Multi-source heterogeneous data fusion pipe network intelligent scheduling decision-making system

Abstract

The invention discloses a multi-source heterogeneous data fusion pipe network intelligent scheduling decision-making system, which comprises a multi-modal data acquisition cabin module, a space-time alignment fusion center module, a digital twin deduction cabin module, a self-adaptive decision-making matrix module, an elastic execution feedback chain module and a credibility tracing platform module, the multi-modal data acquisition cabin module comprises a heterogeneous protocol analysis unit, an unstructured processing engine and an edge preprocessing mechanism, and the space-time alignment fusion center module comprises a space-time reference mapping engine, a federal learning cleaning tower and a dynamic semantic association library. The problem that data of a traditional system cannot be effectively integrated is solved, fusion of multi-source heterogeneous data is achieved, data islands are broken, the response speed is increased, the dynamic response capacity is enhanced, in addition, decision making efficiency and accuracy can be improved, decision making intellectualization can be enhanced, optimal configuration of pipe network energy efficiency can be achieved, and the system is suitable for popularization and application. And the energy efficiency of the pipe network is greatly optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of pipe network intelligent dispatching decision systems, and in particular to a pipe network intelligent dispatching decision system integrating multi-source heterogeneous data. Background Art

[0002] The current existing technologies for urban water supply network management have many shortcomings: 1. Serious data silos: Traditional systems often use a single protocol (such as SCADA) for access, making it difficult to integrate unstructured data sources such as IoT sensors, BIM models, and social media sentiment. This results in ineffective data integration. 2. Delayed dynamic response: Relying on periodic manual inspection data, it lacks the ability to integrate and analyze real-time data such as pressure fluctuations and water quality changes, and cannot respond to sudden situations in the pipeline network in a timely manner; 3. Insufficient intelligent decision-making: Most patents still use a threshold alarm plus manual scheduling model, failing to implement adaptive decision-making based on deep reinforcement learning, resulting in low decision-making efficiency and accuracy. 4. Limitations of energy efficiency optimization: Most systems only consider the pipe network topology and fail to integrate external heterogeneous data such as weather forecasts and user water usage habits, making it difficult to achieve optimal configuration of pipe network energy efficiency. To this end, we proposed a pipeline network intelligent scheduling decision-making system that integrates multi-source heterogeneous data to solve the above problems. Summary of the Invention

[0003] The purpose of the present invention is to solve the shortcomings of the existing technology and propose a pipeline network intelligent scheduling decision system that integrates multi-source heterogeneous data.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions: The intelligent dispatching and decision-making system for pipeline networks with multi-source heterogeneous data fusion includes a multimodal data acquisition cabin module, a spatiotemporal alignment fusion hub module, a digital twin deduction cabin module, an adaptive decision matrix module, an elastic execution feedback chain module and a credibility traceability platform module. The multimodal data acquisition cabin module includes a heterogeneous protocol parsing unit, an unstructured processing engine and an edge preprocessing mechanism. The spatiotemporal alignment fusion hub module includes a spatiotemporal benchmark mapping engine, a federated learning cleaning tower and a dynamic semantic association library. The digital twin deduction cabin module includes a multi-scale fluid modeler, a multi-objective optimization engine and a virtual-reality linkage interface. The adaptive decision matrix module includes a two-layer reinforcement learning architecture, a case reasoning mechanism and a dynamic weight distributor. The elastic execution feedback chain module includes a multi-protocol reverse control bridge and a compensation learning mechanism. The credibility traceability platform module includes a blockchain evidence storage system and an explainable visualization interface.

[0005] Preferably, the heterogeneous protocol parsing unit integrates Modbus-TCP, OPC-UA, MQTT, and LoRaWAN industrial protocols, uses protocol stack dynamic loading technology, supports parallel access of field programmable logic controllers and smart sensors, adds non-standard protocol adapters, and parses private format data streams through reverse engineering. The unstructured processing engine deploys a video leakage detection model based on YOLOv7, combines the social media public opinion sentiment analysis BERT model, constructs a text-image joint feature extraction network, adds a laser point cloud data parsing module, and processes the spatial alignment of BIM models and LiDAR scanning data in real time. The edge preprocessing mechanism introduces a time series compression algorithm to complete data noise reduction and outlier elimination at the edge node, thereby reducing cloud transmission bandwidth consumption.

