8804results about "Pipeline systems" patented technology

A system for AI-supported leak detection and localization in water distribution infrastructures, consisting of: a large number of distributed sensor nodes mounted along a water pipe, each sensor node comprising the following: a pressure sensor configured to measure local hydraulic pressure fluctuations within the pipeline with a resolution of at least 0.01 bar; a flow sensor configured to measure the volume flow within the pipeline with an accuracy of at least ±0.5% of the measured value; an acoustic sensor configured to detect vibration signatures caused by leaks in a frequency range between 50 Hz and 20 kHz; an embedded microcontroller with integrated analog-to-digital conversion circuitry for digitizing sensor outputs; a wireless communication module configured to transmit time-synchronized sensor data to a cloud-based processing platform; and a local energy subsystem with a rechargeable battery and an optional circuit for generating photovoltaic energy; the cloud-based processing platform includes an artificial intelligence engine that comprises the following: a data acquisition module configured to receive and decode the transmitted sensor data and to perform time alignment; a supervised learning module that is trained on historical data of flagged leaks and non-leaks to classify incoming sensor patterns; an unsupervised learning module configured to detect anomalies by modeling normal operating baselines of the pipeline; and a topology-aware localization module configured to determine leak coordinates using the topology of the pipeline network, modeling the propagation of hydraulic waves, and estimating the arrival time difference from multi-node acoustic detections; and wherein the system is configured to provide real-time leak alerts and georeferenced visualization via a remote monitoring interface.

The invention relates to the technical field of fire-fighting monitoring, in particular to a fire-fighting water system fault detection and early warning method and system based on the fire-fighting Internet of Things, and the method comprises the steps: 1, periodically collecting pressure data through pressure sensors disposed at a fire-fighting water pump outlet, a pipe network main pipe branch point and the most unfavorable tail end; 2, according to the current valve opening degree, the pump state and historical normal working condition data, theoretical pressure expected values and dynamic allowable deviation zones of all nodes are generated; 3, when actually measured pressure deviates from a theoretical pressure expected value and exceeds a dynamic allowable deviation band, marking abnormal nodes, reversely constructing a fault propagation tree along the topological model, and allocating weight factors for associated nodes according to fault types; and 4, triggering graded early warning based on the number of the abnormal nodes, the fault propagation path and the weight factor accumulated value. And the system can automatically take measures when a fault occurs through an equipment linkage function, so that the efficient operation of the fire fighting water system is guaranteed, and the efficiency and safety of fire emergency response are improved.

The invention relates to the technical field of pipeline safety monitoring, and discloses an online lossless real-time monitoring system for micro-strain of an in-service natural gas pipeline. A micro-strain data acquisition unit of the system acquires a micro-strain data set on the surface of the in-service natural gas pipeline in real time. And the three-dimensional strain field reconstruction unit receives the data set and executes three-dimensional strain field reconstruction processing to generate strain distribution characteristics of the pipeline. And the life prediction model analysis unit calls a pre-trained life prediction model to carry out nonlinear analysis processing on the strain distribution characteristics, and outputs a residual life prediction value and a key risk area identifier of the pipeline. The environmental factor compensation unit performs environmental factor compensation correction processing on the residual life prediction value to generate a corrected residual life prediction value. And the maintenance strategy generation unit generates a pipeline maintenance strategy set according to the key risk area identifier. According to the invention, real-time and accurate evaluation and intelligent maintenance decision support of the health condition of the pipeline are realized.

The invention provides an intelligent monitoring method and system for the full life cycle safety state of an oil and gas pipeline, and relates to the technical field of oil and gas pipelines, and the method comprises the steps: collecting the operation state data of the oil and gas pipeline, building a digital twin model, constructing a multi-dimensional feature vector, and carrying out the feature fusion; and constructing a fault evolution prediction model based on the fusion feature vector in combination with a graph neural network, calculating a residual life prediction value, continuously optimizing the safety state evaluation accuracy through online iterative training, and generating a safety state evaluation report and a maintenance decision suggestion. Intelligent monitoring and prediction of the safety state of the oil and gas pipeline are achieved, and the reliability of safe operation of the pipeline is improved.

