Water conservancy emergency resilience control method and system based on digital twinning and knowledge graph

Abstract

This invention relates to the field of intelligent water conservancy engineering technology, specifically to a water conservancy emergency resilience control method and system based on digital twins and knowledge graphs. It includes a spatiotemporal knowledge graph based on integrated engineering entities, historical cases, and expert rules; real-time matching of disaster scenarios using graph computing to generate preliminary emergency strategies and risk predictions; extraction of features from the preliminary strategies using few-sample learning; and fine-tuning and hardening of the strategies using an optimizer embedded with physical information constraints to obtain physically reliable refined control plans. Multiple refined plans are simulated and deduced in parallel within a digital twin; based on the deduction results, the optimal emergency control instruction set is selected and output with the goal of maximizing system resilience, while simultaneously updating the spatiotemporal knowledge graph. This invention, through a three-level intelligent pipeline, achieves second-level experience reuse, scientific customization and adaptation, and global optimization through simulation in emergency decision-making, significantly improving the emergency response speed, scheme reliability, and overall resilience of water conservancy systems.

Description

Technical Field

[0001] This invention relates to the field of intelligent water conservancy engineering technology, specifically to a water conservancy emergency resilience control method and system based on digital twins and knowledge graphs. Background Technology

[0002] my country's water conservancy system is vast and complex. When responding to extreme weather and sudden disasters, the timeliness, scientific rigor, and comprehensiveness of emergency decision-making are crucial. Traditional emergency management models primarily rely on expert experience, historical contingency plan databases, and limited numerical simulations. Expert experience-based decision-making is highly dependent on individual capabilities, resulting in slow response times and difficulty in quickly formulating effective solutions when facing new or complex disasters. Static historical contingency plan databases lack flexibility and cannot adapt to real-time scenarios with subtle changes in the specific location and intensity of disasters. Furthermore, single numerical simulations based on deterministic parameters cannot cover the wide range of uncertainties such as future rainfall and equipment status, failing to adequately stress test and optimize contingency plans globally. While existing technologies have attempted to apply artificial intelligence to disaster prediction, they are mostly focused on single aspects, such as using machine learning for hydrological forecasting or simulation for scheme comparison, lacking a closed-loop intelligent decision-making framework that deeply couples historical experience, physical mechanisms, and forward-looking simulations. This results in existing systems often having "data silos" and "model barriers," and emergency response remains in an "experience-driven" or "semi-experience-semi-simulation" state, making it difficult to achieve the decision-making goals of second-level cognition, scientific adaptation, and optimal resilience. When facing increasingly frequent extreme disasters, the overall resilience of the system faces bottlenecks.

[0003] Therefore, the existing technology still needs further development. Summary of the Invention

[0004] The purpose of this invention is to overcome the above-mentioned technical deficiencies and provide a water conservancy emergency resilience control method and system based on digital twins and knowledge graphs to solve the problems existing in the prior art.

[0005] To achieve the above-mentioned technical objectives, according to a first aspect of the present invention, the present invention provides a water conservancy emergency resilience control method based on digital twins and knowledge graphs, comprising: S1. Based on a spatiotemporal knowledge graph that integrates engineering entities, historical cases, expert rules and hydrodynamic relationships, graph computing is used to match disaster scenarios in real time and generate a preliminary set of emergency strategies and risk predictions. S2. Combine small sample learning to extract transferable features from the preliminary emergency strategy set, and use an optimizer with embedded physical information constraints to fine-tune and harden the strategies to obtain a physically reliable refined control plan. S3. Perform parallel simulation and deduction of multiple refined control plans in a digital twin, evaluate and select the optimal emergency control command with the goal of maximizing system resilience based on the simulation results, and update the spatiotemporal knowledge graph.

[0006] Specifically, the method further includes: Acquire entity data of water conservancy projects, historical disaster case data, expert emergency rules data, and hydrological and hydrodynamic correlation data; perform entity extraction, relation extraction, and attribute fusion on multi-source heterogeneous data to construct a multimodal knowledge network of "engineering-disaster-emergency"; after the emergency decision is executed, the real-time monitoring data of this event, the execution process, and the final decision result are used as new cases for structured processing and are associated and integrated into the knowledge network to perform autonomous incremental updates of the graph and evolution of emergency knowledge.

[0007] Specifically, in step S1, the real-time matching of disaster scenarios using graph calculation includes: The real-time acquired disaster event feature information is mapped to query nodes and relationships in the spatiotemporal knowledge graph; the knowledge graph is encoded using a graph neural network model to capture the deep semantic features of entities and relationships; through a multi-hop reasoning mechanism, traversal and reasoning are performed along the associated path starting from the query node, automatically matching similar historical cases, identifying upstream risk sources and downstream disaster-bearing bodies, and forming a logical chain describing the key risk propagation path.

[0008] Specifically, step S2 includes: From the set of similar historical cases matched by the spatiotemporal knowledge graph, disaster pattern feature vectors and response strategy feature vectors are extracted; using a metric-based prototype network, the feature distance between the disaster features represented by the current small amount of real-time monitoring data and the disaster pattern prototypes of each historical case is calculated; based on the feature distance, the historical response strategy features are weighted and aggregated to generate optimization strategy parameters that are initially adapted to the specific intensity, location and environmental parameters of the current disaster.

[0009] Specifically, in step S2, policy hardening using an optimizer with embedded physical information constraints includes: A neural network optimizer model is constructed with the basic governing equations of hydrodynamics as physical constraints. The strategy parameters to be optimized are used as inputs to the optimizer model. The optimizer model takes the strategy adjustment as output and performs iterative optimization with the goal of satisfying the constraints of the physical equations and the engineering safety boundary conditions. The strategy parameters are corrected and a control plan that is feasible in the sense of conservation of mass, energy and momentum is output.

[0010] Specifically, in step S3, performing parallel simulation and deduction in the digital twin includes: A high-fidelity hydraulic simulation digital twin is established to operate synchronously with the physical hydraulic engineering project; multiple refined control plans are used as the initial control conditions for parallel simulation tasks; in the digital twin, different uncertainty scenario parameters are injected into each plan simultaneously, and a large-scale simulation calculation thread is started concurrently to simulate the execution process of the plan and the evolution of the system state under different random disturbances.

