A smart operation and maintenance management system for reservoir dam safety

Abstract

This invention relates to the field of reservoir dam safety operation and maintenance technology, and discloses an intelligent operation and maintenance management system for reservoir dam safety, comprising: a data integration module, a geographic processing service module, a spatial intelligent analysis and orchestration module, a spatiotemporal knowledge graph module, and a human-machine collaborative decision-making module. This invention automatically converts users' natural language requirements into specific spatial analysis tasks, intelligently matches the optimal algorithm based on task type and data characteristics, then converts the spatial analysis results into a structured knowledge graph in real time, and uses graph neural networks for intelligent reasoning and to generate decision suggestions, providing intelligent decision support for reservoir dam safety operation and maintenance.

Description

Technical Field

[0001] This invention relates to the field of reservoir dam safety operation and maintenance technology, specifically to a reservoir dam safety intelligent operation and maintenance management system that integrates spatiotemporal knowledge graph construction, spatial intelligent analysis, and collaborative decision-making. Background Technology

[0002] Reservoirs and dams are crucial infrastructure for ensuring public safety, and they currently face severe challenges due to their large scale, numerous structures, complex management, aging equipment and facilities, and frequent extreme disasters. Traditional operation and maintenance models for reservoirs and dams have long relied on manual inspections, periodic monitoring, and experience-based judgment. Daily maintenance requires significant investment of manpower and resources and is characterized by significant delays and passivity, making it difficult to address complex and ever-changing engineering safety risks. Furthermore, grassroots reservoir and dam management units generally face problems such as insufficient professional and technical personnel, a severely aging workforce, and outdated knowledge structures, resulting in enormous maintenance and management costs. Therefore, there is an urgent need to improve efficiency and effectiveness to achieve better safety assurance with limited resources.

[0003] To address the aforementioned operational and maintenance challenges, reservoirs and dams in the new era have begun to widely apply next-generation information technologies such as the Internet of Things, big data, artificial intelligence, and digital twins, and have built a large number of digital management systems. For example, the prior art with publication number CN119469249A proposes a reservoir rainfall and water level monitoring and dam safety monitoring system and method; the prior art with announcement number CN115410340B proposes a reservoir safety monitoring and early warning method integrating video, satellite, and sensor monitoring; and the prior art with announcement number CN117689216B proposes a water conservancy project operation and maintenance management system based on digital twins, etc. These systems, empowered by digitalization, networking, and intelligent technologies, promote more thorough perception of the status of reservoir dam projects and more efficient handling and execution. However, as typical spatially constrained engineering structures, the safety status of reservoir dams is closely related to multi-dimensional spatiotemporal factors such as three-dimensional spatial location, geological environmental constraints, and hydrological and meteorological conditions. Existing systems lack a deep understanding of the complex spatial constraints between reservoir dams and their surrounding environment, making it difficult to identify abnormal patterns and potential risks hidden in spatiotemporal information. This keeps reservoir dam operation and maintenance management in a passive "post-event response" mode. Furthermore, existing digital systems are mainly differentiated by professional fields, such as dam safety monitoring systems, hydrological and meteorological systems, and geological environment monitoring systems. Due to inconsistent data formats and spatiotemporal benchmarks, there is a lack of effective interconnection mechanisms between these systems. This makes it difficult to effectively fuse and correlate multi-source information on the same management object, hindering comprehensive safety assessments and accurate decision-making. These technological limitations not only restrict the application effectiveness of digital systems but also become key bottlenecks hindering the improvement of intelligent operation and maintenance of reservoir dams under the new circumstances.

[0004] In summary, while existing methods and systems have achieved the construction of digital infrastructure capabilities for the operation and maintenance management of reservoirs and dams, they still have significant shortcomings in spatial intelligent cognition and system collaboration. Furthermore, they do not utilize the latest technologies such as large language models and spatiotemporal knowledge graphs, making it difficult to form a global spatiotemporal security situational awareness and comprehensive decision support. Summary of the Invention

[0005] To address the technical problems in existing reservoir dam operation and maintenance management systems, such as the lack of technical means to automatically convert natural language requirements into professional spatial analysis tasks, the lack of a dynamic construction and incremental update mechanism for spatiotemporal knowledge graphs for dam safety, and the lack of deep integration and collaborative decision-making capabilities for spatial intelligent analysis and knowledge graph reasoning, this invention provides an intelligent operation and maintenance management system for reservoir dam safety based on spatiotemporal knowledge graphs and spatial intelligent collaboration. The system aims to shift from passive response to proactive risk perception, providing intelligent decision support for reservoir dam safety operation and maintenance.

[0006] This application provides an intelligent operation and maintenance management system for reservoir dam safety, comprising: Data integration module: used for unified access, standardized processing and storage management of multi-source heterogeneous data of the dam, and to establish a geographic database; Geoprocessing service module: used to integrate geoprocessing algorithms, process data from geographic databases, and execute and manage analysis tasks; the analysis tasks include: time series analysis, spatial anomaly detection, environmental correlation analysis, and trend prediction; Spatial intelligent analysis and orchestration module: used to analyze user natural language needs, match geographic data processing algorithm combinations according to user natural language needs, orchestrate the algorithm combinations into an analysis process in the form of a directed acyclic graph with data dependencies, formulate execution strategies, and output spatial analysis results; the spatial intelligent analysis and orchestration module includes a large language model decision reasoning unit, a natural language needs understanding unit, an algorithm matching unit, and an analysis process dynamic orchestration unit; The human-machine collaborative decision-making module obtains the user's natural language requirements from the user terminal, the spatial intelligent analysis and orchestration module analyzes the user's natural language requirements, orchestrates and executes the strategy to call the geoprocessing algorithm in the geoprocessing service module, and the data integration module provides data support to the geoprocessing algorithm to generate spatial analysis results. The spatial intelligent analysis and orchestration module outputs spatial analysis results and transmits them to the spatiotemporal knowledge graph module. The spatiotemporal knowledge graph module generates a structured knowledge graph. The human-machine collaborative decision-making module generates knowledge graph reasoning results based on the structured knowledge graph and spatial analysis results, and outputs the structured knowledge graph and knowledge graph reasoning results.

[0007] Furthermore, the data integration module includes: Data acquisition unit: used to collect multi-source heterogeneous data about the dam; Standardization processing unit: Performs standardization processing on multi-source heterogeneous data of the dam to obtain standardized dam data; Data Quality Assessment Unit: Used to establish a data quality assessment mechanism and evaluate the quality of standardized dam data; Hybrid storage and management unit: used to store and manage dam standardized data and establish a geographic database; The multi-source heterogeneous data of the dam includes: dam safety monitoring data, hydrological and meteorological data, geological environment data, equipment operation data, and geospatial data; The standardized dam data includes both structured and unstructured data.

[0008] Furthermore, the dam safety monitoring data includes: monitoring point coordinates, seepage pressure, stress and strain, and vibration frequency; The hydrological and meteorological data include: reservoir water level, inflow, rainfall, and ambient temperature; The geological environment data includes: earthquake monitoring, landslide displacement, and debris flow early warning; The equipment operation data includes: generator set status, gate opening degree, and monitoring equipment operating status; The geospatial data includes: three-dimensional models of engineering structures, topography, and geological features; The structured data includes: monitoring values, equipment status, and metadata; The unstructured data includes: 3D models, remote sensing images, and document files; The hybrid storage and management unit uses a relational database to store structured data and an object storage system to store unstructured data.

