Hydraulic engineering equipment data intelligent management system based on digital twinning

Abstract

The invention discloses a hydraulic engineering equipment data intelligent management system based on digital twinning, and belongs to the technical field of hydraulic engineering. Comprising an intelligent perception and data fusion module for realizing real-time acquisition and standardized processing of cross-modal data; the knowledge graph construction and causal reasoning module is used for constructing an intelligent knowledge system capable of autonomously learning and semantic reasoning; the digital twin modeling and simulation module is used for realizing dynamic simulation and scene deduction of a full life cycle and providing limit working condition simulation and risk assessment support; the intelligent prediction and health management module is responsible for performing real-time monitoring, fault early warning and residual service life prediction on the equipment state, and generating personalized intelligent maintenance strategies for different working conditions; the visualization and decision support module is used for visually presenting the equipment operation data and the analysis result and providing intelligent decision recommendation; and the cloud edge collaboration and system integration module realizes cross-platform interoperation and continuous integration through distributed computing and micro-service architecture.

Description

Technical Field

[0001] The present application relates to the field of water conservancy engineering technology, and more specifically, to an intelligent management system for water conservancy engineering equipment data based on digital twins. Background Art

[0002] As modern water conservancy projects continue to expand, the types and quantity of water conservancy equipment are rapidly increasing. Traditional management and maintenance methods are no longer able to meet actual needs. Under existing technical conditions, fault monitoring and maintenance of water conservancy equipment mainly rely on regular inspections and empirical judgment. This is not only time-consuming and labor-intensive, but also fails to achieve real-time monitoring and early warning of equipment status, resulting in a high equipment failure rate and affecting the safety and efficiency of project operations.

[0003] Digital twin technology, an emerging intelligent technology, builds virtual models of equipment and synchronizes them with the actual equipment. This allows for monitoring, simulation, and analysis of equipment operating status in a virtual space, thereby enhancing the intelligence of equipment management. Digital twin technology has achieved significant results in fields such as manufacturing and aerospace, providing valuable insights and references for other sectors.

[0004] However, in the field of water conservancy projects, the diverse range of equipment and complex operating environments place higher demands on the application of digital twin technology. Building high-precision virtual models of equipment based on multi-source heterogeneous data, and implementing intelligent management and maintenance of the equipment throughout its lifecycle, have become pressing technical challenges.

[0005] To sum up, how to achieve real-time monitoring, fault warning and personalized maintenance of water conservancy project equipment has become a technical problem that needs to be solved urgently. Summary of the Invention

[0006] In order to overcome the shortcomings of the existing technology, the purpose of this application is to provide an intelligent management system for water conservancy project equipment data based on digital twins, including the following modules:

[0007] The intelligent perception and data fusion module realizes real-time collection and standardized processing of cross-modal data through access, preprocessing, feature extraction and fusion of multi-source heterogeneous sensor data;

[0008] The knowledge graph construction and causal reasoning module formalizes expert knowledge and establishes a dynamic causal association network to build an intelligent knowledge system capable of autonomous learning and semantic reasoning;

[0009] The digital twin modeling and simulation module achieves dynamic simulation and scenario deduction throughout the entire life cycle through reconstruction of the equipment's physical model and synchronization of virtual and real models, providing support for extreme working condition simulation and risk assessment.

[0010] The intelligent prediction and health management module is responsible for real-time monitoring of the device status, fault warning, and prediction of the remaining service life, and generating personalized intelligent maintenance strategies for different working conditions;

[0011] The visualization and decision support module uses immersive and interactive multi-dimensional visualization technologies to intuitively present the device operation data and analysis results, and provides intelligent decision recommendations;

[0012] The cloud-edge collaboration and system integration module realizes cross-platform interoperability and continuous integration through distributed computing and microservices architecture, supports containerized deployment and automatic scaling, and ensures efficient operation of cloud-edge collaboration.

[0013] Furthermore, the intelligent perception and data fusion module includes the following components:

[0014] The sensor data access unit is responsible for receiving the raw data from different types of sensors and performing format conversion;

[0015] The data preprocessing unit cleans, denoises, and fills in missing values for the accessed data to improve data quality and ensure data consistency and accuracy;

[0016] The data synchronization unit aligns the timestamps from different sensors to achieve cross-time synchronization and ensure temporal consistency between different data sources;

[0017] The feature extraction unit extracts key features from the preprocessed data to identify potential patterns and regularities;

[0018] The cross-modal fusion unit merges the data from different sensors to form a unified data view and realizes collaborative processing of cross-modal information;

[0019] The data standardization unit standardizes the fused data to ensure consistent data range and scale;

[0020] The data monitoring and feedback unit monitors the data flow status in real time, timely feedbacks the processing results and potential problems, and ensures the real-time performance and accuracy of the data processing process to adapt to the dynamic changing environmental requirements.

[0021] Furthermore, the knowledge graph construction and causal reasoning module includes the following components:

[0022] The knowledge extraction unit adopts a multi-source knowledge fusion strategy to automatically extract heterogeneous knowledge from expert literature, structured databases, and sensor data, realizing cross-domain and multi-dimensional knowledge acquisition;

[0023] Knowledge Representation Unit, which converts heterogeneous knowledge into a standardized graph structure representation, and constructs a knowledge graph with rich semantics and clear logic through precise mapping of nodes, edges, and attributes;

[0024] Dynamic Causal Network Construction Unit, which constructs a multi-level and dynamically evolving causal association network based on expert knowledge and data-driven analysis methods, and precisely captures the causal dependencies and information flow patterns between knowledge entities;

[0025] Causal Reasoning Unit, which uses Bayesian networks to reason about the causal relationships in the knowledge graph, and generates new knowledge and reasoning results with confidence through evidence reasoning and probability inference;

[0026] Knowledge Update and Learning Unit, which continuously optimizes the knowledge graph according to new data and out-of-domain experience through incremental learning, knowledge fusion, and conflict resolution algorithms, and dynamically adjusts knowledge nodes, attributes, and causal relationships;

[0027] Semantic Reasoning Unit, which integrates logical reasoning, ontology reasoning, and semantic matching technologies based on the constructed knowledge graph to achieve automated semantic reasoning across ontologies and contexts, and effectively discovers implicit knowledge and potential associations.