The invention provides an underground pipe network defect intelligent identification and early warning method and system based on deep learning, and relates to the technical field of pipe network detection, and the method comprises the steps: employing a pipeline detection robot to obtain multi-source data, and carrying out the preprocessing; mapping the features to a manifold space, constructing a similarity matrix, and carrying out geometric difference weighted fusion; constructing an incidence matrix to calculate a spatial distance and mechanical strength, and carrying out iterative propagation to generate a defect characteristic spectrum; and acquiring an evolution sequence by adopting a self-adaptive sliding window, determining influence factors based on anti-fact intervention, and calculating a state transition probability to determine an optimal maintenance scheme. According to the method, the pipe network defect identification accuracy is improved, and pipeline state prediction and maintenance decision intelligence are realized.

A natural gas pipeline multi-working-condition fault diagnosis method and system based on Bayesian adversarial attack and single-source domain transfer, relating to the technical field of mechanical fault detection and diagnosis. The core of the method is using the transfer learning technology to solve the problem of insufficient generalization ability of existing deep reasoning models when processing pipeline fault diagnosis tasks under different working conditions. The method mainly comprises the following steps: constructing an attack sample generator on the basis of a Bayesian network, wherein the attack sample generator is used for generating, by adding delicately designed tiny disturbance into an input sample, an attack sample that can cause an reasoning model to make an incorrect decision, so as to mine and analyze a defect of the reasoning model; constructing a domain discriminator on the basis of the Bayesian network, wherein the domain discriminator is used for assist in generating a high-concealment attack sample by means of adversarial learning between the domain discriminator and the generator, that is, there is almost no visible difference between the high-concealment attack sample and an original sample; and constructing a classifier on the basis of the Bayesian network, and by expanding the distance between the attack sample and an original decision boundary of the reasoning model, constraining the posterior distribution of network parameters of the reasoning model to be adjusted towards a higher score of the attack sample, thereby enhancing the adaptability and robustness of the model when facing disturbance in different domains. By means of the steps, the present invention effectively solves the problem of missing reporting and false reporting risk improvement caused by poor generalization ability of traditional deep learning models under different working conditions.

The invention relates to the technical field of pipeline detection, and discloses a pipeline weld defect analysis method and device based on magnetic flux leakage detection.The method comprises the steps that a composite excitation signal is generated and optimized through a multi-frequency excitation source, a magnetic flux leakage attenuation coefficient and scattering characteristics are calculated in combination with pipeline parameters, and if the attenuation coefficient exceeds a preset attenuation threshold value, the pipeline weld defect is detected; if yes, adjusting the excitation signal for optimization, extracting high-frequency transient and low-frequency stable features of the optimized magnetic flux leakage signal, and generating a preliminary feature vector of the weld defect; mapping the preliminary feature vector to a three-dimensional defect space position, generating a space distribution diagram containing the defect position, optimizing the preliminary feature vector to make a fuzzy region clear, and obtaining the depth and direction of the defect; and according to the depth and direction of the defect, analyzing the spatial distribution characteristics of the preliminary feature vectors, determining the type of the defect, dynamically adjusting excitation signal parameters for optimization, recalculating the attenuation coefficient of the signal, extracting the optimized scattering features, and obtaining a high-precision weld defect positioning result.

The invention discloses an optical fiber embankment underwater piping leakage event time-space correlation analysis method, and relates to the technical field of leakage event intelligent identification and risk assessment in embankment safety monitoring, and the method comprises the following steps: S1, obtaining continuous time-space monitoring data of an embankment underwater region obtained through monitoring by a distributed optical fiber sensing system; and S2, processing the continuous space-time monitoring data by adopting a feature recognition model based on a neural network, and recognizing a suspected leakage event. According to the time-space correlation analysis method for the underwater piping leakage event of the optical fiber embankment, by fusing multi-level data processing and self-adaptive feature learning, false alarms caused by environmental interference are effectively restrained, and the recognition accuracy of a real leakage event in a complex underwater scene is improved. An analysis framework combining space-time association diagram construction and physical mechanism verification is adopted, the internal relation between events can be deeply mined, and the space-time evolution rule of a seepage path is accurately restored.