[0009] Furthermore, the geoprocessing service module includes: Unified access unit for multi-source geoprocessing services: used to generate geoprocessing services based on geoprocessing algorithms, standardize service interfaces, realize unified invocation and management of geoprocessing services, and maintain the service registry; Service Containerization Deployment Unit: Used for containerizing geoprocessing services; Resource pool management unit: Constructs computing resource pools, dynamically allocates computing resources, and generates resource allocation strategies through large language models; The geographic processing algorithms include: geographic information system algorithm library, spatial database functions, general geographic computing library and professional hydrological analysis algorithms; The maintenance service registry includes: function description, parameter definition, performance characteristics, and applicable scenarios.

[0010] Furthermore, the large language model decision reasoning unit is used to: construct a large language model, interpret spatial analysis results through the large language model, and perform semantic interpretation and empirical verification of the structured knowledge graph through the large language model; The natural language requirement understanding unit is used to: understand natural language requirements through a large language model, simultaneously construct a domain knowledge base and prompt word templates for the reservoir dam, obtain key task parameters, and analyze the task list; The algorithm matching unit is used to: match geoprocessing algorithms based on key task parameters and generate the optimal algorithm combination; The dynamic orchestration unit for the analysis process is used to: organize the optimal algorithm combination into an analysis process in the form of a directed acyclic graph, identify the data dependencies and execution order between algorithms, and formulate execution strategies; The spatial intelligent analysis and orchestration module also includes: Rule parameter configuration unit: used to establish a parameter configuration knowledge base and select parameter configurations based on the analysis task list; Execution process monitoring unit: used to monitor the execution process of geoprocessing services and acquire monitoring data; Strategy execution dynamic adjustment unit: used to analyze monitoring data through large language models and dynamically adjust the execution strategy; Analysis Result Quality Assessment Unit: Used to assess the quality of spatial analysis results.

[0011] Furthermore, the method of understanding natural language requirements through a large language model supports a multi-layered parsing strategy. The multi-layered parsing strategy includes: identifying task types, extracting key task parameters, determining analysis objects, and generating an analysis task list; The parameter configuration selection based on the analysis task list specifically refers to selecting parameter configuration based on the data characteristics and business requirements of the analysis task list. The data characteristics include data size, quality level, and update frequency, and the business requirements include real-time requirements and accuracy requirements. The monitoring data includes: execution status, resource usage, processing progress, and intermediate results; The quality assessment includes: data quality assessment, algorithm performance assessment, result accuracy assessment, and reliability assessment.

[0012] Furthermore, the spatiotemporal knowledge graph module includes: Dual database unit: used to build a dual database linkage architecture, constructing spatial entities and spatial relationships based on spatial analysis results; Knowledge graph node construction unit: used to establish an automatic mapping mechanism from spatial entities to knowledge graph nodes, mapping spatial analysis results to knowledge graph nodes; Knowledge graph relation edge construction unit: used to establish an automatic identification and construction mechanism for spatial relations to knowledge graph relation edges, converting spatial analysis results into knowledge graph relation edges; Structured Units: Used to combine knowledge graph nodes and knowledge graph relation edges to form a structured knowledge graph; Incremental update unit: used to build an incremental update mechanism for the knowledge graph, enabling real-time synchronization of spatial analysis results to the structured knowledge graph.

[0013] Furthermore, the knowledge graph nodes include: monitoring point nodes, abnormal event nodes, dam structure nodes, and environmental factor nodes; The mapping source of the monitoring point nodes is dam safety monitoring data, the mapping source of the abnormal event nodes is spatial analysis results, the mapping source of the dam structure nodes is geospatial data, and the mapping source of the environmental factor nodes is hydrological and meteorological data. The knowledge graph relationship edges include: containment relationship, overlimit relationship, proximity relationship, correlation relationship, and causal relationship.

[0014] Furthermore, the human-machine collaborative decision-making module includes: Knowledge Graph Reasoning and Analysis Unit: Used to perform graph reasoning based on structured knowledge graphs and spatial analysis results, and generate knowledge graph reasoning results; Multi-dimensional decision constraint modeling unit: used to construct multi-dimensional decision constraint models to constrain the reasoning results of knowledge graphs; Human-Computer Interaction Unit: Used to generate human-computer interaction interfaces, realize the input of users' natural language requirements, and output structured knowledge graphs and knowledge graph reasoning results; Furthermore, the user's natural language requirements support rainstorm analysis; When the user's natural language requirement is rainstorm analysis, the spatial analysis result is the rainstorm analysis result; the rainstorm analysis result includes: flood prediction result, inundation analysis result, and risk level information. The spatiotemporal knowledge graph module converts the rainstorm analysis results into a structured knowledge graph, including the following steps: The flood forecast results are converted into flood evolution entities; the flood evolution entities include: flood hydrograph, peak flow, and arrival time; The flooding analysis results are converted into flooding extent entities, which include: flooded area, flooding depth, and affected population. The risk level information is converted into a risk level entity, which includes attributes such as risk type, probability level, and degree of impact. Based on the results of the rainstorm analysis, a rainstorm knowledge graph relationship edge is constructed; the rainstorm knowledge graph relationship edge includes: the causal relationship between flood and risk, the influence relationship between scheduling and effect, and the relationship between emergency response and resource allocation.

[0015] The beneficial effects of this invention are as follows: 1. This invention automates and intelligently converts users' natural language requirements into specific spatial analysis tasks, and intelligently matches the optimal algorithm based on task type and data characteristics. Compared with traditional manual algorithm selection, this significantly improves the efficiency and accuracy of spatial analysis. 2. A spatiotemporal knowledge graph model specifically for reservoir dams was constructed through the spatiotemporal knowledge graph module. It supports the unified representation of multiple types of nodes such as facilities, monitoring, environment, and events, and defines multi-dimensional relationship types such as space, temporality, semantics, and logic. It realizes the dynamic construction and incremental update of knowledge. Compared with the traditional static knowledge management method, it can reflect the changes in the status of the project in real time and support intelligent reasoning and decision support. 3. By using a human-machine collaborative decision-making module to perform intelligent reasoning on the spatiotemporal knowledge graph, the spatial relationships and impact paths between facilities are identified. Combined with real-time monitoring data and historical knowledge, risk warnings and decision-making suggestions are automatically generated. Compared with traditional experience-based decision-making methods, this technology provides a scientific basis for operation and maintenance decisions, improving the accuracy and timeliness of decision-making. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the system structure provided in an embodiment of the present invention. Detailed Implementation

[0017] The specific implementation of the present invention will now be described in detail with reference to the accompanying drawings.

[0018] Example 1: like Figure 1 As shown, a smart operation and maintenance management system for reservoir dam safety includes: Data integration module: used for unified access, standardized processing and storage management of multi-source heterogeneous data of the dam, and to establish a geographic database; The data integration module includes: Data acquisition unit: used to collect multi-source heterogeneous data about the dam; Standardization processing unit: Performs standardization processing on multi-source heterogeneous data of the dam to obtain standardized dam data; Data Quality Assessment Unit: Used to establish a data quality assessment mechanism and evaluate the quality of standardized dam data; Hybrid storage and management unit: used to store and manage dam standardized data and establish a geographic database; The multi-source heterogeneous data of the dam includes: dam safety monitoring data, hydrological and meteorological data, geological environment data, equipment operation data, and geospatial data; The standardized dam data includes both structured and unstructured data.

