A safety inspection and troubleshooting system for oil and gas pipelines

Abstract

This invention discloses a safety inspection and investigation system for oil and gas pipelines, belonging to the field of pipeline safety inspection and investigation technology. It includes: an integrated air-space-ground three-dimensional perception module, a multimodal data fusion module, an intelligent analysis module, a digital twin and 3D visualization command module, a closed-loop management module, and an operation and maintenance support module. The integrated air-space-ground three-dimensional perception module is used to construct a multi-dimensional collaborative monitoring network covering space-based, air-based, and ground-based systems, achieving comprehensive data collection along the entire oil and gas pipeline and its surrounding environment. The multimodal data fusion module performs spatiotemporal alignment, feature extraction, and cross-modal deep fusion on the data collected by the integrated air-space-ground three-dimensional perception module to generate fused data. This invention, in addition to enabling safety inspection and investigation of oil and gas pipelines, can also perform inspections using various sensing methods and can predict and optimize pipeline safety.

Description

Technical Field

[0001] This invention relates to the field of pipeline safety inspection and investigation technology, and more specifically, to an oil and gas pipeline safety inspection and investigation system. Background Technology

[0002] Petroleum (oil) refers to a mixture of gaseous, liquid, and solid hydrocarbons. It is a viscous, dark brown liquid that occurs naturally and is often referred to as the "blood of industry." Natural gas pipelines are pipelines that transport natural gas from extraction sites or processing plants to urban gas distribution centers or industrial users; they are also called gas transmission pipelines. Transporting natural gas via pipelines is the primary method for transporting large quantities of natural gas overland. Natural gas pipelines account for approximately half of the world's total pipeline length.

[0003] Many existing solutions primarily focus on drone inspection systems. While these solutions address the dangers and low efficiency of manual inspections, drones are heavily influenced by battery life and weather conditions, making it impossible to achieve all-weather, all-area coverage. Furthermore, these solutions are often limited to the acquisition and transmission of images and videos, lacking effective means of sensing underground pipeline conditions, minor leaks, and soil corrosion. They also lack the ability to directly monitor the condition of the pipeline itself. These solutions generally treat data from different sources as isolated information, lacking mechanisms for deep fusion and correlation analysis of multi-source spatiotemporal data such as satellite remote sensing, drone imagery, ground sensor data, and acoustic signals. This results in fragmented information, making it difficult to form a comprehensive and unified understanding of the pipeline's safety status. Summary of the Invention

[0004] In view of the problems existing in the prior art, the purpose of this invention is to provide a safety inspection and investigation system for oil and gas pipelines. In addition to realizing the safety inspection and investigation of oil and gas pipelines, this invention can also carry out inspections using various sensing methods, and can predict and optimize pipeline safety.

[0005] A high-precision safety inspection and troubleshooting system for oil and gas pipelines with no blind spots

[0006] To solve the above problems, the present invention adopts the following technical solution:

[0007] An oil and gas pipeline safety inspection and investigation system includes: an integrated air-space-ground three-dimensional perception module, a multimodal data fusion module, an intelligent analysis module, a digital twin and three-dimensional visualization command module, a closed-loop management module, and an operation and maintenance support module;

[0008] The integrated air-space-ground three-dimensional sensing module is used to construct a multi-dimensional collaborative monitoring network covering space-based, air-based, and ground-based systems, enabling data collection of the entire oil and gas pipeline and its surrounding environment without blind spots.

[0009] The multimodal data fusion module is used to perform spatiotemporal alignment, feature extraction, and cross-modal deep fusion on the data collected by the integrated air-space-ground three-dimensional perception module to generate fused data.

[0010] The intelligent analysis module is used to perform real-time analysis, dynamic risk assessment and intelligent early warning of the fused data based on artificial intelligence models, and output inspection and dispatch instructions.

[0011] The digital twin and 3D visualization command module is used to construct and drive a pipeline digital twin based on geographic information system, building information model and real-time monitoring network data, so as to realize the visualization monitoring and emergency command of the pipeline.

[0012] The closed-loop management module is used to automatically convert the analysis results into executable operation and maintenance work orders and track the entire task processing process, forming a management closed loop from problem discovery to solution verification.

[0013] The operation and maintenance support module is used to generate decision support reports, equipment health records, and risk prediction analyses based on historical and real-time monitoring data, supporting the scientific operation and maintenance of the pipeline network. As a preferred embodiment of the present invention, the integrated air-space-ground sensing module includes a space-based sensing unit, an air-based sensing unit, a ground-based sensing unit, and a central processing server.

[0014] The space-based sensing unit is used to perform wide-area scanning and deformation monitoring along the pipeline to macroscopically identify geological subsidence, third-party construction activities, and suspected leakage areas.

[0015] The space-based sensing unit includes a drone hangar group deployed along the pipeline and a drone swarm dispatched by it. The drones are equipped with lidar, infrared thermal imagers and gas leak detectors, which are used to perform centimeter-level three-dimensional modeling, temperature anomaly location and direct detection of leaked gas in suspected areas detected by the space-based sensing unit.

[0016] The foundation sensing unit is used to sense construction vibrations from third parties, perform preliminary leak location through acoustic signals, and conduct intelligent video inspection and tracking of personnel and vehicle activities.

[0017] The central processing server is used to receive and fuse the multi-source spatiotemporal data. Based on the macroscopic coordinates provided by the space-based sensing unit and the early warning signals provided by the ground-based sensing unit, it intelligently dispatches the UAV cluster in the space-based sensing unit to the target area for verification. It also performs cross-verification and comprehensive analysis on the laser point cloud, infrared thermal image, gas concentration data and ground-based video data transmitted back by the UAVs, and finally outputs a decision support report containing accurate leak point coordinates, risk level and disposal recommendations.

[0018] As a preferred embodiment of the present invention, the multimodal data fusion module includes an input and preprocessing unit, a single-modal feature extraction unit, an intermodal feature interaction unit, an adaptive feature fusion unit, and an output and optimization unit;

[0019] The input and preprocessing unit is used to receive multi-source spatiotemporal data streams from the integrated air-space-ground three-dimensional sensing module, and to perform spatiotemporal alignment, data cleaning and standardization processing on the data streams to generate spatiotemporally consistent preprocessed data.

[0020] The single-modal feature extraction unit is used to extract abstract feature vectors of each modality from the input and the data preprocessed by the preprocessing unit, respectively, using a deep neural network model.

[0021] The intermodal feature interaction unit is used to explicitly model the correlation between features of different modalities through a cross-modal attention mechanism, and further introduces a causal decoupling mechanism to separate and retain cross-modal joint semantic information while eliminating potential false correlations.

[0022] The adaptive feature fusion unit is used to receive interactively enhanced features and generate a joint feature vector based on a fusion strategy that combines dynamic subspace routing and counterfactual confusion pooling.

