AI-driven hydraulic model real-time checking and optimizing system

Abstract

The invention discloses an AI-driven hydraulic model real-time checking and optimizing system which comprises a cloud platform, an edge layer and a sensing layer, the edge layer comprises a data preprocessing module, a local reasoning module and a network disconnection continuous transmission module, and the sensing layer comprises an SCADA pressure point, an IoT water meter, a GIS change log and a maintenance record. The cloud platform comprises a dynamic topology sensing module, a multi-modal space-time fusion module, an incremental dual-channel checking module, a multi-target optimization decision module, a digital twinborn visualization module, an edge-cloud collaboration module and an anomaly propagation deduction module. According to the method, labor dependence is reduced, working efficiency is improved, real-time performance is improved, emergencies such as pipe bursting can be found and processed in time, loss is reduced, the data utilization rate can be increased, data value is fully excavated, electric charge can be saved, annual scheduling cost is greatly reduced, a more accurate model is beneficial to optimization of a pipe network operation strategy, and the method is suitable for popularization and application. The water supply quality and reliability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of real-time calibration of hydraulic models, and in particular to an AI-driven real-time calibration and optimization system for hydraulic models. Background Art

[0002] Existing technologies have three core flaws in hydraulic model optimization: 1. High dependence on manual labor: Traditional calibration requires manual adjustment of parameters such as pipeline friction coefficient, which takes 3-6 months each time and is difficult to adapt to dynamic changes in the pipeline network. Traditional GIS systems rely on manual entry of pipeline changes, resulting in a deviation rate between the model and the actual pipeline network of more than 20%, and a manual entry topology error rate of more than 15%; 2. Insufficient real-time performance: The offline model update cycle exceeds 24 hours, and the offline verification cycle is as long as 24-72 hours. This makes it difficult to capture the impact of sudden events such as pipe bursts and unable to respond to pressure fluctuations caused by sudden pipe bursts. 3. Low data utilization: There is a lack of deep integration of multi-source data such as SCADA, GIS, and IoT. 70% of unstructured data (such as maintenance logs) is not effectively utilized, and SCADA, IoT device and business system data are not aligned in time and space.

[0003] To this end, we proposed an AI-driven hydraulic model real-time calibration and optimization system to solve the above problems. Summary of the Invention

[0004] The purpose of the present invention is to address the shortcomings of the existing technology and propose an AI-driven hydraulic model real-time calibration and optimization system.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: The AI-driven real-time verification and optimization system for hydraulic models includes a cloud platform, an edge layer, and a perception layer. The edge layer includes a data preprocessing module, a local reasoning module, and a network disconnection and resumption module. The perception layer includes SCADA pressure points, IoT water meters, GIS change logs, and maintenance records. The cloud platform includes a dynamic topology perception module, a multimodal spatiotemporal fusion module, an incremental dual-channel verification module, a multi-objective optimization decision module, a digital twin visualization module, an edge-cloud collaboration module, and an anomaly propagation deduction module.

[0006] Preferably, the dynamic topology perception module constructs a topology evolution model based on an improved graph neural network, and parses GIS change logs and drone inspection images in real time. The pipeline feature encoder adopts a residual connection structure, supports adaptive mapping of pipe diameters from DN50 to DN3000, and the topology reconstruction response time is ≤300 seconds. The coordinate error of positioning new / removed pipelines is ≤±0.05m.

[0007] Preferably, the multimodal spatiotemporal fusion module adopts a four-dimensional data engine to achieve multi-dimensional fusion. In the spatial dimension, the Kriging interpolation method is used to fuse the SCADA pressure point data. In the temporal dimension, the IoT device and manual meter reading data are aligned through dynamic time warping. In the modal dimension, the BERT model is used to extract the semantic features of the maintenance log, which are jointly input into the spatiotemporal attention network with the sensor data. The anomaly detection F1 value is ≥0.93 and the false alarm rate is ≤2.1%.

[0008] Preferably, the incremental dual-channel calibration module adopts a dual-channel parameter update mechanism. The fast channel uses a lightweight LSTM network to adjust the friction coefficient in real time, with an update frequency of ≤15 minutes. The deep channel performs global parameter optimization of the CNN-GRU hybrid model every week, and uses transfer learning to reuse historical model weights. The calibration efficiency is improved by 46 times compared with traditional methods, and MAE is ≤0.02MPa.

