1325 results about "Gas leak" patented technology

The invention discloses an SF6 gas leakage detection method and system based on a photoacoustic spectrum analyzer, and the method comprises the steps: arranging a sampling end to collect an SF6 gas sample, and obtaining stable gas input through constant-current sampling and steady-state pretreatment; steady-state gas is guided into the photoacoustic spectrum analyzer, and resonance frequency stabilization and signal amplification output are achieved through the self-tuning unit; executing double-microphone differential detection and digital filtering processing, and outputting a stable SF6 detection signal with a high signal-to-noise ratio; performing time calibration, abnormity elimination and consistency processing on the detection signal to generate standardized detection data; inputting the standardized data to a Transform model, and performing inversion to generate an SF6 leakage source position and a diffusion path; and displaying an inversion result on a monitoring interface, triggering a sound-light alarm when the inversion result exceeds a limit, and uploading the inversion result to a cloud monitoring platform. According to the invention, the intelligent photoacoustic spectrum system combining photoacoustic-fluid steady-state control and self-tuning detection is constructed, so that high-sensitivity detection and accurate positioning of SF6 gas leakage are realized.

The invention relates to the technical field of gas leakage detection, and discloses an infrared imaging radiation correction method and system for gas leakage detection, and the method comprises the steps: obtaining the distribution data of dark current noise, and the mapping relation parameters of an image gray value and a radiation brightness value; acquiring infrared radiation of the gas leakage scene by using an infrared detector to obtain an original infrared image; based on the distribution data of the dark current noise, performing pixel-by-pixel compensation on the original infrared image to generate an infrared image after dark current correction; extracting the radiation intensity distribution of the infrared image after dark current correction, calibrating the radiation value of each pixel in the infrared image based on the mapping relation parameter between the image gray value and the absolute radiation brightness value, converting the image gray value into the absolute radiation brightness value, and generating the infrared image after radiation calibration; and outputting an infrared image after radiometric calibration for subsequent gas leakage area identification and feature analysis. According to the invention, a high-quality and quantifiable radiation quantification image can be output.

The invention relates to a gas leakage early warning method and system for a chemical industry park. The early warning method comprises the following steps: acquiring real-time temperature and pressure data of a reaction kettle area and a storage tank area; obtaining a historical leakage record of a pipeline joint, and determining a diffusion path prediction boundary; extracting a wind direction and a wind speed in a diffusion path prediction boundary, and determining space-time distribution characteristics; analyzing space-time distribution characteristics, determining an overlapping region and setting concentration monitoring nodes; identifying the concentration contribution ratio of the reaction kettle area diffusion characteristic and the storage tank area diffusion characteristic; integrating the operation data of the pipeline joint diffusion characteristics and the loading and unloading area diffusion characteristics, and determining a diffusion path deviation correction coefficient; combining the deviation correction coefficient with the humidity data of the peripheral diffusion characteristics of the cooling tower; and updating diffusion parameters of the separation tower area diffusion characteristics and the public engineering area diffusion characteristics according to a source intensity estimation result. According to the invention, accurate prediction and early warning of gas diffusion in a complex chemical device area are realized, and the safety management level is improved.

The invention discloses a visual gas leakage detection system and method based on a laser spectrum, and relates to the technical field of gas detection visualization. According to the visual gas leakage detection system and method based on the laser spectrum, the method comprises the following steps that 1, gas monitoring data are collected and preprocessed; 2, extracting a characteristic spectrum from the gas monitoring data, obtaining a gas spectrum reduction value, and separating an overlapped absorption spectrum; 3, estimating the diffusion trajectory of the gas, and calculating a gas diffusion coefficient estimation value and a diffusion rate estimation value; outputting a gas concentration estimated value; 4, gas leakage source points are calculated, gas mass tracking is carried out, and different gas leakage sources are separated; and 5, generating a scatter diagram to display gas concentrations at different positions, and marking the position of a leakage source. The problem that a complex multi-gas environment cannot be recognized due to the fact that homogeneity and heterogeneity possibly exist in the same environment due to leakage of different types of gases in the prior art is solved.

