21 results about "Negative pressure wave" patented technology

The invention discloses an underground pipeline intelligent leakage detection method and device, and relates to the field of intelligent leakage detection.The method comprises the steps that a high-frequency pressure sequence of a target pipe section is collected, and based on the Boltzmann superposition principle, the high-frequency pressure sequence is used for constructing a neural lag operator network by taking the environment temperature as a heat rheological regulation factor; taking the full historical stress-strain memory field tensor as input, carrying out non-local memory characteristic and hysteresis loop analysis, carrying out point-by-point differential processing on a high-frequency pressure sequence and the intrinsic nonlinear hysteresis response signal of the pipe, stripping non-stationary background fluctuation, avoiding modulation interference of material nonlinearity on a fluid signal, and obtaining a non-linear hysteresis response signal of the pipe. The method comprises the following steps: extracting the abnormal residual error of the fidelity fluid dynamics, constructing a spatio-temporal evolution Poincare section, inputting the spatio-temporal evolution Poincare section into a convolutional neural network, judging the real leakage negative pressure wave and the random drift of the sensor through the morphological difference of attractors on the spatio-temporal evolution Poincare section, and eliminating the periodic false alarm caused by the rheological property of the material.

The invention relates to a water pipe leakage detection method based on a distributed fiber bragg grating sensing network, and belongs to the technical field of cross application of optical fiber sensors and artificial intelligence. In the process of the method, firstly, a three-dimensional fluid dynamic simulation model of the water conveying pipeline system is established, and training data are generated; secondly, the fiber grating sensors are laid along the pipeline in a distributed mode to form a real-time monitoring network which is used for collecting negative pressure wave signals generated by leakage; then, aiming at a signal noise problem, innovatively adopting a variation mode decomposition method (VMD) combining a genetic algorithm (GA) and a correlation coefficient (CC) to carry out intelligent noise reduction and reconstruction on the signal; then, dimension reduction is carried out on the reconstructed signal by using a compressed sensing (CS) technology so as to reduce the calculation complexity; then, the signals after dimension reduction are converted into logarithmic spectrograms, the logarithmic spectrograms are input into a deep residual neural network (ResNet) for feature extraction, and then the signals are accurately classified into noise, normal, slight leakage, moderate leakage and serious leakage through a two-dimensional convolutional neural network (2D CNN); and finally, according to a classification result and the position of the sensor, calculating and positioning a leakage point by using a negative pressure wave method. According to the method, by fusing advanced signal processing and deep learning technologies, the accuracy, robustness and intelligent level of leakage detection are remarkably improved, and effective monitoring of the health state of the water conveying pipeline is achieved.

A physics-informed data-driven oil and gas pipeline network fault diagnosis method and system for type-imbalanced data scenarios, relating to the field of oil and gas pipeline network fault diagnosis, and aiming at solving the overfitting problem of current intelligent fault diagnosis models during processing of oil field type-imbalanced data sets, and reducing the risks of false alarms and missed alarms. Main steps are as follows: deeply understanding propagation and attenuation mechanisms of negative pressure waves when oil and gas move in a pipeline network, and establishing a negative pressure wave attenuation physical model reflecting the operating state of the pipeline network; on the basis of a long short-term memory network, constructing a deep generative adversarial model suitable for processing time series data; designing a reasonable series-parallel mechanism to fuse the physical model and a data-driven model, so as to construct a hybrid generative adversarial model; using the trained hybrid generative adversarial model to generate pipeline network fault data, and balancing an original training set; and training an intelligent fault diagnosis model to achieve pipeline fault type identification. The present invention considers both the prior knowledge from the physical model and the learning capability of the data-driven model and integrates same, and compared with simple data-driven models, uses the prior knowledge of the pipeline network contained in the physical model to reduce a parameter space search domain, thus reducing the number of estimated parameters, improving the interpretability and generalization performance of a deep generation model, and improving the physical rationality and feature distinguishability of generated fault data. Therefore, the present invention effectively overcomes the negative impact of type-imbalanced data sets on the performance of diagnosis models, further improving the accuracy of intelligent fault diagnosis of pipelines.