[0028] Furthermore, the causal dependence is evaluated by the following formula: where I(E i ,E j ) represents the degree of mutual information dependence between entities E i and E j ; P(E i ,E j ) represents the joint probability of nodes E i and E j occurring simultaneously; P(E i ) represents the probability of node E i occurring; P(E j ) represents the probability of node E j occurring;

[0029] The information flow pattern is expressed by the formula: where F ij (t) represents the information flow from node E i to node E j at time t; w kj (t) represents the weight of the edge from node E k to node E j at time t; w ik (t) represents the weight of the edge from node E (t) represents the information flow from node E i to node E k at time t; d k (t) represents node E kThe information capacity at time t; n represents the total number of nodes in the causal network.

[0030] Furthermore, cross-ontology and cross-context automated semantic reasoning is achieved through the following formula: Among them, P(K∣D) represents the probability of deriving knowledge K given data D, where K here includes implicit knowledge and potential associations; represents reasoning through the rules in rule set R to derive new hypothesis A from known fact B; P(C∣D) represents the probability of knowledge C obtained based on ontology reasoning given data D; S i m(A,C) represents the similarity between A and C calculated through semantic matching; rg represents the g-th rule in rule set R; P(A∣B) represents the probability of event A occurring given B.

[0031] Furthermore, the digital twin modeling and simulation module includes the following components:

[0032] Physical model construction unit, which reconstructs the digital twin model according to the geometric structure and physical characteristics of the device to ensure that the virtual model is consistent with the actual device;

[0033] Data synchronization and transmission unit, which obtains the operation data of the device in real time and transmits it to the digital twin model to achieve dynamic synchronization between the physical device and the digital twin;

[0034] Scene modeling unit, which constructs a virtual environment to provide a real scene basis for the simulation of the entire life cycle;

[0035] Dynamic simulation unit, which conducts dynamic simulation based on the digital twin model and real-time data to simulate the operation process, state changes, and potential faults of the device to predict the performance of the device under different working conditions;

[0036] Risk assessment unit, which conducts comprehensive risk analysis and assessment based on the simulation results, calculates the comprehensive risk index to identify potential failure modes, bottlenecks, fault propagation risks, and other potential risks;

[0037] Feedback and optimization unit, which automatically optimizes the device parameters, operation strategies, and maintenance plans according to the simulation and evaluation results to ensure that the device achieves optimal performance and minimum risk throughout its life cycle.

[0038] Furthermore, the comprehensive risk index is expressed by the formula: Among them, SRI is the comprehensive risk index; W fail,l represents the degree of influence of the failure of the l-th device on the risk; W bottleneck,l represents the degree of influence of the bottleneck of the l-th device on the risk; W risk,lIndicates the degree of influence of the potential risk source of the l-th device on the risk; W propagation,l Indicates the degree of influence of the fault propagation chain of the l-th device on the risk; γ propagate Is the propagation weighting coefficient, reflecting the depth and scope of influence of fault propagation; R fail,l Indicates the risk score of the fault of the l-th device, and the formula is: R fail,l = P fail,l ·D fail,l , where, P fail,l Is the probability of the l-th device failing, and D fail,l Is the degree of loss after the l-th device fails; B bottleneck,l Indicates the risk score of the bottleneck of the l-th device, and the formula is: B bottleneck,l = I bottleneck,l / C bottleneck,l , where, I bottleneck,l Indicates the degree of influence of the bottleneck of the l-th device on the water conservancy project, and C bottleneck,l Indicates the fault tolerance ability of the bottleneck of the l-th device; E risk,l Indicates the risk exposure degree of the potential risk source of the l-th device, and the formula is: E risk,l = P exposure,l ·S impact,l , where, P exposure,l Indicates the probability of the potential risk source of the l-th device being exposed, and S impact,l Indicates the degree of influence of the potential risk source of the l-th device on the water conservancy project; R propagation,l Indicates the propagation risk of the fault of the l-th device, and the formula is: R propagation,l = P fail,l ·P propagate,l ·D propagate,l , where, P propagate,l Indicates the probability of the fault of the l-th device spreading to other devices, and D propagate,l Indicates the overall impact on the water conservancy project after the fault of the l-th device spreads; L represents the number of devices in the water conservancy project.

[0039] Furthermore, the intelligent prediction and health management module includes the following components:

[0040] Status diagnosis unit, using machine learning and pattern recognition algorithms, to conduct real-time evaluation and accurate diagnosis of the current operating status of the device, and quickly identify potential abnormalities and fault risks;

[0041] Remaining life prediction unit, based on deep learning and probability models, to predict the remaining service life and performance degradation trend of the device, and provide a future-oriented status estimate;

[0042] Risk grading unit, based on the diagnosis results and life prediction, conducts multi-dimensional quantitative assessment of equipment risks and classifies them into different levels;

[0043] Intelligent early warning unit, according to the risk grading results, generates targeted early warning information, including risk level, early warning level, potential failure mode and preliminary handling suggestions;

[0044] Maintenance strategy generation unit, combining risk grading and early warning information, formulates differentiated maintenance plans, including preventive maintenance, condition-based repair and emergency response strategies, and provides detailed implementation guidance for each strategy;

[0045] Health assessment unit, comprehensively considering equipment status, life prediction and historical maintenance data, conducts quantitative assessment of the equipment's health level and provides an intuitive comprehensive health score.

