Reservoir dam safety monitoring system based on AI wisdom field and operation method

Abstract

The application discloses a reservoir dam safety monitoring system and operation method based on an AI wisdom field, and relates to the technical field of reservoir dam safety monitoring. The system comprises a data access and management module, a large model layer, an intelligent agent layer, a wisdom field layer, a collaborative scheduling engine, a digital twin carrier, a unified service interface and a feedback iteration module. The system accesses dam body deformation, seepage, stress and strain, vibration, environmental quantity, inspection images and operation and maintenance data, uses a special multi-dimensional large model for reservoir dams to perform multi-modal fusion analysis, risk prediction and abnormal source tracing, and completes early warning, decision making, execution and feedback through multiple autonomous collaborative intelligent agents. The wisdom field layer realizes virtual-real linkage of physical dams, monitoring equipment, disposal equipment and management processes based on digital twinning, forms a closed-loop management and control system of "perception-cognition-early warning-decision making-execution-feedback", and improves the intelligent, accurate and self-evolution capabilities of reservoir dam safety monitoring.

Description

Technical Field

[0001] This invention relates to the field of water conservancy project safety monitoring technology, specifically to a reservoir dam safety monitoring system and operation method based on AI smart field. Background Technology

[0002] Reservoirs and dams are crucial infrastructure for flood control, water supply, irrigation, power generation, and ecological regulation. Their operational safety directly impacts the safety of life and property downstream and the stability of the regional economy and society. As many reservoirs and dams enter their long-term service phase, issues such as dam deformation, abnormal seepage, crack propagation, settlement accumulation, structural aging, and the evolution of risks under extreme weather conditions are becoming increasingly prominent, placing higher demands on dam safety monitoring, risk identification, and emergency response capabilities. Traditional safety management methods relying on manual inspections, single-point sensor monitoring, and threshold alarms are no longer sufficient to meet the needs of refined, intelligent, and forward-looking management of reservoirs and dams.

[0003] Existing reservoir dam safety monitoring systems are typically based on an Internet of Things (IoT) architecture. They collect data on dam deformation, seepage, stress, and environmental parameters by deploying monitoring equipment such as piezometers, displacement gauges, crack gauges, stress-strain gauges, rain gauges, and water level gauges. This data is then transmitted to a central platform for display, statistical analysis, and alarm functions. While these systems achieve a certain degree of automatic data collection and anomaly alerts, their analytical logic largely relies on threshold comparisons of single indicators. They lack in-depth analysis of the spatiotemporal coupling relationships between multi-source heterogeneous data, making it difficult to accurately distinguish between normal fluctuations caused by environmental factors such as water level, rainfall, and temperature, and abnormal responses caused by dam structural damage.

[0004] In recent years, technologies such as digital twins, knowledge graphs, machine learning, and multimodal large models have begun to be applied in the field of water conservancy project safety monitoring. Some systems can visualize dam operation status, analyze hazard propagation paths, fuse and analyze monitoring data, or assist in risk assessment. However, the related technologies still have significant shortcomings: digital twin systems mostly remain at the level of 3D display and status presentation, lacking two-way linkage with actual equipment, operation and maintenance processes, and emergency response; knowledge graph methods are mostly geared towards specific hazard analysis tasks, lacking general reasoning and multi-task collaboration capabilities; and large model methods mainly focus on risk identification and auxiliary assessment, and have not yet formed an autonomous closed loop with inspection equipment, monitoring terminals, emergency response equipment, and management processes.

[0005] Therefore, existing technologies generally suffer from the following problems: First, monitoring data sources are scattered and formats are inconsistent, making it difficult to unify and integrate data such as deformation, seepage, stress, environmental quantities, inspection images, and operation and maintenance records, resulting in insufficient accuracy in risk assessment; Second, after an alarm is triggered, manual review, decision-making, and dispatching are still the primary methods relied upon, with a lack of linkage mechanisms between the monitoring system and execution terminals such as drones, underwater robots, gates, and drainage equipment, making it difficult to form a closed loop of "perception-assessment-early warning-decision-execution-feedback"; Third, the systems are mostly customized, with weak adaptability to different dam types, reservoir sizes, and operating conditions; Fourth, there is a lack of a continuous learning mechanism based on historical cases, treatment effects, and on-site feedback, making it difficult for the system to self-optimize as operational data accumulates; Fifth, industry standards, expert experience, historical cases, and emergency plans are not effectively integrated into the monitoring, assessment, and decision-making process, resulting in insufficient decision-making basis, weak interpretability, and weak traceability.

[0006] In summary, traditional reservoir dam safety monitoring systems remain passive control tools primarily based on data collection and over-limit alarms, failing to meet the demands of modern reservoir dams for proactive prediction, precise control, intelligent response, and continuous evolution. Therefore, there is an urgent need to propose a reservoir dam safety monitoring system and operational method based on AI-powered intelligent fields. This system should organically integrate multi-dimensional large-scale models, autonomous collaborative intelligent agents, digital twin intelligent fields, and domain knowledge systems to construct an intelligent closed-loop control system covering the entire dam safety monitoring process, thereby enhancing the reservoir dam's risk identification, early warning decision-making, collaborative response, and long-term operation and maintenance capabilities. Summary of the Invention

[0007] Based on the above description, this invention provides a reservoir dam safety monitoring system and operation method based on AI smart field, in order to solve the problems existing in the reservoir dam safety monitoring system, such as insufficient fusion of multi-source monitoring data, lagging risk assessment, fragmented early warning and response, weak equipment coordination capability, poor system scenario adaptability, difficulty in accumulating operating experience, and lack of continuous self-evolution mechanism.

[0008] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: A reservoir dam safety monitoring system based on AI-powered smart fields includes: The system includes a data access and governance module, a large model layer, an intelligent agent layer, a smart field layer, a multi-intelligent agent library, a collaborative scheduling engine, a digital twin carrier, a unified service interface, and a feedback and iteration module. The data access and governance module is configured to access the monitoring data of the physical effects of the reservoir dam, the data of the engineering operation environment, the operation and maintenance management data, the inspection image data, the equipment status data, the standard data and the historical case data, and to clean, align, complete, mark the quality and manage the accessed data in a unified manner. The large model layer is configured to construct a dedicated multi-dimensional large model for reservoir dams based on data from the field of reservoir dam safety monitoring, and to perform multimodal fusion analysis, anomaly identification, risk prediction, anomaly tracing, and generation of handling strategies on the ontological effect quantity monitoring data, engineering operation environment quantity data, operation and maintenance management data, inspection image data, and domain knowledge data. The intelligent agent layer is configured to invoke the corresponding reservoir dam safety intelligent agent to perform perception, cognition, early warning, prediction, rehearsal, contingency planning, decision-making, execution and feedback tasks based on the judgment results and strategy instructions output by the large model layer. The intelligent field layer is configured to integrate the physical entity of the reservoir dam, sensing terminals, execution equipment, operation and maintenance personnel, management processes and digital twin models to form a virtual and real linkage control scenario for reservoir dam safety monitoring; The multi-agent library is configured to store agent templates, task flows, calling interfaces and permission configurations for different business scenarios; The collaborative scheduling engine is configured to perform task splitting, task allocation, resource scheduling, and conflict coordination for multiple intelligent agents based on risk level, task type, device status, resource occupancy status, and handling process. The digital twin carrier is configured to construct a three-dimensional scene model, operation status model, monitoring point model, risk evolution model, and emergency response simulation model of the reservoir dam, and is updated synchronously with on-site monitoring data, risk assessment results, and execution feedback results; The unified service interface is configured to connect monitoring equipment, inspection equipment, gate control equipment, drainage equipment, emergency response equipment, operation and maintenance management system, early warning release system, and human-machine interaction terminal. The feedback iteration module is configured to receive on-site monitoring feedback, agent execution feedback, handling effect feedback, and manual review feedback, and input the feedback results into the large model layer, agent layer, and smart field layer to update model parameters, agent strategies, and digital twin states.

[0009] Based on the above technical solution, the present invention can be further improved as follows.

[0010] Furthermore, the monitoring data of the bulk effect includes one or more of the following: dam body horizontal displacement, vertical displacement, deflection, tilt, joint opening and closing degree, seepage flow, seepage pressure, phreatic line, stress, strain, vibration frequency, amplitude, and acceleration; the engineering operation environment data includes one or more of the following: upstream water level, downstream water level, rainfall, air temperature, reservoir water temperature, ground motion, wind force and direction, ice jams, waves, and solar radiation; the operation and maintenance management data includes one or more of the following: inspection records, image records, video records, infrared records, defect repair records, equipment logs, inspection reports, and operation and maintenance work orders; the domain knowledge data includes one or more of the following: reservoir dam safety monitoring specifications, operation and management procedures, historical defect cases, emergency response cases, expert experience, equipment failure modes, and domain knowledge graphs.

[0011] Furthermore, the large model layer includes a multimodal data embedding unit, a spatiotemporal coupling analysis unit, a domain knowledge fusion unit, a risk prediction unit, a solution generation unit, and a model optimization unit. The multimodal data embedding unit is configured to embed features from text, images, videos, numerical time-series data, and structured knowledge data. The spatiotemporal coupling analysis unit is configured to establish the spatiotemporal correlation between ontological effect quantities and engineering operating environment quantities. The domain knowledge fusion unit is configured to fuse standards, historical cases, expert experience, and domain knowledge graphs. The risk prediction unit is configured to output future deformation, seepage, stress-strain, or vibration risk prediction results based on historical monitoring data, real-time monitoring data, and environmental quantity data. The solution generation unit is configured to generate candidate disposal strategies based on risk prediction results, anomaly tracing results, and domain knowledge data. The model optimization unit is configured to perform incremental fine-tuning, effect verification, and model version management based on feedback data.

[0012] Furthermore, the intelligent agent layer includes a vertical business process intelligent agent and a horizontal empowerment support intelligent agent; the vertical business process intelligent agent includes a perception intelligent agent, a cognitive intelligent agent, an early warning intelligent agent, a prediction intelligent agent, a pre-simulation intelligent agent, a contingency plan intelligent agent, a decision-making intelligent agent, an execution intelligent agent, and a feedback intelligent agent; the perception intelligent agent is configured to collect or retrieve data on dam deformation, seepage, stress and strain, vibration, environmental quantities, and apparent defects; the cognitive intelligent agent is configured to identify the dam's operating status, anomaly types, structural weaknesses, and risk causes based on the large model layer and domain knowledge data; the early warning intelligent agent is configured to generate tiered early warning information based on anomaly identification results, risk prediction results, and early warning rules; the prediction intelligent agent is configured to... The system is configured to predict the evolution trend of key monitoring indicators of the dam; the pre-simulation intelligent agent is configured to construct risk evolution scenarios and disposal measure simulation scenarios based on the digital twin carrier; the contingency plan intelligent agent is configured to match or generate disposal procedures corresponding to risk type, risk level and on-site conditions from the emergency plan library; the decision intelligent agent is configured to generate disposal plans based on risk level, disposal pre-simulation results, contingency plan matching results, equipment status and resource conditions; the execution intelligent agent is configured to convert the disposal plans into equipment control commands, inspection tasks, work order tasks or emergency disposal tasks; and the feedback intelligent agent is configured to collect disposal effects, equipment execution status, monitoring changes and manual confirmation results, and form feedback data.

[0013] Furthermore, the horizontally empowering supporting intelligent agents include one or more of the following: twin intelligent agents, knowledge intelligent agents, collaborative intelligent agents, writing intelligent agents, instrument management and maintenance intelligent agents, network security intelligent agents, immersive training intelligent agents, and interactive intelligent agents; the twin intelligent agent is configured to build, update, and invoke the digital twin model of the reservoir dam; the knowledge intelligent agent is configured to build a knowledge graph, case library, standard library, and equipment knowledge library in the field of reservoir dam safety; the collaborative intelligent agent is configured to perform information sharing, task collaboration, resource collaboration, and conflict coordination among multiple intelligent agents; the writing intelligent agent is configured to... The system generates daily monitoring reports, early warning briefings, emergency response reports, and annual monitoring reports. The instrument management and maintenance intelligent agent is configured to manage the asset ledger, operating status, fault diagnosis, and maintenance work orders of monitoring instruments and execution equipment. The network security intelligent agent is configured to provide security protection for system communication, industrial control equipment, data transmission, and operation logs. The immersive training intelligent agent is configured to conduct operation and maintenance training, emergency drills, and operational assessments based on digital twin scenarios. The interactive intelligent agent is configured to provide data queries, knowledge Q&A, business operations, and scenario explanations through natural language, voice, gestures, or a digital human interface.

[0014] Furthermore, the intelligent field layer includes a data integration module, a digital twin module, a virtual-real mapping module, a business collaboration module, a pre-simulation and deduction module, and a visualization and interaction module. The data integration module is configured to uniformly manage the monitoring data, equipment data, business data, and knowledge data of the reservoir dam. The digital twin module is configured to construct the geometric model, monitoring point model, physical response model, and operational status model of the reservoir dam. The virtual-real mapping module is configured to map on-site monitoring data, equipment operating status, early warning information, and disposal results to the digital twin model. The business collaboration module is configured to link the processes of early warning issuance, inspection and verification, emergency response, equipment maintenance, report generation, and manual review. The pre-simulation and deduction module is configured to generate risk evolution paths, disposal effect simulation results, and scheme comparison results based on prediction results and disposal measures. The visualization and interaction module is configured to display the dam's three-dimensional scene, monitoring indicators, risk level, disposal process, equipment status, and feedback results.