[0023] The output and optimization unit is used to map the joint feature vector to the target space required by the intelligent analysis module, and minimize the preset loss function through a joint optimization strategy during the training phase, so as to iteratively update the parameters of the entire fusion subunit.

[0024] As a preferred embodiment of the present invention, the adaptive feature fusion unit adopts a fusion strategy combining dynamic subspace routing and counterfactual confusion pooling, specifically including:

[0025] The dynamic subspace routing module is used to dynamically select at least one target subspace that is most relevant to the current target task from multiple pre-built feature subspaces based on the context content of the current multimodal input data, so as to filter irrelevant or redundant feature information.

[0026] The counterfactual confusion pooling module is connected to the dynamic subspace routing module and is used to receive features after routing selection. By constructing counterfactual samples and performing comparative analysis, it actively identifies and suppresses non-causal feature activation caused by data bias.

[0027] As a preferred embodiment of the present invention, the target subspace of the dynamic subspace routing selection provides a focal point for comparison of the counterfactual confusion pooling. The counterfactual confusion pooling mainly targets the features routed to the current target subspace, generates and evaluates their counterfactual samples, making the confusion analysis more targeted and computationally more efficient.

[0028] The confusion evaluation result output by the counterfactual confusion pooling will be sent as a feedback signal to the dynamic subspace routing. In the next iteration or in the routing decision for subsequent data, the dynamic subspace routing will reduce its dependence on these highly confusing features, thereby guiding the routing mechanism to learn more stable and causal feature association patterns.

[0029] As a preferred embodiment of the present invention, the intelligent analysis module includes a real-time intelligent early warning anomaly identification unit, a dynamic risk assessment predictive maintenance unit, an intelligent scheduling decision optimization unit, and an asset integrity management expert knowledge base unit;

[0030] The real-time intelligent early warning anomaly identification unit is used to receive fused state tensors from multiple sensors, split the fused state tensors into multiple sequences, add sign vectors to form the final input sequence, add the final input sequence to the position code, and then input it to the encoder network for encoding.

[0031] The dynamic risk assessment and predictive maintenance unit is used to establish multi-level risk thresholds based on the overall risk value and the dynamic anomaly scoring threshold, and to dynamically assess the risk into different levels based on the range in which the overall risk value falls.

[0032] The intelligent scheduling decision optimization unit is used to receive the early warning event and the risk assessment result, and automatically generate maintenance priority order, inspection work order and resource scheduling strategy based on linear programming optimization algorithm;

[0033] The asset integrity management expert knowledge base unit is used to build and maintain a dynamic knowledge graph of the entire lifecycle data of the integrated pipeline.

[0034] As a preferred embodiment of the present invention, the digital twin and 3D visualization command module includes a unified data fusion and real-time mapping unit, a high-fidelity 3D scene construction and dynamic rendering unit, an intelligent space analysis and simulation unit, a collaborative command and closed-loop disposal unit, and a parallel execution and management unit.

[0035] The unified data fusion and real-time mapping unit is used to access and manage multi-source heterogeneous spatiotemporal data from integrated air-space-ground sensing networks, IoT sensors, business systems and geographic information systems. Through stream processing and spatiotemporal alignment, it achieves second-level synchronization from physical pipelines to virtual models, and constructs the unique data truth source of the digital twin.

[0036] The high-fidelity 3D scene construction and dynamic rendering unit is used to integrate oblique photogrammetry, laser point cloud, building information model and geographic information system data to construct a multi-level, interactive 3D virtual scene from macro pipeline corridor to micro equipment weld. Each entity object in the scene is dynamically bound to real-time business data, supporting users to freely zoom, rotate and select information points to view the station and equipment.

[0037] The intelligent space analysis and simulation unit is used to perform leakage diffusion simulation based on computational fluid dynamics, emergency resource scheduling based on path planning algorithm, and pipeline structure safety simulation based on finite element analysis in a three-dimensional space context, providing quantitative analysis support for decision-making.

[0038] The collaborative command and closed-loop response unit is used to digitize the text emergency plan and perform dynamic simulations in a three-dimensional scene. It automatically generates and dispatches response work orders based on intelligent analysis results, supports multi-party audio and video collaboration and remote control between mobile terminals and the command center, and realizes closed-loop management of the entire process from early warning, decision-making, response to review.

[0039] The parallel execution and management unit is used to construct an artificial pipeline system that runs in parallel with the descriptive digital twin, with adjustable parameters and independent operation. It trains the artificial system using historical and real-time measured data, and drives continuous data interaction and parallel interaction among the digital twin, the artificial system, and the physical pipeline. At the same time, it provides simulation guidance and optimization recommendations for different operation and maintenance strategies and emergency plans.

[0040] As a preferred embodiment of the present invention, the parallel execution and management unit includes an artificial system construction subunit, a parallel interaction engine, and a strategy experimentation and optimization subunit:

[0041] The artificial system construction subunit is used to construct an independently operable artificial pipeline system model that runs in parallel with the digital twin, based on historical operating data, physical mechanism models, and real-time monitoring data. The parallel interaction engine is used to drive the interaction of data and instructions between the digital twin, the artificial system, and the physical pipeline, specifically performing the description of pipeline operating status, the prediction of potential risks, and the guidance of operation and maintenance strategies. The strategy experimentation and optimization subunit is used in the artificial system to simulate and evaluate multiple candidate operation and maintenance strategies or emergency solutions from the intelligent analysis module, and output the optimal strategy to the digital twin and the closed-loop management unit to guide the execution of the physical pipeline.

[0042] As a preferred embodiment of the present invention, the closed-loop management module includes an automatic work order generation and intelligent dispatch unit, a task execution and process tracking unit, a result feedback and verification closed-loop unit, and a performance evaluation and continuous optimization unit.

[0043] The automatic work order generation and intelligent dispatch unit is used to automatically create structured electronic work orders based on risk events output by the intelligent analysis module, and intelligently dispatch the work orders to the execution terminal according to a multi-dimensional optimization strategy.

[0044] The task execution and process tracking unit is used to provide navigation and operation support for the execution terminal, and to collect, synchronize and visualize multimodal data of the entire task execution process in real time;

[0045] The result feedback and verification closed-loop unit is used to receive and review the processing results, verify the processing effect through digital twin collaboration, update the system status and archive the data, and complete the closed loop of a single task.

[0046] The performance evaluation and continuous optimization unit is used to dynamically evaluate operation and maintenance performance based on the closed-loop data of the entire process, and to autonomously optimize the dispatch strategy and inspection plan through large language model and reinforcement learning technology, so as to realize the system-level strategy closed loop and continuous evolution.

[0047] As a preferred embodiment of the present invention, the specific steps of the performance evaluation and continuous optimization unit in generating a structured report include:

[0048] Acquire full-process data on the dispatch, execution, and closure of all work orders within an evaluation period, as well as the triggering and verification data of all early warning events during the same period;

[0049] Based on the full-process data, key performance indicators are calculated, including average work order response time, work order processing timeout rate, false alarm rate, missed alarm rate, and regional inspection coverage rate.

