Hydraulic engineering operation and maintenance monitoring system based on digital twinning

Abstract

The invention discloses a hydraulic engineering operation and maintenance monitoring system based on digital twinning, and the system comprises the steps: S1, a data collection module which is used for obtaining multi-modal actual detection data from a plurality of monitoring points of a hydraulic engineering; s2, a data generation module which simulates and generates multi-dimensional virtual monitoring data and an abnormal sample library related to hydraulic engineering operation and maintenance; s3, a data fusion module which fuses the multi-dimensional virtual monitoring data and the multi-modal actual detection data; s4, a digital twinborn model construction module which dynamically constructs a multi-dimensional digital twinborn model of the water conservancy project based on the fused multi-modal data set; s5, an anomaly detection module which detects potential anomaly and sudden faults through an anomaly sample library generated by the generative adversarial network in combination with a multi-dimensional spatial-temporal trajectory clustering analysis method; and S6, an operation and maintenance monitoring module which is used for providing real-time operation and maintenance monitoring service for the multi-dimensional digital twin model. The method has the advantages of being high in dynamic adaptive capacity, high in monitoring accuracy and high in abnormal response speed.

Description

Technical Field

[0001] The present invention relates to the technical field of operation and maintenance monitoring, and in particular to a water conservancy project operation and maintenance monitoring system based on digital twins. Background Art

[0002] With the continuous development and increasing complexity of water conservancy engineering technology, water conservancy engineering operation and maintenance monitoring has become an important part of ensuring engineering safety and optimizing operation. In complex water conservancy engineering systems, real-time collection, analysis and processing of multi-source data are the key to achieving accurate engineering monitoring and efficient operation and maintenance. However, due to the diversity of data types and the complexity of engineering scenarios, traditional water conservancy engineering operation and maintenance monitoring technology faces many challenges in practical applications.

[0003] In the existing technology, traditional water conservancy project operation and maintenance monitoring methods mostly rely on fixed sensor networks, single data processing models and rule-driven analysis systems. These methods have significant limitations when facing increasingly complex engineering environments and sudden anomalies, which are mainly reflected in the following aspects: 1. Insufficient multi-source data fusion capabilities: Existing technologies usually monitor and analyze a single data source, lacking effective fusion of multi-modal data such as hydrological data, structural data, environmental data, and equipment operation data. This isolated data processing method cannot fully reflect the real-time operation status of water conservancy projects, resulting in incomplete monitoring results.

[0004] 2. Lack of dynamic modeling capabilities: Traditional monitoring systems mostly rely on static modeling methods and are slow to respond to dynamic changes in the operation of water conservancy projects. Especially in abnormal conditions or emergencies, model updates are delayed and changes in project status cannot be accurately predicted.

[0005] 3. Low accuracy of anomaly detection: Existing anomaly detection methods are usually based on historical data or preset rules, which are difficult to deal with complex abnormal patterns and rare failures in water conservancy projects. The lack of in-depth analysis of multi-dimensional spatiotemporal data characteristics makes it difficult for the accuracy and reliability of detection results to meet actual needs.

[0006] 4. Lack of intelligent optimization mechanism: Traditional systems lack adaptive intelligent optimization capabilities when dealing with complex working conditions or nonlinear changes. Especially when data is sparse or there are insufficient abnormal events, the system’s ability to predict potential risks is limited.

[0007] Therefore, how to provide a water conservancy project operation and maintenance monitoring system based on digital twins is a problem that technical personnel in this field urgently need to solve. Summary of the invention

[0008] One purpose of the present invention is to propose a water conservancy project operation and maintenance monitoring system based on digital twins. The present invention fully combines generative adversarial networks, multimodal data fusion, dynamic digital twin modeling and multidimensional spatiotemporal trajectory analysis technology, and describes in detail the technical solution for realizing real-time fusion of multi-source data of water conservancy projects, anomaly detection and precise analysis. It has the advantages of strong dynamic adaptability, high monitoring accuracy and fast abnormal response speed.

[0009] A water conservancy project operation and maintenance monitoring system based on digital twin according to an embodiment of the present invention includes: S1, data acquisition module, used to obtain multi-modal actual detection data including hydrological data, structural data, environmental data and equipment operation data from multiple monitoring points of water conservancy projects; S2, data generation module, based on generative adversarial network technology, simulates and generates multi-dimensional virtual monitoring data and abnormal sample library related to water conservancy project operation and maintenance; S3, a data fusion module, connected to the data generation module, fuses the multi-dimensional virtual monitoring data generated by the generative adversarial network and the multi-modal actual detection data to form a unified multi-modal data set; S4, a digital twin model construction module, connected with the data fusion module, dynamically constructs a multi-dimensional digital twin model of the water conservancy project based on the fused multi-modal data set; S5, anomaly detection module, connected with the digital twin model construction module, detects potential anomalies and sudden failures through the anomaly sample library generated by the generative adversarial network combined with the multi-dimensional spatiotemporal trajectory clustering analysis method; S6, operation and maintenance monitoring module, is connected with the digital twin model construction module and the anomaly detection module, and is used to provide real-time operation and maintenance monitoring services for the multi-dimensional digital twin model.