[0015] Furthermore, the collaborative scheduling engine includes an information sharing unit, a task coordination unit, a resource coordination unit, and a conflict coordination unit. The information sharing unit is configured to synchronize the perception data, cognitive results, early warning information, decision instructions, execution data, and feedback opinions generated by each intelligent agent. The task coordination unit is configured to generate a task chain according to the business sequence of perception, cognition, early warning, prediction, rehearsal, contingency plan, decision, execution, and feedback. The resource coordination unit is configured to schedule monitoring equipment, inspection equipment, emergency equipment, computing resources, human resources, and knowledge resources. The conflict coordination unit is configured to adjust the task execution order, resource allocation method, or parallel task splitting method when multiple intelligent agents simultaneously call the same device, interface, computing resources, or business process.

[0016] Furthermore, the feedback iteration module includes a feedback data receiving unit, a deviation analysis unit, an incremental training unit, a strategy optimization unit, and a version management unit. The feedback data receiving unit is configured to receive the latest monitoring data, inspection and verification results, disposal effect evaluation results, equipment operation results, and manual review results. The deviation analysis unit is configured to compare the deviation between the model prediction results and the on-site observation results, and determine the update targets for model parameters, agent strategies, or digital twin states. The incremental training unit is configured to perform incremental fine-tuning of the large model layer based on the filtered feedback data. The strategy optimization unit is configured to update the agent's task rules, calling order, disposal process, and resource scheduling strategy according to the task execution effect. The version management unit is configured to record the model version, agent version, data source, update time, performance verification results, and rollback information.

[0017] The operation method of the reservoir dam safety monitoring system based on AI smart field includes the following steps: S1 connects to the monitoring data of the dam's physical effects, engineering operation environment data, operation and maintenance management data, inspection image data, equipment status data, standard data and historical case data, and performs cleaning, alignment, completion, quality marking and unified management. S2, the processed data is input into the large model layer, which performs multimodal fusion analysis, spatiotemporal coupling analysis, domain knowledge fusion, anomaly identification, risk prediction, anomaly tracing, and candidate handling strategy generation. S3. Based on the dam safety status, anomaly type, risk level, anomaly cause, and candidate handling strategy output by the large model layer, the collaborative scheduling engine calls the corresponding intelligent agents in the intelligent agent layer to generate a task chain covering perception, cognition, early warning, prediction, rehearsal, contingency plan, decision-making, execution, and feedback. S4 is generated by the intelligent field layer based on the digital twin carrier, which generates virtual scenarios, risk evolution paths and disposal simulation results corresponding to the risk location, risk type and risk level; S5 is generated by the decision-making intelligent agent by combining risk prediction results, disposal simulation results, emergency plans, domain knowledge, equipment status and resource conditions to generate disposal plans; S6, the executing intelligent agent transforms the disposal plan into inspection tasks, equipment control instructions, work order tasks or emergency disposal tasks, and drives the corresponding equipment or business system to execute them through a unified service interface; S7, the feedback agent collects on-site monitoring feedback, equipment execution feedback, handling effect feedback and manual review feedback, and inputs the feedback results into the feedback iteration module; S8, through the feedback iteration module, updates the large model layer, intelligent agent layer, and intelligent field layer based on the feedback results, forming a closed-loop operation for reservoir dam safety monitoring.

[0018] Furthermore, steps S3 to S8 form a closed-loop chain of "perception-cognition-early warning-decision-execution-feedback". In the perception stage, data on dam deformation, seepage, stress and strain, vibration, environmental quantities, and apparent defects are collected. In the cognition stage, the dam's operating status, anomaly types, structural weak areas, and risk causes are identified. In the early warning stage, tiered early warning information is generated based on anomaly identification results, risk prediction results, and early warning rules. In the decision-making stage, a response plan is generated based on the risk level, response simulation results, contingency plan matching results, equipment status, and resource conditions. In the execution stage, the response plan is transformed into equipment control commands, inspection tasks, work orders, or emergency response tasks. In the feedback stage, the response results, monitoring changes, equipment status, and manual confirmation results are fed back to the large model layer, intelligent agent layer, and intelligent field layer.

[0019] Compared with the prior art, the technical solution of this application has the following beneficial technical effects: 1. Through the intelligent field layer data integration module, the spatiotemporal benchmark of multi-source heterogeneous data is unified, and data cleaning, fusion, and quality verification are completed, completely breaking down the "information silos" of deformation, seepage, and environmental quantity data in existing technologies. Relying on the multimodal fusion capability of the large model layer and the spatiotemporal coupling analysis subnet of environmental quantity-effect quantity, the complex nonlinear correlation between the ontological effect quantity and the environmental quantity is deeply explored, accurately distinguishing between normal fluctuations and abnormal hidden dangers in the dam's operating status. Compared with existing technologies (the accuracy rate of hidden danger identification is generally below 75%, and the advance time for risk prediction is less than 24 hours), the accuracy rate of anomaly identification is improved to over 95%, and the advance time for risk prediction is extended to over 72 hours. It can accurately identify difficult-to-detect hidden dangers such as deep water seepage and hidden cracks, truly achieving "early detection, early prediction, and early handling" of hidden dangers, and significantly improving the scientificity, accuracy, and foresight of dam safety assessment. 2. Driven by a dedicated multi-dimensional model for reservoir dams, the intelligent agent layer constructs a vertical closed-loop link of "perception-cognition-early warning-decision-execution-feedback," coupled with comprehensive support from horizontal empowerment agents. This enables fully autonomous closed-loop execution of the entire process, from "data collection-state analysis-command issuance-action execution-result feedback," without real-time human intervention. It can replace manual labor in managing high-risk scenarios such as deep water, high altitudes, and steep slopes, solving the problems of disconnect between "monitoring and control" and delayed emergency response in existing technologies. Through the linkage between the intelligent field-layer collaborative scheduling module and collaborative intelligent agents, and relying on an event-driven architecture, deep collaboration among various intelligent agents, devices, and business processes is achieved, overcoming the "lone wolf" dilemma of existing systems. Compared to existing technologies, inspection efficiency, emergency response efficiency, and operation and maintenance efficiency are improved by more than 60%, 60%, and 70%, respectively. Emergency response time is shortened from hours to minutes, significantly reducing the cost of manual intervention and the risks of high-risk operations. 3. Employing a large-scale model training strategy of "pre-training + domain fine-tuning," combined with modular and containerized agent design and a standardized encapsulated multi-agent library, the system architecture can quickly adapt to different types (earth-rock dams, concrete dams, etc.), scales (large high dams, medium and small reservoir dams), and control scenarios without large-scale system reconstruction, significantly reducing deployment and upgrade costs and addressing the pain points of high customization and poor versatility in existing systems. Through the linkage of the large-scale model layer's autonomous optimization mechanism, the agent layer's autonomous upgrade mechanism, and the intelligent field layer's feedback iteration module, combined with the parameter correction and knowledge accumulation capabilities of the feedback agents, the system's performance can be continuously and autonomously optimized. This overcomes the shortcomings of existing systems that are "developed once and run inefficiently for a long time," ensuring continuous adaptation to the dynamic changes in dam operation status and the upgrading of control requirements, thus achieving system self-evolution. 4. Construct a human-machine integrated operation mode that prioritizes unmanned operation and supplements it with manual intervention. The intelligent agent autonomously undertakes high-risk and tedious management tasks such as deep-water exploration and high dam inspection. Combined with the multimodal interaction capabilities and visual interface of the interactive intelligent agent, the reliance on the combined water conservancy and IT professional skills of the operation and maintenance personnel is significantly reduced. Through the preventive operation and maintenance capabilities of the instrument management and maintenance intelligent agent, coupled with the collaborative optimization of the large model and the intelligent agent, the cost of manual operation and maintenance is reduced by more than 65% (currently, the cost of manual operation and maintenance accounts for more than 60%), the integrity rate of core equipment is increased to more than 99%, and the equipment failure rate is significantly reduced. This effectively solves the problem that it is difficult to deploy intelligent systems in small and medium-sized reservoirs due to excessively high operation and maintenance costs, significantly improves the engineering practicality and widespread promotion value of the system, and helps to promote the universal upgrading of intelligent management and control of reservoir dam safety. 5. By constructing a knowledge graph for reservoir dam safety through knowledge agents, structured and unstructured knowledge, including industry standards, historical cases, expert experience, and mechanical theories, is deeply injected into the large model and various agents. Combined with technologies such as Retrieval Augmentation (RAG), real-time linkage and reasoning between knowledge and data are achieved, addressing the pain point of existing technologies that "have data but no knowledge." The interpretability of the large model's knowledge reasoning ability is ≥90%, enabling full traceability of the decision-making process. The generated emergency response plans and operation and maintenance plans not only comply with industry standards but also draw on historical experience, ensuring the rationality, feasibility, and compliance of decisions. At the same time, it supports refined scheduling that takes into account multiple objectives such as ecological flow and fish protection, contributing to the integrated development of "safety, ecology, and intelligence." 6. The intelligent field layer integrates the dam's physical entity, comprehensive monitoring data, all types of intelligent agents, and all business processes. Combined with the digital twin module's four-dimensional high-fidelity model of "geometry-physical-behavior-rules" and a virtual-real linkage mechanism, it achieves integrated management and control of the dam throughout its entire lifecycle—planning, construction, operation, maintenance, and decommissioning—across all areas and time periods. Through virtual-real collaborative simulations and multi-entity collaborative management, it overcomes the shortcomings of existing technologies in terms of incomplete coverage of management scenarios and disconnected management links. This significantly improves the comprehensiveness, reliability, and stability of dam safety management, providing strong technical support for the safe and stable operation of reservoir dams and contributing to the modernization of the national water security system. Attached Figure Description

[0020] Figure 1 This is an overall architecture diagram of the reservoir dam safety monitoring system based on AI smart field architecture provided in this embodiment of the invention; Figure 2 This is a large-scale safety model architecture diagram of a reservoir dam provided in an embodiment of the present invention; Figure 3 This is a flowchart of the autonomous optimization process of the large-scale safety model for reservoir dams provided in this embodiment of the invention; Figure 4 This is a flowchart of the intelligent agent collaboration process provided in an embodiment of the present invention; Figure 5 This is an interaction diagram of the core modules of the smart field layer provided in this embodiment of the invention; Figure 6 This is a structural diagram of the four-dimensional digital twin model of the dam provided in this embodiment of the invention; Figure 7 This is a diagram of the two-dimensional classification architecture of the multi-agent library provided in this embodiment of the invention; Figure 8 This is a diagram of the metadata organization structure of the intelligent entity library provided in this embodiment of the invention; Figure 9 This is an internal workflow diagram of the intelligent agent collaborative scheduling engine provided in an embodiment of the present invention. Detailed Implementation

[0021] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings, which illustrate embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of this application will be thorough and complete.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0023] Example 1: Reference Figures 1-9 This is a reservoir dam safety monitoring system based on AI-powered intelligent fields. The system adopts a distributed, modular design, constructing a three-tiered architecture: a large model layer (bottom layer), an intelligent agent layer (middle layer), and an intelligent field layer (top layer). These three layers form a close hierarchical support and bidirectional interaction relationship, complemented by four auxiliary modules: a multi-intelligent agent library, a collaborative scheduling engine, a digital twin carrier, and a unified service interface. Together, they constitute an intelligent closed-loop management and control system for the entire dam safety monitoring business chain. Specific definitions are as follows: Large Model Layer (Underlying Intelligent Core): Defined as a collection of dedicated multi-dimensional large models (Dam Safety LLMs) for reservoir dams, trained based on massive historical data, real-time sensing data, and operation and maintenance management data in the field of reservoir dam safety monitoring. As the "intelligent brain" of the system, this layer possesses multi-modal data fusion, anomaly identification, risk prediction, decision generation, and self-iterative learning capabilities. It is responsible for transforming complex engineering safety problems into parsable and executable policy instructions, providing core algorithms and cognitive support for the upper layers.

[0024] Intelligent Agent Layer (Mid-Layer Execution Core): Defined as a cluster of autonomous and collaborative intelligent agents (Autonomous Agents) with the capabilities of autonomous perception, task decomposition, collaborative cooperation, and command execution. Serving as the hub connecting the underlying intelligence and the top-level applications, this layer consists of multiple modular intelligent units (such as perception agents, decision-making agents, and execution agents). It receives policy instructions from the larger model layer, completes specific business operations (such as drone inspection, gate control, and report generation), and feeds the execution results back to the larger model layer, achieving a closed loop of "command-execution-feedback".

[0025] The Smart Field Layer (Top-Level Ecosystem Core): Defined as an Omni-domain Digital Smart Field that integrates the dam's physical entity, sensing terminals, maintenance personnel, and management processes. As the system's "ecological carrier," this layer uses high-fidelity digital twin technology to achieve visualized presentation, full-process control, and multi-role collaboration of all dam safety monitoring services; it also receives the execution results from the intelligent agent layer and applies them to the physical dam, forming a closed loop of virtual-physical linkage between "virtual simulation - physical execution - data feedback."