[0046] Furthermore, the comprehensive health score is expressed by the formula: where w1 is the weight coefficient of the current equipment status; w2 is the weight coefficient of the predicted remaining service life of the equipment; w3 is the weight coefficient of historical maintenance data; H(T) is the comprehensive health score at time T; Q represents the total number of sensors; P q (T) is the measurement value of the qth sensor at time T; P max,q is the safety maximum threshold of the qth sensor, used to standardize the measurement value of each sensor; is the predicted remaining service life of the equipment at time T; R max is the maximum design life of the equipment; F(T) is the number of equipment repairs at time T; F max is the maximum number of repairs of the equipment.

[0047] Furthermore, the cloud-edge collaboration and system integration module includes the following components:

[0048] Distributed computing unit, by allocating computing tasks to the cloud and edge devices, realizes the efficient utilization of computing resources and load balancing;

[0049] Microservices architecture unit, designed with a microservices architecture, splits the system functions into multiple independent services, supports cross-platform interoperability and flexible service updates and expansions;

[0050] Cross-platform interoperability unit, ensures seamless data transmission and function calls between different operating platforms and devices, and supports the integration of devices and applications in heterogeneous environments;

[0051] Containerized deployment unit, using containerization technology, packages the application and its dependencies in a container, simplifies deployment and management, and ensures consistency and portability;

[0052] An automatic scaling unit that dynamically adjusts the scale of cloud and edge computing resources according to real-time load and resource requirements, achieving elastic scaling and optimizing resource utilization efficiency;

[0053] A data synchronization and consistency unit that ensures timely and consistent data synchronization between the cloud and edge devices, avoiding data conflicts and losses, and achieving the high efficiency of cloud-edge collaboration;

[0054] A continuous integration and update unit that supports automated continuous integration and deployment processes, ensuring that module updates and upgrades can be quickly and securely pushed to the cloud and edge devices.

[0055] Compared with the prior art, the present application has the following beneficial effects:

[0056] The present application aims to achieve real-time monitoring, fault warning, remaining service life prediction, and generation of personalized maintenance strategies for water conservancy project equipment through multiple modules such as intelligent perception and data fusion, knowledge graph construction and causal reasoning, digital twin modeling and simulation, intelligent prediction and health management, visualization and decision support, and cloud-edge collaboration and system integration, thereby improving the management efficiency and operation reliability of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic structural diagram of an intelligent management system for water conservancy project equipment data based on digital twins disclosed in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] To make the objectives, technical solutions, and advantages of the implementation of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all, of the embodiments of the present invention.

[0059] All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0060] The embodiments described below with reference to the accompanying drawings and directional terms are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0061] As Figure 1 shown, an intelligent management system for water conservancy project equipment data based on digital twins includes the following modules:

[0062] The intelligent perception and data fusion module realizes real-time collection and standardized processing of cross-modal data through access, preprocessing, feature extraction and fusion of multi-source heterogeneous sensor data;

[0063] The knowledge graph construction and causal reasoning module formalizes expert knowledge and establishes a dynamic causal association network to build an intelligent knowledge system capable of autonomous learning and semantic reasoning;

[0064] The digital twin modeling and simulation module achieves dynamic simulation and scenario deduction throughout the entire life cycle through reconstruction of the equipment's physical model and synchronization of virtual and real models, providing support for extreme working condition simulation and risk assessment.

[0065] The intelligent prediction and health management module is responsible for real-time monitoring of equipment status, fault warnings, and remaining service life prediction, and generates personalized intelligent maintenance strategies for different working conditions;

[0066] The visualization and decision support module uses immersive and interactive multi-dimensional visualization technology to intuitively present equipment operation data and analysis results, and provide intelligent decision recommendations;

[0067] The cloud-edge collaboration and system integration module achieves cross-platform interoperability and continuous integration through distributed computing and microservice architecture, supports containerized deployment and automatic scaling, and ensures efficient operation of cloud-edge collaboration.

[0068] The intelligent perception and data fusion module is the foundation of the entire system. Its core goal is to efficiently access, preprocess, extract features, and fuse data from multiple sources of heterogeneous sensors. This module utilizes multimodal sensors to capture real-time device operational status data, including multi-dimensional indicators such as temperature, vibration, pressure, and current. This data comes from diverse sources with varying sampling rates, formats, and accuracy. Therefore, an efficient preprocessing process, including noise filtering, missing value filling, time alignment, and data normalization, is required to ensure the accuracy and robustness of subsequent processing. Furthermore, feature extraction techniques extract key device features from the time, frequency, and time-frequency domains. An algorithmic model then fuses these multimodal data to provide a comprehensive picture of the device's operational status. For example, adaptive weighted fusion techniques unify sensor data within a common reference framework, enabling cross-modal data correlation analysis and information supplementation. These techniques enable the module to reliably and in real time provide high-quality input data to subsequent modules, laying a solid foundation for the overall system performance.