[0009] Preferably, the multi-objective optimization decision module constructs a three-objective Pareto frontier surface, including energy consumption target, water quality target, and life target, adopts NSGA-III algorithm to solve the optimal solution set, and the decision delay is ≤8 seconds.

[0010] Preferably, the digital twin visualization module uses a WebGL engine to render a three-dimensional pressure cloud map, supports real-time rendering of 100,000-level pipe network nodes, has a virtual sensor function, and can perform hydraulic parameter interpolation calculations on any coordinate point.

[0011] Preferably, the edge nodes of the edge-cloud collaboration module deploy a pruned MobileNet model, the local inference delay is ≤150ms, the network disconnection and transmission protocol supports 72 hours of data cache, and the synchronization time difference compensation algorithm accuracy is ≤±1 second.

[0012] Preferably, the anomaly propagation deduction module constructs a hydraulic event knowledge graph and combines reinforcement learning to achieve pipe burst location and pollution diffusion prediction.

[0013] The present invention has the following advantages: 1. Traditional calibration and GIS systems rely on manual operations, which are time-consuming and prone to errors. This system realizes automated data collection, processing, and analysis, reducing manual intervention. For example, manual intervention in model calibration is reduced by 80%, greatly improving work efficiency, reducing errors caused by manual operations, and enabling the model to more accurately reflect the actual pipe network conditions. 2. Traditional offline models have long update and verification cycles, making them difficult to respond to emergencies. This system can collect and process data in real time, quickly responding to dynamic changes in the pipeline network. For example, the average response time for pipe burst location has been reduced from 52 minutes to 6 minutes and 24 seconds, and the response time for pipe burst identification has been shortened from 45 minutes to 8 minutes. This system can promptly detect and handle emergencies such as pipe bursts, reducing losses. 3. The system achieves deep integration of multi-source data such as SCADA, GIS, and IoT through a multimodal spatiotemporal fusion module, effectively utilizing 70% of unstructured data, such as maintenance logs. It also achieves spatiotemporal alignment of SCADA, IoT device, and business system data, fully tapping into the value of data and improving the accuracy and reliability of the model. 4. In terms of energy consumption optimization, it can save electricity costs and greatly reduce annual dispatching costs, bringing considerable economic benefits to the operation and management of urban pipe networks; 5. In terms of model accuracy, the RMSE of pressure prediction is ≤0.018MPa (an improvement of 39%). A more accurate model helps optimize pipe network operation strategies and improve water supply quality and reliability. In summary, the present invention not only reduces manual dependence and improves work efficiency, but also improves real-time performance, can promptly detect and handle emergencies such as pipe bursts, and reduce losses. In addition, it can improve data utilization, fully tap the value of data, and improve the accuracy and reliability of the model. At the same time, it can also achieve electricity bill savings and greatly reduce annual scheduling costs. A more accurate model helps to optimize the pipe network operation strategy and improve water supply quality and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a diagram of the cloud platform system structure of the present invention; Figure 2 This is a diagram of the edge layer system structure of the present invention; Figure 3 This is a structural diagram of the perception layer system of the present invention; Figure 4 This is a diagram of the spatiotemporal attention network structure of the present invention; Figure 5 This is a flow chart of the dual-channel calibration algorithm of the present invention; Figure 6 It is a multi-objective optimization schematic diagram of the present invention; Figure 7 This is a flow chart of the multimodal data fusion of the present invention. DETAILED DESCRIPTION