[0011] Specifically, the parallel simulation and deduction adopts the Monte Carlo simulation method. The uncertain scenario parameters include at least the randomly generated future spatiotemporal variation pattern of rainfall, the probability and location of random failure of key equipment, and the random fluctuation of downstream boundary conditions, so as to obtain its statistical performance distribution.

[0012] Specifically, the evaluation and selection of the optimal instructions based on the simulation results includes: Using a pre-trained deep learning agent model, the simulation input and output are quickly mapped to predict the system performance indicators of each plan under different uncertainty scenarios; an evaluation function with the overall system resilience as the core is constructed, which integrates at least two dimensions: the area under the function maintenance curve and the post-disaster recovery speed; with the goal of maximizing the evaluation function, global optimization is performed from the simulation results of all plans to output the optimal emergency control instruction set.

[0013] Specifically, the calculation of the overall system resilience index includes: During the simulation, the performance status sequence of key engineering facilities and the hydrological and hydraulic status sequence of key points in the watershed are acquired in real time in the digital twin. Based on the performance status sequence, the functional decay and recovery curves of the engineering system are calculated. The area under the comprehensive function curve, the time to reach the lowest point of the curve, and the time required to recover to the preset level are combined to generate a quantitative single resilience evaluation value through weighted fusion, which is used for direct comparison and ranking among different plans.

[0014] According to a second aspect of the present invention, a water conservancy emergency resilience control system based on digital twins and knowledge graphs is provided, comprising: The knowledge graph management module is used to build, store, query, and update the spatiotemporal knowledge graph. The strategy rapid generation and optimization module integrates a graph computing engine, a few-shot learning model, and a physical constraint optimizer to execute steps S1 and S2. The digital twin parallel inference and decision-making module includes a high-fidelity simulation engine, a parallel task scheduler, and a deep learning agent model evaluator, which are used to execute step S3.

[0015] Beneficial effects: The water conservancy emergency resilience control method and system based on digital twins and knowledge graphs provided by this invention bring significant multi-level beneficial effects by constructing a three-level intelligent pipeline of "rapid cognition of knowledge graphs - small sample physical adaptation - parallel optimization of digital twins".

[0016] At the first level, the introduction of spatiotemporal knowledge graphs fundamentally changes the cognitive model in the initial stage of emergency response. It integrates scattered engineering information, historical cases, and expert rules into a structured, reasonable knowledge network, using graph neural networks to achieve second-level semantic matching and multi-hop risk chain reasoning for disaster scenarios. This not only solves the delay problem of "starting from scratch" decision-making, providing valuable "golden response time" for subsequent steps, but also provides decision-makers with unprecedented panoramic risk insights by automatically mining hidden risk paths, realizing a leap from "manual browsing" to "intelligent indexing" of experience reuse.

[0017] At the second level, the deep integration of few-shot learning and physical information constraint optimization overcomes the challenges of customized solutions and scientific reliability. Few-shot learning technology enables the system to rapidly and accurately fine-tune retrieved historical strategies using minimal real-time data, generating draft solutions highly adapted to the specific characteristics of the current disaster situation, thus resolving the drawbacks of static contingency plan libraries that rely on rote application. Subsequently, a neural network optimizer embedded with physical equations acts as a rigorous "scientific verifier," ensuring that the data-driven solutions strictly adhere to fundamental physical laws and engineering safety boundaries. This produces physically credible and engineering-feasible refined control plans, eliminating absurd or dangerous instructions arising from model "illusions" or overfitting, significantly improving the feasibility and safety of the solutions.

[0018] At the third level, large-scale, high-concurrency simulations and resilience assessments within a digital twin environment represent a qualitative leap in decision-making, shifting from "empirical verification" to "simulation optimization." By executing tens of thousands of Monte Carlo simulations in parallel across multiple alternative plans in a synchronous twin, the system can comprehensively evaluate the statistical performance of each plan under extensive uncertainty. Combined with quantitative resilience indicators centered on system function maintenance and recovery speed, a deep learning proxy model is used to rapidly perform global optimization, automatically outputting the most probabilistically resilient optimal instruction set. This constructs a powerful "decision stress test sandbox," enabling decision-makers to virtually rehearse the future in a virtual space, achieving scientific and accurate globally optimal decisions.

[0019] Ultimately, the entire system forms an autonomous closed loop of "perception-decision-learning." The experience and results of each emergency response are automatically accumulated into new cases in the knowledge graph, driving the continuous evolution of the system's intelligence. The three layers of technology are interconnected and synergistic, together forming a hybrid intelligent emergency brain that combines historical wisdom, scientific core, and forward-looking vision. This significantly improves the system resilience, decision-making speed, and scientific rigor of water conservancy projects in the face of unknown and extreme disasters. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the water conservancy emergency resilience control method based on digital twins and knowledge graphs provided in a specific embodiment of the present invention. Figure 2 This is a schematic diagram of the system composition of the water conservancy emergency resilience control system based on digital twins and knowledge graphs provided in a specific embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments in this application, other similar embodiments obtained by those skilled in the art without creative effort should all fall within the scope of protection of this application. Furthermore, directional terms mentioned in the following embodiments, such as "up," "down," "left," and "right," are only for reference to the directions in the accompanying drawings; therefore, the directional terms used are for illustrative purposes and not for limiting the invention.

[0022] The present invention will be further described below with reference to the accompanying drawings and preferred embodiments.

[0023] Please see Figure 1 This invention provides a water conservancy emergency resilience control method based on digital twins and knowledge graphs, comprising: S1. Based on a spatiotemporal knowledge graph that integrates engineering entities, historical cases, expert rules and hydrodynamic relationships, graph computing is used to match disaster scenarios in real time and generate a preliminary set of emergency strategies and risk predictions.

[0024] It should be further explained that step S1 is the initial intelligent step in the emergency response. When the monitoring system triggers an alarm (such as a reservoir water level exceeding the warning level), the system does not start the analysis from scratch, but immediately queries a pre-constructed spatiotemporal knowledge graph of water conservancy emergencies that integrates multi-dimensional knowledge. The entity nodes of this graph include engineering facilities such as reservoirs, gates, and dikes, as well as historical disaster cases, expert experience rules, etc.; the relational edges describe the spatial connections between projects, hydraulic impacts, similarities between historical cases, and "context-action" associations. The system uses graph computing technology, especially graph neural networks (GNNs), to perform real-time matching and multi-hop reasoning between the real-time features of the current disaster (such as rainfall and water level) and the knowledge graph. This process can complete two core tasks within seconds: first, find the historical cases most similar to the current situation, output the effective emergency strategies adopted at that time (such as specific gate opening combinations) as preliminary solutions, forming a strategy set; second, automatically infer the key paths that the current risk may spread (for example, the danger of an upstream reservoir may threaten multiple important towns downstream through the river channel), realizing rapid chain identification of risks. This is equivalent to providing decision-makers with an "experience-based brain" and a "risk map" instantly.