[0069] The Knowledge Graph Construction and Causal Reasoning Module constructs a dynamic causal association network by formalizing domain expert knowledge and combining device operation data, providing the system with the ability of autonomous learning and reasoning. The knowledge graph utilizes information such as device attributes, operation parameters, and fault modes to generate a knowledge base with clear semantics and structure. In terms of dynamic causal reasoning, this module uses technologies such as Bayesian networks to establish causal relationships between device states, thus realizing the transformation from data to knowledge. For example, when a certain sensor detects an increase in abnormal vibration amplitude, the causal reasoning module can trace its potential causes and infer possible fault sources and their influence ranges in combination with the knowledge graph. At the same time, causal reasoning can also be used to generate hypotheses and verify their rationality, thereby optimizing the system's prediction and diagnosis capabilities. Through dynamic updates and semantic associations, this module has the ability to quickly adapt to changes in the device operation environment, providing important support for subsequent digital twin modeling.

[0070] The Digital Twin Modeling and Simulation Module realizes dynamic monitoring, prediction, and analysis throughout the entire life cycle of the device by constructing a digital mirror of the device. Based on the physical characteristics of the device, this module combines real-time operation data and historical data to create a high-precision digital model, keeping the virtual and real in sync with the actual operation state of the device. Its core lies in using physical modeling and machine learning algorithms to model complex device behaviors, and at the same time verifying the accuracy and effectiveness of the model through simulation technology. On this basis, it is possible to simulate extreme operating conditions of the device, such as predicting fatigue fracture or thermal stress distribution under specific conditions, thereby providing support for device design optimization and risk avoidance. In addition, through the scenario deduction function, this module can provide dynamic responses for device operation in different scenarios, assisting decision-makers in formulating scientific intervention measures and realizing the forward-looking and controllability of device management.

[0071] The Intelligent Prediction and Health Management Module realizes fault warning and prediction of the remaining useful life (RUL) by continuously monitoring the device operation state and using technologies such as deep learning and machine learning to analyze the device historical data and current characteristics. While monitoring the device state, this module accurately identifies potential fault modes by constructing a personalized health assessment model. For example, by combining anomaly detection algorithms and neural networks, it can detect early anomalies of the device in a timely manner and generate multi-level warning signals. For different operating conditions, this module can provide differentiated health management strategies, including optimizing maintenance timing, resource allocation, and work order priority ranking. In addition, by combining operation data with the device health state, this module also supports generating adaptive intelligent maintenance strategies, effectively reducing the device failure rate and unplanned downtime, and improving the overall operation efficiency of the system.

[0072] The Visualization and Decision Support Module uses multi-dimensional data presentation methods to construct various display forms including two-dimensional curves, three-dimensional models, and dynamic dashboards, enabling users to quickly grasp the real-time operating status and historical trends of equipment. Especially in fault diagnosis or complex problem analysis, immersive visualization can vividly present the key features, causal relationships, and prediction results of equipment operation, thus helping users more intuitively understand the root causes of problems and potential risks. At the same time, this module also provides an intelligent decision recommendation function. For example, when a fault warning occurs, based on the optimization solutions generated by the Health Management Module, it provides users with multiple options and their risk assessment results, thereby realizing a scientific and transparent equipment management process.

[0073] The Cloud-Edge Collaboration and System Integration Module realizes the efficient operation and continuous integration of the system through distributed computing, microservices architecture, and containerization technology. Distributed computing supports processing large-scale data analysis tasks in the cloud while performing real-time data processing at edge nodes, ensuring a balance between system response speed and analysis depth. The microservices architecture enables each module to be independently deployed, upgraded, and maintained, avoiding the coupling problems between modules in traditional systems, thereby improving the flexibility and scalability of the system. In addition, using containerization technology and auto-scaling functions, this module can dynamically allocate resources according to changes in computing load, optimize system performance, and reduce operation and maintenance costs. By achieving seamless interoperability across multiple platforms, this module not only ensures the effective integration of each functional module but also provides technical support for the iterative upgrade of the digital twin system.

[0074] In summary, the intelligent management system for water conservancy project equipment based on digital twin disclosed in this embodiment constructs a closed-loop intelligent management system through the efficient collaboration among various modules, from data perception to decision support. The Intelligent Sensing and Data Fusion Module ensures the quality and real-time nature of data; the Knowledge Graph and Causal Reasoning Module deepens the knowledgeization of data; digital twin modeling realizes the virtual-real synchronization and full-life-cycle management of equipment; the Intelligent Prediction and Health Management Module improves the reliability of equipment operation; the Visualization and Decision Support Module optimizes the user operation experience; and the Cloud-Edge Collaboration and System Integration Module provides strong technical support. Through the comprehensive action of these modules, this system has the ability of efficient, intelligent, and adaptive equipment management, providing a solid foundation for the safe operation and optimized decision-making of water conservancy projects.

[0075] Furthermore, the Intelligent Sensing and Data Fusion Module includes the following components:

[0076] The Sensor Data Access Unit is responsible for receiving the raw data from different types of sensors and performing format conversion;

[0077] The Data Preprocessing Unit cleans, denoises, and fills in missing values for the accessed data, improves data quality, and ensures data consistency and accuracy;

[0078] A data synchronization unit that aligns the timestamps from different sensors to achieve cross - time synchronization and ensure the temporal consistency between different data sources;

[0079] A feature extraction unit that extracts key features from the pre - processed data to identify potential patterns and regularities;

[0080] A cross - modal fusion unit that merges data from different sensors to form a unified data view and achieve collaborative processing of cross - modal information;

[0081] A data standardization unit that standardizes the fused data to ensure consistent data ranges and scales;

[0082] A data monitoring and feedback unit that monitors the data flow status in real - time, timely feedbacks the processing results and potential problems, ensures the real - time performance and accuracy of the data processing process, and adapts to the dynamic environmental requirements.