[0015] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0016] Reference Figure 1-7, AI-driven real-time verification and optimization system of hydraulic models, including cloud platform, edge layer and perception layer. The edge layer includes data preprocessing module, local inference module and network disconnection and resuming module. The edge layer is the front-line outpost of data processing. The edge layer mainly undertakes the tasks of data preprocessing and preliminary inference, which is an important guarantee for the efficient operation of the system. The data preprocessing module cleans and filters the raw data collected by the perception layer to remove noise and outliers, improve data quality, and ensure the accuracy of subsequent analysis. The local inference module deploys the pruned MobileNet model, which can perform preliminary inference analysis locally without transmitting large amounts of data to the cloud, greatly reducing the data transmission pressure. At the same time, the local inference delay is ≤150ms, ensuring the real-time analysis. The network disconnection and resuming module is a major feature of the edge layer. It supports 72 hours of data caching. Even in the event of network interruption, data will not be lost. When the network is restored, data can be uploaded again, and the synchronization time difference compensation algorithm has an accuracy of ≤±1 second to ensure data continuity and accuracy. The perception layer includes SCADA pressure points, IoT water meters, GIS change logs, and maintenance records. The perception layer is the data source for the entire system and is responsible for collecting various real-time information on the operation of the pipeline network. Among them, the SCADA pressure point can accurately collect pressure data in the pipeline network, providing a basis for subsequent hydraulic analysis, and the IoT water meter records the flow of the pipeline network in real time to help understand the flow status of the water. The GIS change log records the change information of the pipeline in detail, such as addition, removal or modification, etc., providing a basis for the real-time update of the pipeline network topology. The maintenance record comes from the water work order system of the business system, which contains relevant information on pipeline network maintenance and is of great value for analyzing the health status and failure modes of the pipeline network; The cloud platform includes a dynamic topology perception module, a multimodal spatiotemporal fusion module, an incremental dual-channel verification module, a multi-objective optimization decision module, a digital twin visualization module, an edge-cloud collaboration module, and an anomaly propagation deduction module. As the core of the system, it encompasses multiple functional modules that together enable in-depth analysis and optimized decision-making of the pipe network system. The dynamic topology perception module builds a topology evolution model based on an improved graph neural network, capable of real-time analysis of GIS change logs and drone inspection images. The pipeline feature encoder uses a residual connection structure and supports adaptive mapping of pipe diameters from DN50 to DN3000, enabling the system to quickly and accurately perceive changes in pipeline network topology. The topology reconstruction response time is ≤300 seconds, and the coordinate error for locating newly added or removed pipelines is ≤±0.05m, providing precise topological information for real-time management of the pipeline network. The multimodal spatiotemporal fusion module uses a four-dimensional data engine to achieve multi-dimensional fusion, deeply integrating data in three dimensions: space, time, and modality. In the spatial dimension, the Kriging interpolation method is used to fuse SCADA pressure point data to more accurately depict the pressure distribution within the pipe network. In the temporal dimension, dynamic time warping is used to align IoT devices and manual meter reading data, solving the temporal inconsistency problem of different data sources. In the modal dimension, the BERT model is used to extract the semantic features of the maintenance log, which is jointly input into the spatiotemporal attention network with the sensor data to achieve deep fusion of multi-source data. The anomaly detection F1 value is ≥0.93, and the false alarm rate is ≤2.1%, which greatly improves the system's ability to detect abnormal conditions in the pipe network. The incremental dual-channel calibration module adopts a dual-channel parameter update mechanism. The fast channel uses a lightweight LSTM network to adjust the friction coefficient in real time, with an update frequency of ≤15 minutes, which can promptly respond to changes in the pipeline network's operating status. The deep channel performs global parameter optimization of the CNN-GRU hybrid model weekly, using transfer learning to reuse historical model weights, improving calibration efficiency by 46 times compared to traditional methods, with a MAE of ≤0.02MPa, ensuring the accuracy and reliability of model parameters. The multi-objective optimization decision module constructs a three-objective Pareto frontier surface covering energy consumption, water quality, and lifespan objectives, and uses the NSGA-III algorithm to find the optimal solution set, with a decision delay of ≤8 seconds. This module comprehensively considers multiple objectives to provide scientific and reasonable optimization decisions for pipe network operations, achieving efficient and sustainable operation of the pipe network system. The digital twin visualization module uses the WebGL engine to render 3D pressure cloud maps, supports real-time rendering of 100,000-level pipeline network nodes, and features virtual sensor functionality, enabling hydraulic parameter interpolation calculations for any coordinate point. This module intuitively visualizes the operating status of the pipeline network, allowing staff to quickly understand information such as pressure distribution and water flow conditions, providing strong support for decision-making. The edge-cloud collaboration module deploys a pruned MobileNet model on edge nodes, achieving local inference latency of ≤150ms, a network-disconnected retransmission protocol supporting 72-hour data caching, and a synchronization time difference compensation algorithm with an accuracy of ≤±1 second. This module enables efficient collaboration between the edge layer and the cloud platform, ensuring timely data transmission and processing. The Abnormal Propagation Simulation module builds a knowledge graph of hydraulic events and uses reinforcement learning to locate pipe bursts and predict the spread of contamination. This module simulates the propagation of abnormal events within the pipe network, helping personnel prepare for responses and take timely measures to minimize losses.

