4754 results about "Gas concentration" patented technology

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 technical scheme of the invention relates to the technical field of gas detection, in particular to a hazardous chemical storage leakage positioning and tracing method based on a gas array. The method comprises the following steps: collecting gas concentration, components and environmental physical field parameters through a three-dimensional heterogeneous sensor array, and generating a multi-modal data set; and matching the noise mode feature library in the knowledge base by using a transfer learning algorithm, dynamically correcting the baseline drift of the sensor, and outputting calibrated data. A closed-loop adaptive mechanism adjusts sensor parameters and network hyper-parameters through meta reinforcement learning, neural architecture searches and optimizes a model structure, and a differential evolution algorithm updates fluid mechanics boundary conditions. Through cooperation of physical constraint and data driving, the problem of signal distortion in a complex environment is solved, the leakage source positioning precision and robustness are improved, and the method is suitable for the field of hazardous chemical substance storage safety monitoring.

The invention relates to the technical field of data processing, and discloses a method and system for monitoring the running state of reflow soldering equipment. The method comprises the steps of collecting data through a multi-point sensor, extracting temperature gradient, tension fluctuation and gas concentration characteristics, constructing a state recognition model, calculating a health index and setting an early warning threshold value, constructing a fault precursor extraction model to predict a future state, and finally establishing a maintenance strategy optimization system and generating an equipment maintenance plan based on fault early warning information. Through multi-dimensional data fusion and intelligent analysis, early accurate identification of the abnormal state of the equipment, accurate prediction of the fault development trend and active maintenance decision based on quality influence and cost optimization are realized, so that the welding quality stability is improved, the non-planned downtime is shortened, and the maintenance cost is reduced.

The invention relates to the technical field of material physicochemical property monitoring of a multi-source sensing network, in particular to a multi-source sensing storage environment collaborative monitoring and early warning method, which comprises the following steps of: dividing grid units according to material storage types, binding sensor node coordinates, constructing a grid region reference model, and constructing a grid region reference model; integrating the volatilization characteristic parameters analyzed by the laboratory, historical monitoring data and an environment threshold value to generate a substance and environment relation matrix; the method comprises the following steps of: calling model parameters to invert a theoretical gas concentration value, and triggering finite element physical field simulation through residual analysis to generate a three-dimensional simulation field data set containing a temperature gradient and a diffusion path. And the dynamic correlation analysis precision and the early warning reliability of storage environment monitoring are improved.

The invention provides a coal mine goaf coal spontaneous combustion risk intelligent monitoring and early warning method and system, and relates to the technical field of coal mine safety management. A dot-matrix wireless sensor network is deployed in a target area, temperature and gas concentration data are collected in real time, and data processing is carried out through edge calculation. A dynamic characteristic matrix is generated based on transfer learning and a space-time diagram convolutional network, a comprehensive risk index is calculated, four early warning levels are divided, efficient and accurate coal spontaneous combustion risk monitoring and early warning are achieved, and an innovative solution is provided for mine safety management.

The invention relates to a gas sensing compensation method and device and computer equipment, and the method comprises the steps: obtaining original output signals of a gas sensor under various environmental conditions within a preset time, and recording corresponding environmental parameters at the same time; based on the collected original output signals and the corresponding environment data, a gas compensation model used for describing the internal relation of sensor output, environment parameters and actual gas concentration is constructed; the method comprises the following steps: acquiring current environmental parameters in real time through a gas sensor, inputting the current environmental parameters into a gas compensation model, and carrying out correction calculation on output signals of the gas sensor through the gas compensation model to obtain compensated gas concentration signals so as to realize omnibearing correction of measurement errors caused by various factors. The gas sensor can stably and accurately output measurement results under complex and changeable environmental conditions, reliable data support is provided for application in various fields, and the overall performance and practicability of the gas sensor are improved.