The invention belongs to the technical field of petrochemical engineering pipeline monitoring, and discloses an intelligent detection and repair system and method for petrochemical engineering pipeline leakage, and the method comprises the following modules: a data collection module which comprehensively monitors the leakage condition and environmental parameters in a pipeline by deploying a high-precision and low-power-consumption multi-parameter sensor network; the data transmission module is responsible for safely and reliably transmitting the collected data to the cloud processing center; the data processing and analyzing module is used for cleaning, processing and analyzing the collected data and extracting feature information; the leakage identification module is used for identifying and judging leakage events based on various algorithms; the leakage positioning module is used for accurately determining a leakage position; and the leakage evaluation module is used for evaluating the leakage degree, type and potential risk.

The invention discloses a hydrogen sulfide leakage abnormity monitoring method and system, and relates to the technical field of gas monitoring. A hydrogen sulfide leakage abnormity monitoring system comprises a data monitoring module, an airflow correction module, a twinborn inversion module, an association judgment module, a leakage confirmation module, a linkage control module and a monitoring updating module. According to the invention, the gas flow field correction is carried out on the hydrogen sulfide concentration by constructing the gas flow vector model and calculating the gas diffusion disturbance coefficient, so that the monitoring result is closer to the real gas distribution, and the space precision and reliability of leakage detection are improved; by establishing a double-physical-field digital twinborn model of a battery thermal field and an airflow field, corrected concentration data is introduced into a coupling domain for numerical evolution, and space inversion positioning and diffusion path prediction of hydrogen sulfide concentration abnormity are realized.

The invention relates to the technical field of electric vehicle power battery thermal management, and discloses a temperature change-based power battery thermal management system, which comprises a thermal simulation pre-judgment end, a sensor network end, a dynamic liquid cooling control end, a BMS decision end and a fault diagnosis module, aiming at the technical defects of out-of-control temperature difference, response hysteresis and leakage risk caused by static flow channel design of the existing liquid cooling system, breakthrough is realized through a three-dimensional technical architecture: a CFD (Computational Fluid Dynamics) prejudgment layer: automatically calibrating sensor distribution points based on temperature field standard deviation, and improving temperature difference identification precision; according to the dynamic valve control layer, an electromagnetic three-way reversing valve is adopted to execute a directional temperature control strategy, an edge battery cell is preferentially introduced under the low-temperature working condition, and heat dissipation is enhanced under the high-temperature working condition; according to the redundant safety layer, the valve body and the pipeline are integrally formed, the leakage rate is reduced, and flow maintenance under single-point failure is achieved through a double-water-inlet parallel pump source. The method has the effects of improving the convergence speed of temperature difference between modules, reducing the attenuation rate of battery life and reducing the risk probability of thermal runaway.

The invention relates to the technical field of intelligent water affair and urban water supply system informatization, in particular to a water supply pipe network hydraulic model calibration and leakage locating method and system, and the method comprises the steps: obtaining the pressure and flow data of a plurality of monitoring points in a pipe network in real time; taking the model as a boundary condition to drive a hydraulic model to carry out real-time simulation, and calculating a theoretical value; calculating a residual error between a theoretical value and an actual value of the monitoring point; judging a triggering model calibration event or a leakage suspicion event based on a residual error abnormal mode; if calibration is triggered, inverting and updating global parameters of the model through an optimization algorithm to realize self-calibration; if leakage is triggered, a suspected area is determined by combining pressure space distribution analysis, and a leakage pipe section is accurately positioned through analog simulation and correlation analysis. Through closed-loop feedback of real-time data and the model, the model can adapt to changes of a pipe network system, dynamic self-calibration of the water supply pipe network hydraulic model is achieved, and the problem of precision attenuation of a traditional static model is solved.

The invention discloses a pipe network water leakage point detection and distance positioning method based on flow analysis. The method comprises the following steps: S1, constructing a flow balance model of a pipeline network; s2, monitoring the flow state of each node of the pipeline network in real time based on the flow balance model constructed in the step S1, and performing preliminary positioning on a leakage point when pipeline leakage is detected; s3, flow gradient analysis and reverse hydraulic fine positioning: in the suspected leakage area locked in the step S2, meter-scale precision positioning of a water leakage point is realized through a flow gradient analysis and reverse hydraulic iterative model; and S4, a plurality of sensor nodes are arranged on the water leakage pipeline determined in the step S3, detection data are collected, the position of a leakage point is determined according to a related positioning fusion algorithm, comprehensive decision making is carried out on the leakage point and the leakage point determined through flow gradient analysis and reverse hydraulic power in the step S3, and finally the accurate position of the pipeline leakage point is judged. The problems of low precision, weak interference resistance and the like of a traditional method can be solved, and accurate detection of leakage points is realized.