[0017] In this invention, data collection involves the following: SCADA pressure points and IoT water meters at the sensor layer collect real-time data such as pipe network pressure and flow, while GIS change logs record pipeline changes. The water service work order system in the business system provides maintenance records. This data provides the basis for subsequent analysis.

[0018] In this invention, edge layer processing involves a data preprocessing module at the edge layer that performs preprocessing operations such as cleaning and filtering on the collected raw data to improve data quality. The local inference module deploys a pruned MobileNet model, enabling preliminary inference analysis locally, reducing data transmission pressure and achieving local inference latency of ≤150ms. The network interruption and resume module supports 72-hour data caching, ensuring data is not lost during network interruptions and enabling data upload to resume after network restoration. The synchronization time difference compensation algorithm provides an accuracy of ≤±1 second.

[0019] The dynamic topology perception module in this invention builds a topology evolution model based on an improved graph neural network, analyzing GIS change logs and drone inspection images in real time. The pipeline feature encoder uses a residual connection structure and supports adaptive mapping for pipe diameters from DN50 to DN3000. It can quickly and accurately perceive pipeline network topology changes, with a topology reconstruction response time of ≤300 seconds and a coordinate error of ≤±0.05m for locating newly added or removed pipelines.

[0020] In this invention, the multimodal spatiotemporal fusion module uses a four-dimensional data engine to achieve multi-dimensional fusion. In the spatial dimension, Kriging interpolation is used to fuse SCADA pressure point data. In the temporal dimension, dynamic time warping is used to align IoT device and manual meter reading data. In the modal dimension, the BERT model is used to extract semantic features from maintenance logs, which are then combined with sensor data into a spatiotemporal attention network to achieve deep fusion of multi-source data. The anomaly detection F1 score is ≥ 0.93, and the false alarm rate is ≤ 2.1%.

[0021] In the present invention, the incremental dual-channel calibration module adopts a dual-channel parameter update mechanism. The fast channel uses a lightweight LSTM network to adjust the friction coefficient in real time, with an update frequency of ≤15 minutes. The deep channel performs global parameter optimization of the CNN-GRU hybrid model every week, and uses transfer learning to reuse historical model weights to improve calibration efficiency, which is 46 times higher than traditional methods, with MAE ≤0.02MPa.

[0022] In the present invention, the multi-objective optimization decision module constructs a three-objective Pareto frontier surface, including energy consumption target, water quality target, and life target, adopts the NSGA-III algorithm to solve the optimal solution set, and the decision delay is ≤8 seconds, providing optimized decision-making for pipeline network operation.

[0023] In the present invention, the digital twin visualization module uses a WebGL engine to render three-dimensional pressure cloud maps, supports real-time rendering of 100,000-level pipeline network nodes, has a virtual sensor function, can perform hydraulic parameter interpolation calculations on any coordinate point, and intuitively display the operation status of the pipeline network.

[0024] In the present invention, the abnormal propagation deduction module constructs a hydraulic event knowledge graph and combines reinforcement learning to achieve pipe burst location and pollution spread prediction, helping staff to respond to emergencies in a timely manner.

[0025] Deployment test cases of the present invention: Deployed in Qingdao's DN2000 trunk pipeline network.

[0026] Data input: The perception layer includes 258 SCADA pressure points, 1,200 IoT water meters, and daily GIS change logs; the business system includes water service work order system maintenance records (unstructured text).