[0026] Virtual-physical linkage closed-loop mechanism: The physical dam collects multi-source data through sensing terminals and transmits it to the intelligent field layer. After in-depth analysis by the large model layer, a strategy is generated. This strategy is then transformed into specific instructions by the intelligent agent layer, driving physical devices to execute or generating management decisions. The execution results are again sensed and fed back to the system, forming a continuously optimizing closed loop. Specifically: I. Large Model Layer The large model layer is the core intelligent engine of the system. It adopts a large reservoir dam safety model (Dam Safety LLM) based on the Transformer architecture, with a parameter scale of over 10 billion. It follows a two-stage training mode of "pre-training + domain fine-tuning" and has particularly strengthened the ability to analyze the spatiotemporal coupling of ontological effect quantities and environmental quantities.

[0027] (1) Multi-source fusion training data base The training data for the large model employs a multi-source fusion strategy across its entire lifecycle, covering six core data categories to ensure the comprehensiveness and timeliness of the knowledge. Specific data sources and uses are shown in Table 1.

[0028] Table 1. Classification and Application System of Training Data for Large-Scale Safety Model of Reservoir Dams

[0029] (2) Model architecture and training mechanism The model architecture comprises an input layer (multimodal data embedding), an encoder layer (Transformer Blocks), a domain knowledge fusion module, a spatiotemporal attention mechanism module, and an output layer. A spatiotemporal coupling analysis subnet for environmental and effect quantities is specifically designed to capture the complex correlations between multiple physics fields.

[0030] ① Pre-training stage: Based on massive general data (multimodal data such as natural language text, images, and numerical data) and basic data in the dam field (general monitoring data, basic specification data, etc.), pre-training is carried out, focusing on cultivating the model's basic capabilities such as multimodal data processing, general reasoning, and contextual understanding, and building a general intelligent foundation.

[0031] ② Domain Fine-tuning Phase: Fine-tuning training is conducted based on dam-specific data (monitoring data of the target dam's intrinsic effects, environmental monitoring data, operation and maintenance data, etc.). A combination of reinforcement learning and supervised learning is employed to optimize model parameters and algorithm logic, incorporating a domain knowledge graph. In particular, in-depth modeling is performed to address the correlation between effects such as deformation, seepage, stress-strain, and vibration and environmental quantities such as water level, rainfall, and temperature, enhancing the model's ability to perform specific tasks related to the safety status of water conservancy projects.

[0032] The core of the domain fine-tuning phase lies in establishing the mapping relationship between environmental quantities (loads) and effect quantities (responses), the basic mathematical expression of which is shown in the following formula: In the formula, The vector of bulk effect monitoring values ​​at time t, including deformation. seepage flow ,stress Vibration frequency wait; The upstream and downstream water levels at time t; The rainfall intensity at time t; Temperature at time t; Model parameters reflect the structural characteristics and material parameters of the dam.

[0033] For deformation monitoring data, a statistical model can be used for decomposition, as shown in the following formula. This formula is embedded in the loss function of the larger model to guide fine-tuning: In the formula, Total deformation; Water level component, reflecting elastic deformation caused by water pressure; Temperature component: reflects the deformation caused by temperature changes; The aging component reflects irreversible deformations over time, such as concrete creep and rock creep. Random error.

[0034] For seepage monitoring data, the relationship between seepage flow and water level can be expressed as follows, which is used to constrain the physical consistency of the model in seepage analysis: In the formula, : Infiltration flow rate; : Permeability coefficient; : Width of seepage cross section; Upstream and downstream water levels; : Length of seepage path; : Basic seepage flow rate.

[0035] ③ Model Optimization Module: A dedicated model optimization module is set up to receive data feedback from the intelligent field layer and intelligent agent layer (including newly collected monitoring data, field verification results, etc.) and regularly perform incremental fine-tuning training on the model. A model fault tolerance mechanism and anomaly detection threshold are set to ensure the stability and reliability of model operation.

[0036] (3) Core capabilities and performance indicators The large-scale reservoir dam safety model possesses eight core capabilities: multimodal data fusion processing, safety status identification, risk prediction, anomaly tracing, scheme generation, knowledge reasoning, multi-task transfer, and human-computer interaction. In the area of ​​water conservancy project safety monitoring, the model can deeply integrate ontological effect monitoring data with environmental monitoring data to achieve high-precision assessment of dam safety status. Key performance indicators are shown in Table 2. Table 2 Core Capabilities and Key Performance Indicators of the Large-Scale Safety Model for Reservoirs and Dams

[0037] The core algorithm for risk prediction is based on a multi-factor time-series forecasting model, the basic form of which is shown in the following formula: In the formula, :future Predicted effect quantities at any given time (e.g., deformation, seepage flow). Historical environmental time series (water level, rainfall, temperature, etc.); Historical effect size time series; : The mapping function between the i-th environmental quantity and the effect quantity; : The weighting coefficient of the i-th environmental quantity; Effect size autoregressive function; : Autoregressive term weight coefficient.

[0038] (4) Autonomous optimization and iteration mechanism The large model layer establishes a comprehensive self-optimization mechanism, including five stages: feedback data reception, data processing and analysis, incremental fine-tuning training, optimization effect verification, and model version management, forming a closed loop of "feedback → optimization → verification → feedback again".

[0039] ① Feedback data reception: Real-time access to newly collected monitoring data from the smart field layer (including the latest data on deformation, seepage, stress and strain, vibration, water level, rainfall, and temperature) as well as on-site verification results fed back by the smart body layer (such as anomalies found during inspections and evaluations of treatment effectiveness).

[0040] ② Data Processing and Analysis: The feedback data is cleaned, labeled, and its features extracted. It is then compared with historical data to identify the deviation between the model's predictions and actual observations, pinpointing key areas for model optimization. Deviation analysis is performed using the following formula: In the formula, Mean relative deviation; : The actual observed value of the i-th sample; The model prediction value for the i-th sample; : Number of feedback samples.

[0041] ③ Incremental fine-tuning training: Based on the analysis results, high-quality feedback data are selected, and incremental learning technology is used to fine-tune the model, update the model parameters, and optimize the model's adaptability to specific working conditions and specific dam types.

[0042] ④ Optimization Effect Verification: The optimized model is tested on the validation set, focusing on key metrics such as the accuracy of security status identification and risk prediction accuracy, to ensure the optimization is effective and does not introduce negative effects. The verification pass condition is shown in the following formula: In the formula, : Accuracy of the optimized model; Accuracy of the model before optimization; : Preset performance threshold (e.g., 95%).

[0043] ⑤ Model version management: Update the validated optimized model and record version change information (optimization data source, optimization time, performance improvement, etc.) to ensure that the model iteration process is traceable and rollbackable.

[0044] II. Intelligent Agent Layer The intelligent agent layer, as the core carrier for the implementation of large-scale model capabilities, serves as a crucial bridge connecting the large-scale model layer and the intelligent field layer. Driven by the large-scale reservoir dam safety model, it bears the core responsibility of transforming the intelligent analysis results of the large-scale model into actual control actions, synchronously feeding back on-site execution, and supporting the safety management of the dam throughout its entire lifecycle. This layer adopts a three-dimensional architecture of "vertical closed-loop control + horizontal capability support," integrating 17 types of specialized intelligent agents for reservoir dam safety. All agents employ a unified, standardized, and modular design, standardizing and integrating perception, planning, decision-making, execution, and feedback modules to form a complete closed-loop task execution chain of "data acquisition - status analysis - command issuance - action execution - result feedback." Each agent independently undertakes its specific task while also achieving efficient collaboration through a robust coordination mechanism, ensuring the efficient, accurate, and closed-loop implementation of dam safety management tasks.

[0045] (1) Vertical Business Process Intelligent Agent The vertical business process intelligent agents cover the entire process of dam safety management and control, forming a complete closed-loop link of "perception-cognition-early warning-decision-execution-feedback". Various intelligent agents are configured with dedicated hardware devices and software sub-modules based on general modules, with clearly defined core configurations, key performance indicators, and core functions, focusing on the practical implementation of the entire dam safety management and control process, as detailed below: Table 3: Overview of Core Parameters of Vertical Business Process Intelligent Agent

[0046]

[0047]

[0048] (2) Horizontal empowerment to support intelligent agents Horizontal empowerment supports intelligent agents as the backbone of vertical business process intelligent agents, encompassing 11 types of intelligent agents. Based on general modules, it configures dedicated hardware and software sub-modules, focusing on providing technical, knowledge, collaborative, and security support for vertical closed-loop management, ensuring the efficient implementation of vertical workflows. Specifically: Table 4: Overview of Core Parameters for Lateral Empowerment Supporting Intelligent Agents

[0049]

[0050]

[0051]

[0052] (3) Intelligent agent layer collaborative mechanism and autonomous upgrade mechanism The intelligent agent layer, serving as the core bridge connecting the large model layer and the intelligent field layer, adopts a three-dimensional architecture of "vertical closed-loop control + horizontal capability support," integrating 17 types of intelligent agents for reservoir dam safety. The vertical business process intelligent agents focus on the implementation of the entire control process, while the horizontal empowerment support intelligent agents provide comprehensive support and assurance. To ensure efficient collaboration among various intelligent agents and the formation of a unified control force, while simultaneously achieving continuous optimization of the intelligent agent layer's own functions to adapt to the dynamic changes in dam safety control needs and support the steady improvement of dam safety control capabilities and the long-term operation of the system, the intelligent agent layer establishes a comprehensive collaborative mechanism and an autonomous upgrade mechanism, the specific details of which are as follows: 1) Collaboration Mechanism The collaborative mechanism takes the collaborative intelligent agent in the horizontal empowerment support intelligent agent as the core hub, linking all vertical business process intelligent agents with other horizontal empowerment support intelligent agents. Relying on the unified scheduling of the Dam-Safety-LLM (Dam-Safety-LLM) model, it achieves efficient cooperation in all aspects and throughout the entire process. Specifically, it covers four core mechanisms, which are deeply linked and precisely adapted to the core functions of various intelligent agents: ① Information Sharing Mechanism: Breaking down information barriers between intelligent agents, a "vertical + horizontal" full-domain information interconnection link is constructed with the collaborative intelligent agent as the core hub. This enables real-time synchronization of perception data, cognitive results, early warning information, decision-making instructions, execution data, and feedback opinions from each intelligent agent in the vertical workflow (perception, cognition, early warning, decision-making, execution, and feedback). Simultaneously, it opens up information channels between the horizontal empowerment and support intelligent agents and the vertical intelligent agents, synchronously pushing industry knowledge and case experience from the knowledge intelligent agent, virtual simulation data from the twin intelligent agent, and equipment status data from the instrument management and maintenance intelligent agent to all relevant intelligent agents. This provides comprehensive and accurate data and knowledge support for the tasks of each intelligent agent, ensuring the scientific and timely nature of management and control decisions, and aligning with the core functional requirements of the perception intelligent agent ("data output support"), the cognition intelligent agent ("multi-source data fusion"), and the feedback intelligent agent ("data aggregation and feedback").

[0053] ② Task Collaboration Mechanism: Based on the unified scheduling of the Dam Safety LLM model, and combined with the task decomposition, scheduling, and allocation capabilities of collaborative intelligent agents, the task division of vertical business process intelligent agents and horizontal empowerment support intelligent agents is clearly defined, achieving seamless connection of "vertical closed loop and horizontal support". At the vertical level, it ensures the smooth operation of the entire closed-loop link of "perception-cognition-early warning-decision-execution-feedback", with each vertical intelligent agent efficiently connecting and collaborating according to the process. At the horizontal level, it promotes the precise docking of intelligent agents such as twins, prediction, pre-drilling, contingency plans, and knowledge with each vertical link. For example, the pre-drilling intelligent agent provides support for the decision-making intelligent agent in scheme comparison, the contingency plan intelligent agent provides support for the standardized handling process for the decision-making and execution intelligent agents, and the writing intelligent agent links with the perception and feedback intelligent agents to generate various professional reports, ensuring the efficient advancement and closed-loop implementation of various control tasks such as inspection, early warning, handling, operation and maintenance, training, and report preparation.

[0054] ③ Resource Coordination Mechanism: The collaborative intelligent agent coordinates and schedules the hardware, computing, human, and knowledge resources of various intelligent agents. Based on the priority and core requirements of various tasks, resources are dynamically allocated to avoid idleness and waste, significantly improving resource utilization. For emergency tasks such as emergency response, priority is given to scheduling vertical intelligent agents related to emergency response, such as execution and early warning agents, and horizontal intelligent agents such as contingency plan agents, knowledge agents, and network security agents. This rapidly provides contingency plan support, knowledge references, and security assurance, ensuring efficient implementation of emergency responses and minimizing dam safety risks. For routine tasks such as daily operation and maintenance and monitoring, the scheduling of perception agents, instrument management agents, and collaborative intelligent agents is optimized to ensure stable equipment operation and continuous data collection, aligning with the core performance requirements of "full lifecycle equipment management" for instrument management agents and "coordinated resource allocation" for collaborative intelligent agents.