[0019] The dam safety monitoring data includes: monitoring point coordinates, seepage pressure, stress and strain, and vibration frequency; The hydrological and meteorological data include: reservoir water level, inflow, rainfall, and ambient temperature; The geological environment data includes: earthquake monitoring, landslide displacement, and debris flow early warning; The equipment operation data includes: generator set status, gate opening degree, and monitoring equipment operating status; The geospatial data includes: three-dimensional models of engineering structures, topography, and geological features; In this embodiment, for geospatial data, the CGCS2000 plane coordinate system and the 1985 National Elevation Datum are used as unified datums, and coordinate transformation algorithms are used to realize the transformation between different coordinate systems. in, These represent the three-dimensional coordinate components of the geospatial data points in the original coordinate system before the transformation. These represent the three-dimensional coordinate components of the transformed geospatial data points under a unified coordinate datum. This is a coordinate transformation matrix, containing translation, rotation, and scaling parameters.

[0020] The structured data includes: monitoring values, equipment status, and metadata; The unstructured data includes: 3D models, remote sensing images, and document files; The evaluation function expression for assessing the quality of standardized dam data is as follows: Where Q(D) is the data quality score, C(D) is completeness, A(D) is accuracy, I(D) is consistency, T(D) is timeliness, α+β+γ+δ=1, and each weight coefficient is determined based on experience data from specific business scenarios; The hybrid storage and management unit uses a relational database to store structured data and an object storage system to store unstructured data.

[0021] By employing a hybrid storage scheme combining PostgreSQL spatial database and object storage, both efficient querying of structured data and support for large-capacity file storage are ensured. The geoprocessing algorithms include: QGIS algorithm library, PostGIS spatial functions, Python geocomputation library, and professional hydrological analysis algorithms. The Python geospatial computing libraries include: GDAL, Shapely, and Rasterio.

[0022] This example illustrates a situation where a reservoir dam is hit by a severe rainstorm, causing a sharp increase in inflow and a rapid rise in water level, posing a serious risk of dam overflow or even collapse.

[0023] The system activates emergency response mode, quickly integrates relevant data, and makes intelligent decisions.

[0024] The data integration module quickly integrates data related to rainstorm emergencies.

[0025] The data acquisition unit collects hydrological and meteorological data, dam safety monitoring data, watershed geographic data, and emergency resource data. The hydrological and meteorological data includes real-time rainfall, inflow, outflow, and reservoir water level changes. The dam safety monitoring data includes dam deformation monitoring, seepage monitoring, and stress monitoring. The watershed geographic data includes upstream watershed topography, river channel distribution, and land use types. The emergency resource data includes the status of flood discharge facilities, emergency personnel allocation, downstream evacuation routes, and the location of refuge sites.

[0026] The standardized processing unit establishes a unified emergency spatiotemporal benchmark, unifying all data into the same time and space framework for analysis.

[0027] The hybrid storage and management unit employs a hybrid storage scheme. Hydrological and meteorological data with high real-time requirements are stored in an in-memory database; dam monitoring data is stored in a time-series database; geospatial data is stored in a spatial database; and emergency resource data is stored in a knowledge base. A data quality assessment mechanism ensures the reliability and timeliness of emergency data, with particular attention to the accuracy of key data such as rainfall and water levels. Through emergency data integration, a complete information foundation is provided for dam inundation risk assessment during rainstorms, ensuring the timeliness and accuracy of emergency decision-making.

[0028] Geoprocessing service module: used to integrate geoprocessing algorithms, process data from geographic databases, and execute and manage analysis tasks; the analysis tasks include: time series analysis, spatial anomaly detection, environmental correlation analysis, and trend prediction; The geoprocessing service module includes: Unified access unit for multi-source geoprocessing services: used to generate geoprocessing services based on geoprocessing algorithms, standardize service interfaces, realize unified invocation and management of geoprocessing services, and maintain the service registry; Service Containerization Deployment Unit: Used for containerizing geoprocessing services; The geoprocessing service is deployed using containerization technology, with each algorithm running in an independent container, enabling elastic allocation and isolation of resources. The system constructs a computing resource pool to dynamically allocate resources based on algorithm characteristics and task requirements.

[0029] Resource pool management unit: Constructs computing resource pools, dynamically allocates computing resources, and generates resource allocation strategies through large language models; Large language models participate in resource scheduling decisions, comprehensively considering factors such as task priority, data scale, algorithm complexity, and real-time requirements to formulate the optimal resource allocation strategy.

[0030] The geographic processing algorithms include: geographic information system algorithm library, spatial database functions, general geographic computing library and professional hydrological analysis algorithms; The maintenance service registry includes: function description, parameter definition, performance characteristics, and applicable scenarios.

[0031] In this embodiment, the geoprocessing service module constructs a unified geoprocessing service access layer, supporting various emergency geoprocessing services such as rainstorm and flood analysis algorithms, flood discharge scheduling algorithms, inundation range calculation algorithms, and risk assessment algorithms.

[0032] The unified access unit for multi-source geoprocessing services enables unified invocation and management of algorithms from different sources through standardized service interfaces.

[0033] The service containerization deployment unit adopts an emergency containerization deployment strategy, and the resource pool management unit allocates sufficient computing resources to key algorithms. The flood evolution calculation algorithm is deployed in a high-performance computing cluster, the inundation range analysis algorithm is deployed in GPU containers, and the risk assessment algorithm is deployed in memory-optimized containers, ensuring the timeliness of emergency analysis. The system constructs an emergency computing resource pool, prioritizing the resource needs of flood analysis and risk assessment tasks.

[0034] The execution process monitoring unit establishes an emergency monitoring mechanism to track algorithm execution status, computation progress, and result accuracy in real time. The strategy execution dynamic adjustment unit intelligently analyzes monitoring data to identify computational bottlenecks and potential problems, dynamically adjusting the execution strategy. When insufficient computing resources are detected, the system automatically expands computing nodes; when algorithm accuracy does not meet requirements, it automatically adjusts computational parameters. Through priority resource scheduling and intelligent management, the computational needs of flood analysis are guaranteed, ensuring the completion of critical analysis tasks during periods of continuous heavy rainfall.

[0035] Spatial intelligent analysis and orchestration module: used to analyze user natural language needs, match geographic data processing algorithm combinations according to user natural language needs, orchestrate the algorithm combinations into an analysis process in the form of a directed acyclic graph with data dependencies, formulate execution strategies, and output spatial analysis results; the spatial intelligent analysis and orchestration module includes a large language model decision reasoning unit, a natural language needs understanding unit, an algorithm matching unit, and an analysis process dynamic orchestration unit; Human-machine collaborative decision-making module: used to obtain user natural language requirements, interact with spatial intelligent analysis and arrangement module to obtain spatial analysis results, interact with spatiotemporal knowledge graph module to obtain structured knowledge graph, perform graph reasoning based on the structured knowledge graph and the spatial analysis results to generate knowledge graph reasoning results, and output structured knowledge graph and knowledge graph reasoning results to user terminal; The large language model decision reasoning unit is used to: construct a large language model, interpret spatial analysis results through the large language model, and perform semantic interpretation and empirical verification of structured knowledge graphs through the large language model; The method uses a large language model to understand natural language requirements and supports a multi-layered parsing strategy. The multi-layered parsing strategy includes: identifying task types, extracting key task parameters, determining analysis objects, and generating an analysis task list; Specifically, the identified task types include: monitoring and analysis, risk assessment, and emergency response; Key parameters for the extraction task include: time range, spatial range, and accuracy requirements; The objects of analysis include: dam, equipment, and environment; The analysis task list includes: time series analysis, spatial anomaly detection, environmental correlation analysis, and trend prediction.