[0083] In summary, through the collaborative work of its various components, the intelligent perception and data fusion module realizes the full - process data management from sensor data access to standardized processing. The functions of each component not only connect with each other but also provide high - quality data input for subsequent modules. Especially through cross - modal fusion and real - time monitoring, it can meet the complex and dynamic environmental requirements, laying a solid foundation for the intelligent management of water conservancy project equipment. The technical advantages of this module lie in its flexibility, scalability, and reliability, providing key guarantees for the improvement of the overall system performance and also demonstrating the potential for wide application in the industrial field.

[0084] Furthermore, the knowledge graph construction and causal reasoning module includes the following components:

[0085] A knowledge extraction unit that adopts a multi - source knowledge fusion strategy to automatically extract heterogeneous knowledge from expert literature, structured databases, and sensor data, achieving cross - domain and multi - dimensional knowledge acquisition;

[0086] A knowledge representation unit that converts heterogeneous knowledge into a standardized graph - structure representation, constructing a knowledge graph with rich semantics and clear logic through precise mapping of nodes, edges, and attributes;

[0087] A dynamic causal network construction unit that constructs a multi - level and dynamically evolving causal association network based on expert knowledge and data - driven analysis methods, precisely capturing the causal dependencies and information flow patterns between knowledge entities;

[0088] A causal reasoning unit that uses Bayesian networks to reason about the causal relationships in the knowledge graph, generating new knowledge and reasoning results with confidence through evidence reasoning and probability inference;

[0089] Knowledge update and learning unit, which continuously optimizes the knowledge graph according to new data and out-of-domain experience through incremental learning, knowledge fusion, and conflict resolution algorithms, and dynamically adjusts knowledge nodes, attributes, and causal relationships;

[0090] Semantic reasoning unit, based on the constructed knowledge graph, integrating logical reasoning, ontology reasoning, and semantic matching technologies, realizes cross-ontology and cross-context automated semantic reasoning, and effectively discovers implicit knowledge and potential associations.

[0091] In summary, the knowledge graph construction and causal reasoning module constructs a dynamically evolving and semantically rich intelligent knowledge system through the full-chain collaboration of knowledge extraction, representation, update, and reasoning. The knowledge extraction and representation unit ensures the multi-source acquisition and standardized expression of knowledge. The dynamic causal network construction and causal reasoning unit provide strong support for the causal analysis of complex systems. The knowledge update and learning unit enables the system to have the ability of continuous optimization, while the semantic reasoning unit significantly enhances the system's ability in implicit knowledge discovery and intelligent decision-making. Through these functions, it provides strong knowledge support and reasoning ability, enabling the system to show excellent intelligence and adaptability in complex dynamic environments.

[0092] Furthermore, the causal dependence is evaluated by the following formula: where I(E i ,E j ) represents the degree of mutual information dependence between entities E i and E j ; P(E i ,E j ) represents the joint probability that nodes E i and E j occur simultaneously; P(E i ) represents the probability that node E i occurs; P(E j ) represents the probability that node E j occurs;

[0093] The information flow pattern is expressed by the formula: where F ij (t) represents the information flow from node E i to node E j at time t; w kj (t) represents the weight of the edge from node E k to node E j at time t; w ik [[ID=5�]](t) represents the weight of the edge from node E i to node E k at time t; d k (t) represents node E kThe information capacity at time t; n represents the total number of nodes in the causal network.

[0094] Furthermore, cross-ontology and cross-context automated semantic reasoning is achieved through the following formula: where P(K∣D) represents the probability of deriving knowledge K given data D, and here K includes implicit knowledge and potential associations; represents reasoning through the rules in rule set R to derive new hypothesis A from known fact B; P(C∣D) represents the probability of knowledge C obtained based on ontology reasoning given data D; Sim(A,C) represents the similarity between A and C calculated through semantic matching; r g represents the g-th rule in rule set R; P(A∣B) represents the probability of event A occurring given B.

[0095] Furthermore, the digital twin modeling and simulation module includes the following components:

[0096] Physical model construction unit, which reconstructs the digital twin model according to the geometric structure and physical characteristics of the device to ensure that the virtual model is consistent with the actual device;

[0097] Data synchronization and transmission unit, which obtains the operation data of the device in real time and transmits it to the digital twin model to achieve dynamic synchronization between the physical device and the digital twin;

[0098] Scene modeling unit, which constructs a virtual environment to provide a real scene basis for the simulation of the entire life cycle;

[0099] Dynamic simulation unit, which performs dynamic simulation based on the digital twin model and real-time data to simulate the operation process, state changes, and potential faults of the device to predict the performance of the device under different working conditions;

[0100] Risk assessment unit, which conducts a comprehensive risk analysis and assessment based on the simulation results, calculates the comprehensive risk index to identify potential failure modes, bottlenecks, fault propagation risks, and other potential risks;

[0101] Feedback and optimization unit, which automatically optimizes the device parameters, operation strategies, and maintenance plans according to the simulation and assessment results to ensure that the device achieves optimal performance and minimum risk throughout its life cycle.

[0102] In summary, through functions such as physical model construction, data synchronization, dynamic simulation, and feedback optimization, the digital twin modeling and simulation module realizes the precise management and optimization of the entire life cycle of equipment. Each component collaborates with each other to jointly build a management platform that integrates virtual and real worlds and is highly dynamic, which can reflect the equipment status in real time, predict future behaviors, and actively optimize operation strategies. In the management of water conservancy project equipment, the application of this module not only significantly improves the operation efficiency and safety of equipment, but also provides data-driven scientific support for decision-makers, making the management of complex systems more intelligent and controllable.