[0010] Optionally, the S2 specifically includes: S21, data preprocessing unit, used for multi-modal actual detection data Perform cleaning, formatting and standardization to generate standardized data sets , the multi-modal actual detection data Including hydrological data , structure data , Environmental data And equipment operation data ,in Indicates flow rate, Indicates the water level, Indicates flow rate, represents stress, Indicates strain, Indicates the tilt angle, Indicates temperature, Indicates rainfall, Indicates humidity, represents the current, Indicates voltage, Indicates power; S22, Generate adversarial network training unit, connected with data preprocessing unit, based on standardized data set Construct a generative adversarial network model, and optimize the training process through the loss function: ; in, Represents a generator for generating multi-dimensional virtual monitoring data, represents a discriminator, which is used to distinguish multimodal actual detection data from multidimensional virtual monitoring data. Represents the actual multimodal detection data distribution, represents samples drawn from the multimodal actual detection data distribution, represents the random noise distribution of the generator input, represents random noise drawn from a random noise distribution, Represents the discriminator’s discriminant result on the multimodal actual detection data, represents the discriminator's discriminant result on multi-dimensional virtual monitoring data, Represents the adversarial objective function of the generator and the discriminator; S23, a multi-dimensional virtual monitoring data generation unit is connected to the generative adversarial network training unit, using the trained generator Input random noise , generate multi-dimensional virtual monitoring data related to water conservancy project operation and maintenance; S24, abnormal sample library construction unit, connected to the generative adversarial network training unit, generates simulated abnormal samples through the generative adversarial network, and constructs an abnormal sample library in combination with the abnormal feature template ; S25, a data verification unit is connected to the multidimensional virtual monitoring data generation unit and the abnormal sample library construction unit to perform consistency verification on the generated multidimensional virtual monitoring data and the abnormal samples in the abnormal sample library, so that the similarity between the generated multidimensional virtual monitoring data and the multimodal actual detection data reaches a preset threshold.

[0011] Optionally, the S23 specifically includes: S231, random noise generating unit, from random noise distribution Generate random noise in , the random noise is generated by a high-dimensional distribution function that matches the distribution characteristics of water conservancy project operation and maintenance data: ; in, is random noise, represents the dimension of random noise, is the mean vector, is the covariance matrix, which is used to adjust the degree of consistency between random noise and the monitoring characteristics of water conservancy projects; S232, a generator network optimization unit is connected to the random noise generation unit, based on the random noise Through the generator Generate multi-dimensional virtual monitoring data : ; in, is the parameter set of the generator, For the The weight matrix of the layer, For the The bias vector of the layer, is the activation function, is the output activation function, used to generate multimodal virtual monitoring data; S233, data alignment and constraint unit, connected to the generator network optimization unit, is used to pass dynamic constraints Generate multimodal virtual monitoring data Perform multi-dimensional alignment of spatial, temporal, and functional domains: ; in, represents the distribution of the generated multimodal virtual monitoring data in the spatial domain, represents the distribution of the generated multimodal virtual monitoring data in the time domain, represents the regularization term of the generated multimodal virtual monitoring data, , , is the weight coefficient.

[0012] Optionally, the S24 specifically includes: S241, abnormal feature extraction unit, connected to the generative adversarial network training unit, from the standardized data set Extract the feature vector of abnormal samples from : ; in, Indicates feature components, is the dimension of the feature vector, It is a feature extraction function used to extract the key features of temporal trends, spatial deviations and physical parameter anomalies; S242, abnormal sample generation unit, connected to the abnormal feature extraction unit, based on the generator of the generative adversarial network Input abnormal feature vector and random noise , generate abnormal samples : ; in, represents the parameter set of the generator, represents random noise; S243, the abnormal feature template unit is connected to the abnormal sample generation unit, and the generated abnormal sample With predefined abnormal signature templates Match and classify to build an abnormal sample library , where the abnormal feature template Includes typical failure mode feature sets and potential failure mode feature sets.

[0013] Optionally, the S4 specifically includes: S41, digital twin model feature initialization unit, connected with the data fusion module, based on the fused multimodal data set The multi-dimensional feature space of the digital twin model is initialized using a global optimization strategy: ; in, represents the initial state of the digital twin model, For digital twin models in multimodal datasets The predicted value on represents the smoothness regularization of the digital twin model state in the spatial domain, is the domain, is the regularization coefficient; S42, a feature weight dynamic allocation unit, connected to the digital twin model feature initialization unit, allocates dynamic weights to multimodal actual detection data through a distributed attention mechanism according to the operation and maintenance characteristics of the water conservancy project; S43, the model multi-dimensional dynamic update unit, is connected to the feature weight dynamic allocation unit, and updates the digital twin model based on the dynamically allocated weights and multi-modal actual detection data: ; in, Indicates that the digital twin model is Time, point The state of being is the kernel function used to capture spatial correlation, and is the weight coefficient, represents the weight of the i-th input feature in the multimodal actual detection data, Represents the specific value of the i-th input feature at point y; S44, abnormal working condition modeling unit, connected with the model multi-dimensional dynamic update unit, combined with abnormal samples generated by the generative adversarial network And real-time dynamic update status, expand the digital twin model's ability to characterize abnormal working conditions, and obtain a multi-dimensional digital twin model.

