth aspect of the present application provides a computer-readable storage medium, in which instructions are stored. When the computer-readable storage medium is run on a computer, the computer is enabled to execute the above-mentioned intelligent water conservancy inspection method.

[0021] In the technical solution provided by this application, the target digital twin is obtained by dynamically fusion processing of multi-dimensional water conservancy environmental data, and a high-precision digital expression of a complex water conservancy system is achieved. This digital twin can simultaneously consider the interaction of multiple factors such as hydrology, geology, and meteorology, greatly improving the accuracy and prediction ability of the model. Secondly, the digital twin is subjected to intelligent collaborative analysis to obtain an adaptive inspection strategy, so that the inspection process can be dynamically adjusted according to real-time environmental changes, improving the flexibility and pertinence of the inspection. Inspection resources can be allocated more efficiently, reducing unnecessary waste of manpower and material resources. Through the adaptive inspection strategy, multimodal sensing data is collected in the target area to obtain 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, the multimodal data is processed by federated learning 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. This application is suitable for processing water conservancy systems with dispersed geographical locations and huge amounts of data. The holographic situational awareness data is subjected to causal reasoning analysis to obtain a dynamic risk map, which realizes a comprehensive assessment and dynamic prediction of the risks of the water conservancy system, greatly improving the foresight and accuracy of risk prevention and control. By identifying potential causal relationships, this method can discover hidden risks that are difficult to detect with traditional methods. Finally, the dynamic risk map is input into the preset deep Q network for prevention and control strategy analysis to obtain active prevention and control strategies and inspection reports, realizing closed-loop management from risk identification to strategy formulation. This method based on deep reinforcement learning can continuously learn and optimize prevention and control strategies to adapt to complex and changeable water conservancy environments. The present invention realizes the comprehensive intelligence of water conservancy inspections through the comprehensive application of advanced technologies such as digital twins, multi-agent collaboration, multimodal data fusion, federated learning, causal reasoning and deep reinforcement learning. It not only improves the efficiency and accuracy of inspections, but also enhances the risk prediction and prevention and control capabilities of water conservancy systems. The present application can timely discover potential hidden dangers, take preventive measures in advance, and significantly reduce the possibility of water conservancy disasters. At the same time, through adaptive inspection strategies and intelligent data analysis, the investment in human resources is greatly reduced and the inspection costs are reduced. More importantly, this application establishes an intelligent system that continuously learns and optimizes, which can continuously accumulate experience and improve the accuracy of decision-making over time. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying 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] Embodiments of the present application provide a method, device, equipment and storage medium for intelligent water conservancy inspection. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. 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 comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or 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 , an embodiment of the intelligent water conservancy inspection method in the embodiment of the present application includes:

[0027] Step S101, dynamically fusion processing is performed on the acquired multi-dimensional water conservancy environment data of the preset target area to obtain a target digital twin;

[0028] Step S102: Performing intelligent agent 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 the present application may be an intelligent water conservancy inspection device, or a terminal or a server, which is not limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0034] Specifically, data including hydrology, geology, meteorology, etc. 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 time-space alignment, and then outlier detection and cleaning are performed. Next, the time-frequency features are extracted by multi-scale wavelet transform, and the autoencoder is used for feature extraction and dimensionality reduction. After principal component analysis, the 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 the geographic information system data, and finally dynamically fused through the graph neural network to form the target digital twin.