The invention discloses an intelligent pipe network leakage detection method based on edge calculation, and relates to the technical field of pipe network monitoring and leakage detection, and the method comprises the steps: carrying out the feature extraction of a preprocessing data set through an edge analysis terminal, and obtaining a pressure stability coefficient, a sound wave correlation coefficient and a flow mutation coefficient; inputting the pressure stability coefficient, the sound wave correlation coefficient and the flow abrupt change coefficient into a leakage probability evaluation model, and outputting a leakage risk index; and the edge decision terminal generates a leakage level signal according to the leakage risk index and triggers a positioning instruction. The pressure stability coefficient, the sound wave correlation coefficient and the flow abrupt change coefficient are extracted through the edge analysis terminal and input into the leakage probability evaluation model, the leakage risk index is output, accurate quantitative evaluation of the leakage risk is achieved, the model can adapt to changes of different working conditions through introduction of the dynamic weight, and the reliability of the model is improved. And the adaptability and accuracy of evaluation are improved.

The invention discloses an intelligent water affair management method and system based on artificial intelligence, and relates to the technical field of intelligent water affair management, and the method comprises the steps: initializing a pipe network digital twinborn model, loading pre-stored pipe network GIS data, collecting pipeline pressure data, water quality parameters and user water consumption in real time, inputting a risk assessment model, calculating a real-time risk probability, and carrying out the real-time risk assessment. When the real-time risk probability exceeds an early warning threshold value, generating a high-risk early warning signal; based on the high-risk early warning signal, a differential order parameter and an integral gain parameter are calculated through a pipe state fractional order controller, a voltage stabilization control instruction is generated and executed, and meanwhile a control error signal in the voltage stabilization process is extracted; inputting the control error signal and the water source type label into the element reinforcement learning model, and dynamically optimizing the differential order parameter and the integral gain parameter to obtain a water source risk coefficient; according to the method, personalized modeling and dynamic updating of risk assessment are realized by outputting the dynamic response characteristic data.

The invention relates to the technical field of pipeline safety, and discloses a hydrogen-doped pipeline hydrogen damage grading evaluation method based on fracture mechanics. Comprising the following steps: acquiring initial pipeline state data by establishing a hydrogen-doped pipeline digital twin model; then introducing a hydrogen concentration dependency term to correct the J integral expression, and performing stress analysis based on the J integral expression; generating a material toughness attenuation curve based on a stress analysis result; hydrogen diffusion and accumulation rate parameters are obtained based on a material toughness attenuation curve; predicting a damage development track in combination with real-time operation data; establishing a grading standard system of dynamic response based on the model for risk assessment; and finally, real-time monitoring and scientific grading of the hydrogen damage of the hydrogen-doped pipeline are realized. According to the invention, comprehensive dynamic evaluation of hydrogen damage of the hydrogen-doped pipeline is realized, and scientific decision support is provided for safe operation of the pipeline.

The invention relates to the technical field of water supply pipeline detection, and discloses an underground water supply pipeline health grade evaluation and risk prediction method, which comprises the following steps: sensor arrangement: arranging a flow sensor, a pressure sensor and a sonic sensor at key positions of an underground water supply pipeline; and data acquisition: acquiring signals of the sensor in real time through a data acquisition module, wherein the signals comprise flow, pressure and sound wave signals. Preprocessing the data: carrying out preprocessing such as denoising and normalization on the collected signals; and multi-source data fusion: inputting flow, pressure and sound wave signals into a deep neural network model, and performing feature extraction and fusion analysis. And health level assessment: assessing the health level of the pipeline based on the output result of the deep neural network model. And risk prediction: predicting abnormal working conditions possibly occurring in the future and risk levels of the abnormal working conditions by analyzing the current pipeline state and historical data. And abnormal positioning: accurately positioning an abnormal position in combination with the propagation time of the sensor signal and a positioning model of the deep neural network.