The invention discloses a hazardous chemical substance safety production risk monitoring and dynamic early warning system, particularly relates to the technical field of industrial safety monitoring, and is used for solving the problem of monitoring failure caused by signal interference or offset in a mixed leakage scene of various hazardous chemical substances in the prior art. Multi-source monitoring data such as gas concentration and temperature are acquired in real time through a data acquisition module, and a conflict identification module detects reverse fluctuation characteristics of sensor data; the leakage verification module is used for reversely positioning a leakage source and verifying path shielding interference based on production pipe network topology and real-time flow data; after the physical shielding is eliminated, the physicochemical analysis module analyzes the signal counteracting characteristic of the hazardous chemical substance combination and verifies the matching between the diffusion path and the sensor layout; the dynamic compensation module dynamically corrects the monitoring data according to a physicochemical reaction mechanism; the risk assessment module generates a mixed leakage risk grade and triggers a graded emergency response instruction; accurate identification and dynamic early warning of mixed leakage risks are realized.

The invention discloses a tunnel fan energy-saving control method and system, a storage medium and a program product, and relates to the field of general control or regulation systems.The method comprises the steps that historical train flow data are obtained, and the predicted train flow of the next time period is obtained through calculation according to the historical train flow data; collecting real-time gas concentration data and wind speed and direction data; calculating a theoretical gas concentration increment based on an aerodynamic model, the predicted train flow, the real-time gas concentration data and the wind speed and direction data; calculating according to the theoretical gas concentration increment and a pollutant diffusion model to obtain a pollutant diffusion trend; determining a target section according to the pollutant diffusion trend and a preset gas concentration threshold value; according to the position information of the target section and the pollutant diffusion trend, the minimum starting number of tunnel fans and the corresponding minimum rotating speed are obtained through calculation; and generating a fan control instruction according to the minimum number of started fans and the minimum rotating speed. According to the method, the effect of reducing the energy consumption of the tunnel fan while the ventilation effect of the tunnel is maintained is achieved.

The invention relates to the technical field of storage visual management, in particular to a chemical storage digital visual management system and method. The method comprises the following steps: deploying environment monitoring equipment for a storage area, constructing a multi-parameter fusion intelligent sensing network, and collecting temperature and humidity, gas concentration, pressure and vibration parameters in real time to obtain real-time environment data; based on the real-time environment data, evaluating a storage safety state, identifying an abnormal behavior and evaluating an environment risk to obtain a safety risk evaluation result; and deploying a distributed emergency response network based on a security risk assessment result, dividing risk levels and formulating a multi-level response strategy to obtain an emergency response execution scheme. According to the invention, a chemical warehouse management closed-loop system integrating multi-dimensional perception, intelligent analysis, dynamic control and three-dimensional visualization is constructed, so that accurate and safe management and control of a whole process, a whole space and a whole state are realized.

The invention discloses a BMS-based multifunctional integrated monitoring and fire control method and system, and the method comprises the steps: synchronously collecting the temperature, combustible gas concentration and dynamic environment monitoring data in a container through a BMU battery box management unit of a BMS, and generating a space-time correlated container environment state matrix; based on the environment state matrix, extracting temperature anomaly features, gas concentration gradient features and dynamic environment fluctuation features through a multi-modal feature fusion model, and constructing a multi-dimensional risk feature vector; inputting the multi-dimensional risk feature vector into a fire-fighting early warning optimization algorithm to generate a graded fire-fighting early warning signal; and according to the graded fire-fighting early warning signal, an active fire-fighting intervention strategy is triggered through a BMU unit of the BMS, a fire extinguishing device, a ventilation system and a power cut-off module are dynamically controlled, and a fire-fighting response path is synchronously optimized to be matched with the current risk grade. According to the embodiment of the invention, graded fire-fighting early warning can be realized, and the accuracy and timeliness of fire early warning are improved.