[0025] S2. Combine small sample learning to extract transferable features from the preliminary emergency strategy set, and use an optimizer with embedded physical information constraints to fine-tune and harden the strategies to obtain a physically reliable refined control plan.

[0026] It should be further explained that step S2 aims to address "how to customize and optimize historical experience with a very small amount of new data" and ensure the scientific feasibility of the solution. While the initial strategy set generated in step S1 is rapid, it may not fully adapt to the new characteristics of the current disaster (such as precise location and intensity). Therefore, the system employs few-shot learning techniques (e.g., prototype networks) to extract transferable "disaster pattern features" and "response strategy features" from similar historical cases. The system compares and performs metric learning with the current limited real-time monitoring data (such as future short-term rainfall forecasts) against these historical features, quickly fine-tuning the parameters of the initial strategy to better align with the current reality. However, data-driven fine-tuning alone may produce solutions that violate physical laws (such as mass non-conservation) or exceed engineering safety limits. Therefore, the system introduces a neural network optimizer embedded with physical information constraints. This optimizer incorporates fundamental hydrodynamic equations (such as the Saint-Venant equations) as "hard constraints" into the optimization process, performing secondary corrections on the fine-tuned strategies to ensure that each generated control plan (such as gate opening and closing sequences and pump station start-up schemes) is physically reliable and meets all engineering safety boundary conditions (such as maximum gate opening and amplitude variation rate). This step is equivalent to a "scientific verifier," transforming empirical solutions into safe, reliable, and feasible refined technical solutions.

[0027] S3. Perform parallel simulation and deduction of multiple refined control plans in a digital twin, evaluate and select the optimal emergency control command with the goal of maximizing system resilience based on the simulation results, and update the spatiotemporal knowledge graph.

[0028] Step S3 serves as the "intelligent sandbox" and "stress test field" for final decision-making. While the multiple refined alternative plans generated in Step S2 are guaranteed in terms of quality and safety, their overall effectiveness and robustness under complex and uncertain future scenarios are unknown. The system synchronously injects these plans into a digital twin that maintains high-fidelity synchronization with the physical hydraulic system. Within the twin, the system simultaneously initiates thousands to tens of thousands of Monte Carlo simulations for each plan, with each simulation randomly injecting different uncertainties (such as multiple possibilities for future rainfall, random equipment failures, and downstream boundary fluctuations). Through this large-scale parallel "hypothesis analysis," the performance of each plan is comprehensively tested under various possible future scenarios. Next, the system utilizes a pre-trained deep learning proxy model to rapidly analyze the massive simulation results and intelligently evaluates and makes decisions with the core objective of maximizing "system resilience." "Resilience" is a quantitative indicator that comprehensively considers the system's ability to maintain function during disasters (such as ensuring water supply and controlling floods) and the speed of post-disaster recovery. The system automatically compares the resilience distribution of all contingency plans under different risk scenarios, and finally selects the globally optimal and most resilient emergency control instruction set in a probabilistic sense, and issues it for execution. At the same time, the entire process data and decision results of this event are automatically structured and updated into the knowledge graph of step S1 as new knowledge feedback, realizing the autonomous evolution of the system's capabilities.

[0029] It should be further explained that this method is a three-level pipeline-style intelligent decision-making method, the core of which lies in the deep integration of historical experience, physical laws, and forward-looking simulation. Step S1 addresses the question of "where to quickly obtain experience" in the early stages of an emergency. The system continuously runs a background service that polls the connected water conservancy IoT platform every 1 minute to monitor data streams such as water level, flow rate, rainfall, gate opening, and video surveillance in real time. When one or more monitoring indicators (such as the reservoir water level exceeding the flood limit by 0.5 meters, which is a specific and adjustable threshold; 0.5 meters is chosen as the warning threshold because it can provide sufficient warning time before the reservoir capacity risk occurs without being too sensitive and generating too many false alarms) exceed the preset threshold, the system automatically packages all relevant monitoring data at the current time point (such as the current timestamp, the water level of Reservoir A at 215.5m, the inflow rate at 1500m³ / s, and the cumulative rainfall of 120mm in the upstream B area over the past 3 hours) into a "disaster scenario description" and immediately triggers step S1.

[0030] Understandably, this step uses an integrated knowledge graph as an "intelligent index" to shorten the time-consuming process of manually flipping through files and recalling experiences to a second-level automated process, winning crucial "golden response time" for subsequent decision-making and realizing "rapid reuse of experience" in emergency response.

[0031] Specifically, in step S1, constructing and updating the spatiotemporal knowledge graph includes: acquiring entity data of water conservancy projects, historical disaster case data, expert emergency rule data, and hydrological and hydrodynamic correlation data; performing entity extraction, relation extraction, and attribute fusion on multi-source heterogeneous data to construct a "project-disaster-emergency" multimodal knowledge network; after the emergency decision is executed, the real-time monitoring data, execution process, and final decision result of this event are treated as new cases for structured processing and fused into the knowledge network to achieve autonomous incremental updates of the graph and evolution of emergency knowledge.

[0032] It should be further explained that the construction of the map is a systematic project, specifically divided into two stages: 1. Initial Construction Phase: Data sources include: (a) Engineering BIM / GIS database, extract the geometric, material and design parameters of entities such as dams, spillways, dikes, and pumping stations, and establish relationships such as "connection" and "location" based on spatial topology; (b) Historical document library: For unstructured texts such as flood control summary reports and emergency response records from the past 50 years, use a BERT-based named entity recognition model to extract entities and attributes such as "time", "location", "disaster type", "response measures", and "loss". (c) Expert experience base, through structured interviews, encodes rules such as "If the reservoir water level is higher than 217.0m and the inflow is greater than the design flood discharge capacity, the emergency spillway shall be activated first" into a graph structure of (entity: water level, relation: higher than, entity: threshold) and (entity: situation, relation: trigger, entity: action); (d) Hydrodynamic model: The model mesh or river cross section is abstracted as nodes, and the water flow exchange relationship is abstracted as edges. The weight of the edges can be set as the river roughness coefficient or the pipeline resistance coefficient. All data is integrated into a unified graph database through preset mapping rules, such as using Neo4j, whose query language Cypher facilitates complex relational queries.