This invention discloses a method and system for monitoring leaks in urban low-pressure gas pipelines based on negative pressure waves, comprising: S1, acquiring the medium-pressure signal of the medium-pressure pipeline upstream of the pressure regulating box and the low-pressure signal of the low-pressure pipeline downstream of the pressure regulating box; S2, performing discrete wavelet transform on the medium-pressure signal and the low-pressure signal; S3, if the low-pressure signal shows a sudden pressure drop, it is determined that a low-pressure gas leak has occurred under no-gas-use conditions; otherwise, proceed to step S4; S4, if the maximum fluctuation amplitude of the detail signal after two splits of the medium-pressure signal exceeds a set value or the low-pressure signal shows low-frequency changes, it is determined that a low-pressure gas leak has occurred under gas-use conditions. This invention, based on negative pressure waves, only requires collecting and analyzing the internal pressure of the pipeline to achieve real-time online monitoring. Compared with traditional manual inspections, it saves manpower, is highly accurate, safe, and convenient, and has a short response time and high processing efficiency in leak situations.

The invention discloses a pipeline leakage identification system and method and a storage medium, the system comprises a plurality of data acquisition modules, a remote data transmission module and a cloud server, the plurality of data acquisition modules are respectively and correspondingly arranged on a plurality of detection points of a pipeline, and each data acquisition module comprises a pressure sensor and a flowmeter; the sensors are respectively used for collecting negative pressure wave signals of leakage points and flow signals of corresponding detection points; the remote data transmission module is used for remotely controlling the running state of each data acquisition module and controlling to upload the acquired data in real time; and the cloud server is used for determining the time difference of the negative pressure wave signal of the leakage point reaching each pressure sensor based on the received acquisition data, determining the position of the leakage point according to the time difference and the pre-calibrated negative pressure wave signal propagation speed, and verifying the position of the leakage point through the change of the flow signal. According to the method, the pipeline leakage identification accuracy and efficiency can be improved, and accurate positioning of leakage points can be realized.

This invention relates to the field of analysis and early warning, and discloses an IoT-based regional water supply network leakage analysis and early warning system. The system includes a partitioning module, a fusion analysis module, and an early warning module. The system collects data through sensor arrays deployed in the network partitions. The fusion analysis module executes a negative pressure wave localization algorithm and an acoustic correlation analysis algorithm in parallel, generating first and second localization results respectively. By calculating the Euclidean distance between the two results and comparing it with a preset tolerance, it determines whether to output localization information or trigger model reverse verification. During verification, a dynamic hydraulic model takes real-time data as input, iteratively fits and outputs a virtual leak location, and outputs the final result after a second distance verification. The early warning module generates tiered early warning information based on the analysis results and displays it visually. This invention, by combining multi-algorithm fusion verification with hydraulic model reverse verification, significantly improves the accuracy and reliability of leakage localization and effectively reduces the false alarm rate.

The application discloses a long-distance water delivery pipeline online water hammer tracing method based on negative pressure waves, comprising the following steps: obtaining a pipeline pressure signal of a long-distance water delivery pipeline; preprocessing the pipeline pressure signal of each key node; smoothing filtering the preprocessed signal through an S-G filter; taking the window length, polynomial order and derivative order of the filter as adjustable parameters; adaptively searching and adjusting the adjustable parameter combination; obtaining a filtering result; constructing a comprehensive score function based on the filtering result; screening an optimal parameter combination according to the comprehensive score function; performing difference operation and continuous same sign judgment on the filtering signal of the optimal parameter combination; obtaining a negative pressure wave arrival time; and obtaining a water hammer tracing result according to the negative pressure wave arrival time. The method utilizes adaptive filtering, continuous same sign criterion and the collaborative calculation of a multi-sensor pair, reduces the influence caused by the abnormality of individual sensors or local errors, and improves the real-time performance and reliability of water hammer tracing in long-distance water delivery projects.

A system (3) utilizing internal and external online, real-time direct leak detection based on applying fiber optic sensors (4) to detect leak generated acoustic waves (6) or negative pressure wave inside the pipeline (5) and utilizing the same fiber optic sensing cable (9) or a different fiber optic sensing cable (14) to detect leak outside of the pipeline. This approach encompasses two different fiber optic technologies to detect leaks both internally and externally and involves quasi¬ distributed and / or distributed sensing techniques. This system will detect leaks internally or intrinsically by sensing the leak generated acoustic signals propagated inside tubulars, pipes, pipelines, flow channels, or any pressurized system, as well as externally or extrinsically by detecting leak released fluid or leak event associated phenomena outside of the leaking systems respectively. These two technologies may detect leaks separately or jointly.