[0103] Furthermore, the comprehensive risk index is expressed by the formula: where SRI is the comprehensive risk index; W fail,l represents the impact degree of the failure of the l-th equipment on the risk; W bottleneck,l represents the impact degree of the bottleneck of the l-th equipment on the risk; W risk,l represents the impact degree of the potential risk source of the l-th equipment on the risk; W propagation,l represents the impact degree of the failure propagation chain of the l-th equipment on the risk; γ propagate is the propagation weighting coefficient, reflecting the depth and scope of influence of failure propagation; R fail,l represents the risk score of the failure of the l-th equipment, and the formula is: R fail,l =P fail,l ·E fail,l where P fail,l is the probability of the l-th equipment failing, and D fail,l is the loss degree after the l-th equipment fails; B bottleneck,l represents the risk score of the bottleneck of the l-th equipment, and the formula is: B bottleneck,l =I bottleneck,l / C bottleneck,l where I bottleneck,l represents the impact degree of the bottleneck of the l-th equipment on the water conservancy project, and C bottleneck,l represents the fault tolerance ability of the bottleneck of the l-th equipment; E risk,l represents the risk exposure degree of the potential risk source of the l-th equipment, and the formula is: E risk,l =P exposure,l ·S impact,l where P exposure,l represents the probability of the potential risk source of the l-th equipment being exposed, and S impact,l represents the impact degree of the potential risk source of the l-th equipment on the water conservancy project; R propagation,l [[ID=5,4]]represents the propagation risk of the failure of the l-th equipment, and the formula is: R propagation,l =P fai l ,l ·P propagate,l ·D propagate,l, where P propagate,l represents the probability that the failure of the l-th device spreads to other devices, and D propagate,l represents the overall impact on the water conservancy project after the failure of the l-th device spreads; L represents the number of devices in the water conservancy project.

[0104] Furthermore, the intelligent prediction and health management module includes the following components:

[0105] The status diagnosis unit uses machine learning and pattern recognition algorithms to conduct real-time evaluation and accurate diagnosis of the current operating status of the device, and quickly identify potential anomalies and failure risks;

[0106] The remaining useful life prediction unit, based on deep learning and probability models, predicts the remaining useful life and performance degradation trend of the device, and provides a future-oriented status estimate;

[0107] The risk grading unit conducts multi-dimensional quantitative evaluation of the device risk according to the diagnosis results and remaining useful life prediction, and classifies it into different levels;

[0108] The intelligent warning unit generates targeted warning information according to the risk grading results, including risk level, warning level, potential failure mode and preliminary handling suggestions;

[0109] The maintenance strategy generation unit combines risk grading and warning information to formulate differentiated maintenance plans, including preventive maintenance, status repair and emergency disposal strategies, and provides detailed implementation guidance for each strategy;

[0110] The health assessment unit comprehensively evaluates the health level of the device by integrating device status, remaining useful life prediction and historical maintenance data, and provides an intuitive comprehensive health score.

[0111] In summary, the intelligent prediction and health management module provides comprehensive support for the management of the entire life cycle of the device through functions such as status diagnosis, remaining useful life prediction, risk assessment, intelligent warning and maintenance strategy generation. Each component cooperates with each other, from real-time status monitoring to forward-looking prediction, from accurate risk identification to optimized maintenance decision-making, realizing the intelligentization, automation and high efficiency of device management. In the management of water conservancy project equipment, this module can significantly improve the operation efficiency of the equipment, reduce the economic losses caused by sudden failures, and provide data-driven decision support for the optimal allocation of maintenance resources. At the same time, it lays a scientific foundation for the management of the entire life cycle of the device, and promotes the deep integration of intelligentization and digitalization in engineering practice.

[0112] Furthermore, the comprehensive health score is expressed by the formula: Among them, w1 is the weight coefficient of the current state of the device; w2 is the weight coefficient of the predicted remaining service life of the device; w3 is the weight coefficient of historical maintenance data; H(T) is the comprehensive health score at time T; Q represents the total number of sensors; P q (T) is the measurement value of the q-th sensor at time T; P max,q is the safety maximum threshold of the q-th sensor, used to standardize the measurement value of each sensor; is the predicted remaining service life of the device at time T; R max is the maximum design life of the device; F(T) is the number of device repairs at time T; F max is the maximum number of repairs of the device.

[0113] Furthermore, the cloud-edge collaboration and system integration module includes the following components:

[0114] Distributed computing unit, by allocating computing tasks to the cloud and edge devices, realizes the efficient utilization of computing resources and load balancing;

[0115] Microservices architecture unit, designed with a microservices architecture, splits the system functions into multiple independent services, supports cross-platform interoperability and flexible service updates and expansions;

[0116] Cross-platform interoperability unit, ensures that data transmission and function calls between different operating platforms and devices can be seamlessly connected, supports the integration of devices and applications in heterogeneous environments;

[0117] Containerized deployment unit, uses containerization technology to package the application and its dependencies in a container, simplifies deployment and management, and ensures consistency and portability;

[0118] Auto-scaling unit, dynamically adjusts the scale of cloud and edge computing resources according to real-time load and resource requirements, realizes elastic scaling, and optimizes the resource utilization efficiency;

[0119] Data synchronization and consistency unit, ensures that data synchronization between the cloud and edge devices is timely and consistent, avoids data conflicts and losses, and realizes the high efficiency of cloud-edge collaboration;

[0120] Continuous integration and update unit, supports automated continuous integration and deployment processes, and ensures that module updates and upgrades can be quickly and securely pushed to the cloud and edge devices.