The invention provides a hydrogen energy storage equipment leakage fault early warning method and system based on deep learning, and the method comprises the steps: collecting the multi-source sensing data of hydrogen energy storage equipment in real time, constructing a multi-dimensional feature matrix, building the nonlinear mapping of pressure fluctuation and gas concentration through a bidirectional interaction network, and carrying out the early warning of the leakage fault of the hydrogen energy storage equipment. And performing spatial modeling on the temperature gradient and the vibration spectrum to generate a fusion feature tensor. And then, inputting the fused feature tensor into a spatio-temporal reasoning model, predicting equipment leakage and energy loss, and outputting a leakage probability cloud picture and an energy loss vector. And an entropy sudden change area is identified based on an energy loss vector, when the high-density leakage area and the entropy sudden change area are overlapped in a continuous detection period, a multi-stage early warning signal is triggered, a leakage source thermodynamic diagram and a pressure balance parameter set are generated, a self-adjusting system is activated to dynamically correct the valve opening degree, network weight is updated, and a closed-loop control link is formed. The early warning accuracy and the response speed of the leakage fault of the hydrogen energy storage equipment are improved.

The invention relates to the field of grain depot ventilation management, and discloses an automatic management system for ventilation in intelligent grain depot production, which comprises an environmental parameter monitoring module, a data processing and analysis module, an intelligent ventilation decision module, a ventilation equipment linkage control module and a dynamic feedback optimization module, the environmental parameter monitoring module is used for collecting temperature, humidity and gas concentration data in the bin in real time through the Internet of Things communication unit; the data processing and analyzing module is connected with the environment parameter monitoring module; the intelligent ventilation decision-making module receives the multi-dimensional data atlas, analyzes the temperature gradient, the humidity sensitivity index and the mildew risk value in the atlas through a dynamic classification algorithm, and fuses external meteorological data; the ventilation equipment linkage control module is used for receiving the instruction sequence of the intelligent ventilation decision module; the dynamic feedback optimization module is used for monitoring the efficiency coefficient and the grain pile environment change rate in the ventilation process; the system has the advantages that the ventilation precision and efficiency are improved, and granary environment management is optimized.

The invention relates to the technical field of signal acquisition, and discloses a signal acquisition method and system for an MEMS gas sensor array, and the method comprises the steps: carrying out the analog-to-digital conversion and temperature and humidity compensation of resistance value signals synchronously acquired by a plurality of sensing units in the MEMS gas sensor array, and obtaining a compensated response matrix; performing fast Fourier transform and dynamic feature extraction to obtain a frequency domain feature matrix of each sensing unit; performing sensing unit crosstalk coefficient calculation and response component decoupling to obtain a decoupling feature vector; carrying out Hilbert transform and adsorption-desorption characteristic extraction to obtain a gas sensitive characteristic sequence; inputting a self-adaptive power spectrum analysis model to carry out cross sensitivity identification processing to obtain a target gas response component; according to the method, the accuracy of target gas detection and concentration quantitative analysis is improved.

The invention relates to the technical field of mine mining, and discloses an underground multi-parameter environment monitoring method, which comprises the following steps of: acquiring equipment pose, vibration spectrum and environment parameters in real time through a multi-source sensor, performing space-time alignment, filtering noise reduction and feature extraction on original data, outputting a standardized state vector, and performing data processing on the standardized state vector; fusing the pose data, the vibration signals and the environmental parameters under a unified space-time reference to construct a multi-dimensional feature matrix; and carrying out dimension reduction and redundant information elimination by adopting principal component analysis, and outputting a fused equipment state vector. According to the method, through real-time fusion of the equipment pose and the airflow dynamic state, the ventilation equipment is cooperatively regulated and controlled, the interference of mechanical vibration on the gas monitoring precision is eliminated and the reliability of gas concentration detection is guaranteed based on coupling modeling of vibration spectrum and pose drift and intelligent compensation of sensor reading errors, and through space correlation analysis and dynamic air volume optimization, the gas concentration detection accuracy is improved. Wind speed sudden drop caused by tramcar passing is actively eliminated, and roadway global continuous monitoring is achieved.