[0050] By comparing the deviations of the performance indicators with the preset target values ​​and combining them with association rule mining, the systemic causes of the deviations are located, and a structured report containing the specific parameter adjustment direction and magnitude is generated.

[0051] Compared with the prior art, the advantages of this invention are:

[0052] (1) This invention can actively identify and remove false statistical correlations in the data to ensure that the fused features truly reflect the causal relationship between the physical states of the pipeline, which greatly improves the accuracy and robustness of the subsequent risk identification model and solves the problem of high false alarm rate in complex scenarios. At the same time, the system not only constructs a descriptive virtual copy, but also creates an artificial system for experiments, so that various maintenance strategies and emergency plans can be simulated and optimized in the virtual space at zero cost and infinitely many times. Then the optimal strategy is guided to the physical world for execution, realizing the leap from describing the current situation to predicting the future and guiding optimization. Through reinforcement learning and large language model analysis, the historical operation and maintenance effectiveness data can be automatically transformed into optimization strategies, dynamically adjusting core parameters such as work order dispatch rules, risk warning thresholds, and inspection plans, so that the whole system becomes an intelligent life form that can continuously learn from experience and iterate itself.

[0053] (2) This invention eliminates monitoring blind spots through a space-air-ground collaborative sensing network, enabling early detection of hidden dangers that are difficult to detect by traditional means, such as geological disaster precursors, minor leaks, and concealed construction. Combined with second-level intelligent early warning and dynamic risk assessment, accident prevention can be transformed from post-event response to pre-event prediction, and even risk warnings can be achieved dozens of hours in advance, fundamentally curbing the occurrence of major accidents. Secondly, it achieves huge benefits in terms of operational efficiency and economy. The collaborative inspection of drones and robots and intelligent path planning can improve inspection efficiency several times and greatly reduce labor costs and safety risks. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of a module of an oil and gas pipeline safety inspection and investigation system according to the present invention;

[0055] Figure 2 This is a schematic diagram of a unit in the integrated air-ground-space three-dimensional perception module of the oil and gas pipeline safety inspection and investigation system of the present invention. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0057] Example:

[0058] Please see Figure 1-2 A safety inspection and investigation system for oil and gas pipelines includes: an integrated air-space-ground three-dimensional perception module, a multimodal data fusion module, an intelligent analysis module, a digital twin and three-dimensional visualization command module, a closed-loop management module, and an operation and maintenance support module.

[0059] The integrated air-space-ground three-dimensional sensing module is used to build a multi-dimensional collaborative monitoring network covering space-based, air-based, and ground-based systems, enabling high-frequency data collection of the entire oil and gas pipeline and its surrounding environment without blind spots.

[0060] The multimodal data fusion module is connected to the stereo perception subunit and is used to perform spatiotemporal alignment, feature extraction and cross-modal deep fusion of multi-source spatiotemporal data to generate a unified joint feature representation.

[0061] The intelligent analysis module is connected to the multimodal data fusion module, which is used to perform real-time analysis, dynamic risk assessment and intelligent early warning of the fused data based on artificial intelligence models, and output inspection and scheduling instructions.

[0062] The digital twin and 3D visualization command module is connected to the intelligent analysis module to build and drive the pipeline digital twin based on geographic information system, building information model and real-time monitoring data, so as to realize global situation visualization, intelligent scheduling and emergency simulation command.

[0063] The closed-loop management module is connected to the intelligent analysis module and the digital twin and 3D visualization command module to automatically convert analysis results into executable operation and maintenance work orders and track the entire task processing process, forming a management closed loop from problem discovery to solution verification.

[0064] The operation and maintenance support module is connected to the closed-loop management module to generate decision support reports, equipment health records and risk prediction analysis based on historical and real-time monitoring data, supporting the long-term scientific operation and maintenance of the pipeline network.

[0065] Specifically, the integrated air-space-ground three-dimensional sensing module includes a space-based sensing unit, an air-based sensing unit, a ground-based sensing unit, and a central processing server;

[0066] The space-based sensing unit, air-based sensing unit, and ground-based sensing unit are respectively connected to the central processing server to upload the collected multi-source spatiotemporal data in real time.

[0067] The central processing server is used to fuse and analyze the received multi-source spatiotemporal data to generate a pipeline safety status assessment report.

[0068] The space-based sensing unit includes an on-orbit satellite equipped with synthetic aperture radar (SAR) and a multispectral imager. The SAR is used to penetrate cloud and rain weather barriers to monitor and identify geological subsidence and third-party construction activities along the pipeline. The multispectral imager is used to perform periodic wide-area scanning and analyze abnormal vegetation and soil moisture changes along the pipeline through time-series image comparison and analysis to macroscopically identify suspected leak areas or external threats.

[0069] The airborne sensing unit includes a cluster of drone hangars deployed at pre-set locations along the pipeline, and a drone swarm managed by the drone hangars. The drones are equipped with lidar, infrared thermal imagers, and gas leak detectors. The lidar is used to perform centimeter-level precision 3D terrain modeling of suspected areas or complex terrain sections detected by satellites, assisting in path planning and obstacle avoidance. The infrared thermal imager is used to locate minute leaks or equipment overheating faults by identifying temperature anomalies on the pipeline surface or in the surrounding environment at night or in severe weather. The gas leak detector is used to sample and analyze the concentration of specific gases in the atmosphere above the pipeline when flying at low altitudes, enabling direct detection and confirmation of leaked gases.

[0070] The ground-based sensing unit includes valve chambers, stations, and monitoring stations along the pipeline. The monitoring station integrates vibration sensors, acoustic wave transmitting transducers, receiving transducers, and high-definition photoelectric turntables.

[0071] Vibration sensors are buried on the surface of the pipeline to sense vibration signals generated by third-party construction such as excavation and drilling, and to realize intrusion early warning. Acoustic wave transmitting transducers and receiving transducers are respectively set at both ends of the pipeline to form a pipeline acoustic monitoring network. Acoustic wave transmitting transducers and receiving transducers are used to transmit and receive ultrasonic signals propagating along the pipe wall. By analyzing the signal attenuation and frequency change characteristics, preliminary interval location is performed when a leak occurs.

[0072] The high-definition photoelectric turntable is equipped with dual-spectrum cameras for visible light and thermal imaging. With the help of AI recognition algorithms, it can perform all-weather intelligent video inspection and tracking of personnel, vehicle activities, and pipeline surface conditions within the monitoring field of view.

[0073] The central processing server performs the following operations:

[0074] Receive macroscopic suspected coordinates and threat types provided by space-based sensing units;

[0075] Receive vibration early warning signals and acoustic leak location ranges provided by the ground-based sensing unit;

[0076] Based on the above information, the system intelligently generates inspection tasks and dispatches a cluster of drones in the airborne sensing unit to fly to the target area.