[0121] In summary, the cloud-edge collaboration and system integration module realizes efficient, flexible, and reliable system integration and management through technologies such as distributed computing, microservices architecture, cross-platform interoperability, containerized deployment, and auto-scaling. It can not only adapt to complex heterogeneous environments but also dynamically adjust resource allocation and function configuration according to real-time requirements, providing strong technical support for the intelligent management of water conservancy project equipment. Through data synchronization and consistency, as well as continuous integration and update, this module ensures the efficient collaboration and stable operation of the system, thus significantly improving equipment management efficiency and system response capabilities. The implementation of this module lays an important foundation for realizing the informatization and digitalization of the whole life cycle management of water conservancy projects and provides a reference example for the design of cloud-edge collaboration systems in other fields.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent management system for water conservancy project equipment data based on digital twins, characterized in that, It includes the following modules: The intelligent perception and data fusion module realizes the real-time acquisition and standardized processing of cross-modal data through the access, preprocessing, feature extraction, and fusion of multi-source heterogeneous sensor data; The knowledge graph construction and causal reasoning module constructs an intelligent knowledge system capable of autonomous learning and semantic reasoning by formalizing expert knowledge and establishing a dynamic causal association network; The digital twin modeling and simulation module realizes the dynamic simulation and scenario deduction of the entire life cycle through the reconstruction of the device physical model and virtual-real synchronization, and provides support for extreme working condition simulation and risk assessment; The intelligent prediction and health management module is responsible for real-time monitoring of the device status, fault warning, and prediction of the remaining service life, and generates personalized intelligent maintenance strategies for different working conditions; The visualization and decision support module uses immersive and interactive multi-dimensional visualization technologies to intuitively present the device operation data and analysis results, and provides intelligent decision recommendations; The cloud-edge collaboration and system integration module realizes cross-platform interoperability and continuous integration through distributed computing and microservice architecture, supports containerized deployment and automatic scaling, and ensures the efficient operation of cloud-edge collaboration.

2. The intelligent management system for water conservancy project equipment data based on digital twin according to claim 1, wherein The intelligent perception and data fusion module includes the following components: The sensor data access unit is responsible for receiving the raw data from different types of sensors and performing format conversion; The data preprocessing unit cleans, denoises, and fills in missing values for the accessed data to improve data quality and ensure data consistency and accuracy; The data synchronization unit aligns the timestamps from different sensors to achieve cross-time synchronization and ensure the temporal consistency between different data sources; The feature extraction unit extracts key features from the preprocessed data to identify potential patterns and regularities; The cross-modal fusion unit merges the data from different sensors to form a unified data view and realizes the collaborative processing of cross-modal information; The data standardization unit standardizes the fused data to ensure the consistency of the data range and scale; The data monitoring and feedback unit monitors the data flow status in real time, timely feedbacks the processing results and potential problems, and ensures the real-time performance and accuracy of the data processing process to adapt to the dynamic changing environmental requirements.

3. The intelligent management system for water conservancy project equipment data based on digital twin according to claim 1, characterized in that The knowledge graph construction and causal reasoning module includes the following components: The knowledge extraction unit adopts a multi-source knowledge fusion strategy to automatically extract heterogeneous knowledge from expert literature, structured databases, and sensor data, realizing cross-domain and multi-dimensional knowledge acquisition; The knowledge representation unit converts heterogeneous knowledge into a standardized graph structure representation, and constructs a knowledge graph with rich semantics and clear logic through the precise mapping of nodes, edges, and attributes; The dynamic causal network construction unit constructs a multi-level and dynamically evolving causal association network based on expert knowledge and data-driven analysis methods, accurately capturing the causal dependencies and information flow patterns between knowledge entities; The causal reasoning unit uses a Bayesian network to reason about the causal relationships in the knowledge graph, and generates new knowledge and reasoning results with confidence through evidence reasoning and probability inference. Knowledge update and learning unit, which continuously optimizes the knowledge graph according to new data and out-of-domain experience through incremental learning, knowledge fusion, and conflict resolution algorithms, and dynamically adjusts knowledge nodes, attributes, and causal relationships; Semantic reasoning unit, which integrates logical reasoning, ontology reasoning, and semantic matching technologies based on the constructed knowledge graph to achieve cross-ontology and cross-context automated semantic reasoning, and effectively discovers implicit knowledge and potential associations.

4. The intelligent management system for water conservancy project equipment data based on digital twin according to claim 3, characterized in that, The causal dependence is evaluated by the following formula: where I(E i , E j ) represents the degree of mutual information dependence between entities E i and E j ; P(E i , E j ) represents the joint probability of nodes E i and E j occurring simultaneously; P(E i ) represents the probability of node E i occurring; P(E j ) represents the probability of node E j occurring; The information flow pattern is expressed by the formula: where F ij (t) represents the information flow rate from node E i to node E j ; w kj (t) represents the weight of the edge from node E k to node E j ; w ik (t) represents the weight of the edge from node E i to node E k ; d k (t) represents the information capacity of node E k at time t; n represents the total number of nodes in the causal network.

5. The intelligent management system for water conservancy project equipment data based on digital twin according to claim 4, characterized in that, Automated semantic reasoning across ontologies and contexts is achieved through the following formula: Among them, P(L∣D) represents the probability of knowledge K derived from given data D, where K here includes implicit knowledge and potential associations; represents reasoning through the rules in rule set R to derive new hypothesis A from known fact B; P(C∣D) represents the probability of knowledge C obtained according to ontology reasoning under the condition of given data D; Sim(A, C) represents the similarity between A and C calculated through semantic matching; r g represents the g-th rule in rule set R; P(A∣B) represents the probability of event A occurring under the condition of given B.