[0055] ④ Conflict Coordination Mechanism: The collaborative agent monitors the task execution status and resource usage of each agent in real time. An automated coordination and scheduling mechanism is established to address various conflicts that may arise when multiple agents execute tasks simultaneously. This mechanism focuses on resolving resource conflicts (such as multiple agents simultaneously accessing the same hardware device or computing resources) and task conflicts (such as overlapping tasks between the sensing agent's inspection and the instrument maintenance agent's equipment operation and maintenance, or overlapping actions of the executing agent with tasks of other agents). By automatically adjusting the task execution order, optimizing resource allocation schemes, and splitting parallel tasks, the mechanism ensures that various control tasks are carried out in an orderly and efficient manner, avoiding control blind spots and task disconnections, and fully leveraging the core role of the collaborative agent as the "general coordinator."

[0056] 2) Self-upgrade mechanism To adapt to the dynamic changes in dam safety management and control needs, the technological iteration of the large model layer, and the upgrading of hardware equipment in the intelligent field layer, and to ensure the continuous optimization of the intelligent agent layer's functions and the steady improvement of its performance, thereby achieving self-evolution of itself and the entire management and control system, the intelligent agent layer has established and improved an autonomous upgrade mechanism, linking the large model layer and the intelligent field layer to form an upgrade closed loop. The specific implementation path is as follows: First, it links with the large model layer to upgrade algorithms and logic. The agent layer receives optimized parameters, updated judgment logic, and cutting-edge industry algorithms pushed by the large model layer in real time, and adapts synchronously to the upgrade results of the large model. For example, it synchronously updates the AI ​​algorithm of the cognitive agent, the multi-model fusion algorithm of the predictive agent, and optimizes the inference logic of the decision agent. This ensures that the core capabilities of each agent are synchronized with the large model layer, continuously improving the accuracy and efficiency of task execution, and meeting the needs of improving key performance indicators such as "interpretability of mechanism diagnosis" for the cognitive agent and "prediction accuracy" for the predictive agent.

[0057] Second, execution optimization is achieved by leveraging data from the intelligent field layer. This involves continuously collecting on-site execution data, equipment operation data, changes in control scenarios, and actual needs from the intelligent field layer. Combined with the intelligent analysis capabilities of the large model, this automatically optimizes the module parameters, execution logic, and task planning schemes of each intelligent agent. For example, based on historical monitoring data and on-site feedback collected by the sensing agent, the collection frequency and monitoring range of the sensing agent are optimized; based on the handling effect data of the execution agent, the action flow of the execution agent and the equipment linkage logic are adjusted; and based on the equipment operation and maintenance data of the instrument management and maintenance agent, equipment control strategies are optimized, achieving a closed-loop upgrade of "data feedback - analysis and optimization - practical application."

[0058] Third, it achieves its own software and hardware iterative upgrades. It regularly completes software version updates and hardware adaptation adjustments for the intelligent agent layer, and optimizes and upgrades the core modules of various intelligent agents. For example, it upgrades the multimodal recognition capabilities of interactive intelligent agents, the report generation efficiency of writing intelligent agents, and the vulnerability detection capabilities of network security intelligent agents. At the same time, it adapts to newly added hardware devices in the intelligent field layer, expands the functional boundaries of intelligent agents, and continuously improves the task execution accuracy, efficiency, and compatibility of each intelligent agent, thereby achieving iterative upgrades of its own functions.

[0059] Fourth, experience accumulation drives continuous evolution. By summarizing the task execution experience and handling cases of each intelligent agent through feedback agents, the experience is simultaneously accumulated in the case library and knowledge graph of the knowledge intelligent agent, providing experience support for the optimization and upgrading of each intelligent agent; at the same time, combined with the industry standards and expert experience of the knowledge intelligent agent, the task execution standards of each intelligent agent are continuously optimized, promoting the transformation of the intelligent agent layer from "passive execution" to "proactive optimization", and realizing the long-term evolution of itself and the entire dam safety management and control system.

[0060] III. Smart Field Layer The intelligent field layer is the top-level ecological core of this invention, defined as a digital ecological scenario integrating the physical entity of the dam, sensing terminals, operation and maintenance personnel, management processes, and full data resources. It is also the core carrier of the intelligent field for reservoir dam safety. This layer serves as a key carrier for realizing the value of the intelligent judgment capabilities of the large model layer and the task execution capabilities of the intelligent agent layer. It integrates all 17 types of core intelligent agents in the intelligent agent layer, multi-source monitoring data throughout the dam's lifecycle, the dam's physical control environment, and safety business management processes. It also integrates emerging technologies such as the Internet of Things, digital twins, 5G, big data, and cloud computing to construct an intelligent, collaborative, and integrated safety management ecosystem characterized by "full-domain perception, virtual-real collaboration, multi-entity linkage, and full lifecycle control." The intelligent field layer is not only a visual presentation window of the dam's safety status but also the final implementation link in the "virtual control-physical execution" linkage closed loop. It realizes the transformation from "passive response" to "proactive prediction, precise control, and closed-loop operation and maintenance," ensuring the efficiency, scientific nature, and sustainability of reservoir dam safety management.

[0061] (1) Core Component Modules The intelligent field layer adopts a modular design, with core components including a data integration module, a digital twin module, a collaborative scheduling module, a business management module, and a feedback iteration module. Each module functions independently yet interacts closely, forming a complete top-level control and support system. The functions and interaction relationships of each module are shown in the attached figure. Figure 5 As shown: The specific functions and interaction logic of each module are as follows: Data Integration Module: Serving as the data foundation of the intelligent field layer, this module is responsible for accessing multi-source heterogeneous data throughout the dam's entire lifecycle. This includes on-site effects (deformation, seepage, stress-strain, vibration), environmental parameters (water level, rainfall, air temperature, reservoir water temperature, etc.), maintenance records (inspections, repairs, work orders, etc.), video images, third-party system data, and manually entered data. The core functions of this module are data cleaning, spatiotemporal benchmark alignment, multimodal fusion, and quality verification. It transforms the chaotic raw data into standardized and regulated data resources, simultaneously pushing them to the digital twin module, collaborative scheduling module, and business management module. This provides high-quality, unified data support for upper-layer applications, the intelligent agent layer, and the large model layer, forming an efficient connection with the data output of the sensing intelligent agent.

[0062] Digital Twin Module: The core technology carrier of the intelligent field layer, constructing a high-fidelity digital twin of the dam integrating "geometry, physics, behavior, and rules" in four dimensions. This enables real-time bidirectional mapping between the physical dam and the virtual model, with a one-to-one correspondence between spatial coordinates and parameters, providing virtual support for intelligent agent collaboration and risk simulation. Its model structure is shown in the attached figure. Figure 6 As shown: Collaborative Scheduling Module: With a built-in intelligent agent collaborative scheduling engine, it serves as the core hub for the linkage between the intelligent field layer and the intelligent agent layer. Based on event-driven architecture (EDA), it dynamically orchestrates vertical business process intelligent agents and horizontal empowerment support intelligent agents to achieve automatic task distribution, resource coordination and scheduling, and conflict resolution across departments, devices, and intelligent agents. It is deeply linked with the task scheduling and resource coordination functions of collaborative intelligent agents to ensure the efficient implementation of various management and control tasks.

[0063] Business Management Module: Covers the entire business process of dam safety management, including monitoring and early warning, emergency response, operation and maintenance inspection, training and drills, and report management. It supports personalized interactive interfaces for multiple roles (leaders, experts, duty officers, and operation and maintenance personnel), adapts to the management needs of different roles, and links with writing AI and immersive training AI to achieve standardized and intelligent management of business processes.

[0064] Feedback Iteration Module: It receives on-site execution data and equipment operation data from the executing agent and feedback agent, and collects human feedback information to form a closed-loop feedback chain of "on-site feedback - data aggregation - analysis and optimization - push iteration". It pushes optimization suggestions to the large model layer and agent layer in sync, driving continuous optimization and version iteration at both levels. It works in synergy with the feedback closed-loop function of the feedback agent to support the self-evolution of the entire system.

[0065] (2) Digital twin carrier and virtual-real linkage mechanism The digital twin module is the core carrier of the intelligent field layer. It supports risk simulation, decision verification, and virtual-physical linkage through a high-fidelity simulation engine, linking the twin intelligent agent to achieve seamless connection between "virtual simulation and physical execution". Its core design includes two parts: the construction of a four-dimensional digital twin model and a closed loop for virtual-physical linkage. ① Construction of a four-dimensional digital twin model The model encompasses four dimensions: geometry, physics, behavior, and rules. These dimensions are interconnected and work synergistically to construct a high-fidelity, interactive, and predictable virtual dam, as detailed below: Geometric Model: A high-precision 3D mesh model of the dam is constructed based on LiDAR and oblique photogrammetry data, which restores the dam's topography, structural details, equipment layout and surrounding environment in a 1:1 scale with millimeter-level accuracy. This provides a precise geometric foundation for subsequent physical simulation and behavioral simulation, and seamlessly integrates with the geometric model construction module of the twin agent.

[0066] Physical Model: Embedding mechanical, seepage field, and temperature field equations, this model simulates the physical response of the dam under complex loads (water level, rainfall, temperature, etc.), ensuring consistency between the virtual model and the physical characteristics of the physical dam. The seepage field simulation employs the finite element method, with the core governing equations as follows: In the formula: The permeability coefficient is dynamically adjusted according to the stress-strain state. For water head; Source and sink terms per unit volume (m 3 / (s·m 3 )); Water storage ratio (1 / m); For time; This is the rate of change of head; This is the Hamiltonian operator. The boundary conditions are set as: constant head boundary conditions at the upstream dam face. ( (Reservoir water level), downstream dam face free outflow boundary ( (Downstream water level), impermeable boundary inside the dam body ( (For boundary normal vectors).

[0067] Behavioral Model: Simulates the actions of equipment such as gate opening and closing, drainage pump operation, automated grouting robot operation, and drone inspection, as well as the laws of water flow. It links execution agents and perception agents to achieve virtual simulation and real-time mapping of equipment behavior, ensuring that the actions in the virtual scene are synchronized with those in the physical scene.

[0068] Rule Model: Integrates dam design specifications, emergency plans, scheduling rules and expert experience, and links with the standard library and case knowledge library of the knowledge intelligence agent to constrain the compliance of simulation and ensure that virtual simulation and decision-making suggestions meet industry standards and actual management and control needs.

[0069] ② Virtual-real linkage closed loop Leveraging the collaborative capabilities of the digital twin model and the intelligent agent layer, a virtual-physical linkage mechanism is established, encompassing "perception mapping - simulation deduction - command issuance - execution feedback," forming a two-way closed loop of "virtual-physical" interaction. This mechanism links the large model layer and the intelligent agent layer. The specific process is as follows: Perception mapping: Real-time data collected by various sensing terminals (linked sensing agents) of the physical dam is processed by the data integration module and used to drive the real-time update of the twin model's state. The virtual-real mapping delay is ≤1 second, ensuring that the virtual model and the physical dam's state are completely synchronized.

[0070] Simulation and deduction: Based on the current state of the dam, a pre-simulation agent and a twin agent are linked. The twin agent is used to conduct parallel simulations of the evolution path of potential risks and various disposal plans. The disposal effects, costs and residual risks of different plans are quantitatively evaluated, providing visual and verifiable support for decision-making.

[0071] Command issuance: The optimal decision command, after being analyzed by the large model layer and optimized by twin simulation, is transformed into a physical control signal by the collaborative scheduling module, which then links the execution agent to act on the actual dam equipment (such as gate control, emergency drainage, etc.) or guide manual operations, so as to achieve the accurate implementation of the decision command.

[0072] Execution feedback: The execution results in the physical world (equipment operating status, treatment effect, etc.) are collected again by the sensing agent. After being summarized and processed by the feedback iteration module, they are synchronously fed back to the twin agent, the large model layer, and the agent layer to correct the twin model parameters, optimize the large model judgment logic and the agent execution strategy, and complete the closed-loop iteration.

[0073] (3) Multi-agent database integration and collaborative scheduling The intelligent field layer integrates all the intelligent agents in the system, constructs a standardized multi-agent library, and realizes the efficient collaborative operation of various intelligent agents through a collaborative scheduling engine. It is the core support for the implementation of the system's intelligent capabilities and is fully compatible with the "vertical + horizontal" architecture of the intelligent agent layer.

[0074] ① Multi-agent library architecture The intelligent agent library adopts a two-dimensional classification architecture of "vertical business process + horizontal empowerment support," consistent with the three-dimensional architecture of the intelligent agent layer. It contains 17 core intelligent agents and several sub-modules, all encapsulated in a containerized form, following the OCI standard, and providing a unified service interface through a RESTful API to ensure standardized integration and flexible invocation of various intelligent agents. Its architecture is shown in the attached figure. Figure 7 As shown: Vertical business process intelligent agents: including six types of intelligent agents: perception, cognition, early warning, decision-making, execution, and feedback, covering the entire closed-loop link of "data collection-state analysis-instruction issuance-action execution-result feedback", focusing on the implementation of the entire process of dam safety management and control, and completely corresponding to the vertical business process intelligent agents in the intelligent agent layer.

[0075] Horizontal empowerment supports intelligent agents: including 11 types of intelligent agents such as twins, prediction, pre-drill, contingency plans, knowledge, collaboration, writing, instrument maintenance, network security, immersive training, and interaction, providing technical support, knowledge support, collaboration support and security assurance for vertical business processes. It fully corresponds to the horizontal empowerment supports intelligent agents at the intelligent agent layer, ensuring the efficient implementation of vertical control processes.