The invention discloses a coal mine ventilation and heat reduction integrated control method based on intelligent scheduling, and relates to the technical field of coal mine ventilation and heat reduction control, and the method comprises the following steps: arranging a plurality of monitoring devices on a ventilation path and a standby ventilation path of a mine, and collecting temperature, humidity, gas concentration and wind speed and volume data; the central control system gathers monitoring data in real time, analyzes the ventilation condition in the air duct and identifies whether abnormal changes exist or not. Continuous ventilation is ensured through multi-path redundancy and dynamic switching, risks of gas explosion and the like are reduced, early warning is performed by means of the airflow simulation model, and safety of miners is guaranteed. And in combination with a reinforcement learning algorithm and a simulation model, quick response and self-adaptive adjustment are realized, air distribution is optimized, shutdown is avoided, and economic benefits are improved. Meanwhile, by intelligently planning a ventilation path and dynamically adjusting the fan load, energy consumption and maintenance cost are reduced, the service life of equipment is prolonged, and sustainable operation of a mine is promoted.

The invention discloses a chemical safety risk management and control method and system, and relates to the technical field of risk management and control. Comprising the following steps: acquiring climate data of each region in a chemical plant in real time to obtain microclimate information of different regions; deploying a gas sensor to monitor the gas concentration change and calculate the gas leakage rate; combining the climate data and the gas leakage rate, and adopting a computational fluid mechanics method to establish a gas diffusion prediction model; and in combination with the output of the diffusion prediction model, judging the gas leakage risk level of each region, setting different early warning thresholds according to the risk levels, and automatically triggering an early warning mechanism when the set thresholds are reached. The method not only considers climatic factors, but also combines the change of the factory environment, and can update the prediction result of the diffusion path and the potential dangerous area in real time.

The invention discloses a multi-source data fusion vehicle-mounted gas leakage traceability detection system and method, and relates to the technical field of gas leakage traceability detection. According to the vehicle-mounted gas leakage traceability detection method based on multi-source data fusion, the diffusion coefficient is dynamically corrected by obtaining the gas concentration and environmental data in the vehicle running process in real time, and the leakage source position and the leakage rate are reversely calculated in combination with the self-adaptive particle swarm optimization algorithm; the method can dynamically correct the diffusion coefficient according to the gas concentration and environmental data obtained in real time when the vehicle runs, and accurately and reversely calculate the leakage source position and the leakage rate through the adaptive particle swarm optimization algorithm, and the traditional detection method generally depends on static parameters and preset models, lacks the ability to cope with the fast changing environment, and is difficult to detect. According to the method, the model is adjusted in real time through multi-dimensional environment data obtained in real time, such as information of wind speed, temperature and humidity, air pressure and the like, and it is ensured that the leakage source can still be rapidly and accurately positioned in a dynamic environment.

The invention discloses an intelligent multi-gas detection module data processing system and method based on NDIR, and relates to the technical field of gas detection.The method comprises the steps that a multi-wavelength NDIR sensor is used for obtaining light intensity changes of a gas sample under different wavelengths, and original spectral signals are generated; performing denoising processing on the spectral signal by adopting wavelet transform, and performing zero calibration and dynamic baseline deduction; based on the Beer-Lambert law in combination with environmental factors, establishing a relation model between gas absorption and spectral signals; updating model parameters by using a recursive least square algorithm, and continuously optimizing gas concentration prediction; separating gas signals by adopting a non-negative matrix factorization algorithm, and predicting the concentration of each gas; and the edge end operates the lightweight model in real time, and regularly uploads the model to the cloud end for global optimization and federated learning. The method can effectively improve the condition that the precision is insufficient when the model is used for a long time in the prior art.

The invention relates to the technical field of anomaly detection, in particular to an intelligent gas hidden danger detection system which comprises a leakage monitoring module, a time difference extraction module, a direction positioning module, a color temperature analysis module and a risk aggregation module. According to the method, the time and space relation of gas concentration response nodes is extracted, a leakage diffusion path is constructed, and a dominant propagation direction is identified, so that a leakage trend region has a clear positioning basis, regional brightness and hue changes in an image frame are combined, the time sequence stability proportion is counted, and a cross-modal trend verification channel is established; the defect of a single sensing dimension is effectively made up, in the image and trend direction matching process, abnormal area numbers are recognized through stable proportion difference value changes, intersection confirmation is carried out on trend areas, the accuracy and spatial directivity of hidden danger marking are remarkably improved, the risk recognition chain is more complete through cooperative calculation of multi-source data, and the accuracy of hidden danger marking is improved. And the method has higher linkage response capability and dynamic judgment depth.