The invention relates to the technical field of gas leakage traceability positioning, in particular to a dangerous gas leakage traceability positioning method and system based on artificial intelligence, and the method comprises the steps: data collection: collecting environment parameters and gas concentration data from fixed and mobile sensors, and deploying the fixed sensors at key nodes to monitor gas diffusion; the mobile sensor is carried on the inspection robot or the unmanned aerial vehicle to dynamically fill the dead zone of the fixed sensor; preprocessing the data, and performing denoising, normalization and feature enhancement on the data; feature extraction and selection: extracting key features by using a machine learning algorithm; model training: training a deep neural network model to predict a leakage source position; monitoring and triggering: monitoring data and triggering traceability positioning when the data is abnormal; accurate positioning is carried out, and a leakage source is positioned by using the model and real-time data; intelligent decision making is achieved, and emergency response suggestions and optimization schemes are provided.

The invention relates to an industrial gas tank storage place early warning method and system. The method comprises the following steps: acquiring image time sequence data at least representing temperature field distribution of a target monitoring area, generating a temperature image sequence, determining area temperature change characteristics and tank body surface reflection characteristics in the area, and generating reflection area probability distribution of the target monitoring area to determine initial gas leakage probability data of the target monitoring area; generating baseline feature data at least representing background thermal field distribution of the target monitoring area; and performing association processing on the initial gas leakage probability data and the baseline feature data to generate fusion time sequence features and determine a gas leakage diffusion weight so as to correct the initial gas leakage probability data to obtain corrected gas leakage probability data. By adopting the method, the gas leakage probability can be accurately judged by comprehensively utilizing temperature field time sequence information, tank body surface reflection characteristics and background thermal field distribution characteristics under the condition of a complex background thermal field, so that early warning is realized.

The invention discloses an indoor gas leakage monitoring and intelligent joint control system, and belongs to the technical field of intelligent safety monitoring systems. Comprising a sensing layer, a network layer, a control layer and a platform layer, the sensing layer comprises a multi-sensor array and an edge computing node, the multi-sensor array is combined with a catalytic combustion sensor, an infrared sensor, a semiconductor sensor and a temperature and humidity sensor, and sensor data are fused through a weighting algorithm so as to reduce the false alarm rate; a temperature and humidity sensor dynamically adjusts a detection threshold value to adapt to different climate conditions, and an edge computing node is deployed with a lightweight machine learning model. Through multi-sensor data fusion and a weighting algorithm, the false alarm rate is reduced to be smaller than 0.1% per year, environment self-adaptive calibration and AI auxiliary decision making are performed, interference of alcohol, perfume and the like is effectively eliminated, different climate conditions are adapted, leakage judgment is completed at an equipment end through edge calculation, cloud delay is avoided, a multi-stage emergency strategy is provided, and intelligent safe ecology driven by the Internet of Things is constructed.

The invention relates to the technical field of leakage detection, and discloses an SF6 gas pressure early warning method, device and equipment, a storage medium and a program product.The SF6 gas pressure early warning method comprises the steps that for target electrical equipment, environment temperature data of the environment where the target electrical equipment is located is used for conducting environment compensation on measured pressure data of SF6 gas, and compensation pressure data is obtained; calculating a corrected pressure change rate based on the compensated pressure data at the plurality of moments; if the compensation pressure data at the current moment does not meet the pressure standard and / or the correction pressure change rate at the current moment does not meet the change rate standard, feature matching is carried out based on the data set at the current moment and the data set at the historical moment, and a target data set meeting the matching range is determined from the data set at the historical moment, taking the SF6 gas leakage condition at the historical moment corresponding to the target data set as the SF6 gas leakage condition at the current moment; and performing early warning based on the SF6 gas leakage condition at the current moment. According to the invention, the accuracy of SF6 gas early warning can be improved.