[0014] Optionally, the S42 specifically includes: S421, query vector generation unit, connected to the digital twin model feature initialization unit, constructs a query vector through weighted aggregation based on the multi-modal actual detection data of water conservancy project operation and maintenance : ; ; in, is the query vector, For the feature vectors, is the weight distribution coefficient, represents the feature importance scoring function, The number of input features for multimodal actual detection data; S422, the attention weight calculation unit is connected to the query vector generation unit, based on the input feature vector and query vector Calculate the weight of each input feature through the distributed attention mechanism : ; in, For the The weights of the input features, is the feature vector and query vector The matching score of and are the query vector and the weight matrix of the key corresponding to the query vector, is the feature dimension; S423, a dynamic weight allocation unit is connected to the attention weight calculation unit to dynamically allocate weights to all input features. With corresponding features Combining to generate weighted feature representation : ; in, Update the multi-dimensional feature state in the digital twin model for weighted feature representation.

[0015] Optionally, the S44 specifically includes: S441, an abnormal sample input unit, connected to the model multi-dimensional dynamic update unit, receives abnormal samples generated by the generative adversarial network ; S442, an abnormal feature mapping unit, connected to the abnormal sample input unit, maps the abnormal sample to the multi-dimensional feature space of the abnormal model: ; in, The mapping result representing the abnormal features, For abnormal samples at point The characteristic value of For the digital twin model at the last moment status, is the abnormal feature mapping function; S443, an abnormal model optimization unit, connected to the abnormal feature mapping unit, optimizes the abnormal model based on the abnormal feature mapping result: ; in, Indicates that the abnormal model is at time status, For the abnormal model at point The predicted value of represents the smoothness regularization of the anomaly model in the spatial domain, is the regularization coefficient, is the domain; S444, an abnormal model output unit, connected to the abnormal model optimization unit, outputs the optimized abnormal model and compare it with the real-time updated digital twin model Combined to form a multi-dimensional digital twin model.

[0016] Optionally, the S5 specifically includes: S51, abnormal sample library input unit, connected to the digital twin model construction module, receives the abnormal sample library generated by the generative adversarial network ; S52, a multi-dimensional trajectory feature extraction unit, connected to the abnormal sample library input unit, based on multi-modal actual detection data and abnormal sample library Extract spatiotemporal trajectory features: ; in, Representing a spatial point and time point The space-time trajectory characteristics of For location, For speed, is the acceleration; S53, a trajectory clustering analysis unit, connected to a multi-dimensional trajectory feature extraction unit, based on spatiotemporal trajectory features and trajectory templates in the abnormal sample library Perform multidimensional spatiotemporal trajectory clustering: ; in, and are two space-time trajectories, is the domain, represents the space-time distance between space-time trajectories; S54, an anomaly detection unit, connected to the trajectory clustering analysis unit, identifies potential anomalies and sudden failures based on the trajectory clustering results, and determines the abnormal state through an abnormality discrimination function: ; in, Indicates the abnormal judgment result. is the distance threshold for abnormality judgment, is the trajectory to be detected, It is the abnormal trajectory template.

[0017] The beneficial effects of the present invention are: (1) The present invention combines generative adversarial networks, multimodal data fusion, dynamic digital twin modeling, and multidimensional spatiotemporal trajectory analysis methods to achieve deep modeling of the operating status of water conservancy projects and improve their dynamic adaptability, enabling the system to fully adapt to complex and changing engineering environments and effectively respond to various potential anomalies and sudden failures, especially the difficult problem of abnormal fusion analysis of complex multi-source data.

[0018] (2) By building a real-time dynamic digital twin model, combined with an abnormal sample library and trajectory clustering analysis technology, the present invention not only improves the accuracy and real-time performance of anomaly detection, but also enables efficient analysis of dynamic changes and rare faults in water conservancy projects. This greatly reduces the system response time and improves the ability to process complex engineering data.

[0019] (3) The present invention comprehensively improves the coverage capability of the water conservancy project operation and maintenance monitoring system through multi-dimensional modeling and intelligent optimization technology, enabling the system to fully understand the status of water conservancy projects from multiple dimensions of time, space, and function, achieve accurate prediction and proactive maintenance of potential risks, reduce manual intervention, and significantly improve the intelligence level and efficiency of monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1This is the overall framework diagram of a water conservancy project operation and maintenance monitoring system based on digital twin proposed in the present invention. DETAILED DESCRIPTION

[0021] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0022] refer to Figure 1 , a water conservancy project operation and maintenance monitoring system based on digital twin, including: S1, data acquisition module, used to obtain multi-modal actual detection data including hydrological data, structural data, environmental data and equipment operation data from multiple monitoring points of water conservancy projects; This implementation method uses a data acquisition module to obtain multi-modal actual detection data including hydrological data, structural data, environmental data and equipment operation data from multiple monitoring points of the water conservancy project, thereby realizing comprehensive monitoring of the operation status of the water conservancy project. It can not only capture the current engineering status characteristics, but also provide high-quality, multi-dimensional input data for subsequent data fusion and model building. This module effectively solves the problem of monitoring blind spots caused by single data in traditional monitoring methods, provides a solid foundation for accurate monitoring and dynamic modeling of water conservancy projects, and improves the data integrity and real-time performance of the system.