[0006] Preferably, the spatiotemporal reference mapping engine designs a four-dimensional spatiotemporal coordinate system converter, adopts an improved ICP algorithm to achieve millimeter-level spatial alignment, and has a built-in atomic clock synchronization module to eliminate multi-source data timestamp deviation. The federated learning cleaning tower constructs a cross-domain data quality assessment model based on homomorphic encryption, sets a 7-level quality scoring system, adds a data lineage tracking function, records the cleaning path and correction parameters of each data point, and the dynamic semantic association library establishes a semantic ontology library containing more than 2,000 industry terms, and realizes the semantic mapping of text descriptions and sensor codes through knowledge graph embedding technology.

[0007] Preferably, the multi-scale fluid modeler couples the CFD fluid dynamics model with the graph neural network, supports multi-resolution simulation from single pipelines to regional pipeline networks, adds a corrosion evolution prediction module, and combines material stress data and water quality parameters to predict changes in pipe wall thickness. The multi-objective optimization engine uses the NSGA-III algorithm to construct a four-dimensional target space, generate a Pareto front solution set, integrate Monte Carlo random simulation, and evaluate the scheduling robustness of 12 types of extreme weather risk scenarios. The virtual-reality linkage interface realizes millisecond-level data bidirectional synchronization between the digital twin and the SCADA system through the OPC-UA and FMI joint simulation interface.

[0008] Preferably, in the two-layer reinforcement learning architecture, the upper-layer policy network adopts RainbowDQN to generate the scheduling strategy, the lower-layer execution network uses the PPO algorithm to optimize the valve opening, and a transfer learning module is added to support the knowledge transfer of cross-regional scheduling strategies. A causal knowledge graph containing more than 3,000 historical accident cases is constructed in the case reasoning mechanism, and the GAT network is used to achieve similar case matching. An emergency plan generator is added to automatically output the disposal steps in combination with the current working conditions. The dynamic weight allocator quantifies the contribution of each data source to the decision-making based on the Shapley value algorithm, and realizes dynamic confidence weighting of multi-source information.

[0009] Preferably, the multi-protocol reverse control bridge supports triple instruction verification of Profibus, EtherCAT, and 4-20mA signals, and adopts redundant coding technology to improve the reliability of instruction transmission. The compensation learning mechanism adopts the LSTM-Attention deviation prediction model, automatically updates the digital twin parameters according to the execution results, and adds an adversarial training module to improve the generalization ability of the model in data missing scenarios.

[0010] Preferably, the blockchain evidence storage system adopts Hyperledger Fabric to build a consortium chain, sets up a dual-chain structure to store decision-making process data and execution logs respectively, adopts zero-knowledge proof technology to realize privacy protection query of audit data, and the explainable visualization interface integrates Grad-CAM and LIME technologies to generate decision heat maps and feature contribution radar maps, adopts spatiotemporal situation deduction sandbox, and supports dynamic backtracking of four-dimensional data.