The invention relates to the technical field of digital twinning, and discloses a gas leakage accurate early warning method and system based on digital twinning. The method comprises the following steps: generating a digital twinborn model of a target area based on three-dimensional point cloud data of the target area; a first spatial position of each target object in the target area is acquired, each target object comprises a plurality of target parts, and a second spatial position of each target part is identified; judging whether gas leakage occurs in a surrounding preset range, if so, acquiring shape information of the gas, identifying a gas leakage source based on the shape information, determining a third spatial position of the gas leakage source, and mapping the first spatial position, the second spatial position and the third spatial position into a digital twin model; and establishing a gas diffusion model, simulating a diffusion path of gas leakage based on the environmental data of the target area, and superposing the diffusion path to the digital twinborn model to form a visual diffusion path, so that the accuracy of gas leakage source position identification can be improved.

The invention belongs to the technical field of tunnel catastrophe, and particularly relates to an intelligent feedback type tunnel catastrophe monitoring method, which comprises the following steps of: 1, deploying a tunnel monitoring system; a temperature sensor, a smoke sensor, an air velocity sensor and a gas concentration sensor are arranged in the tunnel; the temperature sensor is used for monitoring temperature changes in the tunnel. According to the intelligent feedback type tunnel catastrophe monitoring method, the safety and the emergency response capability of the tunnel are effectively improved through real-time data monitoring and analysis and an automatic control mechanism. Firstly, through temperature, smoke, gas concentration and air velocity sensors, the system can detect and identify potential catastrophe risks in a tunnel in time, such as fire, smoke diffusion and harmful gas leakage. Once an abnormality occurs, the system performs catastrophe prediction through an algorithm model, and performs accurate evaluation on a fire propagation path and smoke diffusion in combination with a simulation analysis result, thereby providing reliable data support for quick response.

The invention relates to the technical field of gas sensing detection, and discloses a gas detection method and system based on a quartz tuning fork enhanced photoacoustic spectrum. The method comprises the following steps: constructing a standardized continuous tuning fork signal data stream; dynamically segmenting the data stream based on the gas concentration interval, and extracting time domain and frequency domain features in parallel in each segment to form an initial feature vector; dynamically adjusting an anomaly judgment threshold according to the jump amplitude of the adjacent segment feature vectors, and scanning and screening out an effective analysis interval of a stable concentration state according to the anomaly judgment threshold; performing cross-domain coupling compensation on the feature vectors in the effective interval to generate enhanced feature vectors; and finally, performing reverse traceability reasoning through a gas leakage propagation model based on the enhanced feature vector, and outputting a leakage source position identifier. The device can adapt to a dynamically changing concentration field, and the reliability and traceability positioning capability of gas detection in a complex environment are improved.

The invention relates to the technical field of gas detection, and relates to an optimization method and system for SF6 gas detection in a multi-temperature environment, and the optimization method comprises the steps: obtaining dual-band infrared images of a scene to be detected at the same time, the dual-band infrared images comprising a detection band infrared image and a reference band infrared image; preprocessing the dual-band infrared image; performing registration processing on the preprocessed dual-band infrared image; performing differential operation on the registered dual-band infrared image to obtain a differential image; and determining a dynamic segmentation threshold according to the current environment temperature, and performing threshold segmentation on the differential image according to the dynamic segmentation threshold so as to identify the SF6 gas leakage area. According to the optimization method and the optimization system, the dynamic segmentation threshold value is determined according to the current environment temperature, the problem that the detection performance of a fixed threshold value is reduced due to background radiation changes at different environment temperatures can be solved, and stable and reliable detection sensitivity can be kept within a wide temperature range.

The invention discloses a gas leakage monitoring system for an environment-friendly ring main unit, and belongs to the technical field of environment-friendly ring main unit monitoring. The system comprises a real-time acquisition looped network supervision platform, and a joint data real-time acquisition and storage module, an external environment gas dynamic analysis module, an internal environment gas risk analysis module and an emergency regulation and control decision processing module which are in communication connection with the real-time acquisition looped network supervision platform; multi-source data fusion monitoring of internal and external environments of the ring main unit is realized through the joint data real-time acquisition and storage module, the one-sidedness problem of single parameter monitoring is solved, and the leakage risk identification accuracy is improved; second-level data processing and grading risk early warning are realized by means of a real-time algorithm, and the fault response time is shortened; a grading emergency response mechanism is established based on the comprehensive risk index, different risks are precisely handled, and the operation and maintenance cost is reduced while the equipment safety is guaranteed.