[0035] The digital twin is analyzed by agents in a collaborative way to obtain an adaptive inspection strategy. This process first decomposes the digital twin into multiple sub-region agents, such as dividing the reservoir into dam, spillway, water inlet and other areas. Then, these agents are assigned preliminary tasks and trained with a reinforcement learning network to obtain a collaborative strategy. This strategy is verified by simulation and optimized by genetic algorithm, and then a decision rule set is generated through fuzzy decision tree analysis. These rules are constructed into an inspection knowledge graph, and after the key features are extracted by the graph attention network, the final adaptive inspection strategy is obtained through dynamic programming calculation. According to the adaptive inspection strategy, the system collects multimodal sensor data for the target area. This step first decomposes the inspection strategy into specific subtasks, such as checking dam leakage and monitoring water quality changes. Then, a sensor deployment plan is formulated through a resource allocation algorithm to optimize the sensor network topology. According to this topology, the system calibrates the sensor, sets the data collection cycle, and starts collecting multimodal data. These raw data are processed by noise filtering, data fusion and anomaly detection to form a high-quality multimodal data set. These multimodal data are processed by federated learning to obtain holographic situation awareness data. First, the data is divided into multiple local data subsets and distributed on different edge computing nodes. Each subset is processed by the Laplace differential privacy algorithm to protect privacy, and then feature extraction is performed. The local feature vectors of each node are weighted averaged by the federated averaging algorithm to obtain a global parameter set. This parameter set is weighted 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 update feature. The system cross-validates and evaluates the performance of this feature, and decides whether further iteration is needed based on the evaluation results. Finally, the global features are optimized by the Adam optimization algorithm to obtain holographic situational awareness data.

[0036] The holographic situational awareness data is then analyzed for causal reasoning to obtain a dynamic risk map. This process first performs feature selection and dimensionality reduction on the data to extract key features. Then, the initial Bayesian network structure is constructed using a structural learning algorithm and optimized in combination with expert knowledge. The optimized network structure is input into the parameter learning algorithm together with the holographic situational awareness data to calculate the conditional probability table. After verification and outlier processing, this probability table is integrated with the network structure to form a complete Bayesian network. The system 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. This result is then used to predict the risk evolution trend through time series analysis, and finally a dynamic risk map is formed through spatial interpolation and visualization.

[0037] Finally, the dynamic risk map is input into the preset deep Q network for prevention and control strategy analysis to obtain active prevention and control strategies and inspection reports. First, the risk map is graded and prioritized, and then the discrete state space is constructed using the state encoding algorithm. The action set and reward function are defined in this space to form a reinforcement learning environment. The deep Q network performs iterative calculations of Q values ​​in this environment to obtain the initial Q value table. The system performs greedy strategy exploration on this table to generate action sequences. These sequences are evaluated through Monte Carlo tree search to obtain strategy scores. The scoring results are subjected to multi-objective trade-offs and Pareto optimization to form a non-dominated solution set. The decision tree algorithm is used to extract prevention and control decision rules from this solution set, and after interpretability analysis and visualization processing, an active prevention and control strategy is finally formed. This strategy is associated with the dynamic risk map to generate the final inspection report.

[0038] In the embodiment of the present application, by dynamically fusion processing the multi-dimensional water conservancy environment data, the target digital twin is obtained, and the high-precision digital expression of the complex water conservancy system is realized. This digital twin can simultaneously consider the interaction of multiple factors such as hydrology, geology, and meteorology, greatly improving the accuracy and prediction ability of the model. Secondly, the digital twin is subjected to intelligent collaborative analysis to obtain an adaptive inspection strategy, so that the inspection process can be dynamically adjusted according to real-time environmental changes, improving the flexibility and pertinence of the inspection. Inspection resources can be allocated more efficiently, reducing unnecessary waste of manpower and material resources. Through the adaptive inspection strategy, multimodal sensing data is collected in the target area to obtain 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, the multimodal data is federated learning processed 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. This application is suitable for processing water conservancy systems with dispersed geographical locations and huge amounts of data. The holographic situational awareness data is subjected to causal reasoning analysis to obtain a dynamic risk map, which realizes a comprehensive assessment and dynamic prediction of the risks of the water conservancy system, greatly improving the foresight and accuracy of risk prevention and control. By identifying potential causal relationships, this method can discover hidden risks that are difficult to detect with traditional methods. Finally, the dynamic risk map is input into the preset deep Q network for prevention and control strategy analysis to obtain active prevention and control strategies and inspection reports, realizing closed-loop management from risk identification to strategy formulation. This method based on deep reinforcement learning can continuously learn and optimize prevention and control strategies to adapt to complex and changeable water conservancy environments. The present invention realizes the comprehensive intelligence of water conservancy inspections through the comprehensive application of advanced technologies such as digital twins, multi-agent collaboration, multimodal data fusion, federated learning, causal reasoning and deep reinforcement learning. It not only improves the efficiency and accuracy of inspections, but also enhances the risk prediction and prevention and control capabilities of water conservancy systems. The present application can timely discover potential hidden dangers, take preventive measures in advance, and significantly reduce the possibility of water conservancy disasters. At the same time, through adaptive inspection strategies and intelligent data analysis, the investment in human resources is greatly reduced and the inspection costs are reduced. More importantly, this application establishes an intelligent system that continuously learns and optimizes, which can continuously accumulate experience and improve the accuracy of decision-making over time.

