5923 results about "Gas pipeline" patented technology

The invention provides a gas leakage time-space correlation early warning method and system based on multi-modal data fusion, and relates to the technical field of data processing, and the method comprises the steps: collecting real-time concentration data and a historical leakage accident database through an NB-IoT combustible gas sensor network disposed at a key node of a gas pipe network; according to the real-time concentration data and a historical leakage accident database, a UTM projection coordinate system is adopted to carry out registration on geographic space data, a three-dimensional space mapping model of a pipe network topological structure is established, timestamp alignment is carried out on all dynamic monitoring data streams through an NTP clock synchronization protocol, and a time-space correlation standardized data set is formed. According to the invention, full-chain intelligent management of the gas leakage risk from sensing, prediction to disposal is realized.

The invention discloses a gas pipe network fault positioning method based on a space-time diagram neural network, and the method employs an edge-cloud collaborative architecture, and achieves the precise detection of leakage points through simulation data generation, multi-modal perception, dynamic space-time modeling and hierarchical positioning. Firstly, simulation modeling is conducted on a pipe network, a multi-working-condition leakage data set is generated, and monitoring point layout is optimized through fuzzy clustering; a multi-modal sensing unit and a lightweight anomaly detection module are deployed at an edge end to realize coarse-grained anomaly detection and data hierarchical transmission; the cloud constructs a dynamic space-time diagram neural network, integrates a pipe network topological structure, multi-source time sequence data and physical constraints, and realizes high-precision positioning of leakage points through a hierarchical positioning strategy; and finally, realizing online evolution of the model through elastic weight solidification and hierarchical parameter updating. According to the method, the space-time diagram neural network and the physical characteristics of the pipe network are deeply fused, and the problems that space-time coupling features are difficult to extract and physical constraints are missing in complex pipe network fault positioning are solved.

The invention provides an urban gas pipeline micro-leakage early warning method and system, and relates to the technical field of data processing, and the method comprises the steps: employing a fuzzy analytic hierarchy process to calculate a dynamic risk index based on an evaluation matrix, and triggering the three-stage linkage alarm of an audible and visual alarm, a mobile terminal and an emergency platform when the index exceeds a threshold value; for three-level linkage alarm, a mobile robot is dispatched, a target area is navigated based on thermodynamic diagram positioning and reference coordinates, gas concentration is remeasured through a methane laser detector, a pipeline surface temperature field is scanned in combination with infrared thermal imaging, and a verification result and correction data are bound through a block chain evidence storage technology. And a tamper-resistant full-link evidence chain is formed. According to the invention, through cooperation of early warning and calculation, full-process automation of gas pipeline monitoring, early warning and emergency verification is realized, and the intelligent level of safety monitoring is improved.

The invention relates to the technical field of gas pipeline safety assessment, in particular to a gas pipeline safety assessment early warning system and method based on data analysis. The method comprises the following steps: collecting gas pressure data of a gas pipeline at multiple points at the same time, carrying out time-space synchronization processing to obtain aligned gas pressure data so as to construct a continuous gas pressure field of the pipeline, based on the gas pressure field, identifying the pressure distribution difference of the pipeline, estimating the transient flow velocity of the gas, continuously recording the transient flow velocity of the gas, and deducing the change of the flow velocity. Judging the deformation condition of the inner wall of the pipeline in combination with air pressure field data, positioning a deformation area, reconstructing a pipeline frame, analyzing deformation response, identifying a deformation type, identifying a corrosion evolution trend, performing safety evaluation, judging the gas circulation degree and performing physical safety evaluation. According to the invention, dynamic monitoring of the gas pipeline is realized, a data-driven safety management mode is promoted, the early warning capability of potential risks is improved, and a more intelligent safety assessment and early warning mechanism is formed.