The invention discloses an acousto-optic combined detection and fusion positioning method and device for underground gas leakage, and belongs to the technical field of industrial safety monitoring. In order to solve the problems of high false alarm rate, insufficient positioning precision, poor dynamic environment adaptability and the like of a single sensor in the prior art, the invention provides a multi-modal signal space-time collaborative fusion technical scheme which specifically comprises an acousto-optic heterogeneous sensor, an integrated MEMS ultrasonic array and a tunable laser spectrum module. Microsecond-level synchronous acquisition of acousto-optic signals is realized through a GPS taming clock, and a cross-correlation time delay compensation model is designed to eliminate space-time mismatch errors. And designing a three-stage dynamic fusion framework: improving LMS adaptive filtering to suppress industrial noise, establishing a light intensity attenuation model in combination with dust environment concentration, carrying out multi-modal feature extraction of acousto-optic signals, carrying out signal preliminary fusion, and judging a gas leakage condition. And constructing an acousto-optic Bayesian joint probability model, and calculating the leakage posterior probability. And optimizing positioning parameters based on a double-Kalman filter group, predicting sound wave time delay by forward filtering, and correcting a light diffusion coefficient by backward filtering to realize three-dimensional coordinate solution. Temperature, humidity and dust concentration parameters are fused in real time, a sound velocity calculation model and a light intensity attenuation factor are corrected, and the robustness of complex working conditions is improved. According to the invention, the advantages of acousto-optic signals are combined, errors of various complex environmental factors are further fused and compensated, the overall error is greatly reduced, and the method has the characteristic of dynamic adaptation, and is very suitable for gas leakage detection and positioning in complex environments such as underground environments and the like.

The invention provides a gas leakage detection method and device, equipment, a storage medium and a program product. The method comprises the following steps: obtaining first light intensity attenuation data of a target area, and carrying out inversion reconstruction processing on the first light intensity attenuation data to obtain a laser concentration field; acquiring traffic data in a low peak period in the target area, and determining traffic abnormal data in the traffic data through an anomaly detection model; obtaining discrete pressure sensor data in the target area, and determining pressure topology low point data based on the discrete pressure sensor data; and then fusion processing is performed on the laser concentration field, the flow abnormal data and the pressure topology low point data to obtain a leakage confidence coefficient, an alarm notification and a leakage point position are output when the leakage confidence coefficient exceeds a preset threshold value, and the method is used for achieving the effects of reducing a gas detection blind area and improving the accuracy of a gas leakage detection result.

The invention provides a chemical storage tank gas leakage monitoring system and a gas leakage positioning method. A leakage diffusion model in a chemical storage tank area is constructed; extracting chemical gas diffusion characteristics at each monitoring position according to the leakage diffusion model, and performing inversion calculation on the chemical gas leakage concentration of the chemical storage tank area to obtain different pre-estimated leakage source positions and pre-estimated leakage source intensities in the chemical storage tank area; extracting infrared absorption characteristics of chemical gas at each high-risk leakage point at different moments from an infrared image sequence at different high-risk leakage points in the chemical storage tank area, and performing spatial constraint on each estimated leakage source position to obtain a spatial constraint value of each estimated leakage source position; and performing positioning evaluation on each estimated leakage source position to obtain the positioning confidence of each estimated leakage source position. By adopting the scheme of the invention, the success rate of leakage source positioning search can be improved under the scene of early weak leakage of the chemical gas.

The invention relates to the technical field of image processing, in particular to an intelligent monitoring and early warning method for an under-pressure workover operation site, and the method comprises the steps: obtaining a real-time monitoring video of the under-pressure workover operation site; extracting a dynamic change frame from the real-time monitoring video; carrying out clustering processing on the pixel points of each dynamic change frame to obtain a plurality of clusters; determining a gas purity index of each cluster; based on a gas purity index, determining a gas leakage discrimination degree; and performing leakage early warning based on the gas leakage discrimination degree. According to the method, the characteristic difference of the gas leakage and the water vapor in the dynamic change frame is analyzed, and the dynamic change and the texture characteristics during the gas leakage are combined, so that the gas leakage and the water vapor are accurately distinguished, and the accuracy and the credibility of monitoring and early warning are improved; and on the other hand, by accurately recognizing gas leakage, the risks of environmental pollution and resource loss caused by gas leakage can be reduced, and the efficiency and economic benefits of workover treatment are improved.