[0077] The laser point cloud, infrared thermal image, and gas concentration data transmitted back by the UAV, along with ground-based high-definition video data, are cross-validated and comprehensively analyzed.

[0078] The final output includes a decision support report with accurate leak point coordinates better than 0.5 meters, risk level assessment, and maintenance priority recommendations, and automatically triggers associated alarm devices.

[0079] Specifically, the multimodal data fusion module includes an input and preprocessing unit, a single-modal feature extraction unit, an intermodal feature interaction unit, an adaptive feature fusion unit, and an output and optimization unit;

[0080] The input and preprocessing unit is used to receive multi-source spatiotemporal data streams from the integrated air-space-ground three-dimensional sensing module, and to perform spatiotemporal alignment, data cleaning and standardization on the data streams to generate spatiotemporally consistent preprocessed data;

[0081] The single-modal feature extraction unit is used to extract high-level abstract feature vectors of their respective modalities from the input and preprocessed data of the preprocessing unit, respectively, using a deep neural network model.

[0082] The intermodal feature interaction unit is used to explicitly model the correlation between features of different modalities through a cross-modal attention mechanism, and further introduces a causal decoupling mechanism to separate and retain cross-modal joint semantic information while eliminating potential false associations;

[0083] The adaptive feature fusion unit is used to receive interactively enhanced features and generate a joint feature vector based on a fusion strategy that combines dynamic subspace routing with counterfactual confusion pooling.

[0084] The output and optimization unit is used to map the joint feature vector to the target space required by the intelligent analysis module, and minimize the preset loss function through the joint optimization strategy during the training phase, so as to iteratively update the parameters of the entire fusion subunit;

[0085] The single-modal feature extraction unit employs a specific network model for different modalities:

[0086] Spatial texture features are extracted from image or video modal data using convolutional neural networks (CNNs) or vision transformers (Vision Transformers).

[0087] Semantic vectors are extracted from text or report modal data using the pre-trained language model BERT;

[0088] One-dimensional convolutional neural network (1D-CNN), long short-term memory network (LSTM), or transformer are used to extract temporal dynamic features from temporal sensor modal data.

[0089] The acoustic or infrared sequence data is first converted into a spectrogram or heat map, and then a two-dimensional convolutional neural network (2D-CNN) is used for feature extraction.

[0090] The intermodal feature interaction unit includes a cross-modal attention subunit and a causal decoupling subunit. The cross-modal attention subunit is calculated using the cross-modal attention formula, specifically: Q is the query vector, derived from the features of the first modality. K and V are the key vector and value vector, respectively, derived from the features of the second modality. , The dimension of the key vector is used to scale the dot product result, the softmax function normalizes the weights, V is the weighted aggregate information, and the causal decoupling subunit is used to construct a cross-modal causal graph. Through intervention and counterfactual reasoning, it identifies and weakens spurious associations caused by data distribution bias in the data.

[0091] The adaptive feature fusion unit employs a fusion strategy combining dynamic subspace routing and counterfactual confusion pooling, specifically including:

[0092] Based on the content and context of the current input data, dynamically select the feature subspace most relevant to the target task for fusion to filter out irrelevant or redundant information;

[0093] By generating counterfactual samples and performing comparative analysis at the attention layer, the model can proactively identify and suppress non-causal feature activation caused by data bias, thereby enhancing its ability to capture true causal relationships.

[0094] The formula for calculating the joint feature vector F_fused generated by the adaptive feature fusion unit is as follows: ,in It is the feature vector of the i-th mode after processing by the interaction unit. It is its corresponding adaptive weight, and the sum of all weights satisfies =1, weight By a learnable gating function Calculations show that Context represents global context information;

[0095] When the downstream task is a classification task, the output and optimization unit uses the cross-entropy loss function to calculate the output space, and the formula is as follows: ,in It is the one-hot encoding of the actual label in category c. It is the probability predicted by the model that the class belongs to category c.

[0096] The specific operations performed by the input and preprocessing unit include:

[0097] Spatial registration is performed based on GPS and IMU data to unify data from different sources into the same geographic coordinate system;

[0098] Use sliding window or interpolation methods to align asynchronous time series data streams in time, ensuring data consistency in timestamps;

[0099] Pixel value normalization is performed on image data, and Z-score normalization or minimum-maximum scaling is performed on sensor time-series data.

[0100] Specifically, the intelligent analysis module includes a real-time intelligent early warning and anomaly identification unit, a dynamic risk assessment and predictive maintenance unit, an intelligent scheduling and decision optimization unit, and an asset integrity management expert knowledge base unit;

[0101] The real-time intelligent early warning anomaly identification unit is used to process the multi-source real-time monitoring data streams in seconds. Based on deep learning and multi-source signal fusion decision algorithm, it automatically identifies leakage, third-party intrusion and equipment failure, and generates primary early warning events.

[0102] The dynamic risk assessment and predictive maintenance unit is used to calculate the weighted sum as the overall risk value of the pipe segment according to the application scenario. Based on the historical anomaly scoring sequence, a probability density function is generated through kernel density estimation. On this basis, a static risk threshold is constructed by combining the preset confidence quantile. An expected economic loss function is introduced. By minimizing the expected economic loss function and combining it with historical data verification, the static risk threshold is dynamically adjusted to generate a dynamic anomaly scoring threshold for final risk classification, so as to balance the economic impact of missed and false alarms. Based on the overall risk value and the dynamic anomaly scoring threshold, a multi-level risk threshold is established, and the risk is dynamically assessed into different levels according to the range in which the overall risk value falls.

[0103] The intelligent scheduling decision optimization unit is used to receive early warning events and risk assessment results, optimize risk thresholds based on the expected economic loss function, and automatically generate maintenance priority order, inspection work order and resource scheduling strategy accordingly.

[0104] The asset integrity management expert knowledge base unit is used to build and maintain a dynamic knowledge graph of integrated pipeline lifecycle data, providing knowledge reasoning support for risk assessment, fault diagnosis and maintenance decisions, and forming a closed-loop management archive from event perception to handling verification;

[0105] The real-time intelligent early warning anomaly identification unit performs the following operations:

[0106] Parallel processing of pipeline pressure, flow, temperature, vibration, and video image data is performed. For video image data, an improved YOLOR-CNN target detection model is used to identify personnel, vehicles, and excavating machinery intrusion targets around the pipeline in real time. For time-series signals such as pressure and flow, the Empirical Mode Decomposition (EMD) algorithm is used to decompose non-stationary signals into multiple Intrinsic Mode Functions (IMFs), and then combined with pattern recognition to determine abnormal operating conditions.

[0107] By integrating evidence from multiple sources, including sound waves, negative pressure waves, and infrared thermal imaging, a Bayesian inference method is used for fusion decision-making. The formula for calculating the posterior probability of leakage is as follows: ,in This is the posterior probability of leakage occurring when E1, E2, etc., occur simultaneously. Let Ei be the conditional probability of observing evidence Ei when the leak occurs. This is the prior probability;

[0108] The dynamic risk assessment and predictive maintenance unit uses the spatiotemporal graph attention network ST-GAT for learning and prediction, specifically including:

[0109] A pipeline network diagram model is constructed based on the pipeline network topology, with stations and test piles as nodes and pipe segments as edges, and a multi-scale temporal feature set is embedded into the model.