The invention discloses an air-ground cooperative monitoring and early warning method and system for a buried gas pipeline in a landslide hidden danger area, and the method comprises the steps: carrying out the ground monitoring through employing distributed optical fiber sensing, and enabling an optical fiber sensor to be embedded in the outer wall of the buried gas pipeline or to be laid with an accompanying optical fiber pipeline; collecting multiple physical quantity data of strain, temperature and vibration of the pipeline; in-air monitoring, a multi-sensor platform carried by an unmanned aerial vehicle is used for carrying out in-air inspection; the air monitoring data and the ground monitoring data are fused, and a landslide hidden danger area pipeline safety state evaluation model is constructed; performing real-time intelligent analysis on the fusion data by using an advanced mixed attention convolutional neural network, and identifying a potential catastrophe hidden danger area of the pipeline; and according to a risk assessment result, early warning grading is carried out, and a corresponding emergency plan is formulated. According to the method, real-time and accurate monitoring of the pipeline state can be realized, the landslide hidden danger can be found in time, accurate early warning can be realized, and safe operation of the buried gas pipeline in the landslide hidden danger area can be effectively guaranteed.

The invention relates to the technical field of intelligent inspection, in particular to a pipeline inspection abnormal hidden danger intelligent identification system based on an unmanned aerial vehicle. According to the invention, the three-dimensional grid construction is carried out on the data collected by the unmanned aerial vehicle in the inspection process, so that the space division of the inspection area is realized, and the detection of the gas pipeline has higher precision. In combination with hyperspectral data analysis, measurement of methane concentration can be accurately mapped to space grid points, interference of environmental factors is reduced, and preliminary recognition accuracy of a leakage area is improved. On the basis of local concentration gradient calculation, through a data smoothing and diffusion optimization method, the boundary of a leakage area is clearer, interference of environmental noise is eliminated, meanwhile, the concentration value is dynamically adjusted, and the adaptive capacity to the change trend of leakage points is enhanced. By means of the construction of the virtual diffusion field and in combination with the space occupation condition of terrain obstacles, the analysis of the gas flow path is more accurate, and the problem of misjudgment caused by terrain complexity is solved.

The invention provides an intelligent fuel gas accessory part safety monitoring method, an Internet of Things system and a medium, and relates to the field of fuel gas pipeline accessory facility monitoring. The method is executed by an intelligent gas accessory safety monitoring Internet of Things system, and comprises the following steps: determining a first abnormal value; determining a target acquisition parameter based on the first abnormal value; determining an acquisition instruction based on the target acquisition parameter; determining a second abnormal value; and determining a maintenance instruction and / or a parameter adjustment instruction based on the second abnormal value. The Internet of Things system comprises a government safety supervision management platform, a government safety supervision sensing network platform, a government safety supervision object platform, a gas company management platform, a gas company sensing network platform, a gas equipment object platform and a gas pipeline maintenance object platform. The method can be operated after a computer instruction stored in a computer readable storage medium is read. According to the method, real-time monitoring and intelligent management of the appurtenances can be realized, so that the detection result is more comprehensive and accurate.

The invention discloses a three-dimensional GIS (geographic information system) system for safety assessment of an urban gas pipeline internet of things. A gas pipe network monitoring and assessment system combining the internet of things, a mobile internet and BIM / GIS information is established. Firstly, an overall framework based on sensing, transmission, knowing and use of the internet of things is established; secondly, a front-end sensing system deployment scheme is established, a database system is built, and index data are collected and stored; thirdly, a gas leakage hazard model is constructed, and possible hazard consequences are evaluated; 4, a pipe network monitoring system application layer module is constructed, and underground space information management, safety analysis, prediction and early warning and an auxiliary decision making module are achieved; and establishing a gas pipe network risk identification module, a basic information checking module, a monitoring and explosion early warning module, a three-dimensional visualization module and an auxiliary decision-making module. The system monitors the operation condition of the gas pipe network in real time, analyzes the operation rule of the gas pipe network, evaluates the risk of the gas pipe network, predicts, early warns and decides in time, changes passive first-aid repair into active protection, and is high in monitoring stability, precision and efficiency.