[0023] S2, data generation module, based on generative adversarial network technology, simulates and generates multi-dimensional virtual monitoring data and abnormal sample library related to water conservancy project operation and maintenance; In this implementation, S2 specifically includes: S21, data preprocessing unit, used for multi-modal actual detection data Perform cleaning, formatting and standardization to generate standardized data sets , the multi-modal actual detection data Including hydrological data , structure data , Environmental data And equipment operation data ,in Indicates flow rate, Indicates the water level, Indicates flow rate, represents stress, Indicates strain, Indicates the tilt angle, Indicates temperature, Indicates rainfall, Indicates humidity, represents the current, Indicates voltage, Indicates power; S22, Generate adversarial network training unit, connected with data preprocessing unit, based on standardized data set Construct a generative adversarial network model, and optimize the training process through the loss function: ; in, Represents a generator for generating multi-dimensional virtual monitoring data, represents a discriminator, which is used to distinguish multimodal actual detection data from multidimensional virtual monitoring data. Represents the actual multimodal detection data distribution, represents samples drawn from the multimodal actual detection data distribution, represents the random noise distribution of the generator input, represents random noise drawn from a random noise distribution, Represents the discriminator’s discriminant result on the multimodal actual detection data, represents the discriminator's discriminant result on multi-dimensional virtual monitoring data, Represents the adversarial objective function of the generator and the discriminator; S23, a multi-dimensional virtual monitoring data generation unit is connected to the generative adversarial network training unit, using the trained generator Input random noise , generate multi-dimensional virtual monitoring data related to water conservancy project operation and maintenance; S24, abnormal sample library construction unit, connected to the generative adversarial network training unit, generates simulated abnormal samples through the generative adversarial network, and constructs an abnormal sample library in combination with the abnormal feature template ; S25, a data verification unit is connected to the multidimensional virtual monitoring data generation unit and the abnormal sample library construction unit to perform consistency verification on the generated multidimensional virtual monitoring data and the abnormal samples in the abnormal sample library, so that the similarity between the generated multidimensional virtual monitoring data and the multimodal actual detection data reaches a preset threshold.

[0024] S23 specifically includes: S231, random noise generating unit, from random noise distribution Generate random noise in , the random noise is generated by a high-dimensional distribution function that matches the distribution characteristics of water conservancy project operation and maintenance data: ; in, is random noise, represents the dimension of random noise, is the mean vector, is the covariance matrix, which is used to adjust the degree of consistency between random noise and the monitoring characteristics of water conservancy projects; S232, a generator network optimization unit is connected to the random noise generation unit, based on the random noise Through the generator Generate multi-dimensional virtual monitoring data : ; in, is the parameter set of the generator, For the The weight matrix of the layer, For the The bias vector of the layer, is the activation function, is the output activation function, used to generate multimodal virtual monitoring data; S233, data alignment and constraint unit, connected to the generator network optimization unit, is used to pass dynamic constraints Generate multimodal virtual monitoring data Perform multi-dimensional alignment of spatial, temporal, and functional domains: ; in, represents the distribution of the generated multimodal virtual monitoring data in the spatial domain, represents the distribution of the generated multimodal virtual monitoring data in the time domain, represents the regularization term of the generated multimodal virtual monitoring data, , , is the weight coefficient.

[0025] S24 specifically includes: S241, abnormal feature extraction unit, connected to the generative adversarial network training unit, from the standardized data set Extract the feature vector of abnormal samples from : ; in, Indicates feature components, is the dimension of the feature vector, It is a feature extraction function used to extract the key features of temporal trends, spatial deviations and physical parameter anomalies; S242, abnormal sample generation unit, connected to the abnormal feature extraction unit, based on the generator of the generative adversarial network Input abnormal feature vector and random noise , generate abnormal samples : ; in, represents the parameter set of the generator, represents random noise; S243, the abnormal feature template unit is connected to the abnormal sample generation unit, and the generated abnormal sample With predefined abnormal signature templates Match and classify to build an abnormal sample library , where the abnormal feature template Includes typical failure mode feature sets and potential failure mode feature sets.

[0026] This implementation method generates multi-dimensional virtual monitoring data and abnormal sample libraries through generative adversarial network simulation, providing comprehensive modeling capabilities for the operation and maintenance status of water conservancy projects. It not only covers the multi-modal characteristics of current actual detection data, but also expands the breadth and depth of data, enabling virtual data to cover multiple modes of rare faults and potential anomalies. This method improves the richness and authenticity of data input and provides a more accurate foundation for the construction of digital twin models.