[0011] The workflow of the present invention is: 1. Data acquisition: The heterogeneous protocol parsing unit in the multimodal data acquisition module integrates multiple industrial protocols, supports access from different devices, and can parse proprietary data streams. The unstructured processing engine deploys specific models to process unstructured data such as video and text, as well as BIM and LiDAR data. The edge preprocessing mechanism performs data noise reduction and outlier removal at the edge node. 2. Data fusion and spatiotemporal alignment: The spatiotemporal reference mapping engine of the fusion hub module achieves spatiotemporal alignment of multi-source data, eliminating timestamp bias. The federated learning cleaning tower assesses data quality and records the cleaning path. The dynamic semantic association library achieves semantic mapping between text and sensor encoding. 3. Model deduction: The multi-scale fluid modeler of the digital twin deduction cabin module performs multi-resolution simulation and corrosion evolution prediction. The multi-objective optimization engine generates Pareto front solutions and evaluates risk scenarios. The virtual-real linkage interface realizes bidirectional data synchronization between the digital twin and the SCADA system. 4. Decision generation: The two-layer reinforcement learning architecture of the adaptive decision matrix module generates scheduling strategies and optimizes valve openings, supports knowledge transfer, and the case-based reasoning mechanism matches similar cases and generates emergency plans. The dynamic weight allocator dynamically weights the confidence of multi-source information. 5. Execution feedback: The multi-protocol reverse control bridge of the elastic execution feedback chain module performs instruction verification and reliable transmission. The compensation learning mechanism updates the digital twin parameters based on the execution results to improve the model generalization capability. 6. Traceability evaluation: The blockchain evidence storage system of the credibility traceability platform module stores decision and execution data, protects privacy, and generates decision heat maps through an explainable visualization interface, supporting four-dimensional data backtracking.

[0012] The present invention has the following advantages: 1. The system integrates multiple protocols through a multimodal data collection module, making it compatible with unstructured data sources such as IoT sensors, BIM models, and social media public opinion. This solves the problem of traditional system data being unable to be effectively integrated, enabling the fusion of multi-source heterogeneous data and breaking down data silos. 2. The system collects data such as pressure fluctuations and water quality changes in real time and performs integrated analysis, enabling timely detection of sudden pipe network conditions. This changes the current situation of relying on periodic manual inspection data, improves response speed, and enhances dynamic response capabilities. 3. The system adopts technologies such as a two-layer reinforcement learning architecture to achieve adaptive decision-making based on deep reinforcement learning, replacing the traditional threshold alarm plus manual scheduling model, improving decision-making efficiency and accuracy, and enhancing the intelligence of decision-making; 4. The system integrates external heterogeneous data such as weather forecasts and user water usage habits, not only considering the pipe network topology, but also achieving the optimal configuration of pipe network energy efficiency, greatly optimizing the energy efficiency of the pipe network; To sum up, the present invention solves the problem that traditional system data cannot be effectively integrated, realizes the fusion of multi-source heterogeneous data, breaks the data silos, and at the same time improves the response speed and enhances the dynamic response capability. In addition, users can improve decision-making efficiency and accuracy, enhance the intelligence of decision-making, and achieve the optimal configuration of pipeline network energy efficiency, greatly optimizing the energy efficiency of the pipeline network. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a system workflow diagram of the present invention; Figure 2 This is a module structure diagram of the multimodal data acquisition cabin of the present invention; Figure 3 This is a structural diagram of the spatiotemporal alignment fusion hub module of the present invention; Figure 4 This is a module structure diagram of the digital twin deduction cabin of the present invention; Figure 5 This is a structural diagram of the adaptive decision matrix module of the present invention; Figure 6 This is a module structure diagram of the elastic execution feedback chain of the present invention; Figure 7 This is a module structure diagram of the credibility tracing platform of the present invention. DETAILED DESCRIPTION

[0014] 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.

[0015] The intelligent dispatching and decision-making system for pipeline networks with multi-source heterogeneous data fusion includes a multimodal data acquisition cabin module, a spatiotemporal alignment fusion hub module, a digital twin deduction cabin module, an adaptive decision matrix module, an elastic execution feedback chain module and a credibility traceability platform module. The multimodal data acquisition cabin module includes a heterogeneous protocol parsing unit, an unstructured processing engine and an edge preprocessing mechanism. The spatiotemporal alignment fusion hub module includes a spatiotemporal benchmark mapping engine, a federated learning cleaning tower and a dynamic semantic association library. The digital twin deduction cabin module includes a multi-scale fluid modeler, a multi-objective optimization engine and a virtual-reality linkage interface. The adaptive decision matrix module includes a two-layer reinforcement learning architecture, a case reasoning mechanism and a dynamic weight allocator. The elastic execution feedback chain module includes a multi-protocol reverse control bridge and a compensation learning mechanism. The credibility traceability platform module includes a blockchain evidence storage system and an explainable visualization interface.