The invention relates to the field of urban gas pipeline integrity management, and discloses a ground gas leakage diffusion simulation calculation method, which comprises the following steps of: initializing environmental parameters and leakage physical conditions, constructing a rotation alignment wind field calculation grid, and dividing dense and sparse time sequences by adopting a dynamic time step strategy; a leakage flow dynamic sequence is generated in combination with linear interpolation, diffusion coefficients are calculated based on atmospheric stability level sections, and an intermediate coefficient matrix is pre-calculated to optimize the calculation efficiency of a Gaussian puff model; an RK4 numerical solution is introduced to process an unsteady state diffusion process, and fusion of an analytical model and a numerical method is realized; and performing standard condition conversion, anomaly correction and structured storage on the concentration data to generate a multi-dimensional visualization result. According to the method, minute-level high-resolution grid leakage diffusion simulation is realized, leaked gas volume concentration distribution and diffusion trend are accurately output, and reliable technical support is provided for smart city operation, city gas safety management, real-time early warning and emergency decision making.

Embodiments of the present disclosure provide a method, an Internet of Things (IoT) system, and a medium for pipeline repair based on smart gas. The method is executed based on a government supervision and management platform of the IoT system for pipeline repair based on smart gas. The method includes: obtaining pipeline repair equipment information and pipeline data to be repaired; determining a pipeline repair sequence based on the pipeline repair equipment information and the pipeline data to be repaired; based on the repair commands, performing at least one of the following operations: receiving feedback information; determining an updated collection frequency for a pressure regulating equipment and a gas metering device deployed on a target gas pipeline based on the feedback information; and generating a repair update command based on the pipeline data to be repaired, a pressure regulating parameter, a gas supply parameter, and a monitoring data.

The invention relates to the technical field of urban public safety, in particular to an underground gas pipe network safety pre-warning system, which comprises an acquisition module, a monitoring module, a monitoring module and a control module, and is characterized in that the acquisition module is used for arranging sensing nodes at key positions of a gas pipeline body and a surrounding environment to form a distributed monitoring network; the method comprises the following steps: synchronously acquiring mechanical deformation, methane concentration and mechanical vibration data of a pipeline through a distributed monitoring network to obtain a multi-modal sensing data set; the fusion module is used for performing humidity and temperature interference correction on the methane concentration data in the multi-modal sensing data set to obtain corrected methane concentration data; and performing fusion processing on the mechanical deformation data, the mechanical vibration data and the corrected methane concentration data to obtain an initial pipeline safety state evaluation index. Through multi-source data fusion, environmental interference correction and spatial domain calibration, transmission and dynamic analysis are combined, and precise early warning and efficient linkage disposal of the safety state of the gas pipe network are achieved.

The invention discloses a gas pipeline and subway safety distance optimization method based on numerical simulation, and relates to the technical field of subway tunnel safety, and the method comprises the steps: simulating gas pipeline explosion data through a KFX Exsim gas explosion model; establishing a fluid-solid coupling model, a soil dynamic model and a thermodynamic coupling model, and coupling the three models to obtain a multi-physics coupling model; based on a multi-physics field coupling model, performing decomposition and region division on a numerical simulation task in combination with a parallel computing framework, and simulating the influence of gas pipe explosion on the subway; based on a numerical simulation result, evaluating the safety of the tunnel under the action of the explosion load; based on a safety evaluation result, optimizing a safety distance between the gas pipeline and the tunnel; according to the method, the fluid-solid coupling model, the soil dynamic model and the thermodynamic coupling model are established and coupled to form a multi-physical field coupling model comprehensively covering the explosion environment, and accurate simulation of complex behaviors of the subway tunnel in the explosion environment is achieved.