[0033] 2. Dynamic Update Phase: After each emergency decision-making loop (i.e., after step S3 outputs and executes the command), the system initiates a "Case Consolidation" sub-process. This process first cleans and labels the time-series data of this event (alarm, decision, execution, and result). Then, using the same entity relationship extraction model as the initial construction phase, it automatically identifies new entities (such as newly emerging piping points) and new relationships (such as the "effective mitigation" relationship between "a certain scheduling strategy" and "reduction of flooding in a certain urban area"). Next, the system calculates the similarity between the feature vector of the new case and the vector of existing case nodes in the graph, finds the K (K=3) most similar old case nodes, and establishes "similar to" relationship edges between them, thereby organically linking new knowledge into the existing knowledge system and realizing the autonomous growth of the graph.

[0034] Understandably, this dynamic, closed-loop knowledge update mechanism is the core of the system's "learning and evolution" capability. It enables the system to break through the static limitations of traditional contingency plan databases, transforming each practical operation into valuable digital experience, continuously enriching and improving its knowledge base. As a result, when facing unprecedented new and complex disasters that may occur in the future, the system has stronger analogical and adaptive capabilities, significantly improving its long-term intelligence level.

[0035] Specifically, in step S1, the real-time matching of disaster scenarios using graph computing includes: mapping the real-time acquired disaster event feature information to query nodes and relationships in the spatiotemporal knowledge graph; encoding the knowledge graph using a graph neural network model to capture the deep semantic features of entities and relationships; and through a multi-hop reasoning mechanism, traversing and reasoning along the associated path from the query node to automatically match similar historical cases, identify upstream risk sources and downstream disaster-bearing bodies, and form a logical chain describing the key risk propagation path.

[0036] It should be further explained that the specific algorithm flow for this step is as follows: 1. Scenario Mapping and Query Construction: The system converts the "disaster scenario description" packaged in step S1 into a subgraph query. For example, the entities include "Reservoir A (water level: 215.5m)" and "heavy rainfall event (intensity: 40mm / h)", with the relationship "occurred at (Reservoir A, heavy rainfall event)". This subgraph serves as the query input.

[0037] 2. Graph Neural Network Encoding and Matching: The system uses a pre-trained Relational Graph Convolutional Network (R-GCN) model. The training method for this model is as follows: positive and negative sample pairs are constructed from historical cases. Positive samples are entity relationship subgraphs within the same case, while negative samples are randomly combined subgraphs from different cases. The training objective is to make the distance between positive samples in the graph vector space closer. The model encodes the entire knowledge graph, obtaining a low-dimensional vector representation of each node. (in (Represents the node index). For the query subgraph, compute its graph-level representation vector. (For example, by averaging the vectors of all nodes in the query subgraph). Next, the system calculates... Cosine similarity between the vectors and all nodes labeled "historical cases" in the graph .

[0038] in, It is the first Vector representations of historical case nodes. Select nodes with similarity greater than a threshold. (The preferred value is 0.7, which is determined after balancing recall and precision on the validation set.) All cases constitute the "similar historical case set".

[0039] 3. Multi-hop Reasoning and Risk Chain Generation: Starting from the core entity node of the current alarm (e.g., "Reservoir A"), the system performs a breadth-first search (BFS) along the relationship edges in the knowledge graph, setting a maximum number of hops of 3. For example, the path might be: Reservoir A (potentially leading to), downstream river section B (flowing through), and city flood control wall C. The system will count all frequently accessed downstream disaster-bearing entity nodes (e.g., more than 50% of the reasoning paths pass through a certain node) and list them as key risk points. At the same time, starting from similar historical case nodes, along the "taken" relationship edges, the system finds the corresponding "emergency strategy" nodes. The specific control measures associated with these strategy nodes (e.g., "opening floodgate No. 1 to 30% opening") are extracted to form a "preliminary emergency strategy set".

[0040] Understandably, this process not only achieves rapid case matching based on semantics, but more importantly, it automatically reveals potential, indirect, and cross-spatial risk transmission paths through multi-hop reasoning. This is equivalent to equipping decision-makers with a "risk perspective lens," enabling them to discover in advance hidden risk chains such as "the danger at Reservoir A may threaten key economic zones dozens of kilometers downstream through the complex river network within hours," thereby supporting more forward-looking and holistic emergency decision-making.

[0041] Specifically, in step S2, fine-tuning by combining few-sample learning includes: extracting disaster pattern feature vectors and response strategy feature vectors from the set of similar historical cases matched by the spatiotemporal knowledge graph; using a metric-based prototype network to calculate the feature distance between the disaster features represented by the current small amount of real-time monitoring data and the disaster pattern prototypes of each historical case; and weighting and aggregating historical response strategy features based on the feature distances to generate optimization strategy parameters that are initially adapted to the current specific intensity, location, and environmental parameters of the disaster.

[0042] It should be further noted that this step relies on a pre-trained few-shot learning framework, and the specific implementation is as follows: 1. Feature Vector Extraction and Prototype Calculation: When each historical case is stored in the graph, two fixed-length feature vectors are generated through a feature encoder (a three-layer fully connected neural network): disaster mode feature vector. (Dimensions set to 128) and coping strategy feature vectors (Dimensions set to 64). For the K similar historical cases retrieved in step S1 (support set, K is preferably 5, a small sample size that provides sufficient diversity while avoiding information redundancy), calculate the mean of their disaster pattern feature vectors, which serves as the "prototype" vector for that category. .

[0043] in, It represents the number of cases in the k-th category.

[0044] 2. Current Disaster Feature Encoding and Distance Calculation: Current real-time monitoring data (such as the forecast rainfall sequence for the next hour, the current watershed soil moisture index, etc.) are input into the same feature encoder as historical cases to obtain the feature vector of the current disaster. Then, calculate. To each prototype Euclidean distance (Euclidean distance was chosen because it can intuitively reflect the magnitude of differences in a continuous feature space.)

[0045] 3. Weighted generation of initial policy parameters: The distance is normalized using the softmax function to obtain the weights. The closer the categories are, the greater their weight.