The invention discloses an intelligent regulation and control method and system for dust removal and air control of an underground high-gas heading face, and relates to the field of intelligent control. Firstly, an environment parameter set and an equipment parameter set are obtained in real time; constructing a digital twinborn model of the tunneling face and the ventilation system based on the environmental parameter set and the equipment parameter set, and simulating gas concentration and dust concentration under different working conditions; the prediction model predicts the gas emission amount trend and the dust concentration trend; and inputting the environment parameter set, the equipment parameter set and the risk level into an optimization model, and generating a target decision instruction by the optimization model. The operation parameters in the ventilation system can be timely regulated and controlled in real time based on the monitored environment parameter set of the tunneling face and the equipment parameter set in the ventilation system, so that the ventilation and dust removal efficiency of the ventilation system is improved. Moreover, the decision instruction is generated by referring to the trend of the gas emission amount and the dust concentration in the future, so that the accuracy of the decision instruction of the ventilation system is further improved.

The invention relates to the technical field of mine ventilation control, in particular to a mine fan ventilation control method and system based on digital twinning. The method comprises the following steps: carrying out data preprocessing on acquired mine monitoring data; performing feature extraction on the preprocessed mine monitoring data; utilizing a random forest algorithm to predict the relationship between the data characteristics and the fan air volume; a digital twinborn model is used for resolving the relation between the data characteristics and the fan air volume, and a fan ventilation control strategy is obtained; and generating a PLC control instruction based on the fan ventilation control strategy. And through multi-source data fusion and deep analysis, the real-time ventilation requirement of the mine can be accurately grasped. Based on random forest algorithm prediction and digital twinborn model calculation, dynamic and accurate adjustment of the air volume of the draught fan is achieved, it is ensured that the concentration of harmful gas in all underground areas is maintained within a safety threshold value, the oxygen content is sufficient, an ideal environment is created for personnel operation and equipment operation, and the safety and reliability of a ventilation system are remarkably improved.

The invention discloses a mine disaster prediction method based on multi-source data, and relates to the technical field of mine safety, and the method comprises the following steps: collecting original data from different types of sensors in a mine, the data types comprising gas concentration, temperature, humidity, wind speed, ground pressure, water level and vibration information; each type of data is provided with a corresponding timestamp and a spatial position identifier. According to the method, time resampling and space mapping standardization of multi-source data are realized, so that the time-space consistency of data fusion is remarkably improved, and the accuracy of disaster prediction model input is ensured. Meanwhile, a dynamic feature matrix is constructed and a high-precision position weight mechanism is introduced, so that the sensitivity of the model to key areas and key parameters is enhanced, the real-time performance and accuracy of mine disaster prediction are effectively improved, and the risk of missing report and false report of an early warning system is remarkably reduced.

The invention discloses a toxic and harmful substance leakage reinspection method, device and equipment based on an unmanned aerial vehicle, and a storage medium, relates to the technical field of Internet of Things and intelligent factories, and aims to solve the problem that the detection accuracy and efficiency of toxic and harmful substances are low. The method comprises the following steps: dividing a target area into a plurality of grids, and acquiring initial gas concentration data of each grid in real time through a fixed foundation sensing array; the grids with the initial gas concentration data larger than the preset concentration are determined as abnormal grids, and a reinspection flight path is dynamically generated according to the unmanned aerial vehicle parking position, the abnormal grid position and the environment parameters; the unmanned aerial vehicle is controlled to fly to the abnormal grid according to the reinspection flight path, and multi-dimensional data including visible light images, infrared thermal imaging data, gas component spectrums and space coordinate data of the abnormal grid are collected; and fusing the initial gas concentration data and the multi-dimensional data of the abnormal grid, constructing a three-dimensional dynamic concentration field model, and outputting leakage source position information and a diffusion trend.