In order to overcome the defects in the prior art, the invention provides an urban gas pipeline risk assessment method based on a knowledge graph, and the method comprises the following steps: 1, collecting historical information of gas pipeline accidents, including detailed information of accident causes, types and consequences, and collecting images, videos and text reports of accident sites, needed information is identified and extracted, then multi-modal fusion is carried out to obtain a comprehensive feature vector, and an urban gas pipeline-risk source knowledge graph is established. And step 2, constructing an urban gas pipeline digital model. And step 3, acquiring related information of the running state of the urban gas pipeline in real time, and inputting the information into the urban gas pipeline-risk source knowledge graph for risk assessment. And for risk points which may form risks, marking and prompting the risk points on the urban gas pipeline digital model, and / or forming alarm information. Through the advanced knowledge graph technology, early warning and real-time response to gas pipeline accidents are achieved, and the overall emergency management efficiency and scientificity are improved.

The invention provides a verification method of a mass flow controller and a chemical vapor deposition machine. The method comprises the following steps: in a gap between two adjacent process treatments, setting a first mass flow controller to be a preset flow, introducing first gas into a vacuum reaction chamber through a first gas pipeline, and measuring the gas pressure change of the reaction chamber to obtain a first pressure change rate; setting a second mass flow controller as a preset flow, introducing a second gas into the vacuum reaction chamber through a second gas pipeline, and measuring the gas pressure change of the reaction chamber to obtain a second pressure change rate; obtaining a detection relation coefficient based on the first pressure change rate and the second pressure change rate; and when the deviation ratio between the detection relation coefficient and a pre-obtained reference relation coefficient is greater than a preset threshold value, determining that the at least one mass flow controller has abnormal deviation. Through the method, the gas flow deviation of the machine can be detected, and the flow control condition of the mass flow controller is fed back, so that the use efficiency of the machine is improved, and the detection cost is reduced.

The invention discloses a community fuel gas system management method and system based on fuel gas safety, and relates to the technical field of fuel gas system management.The method comprises the following steps that basic data of an old community fuel gas system are collected, field inspection is conducted on fuel gas pipelines, valves and combustion appliances in a community, and current situation data are recorded; risk assessment is carried out based on the on-site inspection result and basic data of the gas system, and potential safety hazards and weak links are identified; and potential safety hazards and weak links are identified according to risk assessment. Sensors are installed on gas pipelines and key equipment, key data of pressure, flow and leakage detection are collected and processed in real time, early warning of different levels is triggered once abnormal data are monitored, a corresponding early warning mechanism is generated, and therefore the safety monitoring level of a gas system is improved, and the safety of the gas system is guaranteed. Potential safety hazards can be found and early warned in time, and accidents are effectively avoided.

The invention discloses an intelligent ground mobile high-precision gas leakage inspection and source tracking system, and relates to the technical field of gas pipe network leakage detection and leakage point positioning analysis. The system is mainly composed of a mobile inspection platform, a mid-infrared laser detection device, a three-dimensional anemograph, an inertial navigation module, a GIS system, a GNSS navigation positioning system, a wireless communication module and an intelligent detection comprehensive platform, and multi-source information fusion calculation, a dynamic adaptive analysis model and a maximum likelihood estimation method are combined. According to the intelligent gas leakage detection system, autonomous inspection can be achieved in a complex environment, abnormal gas component data can be processed in real time, detected gas space-time distribution information is transmitted to an intelligent comprehensive platform through a wireless communication module, data sharing and monitoring are completed, and efficient detection and accurate positioning of gas leakage are achieved. The method is suitable for gas leakage monitoring and component tracking in the fields of urban gas pipe networks, industrial parks and environmental protection, and the efficiency and precision of gas detection are remarkably improved.

The invention provides an intelligent gas pipeline sampling monitoring method, an Internet of Things system and a storage medium, and relates to the technical field of gas supervision. The method is executed by a government safety supervision management platform, and comprises the following steps: acquiring pipeline sensing data; determining sampling parameters based on the pipeline sensing data; and in response to acquisition of the sampling data from the sampling device, determining a fault detection instruction based on the sampling data, and sending the fault detection instruction to the gas maintenance object platform to arrange manual inspection. The Internet of Things system comprises a government safety supervision management platform, a government safety supervision sensing network platform, a government safety supervision object platform, a gas company sensing network platform, a gas equipment object platform and a gas maintenance object platform. The method can be operated after a computer instruction stored in a computer readable storage medium is read. The gas in the pipeline can be reasonably sampled, so that the running state condition of the pipeline can be accurately evaluated, and the safety and stability of the pipeline are favorably maintained.