[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 data set 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 characteristic 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 aligning the smoothed state estimate with pre-acquired GIS 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, the multi-dimensional water conservancy and environmental data are processed in time and space. For example, for a large reservoir, water level, water quality, rainfall, and surface deformation data may be collected at the same time. These data may come from different sensors with different time resolutions and spatial reference systems. The time and space alignment process will unify all data into a common time scale (such as one data point per hour) and spatial coordinate system (such as WGS84 coordinate system), thereby obtaining a water conservancy and environmental data set under a unified coordinate system. Outliers are detected and removed from this unified data set. Statistical methods such as Z-score or machine learning methods such as the isolation forest algorithm may be used here 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 data set is obtained.

[0051] The cleaned data set is subjected to a multi-scale wavelet transform to obtain a time-frequency feature representation. Wavelet transform can analyze signals at different time scales and is particularly suitable for processing non-stationary time series such as hydrological data. For example, it can 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 trying to reconstruct the input. In this process, the autoencoder may learn the underlying relationship between, for example, water level and rainfall, resulting in a low-dimensional feature vector.

[0052] This low-dimensional feature vector is subjected to principal component analysis to further reduce the dimension and extract the main characteristic components. Principal component analysis can find the main direction of change in the data. For example, it may find that the change in reservoir water level is mainly affected by two factors: rainfall and upstream water flow. The main characteristic components obtained are input into the long short-term memory (LSTM) network for time series modeling. The LSTM network is good at capturing long-term dependencies and is very suitable for processing time series with long-term memory effects such as hydrological data. For example, it can learn how upstream rainfall affects downstream water levels over time. Through the LSTM network, a dynamic feature sequence is obtained.

[0053] This dynamic feature sequence is Kalman filtered 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, surrounding geological information, etc. Spatial alignment associates 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 obtains geographic correlation features. These geographic correlation features are tensor decomposed to obtain multimodal interaction features. Tensor decomposition can capture high-order correlations between different modal data. For example, it may find 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 input into the graph neural network for dynamic fusion to obtain the target digital twin. Graph neural networks are particularly suitable for processing data with spatial relationships. In the water conservancy system, there are spatial associations between different monitoring points, and graph neural networks can effectively model this relationship. For example, it can learn how upstream water level changes propagate to downstream, or how geological conditions in different regions affect the 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) Allocate tasks to multiple sub-region agents and obtain an initial task allocation plan;

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

[0059] (4) Conduct simulation verification on the agent collaboration strategy to 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 collaboration 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 agents to obtain multiple sub-area agents. Taking a large reservoir as an example, it may be decomposed into multiple sub-areas such as dam, spillway, water inlet, reservoir area, etc. Each sub-area is regarded as an agent with its own state, behavior and goal. For example, the dam agent may focus on structural integrity and leakage, while the reservoir agent may focus on water quality and ecological environment. Tasks are assigned to these sub-area agents to obtain an initial task allocation scheme. Task allocation takes into account the characteristics and current state of each agent. For example, if there is heavy rainfall in the near future, more resources may be allocated to the dam and spillway agents for inspection; if water quality is abnormal somewhere in the reservoir area, the inspection frequency of the area may be increased. The initial task allocation scheme includes inspection routes, frequencies, key focus items, etc.