[0036] The natural language requirement understanding unit is used to: understand natural language requirements through a large language model, simultaneously construct a domain knowledge base and prompt word templates for the reservoir dam, obtain key task parameters, and analyze the task list; The algorithm matching unit is used to: match geoprocessing algorithms based on key task parameters and generate the optimal algorithm combination; The dynamic orchestration unit for the analysis process is used to: organize the optimal algorithm combination into an analysis process in the form of a directed acyclic graph, identify the data dependencies and execution order between algorithms, and formulate execution strategies; In the dynamic orchestration unit of the analysis process, the large language model participates in process optimization, and formulates the optimal execution strategy based on factors such as data scale, computing resources, time constraints, and algorithm complexity.

[0037] The system supports parallel processing of tasks, automatically identifies branch tasks that can be executed in parallel, and optimizes resource allocation.

[0038] The optimization objectives of resource allocation include: minimizing total execution time, balancing computational resource load, meeting time constraints, and ensuring result quality requirements.

[0039] The spatial intelligent analysis and orchestration module also includes: Rule parameter configuration unit: used to establish a parameter configuration knowledge base and select parameter configurations based on the analysis task list; The parameter configuration selection based on the analysis task list specifically refers to selecting parameter configuration based on the data characteristics and business requirements of the analysis task list. The data characteristics include data size, quality level, and update frequency, and the business requirements include real-time requirements and accuracy requirements. For key parameters, the system provides adjustment suggestions and explanations, and supports manual confirmation and fine-tuning; the system records the historical effects of parameter selection and continuously optimizes parameter configuration rules.

[0040] Execution process monitoring unit: used to monitor the execution process of geoprocessing services and acquire monitoring data; The monitoring data includes: execution status, resource usage, processing progress, and intermediate results; The execution process monitoring unit establishes a full-link monitoring mechanism from task submission to result return, and tracks the execution status, resource usage, data transmission speed, computing progress and other indicators of each task in real time.

[0041] Strategy execution dynamic adjustment unit: used to analyze monitoring data through large language models and dynamically adjust the execution strategy; By analyzing monitoring data using large language models, we can identify performance bottlenecks and optimization opportunities.

[0042] The system has adaptive optimization capabilities, which can dynamically adjust the execution strategy according to the running status, such as adjusting the parallelism, optimizing the data transmission path, and selecting the optimal algorithm parameters.

[0043] At the same time, a comprehensive fault handling mechanism should be established, including task retry, degradation processing, and switching to alternative algorithms.

[0044] When an algorithm execution anomaly is detected, the system automatically selects an alternative algorithm or adjusts the parameters; when resources are insufficient, the system automatically adjusts the task priority; when the result quality is substandard, the system automatically adds verification steps or adjusts the algorithm parameters.

[0045] Analysis Result Quality Assessment Unit: Used to assess the quality of spatial analysis results.

[0046] The quality assessment includes: data quality assessment, algorithm performance assessment, result accuracy assessment, and reliability assessment.

[0047] Data quality assessment includes: the completeness and accuracy of input data; algorithm performance assessment includes: the efficiency of the execution strategy and resource consumption; result accuracy assessment includes: the degree of agreement between spatial analysis results and engineering practice; and reliability assessment includes: the stability and repeatability of spatial analysis results.

[0048] The system verifies the spatial analysis results through methods such as cross-validation, sensitivity analysis, and expert evaluation to ensure the reliability of the spatial analysis results.

[0049] In this embodiment, emergency command personnel input via voice: "Emergency analysis of the safety threat posed by heavy rain to the reservoir dam, prediction of water level changes in the next 6 hours, formulation of the optimal flood discharge scheduling plan, and assessment of downstream flooding risk." The natural language requirement understanding unit parses the emergency requirement based on a special prompt word template for heavy rain emergencies, quickly generating an emergency analysis task list.

[0050] The multi-layered analysis of emergency tasks includes: the first layer identifies the task type as a rainstorm emergency response; the second layer extracts key parameters, including time constraints and safety requirements; the third layer determines the analysis objects, including reservoir dams, upstream basins, and downstream areas; and the fourth layer generates a specific list of emergency analysis tasks, including sub-tasks such as flood evolution prediction, flood discharge scheduling optimization, inundation risk assessment, and emergency evacuation planning.

[0051] In this embodiment, the time constraint is a 6-hour prediction, and the safety requirement is to prevent dam overflow.

[0052] The algorithm matching unit intelligently selects the optimal combination of algorithms from the emergency algorithm pool based on the characteristics of the emergency task. The dynamic orchestration unit organizes the selected algorithms into an analysis process in the form of a directed acyclic graph, identifying the data dependencies and execution order between algorithms.

[0053] The large language model decision-making and reasoning unit participates in emergency process optimization, formulating optimal execution strategies based on time constraints and safety requirements to ensure that flood control safety analysis is completed with priority. The parameter optimization function automatically adjusts algorithm parameters according to the characteristics of the emergency scenario, improving analysis efficiency. Through intelligent orchestration in emergency mode, rapid analysis and risk assessment of rainstorms and floods are achieved, providing a scientific basis for flood control decision-making.

[0054] Spatiotemporal Knowledge Graph Module: Used to establish an automatic mapping mechanism from spatial entities to knowledge graph nodes, converting spatial analysis results into structured knowledge graphs, and realizing semantic representation and dynamic updating of spatial analysis results; The spatiotemporal knowledge graph module includes: Dual database unit: used to build a dual database linkage architecture, constructing spatial entities and spatial relationships based on spatial analysis results; In this embodiment, a dual-database linkage architecture of PostgreSQL+PostGIS and Neo4j is constructed. PostgreSQL is responsible for storing space monitoring data and performing space analysis calculations, while Neo4j is responsible for storing knowledge graphs and performing graph query reasoning.

[0055] The PostgreSQL database stores raw data for dam safety monitoring, including numerical data such as monitoring point coordinates, deformation rates, seepage pressure, stress and strain values, and vibration frequencies. PostGIS extends the storage with geometric object data, including spatial data such as the point geometry of monitoring points, the polygon geometry of anomalous areas, and the multi-polygon geometry of the dam structure. Spatial indexes are created to accelerate spatial queries; these indexes are GiST and SP-GiST indexes.

[0056] Neo4j graph database stores anomaly knowledge graphs, including entity nodes and relation edges.

[0057] A two-phase commit protocol is employed to ensure data consistency between the two databases. When spatial data in PostgreSQL changes, the system automatically triggers a synchronization mechanism to synchronize the relevant data to the Neo4j knowledge graph. The synchronization process uses a transactional message mechanism, transmitting data change events through an Apache Kafka message queue to ensure reliable message delivery.

[0058] Define a dual-database consistency function: C_consistency=N_valid / (N_total-N_conflict) Where C_consistency is the consistency score, N_valid is the number of valid transactions, N_total is the total number of transactions, N_conflict is the number of conflicting transactions, and the target consistency score is >0.99.