[0110] A temporal coding layer is used to encode the temporal features of nodes and edges, and a spatial attention layer is used with edge weight coefficients as initial adjacency weights to aggregate information of adjacent nodes through a graph attention mechanism.

[0111] In the output prediction layer, the probability prediction value of corrosion events, the corrosion rate estimate value, and the uncertainty estimate value at future time moments are generated as the target prediction results.

[0112] The intelligent scheduling decision optimization unit performs the following operations:

[0113] Receive the risk level and maintenance priority order output by the dynamic risk assessment predictive maintenance unit;

[0114] Repair work orders are automatically generated based on repair priority.

[0115] Based on personnel, material, time constraints and the importance of pipeline sections, operations research optimization algorithms are used for resource scheduling and task allocation, and scheduling instructions are sent to inspection robots or drone clusters.

[0116] When the intelligent scheduling decision optimization unit performs resource scheduling, it adopts a dynamic strategy based on risk scoring to calculate the risk score Ri,i+1 of the pipeline section between adjacent sensor acquisition points. The calculation formula is as follows: ,in These are the standardized values ​​of the differences between pressure, temperature, vibration, and flow rate data, respectively. , , K represents the corresponding weight, and K is the adjustment factor.

[0117] Based on the threshold range into which the risk score falls, the inspection strategy is dynamically determined as either routine inspection or rapid inspection, and the minimum number of inspection robots required is calculated accordingly to generate a cross-regional robot scheduling strategy.

[0118] The asset integrity management expert knowledge base unit includes a dynamic knowledge graph construction subunit and a knowledge reasoning and case matching subunit. The dynamic knowledge graph construction subunit is used to define pipeline entity types and relationship types, and to build and update the dynamic knowledge graph based on real-time and historical data. The entity types include pipe segments, valves, and welds, and the relationship types include adjacency, corrosion impact, and historical maintenance. When a leak or anomaly is detected, the knowledge reasoning and case matching subunit determines the target subgraph corresponding to the leak point based on the dynamic knowledge graph, matches it with the subgraphs in the historical knowledge graph library, obtains similar historical cases, and assists in diagnosing the cause of the failure and predicting the probability of failure.

[0119] The dynamic risk assessment and predictive maintenance unit also employs a dynamic integrated weighting method to calculate risk factor weights, specifically including:

[0120] The subjective weighting method is used to determine the subjective weights of risk factors, and the objective weighting method is used to determine the objective weights of risk factors.

[0121] By using a dynamic integrated weighting method, subjective and objective weights are integrated to obtain a dynamic integrated weight for risk failure probability, thereby enabling dynamic adjustment of risk assessment weights and improving the accuracy of assessment.

[0122] Specifically, the digital twin and 3D visualization command module includes a unified data fusion and real-time mapping unit, a high-fidelity 3D scene construction and dynamic rendering unit, an intelligent spatial analysis and simulation unit, a collaborative command and closed-loop handling unit, and a parallel execution and management unit.

[0123] The unified data fusion and real-time mapping unit is used to access and manage multi-source spatiotemporal data from the integrated air-space-ground sensing network, IoT sensors, business systems and geographic information systems. Through spatiotemporal alignment and stream processing, it achieves second-level synchronization from physical pipelines to virtual models, and constructs the unique data truth source of the digital twin.

[0124] The high-fidelity 3D scene construction and dynamic rendering unit is used to integrate oblique photography, laser point cloud, BIM and GIS data to construct a multi-layered, interactive 3D scene from macro-pipeline network to micro-equipment, and to achieve dynamic rendering using the level of detail (LOD) algorithm, in which scene objects are bound to real-time business data.

[0125] The intelligent space analysis and simulation unit is used to perform leakage diffusion simulation based on computational fluid dynamics (CFD), emergency dispatch based on path planning algorithms, and structural safety simulation based on finite element analysis (FEA) in a three-dimensional spatial context, providing quantitative analysis support for decision-making.

[0126] The collaborative command and closed-loop response unit is used to digitize emergency plans and dynamically simulate them, intelligently generate and dispatch work orders based on analysis results, support mobile collaboration and remote control, and realize closed-loop management of the entire process from early warning, decision-making, response to debriefing.

[0127] The parallel execution and management unit is used to build an artificial system parallel to the digital twin, train the artificial system using measured data, and describe, predict and guide the operation status of the pipeline through the parallel interaction between the digital twin, the artificial system and the physical pipeline.

[0128] The parallel execution and management unit includes a manual system construction subunit, a parallel interaction engine, and a strategy experimentation and optimization subunit.

[0129] The artificial system construction subunit is used to construct an independently operable artificial pipeline system model that runs in parallel with the digital twin, based on historical operating data, physical mechanism models, and real-time monitoring data. The parallel interaction engine is used to drive the interaction of data and instructions between the digital twin, the artificial system, and the physical pipeline, specifically performing the description of pipeline operating status, the prediction of potential risks, and the guidance of operation and maintenance strategies. The strategy experimentation and optimization subunit is used in the artificial system to simulate and evaluate multiple candidate operation and maintenance strategies or emergency solutions from the intelligent analysis module, and output the optimal strategy to the digital twin and the closed-loop management unit to guide the execution of the physical pipeline.

[0130] The specific operation steps of the artificial system construction module include:

[0131] It receives dynamic risk assessment model parameters, equipment degradation law functions, and joint feature representation patterns from the intelligent analysis module;

[0132] Based on the above inputs, a parameterized artificial system model with adjustable parameters and multiple failure mode simulation capabilities is constructed. The model maintains the same macroscopic behavior as the physical pipeline, but has configurable differences in microscopic parameters and response logic to simulate different operating conditions and risk scenarios.

[0133] Specifically, the closed-loop management module includes an automatic work order generation and intelligent dispatch unit, a task execution and process tracking unit, a result feedback and verification closed-loop unit, and a performance evaluation and continuous optimization unit.

[0134] The automatic work order generation and intelligent dispatch unit is used to automatically create structured electronic work orders based on risk events output by the intelligent analysis module, and intelligently dispatch the work orders to the execution terminal according to multi-dimensional optimization strategies.

[0135] The task execution and process tracking unit is used to provide navigation and operation support for the execution terminal, and to collect, synchronize and visualize multimodal data of the entire task execution process in real time;

[0136] The result feedback and verification closed-loop unit is used to receive and review the handling results, verify the handling effect through digital twin collaboration, update the system status and archive the data, and complete the closed loop of a single task.