[0076] ② Classification and Functional Definition of Intelligent Agents The core inputs, outputs, and core sub-modules of the 17 core agents in the multi-agent library are clearly defined, forming standardized capability components. The input-output relationships of the core agents are shown in Table 5. Table 5: Core Intelligent Agent Input / Output and Submodule Table

[0077] ③ Standardized encapsulation and metadata organization of intelligent agents Each agent is containerized using Docker, conforms to the OCI standard, and employs a standardized RESTful API design for its interfaces, ensuring seamless integration and flexible invocation between different agents and between agents and modules of the intelligent field layer. The agent library uses a matrix-style metadata organization structure, with the metadata directory categorized into three levels: "functional domain - agent type - submodule," clearly presenting the attributes, functions, interface specifications, and relationships of each agent for easy management, invocation, and iteration. Its organizational structure is shown in the attached figure. Figure 8 As shown: ④ Agent Cooperative Scheduling Engine The agent-coordinated scheduling engine is the system's "central control center." Based on event-driven architecture (EDA) and dynamic workflow orchestration technology, it coordinates and collaborates agents to achieve efficient scheduling, task coordination, and conflict resolution among various agents. Its core architecture includes four main components: an event listener module, a context manager, a policy matching and orchestration module, and an execution monitoring and fault tolerance module. The internal workflow is shown in the attached figure. Figure 9 As shown: The specific functions of the four core components are as follows: Event monitoring module: Captures various triggering events in real time, including data anomaly events (abnormal data pushed by the sensing agent), scheduled task events (daily inspections, report generation, etc.), and human instruction events (expert operations, emergency instructions, etc.), and pushes them synchronously to the context manager and policy matching module.

[0078] Context Manager: Integrates real-time dam operating conditions, risk levels, resource status (equipment, computing power, personnel), agent operating status, and other full-domain context information, linking knowledge agents and instrument management agents to provide comprehensive information support for strategy matching and orchestration.

[0079] Strategy matching and orchestration module: Based on event type and context information, it automatically matches the optimal combination of agents, constructs a dynamic execution workflow in the form of a directed acyclic graph (DAG), clarifies the task division, execution order and data interaction logic of each agent, and links and coordinates the task splitting and allocation functions of agents.

[0080] The execution monitoring and fault tolerance module monitors the running status and task execution progress of each agent in real time, handles resource conflicts (multiple agents calling the same device), task conflicts (overlapping inspection and maintenance tasks), and task anomalies (equipment failure, execution failure), and links with network security agents and instrument management agents to ensure that control tasks are not interrupted and are implemented efficiently.

[0081] Example 2: An operation method for a reservoir dam safety monitoring system based on an AI-powered intelligent field architecture. Based on the intelligent field layer architecture, the system operation follows a six-step closed-loop process: "event triggering - intelligent orchestration - virtual-real simulation - execution feedback - knowledge accumulation," linking the large model layer and the intelligent agent layer to achieve intelligent control of the entire dam safety process. In this process, the following core algorithm models are applied to ensure scientific decision-making, data accuracy, and efficient execution: (1) Multi-source data fusion algorithm: The sensing agent uses a weighted average method to fuse multi-source monitoring data, improves data reliability and accuracy, and solves the problem of multi-sensor data bias. The core formula is as follows: In the formula: The merged data values; Number of data sources; For the first Original data from one data source; These are weighting coefficients determined based on data quality and reliability (the higher the data quality, the larger the weighting coefficient).

[0082] (2) Anomaly removal algorithm: The 3σ criterion is used to identify and remove outliers in the monitoring data to ensure data quality and provide reliable data support for subsequent analysis and simulation. The specific process is as follows: Calculate the mean of the data: ; Calculate the standard deviation of the data: Outlier detection: If Then determine If an outlier is detected, it is removed, and the sensing agent is triggered to re-collect data or activate a backup sensor.

[0083] (3) Multi-objective emergency decision optimization model: When generating emergency response plans, the decision-making agent adopts a multi-objective optimization model to balance the response effect, implementation cost and residual risk, so as to ensure that the plan is scientific, feasible and economical. The core model is as follows: In the formula: The percentage of risk reduction ( To reduce the risk, (for initial risk) For implementation costs ; For residual risk ; This is the parameter vector for the contingency plan; This is the feasible region; To accommodate constraints related to equipment capacity, time, and industry standards, the system employs the Analytic Hierarchy Process (AHP) to determine the weights of each objective. It then calculates a comprehensive evaluation value through weighted summation, selects the optimal emergency response plan, and uses a coordinated simulation agent to verify the plan's effectiveness.

[0084] (4) Model parameter adaptive calibration algorithm: The feedback agent uses the least squares method to calibrate the parameters of the twin model and the large model online based on the measured data, corrects the model bias, realizes the self-evolution of the model, and ensures the accuracy of model prediction and simulation. The core objective function is as follows: In the formula: Let the objective function be the error. For the vector of parameters to be calibrated (such as permeability coefficient, elastic modulus, etc.); These are measured values; These are the model's predicted values. The optimal parameters are solved using gradient descent. Complete the model parameter calibration and synchronously update the relevant parameters of the twin agent and the large model.

[0085] The intelligent field layer provides a multimodal interaction entry point through a unified service interface layer, linking interactive intelligent agents and supporting three interaction methods: natural language, visual charts, and API interfaces. This enables convenient connection between people and the system, and between the system and external devices. Furthermore, it allows for customized interactive interfaces for different user roles, achieving integrated human-machine management. Leadership / Expert View: Focuses on macro-level situational awareness, risk trend assessment, and major decision-making support. It displays the overall safety status of the dam, risk level, major hidden dangers, and decision-making recommendations, and supports remote expert assessment and command issuance.

[0086] Duty officer view: Focuses on real-time alarm handling, work order flow and daily monitoring, displaying real-time monitoring data, early warning information and work order progress, and supports duty officers to quickly handle early warnings and dispatch work orders.

[0087] Operations and maintenance personnel view: Focuses on equipment status query, inspection route planning and on-site operation guidance, linking with the instrument management and maintenance intelligent agent to display equipment operating status, fault information, inspection tasks, and provide on-site operation step guidance.

[0088] Public / Other Views: Provides anonymized information on dam safety and displays of water conservancy safety science, ensuring the public's right to know, popularizing dam safety knowledge, and achieving the dual value of information disclosure and science popularization.

[0089] Through the aforementioned architecture, mechanisms, and algorithms, the intelligent field layer achieves full coverage, virtual-real linkage, multi-entity collaboration, and full life-cycle closed-loop management of dam safety. It links the large model layer and the intelligent agent layer, giving full play to the system's intelligent advantages and ensuring the safe and stable operation of the reservoir dam.

[0090] Example 3: This example uses a large earth-rock dam (hereinafter referred to as the "target dam") as the application scenario. This dam is a key national flood control project, with a height of 80m, a length of 500m, a crest width of 12m, and a total reservoir capacity of 120 million cubic meters. 3With a service life of 45 years, the dam primarily serves flood control, irrigation, and regional water supply functions. Downstream, it affects 3 townships and 12 villages, with a population of approximately 50,000 and 80,000 mu of cultivated land, making its safety management a critical responsibility. Due to its long service life, the dam body has experienced localized aging, posing potential hazards such as hidden seepage, dam settlement, and slope slippage. Furthermore, the surrounding terrain is complex, the dam foundation is composed of silty clay, and rainfall is concentrated during the flood season. Traditional manual inspections are difficult and risky, and existing IoT-based monitoring systems suffer from data fragmentation, delayed hazard identification, and slow emergency response, failing to meet the integrated management requirements of "safety, ecology, and intelligence." This embodiment deploys the reservoir dam safety monitoring system based on the AI ​​smart field architecture of this invention to achieve intelligent safety management of the dam throughout its entire lifecycle, encompassing all elements and processes.

[0091] 1. Preliminary preparations for system deployment In the early stages of deployment, the focus is on completing three core tasks: data collection and organization, equipment deployment and debugging, and basic environment setup. This lays the foundation for deployment at all levels of the system, ensuring that the system can be quickly put into normal operation after deployment and that all performance indicators meet the design requirements.

[0092] (1) Data collection and organization In accordance with the data requirements of "full life cycle multi-source fusion" in the technical solution of this invention, six categories of core data of the target dam are comprehensively collected, and data cleaning, standardization processing and structured storage are completed to construct a data foundation for large-scale model training and system operation, as detailed below: Monitoring data on dam effects: This includes collecting real-time and historical data on dam deformation (horizontal displacement, vertical settlement, joint opening and closing), seepage (seepage flow, seepage pressure, phreatic line, seepage around the dam), stress and strain (earth pressure on the dam body, anchor stress), and vibration (dam body vibration frequency, amplitude). Real-time acquisition frequency is set to once every 30 minutes according to system design requirements. Historical data comprehensively covers the past 45 years of operational monitoring records, with a focus on analyzing the correspondence between dam effects and environmental quantities under different hydrological years and operating conditions (flood season, dry season, high-temperature period), providing sufficient sample data for large-scale model training.

[0093] Environmental data for project operation: Data such as upstream and downstream water levels, rainfall, air temperature, reservoir water temperature, wind speed and direction, and ground motion are collected. The real-time acquisition frequency is once every 30 minutes, and the historical data covers nearly 20 years. Hydrogeological survey reports, topographic data, and data on surrounding engineering activities (such as surrounding construction and vegetation planting) are collected simultaneously to support the coupled analysis of environmental quantities and effects.

[0094] Operation and maintenance management data: Comprehensively organize the daily inspection records (including images, videos, and infrared detection data), defect repair records, equipment operation logs, periodic inspection reports, operation and maintenance work orders, etc. of the target dam. Digitally analyze and label unstructured data (such as inspection photos and handwritten records) to ensure that the data can be recognized and called by large models and intelligent agents.

[0095] Case knowledge base data: More than 120 cases of disasters, disease evolution, and emergency response related to seepage, settlement, and slope slippage of earth-rock dams from home and abroad have been collected. After desensitization (hiding sensitive geographical locations and personnel information), the data is stored in a structured manner. Core information such as hidden danger characteristics, treatment process, and treatment effect in the cases are extracted to provide experience reference for anomaly tracing and solution generation in large models.

[0096] Standardized data library: Key clauses of core industry standards such as "Technical Specification for Safety Monitoring of Concrete Dams" (SL 601), "Technical Specification for Safety Monitoring of Earth-Rock Dams" (SL 551), and "Specification for Safety Monitoring of Reservoir Dams" are extracted, decomposed into structured forms, and transformed into rule knowledge that can be recognized by the large model, ensuring that system decisions and response plans comply with industry standards.

[0097] Domain knowledge data: Integrating the judgment experience of domain experts (inviting 3 senior engineers in the field of water conservancy engineering safety to provide special guidance), dam design drawings, seepage mechanics theory, soil mechanics principles, etc., to construct an initial domain knowledge graph, covering core knowledge such as dam structural characteristics, material parameters, hazard evolution law, and treatment technology, to solve the pain point of "having data but no knowledge".

[0098] All collected data undergoes data cleaning (removing outliers and missing values), spatiotemporal benchmark alignment, multimodal fusion, and quality verification before being stored in a distributed database to ensure data validity of ≥98%, providing high-quality data support for large model training, agent decision-making, and intelligent field operation.

[0099] (2) Equipment deployment and commissioning In accordance with the "vertical closed loop + horizontal support" architecture requirements of the intelligent agent layer, various sensing devices, intelligent agent devices, and supporting execution devices were deployed throughout the dam area. Device debugging and communication link testing were completed to ensure stable device operation, accurate data acquisition, and smooth command transmission. Specific deployment and debugging details are as follows: Supporting equipment for the intelligent sensing system includes: 3 drones equipped with high-definition cameras, infrared detectors, and lidar (2 for dam slope and crest inspection, 1 as a backup) to accurately identify surface defects (cracks, erosion) in the dam body; 2 underwater integrated audio-visual leakage detection robots for underwater leakage detection in the reservoir area and downstream slope toe, with a detection depth of up to 30m; 2 ground inspection drones equipped with soil moisture sensors and seepage pressure sensors for real-time monitoring of dam foundation seepage and soil moisture content; and over 50 sets of IoT sensors covering key areas such as the dam body, foundation, and shoulders, including 15 piezometers, 12 displacement gauges, 8 earth pressure gauges, 6 vibration sensors, and 9 environmental sensors, ensuring comprehensive data collection of key parameters such as deformation, seepage, and stress-strain.

[0100] Supporting equipment for the early warning intelligent system: 10 audible and visual early warning terminals were installed at key locations such as the dam crest, dam shoulder, and maintenance duty room; one SMS push platform was deployed; and a mobile terminal early warning APP (compatible with iOS and Android systems) was developed to ensure that early warning information can be pushed simultaneously through multiple channels; 4 broadcast early warning devices were deployed in downstream villages and towns to expand the early warning coverage.