[0027] S3, a data fusion module, connected to the data generation module, fuses the multi-dimensional virtual monitoring data generated by the generative adversarial network and the multi-modal actual detection data to form a unified multi-modal data set; This implementation method constructs a unified multimodal data set by integrating the multi-dimensional virtual monitoring data generated by the generative adversarial network with the multi-modal actual detection data. It not only enriches the dimension and expression of the data, but also optimizes the quality and consistency of the data through intelligent fusion. It can more comprehensively reflect the real-time operation status of the water conservancy project, thereby improving the fusion efficiency and accuracy of multi-source data.

[0028] S4, a digital twin model construction module, connected with the data fusion module, dynamically constructs a multi-dimensional digital twin model of the water conservancy project based on the fused multi-modal data set; In this implementation, S4 specifically includes: S41, digital twin model feature initialization unit, connected with the data fusion module, based on the fused multimodal data set The multi-dimensional feature space of the digital twin model is initialized using a global optimization strategy: ; in, represents the initial state of the digital twin model, For digital twin models in multimodal datasets The predicted value on represents the smoothness regularization of the digital twin model state in the spatial domain, is the domain, is the regularization coefficient; S42, a feature weight dynamic allocation unit, connected to the digital twin model feature initialization unit, allocates dynamic weights to multimodal actual detection data through a distributed attention mechanism according to the operation and maintenance characteristics of the water conservancy project; S43, the model multi-dimensional dynamic update unit, is connected to the feature weight dynamic allocation unit, and updates the digital twin model based on the dynamically allocated weights and multi-modal actual detection data: ; in, Indicates that the digital twin model is Time, point The state of being is the kernel function used to capture spatial correlation, and is the weight coefficient, represents the weight of the i-th input feature in the multimodal actual detection data, Represents the specific value of the i-th input feature at point y; S44, abnormal working condition modeling unit, connected with the model multi-dimensional dynamic update unit, combined with abnormal samples generated by the generative adversarial network And real-time dynamic update status, expand the digital twin model's ability to characterize abnormal working conditions, and obtain a multi-dimensional digital twin model.

[0029] S42 specifically includes: S421, query vector generation unit, connected to the digital twin model feature initialization unit, constructs a query vector through weighted aggregation based on the multi-modal actual detection data of water conservancy project operation and maintenance : ; ; in, is the query vector, For the feature vectors, is the weight distribution coefficient, represents the feature importance scoring function, The number of input features for multimodal actual detection data; S422, the attention weight calculation unit is connected to the query vector generation unit, based on the input feature vector and query vector Calculate the weight of each input feature through the distributed attention mechanism : ; in, For the The weights of the input features, is the feature vector and query vector The matching score of and are the query vector and the weight matrix of the key corresponding to the query vector, is the feature dimension; S423, a dynamic weight allocation unit is connected to the attention weight calculation unit to dynamically allocate weights to all input features. With corresponding features Combining to generate weighted feature representation : ; in, Update the multi-dimensional feature state in the digital twin model for weighted feature representation.

[0030] S44 specifically includes: S441, an abnormal sample input unit, connected to the model multi-dimensional dynamic update unit, receives abnormal samples generated by the generative adversarial network ; S442, an abnormal feature mapping unit, connected to the abnormal sample input unit, maps the abnormal sample to the multi-dimensional feature space of the abnormal model: ; in, The mapping result representing the abnormal features, For abnormal samples at point The characteristic value of For the digital twin model at the last moment status, is the abnormal feature mapping function; S443, an abnormal model optimization unit, connected to the abnormal feature mapping unit, optimizes the abnormal model based on the abnormal feature mapping result: ; in, Indicates that the abnormal model is at time status, For the abnormal model at point The predicted value of represents the smoothness regularization of the anomaly model in the spatial domain, is the regularization coefficient, is the domain; S444, an abnormal model output unit, connected to the abnormal model optimization unit, outputs the optimized abnormal model and compare it with the real-time updated digital twin model Combined to form a multi-dimensional digital twin model.

[0031] This implementation method uses the dynamic construction of a digital twin model to fuse multimodal data inputs and provide a comprehensive analysis of the operating status of water conservancy projects through global optimization strategies and dynamic update mechanisms. It can not only accurately characterize the current spatial, temporal and functional dimensional characteristics, but also dynamically capture its evolutionary trends over time. By combining real-time data input and historical data status updates, potential anomalies and changes in operating status in water conservancy projects can be more accurately identified, improving the model's predictive ability and adaptability, and providing intelligent and comprehensive technical support for water conservancy project operation and maintenance monitoring.