The invention discloses a fuel gas intelligent valve with point type laser methane sensors, and relates to the field of fuel gas intelligent valves, and the fuel gas intelligent valve comprises an equipment installation module which is used for installing three point type laser methane sensors and collecting interface methane concentration values; the data acquisition module is used for acquiring pipeline parameters and environment parameter data inside and outside the interface; the risk assessment module is used for calculating the methane concentration change rate according to the methane concentration value and assessing whether gas leakage exists at each interface; the fuel gas leakage positioning module is used for constructing a fuel gas leakage positioning model based on a neural network and acquiring the probability of the fuel gas leakage risk at each interface; the valve self-adjusting module is used for determining whether the gas leakage point is a leakage point or not according to the gas leakage risk probability; and the historical data analysis module is used for calculating the precision ratio, the recall rate and the F1-Score value by using the historical data and the field actual measurement result. The leakage risk of the gas pipeline is effectively diagnosed and positioned, and the opening degree of the valve is intelligently controlled.

The invention relates to the technical field of pipe network data processing, in particular to an automatic monitoring data processing method for pipe network pressure. The method comprises the following steps: acquiring pipe network structure data; constructing a pipeline topology network based on the pipe network structure data to generate the pipeline topology network; screening main pipelines and branch pipes of the pipeline topology network to obtain pipe network circulation data; carrying out pipe wall microwave resonator deployment on the main pipeline and the branch pipelines based on the pipe network circulation data, and capturing pipeline circumferential deformation harmonic waves in real time through the deployed microwave resonators; longitudinal wave propagation time delay of pipe network circulation data is analyzed, and a pipe body structure dynamic resonance atlas is constructed in combination with pipeline circumferential deformation harmonic waves; and carrying out pipe network internal pressure and pipe wall equivalent stress calculation on the pipeline topology network through a pipe body structure dynamic resonance map. By combining pipeline topology analysis, microwave resonator monitoring, dynamic resonance spectrum and bidirectional fluid-solid coupling analysis, the accuracy of pipe network pressure monitoring and identification is improved.

The invention relates to the technical field of environmental pollution monitoring, in particular to an on-line environmental pollution monitoring method and system.The method comprises the steps that original acoustic signals of the inner side wall and the outer side wall of a curve are collected in real time in a high-risk monitoring area, propagation path compensation is conducted, and an inner side acoustic characteristic parameter set and an outer side acoustic characteristic parameter set are obtained; analyzing to obtain thickness values and adhesion strength values of biological membranes on the inner side and the outer side of the curve; inner and outer side falling risk factors are obtained through calculation, and a comprehensive falling risk value of the inner and outer side falling risk factors is calculated; when the comprehensive shedding risk value exceeds a preset shedding risk threshold value, generating biological membrane shedding risk early warning information; the monitoring problem of spatial distribution heterogeneity of the biological membrane at the pipe network bend is effectively solved, and accurate early warning of the falling risk of the biological membrane in the bend area is achieved.

The invention relates to the technical field of pipeline detection, and discloses a pipeline magnetic flux leakage detection method and device, and the method comprises the steps: obtaining sensor array arrangement parameters and original magnetic field data; establishing a three-dimensional sensor network according to the sensor array arrangement parameters to obtain space coordinate information; performing adaptive signal conditioning and feature extraction according to the original magnetic field data and the space coordinate information to obtain a magnetic flux leakage defect candidate region; and comparing the magnetic flux leakage defect candidate region with a magnetic flux leakage characteristic database to obtain a defect position and a severity level. According to the method, accurate positioning and automatic grading evaluation of pipeline defects can be realized.

The invention relates to the technical field of water supply pipe network monitoring and computers, and discloses a precise water leakage positioning method and system based on water supply pipe network leakage monitoring, and the method comprises the steps: obtaining pipe network time sequence data and a topological structure, and constructing a structured model; performing multi-source data cross validation according to the model, and determining a deviation source node; historical data of the deviation source node are extracted for simulation, and a confirmation result of the leakage event is obtained; analyzing the space-time gradient change of the upstream pipeline flow, and accurately positioning the coordinates of the water leakage point; the water leakage point coordinate enhancement model is fed back, consistency verification is carried out, and accurate coordinates are obtained; and integrating physical details of the pipe network to generate a final positioning result. By establishing a set of intelligent analysis closed loop from data fusion positioning to feedback verification, the problem of insufficient water leakage point positioning accuracy in the prior art is solved.