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 discloses a risk assessment method, system and terminal based on a gas pipeline, and the method comprises the steps: obtaining the sample monitoring data of a plurality of gas monitoring devices attached to the gas pipeline, and building a risk basic model of the gas pipeline according to the sample monitoring data; performing index weight distribution on the risk basic model by using a weight analysis method to obtain a target risk basic model, and training the target risk basic model according to the sample monitoring data to obtain a risk assessment model; and acquiring current sample monitoring data, inputting the current sample monitoring data into the risk assessment model, and outputting a gas risk assessment result of the gas pipeline. According to the method, the risk model is established through the monitoring data, the weight analysis and training are performed, the gas pipeline risk is finally evaluated, and the potential risk of the gas pipeline can be effectively identified and pre-warned by establishing the risk evaluation model, so that the safety management level is improved, and the accident occurrence probability is reduced.

A method (100) of monitoring and controlling for hydraulic and gas distribution pipeline networks comprises the steps of: providing a pipe (1) of polymeric material for water or gas installations suitable for connection in series with other pipes; Arranging and fixing on the diametral surface of said pipes (1) at least a fiber optic conductor (12) provided with an optical transmitting core (15) and at least a Fiber Bragg Grating FBG (14) sensor; arranging coating means (30) around said pipe for the protection of said fiber optic conductor (12); configuring said optical fiber conductor (12) to be connected to a control unit (20) capable of sending a light signal; and configuring said fiber optic FBG sensor (14) to send a reflected light signal dependent on the variation in size of said pipe (1). The invention also includes a pipe (1) or conduit for hydraulic or gas distribution pipeline networks comprising at least one fiber optic conductor (12) and a distribution pipeline network (10) comprising at least a pipe (1).

The invention discloses an intelligent gas pipeline inspection method based on an unmanned vehicle, and relates to the technical field of unmanned detection, and the method comprises the steps: making an inspection plan, and planning an inspection route; establishing a precise map to realize autonomous navigation and positioning, and performing autonomous inspection according to a planned inspection route; an environment monitoring sensor module carried on an inspection unmanned vehicle is used for monitoring the operation environment of a gas pipeline, and when an environment air index is detected to be abnormal, an ultrasonic sensor array is used for identifying a sound wave signal generated by gas pipeline leakage, positioning a gas leakage point and recording and reporting early warning information and on-site conditions to a remote monitoring station; meanwhile, sound-light alarm information is generated; data of sensors along the gas pipeline and in the gas pipeline are read, an inspection video is uploaded to a server, and a special inspection task is completed through remote control. According to the invention, autonomous inspection can be realized, gas leakage can be accurately identified, a gas leakage point can be positioned, and the harm of gas leakage accidents can be reduced.

The invention belongs to the technical field of material life prediction, and particularly relates to an X70 steel gas pipeline multi-working-condition life prediction method based on transfer learning. Aiming at the problem of low prediction precision caused by dependence on single working condition data and large distribution difference between laboratory and actual service environment data in a traditional life prediction method, the invention provides a cross-domain transfer learning framework. The method comprises the following steps: acquiring mechanical property data through slow strain tensile tests of different hydrogen doping ratios in a laboratory environment, and constructing a domain self-adaptive life prediction model in combination with multi-modal feature decoupling, dynamic distribution alignment and physical constraint fusion strategies. The method significantly reduces actual scene data requirements, realizes high-precision prediction of the residual life of the pipeline under multiple working conditions, is suitable for oil and gas pipeline health monitoring and hydrogen embrittlement risk assessment, and has the advantages of high efficiency, low cost and high reliability.