[0032] S5, anomaly detection module, connected with the digital twin model construction module, detects potential anomalies and sudden failures through the anomaly sample library generated by the generative adversarial network combined with the multi-dimensional spatiotemporal trajectory clustering analysis method; In this implementation, S5 specifically includes: S51, abnormal sample library input unit, connected to the digital twin model construction module, receives the abnormal sample library generated by the generative adversarial network ; S52, a multi-dimensional trajectory feature extraction unit, connected to the abnormal sample library input unit, based on multi-modal actual detection data and abnormal sample library Extract spatiotemporal trajectory features: ; in, Representing a spatial point and time point The space-time trajectory characteristics of For location, For speed, is the acceleration; S53, a trajectory clustering analysis unit, connected to a multi-dimensional trajectory feature extraction unit, based on spatiotemporal trajectory features and trajectory templates in the abnormal sample library Perform multidimensional spatiotemporal trajectory clustering: ; in, and are two space-time trajectories, is the domain, represents the space-time distance between space-time trajectories; S54, an anomaly detection unit, connected to the trajectory clustering analysis unit, identifies potential anomalies and sudden failures based on the trajectory clustering results, and determines the abnormal state through an abnormality discrimination function: ; in, Indicates the abnormal judgment result. is the distance threshold for abnormality judgment, is the trajectory to be detected, It is the abnormal trajectory template.

[0033] This implementation combines the abnormal sample library generated by the generative adversarial network and the multi-dimensional spatiotemporal trajectory clustering analysis method to provide a comprehensive analysis of the operating status of the water conservancy project. It not only covers the current spatiotemporal trajectory characteristics, but also captures the dynamic evolution pattern of the abnormal state. It can more accurately detect potential anomalies and sudden failures from complex multi-source data.

[0034] S6, operation and maintenance monitoring module, is connected with the digital twin model construction module and the anomaly detection module, and is used to provide real-time operation and maintenance monitoring services for the multi-dimensional digital twin model.

[0035] This implementation provides comprehensive monitoring and dynamic update capabilities for the operating status of water conservancy projects by combining the digital twin model and the real-time operation and maintenance monitoring module, which not only includes the current project status characteristics, but also covers the dynamic evolution trends in time, space and functional dimensions. Embodiment 1:

[0036] In order to verify the feasibility of the present invention, the present invention is applied to a large-scale water conservancy project, which is located in East China and is a comprehensive water conservancy hub integrating flood control, irrigation, water supply and power generation. Due to its large scale, complex system and diverse data sources, the operation and maintenance monitoring of the project faces severe challenges, especially in the real-time fusion of multimodal data and accurate detection of abnormal faults. In order to solve the above problems, the operation and maintenance team of this project deployed the water conservancy project operation and maintenance monitoring system based on digital twins of the present invention.

[0037] During the deployment phase, the system first accesses the hydrological data, structural monitoring data, environmental monitoring data, and equipment operation data of the water conservancy project through the multimodal data acquisition module. The multimodal data collected in real time is standardized through the data fusion module and provided as input to the digital twin model construction module for dynamically building the digital twin model of the project. During the operation of the system, the real-time generated digital twin model is used to monitor the operating status of the water conservancy project, and the potential anomalies and sudden failures are analyzed and identified through the anomaly detection module.

[0038] In this scenario, the operation and maintenance monitoring system of the present invention is mainly used for flood control management during the flood season. At that time, the water level in the reservoir area of ​​the project was close to the warning line, and the operation and maintenance team needed to grasp the operating status of the reservoir in real time and detect possible structural anomalies and equipment failures. The system generates multidimensional virtual data related to water conservancy projects through generative adversarial networks, including abnormal samples under various extreme working conditions, and conducts comparative analysis in combination with actual monitoring data collected in real time. Subsequently, the system analyzes the dynamic change trajectory of hydrological characteristics and dam structure monitoring data through a trajectory clustering algorithm, and identifies the potential leakage risk in a certain area upstream of the dam.

[0039] To verify the performance of the system, the engineering team conducted a statistical analysis of the system's operating data over a three-month period. The data is shown in Table 1 below.

[0040] Table 1 Comparison of the operation effects of water conservancy project operation and maintenance monitoring system ; As can be seen from Table 1, before the system was deployed, the engineering team received an average of about 25 abnormal alarms per month, most of which were false alarms, and the accuracy of abnormal detection was only 78.6%. After the system of the present invention was deployed, the average number of abnormal alarms per month dropped significantly to 2 times, and the accuracy of abnormal detection increased to 96.4%. In an actual case, the system successfully detected slight structural changes in the downstream area of ​​the dam during a rainstorm. Through trajectory clustering analysis, it was found that the strain data trajectory in the area deviated from the normal mode, and further located the potential leakage point. The system responded quickly and issued a high-risk alarm to the operation and maintenance team in just 5 minutes. In the end, the team took reinforcement measures in time to avoid the occurrence of dam leakage accidents.

[0041] In addition, the system has improved the processing efficiency of multimodal data through an efficient data fusion module, increasing the processing of 50GB of data per hour to 150GB of data, providing strong data support for real-time monitoring of the project. At the same time, the system has performed well in predicting potential faults, with the prediction accuracy increased from 72.3% to 94.7%, significantly reducing the threat of sudden faults to project operations.

[0042] The present invention realizes the comprehensive intelligent upgrade of water conservancy project operation and maintenance monitoring by combining generative adversarial networks, multimodal data fusion, digital twin model construction and multidimensional trajectory clustering analysis technology. In practical applications, the present invention not only effectively solves the technical problems of real-time fusion and precise analysis of multi-source data, but also greatly improves the real-time and accuracy of anomaly detection, providing important guarantees for the safe operation and efficient maintenance of water conservancy projects.