[0046] in, This is a temperature coefficient, preferably 10, used to control the smoothness of the weight distribution. Finally, weighted aggregation supports the collection of coping strategy feature vectors, generating a preliminary adapted strategy parameter vector. .

[0047] It can be decoded into a specific sequence of control parameters, such as a set of recommended gate opening values ​​every 15 minutes for the next 6 hours.

[0048] Understandably, this few-shot learning mechanism cleverly resolves the contradiction between "data scarcity" and "customized needs" in emergency scenarios. Instead of relying on massive amounts of current disaster data, it uses metric learning to synthesize a preliminary, adaptable solution from limited but highly relevant historical experiences. This is more robust than directly using the most similar historical plan because it integrates the wisdom of multiple similar cases, reducing decision-making biases caused by the specificity of individual historical cases.

[0049] Specifically, in step S2, the strategy hardening using an optimizer with embedded physical information constraints includes: constructing a neural network optimizer model with the basic equations of hydrodynamics as physical constraints; using the strategy parameters to be optimized as input to the optimizer model; the optimizer model outputting the strategy adjustment amount and performing iterative optimization with the goal of satisfying the physical equation constraints and engineering safety boundary conditions, correcting the strategy parameters, and outputting a refined control plan that is feasible in the sense of conservation of mass, energy, and momentum.

[0050] It should be further explained that the physical hardening step is implemented through a Physical Information Neural Network (PINN) optimizer, the details of which are as follows: 1. Optimizer Model Construction: The optimizer is a neural network containing differentiable physical operators. Its core is an embedded, simplified, but critically conserved "micro-simulator." For example, for river flood evolution, it embeds the difference form of a one-dimensional Saint-Venant equation system. The input to the neural network is the policy parameter vector. (e.g., gate opening sequence), the output is the adjustment amount. .

[0051] 2. Loss Function Design: Total Loss Function It consists of three parts: in: To mitigate data loss, the optimized strategy ensures that the simulation results are consistent with the short-term, high-precision simulation trends based on the current state. For example, it calculates the downstream water level process curve simulated by the optimized strategy. With reference process line The mean square error; This is the physical loss, i.e., the residual of the embedded physical equations. For example, for the continuity equations in the Saint-Venant system of equations: Calculate the residual at each spatiotemporal calculation point in the computation graph. And calculate its mean square value as part of the loss. Wherein, The cross-sectional area of ​​the water passage. For traffic, For lateral inflow, For time, Spatial coordinates; To account for boundary constraint losses, engineering safety constraints (such as maximum / minimum gate opening and amplitude variation rate) are treated as penalty terms. For example, if a gate opening command... Exceeding the maximum value This would increase the loss. ; Furthermore, weighting coefficients , , The preferred values ​​are 1.0, 0.1, and 0.5. This setting ensures that data fitting is the primary objective, while strongly incorporating physical constraints and safety boundaries.

[0052] 3. Optimization Solution: Using the Adam optimizer, with a learning rate of 0.001, the total loss is... Iterative optimization is performed (typically 200-300 epochs), continuously adjusting the parameters of the neural network to obtain the optimal adjustment amount. The final refined control plan is as follows: .

[0053] Understandably, this physical hardening step is a crucial guarantee for generating "feasible and safe" solutions. It acts as a "physical verifier" and "safety filter," ensuring that the initial data-driven strategy not only "looks reasonable" but also strictly adheres to the fundamental laws of nature and the physical limits of engineering facilities. This fundamentally avoids absurd or dangerous directives arising from AI model "illusions" or overfitting to historical data, greatly enhancing the scientific rigor and reliability of decision-making solutions.

[0054] Specifically, in step S3, the parallel simulation and deduction in the digital twin includes: establishing a high-fidelity hydraulic simulation digital twin that operates synchronously with the physical hydraulic engineering project; using multiple refined control plans as the initial control conditions for the parallel deduction task; injecting different uncertainty scenario parameters into each plan simultaneously in the digital twin, and concurrently starting large-scale simulation calculation threads to simulate the execution process and system state evolution of the plan under different random disturbances.

[0055] It should be further explained that the specific setup and task execution process of the digital twin parallel simulation environment are as follows: 1. Digital Twin Construction: A high-fidelity water conservancy engineering simulation model is built based on commercial software (such as the MIKE series, HEC-RAS) or open-source kernels (such as SWMM). During non-emergency periods, the model continuously runs in "data assimilation" mode, automatically assimilating real-time monitoring data (such as water level and flow rate) hourly to ensure its state is synchronized with the physical world. The model's grid resolution, time step, and other parameters have been calibrated and verified, and the Nash efficiency coefficient is required to be greater than 0.85 to guarantee simulation accuracy.

[0056] 2. Contingency Plan and Uncertainty Parameter Preparation: Assume that M refined control plans (e.g., M=4) are generated after step S2. The system prepares a simulation task configuration file for each plan. Within each task, N sets of uncertainty parameters need to be generated. Taking rainfall uncertainty as an example, the system accesses ensemble forecast products from multiple numerical weather prediction (NWP) models, constructs a log-normal distribution for each forecast grid point using ensemble statistics (mean, standard deviation), and randomly samples from it to generate N (N preferably 5000) different spatiotemporal fields of rainfall for the next 72 hours. Equipment failure is simulated based on a Poisson process, given the equipment failure rate. (e.g., the key gate is) / hour), in the length of the simulation time The probability of a failure occurring within is... Monte Carlo sampling is used to determine whether the equipment malfunctions in each simulation.

[0057] 3. Parallel Task Scheduling and Execution: The system uses a Kubernetes-based containerized task scheduling platform. After receiving M tasks, the scheduler dynamically allocates container instances in the computing cluster based on the computing resources (CPU cores, memory) required by each task. Each container instance runs a copy of the simulation model. The scheduler injects the pre-set parameter file and N sets of uncertainty parameters in batches (e.g., 100 sets per batch) into the corresponding containers and executes the simulation concurrently. A cluster with 100 computing nodes can complete a total of M×N=4×5000=20000 simulation runs within 10 minutes.

[0058] Understandably, this massively parallel simulation environment constructs an unprecedented "decision-making laboratory." It allows decision-makers to conduct "tens of thousands of trials" on a limited number of alternative solutions in a virtual space, at extremely low cost and with zero risk, comprehensively testing their performance under various extreme and unexpected conditions. This verification method based on massive simulations elevates decision-making from relying on qualitative experience and local judgments to being based on quantitative statistics and global optimization, representing a revolutionary advancement in the scientific and precise development of emergency decision-making.