The utility model discloses an automatic closing valve for gas pipeline leakage and a self-closing valve thereof, and relates to the technical field of structural design of gas self-closing valves. According to the gas pipeline leakage automatic shut-off valve, a vent hole is formed in the middle of a shell, a leather film is connected to the edge of one side of the shell in a sealed mode, a connecting structure is arranged on the edge of the shell, a gas inlet is formed between the connecting structure and the leather film, and the connecting structure is connected with the inner wall of a gas pipeline in a sealed mode. According to the self-closing valve, a timer is arranged on a valve body, a closing device is arranged at the gas inlet end of the valve body, and a gas pipeline leakage automatic closing valve is arranged at the gas outlet end of the valve body. The problem that in the prior art, automatic closing of the gas pipeline can not be achieved when leakage happens to the gas pipeline, and use safety of gas is affected is solved. According to the gas pipeline leakage automatic closing valve and the self-closing valve thereof, when gas leakage occurs, the gas pipeline leakage automatic closing valve can be automatically closed. And through the closing device of the self-closing valve, the action of the closing valve is further ensured, and the effectiveness of leakage closing is ensured.

The invention provides an intelligent gas pipe network transformation method and system based on the supervision Internet of Things and a medium, and relates to the field of pipe network transformation, the method is executed by a gas company management platform of the intelligent gas pipe network transformation system based on the supervision Internet of Things, and the method comprises the following steps: through a gas company sensing network platform, from an intelligent gas equipment object platform, a gas supply network is established; acquiring gas monitoring data; acquiring historical fault data through the gas database, and determining transformation strategy parameters based on the historical fault data and the gas monitoring data; and on the basis of the transformation strategy parameters, generating a regulation and control instruction, and sending the regulation and control instruction to an intelligent gas equipment object platform through a gas company sensing network platform so as to regulate and control monitoring parameters of gas monitoring equipment of the intelligent gas equipment object platform. The use state of the gas pipeline can be accurately monitored in real time, so that the pipeline needing to be transformed can be found and effectively treated in time, and the overall safety performance of the gas pipeline is remarkably improved.

The invention discloses a standard volume-based mass spectrum signal calibration system and method, and belongs to the field of measurement, the standard volume-based mass spectrum signal calibration system comprises a standard flow generation system and a mass spectrum detection system.The standard flow generation system comprises a standard tank, a first valve, a fifth valve and a micro-leakage valve which are sequentially connected through a gas pipeline; a gas inlet pipeline, a film gauge and a first molecular pump set are connected to a gas pipeline connecting the first valve and the fifth valve in parallel, a second valve is installed on the gas inlet pipeline, and a fourth valve is installed at an inlet of the first molecular pump set. The mass spectrometry detection system comprises a vacuum chamber communicated with the micro-leakage valve through a gas pipeline, a mass spectrometer, a second molecular pump set and a full-scale gauge are connected to the vacuum chamber, and a gate valve is installed at an inlet of the second molecular pump set. The gas release rate can be calculated and continuously adjusted on the premise that a leak hole is not needed, and the problems that in the prior art, a standard leak hole is limited in application range, low-flux measurement is inaccurate, the nominal leak rate is prone to drifting, and cost is high are solved.

The invention provides a multi-scene fusion perception system and method of a humanoid robot olfaction sensor, and relates to the technical field of intelligent sensors, and the system comprises an olfaction perception optimization module which is used for obtaining an olfaction perception optimization network; the first matrix module is used for monitoring a gas pipeline to obtain a first matrix; the second matrix module is used for performing interference compensation according to a real-time environment; the third matrix module is used for correcting according to the state of the sensor; the leakage detection module is used for detecting pipeline leakage; and the suppression verification module is used for carrying out false alarm suppression verification on the pipeline leakage detection result to obtain a pipeline early warning signal. The gas leakage detection method and device can solve the technical problem that the gas leakage detection precision is limited due to the fact that the gas leakage detection device is prone to environmental interference and high in false alarm rate in the prior art, and can adjust the multi-node olfaction sensing network in real time through historical data, conduct multi-level correction on monitoring data and introduce false alarm suppression verification, thereby improving the detection accuracy of gas leakage. The accuracy of gas leakage detection in a multi-scene environment is improved.