6. The intelligent management system for water conservancy project equipment data based on digital twin according to claim 1, wherein, The digital twin modeling and simulation module includes the following components: Physical model construction unit, which reconstructs the digital twin model according to the geometric structure and physical characteristics of the device to ensure that the virtual model is consistent with the actual device; Data synchronization and transmission unit, which obtains the operating data of the device in real time and transmits it to the digital twin model to achieve dynamic synchronization between the physical device and the digital twin; Scene modeling unit, which constructs a virtual environment to provide a real scene basis for the simulation of the entire life cycle; Dynamic simulation unit, which performs dynamic simulation based on the digital twin model and real-time data to simulate the operating process, state changes, and potential faults of the device to predict the performance of the device under different working conditions; Risk assessment unit, which conducts a comprehensive risk analysis and assessment based on the simulation results, calculates the comprehensive risk index to identify potential failure modes, bottlenecks, fault propagation risks, and other potential risks; Feedback and optimization unit, which automatically optimizes the device parameters, operating strategies, and maintenance plans according to the simulation and assessment results to ensure that the device achieves optimal performance and minimum risk throughout its life cycle.

7. The intelligent management system for water conservancy project equipment data based on digital twin according to claim 6, characterized in that The comprehensive risk index is expressed by the formula: where SRI is the comprehensive risk index; W fail,l represents the impact degree of the failure of the l-th device on the risk; W bottleneck,l represents the impact degree of the bottleneck of the l-th device on the risk; W risk,l represents the impact degree of the potential risk source of the l-th device on the risk; W propagation,l represents the impact degree of the failure propagation chain of the l-th device on the risk; γ propagate is the propagation weighting coefficient, reflecting the depth and influence range of the failure propagation; R fail,l represents the risk score of the failure of the l-th device, and the formula is: R fail,l =P fail,l ·D fail,l , where P fail,l is the probability of the l-th device failing, and D fail,l is the loss degree after the l-th device fails; B bottleneck,l represents the risk score of the bottleneck of the l-th device, and the formula is: B bottleneck,\ =I bottleneck,l / C bottleneck,l , where I bottleneck,l represents the impact degree of the bottleneck of the l-th device on the water conservancy project, and C bottleneck,l represents the fault tolerance ability of the bottleneck of the l-th device; E risk,l represents the risk exposure degree of the potential risk source of the l-th device, and the formula is: E risk,l =P exposure,l ·S impact,l , where P exposure,l represents the probability of the potential risk source of the l-th device being exposed, and S impact,l represents the impact degree of the potential risk source of the l-th device on the water conservancy project; R propagation,l represents the propagation risk of the failure of the l-th device, and the formula is: R propagation,l =P fail,l ·P propagate,l ·D propagate,l , where P propagate,l represents the probability of the failure of the l-th device spreading to other devices, and D propagate,l represents the overall impact on the water conservancy project after the failure of the l-th device spreads; L represents the number of devices in the water conservancy project.

8. The intelligent management system for water conservancy project equipment data based on digital twin according to claim 1, wherein, The intelligent prediction and health management module includes the following components: Status diagnosis unit, which uses machine learning and pattern recognition algorithms to conduct real-time evaluation and accurate diagnosis of the current operating state of the device, and quickly identifies potential abnormalities and fault risks; Remaining life prediction unit, which predicts the remaining service life and performance degradation trend of the device based on deep learning and probability models, and provides a future-oriented state estimate; Risk grading unit, which conducts a multi-dimensional quantitative assessment of the device risk based on the diagnosis results and remaining life prediction, and classifies it into different levels; Intelligent warning unit, which generates targeted warning information according to the risk grading results, including risk level, warning level, potential failure mode, and preliminary handling suggestions; Maintenance strategy generation unit, which formulates differentiated maintenance plans in combination with risk grading and warning information, including preventive maintenance, status repair, and emergency response strategies, and provides detailed implementation guidance for each strategy; Health assessment unit, which comprehensively evaluates the health level of the device by integrating device status, remaining life prediction, and historical maintenance data, and provides an intuitive comprehensive health score.

9. The intelligent management system for water conservancy project equipment data based on digital twin according to claim 8, wherein The comprehensive health score is expressed by the formula: where w1 is the weight coefficient of the current state of the device; w2 is the weight coefficient of the predicted remaining service life of the device; w3 is the weight coefficient of the historical maintenance data; H(T) is the comprehensive health score at time T; Q represents the total number of sensors; P q (T) is the measured value of the q-th sensor at time T; P max,q is the safety maximum threshold of the q-th sensor, used to standardize the measured value of each sensor; is the predicted remaining service life of the device at time T; R max is the maximum design life of the device; F(T) is the number of repairs of the device at time T; F max is the maximum number of repairs of the device.

10. The intelligent management system for water conservancy project equipment data based on digital twin according to claim 1, wherein The cloud-edge collaboration and system integration module includes the following components: Distributed computing unit, which realizes the efficient utilization of computing resources and load balancing by distributing computing tasks to the cloud and edge devices; Microservice architecture unit, which adopts a microservice architecture design to split the system functions into multiple independent services, supporting cross-platform interoperability and flexible service update and expansion; Cross-platform interoperability unit, which ensures that data transmission and function calls between different operating platforms and devices can be seamlessly connected, supporting the integration of devices and applications in heterogeneous environments; Containerized deployment unit, using containerization technology, packages the application and its dependencies within a container, simplifies deployment and management, and ensures consistency and portability; Auto-scaling unit, dynamically adjusts the scale of cloud and edge computing resources according to real-time load and resource requirements, realizes elastic scaling, and optimizes resource utilization efficiency; Data synchronization and consistency unit, ensures timely and consistent data synchronization between the cloud and edge devices, avoids data conflicts and losses, and realizes the high efficiency of cloud-edge collaboration; Continuous integration and update unit, supports automated continuous integration and deployment processes, and ensures that module updates and upgrades can be quickly and securely pushed to the cloud and edge devices.

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