[0016] Multimodal data acquisition cabin module, refer to Figure 2 shown The heterogeneous protocol parsing unit integrates common industrial protocols such as Modbus-TCP, OPC-UA, MQTT, and LoRaWAN. Dynamic protocol stack loading technology enables the system to flexibly adapt to different protocols, supporting the parallel integration of field programmable logic controllers and smart sensors. The added non-standard protocol adapter, through reverse engineering, can parse proprietary data streams, ensuring that a variety of data sources can be collected by the system. For example, in some special industrial scenarios, some devices use proprietary communication protocols. This unit can smoothly integrate data from these devices into the system.

[0017] 2. The unstructured processing engine deploys a YOLOv7-based video leak detection model, enabling efficient and accurate detection of pipeline leaks in videos. Combined with the BERT model for social media sentiment analysis, a text-image joint feature extraction network is constructed to comprehensively analyze video and text information. The added laser point cloud data parsing module enables real-time spatial registration of BIM models and LiDAR scan data, supporting 3D modeling and spatial analysis of pipeline networks.

[0018] 3. The edge preprocessing mechanism introduces a time series compression algorithm to perform data noise reduction and outlier removal at the edge node. This reduces cloud transmission bandwidth consumption and reduces data transmission latency. For example, when collecting large amounts of sensor data, noise and outliers may appear. This mechanism can pre-process this data to improve data quality.

[0019] Time-space alignment fusion central module, refer to Figure 3 shown The space-time benchmark mapping engine is designed with a four-dimensional space-time coordinate system converter and uses an improved ICP algorithm to achieve millimeter-level spatial alignment, accurately aligning data from different sources. A built-in atomic clock synchronization module eliminates timestamp deviations in multi-source data and ensures data consistency in the temporal dimension.

[0020] 2. The Federated Learning Cleansing Tower builds a cross-domain data quality assessment model based on homomorphic encryption and establishes a seven-level quality scoring system to quantitatively evaluate data quality. A new data lineage tracking function records the cleaning path and correction parameters of each data point, facilitating subsequent data traceability and quality monitoring.

[0021] 3. Dynamic Semantic Association Library: A semantic ontology library containing over 2,000 industry terms is established, and semantic mapping between text descriptions and sensor codes is achieved through knowledge graph embedding technology. This helps break down semantic barriers between different data, improving data comprehensibility and utilization efficiency.

[0022] Digital twin simulation cabin module, refer to Figure 4 shown 1. The Multiscale Fluid Modeler couples CFD fluid dynamics models with graph neural networks, supporting multi-resolution simulations from individual pipelines to regional pipeline networks. The newly added Corrosion Evolution Prediction module combines material stress data with water quality parameters to predict changes in pipe wall thickness, providing a key basis for pipeline network maintenance and management.

[0023] 2. The multi-objective optimization engine uses the NSGA-III algorithm to construct a four-dimensional objective space and generate a Pareto front solution set, enabling trade-offs and optimization across multiple objectives. It also integrates Monte Carlo stochastic simulation to assess scheduling robustness for 12 extreme weather risk scenarios, improving system reliability in complex environments.

[0024] 3. The virtual-real linkage interface uses OPC-UA and FMI co-simulation interfaces to achieve millisecond-level bidirectional data synchronization between the digital twin and the SCADA system. This enables the virtual model to reflect the operating status of the actual pipeline network in real time, while also feeding back the virtual model's optimization strategies to the actual system.

[0025] Adaptive decision matrix module, refer to Figure 5 shown In the two-layer reinforcement learning architecture, the upper-layer policy network uses RainbowDQN to generate scheduling policies, while the lower-layer execution network uses the PPO algorithm to optimize valve openings. The added transfer learning module supports knowledge transfer of scheduling policies across regions, improving the system's adaptability and scalability.