[0059] Knowledge graph node construction unit: used to establish an automatic mapping mechanism from spatial entities to knowledge graph nodes, mapping spatial analysis results to knowledge graph nodes; The knowledge graph nodes include: monitoring point nodes, abnormal event nodes, dam structure nodes, and environmental factor nodes; The mapping source of the monitoring point nodes is dam safety monitoring data, the mapping source of the abnormal event nodes is spatial analysis results, the mapping source of the dam structure nodes is geospatial data, and the mapping source of the environmental factor nodes is hydrological and meteorological data. Specifically, the mapping source of the monitoring point nodes is dam safety monitoring data, including: monitoring point ID, point geometric coordinates, monitoring type, and monitoring value; mapped to MonitoringPoint nodes, the attributes of which include: point_id, location, type, current_value, and status; Similarly, the mapping source of the abnormal event nodes is the spatial analysis results, including: deformation trend anomalies identified by ARIMA time series analysis and spatial clustering anomalies identified by Getis-Ord Gi hotspot analysis; these are mapped to AnomalyEvent nodes with attributes including event_id, event_type, severity, detected_time, and affected_points. The mapping source for the dam structure nodes is geospatial data, including: dam axis, dam segment division, and structural zoning; the mapping is to DamStructure nodes, with attributes including structure_id, structure_name, geometry, and structural_type; The mapping source for the environmental factor node is hydrological and meteorological data, including meteorological data and hydrological data; it is mapped to the EnvironmentalFactor node, with attributes including factor_id, factor_type, current_value, and impact_level. Knowledge graph relation edge construction unit: used to establish an automatic identification and construction mechanism for spatial relations to knowledge graph relation edges, converting spatial analysis results into knowledge graph relation edges; The knowledge graph relationship edges include: containment relationship, overlimit relationship, proximity relationship, correlation relationship, and causal relationship.

[0060] Among them, the inclusion relationship, namely CONTAINS relationship, is identified by using the ST_Contains function of PostGIS to determine the spatial inclusion relationship; the relationship is established by establishing a CONTAINS relationship edge when the dam structure Polygon contains the monitoring point Point; the relationship attributes include: relationship strength and inclusion time; among them, the relationship strength is calculated based on the geometric area ratio.

[0061] The EXCEEDS_THRESHOLD relationship is identified by comparing the monitored value with a threshold to determine whether it exceeds a preset threshold. The relationship is established when the monitored point's current_value exceeds the threshold. The relationship attributes include: the excess multiple, the excess type, and the excess duration.

[0062] The proximity relationship, or NEARBY relationship, is identified by using the ST_DWithin function in PostGIS to determine spatial proximity. The relationship is established when the distance between anomaly monitoring points is less than a threshold; relationship attributes include distance value, proximity strength, and directional relationship. The correlation relationship, namely CORRELATES_WITH, is identified by calculating the Pearson correlation coefficient between monitoring points through correlation analysis. The relationship is established by creating a CORRELATES_WITH relationship edge when the absolute value of the correlation coefficient is greater than a threshold. Relationship attributes include: correlation coefficient value, correlation type, and confidence level. Causal relationships, or CAUSED_BY relationships, are identified by analyzing the root cause of an anomaly using causal reasoning algorithms. Relationships are established when a causal relationship exists between the anomaly and environmental factors; relationship attributes include causal strength, confidence level, and reasoning basis. Structured Units: Used to combine knowledge graph nodes and knowledge graph relation edges to form a structured knowledge graph; Incremental update unit: used to build an incremental update mechanism for the knowledge graph, enabling real-time synchronization of spatial analysis results to the structured knowledge graph.

[0063] The knowledge graph incremental update mechanism enables real-time synchronization of spatial analysis results to the knowledge graph. When new monitoring data arrives or spatial intelligent analysis identifies new anomalies, the system automatically creates or updates the corresponding nodes and relationships in the knowledge graph. Incremental updates employ a stream processing architecture, transmitting data change events via an Apache Kafka message queue. Neo4j subscribes to relevant topics and updates the graph structure in real time. Update latency is controlled within 30 seconds, ensuring real-time synchronization between the knowledge graph and the actual monitoring status.

[0064] Establish an update consistency verification mechanism to ensure that the updated knowledge graph maintains logical consistency.

[0065] The verification content includes: spatial relationship consistency, topological integrity, and attribute constraint satisfaction. When inconsistency is detected, a rollback mechanism is automatically triggered to restore the state before the update, and the reason for the inconsistency and the handling process are recorded. Among them, spatial relationship consistency refers to whether the spatial relationships in PostGIS are consistent with the relationship edges in Neo4j. Topological integrity includes the integrity of relationships, proximity relationships, and connection relationships. Attribute constraint satisfaction includes numerical range constraints, uniqueness constraints, and business rule constraints.

[0066] Define the knowledge graph update function: Update_KG(Event_change,Graph_knowledge,Strategy_update,Verify_consistency) Among them, Event_change is the change event, Graph_knowledge is the knowledge graph, Strategy_update is the update strategy, and Verify_consistency is the consistency verification flag.

[0067] Human-machine collaborative decision-making module: used to obtain user natural language requirements, interact with spatial intelligent analysis and arrangement module to obtain spatial analysis results, interact with spatiotemporal knowledge graph module to obtain structured knowledge graph, perform graph reasoning based on the structured knowledge graph and the spatial analysis results to generate knowledge graph reasoning results, and output structured knowledge graph and knowledge graph reasoning results to user terminal; The human-machine collaborative decision-making module includes: Knowledge Graph Reasoning and Analysis Unit: Used to perform graph reasoning based on structured knowledge graphs and spatial analysis results, and generate knowledge graph reasoning results; Specifically, based on structured knowledge graphs, spatial analysis results are used as core nodes, and graph algorithms are applied to perform reasoning and analysis within the graph network structure.

[0068] Starting from anomaly nodes, the graph traversal algorithm identifies connected monitoring facility nodes, environmental nodes, and equipment nodes to construct impact paths. The graph search algorithm is used to find historical nodes similar to the current anomaly pattern and extract relevant experience. A graph neural network is used to propagate and aggregate information in the graph structure, identify implicit spatial association patterns and risk propagation mechanisms, and generate knowledge graph reasoning results.

[0069] Based on the reasoning results of the knowledge graph, and combined with engineering safety rules, accurate decision-making suggestions are generated to ensure that the decision-making schemes generated by the reasoning results of the knowledge graph are based on the topological relationships and spatial logic of the graph structure.

[0070] The inference function expression for graph reasoning is: in, For the reasoning results of knowledge graphs, Here, "graph reasoning function" represents a function that performs graph traversal and graph search based on a structured knowledge graph. A knowledge graph that includes the results of algorithm analysis. Based on the current analysis results, These are constraints.

[0071] Multi-dimensional decision constraint modeling unit: used to construct multi-dimensional decision constraint models to constrain the reasoning results of knowledge graphs; By using multi-dimensional decision constraint modeling units, the feasibility of knowledge graph reasoning results in engineering practice is ensured.

[0072] Among them, multi-dimensional decision constraints include: spatial constraints, time constraints, resource constraints, and security constraints; Specifically, spatial constraints consider factors such as spatial limitations, scope of impact, and accessibility; time constraints consider factors such as response time requirements, processing cycles, and time-series dependencies; resource constraints consider factors such as manpower, equipment, and materials; and safety constraints consider factors such as engineering safety standards, risk thresholds, and protection requirements.

[0073] By using multi-dimensional decision constraint modeling units, infeasible solutions are automatically eliminated in knowledge graph reasoning.