The invention discloses a detection device for oil and gas pipeline sealing element production, and belongs to the technical field of pipeline sealing element detection, the detection device comprises a detection table and a gas detection mechanism, the detection table is fixedly provided with a fixing frame, and one end of the fixing frame is provided with a sealing driving mechanism; the gas detection mechanism comprises a gas injection part, a gas detection part and a gas auxiliary detection unit; the gas injection part is used for injecting detection gas into the sealing element body, the gas detection part is used for detecting gas leaked from the sealing element body, the gas auxiliary detection unit comprises a light source and an industrial camera, the light source is inserted into the sealing element body, and the upper portion of the light source is connected with the output end of a first motor; the industrial camera and the gas detection part are both located on the outer side of the sealing part body and rotate around the sealing part body. The device has the advantages that various detection modes are coordinated and matched, and all-directional accurate detection of the defects of the sealing element is realized.

The invention discloses a gas leakage infrared detection algorithm for self-adaptive environmental noise suppression. The gas leakage infrared detection algorithm comprises the following steps: S1, preprocessing input data; s2, adaptive filtering is carried out; s3, carrying out multi-frame fusion, carrying out time-space domain analysis on continuous infrared image frames, separating high-frequency noise and low-frequency gas signals by utilizing wavelet transform, calculating a difference image of adjacent frames by combining an inter-frame difference method, enhancing gas diffusion morphological characteristics, distributing a weight for each frame according to a signal-to-noise ratio, and synthesizing a final image; and S4, post-treatment: concentration quantification and leakage area marking treatment are respectively carried out. According to the gas leakage infrared detection algorithm for self-adaptive environmental noise suppression disclosed by the invention, high sensitivity is kept through a self-adaptive environmental noise suppression algorithm, meanwhile, environmental noise interference is effectively eliminated, and the detection stability and accuracy in a complex scene are improved.

The invention discloses a petroleum pipeline compression resistance detection experiment mechanism, and relates to the technical field of compression resistance detection of novel pipeline materials, the petroleum pipeline compression resistance detection experiment mechanism comprises a workbench, the upper surface of the workbench is fixedly connected with a hydraulic rod, and the output end of the hydraulic rod is fixedly connected with a pressure detection plate; and a working rod is arranged above the side of the pressure detection plate and above the workbench in a nested connection manner. According to the petroleum pipeline pressure resistance detection experiment mechanism, a first air bag can be deformed through movement of an extrusion plate, the inner side and the outer side of a pipeline can be sealed through an expanded fixed air bag, the phenomenon of gas leakage is avoided when internal pressure detection is conducted on the pipeline subsequently, an air compressor supplies air to the interior of a rotating rod through an air supply hose, and the pressure resistance of the pipeline is improved. Therefore, gas is conveyed into the pipeline from the first nozzle and the auxiliary nozzle, gas supply treatment is conducted on different directions in the pipeline, and real-time quantitative evaluation of the internal pressure bearing capacity of the pipeline can be achieved by observing the deformation condition of the pipeline and the gas supply numerical value of the air compressor.

The invention relates to the technical field of infrared thermal imaging, in particular to a gas leakage quantitative calibration method and system based on multispectral feature fusion, and the method comprises the steps: configuring a multiband blackbody radiation source; building a simulated gas cloud cluster environment, obtaining configuration parameters in real time, and performing classified collection on the configuration parameters to construct a four-dimensional parameter space; acquiring infrared thermal imaging data corresponding to the simulated gas cloud cluster environment, and associating the infrared thermal imaging data with the four-dimensional parameter space to form a structured sample set; sharing the structured sample set by using a dual-channel 3D-CNN to extract radiation intensity features and cloud cluster morphological features, and splicing the radiation intensity features and the cloud cluster morphological features to obtain a fusion feature vector; non-linear mapping based on deep learning is carried out on the fusion feature vector to output a concentration prediction value; and introducing an atmospheric transmission correction factor to correct the concentration predicted value so as to obtain a concentration accurate value. The method has the effect of improving the accuracy of gas leakage concentration calibration.