[0026] 2. The case-based reasoning mechanism builds a causal knowledge graph containing over 3,000 historical accident cases and uses a GAT network to match similar cases. The added emergency plan generator automatically outputs the treatment steps based on the current working conditions, providing a fast and effective solution for pipeline network fault handling.

[0027] 3. The dynamic weight allocator quantifies the contribution of each data source to decision-making based on the Shapley value algorithm, achieving dynamic confidence weighting of multi-source information. This ensures that the reliability and importance of different data sources are fully considered during the decision-making process.

[0028] Elastic Execution Feedback Chain Module, refer to Figure 6 shown 1. The multi-protocol reverse control bridge supports triple command verification for Profibus, EtherCAT, and 4-20mA signals, and uses redundant coding technology to improve command transmission reliability, ensuring that control commands can be accurately transmitted to the execution device.

[0029] 2. The compensatory learning mechanism uses the LSTM-Attention bias prediction model to automatically update digital twin parameters based on execution results. The added adversarial training module improves the model's generalization capabilities in scenarios with missing data, enabling the system to better adapt to the uncertainties of actual operations.

[0030] Credibility traceability platform module, refer to Figure 7 shown 1. The blockchain evidence storage system uses Hyperledger Fabric to build a consortium chain, setting up a dual-chain structure to store decision-making process data and execution logs respectively. It uses zero-knowledge proof technology to achieve privacy-protected query of audit data, ensuring data security and traceability.

[0031] 2. The interpretable visualization interface integrates Grad-CAM and LIME technologies to generate decision heat maps and feature contribution radar charts. It uses a spatiotemporal situational analysis sandbox and supports dynamic backtracking of four-dimensional data, allowing users to intuitively understand the decision-making process and results.

[0032] The workflow of the present invention (refer to Figure 1 )for: 1. Data Collection The heterogeneous protocol parsing unit leverages its integrated suite of industrial protocols to provide access to various types of devices. The system can collect data from both common PLCs and smart sensors, as well as specialized devices using proprietary protocols. The unstructured processing engine utilizes models such as YOLOv7 and BERT to process unstructured data like video and text, while also performing spatial registration between BIM and LiDAR data. The edge preprocessing mechanism performs noise reduction and outlier removal at the data source, reducing the burden of subsequent data processing and the amount of data transmitted to the cloud.

[0033] 2. Data Fusion The spatiotemporal reference mapping engine uses a four-dimensional spatiotemporal coordinate system converter and an improved ICP algorithm to achieve precise spatial alignment of multi-source data. The atomic clock synchronization module eliminates timestamp bias. The federated learning cleaning tower assesses and cleanses data quality, recording the cleaning path and correction parameters. The dynamic semantic association library semantically maps text descriptions with sensor codes, enabling cross-correlation and understanding of different types of data.

[0034] 3. Model deduction A multiscale fluid modeler performs multi-resolution simulations and corrosion evolution predictions, providing detailed simulations and predictions of the pipeline network's operational status. A multi-objective optimization engine generates Pareto front solutions to assess scheduling robustness under different risk scenarios. A virtual-physical linkage interface enables bidirectional data synchronization between the digital twin and the SCADA system, enabling real-time interaction between the virtual model and the actual system.

[0035] 4. Decision Making A two-layer reinforcement learning architecture generates scheduling strategies and optimizes valve openings based on real-time data. A transfer learning module supports cross-regional knowledge transfer. A case-based reasoning mechanism matches similar cases from a large number of historical accident cases and generates emergency response plans. A dynamic weight allocator dynamically assigns confidence levels to multi-source information, improving decision accuracy and reliability.

[0036] 5. Execution Feedback The multi-protocol reverse control bridge performs triple verification and reliable transmission of control commands, ensuring they are accurately conveyed to the executing device. A compensatory learning mechanism automatically updates digital twin parameters based on execution results, and an adversarial training module improves the model's generalization capabilities in the presence of missing data, enabling the system to continuously adapt to changes in actual operation.