The invention discloses a gas pipeline leakage detection method based on an unmanned aerial vehicle, and the method comprises the following steps: S1, controlling the unmanned aerial vehicle to fly along a preset pipeline inspection path, and synchronously collecting the following data: S2, carrying out the real-time fusion processing of the data collected in S1, and generating a leakage probability score; s3, when the leakage probability score exceeds a preset threshold value, controlling the unmanned aerial vehicle to hover and marking a leakage point coordinate; and S4, generating an evaluation report containing the leakage aperture, the risk level and the environmental parameters, and encrypting and transmitting the evaluation report to the emergency terminal through the hybrid network. The unmanned aerial vehicle is adopted to carry the laser methane sensor, the multispectral thermal imager and the directional microphone array to detect whether the gas pipeline leaks or not, so that the labor intensity of workers is reduced, and meanwhile, the large-scale pipe network detection time can be shortened by adopting multi-machine cooperative task allocation, so that the detection efficiency is improved.

The invention discloses a gas pipeline leakage acoustic detection device and method based on CPO-VMD and multi-feature extraction, and relates to the technical field of gas pipeline leakage detection, and the device comprises a compressed air storage tank, a pressure control valve, a barometer, a pipeline, a seamless steel pipe, a broadband microphone, a data acquisition card and a data storage device. The method using the device comprises the following steps: collecting a sound wave signal of gas pipeline leakage; carrying out adaptive decomposition on the original signal in combination with a CPO algorithm and variational mode decomposition; performing reconstruction and noise reduction on the key modal component through the correlation coefficient; and multi-dimensional features of the reconstructed signals are extracted, the signal features are screened and preferentially selected, and gas pipeline leakage detection and classification are performed through a classification detector. Therefore, by adopting the method, the decomposition effect and the classification precision of the leakage sound wave signals can be effectively improved, and the strong adaptability of a method framework in different scenes is enhanced.

The invention discloses a flow control method and device, a storage medium and semiconductor processing equipment. The flow control method comprises the steps that a second switch valve is switched on, a first switch valve is switched off, and the first actual flow of preset gas in a gas pipeline is detected when a flow controller is set to be the first current preset target flow; the second switch valve is switched off, the first switch valve is switched on, and the current equivalent flow of the preset gas in the gas pipeline is detected when the flow controller is set to be the first current preset target flow; calculating current temperature change values of the reaction chamber and the virtual chamber; calculating a first flow correction coefficient of a flow controller according to the first current preset target flow and the first actual flow; calculating a second flow correction coefficient of the flow controller according to the current temperature change value; and correcting the first current preset target flow of the flow controller by adopting the first flow correction coefficient and the second flow correction coefficient. The problem that the thickness of the etched film changes can be solved, and the first sheet effect is restrained.

The embodiment of the invention provides a high-temperature and high-humidity flue gas generation device. A first carrier gas pipeline is provided with a first carrier gas flow controller and a permeation humidification module; the standard gas pipeline is provided with a standard gas flow controller; the second carrier gas pipeline is provided with a second carrier gas flow controller; the output ends of the first carrier gas pipeline and the standard gas pipeline are communicated with the input end of the gas mixing pipe; the output end of the gas mixing pipe is communicated with the input end of the target gas pipeline; a water bath temperature control part of the permeation humidification module heats the carrier gas to a first target temperature, wherein the first target temperature is close to the upper limit of the working temperature range of the dew-point instrument; a target gas temperature control part on the target gas pipeline heats the target gas to a second target temperature higher than the first target temperature; the control module is used for controlling the first carrier gas flow controller to convey carrier gas with fixed flow, adjusting the carrier gas flow conveyed by the second carrier gas flow controller and adjusting the standard gas flow conveyed by the standard gas flow controller so as to adjust the humidity and the concentration of target gas.