[0067] This initial task allocation scheme is input into the reinforcement learning network for training to obtain the agent coordination strategy. The reinforcement learning network learns the optimal strategy by constantly trying different inspection strategies and evaluating their effects. For example, the network may learn to increase the frequency of dam inspections during the flood season and pay more attention to water quality monitoring during the dry season. The coordination strategy not only considers the tasks of a single agent, but also the collaboration between agents, such as the collaborative work of dam and spillway agents to deal with flood risks.

[0068] The obtained agent coordination strategy is simulated and verified to obtain simulation result data. Various extreme situations may be simulated during the simulation process, such as sudden heavy rainfall and earthquakes, to test the effectiveness and robustness of the coordination strategy. The simulation result data includes various performance indicators, such as risk detection rate, resource utilization efficiency, response time, etc.

[0069] Then, the simulation result data is input into the genetic algorithm for optimization to obtain the optimized collaborative strategy. The genetic algorithm simulates the biological evolution process, continuously selects, crosses and mutates to generate better strategies. For example, it may be possible to generate a new strategy that performs well in many situations by combining the characteristics of multiple strategies that perform well.

[0070] The optimized collaborative strategy is evaluated by multiple objectives to obtain the strategy evaluation indicators. The evaluation indicators include multiple dimensions such as security (such as risk detection rate), efficiency (such as inspection coverage), and economy (such as resource consumption). This step requires balancing multiple potentially conflicting objectives, such as improving security may increase economic costs.

[0071] These strategy evaluation indicators are input into the fuzzy decision tree for analysis to obtain a set of decision rules. The fuzzy decision tree can handle the fuzzy relationship and uncertainty between indicators. For example, a rule such as "if the rainfall is heavy and the water level is close to the warning line, the frequency of dam inspections will be greatly increased" may be generated.

[0072] The knowledge graph of the decision rule set is constructed to obtain the inspection knowledge graph. The knowledge graph represents various rules, entities and relationships in the form of a graph. For example, it may contain nodes such as "rainfall", "water level", "dam inspection", and the relationships between them. This representation method helps to capture complex causal relationships and dependencies.

[0073] The constructed inspection knowledge graph is input into the graph attention network for feature extraction to obtain key inspection features. The graph attention network can automatically learn the importance of each node in the graph and extract the most critical features. For example, it may be found that in certain cases, the rate of water level change is more important than the absolute water level, or that dam leakage detection should receive special attention under certain conditions.

[0074] Finally, the extracted key inspection features are subjected to dynamic programming calculations to obtain an adaptive inspection strategy. Dynamic programming takes into account decision optimization in time series and can formulate the optimal strategy based on the current state and expected future state. For example, a strategy may be generated to dynamically adjust the inspection focus and frequency based on weather forecasts, upstream water conditions, etc.

[0075] This adaptive inspection strategy is the final output of the entire process, which can flexibly adjust the inspection plan according to real-time conditions. For example, if an abnormal leakage is detected in a certain part of the dam, the strategy may immediately increase the inspection frequency of that area, while adjusting the inspection plan of other areas to ensure the effective use of overall resources. Or, when heavy rainfall is expected, the strategy may increase the inspection of drainage systems and slope stability in advance.

[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) calibrating the sensors 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 on 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 a data fusion algorithm for data fusion to obtain preliminary fused data;

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

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

[0087] Specifically, the adaptive inspection strategy is task-decomposed to obtain multiple subtasks. Taking a large reservoir as an example, the adaptive inspection strategy includes multiple aspects such as dam structure, water quality monitoring, and ecological environment assessment. These tasks are decomposed into more specific subtasks, such as dam leakage detection, water eutrophication monitoring, and fish population surveys. Each subtask has its specific monitoring requirements and importance weights.

[0088] These subtasks are input into the resource allocation algorithm for task allocation, and the sensor deployment plan is obtained. The resource allocation algorithm takes into account the importance of each subtask, the difficulty of monitoring, and the types and number of available sensors. For example, for dam leakage detection, high-precision pressure sensors and fiber optic sensors may be allocated; for water quality monitoring, multi-parameter water quality sensors and automatic samplers may be deployed. The algorithm weighs 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, energy consumption, and data transmission efficiency between sensors. For example, temperature sensors may be set at different depths in the reservoir to form a vertical profile monitoring network; or a series of water level sensors may be arranged along the river to build a flood warning network. The optimized network topology must ensure comprehensive data collection and network reliability and efficiency.