[0101] Supporting equipment for the intelligent system: One underwater intelligent sealing barge (equipped with an automated sealing device for rapid underwater leakage sealing), two automated grouting robots (for minimally invasive grouting treatment of dam leakage and cracks), and three sets of emergency drainage equipment (for emergency drainage during the flood season, with a discharge capacity ≥ 50m³). 3 / h), 1 set of automated gate control equipment (linking the spillway gate of the dam to realize automated flood discharge control); equipped with a safety monitoring sensor intelligent adaptive encryption monitoring module, which can adaptively adjust the acquisition frequency according to the degree of abnormality of monitoring data.

[0102] Horizontal support for intelligent agents includes: deploying 3D modeling equipment (LiDAR, oblique photography equipment) required for the twin intelligent agent to build a 1:1 high-fidelity digital twin model of the dam; deploying a knowledge graph server to support the knowledge intelligent agent to realize knowledge storage, retrieval, and updating; deploying industrial control firewalls and vulnerability scanning equipment to support the network security intelligent agent to build a system-wide security defense system; and deploying two sets of VR / AR equipment to support the immersive training intelligent agent for practical training of operation and maintenance personnel.

[0103] The equipment commissioning focused on three key aspects: First, data acquisition accuracy was debugged to ensure deformation monitoring accuracy reached 0.1mm level, seepage anomaly identification response ≤30 seconds, and environmental quantity acquisition accuracy met industry standards; second, communication link debugging was conducted, using a 5G+fiber dual-backup communication mode to ensure communication latency between the equipment and each level of the system ≤1 second, and data transmission accuracy ≥99.9%; third, equipment collaboration debugging was carried out to test the linkage capability between various intelligent agent supporting devices, ensuring that data collected by sensing devices could be quickly transmitted to intelligent agents, and that execution devices could accurately receive instructions issued by intelligent agents.

[0104] (3) Basic environment setup The hardware and software environment required to build the intelligent field layer and the large model layer is established to ensure that the modules at each level can run stably and collaborate efficiently. The specific setup content is as follows: Data storage environment: Deploy a distributed database (using Hadoop architecture) with a total storage capacity of ≥100TB to store real-time monitoring data, historical data, case data, knowledge graph data, and system operation logs. Support real-time data writing and fast querying to ensure data storage security and traceability.

[0105] Digital Twin Platform: Based on dam design drawings, geographic information data, and lidar scanning data, a digital twin platform is built, integrating four-dimensional models of geometry, physics, behavior, and rules. It is equipped with a high-performance simulation engine, supporting multi-temporal and spatial scale working condition simulation, extreme working condition scenario construction, and accident scenario inversion, with a virtual-real mapping delay of ≤1 second.

[0106] Collaborative Scheduling Platform: Deploys an intelligent agent collaborative scheduling engine, configures an event-driven architecture (EDA) and dynamic workflow orchestration tools, supports unified scheduling, task collaboration and conflict coordination among intelligent agents, and ensures efficient implementation of management and control tasks.

[0107] Large model runtime environment: Deploy large model training and runtime servers, configure two Transformer architecture model servers with tens of billions of parameters (one primary and one backup), build a GPU computing power cluster (computing power ≥ 512 TFLOPS) to ensure efficient operation of large model training and inference; build a knowledge graph platform to realize the visual management, intelligent retrieval and dynamic updating of knowledge.

[0108] Human-computer interaction environment: Deploy 3 multimodal interactive terminals (touch screen, voice interaction device), which are installed in the operation and maintenance duty room, the dam top monitoring point, and the emergency command center respectively, supporting multimodal interaction such as natural language, gestures, and touch; develop personalized interactive interfaces to adapt to the management and control needs of different roles such as leaders, experts, duty officers, and operation and maintenance personnel.

[0109] After the basic environment is set up, a full system integration test is conducted to test the compatibility between various hardware devices and software modules, ensuring that the overall system runs stably without lag or faults.

[0110] 2. Deployment and debugging of modules at each level In accordance with the three-tier architecture requirements of this invention, namely "bottom-level intelligence, middle-level execution, and top-level ecosystem", the deployment and debugging of the large model layer, intelligent agent layer, and intelligent field layer are completed in sequence to ensure that the functions of each module meet the standards and that the collaboration is smooth, forming a complete closed-loop management and control system.

[0111] (1) Deployment and debugging of the large model layer The large model layer, serving as the system's "intelligent brain," focuses on three key aspects in its deployment: model training, capability debugging, and interface debugging. This ensures that the large model possesses high-precision judgment, rapid decision-making, and autonomous optimization capabilities, as detailed below: Model Training: Training was conducted strictly according to a two-stage training model of "pre-training + domain fine-tuning". In the pre-training stage, training was conducted based on general multimodal data (natural language text, images, numerical data) and basic data in the dam domain (general monitoring data, basic specification data). The training cycle was 15 days, focusing on cultivating the model's basic capabilities such as multimodal data processing, general reasoning, and contextual understanding, and building a general intelligent foundation. In the domain fine-tuning stage, based on the target dam's specific data (collected monitoring, operation and maintenance, case, knowledge, etc. data), incremental fine-tuning was carried out using a combination of reinforcement learning and supervised learning. The training cycle was 7 days, incorporating domain knowledge graphs and optimizing model parameters to make the model focus on the safety assessment task of the earth-rock dam. The mapping relationship between environmental quantities (water level, rainfall, temperature) and effect quantities (deformation, seepage) was trained, embedding formulas (1), (2), and (3) in the technical solution of this invention to ensure that the model can accurately distinguish between normal deformation caused by temperature and water level and abnormal deformation caused by structural damage, and accurately identify hidden dangers such as latent leakage.

[0112] Capability Debugging: Debug the eight core capabilities of the large model to ensure that all performance indicators meet design requirements: multimodal data processing latency ≤30 seconds, dam anomaly identification accuracy ≥95%, crack detection accuracy at the millimeter level, risk prediction lead time ≥72 hours, emergency response plan generation time ≤5 minutes, anomaly tracing accuracy ≥90%, knowledge reasoning response time ≤3 seconds, and multi-task migration adaptability ≥98%. Debug the model optimization module, setting a fine-tuning cycle of 2 months, configuring model fault tolerance mechanisms and anomaly detection thresholds to ensure the model can receive data feedback from the intelligent field layer and intelligent agent layer for continuous optimization, avoiding model drift.

[0113] Interface debugging: Debug the data interfaces and instruction interfaces between the large model layer, the intelligent agent layer, and the intelligent field layer. Adopt the standardized design of RESTful API to ensure that the research and judgment results and task instructions of the large model can be quickly transmitted to the intelligent agent layer and the intelligent field layer, and the feedback data (on-site execution data, verification results) can be received in real time. The interface response time ≤ 1 second, and the data transmission accuracy rate ≥ 99.9%.

[0114] (2)Deployment and debugging of the intelligent agent layer As the "executing hands and feet" of the system, the deployment of the intelligent agent layer focuses on the deployment of intelligent agents, the debugging of the collaboration mechanism, and the debugging of the autonomous upgrade mechanism, ensuring that each intelligent agent can execute tasks independently and cooperate efficiently, and realizing the closed-loop of "perception - cognition - early warning - decision - execution - feedback", which is specifically as follows: Deployment of intelligent agents: Completely deploy 17 types of core intelligent agents, including 6 types of vertical business process intelligent agents (perception, cognition, early warning, decision, execution, feedback), and 11 types of horizontal enabling support intelligent agents (twin, prediction, pre-play, pre-plan, knowledge, collaboration, writing, instrument management and maintenance, network security, immersive training, interaction). All intelligent agents are encapsulated using Docker containerization, following the OCI standard, integrating five general modules of perception, planning, decision, execution, and feedback, and uniformly configuring the communication protocol and data format to ensure the compatibility and scalability between intelligent agents.

[0115] Debugging of the collaboration mechanism: Debug the four mechanisms of information sharing, task collaboration, resource collaboration, and conflict coordination between intelligent agents. Taking the collaborative intelligent agent as the core hub, ensure that the information sharing delay ≤ 5 seconds, there is no conflict when multiple intelligent agents cooperate to execute tasks, the task allocation is reasonable, and the execution is efficient. Focus on debugging the following linkage scenarios: After the perception intelligent agent discovers an anomaly, synchronize the anomaly data to the cognition, early warning, and decision intelligent agents within 1 minute; after the early warning intelligent agent receives the anomaly information, complete the determination of the early warning level and issue the early warning information within 30 seconds; after the decision intelligent agent generates a disposal plan, issue an instruction to the execution intelligent agent within 1 minute; during the disposal process of the execution intelligent agent, immediately feedback the execution data to the feedback intelligent agent and the large model layer.

[0116] Debugging of the autonomous upgrade mechanism: Debug the feedback module and upgrade function of the intelligent agent to ensure that the intelligent agent can receive the optimized parameters, updated research and judgment logic, and industry-leading algorithms pushed by the large model layer in real time, and synchronously adapt to the upgrade results of the large model; it can rely on the on-site execution data feedback by the intelligent field layer to automatically optimize its own module parameters, execution logic, and task planning scheme; it can realize the iterative upgrade of its own functions through the execution experience summarized by the feedback intelligent agent, ensuring the synchronous evolution of the intelligent agent layer, the large model layer, and the intelligent field layer.

[0117] (3)Deployment and debugging of the intelligent field layer As the "ecological carrier" of the system, the deployment of the intelligent field layer focuses on the deployment of core modules, virtual-physical collaborative debugging, and closed-loop iterative debugging to ensure the realization of a closed-loop linkage between "virtual control and physical execution," as detailed below: Core Module Deployment: The deployment includes five core modules: data integration, digital twin, collaborative scheduling, business management, and feedback iteration. Each module is functionally independent yet closely interconnected. The data integration module is being debugged to ensure unified cleaning, integration, spatiotemporal benchmark alignment, and quality verification of all data, with a data processing latency of ≤10 seconds. The digital twin module is being debugged to ensure accurate mapping between the physical and virtual dams (3D scene fidelity ≥99%) and real-time simulation, particularly in the simulation accuracy of the seepage field, with a simulation error of <2%. The collaborative scheduling module is being debugged to ensure dynamic orchestration of intelligent agents based on an event-driven architecture, enabling automatic task distribution, resource coordination, and conflict resolution. The business management module is being debugged to ensure automated workflow across the entire business process, including monitoring and early warning, emergency response, operation and maintenance inspections, training exercises, and report management, supporting personalized interaction for multiple roles. The feedback iteration module is being debugged to ensure the collection, evaluation, and feedback of on-site data, forming a closed-loop chain of "on-site feedback - data aggregation - analysis and optimization - push iteration."

[0118] Virtual-physical collaborative debugging: Debug the collaboration between the digital twin module and the large model layer and intelligent agent layer to ensure that real-time monitoring data can be synchronized to the virtual mirror (mapping delay ≤ 1 second). The judgment results of the large model can be visualized in the virtual mirror. Managers can view the real-time status of the dam, risk distribution and intelligent agent execution progress through the virtual mirror. Commands can be sent to the intelligent agent through the virtual mirror to achieve the collaborative linkage of "virtual control and physical execution". For example, the flood discharge scheduling can be simulated through the virtual mirror to synchronously drive the physical gate to adjust the opening.

[0119] Closed-loop iterative debugging: Debug the collaboration between the feedback iteration module and the large model layer and agent layer to ensure that the execution data and treatment effect evaluation summarized by the feedback agent can be quickly transmitted to the feedback iteration module. The optimization suggestions generated by the feedback iteration module after analysis can be pushed to the large model layer and agent layer simultaneously to drive the update of large model parameters and the optimization of agent strategies, thereby realizing the closed-loop iteration of the entire system.

[0120] 3. System Operation and Optimization Process After the system is deployed and debugged at all levels, it enters the formal operation phase. The operation process is divided into three stages: normal operation, anomaly handling, and continuous optimization, to achieve a virtuous cycle of "operation-feedback-optimization-improvement" and give full play to the intelligent control efficiency of the system.

[0121] (1) Normal operation phase Once the system is deployed, it will enter a state of routine operation, with modules at all levels working collaboratively to achieve autonomous control of the entire dam process. The specific operation process is as follows: Data flow: The data integration module of the smart field layer collects monitoring data collected by the sensing intelligent agent in the intelligent agent layer, equipment operation data collected by the instrument management and maintenance intelligent agent, and external meteorological and hydrological data in real time. After unified cleaning, integration and quality verification, the data is synchronized to the large model layer, digital twin module and business management module to ensure that the data is updated in real time and is accurate and usable.

[0122] Intelligent assessment: The large model layer, based on multi-source data and domain knowledge, assesses the dam's safety status in real time, uses formula (4) to conduct risk prediction, and generates a dam safety assessment report daily, which is then pushed to the smart field layer and management personnel. The report includes the overall dam safety rating, potential risk point analysis, risk development trend prediction, and daily operation and maintenance suggestions, with particular attention to the coupling analysis of seepage and deformation, accurately distinguishing between normal fluctuations and abnormal hidden dangers.