The invention belongs to the technical field of pipe network monitoring control, and particularly relates to an urban pipe network leakage detection method and system.The urban pipe network leakage detection method includes the steps that firstly, data are collected at key nodes and pipe sections of a pipe network through a data collection module, and a four-dimensional original data set with spatial positioning attributes is formed; the preprocessing module adopts an improved Kalman filtering algorithm containing a pipe network material attenuation coefficient to reduce noise, and outputs high-quality data; the space-time fusion module is combined with GIS pipe network topological data, the topological weight is calculated through a node degree centrality algorithm, and a time attenuation mechanism and adjacent node features are fused to generate a comprehensive feature matrix; the dynamic judgment module judges suspected leakage based on a multi-parameter weighting model and a sliding window dynamic threshold value; the positioning engine module positions a leakage point through a two-factor algorithm of signal time difference and improved hydraulic model residual error; and finally, the linkage control module starts secondary acoustic verification, valve control, early warning and maintenance scheduling instructions are automatically generated after confirmation, and closed-loop management from detection to disposal is achieved.

The invention discloses a multi-parameter fusion pipe network overflow prediction system and method, relates to the technical field of pipe network overflow prediction, and aims at overflow risks caused by equipment faults, unstable communication and extreme weather in a combined drainage system. A system scheme integrating five steps of predictive maintenance, reliable real-time control, digital twinning check, layered emergency plan and cloud edge cooperation is provided; in the first step, equipment health is monitored and faults are predicted to ensure that key pump stations and gates are available; 2, generating a fault-tolerant scheduling instruction by adopting reliable optimization and multi-agent reinforcement learning; 3, pre-judging and correcting the deviation by utilizing digital twinning check before execution; 4, dealing with a single-point fault and communication paralysis by using a layered distributed emergency mechanism; 5, cross-regional parallel simulation and self-adaptive scheduling are realized through cloud edge collaboration and federated learning; overflow can be reduced, environmental pollution and waterlogging loss under the rainstorm condition can be reduced, and efficient collaborative management of the drainage system is achieved.

The invention relates to the technical field of bearings, and provides a hydrostatic bearing gas supply system which comprises a first gas supply subsystem and a second gas supply subsystem, the first gas supply subsystem comprises a first pipeline, a first check valve, a first reaction mechanism, a second reaction mechanism and a degassing structure, and the first reaction mechanism, the second reaction mechanism and the first pipeline are sequentially connected; a reactant is arranged in the first reaction mechanism, a catalyst is arranged in the second reaction mechanism, and the first check valve and the degassing structure are arranged on the first pipeline; the second gas supply subsystem is connected with the first pipeline; when the gas source is cut off, the reactant and the catalyst are subjected to chemical reaction in the second reaction mechanism to generate water vapor and oxygen, the degassing structure is used for consuming the oxygen and generating superheated steam, and the superheated steam enters the hydrostatic bearing. The air supply system for the hydrostatic bearing provides the pure water vapor working medium for the hydrostatic bearing, so that the normal operation of the hydrostatic bearing is ensured, and the hydrostatic bearing is prevented from being damaged.

The invention relates to the technical field of air leakage big data analysis, in particular to a compressed air pipeline air leakage intelligent analysis system and method based on LLM. A plurality of monitoring nodes in a compressed air pipeline are analyzed, and data normal range parameters and adaptive acquisition frequency are preset for each node in the pipeline operation stage; the clear definition standard avoids misjudgment of original data due to fuzzy threshold values, then data noise is effectively reduced by analyzing the data synchronization degree of each monitoring node and intelligently repairing the problems of missing, deviation and the like, the repaired data better fits the actual operation state of the compressed air pipeline, the missed detection and mispositioning risks are reduced, and the reliability of the compressed air pipeline is improved. And finally fusing the data of the gas leakage node and the adjacent node to supplement multi-dimensional information, and optimizing the fusion data quality. The LLM model depends on high-quality fusion data, deep analysis is carried out in combination with compressed air pipeline multi-monitoring-node correlation characteristics and historical experience, deviation caused by data defects is reduced, and the accuracy of an intelligent air leakage analysis result is greatly improved.