[0059] Specifically, the parallel simulation and deduction adopts the Monte Carlo simulation method. The uncertain scenario parameters include at least the randomly generated future spatiotemporal variation pattern of rainfall, the probability and location of random failure of key equipment, and the random fluctuation of downstream boundary conditions. In this way, each refined control plan is subjected to stress tests under random scenarios thousands to tens of thousands of times to obtain its statistical performance distribution.

[0060] It should be further explained that the specific modeling method for the key uncertainty parameters is as follows: 1. Rainfall uncertainty: An ensemble forecast perturbation method is used. It is assumed that there are 21 ensemble members forecasting rainfall, each providing rainfall estimates for the next 6 hours. Calculate the set mean. and standard deviation For each Monte Carlo sampling, a reference field is first randomly selected from the 21 members. Then apply a random spatially correlated perturbation field. (Generated using a Gaussian random field, with a correlation scale set to 50 km to reflect the spatial continuity of rainfall). Final random rainfall field. for: in, It is the disturbance intensity coefficient, preferably 0.8, so as to appropriately expand the uncertainty range without deviating too far from the forecast.

[0061] 2. Equipment Failure: The system maintains a list of critical equipment, such as "#1 Main Floodgate Hydraulic System" and "#3 Pump Station Main Motor". Each piece of equipment has a failure rate based on maintenance records. At the start of each simulation, a random number uniformly distributed in the interval [0,1] is generated for each device. .like (T is the total simulation time, such as 72 hours), then it is determined that the equipment has a random fault in this simulation (such as being stuck at a certain opening degree), and the time point of the fault is uniformly and randomly selected within T.

[0062] 3. Downstream Boundary Fluctuations: Taking downstream tidal stations as an example, a generalized extreme value distribution (GEV) is fitted based on the historical monthly highest tide level sequence. In each simulation, an extreme water level is randomly selected from this GEV distribution. This serves as the downstream boundary condition for this simulation, designed to simulate extreme conditions such as spring tides and storm surges.

[0063] Understandably, by employing refined and probabilistic modeling of core uncertainties, this method can quantitatively reveal the "vulnerabilities" and "robustness" of each emergency response plan. For example, the analysis might show that "Plan A performs excellently in 95% of cases, but could lead to localized levee breaches in a 5% scenario of extreme rainfall coupled with pump station failure"; while "Plan B is slightly less effective in 80% of cases, but can ensure levee safety in 99.9% of scenarios." This deep risk insight enables decision-makers to make informed trade-offs between "benefits" and "risks," choosing truly resilient solutions.

[0064] Specifically, evaluating and selecting the optimal instructions based on the simulation results includes: using a pre-trained deep learning agent model to quickly map the simulation input and output, and predict the system performance indicators of each plan under different uncertainty scenarios; constructing an evaluation function with the overall system resilience as the core, which integrates at least the area under the function maintenance curve and the post-disaster recovery speed; and, with the goal of maximizing the evaluation function, globally optimizing from the simulation results of all plans, and automatically outputting the optimal emergency control instruction set.

[0065] It should be further explained that the specific technical implementation of the evaluation and decision-making process is as follows: 1. Training and Application of the Proxy Model: To replace time-consuming simulations, the system trains a deep neural network offline as a proxy model. The training data comes from a massive amount of historically accumulated simulation input-output pairs. Input Includes: control plan parameters, rainfall field, equipment status, boundary conditions, etc.; output It is a predefined set of performance indicators, such as the maximum inundation depth of the basin, the duration of water levels exceeding the warning level in key towns, and the highest water level of the reservoir. The network structure adopts a fully connected layer with hidden layer nodes of [256, 128, 64], using the ReLU activation function. After training, during decision-making, for each plan, the plan parameters are combined with the sampled N sets of uncertainty parameters to form N inputs. Batch input into the proxy model, and obtain N sets of prediction performance metrics within milliseconds. This forms the performance distribution of the contingency plan.

[0066] 2. Construction of resilience evaluation function: First, the system function index for a single simulation is calculated based on performance indicators. . It consists of multiple standardized sub-functions (flood control) Water supply Ecology The weighted sum of the values, where the weights are set according to the objectives of each emergency phase, such as the weight vector for flood control emergencies. ,but .based on Curve (time range from the occurrence of the disaster) By the end of the evaluation ,For example (day), calculate two core resilience components: ① Functional maintenance ability That is, the normalized area under the curve (AUC).

[0067] ②Recovery speed ,in It is the minimum functional value. It is the time of its occurrence. It is the recovery threshold (e.g., 0.8). Is the function restored to The time.

[0068] 3. Multi-objective trade-offs and decision-making: The final comprehensive evaluation indicators of the contingency plan Linear weighting method: ,in As weight, and In the event of a major flood emergency, preventing system collapse may be of greater importance, hence the setting... The system calculates N simulations for each contingency plan. Expected value and risk value (e.g.) 5th percentile of the value The decision rule could be: first select... Plans with values ​​greater than a threshold (e.g., 0.7) are then selected from these plans. The largest result is taken as the optimal contingency plan, and its corresponding control instruction set is output. This rule, while pursuing average efficiency, also avoids extreme risks.

[0069] Understandably, this evaluation and decision-making mechanism achieves an intelligent transformation from "simulation results" to "optimal decisions." It not only achieves a qualitative leap in evaluation efficiency through a surrogate model, but more importantly, by introducing a system function index and resilience evaluation function, it transforms a complex, multi-objective engineering problem into a quantifiable, comparable, and optimizable mathematical problem. Ultimately, automated decision-making based on statistical indicators (expected value, risk value) avoids the bias and subjectivity of human decision-making, ensuring that the selected solution is globally optimal in a probabilistic sense, truly realizing intelligent emergency decision-making centered on "system resilience."

[0070] Specifically, the calculation of the overall system resilience index includes: during the simulation process, acquiring in real time the performance status sequence of key engineering facilities and the hydrological and hydraulic status sequence of key points in the watershed in the digital twin; calculating the functional decay and recovery curves of the engineering system based on the performance status sequence; and generating a quantitative single resilience evaluation value by weighted fusion of the area under the comprehensive function curve, the time to reach the lowest point of the curve, and the time required to recover to the preset level, for direct comparison and ranking among different contingency plans.