[0137] The performance evaluation and continuous optimization unit is used to dynamically evaluate operational efficiency and autonomously optimize dispatch strategies and inspection plans based on closed-loop data throughout the entire process, through large language models and reinforcement learning techniques, thereby achieving system-level strategy closed loop and continuous evolution.

[0138] In a specific embodiment of the present invention, the automatic work order generation and intelligent dispatch unit includes a work order generation subunit based on a large language model and a multi-objective optimization dispatch subunit. The work order generation subunit based on the large language model is used to receive early warning information containing the location, type, level, and on-site evidence of the hidden danger, and automatically generate detailed work order description text that conforms to enterprise specifications and contains standardized processing steps and safety requirements. The multi-objective optimization dispatch subunit is used to calculate the comprehensive matching degree between all available execution resources and hidden danger points.

[0139] The task execution and process tracking unit includes a tree-structured task decomposition and tracking subunit and a real-time collaboration and guidance subunit. The tree-structured task decomposition and tracking subunit is used to break down complex work orders into multiple sub-tasks and visualize and track them in the form of a tree diagram. The upper-level node receives the task completion rate transmitted by the lower-level node and calculates the weighted average as the task completion rate of the node based on the weight value of each lower-level node, and feeds back to the root node layer by layer. The real-time collaboration and guidance subunit integrates mobile GIS, audio and video communication and digital twin scenarios, enabling the command center to view the location of personnel or equipment in real time on a 3D map, retrieve on-site videos, and perform remote annotation and voice guidance.

[0140] The results feedback and verification closed-loop unit includes a digital twin collaborative verification subunit and an asset and risk status linkage update subunit. The digital twin collaborative verification subunit is used to compare the uploaded on-site images and sensor data after the disposal with the historical and expected status of the hidden danger point in the digital twin model. It supports multi-level reviewers to view the comparison before and after disposal in the three-dimensional twin scene and conduct online acceptance confirmation. The asset and risk status linkage update subunit is used to automatically trigger the association rules after the work order acceptance is closed, update the last maintenance time and current status of the corresponding pipe section or equipment in the pipeline digital asset health record, and dynamically lower the real-time risk value of the point in the risk database according to the disposal results.

[0141] The performance evaluation and continuous optimization unit includes a performance evaluation report generation subunit based on a large language model and a reinforcement learning strategy optimization subunit.

[0142] The performance evaluation report generation sub-unit based on the large language model is used to obtain full-process operation and maintenance data within the cycle, input it into the pre-trained pipeline operation and maintenance professional large model, and automatically generate a multi-dimensional evaluation report including problem distribution, response timeliness, recurrence rate analysis, performance evaluation and optimization suggestions.

[0143] The reinforcement learning strategy optimization subunit is used to construct work order dispatch rules and inspection plan generation logic into learnable intelligent agent strategies. By using historical closed-loop data as the training environment and the optimization of average response time, hidden danger recurrence rate, and resource utilization key performance indicators as reward signals, the strategy is continuously trained and updated using reinforcement learning algorithms, enabling the system's dispatch and planning capabilities to evolve autonomously.

[0144] Specifically, the dispatch decision function in the automatic work order generation and intelligent dispatch unit is:

[0145] Where R represents the execution resource and T represents the target work order. Let i be the optimization objective function, which includes the estimated arrival time function, the skill matching function, and the current task load function. Adjustable dynamic weights for the corresponding targets;

[0146] When dispatching work orders, the automatic work order generation and intelligent dispatch unit prioritizes selecting execution terminals whose distance from the fault location is less than a preset distance and whose number of pending work orders is less than a first threshold as target terminals. If a target terminal needs to transfer a work order for any reason, the system, upon receiving the transfer request, will only allow the work order transfer if the number of pending work orders for the designated terminal is less than a second threshold.

[0147] Specifically, key performance indicators (KPIs) and performance indicators include:

[0148] ;

[0149] ;

[0150] ;

[0151] The system automatically adjusts the dispatch weights in the automatic work order generation and intelligent dispatch units based on optimization suggestions from the performance evaluation and continuous optimization units. Furthermore, the risk thresholds used to trigger early warnings in the intelligent analysis module and the inspection plans in the digital twin and 3D visualization command module were revised.

[0152] Specifically, the operation and maintenance support module includes a full lifecycle data management and collaborative design support unit, an intelligent management and asset transfer unit for engineering projects, an asset integrity management and intelligent decision optimization unit, an integrated HSE control and emergency intelligent support unit, and an intelligent knowledge base and decision report generation unit.

[0153] The full lifecycle data management and collaborative design support unit is used to standardize, aggregate, model, and store multi-source spatiotemporal data from all stages of pipeline design, procurement, construction, and operation and maintenance based on data lake and knowledge graph technologies, and build a digital twin data base covering the pipeline entity.

[0154] The intelligent management and asset transfer unit for engineering projects is used to centrally control and intelligently verify the progress, quality, and material data of projects during the construction period. Based on blockchain and multi-level traceability code technology, it enables the reliable and complete digital transfer of completed assets to the operation and maintenance system.

[0155] The asset integrity management and intelligent decision optimization unit is used to dynamically assess pipeline risks and predict failure trends based on multimodal data fusion and deep learning models, and to automatically generate optimal maintenance strategies and inspection plans using intelligent optimization algorithms.

[0156] The HSE integrated management and emergency intelligent support unit is used to integrate safety, environment and health management, and realizes intelligent matching of emergency plans and optimized scheduling of emergency resources based on dynamic models and case reasoning.

[0157] The intelligent knowledge base and decision report generation unit is used to accumulate operational knowledge in accordance with best practices such as ITIL, and automatically generate in-depth analysis reports based on large language model technology, providing data-driven insights and suggestions for management decisions.

[0158] The full lifecycle data management and collaborative design support unit includes:

[0159] The data lake construction sub-unit based on the cloud platform is used to store the full life cycle data of key entities of the circumferential weld of oil and gas pipelines. The data enters the data lake in the form of forms and is classified and labeled.

[0160] The knowledge graph construction and fusion subunit is used to collect data on the entire life cycle of pipeline welds. Through knowledge modeling, extraction and fusion technologies, a pipeline weld knowledge graph with weld joints, steel pipes, construction and inspection as entities is constructed to realize semantic association and deep query of data.

[0161] The collaborative design support subunit is used to provide a unified parametric design environment for multiple design institutes, support online collaboration among multiple disciplines, and synchronize the structured design results to the data lake and knowledge graph to provide an initial model for the digital twin.

[0162] The data lake construction sub-unit based on the cloud platform further adopts blockchain technology to store and verify the integrity and credibility of key data of the circumferential weld, and uses smart contracts to automate the management and verification of data entry, modification and query.

[0163] The asset integrity management and intelligent decision optimization unit includes a multi-source fusion risk dynamic assessment subunit, an operation optimization and strategy solving subunit, and an intelligent inspection planning subunit.