[0037] 6. Retrospective evaluation The blockchain evidence storage system securely stores decision-making process data and execution logs, while zero-knowledge proof technology protects data privacy. An interpretable visualization interface generates decision heat maps and feature contribution radar charts, supporting dynamic backtracking of four-dimensional data, making it easy for users to evaluate and trace the decision-making process and results.

[0038] Deployment test cases of the present invention: 1. Data collection and implementation The heterogeneous protocol parsing unit plays a vital role in Beijing's water supply network. This unit seamlessly connects on-site PLCs and smart sensors to the system. Furthermore, sensor data using proprietary protocols can be parsed through reverse engineering. The unstructured processing engine uses the YOLOv7 model to detect pipeline leaks in real time through video surveillance. Once a leak is detected, the system issues a prompt alert. The BERT model is combined with social media analytics to understand public sentiment and concerns regarding the water supply network. Edge preprocessing reduces noise and removes outliers from collected data such as pressure and flow, reducing the amount of data transmitted to the cloud and improving data transmission efficiency.

[0039] 2. Decision-making and implementation When abnormal pressure occurs in the pipeline network, the adaptive decision matrix module responds quickly. A two-layer reinforcement learning architecture generates scheduling strategies based on real-time data and adjusts valve openings to balance pipeline pressure. A case-based reasoning mechanism matches similar cases from over 3,000 historical accident cases, uses the GAT network to find the case closest to the current situation, and automatically generates response steps in conjunction with the emergency plan generator. A dynamic weight allocator weights each data source based on its contribution to the decision. For example, pressure sensor data and flow sensor data are dynamically weighted based on their reliability and importance to the current decision, improving decision accuracy.

[0040] 3. Execution feedback implementation The multi-protocol reverse control bridge in the elastic execution feedback chain module performs triple verification on control commands to ensure accurate transmission to the execution device. Redundant encoding technology further enhances the reliability of command transmission during transmission. A compensatory learning mechanism automatically updates the parameters of the digital twin model based on actual valve opening and pressure changes. For example, when the actual valve opening deviates from the expected opening, the LSTM-Attention deviation prediction model predicts the deviation and updates the model parameters. The adversarial training module simulates data loss scenarios to improve the model's generalization capabilities in real-world applications, enabling the system to better cope with various complex situations.

[0041] The multi-source heterogeneous data fusion pipeline intelligent scheduling decision-making system in the present invention realizes the full-process management of data collection, fusion, deduction, decision-making, execution feedback and retrospective evaluation through the collaborative work of various modules, and has also demonstrated good performance and effects in actual deployment tests.

[0042] 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. The intelligent dispatching decision-making system for pipeline networks with multi-source heterogeneous data fusion includes a multimodal data acquisition module, a spatiotemporal alignment fusion hub module, a digital twin deduction module, an adaptive decision matrix module, a flexible execution feedback chain module, and a credibility traceability platform module. It is characterized by: The multimodal data acquisition cabin module includes a heterogeneous protocol parsing unit, an unstructured processing engine and an edge preprocessing mechanism; the spatiotemporal alignment fusion hub module includes a spatiotemporal benchmark mapping engine, a federated learning cleaning tower and a dynamic semantic association library; the digital twin deduction cabin module includes a multi-scale fluid modeler, a multi-objective optimization engine and a virtual-reality linkage interface; the adaptive decision matrix module includes a two-layer reinforcement learning architecture, a case reasoning mechanism and a dynamic weight allocator; the elastic execution feedback chain module includes a multi-protocol reverse control bridge and a compensation learning mechanism; the credibility traceability platform module includes a blockchain evidence storage system and an explainable visualization interface.