The invention belongs to the technical field of AI fusion positioning methods, and particularly relates to a tiny negative pressure wave-AI fusion positioning method for a long-distance heat supply pipe network, which comprises the following steps: dividing a plurality of logic straight pipe sections along a main line of the long-distance heat supply pipe network by taking a heat source, a tee joint, an elbow and a valve as nodes, and arranging a pair of high-frequency pressure sensors at each of two ends and adjacent nodes of each straight pipe section; a high-frequency pressure sensor outputs a current signal, the current signal is converted into a 1-5V voltage signal through a precision resistor, the 1-5V voltage signal is accessed to a synchronous data acquisition card of an edge AI node to form distributed closed-loop data acquisition, the acquired voltage signal is denoised by adopting a db6 wavelet five-layer soft threshold value, a time difference delta t of a leakage wave reaching an upstream sensor and a downstream sensor is extracted through a cross-correlation function, and the time difference delta t of the leakage wave reaching the upstream sensor and the downstream sensor is calculated. Elbow reflection secondary peak identification is introduced to eliminate reflection wave interference, and a formula is adopted to calculate the real-time wave velocity to carry out wave velocity real-time correction.

A kind of oil pipeline abnormal early warning monitoring method and system based on multi-source data fusion attention mechanism, oil pipeline abnormal early warning technology, to realize the early small leakage anomaly detection of oil pipeline by multi-source data fusion technology and deep learning technology.Technical points: collect pipeline operation state historical data from acoustic wave, temperature, negative pressure wave, vibration sensor, construct data set;Get time series segmentation code for algorithm training;Build fusion attention module for algorithm training;Based on the obtained multi-source data fusion attention Transformer model, adopt the training method of adversarial learning to predict the future operation state of oil pipeline;Use the Gaussian distribution with learnable scale parameter to train the oil pipeline early warning model;Use the trained multi-source data fusion attention Transformer model as the oil pipeline abnormal early warning model to identify the abnormal behavior of future operation state and obtain the failure probability.The present application effectively improves the accuracy and reliability of oil pipeline abnormal early warning by combining multi-source data analysis technology and deep learning technology, and using innovative Gaussian kernel scaling parameter and failure probability calculation method, which has important application value in small leakage detection.

The invention provides a product oil skid-mounted station oil storage tank leakage prevention system, which relates to the technical field of oil storage tank leakage prevention and comprises a distributed negative pressure wave sensing network, a central signal processing unit and a human-computer interaction interface. The sensing network is composed of a plurality of sensors arranged on the outer wall of the storage tank and key nodes of a pipeline, the central processing unit collects background signals to establish a steady-state pressure base line, and abnormal conditions conforming to negative pressure wave characteristics are recognized by dynamically comparing real-time signals; calculating the spatial position of the leakage point by adopting a time difference of arrival algorithm based on the time difference of the signals arriving at different sensors; and multi-stage alarm is carried out according to the ratio of the signal amplitude to the baseline noise. Millisecond-level response and accurate positioning of tiny leakage are achieved, an emergency cut-off system can be linked, and the problems of hysteresis and positioning fuzziness of traditional liquid level monitoring are effectively solved.

The invention discloses a pipeline leakage real-time monitoring system, method and equipment and a medium, and belongs to the technical field of pipeline monitoring. The system comprises a double-pressure wave sensor module used for collecting pressure wave signals of a pipeline, and the pressure wave signals comprise transient negative pressure waves in the initial leakage stage of the pipeline and steady-state pressure waves in the continuous leakage stage of the pipeline; the signal processing module is used for processing the transient negative pressure waves and the steady-state pressure waves and extracting leakage parameter characteristics through time-frequency analysis; the data processing module is used for calculating the position of a leakage point by utilizing a time delay estimation method according to the leakage parameter characteristics; and the alarm communication module is used for sending out a leakage alarm signal when leakage is confirmed, and uploading the leakage point position and the monitoring data to a cloud end. The technical problem that pipeline leakage monitoring and positioning are not accurate is solved.