[0164] The multi-source fusion risk dynamic assessment subunit integrates SCADA real-time monitoring data, internal detection data, and geographic environmental information. It employs a multimodal deep integration network and a cross-modal attention mechanism for feature fusion, calculating the weights of each data source using a weighted complementary correlation matrix. The formula is as follows: ,in Let i be the fusion weight of the i-th data source. This represents the correlation value between the data source and the target failure mode, where n is the total number of data sources.

[0165] The operation optimization and strategy solving subunit is used to establish a mathematical model with the goal of minimizing operating costs. The optimal operating scheme for pump unit combination, speed, temperature, etc., is obtained by using simulated annealing algorithm. The total operating cost of the pipeline, For power costs, For heating costs;

[0166] The intelligent inspection planning subunit is used to automatically associate high-risk pipe sections and high-consequence areas with available inspection resources, dynamically generate and optimize inspection routes and frequencies, and improve the efficiency of risk investigation.

[0167] The HSE integrated management and emergency intelligent support unit includes a digital emergency plan library and case library subunit and an intelligent emergency matching and dispatch subunit. The digital emergency plan library and case library subunit is used to build a standardized safety accident case library, environmental risk scenario library, and structured digital emergency plan library. When the system receives a leak or fire warning, the intelligent emergency matching and dispatch subunit can automatically perform case reasoning and similarity matching in the plan library based on real-time parameters such as accident location, type, scale, and wind direction, and push the most applicable plan. It also uses path planning algorithms such as A* or Dijkstra to plan the optimal arrival route for emergency vehicles.

[0168] The intelligent knowledge base and decision report generation unit includes an ITIL-based process-oriented knowledge management subunit and an intelligent report generation subunit based on a large language model:

[0169] The ITIL-based process-oriented knowledge management subunit is used to establish processes such as configuration management, change management, event management, and problem management, and continuously accumulates fault handling methods, expert experience, and optimization cases generated during operation and maintenance into the knowledge base;

[0170] The intelligent report generation subunit based on the big language model is used to access the full lifecycle data lake and knowledge graph. Through the pre-trained domain big language model, it automatically generates equipment health reports, risk trend analysis reports, performance evaluation reports and economic analysis reports, providing management with multi-dimensional and in-depth decision-making insights.

[0171] The system automatically feeds back and optimizes the risk assessment model parameters, maintenance strategy thresholds, and emergency plan library in the asset integrity management and intelligent decision optimization unit, as well as the emergency plan library in the HSE integrated control and emergency intelligent support unit, through the analysis conclusions and optimization suggestions output by the intelligent knowledge base and decision report generation unit. This forms a self-evolving closed loop from data perception to decision optimization and knowledge accumulation.

[0172] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and its improved concept, should be covered within the scope of protection of the present invention.

Claims

1. A safety inspection and troubleshooting system for oil and gas pipelines, characterized in that, include: The system includes an integrated air-space-ground three-dimensional perception module, a multimodal data fusion module, an intelligent analysis module, a digital twin and 3D visualization command module, a closed-loop management module, and an operation and maintenance support module. The integrated air-space-ground three-dimensional sensing module is used to construct a multi-dimensional collaborative monitoring network covering space-based, air-based, and ground-based systems, enabling data collection of the entire oil and gas pipeline and its surrounding environment without blind spots. The multimodal data fusion module is used to perform spatiotemporal alignment, feature extraction, and cross-modal deep fusion on the data collected by the integrated air-space-ground three-dimensional perception module to generate fused data. The intelligent analysis module is used to perform real-time analysis, dynamic risk assessment and intelligent early warning of the fused data based on artificial intelligence models, and output inspection and dispatch instructions. The digital twin and 3D visualization command module is used to construct and drive a pipeline digital twin based on geographic information system, building information model and real-time monitoring network data, so as to realize the visualization monitoring and emergency command of the pipeline. The closed-loop management module is used to automatically convert the analysis results into executable operation and maintenance work orders and track the entire task processing process, forming a management closed loop from problem discovery to solution verification. The operation and maintenance support module is used to generate decision support reports, equipment health records and risk prediction analysis based on historical and real-time monitoring data, supporting the scientific operation and maintenance of the pipeline network.

2. The oil and gas pipeline safety inspection and troubleshooting system according to claim 1, characterized in that, The integrated air-space-ground three-dimensional sensing module includes a space-based sensing unit, an air-based sensing unit, a ground-based sensing unit, and a central processing server. The space-based sensing unit is used to perform wide-area scanning and deformation monitoring along the pipeline to macroscopically identify geological subsidence, third-party construction activities, and suspected leakage areas. The space-based sensing unit includes a drone hangar group deployed along the pipeline and a drone swarm dispatched by it. The drones are equipped with lidar, infrared thermal imagers and gas leak detectors, which are used to perform centimeter-level three-dimensional modeling, temperature anomaly location and direct detection of leaked gas in suspected areas detected by the space-based sensing unit. The foundation sensing unit is used to sense construction vibrations from third parties, perform preliminary leak location through acoustic signals, and conduct intelligent video inspection and tracking of personnel and vehicle activities. The central processing server is used to receive and fuse the multi-source spatiotemporal data. Based on the macroscopic coordinates provided by the space-based sensing unit and the early warning signals provided by the ground-based sensing unit, it intelligently dispatches the UAV cluster in the space-based sensing unit to the target area for verification. It also performs cross-verification and comprehensive analysis on the laser point cloud, infrared thermal image, gas concentration data and ground-based video data transmitted back by the UAVs, and finally outputs a decision support report containing accurate leak point coordinates, risk level and disposal recommendations.

3. The oil and gas pipeline safety inspection and troubleshooting system according to claim 1, characterized in that, The multimodal data fusion module includes an input and preprocessing unit, a single-modal feature extraction unit, an intermodal feature interaction unit, an adaptive feature fusion unit, and an output and optimization unit. The input and preprocessing unit is used to receive multi-source spatiotemporal data streams from the integrated air-space-ground three-dimensional sensing module, and to perform spatiotemporal alignment, data cleaning and standardization processing on the data streams to generate spatiotemporally consistent preprocessed data. The single-modal feature extraction unit is used to extract abstract feature vectors of each modality from the input and the data preprocessed by the preprocessing unit, respectively, using a deep neural network model. The intermodal feature interaction unit is used to explicitly model the correlation between features of different modalities through a cross-modal attention mechanism, and further introduces a causal decoupling mechanism to separate and retain cross-modal joint semantic information while eliminating potential false correlations. The adaptive feature fusion unit is used to receive interactively enhanced features and generate a joint feature vector based on a fusion strategy that combines dynamic subspace routing and counterfactual confusion pooling. The output and optimization unit is used to map the joint feature vector to the target space required by the intelligent analysis module, and minimize the preset loss function through a joint optimization strategy during the training phase, so as to iteratively update the parameters of the entire fusion subunit.