[0071] It should be further explained that the quantitative resilience evaluation value The calculation formula is detailed below: 1. Function curve generation: In each simulation, the system function index at time t is recorded at intervals of Δt = 1 hour. . The calculation, as mentioned earlier, is a weighted sum of sub-functions such as flood control and water supply. Each sub-function... The calculations are based on simulation output. For example, flood control sub-functions. It can be defined as: in, The number of key protected towns, This represents the simulated flood depth of town i at time t. This is the critical inundation depth of the town (e.g., 0.5 meters). This formula means that when all towns are not inundated, the flood control function is 1; when the inundation depth of any town reaches the critical value, the functional loss contributed by that town is 1.

[0072] 2. Calculation of toughness components: From Three key features were extracted from the curve: The area under the function curve reflects the shock resistance and maintenance capability.

[0073] The function reaches its minimum value. The timing reflects how quickly the system is damaged.

[0074] Restore from the lowest functional level to the preset level (e.g., 0.8) The time required reflects the recovery speed.

[0075] 3. Synthesis of Comprehensive Evaluation Value: The three components are synthesized into a single toughness evaluation value using the following formula. (The range is [0,1]): in, For the theoretical maximum area, The maximum acceptable recovery time (e.g., 14 days). Let be the weight coefficient, and satisfy... The optimal weight value is: This setting emphasizes the system's ability to maintain functionality during disasters. (maximum), while also ensuring it doesn't collapse prematurely ( and faster recovery ).

[0076] Understandably, this quantification method provides a precise, transparent, and reproducible mathematical definition for the abstract concept of "resilience." It not only condenses the multi-temporal, multi-attribute system performance into a single scalar, facilitating objective comparisons between solutions, but more importantly, its composition (AUC, , It has clear physical and engineering significance, enabling decision-makers to understand the specific reasons behind high or low scores (e.g., a low score for a solution may be due to slow recovery), thereby supporting deeper decision analysis and guiding the optimization direction of future engineering design and contingency planning.

[0077] Please see Figure 2 The present invention provides another embodiment, which provides a water conservancy emergency resilience control system based on digital twins and knowledge graphs. The water conservancy emergency resilience control system based on digital twins and knowledge graphs includes: The knowledge graph management module 100 is used to construct, store, query, and update the spatiotemporal knowledge graph. The strategy rapid generation and optimization module 200 integrates a graph computing engine, a few-shot learning model, and a physical constraint optimizer to execute steps S1 and S2. The digital twin parallel inference and decision-making module 300 includes a high-fidelity simulation engine, a parallel task scheduler, and a deep learning agent model evaluator, which are used to execute step S3.

[0078] It should be further explained that the specific deployment and module interaction scheme of this system are designed as follows: 1. Hardware and Infrastructure: The system is deployed on the water resources department's private cloud or high-performance computing center. Hardware includes: (a) Database server cluster for running Neo4j graph database and time series database (such as Influx DB); (b) AI training and inference server (equipped with GPU) for running graph neural networks, few-shot learning models and surrogate models; (c) A massively parallel computing cluster (CPU-intensive) for running the digital twin simulation engine; (d) Application servers and web servers, used to provide APIs and human-computer interaction interfaces. All components are interconnected via a high-speed local area network.

[0079] 2. Detailed Module Structure: ① Knowledge Graph Management Module 100: Provides graphical construction tools, automated data pipelines (interfacing with BIM, document libraries, and real-time data interfaces), version control services, and a query API based on Cypher / Gremlin. It receives "case study" messages from the inference and decision-making module via a message queue (such as Apache Kafka).

[0080] ② Strategy Rapid Generation and Optimization Module 200: This is a collection of microservices. The graph computing engine service receives alarm events, calls the knowledge graph query API to obtain similar cases, and performs multi-hop inference. The few-shot learning service loads a pre-trained feature encoder and prototype network model for rapid feature matching and aggregation. The physical constraint optimizer service runs the PINN model to iteratively optimize the initial strategy. This module receives tasks via a RESTful API and returns multiple refined solutions in JSON format.

[0081] ③ Digital Twin Parallel Inference and Decision Module 300: This is the computational core of the system. The high-fidelity simulation engine is based on Docker containers, with each container containing a pre-configured hydrodynamic model executable file and its dependent libraries. The parallel task scheduler is developed based on Kubernetes. It receives a list of pre-set plans from upstream and dynamically creates and manages the corresponding Pods (container groups) in the cluster according to the resource requirements of each simulation task (e.g., 4 CPU cores, 8GB memory). The deep learning proxy model evaluator is a separate service that can be called before the inference begins to quickly pre-evaluate massive parameter combinations or to perform rapid statistical analysis of the results after the inference. The decision engine is responsible for executing the aforementioned resilience calculations and multi-objective trade-off logic.

[0082] 3. Workflow Integration: All modules are orchestrated through a unified workflow engine (such as Apache Airflow). A typical emergency decision-making workflow is as follows: alarm triggering, invoking the strategy generation module (S1, S2), generating a list of contingency plans, invoking the parallel inference module, inference completion, invoking the proxy model and decision engine to analyze the results, outputting the optimal instruction, and triggering the knowledge graph update task.

[0083] Understandably, this system solidifies methodological innovation into a stable, efficient, and scalable software-defined system. Its modular design decouples high-level functions such as knowledge management, intelligent analysis, and simulation computation, facilitating independent upgrades and maintenance. By leveraging modern cloud-native and containerization technologies, the system can elastically allocate computing resources to cope with explosive growth in computing demands during emergencies. Ultimately, this system provides water conservancy management departments with an end-to-end, complete intelligent decision support platform, from "perception and alarm" to "command output" and "knowledge accumulation," transforming an advanced hybrid intelligent framework into tangible emergency productivity.

[0084] In a preferred embodiment, this application also provides an electronic device, the electronic device comprising: The computer device includes a memory and a processor, wherein the memory stores computer-readable instructions that, when executed by the processor, implement the described water conservancy emergency resilience control method based on digital twins and knowledge graphs. The computer device can be broadly categorized as a server, terminal, or any other electronic device with the necessary computing and / or processing capabilities. In one embodiment, the computer device may include a processor, memory, network interface, communication interface, etc., connected via a system bus. The processor of the computer device can be used to provide the necessary computing, processing, and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and internal memory. The non-volatile storage medium may store an operating system, computer programs, etc. The internal memory can provide an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface and communication interface of the computer device can be used to connect and communicate with external devices via a network. When the computer program is executed by the processor, it performs the steps of the method of the present invention.