The invention relates to the technical field of detection sensors, in particular to dangerous chemical pipeline gas leakage detection protection equipment and a temporary protection method.The dangerous chemical pipeline gas leakage detection protection equipment comprises a supporting plate, the bottom side of the supporting plate is sequentially connected with an exhaust hopper, a gas storage cylinder and a gas inlet hopper, and an electrical sensor structure is installed in the gas storage cylinder; the electrical sensor is further provided with a jacking air inlet structure, a linkage pull-up structure and a protective spraying structure, the electrical sensor structure improves the detection accuracy and sensitivity through a unique electrode design and a gas flow guide mode, the jacking air inlet structure and the linkage pull-up structure cooperatively control air inlet and closing of the air storage cylinder, segmented detection is achieved, and the electrical sensor is protected; the protection spraying structure is used for plugging during leakage, the temporary protection method comprises the steps of detection, isolation, plugging, verification and marking, detection and temporary protection of dangerous chemical pipeline gas leakage are completed through equipment cooperation, the detection accuracy is improved, and the service life of a sensor is prolonged.

The invention discloses a method for detecting, positioning and classifying underwater gas leakage through multiple beams based on deep learning, and relates to the field of underwater acoustic detection and deep learning. And carrying out azimuth correction on the multi-beam data with attitude offset to obtain multi-beam data without attitude offset. And through operations of seabed detection, median filtering, morphological expansion and the like, a high-quality multi-beam sonar image is generated. And constructing a data set for training an instance segmentation model and a flux classification model. And performing gas plume detection and segmentation on the preprocessed sonar image, and outputting mask information. And extracting a target contour based on a mask, determining plume coordinates through minimum circumscribed rotating rectangle fitting, and calculating the position of a seabed leakage point in combination with a conversion factor. And selecting complete plume data near the position right below the sonar equipment, and classifying leakage flux levels based on plume morphological characteristics by adopting a flux classification model. Multi-beam sonar and a deep learning algorithm are integrated, and detection, positioning and classification of underwater gas leakage are realized.

The invention belongs to the technical field of gas leakage monitoring, and particularly relates to a plume model image data enhanced gas leakage visual detection method and system, and the method comprises the steps: obtaining a background image of a to-be-detected site; establishing a pipeline three-dimensional physical model of a natural gas terminal on a to-be-detected site, setting initial conditions of gas leakage, and generating a gas flow concentration field image; superposing the gas flow concentration field image and the background image; performing data enhancement on the superposed image to obtain enhanced image data; and training the enhanced image data by adopting a deep learning method to obtain a gas leakage detection model, and automatically identifying a leakage area by adopting the gas leakage detection model. According to the method and the device, the enhanced image data can be trained by adopting a deep learning method under the condition of no actual leakage data, so that the gas leakage detection model is obtained, and more efficient and more accurate gas leakage detection and early warning are realized.

The invention relates to the field of combustible gas alarm devices, in particular to a combustible gas alarm method and a combustible gas alarm device, and the device comprises an alarm device terminal which is integrated with a combustible gas detection end, an environmental parameter sensor, a scene recognition module and an alarm module; the central control module is electrically connected with the alarm device terminal, receives data acquired by the alarm device terminal, generates a control instruction and issues the control instruction; the air exhaust module is linked with the central control module and is used for receiving a control instruction sent by the central control module to realize intelligent air speed regulation; the wireless communication module is electrically connected with the central control module and the user terminal; and the execution module is electrically connected with the central control module, and an emergency execution unit is arranged in the execution module. Multi-source data is collected through the combustible gas detection end, the environmental parameter sensor and the flow velocity detection unit, gas leakage information is accurately captured in combination with multi-source data analysis, and compared with traditional single concentration detection, the false alarm rate is greatly reduced.