[0089] Sensor calibration is performed according to the determined sensor network topology to obtain the target sensor network. The calibration process ensures that each sensor can provide accurate data at its specific location. For example, a water level sensor needs to be calibrated according to its installation elevation; a water quality sensor needs to be calibrated regularly using a standard solution. This step is critical to ensuring the reliability of the data.

[0090] Set the data collection cycle for the target sensor network to obtain a collection schedule. Different types of sensors and different monitoring targets may require different collection frequencies. For example, water level data may need to be collected once an hour, while water quality data may be collected several times a day. During the flood season, the collection frequency of certain key parameters may increase. The design of the collection schedule needs to balance data accuracy and system resource consumption.

[0091] Based on the established collection schedule, multimodal sensor data is collected in the target area through the target sensor network to obtain raw sensor data. These data include water level, flow, water quality parameters (such as pH, dissolved oxygen, turbidity, etc.), meteorological data (such as rainfall, temperature), and special sensor data (such as dam deformation monitoring data).

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

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

[0094] Anomaly detection is performed on the initially fused data to obtain 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 the surrounding rainfall and upstream water flow have not increased significantly, this may be marked as an anomaly and require further investigation.

[0095] Finally, the anomaly labeled data is fused with the preliminary fused data to obtain the final multimodal data. This step integrates the anomaly information into the overall data to provide richer context for subsequent analysis. For example, if water quality anomalies are detected, the relevant hydrological, meteorological, and ecological data will be marked together to fully analyze 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) Input multiple local data subsets 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) Performing a weighted aggregation process on the global parameter set based on trust to obtain preliminary global features;

[0102] (6) Distribute the preliminary global features to each preset edge computing node 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) According to the iterative decision, the Adam optimization algorithm is used to optimize the global features to obtain holographic situation awareness data.

[0107] Specifically, the multimodal data is sampled and segmented to obtain multiple local data subsets. Taking a large reservoir group as an example, the multimodal data includes multiple types of data such as water level, flow, water quality, and meteorology. The sampling and segmentation process may be based on geographic location or data type, such as dividing the reservoir data of the upstream, midstream, and downstream into different subsets, or classifying the hydrological, water quality, and meteorological data separately. Such segmentation not only ensures the representativeness of the data, 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 the water conservancy inspection scenario, sensitive water conservancy facility data can be protected. For example, for the stress data of the dam, adding noise can still retain the overall trend information without leaking the specific values, which is crucial to protecting the security of critical infrastructure.

[0108] Perform principal component analysis and wavelet transform 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 vector is input into the federated average algorithm for weighted average calculation to obtain a global parameter set. Federated average is one of the core algorithms of federated learning, which allows model training without sharing 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 historical data from multiple reservoirs.

[0109] The global parameter set is weighted and 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 with known 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 securely multi-party computed 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 judgment and trend analysis are performed on the performance indicators to obtain iterative decisions. This step determines whether the model needs to be further optimized. For example, if the model performs poorly in predicting water level changes under extreme weather conditions, more iterative training may be required. Finally, the Adam optimization algorithm is used to optimize the global features according to the iterative decision to obtain holographic situational awareness data. The Adam optimization algorithm is an efficient gradient descent variant that can quickly converge to the optimal solution. In this step, the model is continuously adjusted to improve its prediction accuracy and generalization ability.