[0085] This invention can be implemented as a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, causes the steps of the methods of embodiments of the invention to be performed. In one embodiment, the computer program is distributed across multiple network-coupled computer devices or processors, such that the computer program is stored, accessed, and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be executed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be executed by one or more computer devices or processors, and one or more other method steps / operations may be executed by one or more other computer devices or processors. One or more computer devices or processors may execute a single method step / operation, or execute two or more method steps / operations.

[0086] Those skilled in the art will understand that the method steps of this invention can be performed by a computer program instructing related hardware, such as a computer device or processor, to perform the steps of this invention when executed. Depending on the context, any references herein to memory, storage, databases, or other media may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state drive, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0087] The technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification, provided that such combination does not contain contradictions.

[0088] The specific embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. Any other corresponding changes and modifications made in accordance with the technical concept of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A water conservancy emergency resilience control method based on digital twinning and knowledge graph, characterized in that, include: S1. Based on a spatiotemporal knowledge graph that integrates engineering entities, historical cases, expert rules and hydrodynamic relationships, graph computing is used to match disaster scenarios in real time and generate a preliminary set of emergency strategies and risk predictions. S2. Combine small sample learning to extract transferable features from the preliminary emergency strategy set, and use an optimizer with embedded physical information constraints to fine-tune and harden the strategies to obtain a physically reliable refined control plan. S3. Perform parallel simulation and deduction of multiple refined control plans in a digital twin, evaluate and select the optimal emergency control command with the goal of maximizing system resilience based on the simulation results, and update the spatiotemporal knowledge graph.

2. The method according to claim 1, characterized in that, The method specifically includes: Acquire entity data of water conservancy projects, historical disaster case data, expert emergency rules data, and hydrological and hydrodynamic correlation data; perform entity extraction, relation extraction, and attribute fusion on multi-source heterogeneous data to construct a "project-disaster-emergency" multimodal knowledge network; after the emergency decision is executed, the real-time monitoring data of this event, the execution process, and the final decision result are used as new cases for structured processing and are associated and integrated into the knowledge network to perform autonomous incremental updates of the graph and evolution of emergency knowledge.

3. The method according to claim 2, characterized in that, In step S1, the real-time matching of disaster scenarios using graph calculation specifically includes: The real-time acquired disaster event feature information is mapped to query nodes and relationships in the spatiotemporal knowledge graph; the knowledge graph is encoded using a graph neural network model to capture the deep semantic features of entities and relationships; through a multi-hop reasoning mechanism, traversal and reasoning are performed along the associated path starting from the query node, automatically matching similar historical cases, identifying upstream risk sources and downstream disaster-bearing bodies, and forming a logical chain describing the key risk propagation path.

4. The method according to claim 3, characterized in that, Step S2 includes: From the set of similar historical cases matched by the spatiotemporal knowledge graph, disaster pattern feature vectors and response strategy feature vectors are extracted; using a metric-based prototype network, the feature distance between the disaster features represented by the current small amount of real-time monitoring data and the disaster pattern prototypes of each historical case is calculated; based on the feature distance, the historical response strategy features are weighted and aggregated to generate optimization strategy parameters that are initially adapted to the specific intensity, location and environmental parameters of the current disaster.

5. The method according to claim 4, characterized in that, In step S2, policy hardening using an optimizer with embedded physical information constraints includes: A neural network optimizer model is constructed with the basic governing equations of hydrodynamics as physical constraints. The strategy parameters to be optimized are used as inputs to the optimizer model. The optimizer model takes the strategy adjustment as output and performs iterative optimization with the goal of satisfying the constraints of the physical equations and the engineering safety boundary conditions. The strategy parameters are corrected and a control plan that is feasible in the sense of conservation of mass, energy and momentum is output.

6. The method according to any one of claims 1 to 5, characterized in that, In step S3, parallel simulation and deduction in the digital twin includes: A high-fidelity hydraulic simulation digital twin is established to operate synchronously with the physical hydraulic engineering project; multiple refined control plans are used as the initial control conditions for parallel simulation tasks; in the digital twin, different uncertainty scenario parameters are injected into each plan simultaneously, and a large-scale simulation calculation thread is started concurrently to simulate the execution process of the plan and the evolution of the system state under different random disturbances.

7. The method according to claim 6, characterized in that, The parallel simulation and deduction adopts the Monte Carlo simulation method. The uncertain scenario parameters include at least the randomly generated future spatiotemporal variation pattern of rainfall, the probability and location of random failure of key equipment, and the random fluctuation of downstream boundary conditions, so as to obtain its statistical performance distribution.

8. The method according to claim 7, characterized in that, The optimal instructions were evaluated and selected based on the simulation results, including: Using a pre-trained deep learning agent model, the simulation input and output are quickly mapped to predict the system performance indicators of each plan under different uncertainty scenarios; an evaluation function with the overall system resilience as the core is constructed, which integrates at least two dimensions: the area under the function maintenance curve and the post-disaster recovery speed; with the goal of maximizing the evaluation function, global optimization is performed from the simulation results of all plans to output the optimal emergency control instruction set.

9. The method according to claim 8, characterized in that, The calculation of the overall system resilience index includes: During the simulation, the performance status sequence of key engineering facilities and the hydrological and hydraulic status sequence of key points in the watershed are acquired in real time in the digital twin. Based on the performance status sequence, the functional decay and recovery curves of the engineering system are calculated. The area under the comprehensive function curve, the time to reach the lowest point of the curve, and the time required to recover to the preset level are combined to generate a quantitative single resilience evaluation value through weighted fusion, which is used for direct comparison and ranking among different plans.

10. A water conservancy emergency resilience control system based on digital twins and knowledge graphs, characterized in that, For implementing the method of any one of claims 1 to 9, comprising: The knowledge graph management module is used to build, store, query, and update the spatiotemporal knowledge graph. The strategy rapid generation and optimization module integrates a graph computing engine, a few-shot learning model, and a physical constraint optimizer to execute steps S1 and S2. The digital twin parallel inference and decision-making module includes a high-fidelity simulation engine, a parallel task scheduler, and a deep learning agent model evaluator, which are used to execute step S3.

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