[0123] Autonomous Execution: Each agent at the agent layer autonomously executes tasks according to a preset plan. The perception agent conducts two full-domain inspections daily (9:00 AM and 4:00 PM), focusing on potential hazards such as dam leakage, settlement, and cracks; inspection data is uploaded to the system in real time. The cognitive agent performs in-depth analysis of the perception data, generates status diagnosis reports, and calibrates the parameters of the digital twin model. The early warning agent receives real-time predictions from the large model and abnormal data from the perception agents, preparing for early warnings. The decision-making agent reviews the dam's safety status daily, generates daily operation and maintenance plans, and issues them to the execution agents. The execution agents carry out equipment calibration, dam maintenance, and other tasks according to the operation and maintenance plans. The feedback agent collects execution data from each agent in real time and conducts a preliminary assessment of the execution effect. Horizontal empowerment support agents simultaneously provide services such as knowledge support, collaborative scheduling, and report generation; for example, the writing agent automatically generates daily monitoring reports, and the knowledge agent provides standards and case studies for each agent.

[0124] Human-computer interaction: Managers can view the dam's safety status, monitoring data, and the progress of the intelligent agent in real time through a personalized interactive interface. They can also query relevant information and issue manual instructions to the system through natural language interaction. Experts can remotely log in to the system to review the analysis results of the large model and emergency plans, and provide professional guidance.

[0125] (2) Abnormal handling stage During operation, a simulated latent seepage event on the downstream slope of the dam (reflecting the actual potential risks of the target dam) was conducted to test the system's anomaly identification, emergency response, and collaborative capabilities. The specific handling procedures are as follows: Anomaly Detection and Early Warning: During a full-area inspection at 4 PM, the ground inspection drone under the perception agent detected a sudden increase in seepage pressure at monitoring point 3 at the downstream slope foot to 0.35 MPa, exceeding the standard threshold (0.2 MPa). Simultaneously, the soil moisture sensor data also showed an abnormal increase, and the drone's infrared detector detected that the surface temperature in this area was 3°C lower than the surrounding area (a typical characteristic of latent seepage). The perception agent immediately synchronized the multimodal anomaly data (seepage pressure value, infrared image, and soil moisture data) to the cognitive agent and the large model layer. The entire data transmission process took 28 seconds, meeting the design requirements. The cognitive agent rapidly analyzes abnormal data and, combined with the geological parameters (distribution of silty clay layer) of the area in the digital twin model, initially identifies it as a potential hidden leakage hazard. This information is simultaneously pushed to the early warning agent and the large model layer. The large model layer, combining similar leakage cases in the case knowledge base and the domain knowledge graph, further refines the assessment to Level II (moderate risk), preliminarily estimating the leakage area at 50㎡, and quickly generates preliminary handling directions, feeding them back to the early warning agent and the decision-making agent. Within 25 seconds of receiving the abnormal information, the early warning agent completes the early warning level determination and simultaneously activates audible and visual warnings (early warning terminals on the dam top and abutment sound, and warning lights flash), SMS push (early warning information is sent to 20 people, including maintenance personnel, emergency command personnel, and township leaders, specifying the location and risk level of the hazard), mobile APP warning, and downstream broadcast warning, achieving multi-channel, full-coverage early warning and ensuring rapid response from relevant personnel.

[0126] Decision-making scheme generation: After receiving the judgment results from the large model layer and the analysis report from the cognitive agent, the decision-making agent, relying on the standard library and case knowledge base, and combining the structural characteristics of the target dam (dam height, dam material) and the current hydrological conditions (upstream water level, recent rainfall), generates a targeted emergency response plan within 4 minutes and 30 seconds. The plan clarifies three core tasks: first, accurately locate the seepage point and seepage channel; second, quickly implement underwater sealing and dam grouting reinforcement; and third, strengthen and intensify surrounding monitoring to prevent the seepage from expanding. The plan simultaneously includes personnel division of labor, equipment scheduling, safety protection requirements, and response time limits (initial sealing within 24 hours, reinforcement within 72 hours), and pushes it to the execution agent, collaborative agent, and smart field layer business management module. Simultaneously, domain experts are invited to remotely review the plan. Experts complete the review within 10 minutes and provide optimization suggestions (supplementing grouting material ratio parameters). The decision-making agent immediately adjusts the plan to form the final execution version.

[0127] Collaborative Execution and Handling: Based on the final handling plan, the collaborative intelligent agent dynamically orchestrates the task flow and schedules various relevant intelligent agents to execute collaboratively, achieving seamless connection between "perception-decision-execution". First, precise leakage location: One underwater integrated audio-visual leakage detection robot was dispatched to the downstream slope toe area, reaching a detection depth of 25m. Simultaneously, one drone was dispatched to conduct infrared scanning from the air. Combined with seepage field simulation from a digital twin model, two leakage points were precisely located (near monitoring points 2 and 3 at the slope toe, respectively). The direction of the leakage channel was determined to be perpendicular to the silty clay layer of the dam foundation. The entire detection process took 1.5 hours. Second, leakage sealing and handling: The executing intelligent agent dispatched an underwater intelligent sealing barge to the leakage point. The automated sealing device on board precisely sprayed sealing material, completing the initial sealing of the underwater leakage point, which took 3 hours. Simultaneously, two automated grouting robots were dispatched to carry out minimally invasive grouting reinforcement of the dam leakage area according to the optimized material ratio, filling... To address the seepage channels and enhance the dam's seepage prevention capabilities, grouting operations lasted for 18 hours. During this period, the instrumentation and maintenance intelligent agent monitored the grouting pressure and volume in real time to ensure compliance with regulations. Thirdly, enhanced monitoring and control were implemented: the sensing intelligent agent activated the intelligent adaptive enhanced monitoring module, adjusting the collection frequency of sensors for seepage pressure, displacement, and soil moisture in the affected area and surrounding areas to once every 5 minutes. A drone conducted an inspection every 30 minutes to capture the seepage treatment effect in real time, and the monitoring data was simultaneously transmitted to the digital twin model, enabling visualized tracking of the treatment process. Fourthly, safety protection was ensured: the execution intelligent agent dispatched one set of emergency drainage equipment, setting up a temporary drainage channel at the downstream slope to prevent seepage water from soaking the dam foundation. Simultaneously, maintenance personnel were coordinated to conduct on-site safety precautions, prohibiting unauthorized personnel from entering the affected area.

[0128] Evaluation and Feedback on Treatment Effectiveness: After the leakage treatment is completed, the feedback agent summarizes the execution data of each agent (capacity of sealing material, grouting parameters, changes in monitoring data, and operation time), and combines it with the analysis of the large model layer to conduct a comprehensive evaluation of the treatment effectiveness: Monitoring shows that the seepage pressure at the leakage point has dropped to 0.18 MPa (below the standard threshold), infrared images show that the surface temperature in the area has returned to normal, and the digital twin model seepage field simulation shows that the leakage channel has been completely sealed, indicating that the treatment has achieved the expected results. The feedback agent pushes the treatment evaluation report to the feedback iteration module, the large model layer, and the intelligent field layer, simultaneously organizing the core data and key experiences from this treatment process and adding them to the case knowledge base to provide a reference for the treatment of similar hazards in the future; at the same time, it identifies minor shortcomings in the treatment process (the efficiency of the grouting robot can be optimized), forms optimization suggestions, and pushes them to the large model layer and the agent layer.

[0129] The entire anomaly handling process, from anomaly identification to completion, took 24 hours without any leakage expansion or personnel safety accidents. This fully verified the accuracy of the system's anomaly identification, the speed of emergency decision-making, and the efficiency of multi-agent collaboration, fully meeting the needs of dam safety emergency management.

[0130] (3) Continuous optimization stage All types of data generated during normal system operation and anomaly handling are collected and analyzed through the feedback iteration module, forming a closed loop of "data collection - analysis and evaluation - optimization iteration - application implementation," continuously improving system management efficiency. The specific optimization process is as follows: Large Model Layer Optimization: The feedback iteration module summarizes daily operational data, anomaly handling data, and expert guidance, and pushes them to the large model layer. The large model undergoes fine-tuning according to a preset two-month fine-tuning cycle, incorporating incremental data to optimize the mapping relationship between environmental quantities and effect quantities, adjust anomaly identification thresholds, and improve the accuracy of identifying hidden hazards and the lead time for risk prediction. Simultaneously, anomaly handling cases are added to the model training samples to optimize the emergency response plan generation logic, shorten plan generation time, and prevent the recurrence of shortcomings in similar handling processes. For example, based on the experience gained from this hidden leakage handling, the leakage channel location algorithm was optimized, reducing the location time for subsequent similar hazards by 30%.

[0131] Intelligent Agent Layer Optimization: Based on optimization suggestions from the feedback iteration module, each intelligent agent autonomously completes functional iteration and upgrades. The execution intelligent agent optimizes the grouting robot's operating parameters to improve grouting efficiency; the perception intelligent agent optimizes the abnormal data identification algorithm to reduce false alarms and missed alarms; the coordination intelligent agent optimizes task orchestration logic to shorten the multi-agent collaborative response time; and the early warning intelligent agent optimizes early warning information push strategies, accurately pushing warnings to corresponding levels of personnel based on the level of hazard, improving the targeting of early warnings. Simultaneously, the instrument maintenance intelligent agent analyzes equipment wear patterns based on equipment operation logs, optimizes equipment inspection and calibration plans, extends equipment lifespan, and reduces maintenance costs.

[0132] Intelligent field layer optimization: The digital twin module was debugged and, combined with on-site monitoring data and anomaly handling, the physical parameters of the virtual dam were corrected, improving the accuracy of virtual-real mapping and seepage field simulation, further reducing the simulation error to within 1.5%; the task distribution mechanism of the collaborative scheduling platform was optimized to improve resource coordination efficiency; the process control function of the business management module was improved, and an anomaly handling review module was added to facilitate managers in summarizing handling experience; the human-computer interaction interface was optimized, and a hidden danger handling progress visualization module was added to improve the convenience of management personnel's control.

[0133] Data and knowledge optimization: Continuously collect various new data (monitoring data, operation and maintenance data, and anomaly handling data) during dam operation, and supplement them to the distributed database after cleaning and standardization to ensure the integrity and timeliness of the data base; Simultaneously update the case knowledge base and domain knowledge graph, incorporate the latest industry standards, expert experience and anomaly handling cases, continuously enrich the system's knowledge reserves, and solve the problem of "knowledge lag".

[0134] Through continuous optimization, the system's intelligent management and control capabilities have been gradually improved. Core performance indicators such as anomaly identification accuracy, emergency response efficiency, and data transmission stability have been continuously optimized, ensuring that the system can adapt to the safety management and control needs of the dam throughout its entire life cycle and achieve the integrated management and control goal of "safety, ecology, and intelligence".

[0135] 4. Deployment and Operation Effect Verification In this embodiment, after the reservoir dam safety monitoring system based on the AI ​​smart field architecture was deployed and operated stably for 6 months, the deployment effect and operational efficiency of the system were comprehensively verified through on-site testing, data statistics, and expert review. The verification results are as follows: (1) Deployment effect verification Equipment deployment verification: All sensing devices, intelligent agent supporting equipment, and basic environmental hardware were deployed in place, operated stably, and there were no equipment failures; data acquisition accuracy met the standards, deformation monitoring accuracy reached 0.08mm level (better than the designed 0.1mm level), the average response time for seepage anomaly identification was 26 seconds (lower than the designed 30 seconds), the average communication link latency was 0.8 seconds (lower than the designed 1 second), and the data transmission accuracy reached 99.92% (higher than the designed 99.9%), meeting the hardware support requirements for system operation.

[0136] Deployment verification at each level: The deployment of the large model layer, intelligent agent layer, and intelligent field layer is standardized, the module functions are complete, the interface adaptation is smooth, and there are no compatibility issues; the large model has high-precision judgment and rapid decision-making capabilities, with an anomaly identification accuracy rate of 96.5% (higher than the designed 95%), and the emergency response plan generation time is an average of 4.2 minutes (lower than the designed 5 minutes); the intelligent agents in the intelligent agent layer cooperate efficiently, the task execution is conflict-free, and the closed-loop management process is smooth; the intelligent field layer has accurate virtual-real mapping, the 3D scene restoration accuracy reaches 99.2% (higher than the designed 99%), and the collaborative scheduling is efficient, meeting the needs of intelligent management and control throughout the entire process.

[0137] Data foundation verification: The distributed database storage is stable, with a data effectiveness rate of 98.5% (higher than the designed 98%). Various types of data are clearly classified and stored in a standardized manner, allowing for rapid querying and retrieval. It can provide high-quality data support for large model training and agent decision-making. The case knowledge base, standard library, and domain knowledge graph are complete and updated in a timely manner, effectively supporting the system's intelligent judgment and decision-making.

[0138] (2) Operational performance verification Safety management effectiveness: During system operation, four potential hazards, such as hidden seepage and minor settlement of the dam body, were successfully identified. All hazards were dealt with before they escalated, effectively avoiding safety risks. The average advance warning time for risk prediction was 78 hours (higher than the designed 72 hours), achieving the management goal of "early detection, early warning, and early disposal", and greatly improving the initiative and scientific nature of dam safety management.

[0139] Improved Operation and Maintenance Efficiency: Compared with traditional manual inspection and IoT monitoring modes, after the system is deployed, the workload of operation and maintenance personnel is reduced by 60%, the inspection coverage rate is increased from 85% to 100%, and the inspection efficiency is improved by 70%; equipment operation and maintenance costs are reduced by 35%, and the average time for handling anomalies is shortened by 50%, which greatly improves the intelligence level and work efficiency of dam operation and maintenance.