The invention provides a gas leakage detection method and circuit, and the method comprises the steps: obtaining a plurality of calibration electric signals of calibration concentrations of target gas through employing a gas concentration sensor, and forming a concentration electric signal calibration table; determining the linear correlation between the gas concentration of the target gas and the detection electric signal based on a concentration electric signal calibration table; obtaining a leakage threshold value corresponding to the gas alarm concentration by combining the concentration electric signal calibration table and the linear correlation; a gas concentration sensor is adopted to obtain the real-time detection concentration of target gas in real time, and when the real-time detection concentration reaches a leakage threshold value, an alarm signal is sent out. The method has no destructive influence on the detection environment and no material loss, and is simple in structure, stable in performance, convenient to use, stable in signal and long in service life by detecting the concentration and leakage state of the target gas, and the corresponding detection circuit is small in structure and convenient to mount and dismount.

The invention discloses a mining gas extraction conveying device which comprises at least one protective sleeve used for conveying gas, an extraction pump used for extracting gas and a conveying pipe located in the protective sleeve, and the protective sleeves are fixedly connected through a fixing sleeve in an airtight embedded mode. A first butt joint sleeve in airtight connection with the conveying pipe is fixedly installed in the middle of the fixing sleeve. The inner wall of the protective sleeve is coated with the manganese dioxide coating, ozone is filled in cooperation with the one-way gas pipe, when gas leaks to the protective sleeve in the conveying pipe, the ozone generates hydroxyl free radicals under catalysis of manganese dioxide, the hydroxyl free radicals and methane are subjected to an oxidation reaction, and the hydroxyl free radicals are decomposed into carbon dioxide and water, so that the explosion risk is reduced while the gas concentration is reduced; leaked gas is actively treated by utilizing a chemical catalysis principle, decomposition can be carried out when the gas leaks, the safety of the device is effectively improved, meanwhile, generated water prompts the leakage position through a dyeing patch of a drainage pipe, and timely maintenance is facilitated.

This gas leakage detection method comprises: a temperature distribution acquisition step for acquiring, by means of an infrared camera, the temperature distribution of a detection area including a detection object inside which a gas containing ammonia exists; and a detection step for detecting leakage of the gas containing ammonia in consideration of the temperature distribution of the detection area obtained in the temperature distribution acquisition step.

The invention, which relates to the technical field of monitoring and early warning, discloses a comprehensive monitoring and early warning system and method for fire leaving in a kitchen, and the system comprises a scene sensing module, a preliminary analysis module, a data decision module and an emergency processing module. The scene sensing module is integrated with a camera, a smoke sensor and a gas sensor, and acquired image information, gas concentration and smoke index are transmitted to the preliminary analysis module; face and human body behavior recognition is carried out by adopting a deep learning model, the state of a kitchen fire source is monitored in real time by utilizing a flame state recognition technology, and in a normal state, the system does not intervene and only carries out low-power-consumption monitoring; when temperature abnormity or gas leakage occurs, the system enters an early warning mode, a buzzer alarm device is triggered to prompt a user, emergency disposal is carried out when necessary, timely response can be carried out according to different fire risks, and accidents are reduced.

The invention discloses a multi-modal deep reinforcement learning gas leakage decision optimization method based on optical flow, and the method comprises the steps: carrying out the constraint and correction based on a pixel motion field in combination with wind speed estimation, and obtaining an optical flow reconstruction wind field; based on plume probability distribution, combining with an image intensity and radiation transmission model, obtaining concentration field estimation, fusing gas concentration data and an optical flow reconstruction wind field, and forming full-field state description; establishing a convective diffusion model, predicting the concentration field at the next moment through the convective diffusion model based on the full-field state, establishing a convective diffusion model, and predicting the concentration field at the next moment through the convective diffusion model based on the full-field state. According to the technical scheme of the invention, the dynamic evolution precision of gas leakage can be effectively improved by introducing the wind field reconstruction method of the optical flow and the scatter-free constraint, the prediction stability is improved, the physical consistency of the wind field and the concentration distribution is ensured, and stronger data support is provided for emergency control.