[0074] Human-Computer Interaction Unit: Used to generate human-computer interaction interfaces, realize the input of users' natural language requirements, and output structured knowledge graphs and knowledge graph reasoning results; Specifically, the process of the human-computer interaction unit includes: inputting the user's natural language requirements, generating a structured knowledge graph and knowledge graph reasoning results, having experts review the results through the interactive interface, adjusting the reasoning parameters based on expert feedback, repeatedly regenerating the knowledge graph reasoning results, and finally confirming them with the experts.

[0075] The human-computer interaction unit provides multi-level interaction interfaces: a visual interface to display spatial analysis results and knowledge graph reasoning paths, a natural language interface to explain the basis and details of knowledge graph reasoning results, and a parameter adjustment interface to allow experts to modify knowledge graph reasoning weights and constraints.

[0076] In this embodiment, the knowledge graph reasoning and analysis unit constructs a precise reasoning engine based on a rainstorm emergency knowledge graph. Using the knowledge graph constructed from flood analysis results as its graph structure foundation, it applies graph algorithms for reasoning and analysis. Starting from risk nodes, a graph traversal algorithm identifies connected flood nodes, scheduling nodes, and emergency nodes, constructing risk propagation paths. A graph search algorithm is used to find historical cases similar to the current rainstorm pattern, extracting relevant flood control experience. A graph neural network is employed to propagate and aggregate information within the knowledge graph structure, identifying implicit flood control correlation patterns and risk evolution mechanisms.

[0077] In the emergency response to heavy rain, the system further identifies potential landslide risks and generates corresponding planned emergency responses. Simultaneously, the human-computer interaction unit constructs a spatiotemporal query language design and parsing module to support complex spatiotemporal constraint queries. By designing a query language that includes spatial, temporal, attribute, and logical conditions, the system can accurately identify landslide risk areas.

[0078] Spatial conditions include the safe distance from the dam, elevation difference limits, and slope thresholds; temporal conditions include the duration of the rainstorm and the rate of water level change; attribute conditions include geological type, soil moisture content, and vegetation cover; and logical conditions support complex logical combinations such as AND, OR, and NOT.

[0079] Meanwhile, the large language model decision reasoning unit uses a large language model as an auxiliary tool for flood control decision reasoning. Based on the reservoir flood control knowledge base and historical rainstorm case database, it performs semantic interpretation of the knowledge graph reasoning results. Specific functions include: explaining the severity and evolution trend of flood risk, providing flood control experience based on similar rainstorm histories, generating multiple alternative flood discharge scheduling schemes, and evaluating the safety and effectiveness of the schemes.

[0080] Multi-dimensional decision constraint modeling units ensure the feasibility of decision-making schemes under rainstorm emergencies. Safety constraints consider factors such as dam safety water level, downstream flood control standards, and flood discharge capacity limitations; time constraints consider factors such as flood evolution time, response time requirements, and evacuation time windows; resource constraints consider factors such as the status of flood discharge facilities, emergency personnel allocation, and communication support; and environmental constraints consider factors such as downstream ecological environment impact and river flood discharge capacity.

[0081] The human-computer interaction unit establishes an interactive decision-making process for flood control, effectively combining expert experience with intelligent analysis. The specific process includes the system generating a preliminary flood control plan, experts reviewing the plan through the interactive interface, the system adjusting scheduling parameters based on expert feedback, iteratively regenerating an optimized plan, and experts finally confirming the flood control decision.

[0082] The human-computer interaction unit provides dedicated interactive interfaces for flood control: a visual interface displays the flood evolution process and inundation range, a natural language interface explains the basis for flood control and the scheduling plan, and a parameter adjustment interface allows experts to modify flood discharge constraints. Through collaborative flood control decision-making, scientific and accurate flood risk assessment and scheduling decisions are achieved, ensuring the effectiveness of flood control safety in emergency situations such as rainstorms.

[0083] The specific workflow of this invention can be summarized as follows: After the human-machine collaborative decision-making module obtains the user's natural language requirements from the user terminal, the spatial intelligent analysis and orchestration module analyzes the user's natural language requirements, orchestrates and executes the strategy to call the geoprocessing algorithm in the geoprocessing service module, and the data integration module provides data support to the geoprocessing algorithm to generate spatial analysis results. The spatial intelligent analysis and orchestration module outputs spatial analysis results and transmits them to the spatiotemporal knowledge graph module. The spatiotemporal knowledge graph module generates a structured knowledge graph. The human-machine collaborative decision-making module generates knowledge graph reasoning results based on the structured knowledge graph and spatial analysis results, and outputs the structured knowledge graph and knowledge graph reasoning results.

[0084] This invention has advantages in working analysis during rainstorm analysis, and the user's natural language requirements support rainstorm analysis; When the user's natural language requirement is rainstorm analysis, the spatial analysis result is the rainstorm analysis result; the rainstorm analysis result includes: flood prediction result, inundation analysis result, and risk level information. The spatiotemporal knowledge graph module converts the rainstorm analysis results into a structured knowledge graph, including the following steps: The flood forecast results are converted into flood evolution entities; the flood evolution entities include: flood hydrograph, peak flow, and arrival time; The flooding analysis results are converted into flooding extent entities, which include: flooded area, flooding depth, and affected population. The risk level information is converted into a risk level entity, which includes attributes such as risk type, probability level, and degree of impact. Based on the results of the rainstorm analysis, a rainstorm knowledge graph relationship edge is constructed; the rainstorm knowledge graph relationship edge includes: the causal relationship between flood and risk, the influence relationship between scheduling and effect, and the relationship between emergency response and resource allocation.

[0085] Example 2: This embodiment describes a reservoir dam where abnormal deformation was detected in a localized area during routine safety monitoring. A smart reservoir dam safety operation and maintenance management system was needed for timely intelligent analysis and decision-making. The difference between this embodiment and Embodiment 1 is as follows: The data acquisition unit collects dam safety monitoring data, hydrological and meteorological data, and geological environment data. Dam safety monitoring data includes the coordinates, deformation, seepage pressure, and stress-strain values ​​of each monitoring point; hydrological and meteorological data includes reservoir water level, ambient temperature, and recent rainfall; and geological environment data includes seismic activity records and displacement data of surrounding landslides.

[0086] The standardization unit unifies all source data to the CGCS2000 coordinate system and a unified time base, performs interpolation on missing data, and marks outlier data. The data quality assessment unit scores the standardized data to ensure that the completeness and accuracy of each monitoring sequence meet the analysis requirements.

[0087] The maintenance engineer inputs natural language requirements through the human-computer interaction unit: "Analyze the dam deformation trend and identify abnormal areas, focusing on monitoring points with deformation rates exceeding 2mm / month, and analyze the causes of deformation in conjunction with environmental factors."

[0088] The Natural Language Requirement Understanding Unit performs four-layer parsing of the natural language requirement using a large language model: The first layer identifies the task type as monitoring and analysis; the second layer extracts key parameters: the time range is recent monitoring data, the deformation rate threshold is 2 mm / month, and the focus is on monitoring points whose deformation rate exceeds the threshold; the third layer determines the analysis object as the dam body and surrounding environmental factors; the fourth layer generates an analysis task list, including five sub-tasks: time series analysis, spatial anomaly detection, threshold comparison, environmental factor correlation analysis, and visualization output.