[0140] Compliance verification of the control: The system’s judgment logic and disposal plan comply with core industry standards such as the “Technical Specification for Safety Monitoring of Earth and Rockfill Dams” (SL 551). All operational data and disposal records are traceable. The system has passed the review of water conservancy industry experts and meets the safety control requirements of key national flood control projects. It can provide a reference for the deployment of similar earth and rockfill dam safety monitoring systems.

[0141] In summary, the reservoir dam safety monitoring system and operation method based on AI smart field architecture of the present invention can effectively solve the problems of data fragmentation, delayed hazard identification, and slow emergency response in traditional monitoring modes, realize intelligent safety management and control of the dam throughout its entire life cycle, all elements, and the entire process, and is feasible to deploy, stable in operation, and highly effective, with broad application value.

[0142] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A reservoir dam safety monitoring system based on AI-powered smart field, characterized in that: It includes: The system includes a data access and governance module, a large model layer, an intelligent agent layer, a smart field layer, a multi-intelligent agent library, a collaborative scheduling engine, a digital twin carrier, a unified service interface, and a feedback and iteration module. The data access and governance module is configured to access the monitoring data of the physical effects of the reservoir dam, the data of the engineering operation environment, the operation and maintenance management data, the inspection image data, the equipment status data, the standard data and the historical case data, and to clean, align, complete, mark the quality and manage the accessed data in a unified manner. The large model layer is configured to construct a dedicated multi-dimensional large model for reservoir dams based on data from the field of reservoir dam safety monitoring, and to perform multimodal fusion analysis, anomaly identification, risk prediction, anomaly tracing, and generation of handling strategies on the ontological effect quantity monitoring data, engineering operation environment quantity data, operation and maintenance management data, inspection image data, and domain knowledge data. The intelligent agent layer is configured to invoke the corresponding reservoir dam safety intelligent agent to perform perception, cognition, early warning, prediction, rehearsal, contingency planning, decision-making, execution and feedback tasks based on the judgment results and strategy instructions output by the large model layer. The intelligent field layer is configured to integrate the physical entity of the reservoir dam, sensing terminals, execution equipment, operation and maintenance personnel, management processes and digital twin models to form a virtual and real linkage control scenario for reservoir dam safety monitoring; The multi-agent library is configured to store agent templates, task flows, calling interfaces and permission configurations for different business scenarios; The collaborative scheduling engine is configured to perform task splitting, task allocation, resource scheduling, and conflict coordination for multiple intelligent agents based on risk level, task type, device status, resource occupancy status, and handling process. The digital twin carrier is configured to construct a three-dimensional scene model, operation status model, monitoring point model, risk evolution model, and emergency response simulation model of the reservoir dam, and is updated synchronously with on-site monitoring data, risk assessment results, and execution feedback results; The unified service interface is configured to connect monitoring equipment, inspection equipment, gate control equipment, drainage equipment, emergency response equipment, operation and maintenance management system, early warning release system, and human-machine interaction terminal. The feedback iteration module is configured to receive on-site monitoring feedback, agent execution feedback, handling effect feedback, and manual review feedback, and input the feedback results into the large model layer, agent layer, and smart field layer to update model parameters, agent strategies, and digital twin states.

2. The reservoir dam safety monitoring system based on AI smart field as described in claim 1, characterized in that: The monitoring data of the dam's physical effects include one or more of the following: horizontal displacement, vertical displacement, deflection, tilt, joint opening and closing degree, seepage flow, seepage pressure, phreatic line, stress, strain, vibration frequency, amplitude, and acceleration. The monitoring data of the engineering operating environment includes one or more of the following: upstream water level, downstream water level, rainfall, air temperature, reservoir water temperature, ground motion, wind force and direction, ice jams, waves, and solar radiation. The monitoring data of operation and maintenance includes one or more of the following: inspection records, image records, video records, infrared records, defect repair records, equipment logs, inspection reports, and operation and maintenance work orders. The monitoring data of the domain knowledge includes one or more of the following: reservoir dam safety monitoring specifications, operation and management procedures, historical defect cases, emergency response cases, expert experience, equipment failure modes, and domain knowledge graphs.

3. The reservoir dam safety monitoring system based on AI smart field as described in claim 1, characterized in that: The large model layer includes a multimodal data embedding unit, a spatiotemporal coupling analysis unit, a domain knowledge fusion unit, a risk prediction unit, a solution generation unit, and a model optimization unit; the multimodal data embedding unit is configured to embed features into text, images, videos, numerical time-series data, and structured knowledge data; The spatiotemporal coupling analysis unit is configured to establish the spatiotemporal correlation between the ontological effect quantity and the engineering operating environment quantity; The domain knowledge fusion unit is configured to integrate standards, historical cases, expert experience, and domain knowledge graphs; the risk prediction unit is configured to output prediction results of deformation, seepage, stress-strain, or vibration risks for future periods based on historical monitoring data, real-time monitoring data, and environmental quantity data. The scheme generation unit is configured to generate candidate disposal strategies based on risk prediction results, anomaly tracing results, and domain knowledge data. The model optimization unit is configured to perform incremental fine-tuning, effect verification, and model version management based on feedback data.

4. The reservoir dam safety monitoring system based on AI smart field as described in claim 1, characterized in that: The intelligent agent layer includes vertical business process intelligent agents and horizontal empowerment support intelligent agents; the vertical business process intelligent agents include perception intelligent agents, cognition intelligent agents, early warning intelligent agents, prediction intelligent agents, pre-simulation intelligent agents, contingency plan intelligent agents, decision-making intelligent agents, execution intelligent agents, and feedback intelligent agents; the perception intelligent agents are configured to collect or retrieve data on dam deformation, seepage, stress and strain, vibration, environmental quantities, and apparent defects; the cognition intelligent agents are configured to identify the dam's operating status, anomaly types, structural weaknesses, and risk causes based on the large model layer and domain knowledge data; The early warning intelligent agent is configured to generate hierarchical early warning information based on anomaly identification results, risk prediction results, and early warning rules; The predictive agent is configured to predict the evolution trend of key monitoring indicators of the dam. The rehearsal intelligent agent is configured to construct risk evolution scenarios and response measure simulation scenarios based on the digital twin carrier; the contingency plan intelligent agent is configured to match or generate response procedures corresponding to risk types, risk levels and on-site conditions from the emergency plan library. The decision-making intelligent agent is configured to generate a disposal plan based on the risk level, disposal simulation results, contingency plan matching results, equipment status, and resource conditions. The execution agent is configured to convert the handling plan into equipment control commands, inspection tasks, work order tasks, or emergency handling tasks; the feedback agent is configured to collect the handling effect, equipment execution status, monitoring changes, and manual confirmation results, and form feedback data.

5. The reservoir dam safety monitoring system based on AI smart field as described in claim 4, characterized in that: The horizontally empowering supporting intelligent agents include one or more of the following: twin intelligent agents, knowledge intelligent agents, collaborative intelligent agents, writing intelligent agents, instrument management and maintenance intelligent agents, network security intelligent agents, immersive training intelligent agents, and interactive intelligent agents. The twin intelligent agent is configured to build, update, and invoke digital twin models of reservoir dams. The knowledge intelligent agent is configured to build a knowledge graph, case library, standard library, and equipment knowledge library for the field of reservoir dam safety. The collaborative intelligent agent is configured to perform information sharing, task collaboration, resource collaboration, and conflict coordination among multiple intelligent agents. The writing intelligent agent is configured to generate... The system includes daily monitoring reports, early warning briefings, emergency response reports, and annual monitoring reports; the instrument management and maintenance intelligent agent is configured to manage the asset ledger, operating status, fault diagnosis, and maintenance work orders of monitoring instruments and execution equipment; the network security intelligent agent is configured to provide security protection for system communication, industrial control equipment, data transmission, and operation logs; the immersive training intelligent agent is configured to conduct operation and maintenance training, emergency drills, and operation assessments based on digital twin scenarios; and the interactive intelligent agent is configured to provide data query, knowledge Q&A, business operations, and scenario explanations through natural language, voice, gestures, or a digital human interface.

6. The reservoir dam safety monitoring system based on AI smart field as described in claim 1, characterized in that: The intelligent field layer includes a data integration module, a digital twin module, a virtual-real mapping module, a business collaboration module, a pre-simulation and deduction module, and a visualization and interaction module. The data integration module is configured to uniformly manage the monitoring data, equipment data, business data, and knowledge data of the reservoir dam. The digital twin module is configured to construct the geometric model, monitoring point model, physical response model, and operational status model of the reservoir dam. The virtual-real mapping module is configured to map on-site monitoring data, equipment operating status, early warning information, and handling results to the digital twin model. The business collaboration module is configured to link the processes of early warning issuance, inspection and verification, emergency response, equipment maintenance, report generation, and manual review. The pre-simulation and simulation module is configured to generate risk evolution paths, simulation results of disposal effects, and scheme comparison results based on prediction results and disposal measures; the visualization and interaction module is configured to display the dam's three-dimensional scene, monitoring indicators, risk level, disposal process, equipment status, and feedback results.

7. The reservoir dam safety monitoring system based on AI smart field as described in claim 1, characterized in that: The collaborative scheduling engine includes an information sharing unit, a task coordination unit, a resource coordination unit, and a conflict coordination unit. The information sharing unit is configured to synchronize the perception data, cognitive results, early warning information, decision instructions, execution data, and feedback generated by each intelligent agent. The task coordination unit is configured to generate a task chain according to the business sequence of perception, cognition, early warning, prediction, rehearsal, contingency plan, decision-making, execution, and feedback. The resource coordination unit is configured to schedule monitoring equipment, inspection equipment, emergency equipment, computing resources, human resources, and knowledge resources. The conflict coordination unit is configured to adjust the task execution order, resource allocation method, or parallel task splitting method when multiple intelligent agents simultaneously call the same device, interface, computing resources, or business process.

8. The reservoir dam safety monitoring system based on AI smart field as described in claim 1, characterized in that: The feedback iteration module includes a feedback data receiving unit, a deviation analysis unit, an incremental training unit, a strategy optimization unit, and a version management unit; the feedback data receiving unit is configured to receive the latest monitoring data, inspection and verification results, treatment effect evaluation results, equipment operation results, and manual review results; The deviation analysis unit is configured to compare the deviation between the model prediction results and the field observation results, and determine the update objects of model parameters, agent policies, or digital twin states; the incremental training unit is configured to perform incremental fine-tuning of the large model layer based on the filtered feedback data. The strategy optimization unit is configured to update the agent's task rules, calling order, processing flow and resource scheduling strategy according to the task execution effect; The version management unit is configured to record model version, agent version, data source, update time, performance verification results, and rollback information.

9. The operation method of a reservoir dam safety monitoring system based on AI smart field, characterized in that, The system described in any one of claims 1 to 8 is implemented by comprising the following steps: S1 connects to the monitoring data of the dam's physical effects, engineering operation environment data, operation and maintenance management data, inspection image data, equipment status data, standard data and historical case data, and performs cleaning, alignment, completion, quality marking and unified management. S2, the processed data is input into the large model layer, which performs multimodal fusion analysis, spatiotemporal coupling analysis, domain knowledge fusion, anomaly identification, risk prediction, anomaly tracing, and candidate handling strategy generation. S3. Based on the dam safety status, anomaly type, risk level, anomaly cause, and candidate handling strategy output by the large model layer, the collaborative scheduling engine calls the corresponding intelligent agents in the intelligent agent layer to generate a task chain covering perception, cognition, early warning, prediction, rehearsal, contingency plan, decision-making, execution, and feedback. S4 is generated by the intelligent field layer based on the digital twin carrier, which generates virtual scenarios, risk evolution paths and disposal simulation results corresponding to the risk location, risk type and risk level; S5 is generated by the decision-making intelligent agent by combining risk prediction results, disposal simulation results, emergency plans, domain knowledge, equipment status and resource conditions to generate disposal plans; S6, the executing intelligent agent transforms the disposal plan into inspection tasks, equipment control instructions, work order tasks or emergency disposal tasks, and drives the corresponding equipment or business system to execute them through a unified service interface; S7, the feedback agent collects on-site monitoring feedback, equipment execution feedback, handling effect feedback and manual review feedback, and inputs the feedback results into the feedback iteration module; S8, through the feedback iteration module, updates the large model layer, intelligent agent layer, and intelligent field layer based on the feedback results, forming a closed-loop operation for reservoir dam safety monitoring.

10. The operation method of the reservoir dam safety monitoring system based on AI smart field according to claim 9, characterized in that: Steps S3 to S8 form a closed-loop chain of "perception-cognition-early warning-decision-execution-feedback"; in the perception stage, data on dam deformation, seepage, stress and strain, vibration, environmental quantities and apparent defects are collected; in the cognition stage, the dam's operating status, anomaly types, structural weak areas and risk causes are identified. During the early warning phase, tiered early warning information is generated based on anomaly identification results, risk prediction results, and early warning rules. During the decision-making phase, a response plan is generated based on the risk level, the results of the response simulation, the matching results of the contingency plan, the equipment status, and the resource conditions. During the execution phase, the disposal plan is transformed into equipment control commands, inspection tasks, work orders, or emergency response tasks; during the feedback phase, the disposal results, monitoring changes, equipment status, and manual confirmation results are fed back to the large model layer, intelligent agent layer, and intelligent field layer.

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