2. The intelligent pipeline network scheduling decision system based on multi-source heterogeneous data fusion according to claim 1 is characterized by: The heterogeneous protocol parsing unit integrates Modbus-TCP, OPC-UA, MQTT, and LoRaWAN industrial protocols, uses protocol stack dynamic loading technology, supports parallel access of field programmable logic controllers and smart sensors, adds non-standard protocol adapters, and parses private format data streams through reverse engineering. The unstructured processing engine deploys a video leakage detection model based on YOLOv7, combines the social media public opinion sentiment analysis BERT model, builds a text-image joint feature extraction network, adds a laser point cloud data parsing module, and processes the spatial alignment of BIM models and LiDAR scanning data in real time. The edge preprocessing mechanism introduces a time series compression algorithm to complete data noise reduction and outlier elimination at the edge node, reducing cloud transmission bandwidth consumption.

3. The intelligent pipeline network scheduling decision system based on multi-source heterogeneous data fusion according to claim 1 is characterized by: The spatiotemporal benchmark mapping engine designs a four-dimensional spatiotemporal coordinate system converter, adopts an improved ICP algorithm to achieve millimeter-level spatial alignment, and has a built-in atomic clock synchronization module to eliminate multi-source data timestamp deviation. The federated learning cleaning tower constructs a cross-domain data quality assessment model based on homomorphic encryption, sets up a 7-level quality scoring system, and adds a data lineage tracking function to record the cleaning path and correction parameters of each data point. The dynamic semantic association library establishes a semantic ontology library containing more than 2,000 industry terms, and realizes semantic mapping of text descriptions and sensor codes through knowledge graph embedding technology.

4. The intelligent dispatching and decision-making system for pipeline networks based on multi-source heterogeneous data fusion according to claim 1 is characterized by: The multi-scale fluid modeler couples CFD fluid dynamics models with graph neural networks, supports multi-resolution simulation from single pipelines to regional pipeline networks, adds a corrosion evolution prediction module, and combines material stress data with water quality parameters to predict changes in pipe wall thickness. The multi-objective optimization engine uses the NSGA-III algorithm to construct a four-dimensional target space, generate a Pareto front solution set, and integrates Monte Carlo random simulation to evaluate the scheduling robustness of 12 types of extreme weather risk scenarios. The virtual-reality linkage interface realizes millisecond-level bidirectional synchronization of data between the digital twin and the SCADA system through the OPC-UA and FMI joint simulation interface.

5. The intelligent pipeline network scheduling decision system based on multi-source heterogeneous data fusion according to claim 1 is characterized by: In the two-layer reinforcement learning architecture, the upper-layer policy network uses RainbowDQN to generate scheduling strategies, the lower-layer execution network uses the PPO algorithm to optimize valve opening, and a transfer learning module is added to support knowledge transfer of cross-regional scheduling strategies. The case-based reasoning mechanism constructs a causal knowledge graph containing more than 3,000 historical accident cases, uses the GAT network to achieve similar case matching, and adds an emergency plan generator to automatically output disposal steps based on the current operating conditions. The dynamic weight allocator quantifies the contribution of each data source to the decision-making based on the Shapley value algorithm, realizing dynamic confidence weighting of multi-source information.

6. The intelligent pipeline network scheduling decision system based on multi-source heterogeneous data fusion according to claim 1 is characterized by: The multi-protocol reverse control bridge supports triple instruction verification of Profibus, EtherCAT, and 4-20mA signals, and adopts redundant coding technology to improve the reliability of instruction transmission. The compensatory learning mechanism adopts the LSTM-Attention deviation prediction model to automatically update the digital twin parameters according to the execution results, and adds an adversarial training module to improve the generalization ability of the model in data missing scenarios.

7. The intelligent pipeline network scheduling decision system based on multi-source heterogeneous data fusion according to claim 1 is characterized by: The blockchain evidence storage system uses Hyperledger Fabric to build a consortium chain, sets up a dual-chain structure to store decision-making process data and execution logs respectively, and adopts zero-knowledge proof technology to realize privacy protection query of audit data. The explainable visualization interface integrates Grad-CAM and LIME technologies to generate decision heat maps and feature contribution radar maps, and uses a spatiotemporal situation deduction sandbox to support dynamic backtracking of four-dimensional data.

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