[0089] The algorithm matching unit matches the optimal algorithm combination based on the task list: the time series analysis subtask matches the ARIMA time series analysis algorithm and the deformation trend fitting algorithm; the spatial anomaly detection subtask matches the Getis-Ord Gi hotspot analysis algorithm and the Moran'I spatial autocorrelation analysis algorithm; the threshold comparison subtask matches the threshold determination algorithm; the environmental factor correlation analysis subtask matches the Pearson correlation analysis and multiple regression analysis algorithms; and the visualization output subtask matches the contour line generation algorithm and the heat map generation algorithm.

[0090] The dynamic orchestration unit of the analysis process organizes the above algorithms into a DAG-shaped analysis process: time series analysis and spatial anomaly detection are the first-level nodes that are executed in parallel; the outputs of the two are input to the threshold comparison node; the output of the threshold comparison node and the output of the environmental factor correlation analysis node are input to the visualization output node.

[0091] The execution process monitoring unit monitors the execution status of each algorithm. When the ARIMA algorithm detects a significant acceleration in the deformation trend of a certain monitoring point, the strategy execution dynamic adjustment unit automatically increases the weight of that monitoring point in spatial anomaly detection and extends the time window of time series analysis to obtain more historical data for comparison.

[0092] The analysis results quality assessment unit evaluates the output results: it verifies the stability of the deformation trend analysis through cross-validation; it verifies the credibility of the anomaly detection results by comparing them with historical deformation data; and it assesses the reliability of the environmental factor correlation analysis through sensitivity analysis.

[0093] The spatiotemporal knowledge graph module maps spatial analysis results into a knowledge graph.

[0094] The knowledge graph node building unit establishes the following nodes: Each monitoring point is mapped to a MonitoringPoint node, with attributes including monitoring point ID, spatial coordinates, monitoring type, and current deformation rate; deformation trend anomalies identified by ARIMA time series analysis are mapped to AnomalyEvent nodes, with attributes including anomaly type (deformation trend anomaly), severity level, and detection time; spatial clustering anomalies identified by Getis-Ord Gi hotspot analysis are mapped to AnomalyEvent nodes, with attributes including anomaly type (spatial clustering anomaly) and the set of affected monitoring points; dam structure information is mapped to DamStructure nodes; and environmental factors are mapped to EnvironmentalFactor nodes.

[0095] The knowledge graph relation edge construction unit establishes the following relation edges: CONTAINS relationships between dam structure nodes and monitoring point nodes are established using spatial inclusion functions; EXCEEDS_THRESHOLD relationships between deformation monitoring point nodes and abnormal deformation trend nodes are established using threshold comparisons; NEARBY relationships between abnormal monitoring point nodes are established using spatial distance functions; CORRELATES_WITH relationships between relevant monitoring point nodes are established using Pearson correlation coefficient calculations; and CAUSED_BY relationships between abnormal deformation trend nodes and environmental factor nodes are established using causal reasoning analysis.

[0096] The incremental update unit updates the knowledge graph in real time. When new monitoring data arrives, the attribute values ​​of the monitoring point nodes are automatically updated. When the analysis results change, the abnormal event nodes and relationship edges are automatically updated.

[0097] The knowledge graph reasoning and analysis unit performs graph reasoning on the knowledge graph: starting from the abnormal nodes of deformation trend through the graph traversal algorithm, it identifies the affected dam sections along the CONTAINS relationship, traces the possible environmental causes along the CAUSED_BY relationship, identifies the potential diffusion area along the NEARBY relationship, and constructs a complete deformation impact path.

[0098] Multi-dimensional decision constraint modeling unit applies the following constraints: spatial constraints exclude monitoring points located outside the safe distance; time constraints consider the urgency of deformation development; and safety constraints determine the risk level based on dam safety evaluation standards.

[0099] The human-computer interaction unit demonstrates to operations engineers: a knowledge graph visualization interface showing the impact path and propagation trend of deformation anomalies; a natural language interface explaining the possible causes and development trends of deformation anomalies; and a parameter adjustment interface allowing engineers to adjust deformation rate thresholds and correlation parameters to regenerate analysis results.

[0100] Engineers review the analysis results and decision recommendations output by the system, confirm or adjust parameters through the interactive interface, and ultimately make operation and maintenance decisions.

[0101] This invention automates and intelligently converts users' natural language needs into specific spatial analysis tasks, and intelligently matches the optimal algorithm based on task type and data characteristics, thereby significantly improving the efficiency and accuracy of spatial analysis compared to traditional manual selection algorithms.

[0102] A spatiotemporal knowledge graph model specifically for reservoir dams was constructed using a spatiotemporal knowledge graph module. This model supports unified representation of multiple types of nodes, including facilities, monitoring, environment, and events. It defines multi-dimensional relationship types such as spatial, temporal, semantic, and logical relationships, enabling dynamic construction and incremental updates of knowledge. Compared to traditional static knowledge management methods, this model can reflect changes in the engineering status in real time and supports intelligent reasoning and decision support. By using a human-machine collaborative decision-making module to perform intelligent reasoning on the spatiotemporal knowledge graph, the spatial relationships and impact paths between facilities are identified. Combined with real-time monitoring data and historical knowledge, risk warnings and decision-making suggestions are automatically generated. Compared with traditional experience-based decision-making methods, this technology provides a scientific basis for operation and maintenance decisions, improving the accuracy and timeliness of decision-making.

[0103] The above-disclosed embodiments are merely a few specific examples of the present invention. However, the present invention is not limited thereto, and any variations that can be conceived by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A smart operation and maintenance management system for reservoir dam safety, characterized in that, include: Data integration module: used for unified access, standardized processing and storage management of multi-source heterogeneous data of the dam, and to establish a geographic database; Geoprocessing service module: used to integrate geoprocessing algorithms, process data from geographic databases, and execute and manage analysis tasks; the analysis tasks include: time series analysis, spatial anomaly detection, environmental correlation analysis, and trend prediction; Spatial intelligent analysis and orchestration module: used to analyze user natural language needs, match geographic data processing algorithm combinations according to user natural language needs, orchestrate the algorithm combinations into an analysis process in the form of a directed acyclic graph with data dependencies, formulate execution strategies, and output spatial analysis results; the spatial intelligent analysis and orchestration module includes a large language model decision reasoning unit, a natural language needs understanding unit, an algorithm matching unit, and an analysis process dynamic orchestration unit; Spatiotemporal Knowledge Graph Module: Used to establish an automatic mapping mechanism from spatial entities to knowledge graph nodes, converting spatial analysis results into structured knowledge graphs, and realizing semantic representation and dynamic updating of spatial analysis results; Human-machine collaborative decision-making module: used to obtain user natural language requirements, interact with spatial intelligent analysis and arrangement module to obtain spatial analysis results, interact with spatiotemporal knowledge graph module to obtain structured knowledge graph, perform graph reasoning based on the structured knowledge graph and the spatial analysis results to generate knowledge graph reasoning results, and output structured knowledge graph and knowledge graph reasoning results to user terminal; The human-machine collaborative decision-making module obtains the user's natural language requirements from the user terminal, the spatial intelligent analysis and orchestration module analyzes the user's natural language requirements, orchestrates and executes the strategy to call the geoprocessing algorithm in the geoprocessing service module, and the data integration module provides data support to the geoprocessing algorithm to generate spatial analysis results. The spatial intelligent analysis and orchestration module outputs spatial analysis results and transmits them to the spatiotemporal knowledge graph module. The spatiotemporal knowledge graph module generates a structured knowledge graph. The human-machine collaborative decision-making module generates knowledge graph reasoning results based on the structured knowledge graph and spatial analysis results, and outputs the structured knowledge graph and knowledge graph reasoning results.