The invention discloses a negative-pressure-wave-based online water hammer tracing method for a long-distance water conveying pipeline, and the method comprises the steps: obtaining a pipeline pressure signal of the long-distance water conveying pipeline, carrying out the preprocessing of the pipeline pressure signal of each key node, carrying out the smooth filtering of the preprocessed signal through an S-G filter, and obtaining a pipeline pressure signal of the long-distance water conveying pipeline; and taking the window length, the polynomial order and the derivative order of the filter as adjustable parameters, performing adaptive search and adjustment on an adjustable parameter combination to obtain a filtering result, constructing a comprehensive scoring function based on the filtering result, and screening an optimal parameter combination according to the comprehensive scoring function to obtain a filtering result. And performing differential operation and continuous same-sign judgment on the filtering signal of the optimal parameter combination to obtain a negative pressure wave arrival time, and obtaining a water hammer traceability result according to the negative pressure wave arrival time. According to the method, by means of cooperative calculation of adaptive filtering, continuous same-sign criteria and multiple sensor pairs, the influence caused by abnormity or local errors of individual sensors is reduced, and the real-time performance and reliability of water hammer tracing in the long-distance water conveyance project are improved.

A system utilizing internal and external online, real-time direct leak detection based on applying fiber optic sensors to detect leak generated acoustic waves or negative pressure wave inside the pipeline and utilizing the same fiber optic sensing cable or a different fiber optic sensing cable to detect leak outside of the pipeline. This approach encompasses two different fiber optic technologies to detect leaks both internally and externally and involves quasi-distributed and / or distributed sensing techniques. This system will detect leaks internally or intrinsically by sensing the leak generated acoustic signals propagated inside tubulars, pipes, pipelines, flow channels, or any pressurized system, as well as externally or extrinsically by detecting leak released fluid or leak event associated phenomena outside of the leaking systems respectively. These two technologies may detect leaks separately or jointly.

The invention relates to the field of analysis and early warning, and discloses a regional water supply network leakage analysis and early warning system based on the Internet of Things, which comprises a partition module, a fusion analysis module and an early warning module. The system collects data through a sensor array arranged in a pipe network partition, a fusion analysis module executes a negative pressure wave positioning algorithm and an acoustic correlation analysis algorithm in parallel, and a first positioning result and a second positioning result are generated respectively; and judging whether to output positioning information or trigger model reverse verification by calculating the Euclidean distance between the two and comparing the Euclidean distance with a preset tolerance. And during verification, the dynamic hydraulic model takes real-time data as input, outputs a virtual leakage point position through iterative fitting, and outputs a final result after secondary distance verification. And the early warning module generates graded early warning information according to the analysis result and visually displays the graded early warning information. According to the method, multi-algorithm fusion verification and hydraulic model reverse verification are combined, so that the precision and reliability of leakage positioning are remarkably improved, and the false alarm rate is effectively reduced.

The invention discloses a multi-mode sensing fusion pipeline micro-leakage accurate positioning system and method, and relates to the field of pipeline safety monitoring. The system comprises a multi-mode sensing array, an edge positioning host and a cloud diagnosis platform. The multi-modal sensing array collects acoustic and negative pressure wave modal sensing signals, after the edge positioning host preprocesses the signals, the multi-modal abnormal probability judgment module fuses depth time-frequency features and statistical waveform features, and the self-adaptive space-time filtering and leakage point positioning module adopts a particle filtering algorithm based on an attention mechanism. The above information is fused for iterative convergence, optimal leakage point position estimation is obtained, a leakage event comprehensive research and judgment module outputs a positioning result in combination with a real-time working condition and a historical model, a cloud diagnosis platform gathers data for optimization, and an intelligent closed loop is formed. According to the invention, high-sensitivity, high-reliability and high-precision positioning of pipeline micro-leakage is realized.

The invention relates to the technical field of microfluid precision control, and discloses a micro-droplet generation method based on a three-section type air pressure waveform, which comprises the following steps: a microcontroller sends a time sequence instruction to drive a pneumatic assembly to act, and stages of constant high-pressure pushing, rapid pressure relief and oscillation negative pressure pullback are sequentially executed in a fluid pipeline; in the pushing stage, the stable-pressure gas is used for driving liquid to extend to form a liquid column; in the pressure relief stage, the micro valve is reset to be matched with the front-end one-way pressure relief valve to quickly discharge residual high pressure; in the pull-back stage, a Helmholtz oscillation system formed by a main gas circuit pipeline and a valve port is used for inducing aerodynamic resonance, and reverse negative pressure waves are generated and directly act on the root of a liquid column to promote physical fracture of the liquid column. Through the synergistic effect of an active excitation negative pressure pull-back mechanism and a front-end rapid pressure relief strategy, trailing caused by fluid viscosity is effectively overcome, satellite liquid drops are eliminated, the problem of pressure lag is solved, and high-precision lossless generation of picoliter-to-nanoliter micro-droplets is achieved.