[0043] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A water conservancy project operation and maintenance monitoring system based on digital twins, characterized in that: include: S1, data acquisition module, used to obtain multi-modal actual detection data including hydrological data, structural data, environmental data and equipment operation data from multiple monitoring points of water conservancy projects; S2, data generation module, based on generative adversarial network technology, simulates and generates multi-dimensional virtual monitoring data and abnormal sample library related to water conservancy project operation and maintenance; S3, a data fusion module, connected to the data generation module, fuses the multi-dimensional virtual monitoring data generated by the generative adversarial network and the multi-modal actual detection data to form a unified multi-modal data set; S4, a digital twin model construction module, connected with the data fusion module, dynamically constructs a multi-dimensional digital twin model of the water conservancy project based on the fused multi-modal data set; S5, anomaly detection module, connected with the digital twin model construction module, detects potential anomalies and sudden failures through the anomaly sample library generated by the generative adversarial network combined with the multi-dimensional spatiotemporal trajectory clustering analysis method; S6, operation and maintenance monitoring module, is connected with the digital twin model construction module and the anomaly detection module, and is used to provide real-time operation and maintenance monitoring services for the multi-dimensional digital twin model.

2. A water conservancy project operation and maintenance monitoring system based on digital twin according to claim 1, characterized in that: The S2 specifically includes: S21, data preprocessing unit, used for multi-modal actual detection data Perform cleaning, formatting and standardization to generate standardized data sets , the multi-modal actual detection data Including hydrological data , structure data , Environmental data And equipment operation data ,in Indicates flow rate, Indicates the water level, Indicates flow rate, represents stress, represents strain, Indicates the tilt angle, Indicates temperature, Indicates rainfall, Indicates humidity, represents the current, Indicates voltage, Indicates power; S22, Generate adversarial network training unit, connected with data preprocessing unit, based on standardized data set Construct a generative adversarial network model, and optimize the training process through the loss function: ； in, Represents a generator for generating multi-dimensional virtual monitoring data, represents a discriminator, which is used to distinguish multimodal actual detection data from multidimensional virtual monitoring data. Represents the actual multimodal detection data distribution, represents samples drawn from the multimodal actual detection data distribution, represents the random noise distribution of the generator input, represents random noise drawn from a random noise distribution, Represents the discriminator’s discriminant result on the multimodal actual detection data, represents the discriminator's discriminant result on multi-dimensional virtual monitoring data, Represents the adversarial objective function of the generator and the discriminator; S23, a multi-dimensional virtual monitoring data generation unit is connected to the generative adversarial network training unit, and uses the trained generator Input random noise , generate multi-dimensional virtual monitoring data related to water conservancy project operation and maintenance; S24, abnormal sample library construction unit, connected to the generative adversarial network training unit, generates simulated abnormal samples through the generative adversarial network, and constructs an abnormal sample library in combination with the abnormal feature template ; S25, a data verification unit is connected to the multidimensional virtual monitoring data generation unit and the abnormal sample library construction unit to perform consistency verification on the generated multidimensional virtual monitoring data and the abnormal samples in the abnormal sample library, so that the similarity between the generated multidimensional virtual monitoring data and the multimodal actual detection data reaches a preset threshold.

3. A water conservancy project operation and maintenance monitoring system based on digital twin according to claim 2, characterized in that: The S23 specifically includes: S231, random noise generating unit, from random noise distribution Generate random noise in , the random noise is generated by a high-dimensional distribution function that matches the distribution characteristics of water conservancy project operation and maintenance data: ； in, is random noise, represents the dimension of random noise, is the mean vector, is the covariance matrix, which is used to adjust the degree of consistency between random noise and the monitoring characteristics of water conservancy projects; S232, a generator network optimization unit is connected to the random noise generation unit, based on the random noise Through the generator Generate multi-dimensional virtual monitoring data : ； in, is the parameter set of the generator, For the The weight matrix of the layer, For the The bias vector of the layer, is the activation function, is the output activation function, used to generate multimodal virtual monitoring data; S233, data alignment and constraint unit, connected to the generator network optimization unit, is used to pass dynamic constraints Generate multimodal virtual monitoring data Perform multi-dimensional alignment of spatial, temporal, and functional domains: ; in, represents the distribution of the generated multimodal virtual monitoring data in the spatial domain, represents the distribution of the generated multimodal virtual monitoring data in the time domain, represents the regularization term of the generated multimodal virtual monitoring data, , , is the weight coefficient.

4. According to claim 2, a water conservancy project operation and maintenance monitoring system based on digital twins is characterized in that: The S24 specifically includes: S241, abnormal feature extraction unit, connected to the generative adversarial network training unit, from the standardized data set Extract the feature vector of abnormal samples from : ； in, Indicates feature components, is the dimension of the feature vector, It is a feature extraction function used to extract the key features of temporal trends, spatial deviations and physical parameter anomalies; S242, abnormal sample generation unit, connected to the abnormal feature extraction unit, based on the generator of the generative adversarial network Input abnormal feature vector and random noise , generate abnormal samples : ； in, represents the parameter set of the generator, represents random noise; S243, the abnormal feature template unit is connected to the abnormal sample generation unit, and the generated abnormal sample With predefined abnormal signature templates Match and classify to build an abnormal sample library , where the abnormal feature template Includes typical failure mode feature sets and potential failure mode feature sets.