[0112] Through this series of complex data processing and model training steps, the intelligent water conservancy inspection method realizes the effective use of large-scale, multi-source, heterogeneous data while protecting data privacy and security. The final holographic situational awareness data provides comprehensive, accurate, and real-time information support for water conservancy management. For example, these data can be used to predict future water conditions, optimize water resources scheduling, and timely detect 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 scientificity 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 situation awareness data to obtain a key feature set;

[0115] (2) Inputting 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 situation awareness data into the parameter learning algorithm to obtain the conditional probability table;

[0118] (5) Performing 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) Conduct 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 processing are performed on the holographic situational awareness data to obtain a key feature set. The most representative and predictive features can be extracted from massive water conservancy data. For example, for a large reservoir system, the holographic situational awareness data includes dozens or even hundreds of features such as water level, flow, rainfall, evaporation, upstream water, and downstream water demand. By using principal component analysis (PCA) or correlation-based feature selection methods, it may be found that water level changes are mainly affected by upstream water, rainfall, and evaporation, which are used as key features. These key feature sets are input into the structure learning algorithm to construct the Bayesian network structure to obtain the initial network structure. The Bayesian network is a probabilistic graph model that can effectively represent the causal relationship between variables. For example, the algorithm may automatically learn the causal chain that the upstream water flow affects the reservoir water level, and the reservoir water level affects the downstream flow. In the water conservancy system, this network structure can reflect the complex interaction relationship 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 probability relationship between nodes in the network. For example, the probability that the reservoir water level exceeds 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 some extreme cases is obviously unreasonable (such as a high probability of flood prediction 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 contains both data-driven statistical relationships and expert knowledge, and can fully represent 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 have the greatest impact on the system state. For example, it may be found that during the flood season, the upstream water flow has the most significant impact on the reservoir water level, while during the dry season, evaporation may become a key factor.

[0129] These key node sets are input into the evidence propagation algorithm for probability update calculation to obtain the posterior probability distribution. When new observation data is obtained, such as real-time rainfall data, the algorithm will update the probability distribution of the entire network. This enables 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 sound 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 input into the time series analysis algorithm for dynamic trend prediction to obtain the risk evolution trend. This step takes into account the temporal dynamics of the risk. For example, the trend of flood risk changes in the next 24 hours, 48 ​​hours, and 72 hours may be predicted to provide time margin for flood control decisions. The risk evolution trend is spatially interpolated and visualized to obtain a risk heat map. This maps the risk information to the geographic space. For example, a heat map showing the future flood risk of the 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 obtain a dynamic risk map. This map not only shows the current risk status, but also includes the temporal and spatial evolution trend of the risk. For example, it may show the change of the risk level of a specific location over time, and also show the risk differences between different geographical locations. A comprehensive and dynamic assessment of the risk of the water conservancy system is achieved. This method can not only reflect the current risk status in real time, but also predict the possible evolution of risks in the future. For example, in practical applications, water conservancy management departments can use this dynamic risk map to carry out accurate flood control scheduling: empty the reservoir capacity in advance when high risks are predicted, and store water moderately in low-risk periods to cope with possible droughts. At the same time, this method also provides a scientific basis for the long-term planning of water conservancy projects, such as whether it is necessary to strengthen certain embankments or build new water conservancy facilities based on long-term risk trends. In general, this dynamic risk assessment method based on Bayesian networks has greatly improved the scientific nature and foresight of water conservancy management and provided 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 and 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 a 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) Inputting 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 divided into risk levels and prioritized to obtain a risk priority list. For example, for a large reservoir system, the risk levels may be divided into four levels: low, medium, high, and extremely high, and ranked according to the urgency and potential impact of the risk. For example, dam leakage may be listed as extremely high risk and have the highest priority, while a slight abnormality in water quality may be listed as a medium risk and have a lower priority. The risk priority list is input into the state encoding algorithm to construct the state space and obtain a discrete state space. This step converts the state of a complex water conservancy system into a machine-understandable form. For example, a multidimensional vector can be used to represent the system state, which includes discrete values ​​of key parameters such as water level, flow, and water quality indicators. For the risk of dam leakage, integers from 0 to 3 may be used to represent different states from "no leakage" to "serious leakage". The discrete state space is used to define the action set and design the reward function to obtain a reinforcement learning environment. The action set may include operations such as "increase inspection frequency", "start emergency 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 input into the deep Q network for iterative calculation of Q value to obtain the initial Q value table. The Q value represents the long-term expected benefit of taking a certain action in a certain state. For example, in the state of "water level is close to the warning line", the Q value of the action "increase the discharge flow" may be high because it can effectively reduce the risk of flooding. The initial Q value table is analyzed by greedy strategy exploration to obtain the action sequence. The greedy strategy tends to choose the action with the highest Q value, but it will also randomly select other actions with a certain probability to explore new possibilities. This may produce a series of decisions, such as "increase the discharge flow of the upstream reservoir" → "strengthen the downstream embankment inspection" → "prepare flood control materials". These action sequences are input into the Monte Carlo tree search algorithm for strategy evaluation to obtain the strategy score. Monte Carlo tree search evaluates the effectiveness of the strategy by simulating a large number of possible scenarios. For example, it may simulate the effects of various flood control strategies under different rainfall conditions to obtain a comprehensive score for each strategy.