The invention provides a well leakage simulation device based on negative pressure waves and a well leakage position judgment method and system.The well leakage position judgment method comprises the steps that 1, the negative pressure waves are applied to the interior of a shaft at the position of a shaft outlet, and original negative pressure wave signals at the position of the shaft outlet are obtained; step 2, de-noising the original negative pressure wave signal to obtain a de-noised negative pressure wave signal; step 3, performing cepstrum transform on the denoised negative pressure wave signal to obtain a cepstrum result; and 4, determining the leakage position and the leakage amount of the shaft according to the inverse frequency spectrum result. According to the well leakage position judgment method and system based on the negative pressure waves, downhole tools are not needed, all devices and operations are completed on the ground, underground high-temperature and high-pressure extreme environment conditions are not needed, the system is simple in structure and accurate and timely in response, and a detection result can provide certain reference for well leakage detection on a well drilling site.

The invention relates to a pipeline leakage positioning method and system based on negative pressure waves, and belongs to the technical field of pipeline leakage monitoring. At least two detection terminals are arranged along the pipeline, and each terminal obtains and maintains a unified standard clock through a built-in Beidou satellite time service module, synchronously collects pipeline negative pressure wave data and records an accurate timestamp of the point; carrying out data preprocessing, carrying out mutation feature enhancement and accurate identification, and uploading obtained mutation point timestamps of the upstream and downstream detection terminals to a cloud processing platform; and the cloud processing platform calculates the difference T between the two timestamps, and calculates the accurate position of the leakage point by combining the propagation velocity V of the negative pressure wave in the pipeline medium and the pipeline length L between the upstream terminal and the downstream terminal and utilizing a positioning formula. According to the method, a set of innovative signal processing link and time synchronization mechanism is constructed, so that quick, reliable and meter-scale accurate positioning of a pipeline leakage point is realized.

The invention provides a tailing pipeline leakage detecting and positioning system and method. The system comprises a pressure / flow sensor array, a vibration optical fiber sensor, an unmanned aerial vehicle intelligent inspection unit and a leakage decision center. According to the scheme, firstly, a pipeline full-life-cycle digital twin model is constructed, in the detection stage, multi-source heterogeneous data are collected in real time, and feature extraction and correlation analysis are conducted on negative pressure wave signals, distributed vibration signals and unmanned aerial vehicle inspection images; the credibility of evidences of different sources is dynamically weighted through self-adaptive fusion decision of a large model, and collaborative judgment and high-confidence alarm of leakage events are achieved; in the positioning stage, preliminary area positioning is carried out based on fused sensor signals, then an unmanned aerial vehicle is automatically driven to carry out fine scanning on a high-risk area, leakage points are confirmed through a visual AI model, and accurate geographic coordinates are output. According to the scheme, the problems of fast signal attenuation, high false alarm rate and inaccurate positioning in the complex flow state of the tailing slurry in the traditional method are solved.

The application discloses an underground pipeline intelligent leakage detection method and device, relates to the field of intelligent leakage detection, collects a high-frequency pressure sequence, establishes a time-temperature equivalent equation to calculate a moving factor, maps the high-frequency pressure sequence to a reduced time axis by using the moving factor, obtains a temperature equivalent pressure sequence, and is converted into a full history stress-strain memory field tensor, constructs a neural lag operator network, takes the full history stress-strain memory field tensor as input, carries out non-local memory characteristic and hysteresis loop analysis, carries out point-by-point difference processing on the high-frequency pressure sequence and the pipe material intrinsic nonlinear hysteresis response signal, extracts a fidelity fluid dynamics abnormal residual error, constructs a spatiotemporal evolution Poincare section, inputs into a convolutional neural network, and determines real leakage negative pressure waves and sensor random drift through attractor shape difference on the spatiotemporal evolution Poincare section, so that periodic false alarms caused by material rheological characteristics are eliminated.