5. A water conservancy project operation and maintenance monitoring system based on digital twin according to claim 4, characterized in that: The S4 specifically includes: S41, digital twin model feature initialization unit, connected with the data fusion module, based on the fused multimodal data set The multidimensional feature space of the digital twin model is initialized using a global optimization strategy: ; in, represents the initial state of the digital twin model, For digital twin models in multimodal datasets The predicted value on represents the smoothness regularization of the digital twin model state in the spatial domain, is the domain, is the regularization coefficient; S42, a feature weight dynamic allocation unit, connected to the digital twin model feature initialization unit, allocates dynamic weights to multimodal actual detection data through a distributed attention mechanism according to the operation and maintenance characteristics of the water conservancy project; S43, the model multi-dimensional dynamic updating unit, is connected to the feature weight dynamic allocation unit, and updates the digital twin model based on the dynamically allocated weights and multi-modal actual detection data: ； in, Indicates that the digital twin model is Time, point The state of being is the kernel function used to capture spatial correlation, and is the weight coefficient, represents the weight of the i-th input feature in the multimodal actual detection data, Represents the specific value of the i-th input feature at point y; S44, abnormal working condition modeling unit, connected with the model multi-dimensional dynamic update unit, combined with abnormal samples generated by the generative adversarial network And real-time dynamic update status, expand the digital twin model's ability to characterize abnormal working conditions, and obtain a multi-dimensional digital twin model.

6. A water conservancy project operation and maintenance monitoring system based on digital twin according to claim 5, characterized in that: The S42 specifically includes: S421, query vector generation unit, connected to the digital twin model feature initialization unit, constructs a query vector through weighted aggregation based on the multi-modal actual detection data of water conservancy project operation and maintenance : ； ； in, is the query vector, For the feature vectors, is the weight distribution coefficient, represents the feature importance scoring function, The number of input features for multimodal actual detection data; S422, the attention weight calculation unit is connected to the query vector generation unit, based on the input feature vector and query vector Calculate the weight of each input feature through the distributed attention mechanism : ; in, For the The weights of the input features, is the feature vector and query vector The matching score of and are the query vector and the weight matrix of the key corresponding to the query vector, is the feature dimension; S423, a dynamic weight allocation unit is connected to the attention weight calculation unit to dynamically allocate weights to all input features. With corresponding features Combining to generate weighted feature representation : ； in, Update the multi-dimensional feature state in the digital twin model for weighted feature representation.

7. A water conservancy project operation and maintenance monitoring system based on digital twin according to claim 5, characterized in that: The S44 specifically includes: S441, an abnormal sample input unit, connected to the model multi-dimensional dynamic update unit, receives abnormal samples generated by the generative adversarial network ; S442, an abnormal feature mapping unit, connected to the abnormal sample input unit, maps the abnormal sample to the multi-dimensional feature space of the abnormal model: ； in, The mapping result representing the abnormal features, For abnormal samples at point The characteristic value of For the digital twin model at the last moment status, is the abnormal feature mapping function; S443, an abnormal model optimization unit, connected to the abnormal feature mapping unit, optimizes the abnormal model based on the abnormal feature mapping result: ； in, Indicates that the abnormal model is at time status, For the abnormal model at point The predicted value of represents the smoothness regularization of the anomaly model in the spatial domain, is the regularization coefficient, is the domain; S444, an abnormal model output unit, connected to the abnormal model optimization unit, outputs the optimized abnormal model and compare it with the digital twin model that is updated in real time Combined to form a multi-dimensional digital twin model.

8. The water conservancy project operation and maintenance monitoring system based on digital twin according to claim 1 is characterized in that: The S5 specifically includes: S51, abnormal sample library input unit, connected to the digital twin model construction module, receives the abnormal sample library generated by the generative adversarial network ; S52, a multi-dimensional trajectory feature extraction unit, connected to the abnormal sample library input unit, based on multi-modal actual detection data and abnormal sample library Extract spatiotemporal trajectory features: ； in, Representing a spatial point and time point The space-time trajectory characteristics of For location, For speed, is the acceleration; S53, a trajectory clustering analysis unit, connected to a multi-dimensional trajectory feature extraction unit, based on spatiotemporal trajectory features and trajectory templates in the abnormal sample library Perform multidimensional spatiotemporal trajectory clustering: ; in, and are two space-time trajectories, is the domain, represents the space-time distance between space-time trajectories; S54, an anomaly detection unit, connected to the trajectory clustering analysis unit, identifies potential anomalies and sudden failures based on the trajectory clustering results, and determines the abnormal state through an abnormality discrimination function: ； in, Indicates the abnormal judgment result. is the distance threshold for abnormality judgment, is the trajectory to be detected, It is the abnormal trajectory template.

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