4. The oil and gas pipeline safety inspection and troubleshooting system according to claim 3, characterized in that, The adaptive feature fusion unit employs a fusion strategy combining dynamic subspace routing and counterfactual confusion pooling, specifically including: The dynamic subspace routing module is used to dynamically select at least one target subspace that is most relevant to the current target task from multiple pre-built feature subspaces based on the context content of the current multimodal input data, so as to filter irrelevant or redundant feature information. The counterfactual confusion pooling module is connected to the dynamic subspace routing module and is used to receive features after routing selection. By constructing counterfactual samples and performing comparative analysis, it actively identifies and suppresses non-causal feature activation caused by data bias.

5. The oil and gas pipeline safety inspection and troubleshooting system according to claim 4, characterized in that, The target subspace of the dynamic subspace routing selection provides a focal point for the counterfactual confusion pooling, which mainly targets the features routed to the current target subspace, generates and evaluates their counterfactual samples, making the confusion analysis more targeted and computationally more efficient. The confusion evaluation result output by the counterfactual confusion pooling will be sent as a feedback signal to the dynamic subspace routing. In the next iteration or in the routing decision for subsequent data, the dynamic subspace routing will reduce its dependence on these highly confusing features, thereby guiding the routing mechanism to learn more stable and causal feature association patterns.

6. The oil and gas pipeline safety inspection and troubleshooting system according to claim 1, characterized in that, The intelligent analysis module includes a real-time intelligent early warning and anomaly identification unit, a dynamic risk assessment and predictive maintenance unit, an intelligent scheduling and decision optimization unit, and an asset integrity management expert knowledge base unit. The real-time intelligent early warning anomaly identification unit is used to receive fused state tensors from multiple sensors, split the fused state tensors into multiple sequences, add sign vectors to form the final input sequence, add the final input sequence to the position code, and then input it to the encoder network for encoding. The dynamic risk assessment and predictive maintenance unit is used to establish multi-level risk thresholds based on the overall risk value and the dynamic anomaly scoring threshold, and to dynamically assess the risk into different levels based on the range in which the overall risk value falls. The intelligent scheduling decision optimization unit is used to receive the early warning event and the risk assessment result, and automatically generate maintenance priority order, inspection work order and resource scheduling strategy based on linear programming optimization algorithm; The asset integrity management expert knowledge base unit is used to build and maintain a dynamic knowledge graph of the entire lifecycle data of the integrated pipeline.

7. The oil and gas pipeline safety inspection and troubleshooting system according to claim 1, characterized in that, The digital twin and 3D visualization command module includes a unified data fusion and real-time mapping unit, a high-fidelity 3D scene construction and dynamic rendering unit, an intelligent space analysis and simulation unit, a collaborative command and closed-loop handling unit, and a parallel execution and management unit. The unified data fusion and real-time mapping unit is used to access and manage multi-source heterogeneous spatiotemporal data from integrated air-space-ground sensing networks, IoT sensors, business systems and geographic information systems. Through stream processing and spatiotemporal alignment, it achieves second-level synchronization from physical pipelines to virtual models, and constructs the unique data truth source of the digital twin. The high-fidelity 3D scene construction and dynamic rendering unit is used to integrate oblique photogrammetry, laser point cloud, building information model and geographic information system data to construct a multi-level, interactive 3D virtual scene from macro pipeline corridor to micro equipment weld. Each entity object in the scene is dynamically bound to real-time business data, supporting users to freely zoom, rotate and select information points to view the station and equipment. The intelligent space analysis and simulation unit is used to perform leakage diffusion simulation based on computational fluid dynamics, emergency resource scheduling based on path planning algorithm, and pipeline structure safety simulation based on finite element analysis in a three-dimensional space context, providing quantitative analysis support for decision-making. The collaborative command and closed-loop response unit is used to digitize the text emergency plan and perform dynamic simulations in a three-dimensional scene. It automatically generates and dispatches response work orders based on intelligent analysis results, supports multi-party audio and video collaboration and remote control between mobile terminals and the command center, and realizes closed-loop management of the entire process from early warning, decision-making, response to review. The parallel execution and management unit is used to construct an artificial pipeline system that runs in parallel with the descriptive digital twin, with adjustable parameters and independent operation. It trains the artificial system using historical and real-time measured data, and drives continuous data interaction and parallel interaction among the digital twin, the artificial system, and the physical pipeline. At the same time, it provides simulation guidance and optimization recommendations for different operation and maintenance strategies and emergency plans.

8. The oil and gas pipeline safety inspection and troubleshooting system according to claim 7, characterized in that, The parallel execution and management unit includes a manual system construction subunit, a parallel interaction engine, and a strategy experimentation and optimization subunit. The artificial system construction subunit is used to construct an independently operable artificial pipeline system model that runs in parallel with the digital twin, based on historical operating data, physical mechanism models, and real-time monitoring data. The parallel interaction engine is used to drive the interaction of data and instructions between the digital twin, the artificial system, and the physical pipeline, specifically performing the description of pipeline operating status, the prediction of potential risks, and the guidance of operation and maintenance strategies. The strategy experimentation and optimization subunit is used in the artificial system to simulate and evaluate multiple candidate operation and maintenance strategies or emergency solutions from the intelligent analysis module, and output the optimal strategy to the digital twin and the closed-loop management unit to guide the execution of the physical pipeline.

9. The oil and gas pipeline safety inspection and troubleshooting system according to claim 1, characterized in that, The closed-loop management module includes an automatic work order generation and intelligent dispatch unit, a task execution and process tracking unit, a result feedback and verification closed-loop unit, and a performance evaluation and continuous optimization unit. The automatic work order generation and intelligent dispatch unit is used to automatically create structured electronic work orders based on risk events output by the intelligent analysis module, and intelligently dispatch the work orders to the execution terminal according to a multi-dimensional optimization strategy. The task execution and process tracking unit is used to provide navigation and operation support for the execution terminal, and to collect, synchronize and visualize multimodal data of the entire task execution process in real time; The result feedback and verification closed-loop unit is used to receive and review the processing results, verify the processing effect through digital twin collaboration, update the system status and archive the data, and complete the closed loop of a single task. The performance evaluation and continuous optimization unit is used to dynamically evaluate operation and maintenance performance based on the closed-loop data of the entire process, and to autonomously optimize the dispatch strategy and inspection plan through large language model and reinforcement learning technology, so as to realize the system-level strategy closed loop and continuous evolution.

10. The oil and gas pipeline safety inspection and troubleshooting system according to claim 9, characterized in that, The specific steps for the performance evaluation and continuous optimization unit to generate a structured report include: Acquire full-process data on the dispatch, execution, and closure of all work orders within an evaluation period, as well as the triggering and verification data of all early warning events during the same period; Based on the full-process data, key performance indicators are calculated, including average work order response time, work order processing timeout rate, false alarm rate, missed alarm rate, and regional inspection coverage rate. By comparing the deviations of the performance indicators with the preset target values ​​and combining them with association rule mining, the systemic causes of the deviations are located, and a structured report containing the specific parameter adjustment direction and magnitude is generated.

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