2. The intelligent operation and maintenance management system for reservoir dam safety according to claim 1, characterized in that, The data integration module includes: Data acquisition unit: used to collect multi-source heterogeneous data about the dam; Standardization processing unit: Performs standardization processing on multi-source heterogeneous data of the dam to obtain standardized dam data; Data Quality Assessment Unit: Used to establish a data quality assessment mechanism and evaluate the quality of standardized dam data; Hybrid storage and management unit: used to store and manage dam standardized data and establish a geographic database; The multi-source heterogeneous data of the dam includes: dam safety monitoring data, hydrological and meteorological data, geological environment data, equipment operation data, and geospatial data; The standardized dam data includes both structured and unstructured data.

3. The intelligent operation and maintenance management system for reservoir dam safety according to claim 2, characterized in that, The dam safety monitoring data includes: monitoring point coordinates, seepage pressure, stress and strain, and vibration frequency; The hydrological and meteorological data include: reservoir water level, inflow, rainfall, and ambient temperature; The geological environment data includes: earthquake monitoring, landslide displacement, and debris flow early warning; The equipment operation data includes: generator set status, gate opening degree, and monitoring equipment operating status; The geospatial data includes: three-dimensional models of engineering structures, topography, and geological features; The structured data includes: monitoring values, equipment status, and metadata; The unstructured data includes: 3D models, remote sensing images, and document files; The hybrid storage and management unit uses a relational database to store structured data and an object storage system to store unstructured data.

4. The intelligent operation and maintenance management system for reservoir dam safety according to claim 1, characterized in that, The geoprocessing service module includes: Unified access unit for multi-source geoprocessing services: used to generate geoprocessing services based on geoprocessing algorithms, standardize service interfaces, realize unified invocation and management of geoprocessing services, and maintain the service registry; Service Containerization Deployment Unit: Used for containerizing geoprocessing services; Resource pool management unit: Constructs computing resource pools, dynamically allocates computing resources, and generates resource allocation strategies through large language models; The geographic processing algorithms include: geographic information system algorithm library, spatial database functions, general geographic computing library and professional hydrological analysis algorithms; The maintenance service registry includes: function description, parameter definition, performance characteristics, and applicable scenarios.

5. The intelligent operation and maintenance management system for reservoir dam safety according to claim 1, characterized in that, The large language model decision reasoning unit is used to: construct a large language model, interpret spatial analysis results through the large language model, and perform semantic interpretation and empirical verification of structured knowledge graphs through the large language model; The natural language requirement understanding unit is used to: understand natural language requirements through a large language model, simultaneously construct a domain knowledge base and prompt word templates for the reservoir dam, obtain key task parameters, and analyze the task list; The algorithm matching unit is used to: match geoprocessing algorithms based on key task parameters and generate the optimal algorithm combination; The dynamic orchestration unit for the analysis process is used to: organize the optimal algorithm combination into an analysis process in the form of a directed acyclic graph, identify the data dependencies and execution order between algorithms, and formulate execution strategies; The spatial intelligent analysis and orchestration module also includes: Rule parameter configuration unit: used to establish a parameter configuration knowledge base and select parameter configurations based on the analysis task list; Execution process monitoring unit: used to monitor the execution process of geoprocessing services and acquire monitoring data; Strategy execution dynamic adjustment unit: used to analyze monitoring data through large language models and dynamically adjust the execution strategy; Analysis Result Quality Assessment Unit: Used to assess the quality of spatial analysis results.

6. The intelligent operation and maintenance management system for reservoir dam safety according to claim 5, characterized in that, The method uses a large language model to understand natural language requirements and supports a multi-layered parsing strategy. The multi-layered parsing strategy includes: identifying task types, extracting key task parameters, determining analysis objects, and generating an analysis task list; The parameter configuration selection based on the analysis task list specifically refers to selecting parameter configuration based on the data characteristics and business requirements of the analysis task list. The data characteristics include data size, quality level, and update frequency, and the business requirements include real-time requirements and accuracy requirements. The monitoring data includes: execution status, resource usage, processing progress, and intermediate results; The quality assessment includes: data quality assessment, algorithm performance assessment, result accuracy assessment, and reliability assessment.

7. The intelligent operation and maintenance management system for reservoir dam safety according to claim 1, characterized in that, The spatiotemporal knowledge graph module includes: Dual database unit: used to build a dual database linkage architecture, constructing spatial entities and spatial relationships based on spatial analysis results; Knowledge graph node construction unit: used to establish an automatic mapping mechanism from spatial entities to knowledge graph nodes, mapping spatial analysis results to knowledge graph nodes; Knowledge graph relation edge construction unit: used to establish an automatic identification and construction mechanism for spatial relations to knowledge graph relation edges, converting spatial analysis results into knowledge graph relation edges; Structured Units: Used to combine knowledge graph nodes and knowledge graph relation edges to form a structured knowledge graph; Incremental update unit: used to build an incremental update mechanism for the knowledge graph, enabling real-time synchronization of spatial analysis results to the structured knowledge graph.

8. The intelligent operation and maintenance management system for reservoir dam safety according to claim 7, characterized in that, The knowledge graph nodes include: monitoring point nodes, abnormal event nodes, dam structure nodes, and environmental factor nodes; The mapping source of the monitoring point nodes is dam safety monitoring data, the mapping source of the abnormal event nodes is spatial analysis results, the mapping source of the dam structure nodes is geospatial data, and the mapping source of the environmental factor nodes is hydrological and meteorological data. The knowledge graph relationship edges include: containment relationship, overlimit relationship, proximity relationship, correlation relationship, and causal relationship.

9. The intelligent operation and maintenance management system for reservoir dam safety according to claim 1, characterized in that, The human-machine collaborative decision-making module includes: Knowledge Graph Reasoning and Analysis Unit: Used to perform graph reasoning based on structured knowledge graphs and spatial analysis results, and generate knowledge graph reasoning results; Multi-dimensional decision constraint modeling unit: used to construct multi-dimensional decision constraint models to constrain the reasoning results of knowledge graphs; Human-Computer Interaction Unit: Used to generate human-computer interaction interfaces, realize the input of users' natural language requirements, and output structured knowledge graphs and knowledge graph reasoning results.

10. The intelligent operation and maintenance management system for reservoir dam safety according to claim 9, characterized in that, The user's natural language requirements support rainstorm analysis; When the user's natural language requirement is rainstorm analysis, the spatial analysis result is the rainstorm analysis result; The results of the rainstorm analysis include: flood forecast results, inundation analysis results, and risk level information; The spatiotemporal knowledge graph module converts the rainstorm analysis results into a structured knowledge graph, including the following steps: The flood forecast results are converted into flood evolution entities; the flood evolution entities include: flood hydrograph, peak flow, and arrival time; The flooding analysis results are converted into flooding extent entities, which include: flooded area, flooding depth, and affected population. The risk level information is converted into a risk level entity, which includes attributes such as risk type, probability level, and degree of impact. Based on the results of the rainstorm analysis, a rainstorm knowledge graph relationship edge is constructed; the rainstorm knowledge graph relationship edge includes: the causal relationship between flood and risk, the influence relationship between scheduling and effect, and the relationship between emergency response and resource allocation.

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