The invention discloses an oil product pipeline leakage monitoring method, device and equipment and a medium, and relates to the technical field of pipeline monitoring, the method comprises the steps that a graph model of a pipeline monitoring network is constructed, and each node pipeline in the graph model is provided with a measuring point; collecting pipeline data of each measuring point in real time, wherein the pipeline data comprises temperature, pressure and flow data; performing feature extraction on the pipeline data to obtain various feature data; determining a pipeline working condition for the various characteristic data corresponding to each measuring point, and adjusting a fusion weight for fusing the various characteristic data in real time by adopting a reinforcement learning algorithm based on the pipeline working condition; according to each fusion weight, performing feature fusion on the multiple feature data to obtain a multi-source fusion feature; inputting the multi-source fusion feature of each measuring point into a sensor fault recognition model, and outputting a sensor fault mark; and inputting the multi-source fusion feature of each node carrying the sensor fault mark into a leakage identification and positioning model, and outputting the leakage probability of each node. The prediction accuracy can be improved.

The invention relates to the technical field of safety monitoring, and provides an oil and gas pipeline leakage monitoring method and system, a medium and equipment, and the method comprises the steps: obtaining a leakage vibration signal of each measurement point of an oil and gas pipeline, and extracting a time domain feature, a frequency domain feature, a spatial-temporal feature and an initial distance from a leakage point to a starting end of an optical fiber; performing cross-correlation analysis on the leakage vibration signals of the adjacent measuring points to obtain a propagation time difference and a cross-correlation coefficient, and calculating a correction distance from the leakage point to the starting end of the optical fiber; if the leakage point is in the spiral section, calibrating the correction distance to obtain a calibration distance; calculating the difference value between the initial distance from the leakage point to the optical fiber starting end and the corrected distance to obtain a preliminary positioning error value; and based on the time domain feature, the frequency domain feature and the spatial-temporal feature, in combination with a cross correlation coefficient, a calibration distance, an amplitude enhancement coefficient and a preliminary positioning error value, obtaining a leakage medium type through a medium identification model. The method gives consideration to the positioning efficiency and precision, and greatly improves the accuracy and robustness of a leakage medium type recognition result.

The invention discloses an IoT fire-fighting spraying pipe network intelligent micro-leakage alarm method and system based on time period pressure drop and pressure fluctuation analysis. According to the method, a multi-node IoT pressure sensor is deployed to collect pipe network pressure data in real time, and a pressure drop-fluctuation joint detection model is constructed by adopting frequency domain analysis combining dynamic time period division and pressure drop rate calculation with wavelet transform. Multi-modal feature fusion is realized by using a Transform-LSTM-GNN hybrid model, graded alarm is triggered through a dynamic threshold value, and a leakage point is positioned based on a pipe network topology and a pressure gradient. The system comprises a data acquisition module, a feature analysis module, an AI model training module, an alarm decision module, a maintenance scheme generation module and the like, and a'monitoring-analysis-alarm-maintenance-optimization 'closed loop is formed. Time period dynamic division is realized through a DTW-BIRCH algorithm, and the model is optimized through a multi-task loss function joint; topological feature fusion is enhanced through a graph attention network; and the detection precision is continuously improved through an incremental learning mechanism, and early-stage accurate identification of tiny leakage can be realized.

The embodiment of the invention provides a pipeline blockage monitoring method and system for wet metallurgy slurry conveying, and belongs to the technical field of pressure measurement and pipeline blockage monitoring. The method comprises the following steps: collecting multi-point pressure signals along a slurry pipeline, and extracting pressure pulsation amplitudes and phase characteristics in a pump fundamental frequency and frequency multiplication range based on the multi-point pressure signals so as to establish an on-way pulsation transmission sequence; determining equivalent on-way impedance based on comparison between the on-way pulse transmission sequence and a reference working condition, and outputting an initial judgment result for representing a blockage risk; flow temperature and viscosity estimation parameters are introduced based on the initial judgment result to carry out rheological correction on an impedance threshold value, and a high-frequency phase difference is extracted at an elbow section to update a candidate blockage position; and based on the updated candidate blockage position and the corrected impedance threshold, fusing a phase stability index, and generating and outputting a current blockage evaluation result. According to the scheme, early-stage accurate positioning and stable judgment of slurry pipeline blockage can be achieved under complex working conditions.