[0145] The strategy scores are subjected to multi-objective trade-offs and Pareto optimization to obtain a set of non-dominated solutions. In water management, it is often necessary to balance multiple objectives such as safety, economy, and ecology. Pareto optimization finds a set of solutions that cannot be improved without sacrificing any of the objectives. For example, a set of strategies may be obtained that achieve different balances between flood control safety and ecological protection.

[0146] The non-dominated solution set is input into the decision tree algorithm for rule extraction to obtain the prevention and control decision rules. The decision tree can simplify the complex decision-making process into a series of if-then rules. For example, a rule such as "if the water level exceeds the warning line and the rainfall forecast is greater than 100mm, then start emergency flood discharge."

[0147] Perform interpretable analysis and visualization of prevention and control decision rules to obtain active prevention and control strategies. This step converts the results of machine learning into a form that humans can understand and operate. A decision flowchart may be generated to clearly show the actions that should be taken in different situations.

[0148] Finally, the active prevention and control strategies are correlated with the dynamic risk map to obtain the inspection report. This report combines the risk assessment results and response strategies to provide comprehensive decision-making support for water conservancy managers. For example, the report may state: "In view of the heavy rainfall expected in the next 48 hours, there is a high risk of leakage in area A of the dam. It is recommended to immediately increase the inspection frequency of this area and prepare to initiate an emergency flood discharge plan."

[0149] Through this series of complex calculation and analysis steps, the intelligent water conservancy inspection method realizes the intelligence of the whole process from risk identification to strategy formulation. This method can not only detect potential risks in a timely manner, but also provide targeted response strategies. In practical applications, this can greatly improve the safety and management efficiency of water conservancy systems. For example, before the flood season, the system may automatically generate a set of optimized flood control scheduling plans based on historical data and current conditions, including when to adjust the reservoir water level and how to coordinate the joint scheduling of upstream and downstream reservoir groups. At the same time, the system can also continuously adjust strategies based on real-time monitoring data. For example, when an abnormality is detected in a certain levee, the inspection focus and frequency are immediately adjusted. This dynamic and intelligent management method has greatly enhanced the ability of the water conservancy system 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] An acquisition module 201 is used to dynamically fuse the acquired multi-dimensional water conservancy environment data of a preset target area to obtain a target digital twin;

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

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

[0154] A processing module 204 is used to perform federated learning processing on the multimodal data to obtain holographic situation 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 for prevention and control strategy analysis to obtain active prevention and control strategies and inspection reports.