[0027] Implementation results: The average response time for pipe burst location was reduced from 52 minutes to 6 minutes and 24 seconds. In terms of energy consumption optimization, the pump station combination strategy saved 1.87 million yuan in electricity costs annually. In terms of model accuracy, the RMSE of pressure prediction was ≤ 0.018 MPa (a 39% improvement). Furthermore, the response time for pipe burst identification was shortened from 45 minutes to 8 minutes, annual dispatch costs were reduced by 2.3 million yuan, and manual intervention for model calibration was reduced by 80%.

[0028] The AI-driven real-time hydraulic model verification and optimization system of the present invention realizes real-time monitoring, precise analysis and optimization decision-making of the pipe network system through multi-level architecture design and multi-module collaboration. In the deployment test of Qingdao's DN2000 trunk pipe network, this system demonstrated significant advantages and results, providing strong technical support for the intelligent management of urban pipe networks.

[0029] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. AI-driven hydraulic model real-time calibration and optimization system, including cloud platform, edge layer and perception layer, characterized by: The edge layer includes a data preprocessing module, a local reasoning module and a network disconnection and resumption module; the perception layer includes SCADA pressure points, IoT water meters, GIS change logs and maintenance records; the cloud platform includes a dynamic topology perception module, a multimodal spatiotemporal fusion module, an incremental dual-channel verification module, a multi-objective optimization decision module, a digital twin visualization module, an edge-cloud collaboration module and an anomaly propagation deduction module.

2. The AI-driven hydraulic model real-time calibration and optimization system according to claim 1 is characterized by: The dynamic topology perception module builds a topology evolution model based on an improved graph neural network, and parses GIS change logs and drone inspection images in real time. The pipeline feature encoder adopts a residual connection structure, supports adaptive mapping of pipe diameters from DN50 to DN3000, and has a topology reconstruction response time of ≤300 seconds. The coordinate error of locating newly added / removed pipelines is ≤±0.05m.

3. The AI-driven hydraulic model real-time calibration and optimization system according to claim 1 is characterized by: The multimodal spatiotemporal fusion module uses a four-dimensional data engine to achieve multi-dimensional fusion. In the spatial dimension, the Kriging interpolation method is used to fuse SCADA pressure point data. In the temporal dimension, dynamic time warping is used to align IoT device and manual meter reading data. In the modal dimension, the BERT model is used to extract the semantic features of the maintenance log and jointly input the spatiotemporal attention network with the sensor data. The anomaly detection F1 value is ≥0.93 and the false alarm rate is ≤2.1%.

4. The AI-driven hydraulic model real-time calibration and optimization system according to claim 1 is characterized by: The incremental dual-channel calibration module adopts a dual-channel parameter update mechanism. The fast channel uses a lightweight LSTM network to adjust the friction coefficient in real time, with an update frequency of ≤15 minutes. The deep channel performs global parameter optimization of the CNN-GRU hybrid model every week, and uses transfer learning to reuse historical model weights. The calibration efficiency is improved by 46 times compared with traditional methods, and the MAE is ≤0.02MPa.

5. The AI-driven hydraulic model real-time calibration and optimization system according to claim 1 is characterized by: The multi-objective optimization decision module constructs a three-objective Pareto frontier surface, including energy consumption target, water quality target, and life target, and uses the NSGA-III algorithm to solve the optimal solution set, with a decision delay of ≤8 seconds.

6. The AI-driven hydraulic model real-time calibration and optimization system according to claim 5 is characterized by: The digital twin visualization module uses the WebGL engine to render three-dimensional pressure cloud maps, supports real-time rendering of 100,000-level pipeline network nodes, has virtual sensor functions, and can perform hydraulic parameter interpolation calculations for any coordinate point.

7. The AI-driven hydraulic model real-time calibration and optimization system according to claim 1 is characterized by: The edge nodes of the edge-cloud collaboration module deploy a pruned MobileNet model, with a local inference delay of ≤150ms, a network-disconnected transmission protocol supporting 72 hours of data caching, and a synchronization time difference compensation algorithm with an accuracy of ≤±1 second.

8. The AI-driven hydraulic model real-time calibration and optimization system according to claim 1 is characterized by: The anomaly propagation deduction module constructs a hydraulic event knowledge graph and combines reinforcement learning to achieve pipe burst location and pollution diffusion prediction.

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