[0157] Through the collaborative cooperation of the above-mentioned components, the target digital twin is obtained by dynamically fusion processing of multi-dimensional water conservancy environmental data, and a high-precision digital expression of a complex water conservancy system is realized. 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, so that the inspection process can be dynamically adjusted according to real-time environmental changes, improving the flexibility and pertinence of the inspection. Inspection resources can be allocated more efficiently, reducing unnecessary waste of manpower and material resources. Through the adaptive inspection strategy, multimodal sensing data is collected in the target area to obtain 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, the multimodal data is processed by federated learning 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. This application is suitable for processing water conservancy systems with dispersed geographical locations and huge amounts of data. The holographic situational awareness data is subjected to causal reasoning analysis to obtain a dynamic risk map, which realizes a comprehensive assessment and dynamic prediction of the risks of the water conservancy system, greatly improving the foresight and accuracy of risk prevention and control. By identifying potential causal relationships, this method can discover hidden risks that are difficult to detect with traditional methods. Finally, the dynamic risk map is input into the preset deep Q network for prevention and control strategy analysis to obtain active prevention and control strategies and inspection reports, realizing closed-loop management from risk identification to strategy formulation. This method based on deep reinforcement learning can continuously learn and optimize prevention and control strategies to adapt to complex and changeable water conservancy environments. The present invention realizes the comprehensive intelligence of water conservancy inspections through the comprehensive application of advanced technologies such as digital twins, multi-agent collaboration, multimodal data fusion, federated learning, causal reasoning and deep reinforcement learning. It not only improves the efficiency and accuracy of inspections, but also enhances the risk prediction and prevention and control capabilities of water conservancy systems. The present application can timely discover potential hidden dangers, take preventive measures in advance, and significantly reduce the possibility of water conservancy disasters. At the same time, through adaptive inspection strategies and intelligent data analysis, the investment in human resources is greatly reduced and the inspection costs are reduced. More importantly, this application establishes an intelligent system that continuously learns and optimizes, which can continuously accumulate experience and improve the accuracy of decision-making over time.

[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-mentioned 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 can 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 to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned 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 comprises: Dynamically fuse the acquired multi-dimensional water conservancy environment 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; 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 situation awareness data; Performing causal reasoning analysis on the holographic situation 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 is 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 data set to obtain a cleaned data set; 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 an 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 characteristic 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 the graph neural network for dynamic fusion to obtain the target digital twin.

3. The intelligent water conservancy inspection method according to claim 1 is characterized in that: The intelligent agent collaborative analysis of the digital twin is performed to obtain an adaptive inspection strategy, including: Decomposing the digital twin into multiple agents to obtain multiple sub-region agents; Allocating tasks to the multiple sub-region 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 coordination strategy; Performing simulation verification on the intelligent 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 is characterized in that: The step of collecting multimodal sensing data of the target area according to the adaptive inspection strategy to obtain 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 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 labeled data; The abnormal marked data is fused with the preliminary fused data to obtain the multimodal data.

5. The intelligent water conservancy inspection method according to claim 1 is characterized in that: The performing of federated learning processing on the multimodal data to obtain holographic situation awareness data includes: Sampling and segmenting the multimodal data to obtain multiple local data subsets; Inputting the plurality of local data subsets into the Laplace differential privacy algorithm to add noise, thereby obtaining privacy-preserving data; Performing principal component analysis and wavelet transform on the privacy protection data to obtain a local feature vector; Inputting the local feature vector into a federated averaging algorithm for weighted averaging calculation to obtain a global parameter set; Performing a weighted aggregation process based on the degree of trust on the global parameter set to obtain a preliminary global feature; 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 update feature 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 perform feature optimization processing on the global features to obtain the holographic situation awareness data.

6. The intelligent water conservancy inspection method according to claim 1 is characterized in that: The causal reasoning analysis of the holographic situation awareness data is performed to obtain a dynamic risk map, including: Performing feature selection and dimensionality reduction processing on the holographic situation 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 result to obtain a dynamic risk map.

7. The intelligent water conservancy inspection method according to claim 1 is 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; Perform 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 interpretability 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 comprises: An acquisition module is used to dynamically fuse the acquired multi-dimensional water conservancy environment data of a preset target area to obtain a target digital twin; An analysis module, used to perform intelligent agent collaborative analysis on the digital twin to obtain an adaptive inspection strategy; A collection module, used for collecting multimodal sensing data of the target area according to the adaptive inspection strategy to obtain multimodal data; A processing module, used for performing federated learning processing on the multimodal data to obtain holographic situation awareness data; A reasoning module, used for performing causal reasoning analysis on the holographic situation awareness data to obtain a dynamic risk map; 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.

9. An intelligent water conservancy inspection device, characterized in that: The intelligent water conservancy inspection device comprises: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory so that the intelligent water conservancy inspection device executes the intelligent water conservancy inspection method as described in any one of claims 1-7.

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 as described in any one of claims 1 to 7 is implemented.

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