The invention relates to the technical field of gas detection, and particularly discloses an industrial gas concentration detection system, which is used for solving the problems of signal attenuation, low detection precision, high cost and poor stability caused by environmental interference and multi-laser beam combination since a correlation type optical machine adopts a single-path and fixed compensation method and cannot measure multi-component gas at the same time. Comprising a transmitting end and a receiving end, a laser is arranged in the transmitting end, a red light generator is arranged in the transmitting end, an off-axis parabolic mirror is arranged on one side of the transmitting end, an optical filter is arranged above the off-axis parabolic mirror, a photoelectric detector is arranged above the optical filter, a first window is arranged on one side of the off-axis parabolic mirror, a second window is packaged in the receiving end, and a full-angle reflector is arranged on one side of the second window. The photoelectric detector is sequentially connected with a data inversion analysis module, a dynamic environment compensation module and an environment sensing module; by optimizing optical design and dynamic environment compensation, multi-gas concentration detection is realized.

The invention discloses a nuclear industrial robot body intelligent system based on a large model, which realizes autonomous sensing, decision making and execution of a robot in a nuclear industrial environment by integrating a sensing module, a learning model module, an execution module, a communication module and a security module. The sensing module adopts a multi-sensor fusion technology to collect environmental data such as temperature, radiation intensity and gas concentration in real time; the decision-making module dynamically generates a task execution strategy based on an open source large model base in combination with deep learning and reinforcement learning algorithms; the execution module completes complex tasks such as equipment maintenance, fault processing and nuclear waste carrying through a high-precision mechanical arm and a force feedback control technology; the communication module supports data interaction between the robot and a control center, and remote monitoring and emergency intervention are achieved; the safety module monitors the state of the robot in real time, multiple safety protection mechanisms are provided, and stable operation of the robot in the high-radiation environment is ensured. According to the invention, the operation efficiency and safety of nuclear industry facilities are obviously improved.

The invention provides a gas concentration prediction method of a time convolution network based on a time attention mechanism, and belongs to the technical field of gas concentration prediction. According to the method, time series data are collected through multiple types of sensors, and after normalization and sliding window processing, a hybrid model integrating a time convolutional network, a gating circulation unit and a self-adaptive time attention mechanism is constructed. The time convolution network adopts an expansion causal convolution hierarchy to expand a receptive field, and captures a gas long-term accumulation effect; the gating circulation unit separates instantaneous fluctuation and trend characteristics through a double-time-scale architecture; the time attention mechanism dynamically distributes weights to strengthen key time steps. Through long-term and short-term feature fusion and dynamic optimization, the problem that a traditional model cannot give consideration to long-period dependence and short-term mutation is solved, the precision, stability and real-time performance of gas concentration prediction are remarkably improved, and the method is suitable for multi-component gas monitoring in a complex industrial environment.

The invention discloses a multi-mode end point detection system and method for a semiconductor dry process, and relates to the field of gas concentration detection. A gas absorption cell; the reference light path adjusting unit comprises an adjustable attenuator used for adjusting signals; the first photoelectric detector is used for detecting a transmission signal; the second photoelectric detector is used for detecting a reference signal; the lateral scattered light detector is used for detecting a scattered signal of the particulate matters in the gas absorption tank; and the signal processing unit is in signal connection with the first photoelectric detector, the second photoelectric detector and the lateral scattering light detector and is used for performing compensation calculation on the transmission signal based on the reference signal and the scattering signal so as to obtain a corrected gas absorption signal. By adopting the scheme, measurement signal distortion caused by optical mirror surface pollution and particulate matter scattering interference in a semiconductor tail gas optical detection technology is avoided.

The invention discloses a tunnel fire position positioning and fire source risk analysis method and system, and relates to the technical field of tunnel fire detection, and the method comprises the steps: periodically collecting image, temperature, smoke, harmful gas concentration, infrared radiation and humidity data at each monitoring point in a tunnel, and dynamically adjusting the monitoring frequency according to the humidity; feature extraction is performed on the collected data, a factor set and a fuzzy evaluation set for fire judgment are constructed, and a fuzzy algorithm is utilized to calculate the membership degree of each feature to fire evaluation; a comprehensive membership value vector is generated in combination with the feature weight and is used for judging whether a fire occurs or not and performing preliminary positioning; initial monitoring equipment and front and back adjacent equipment are set, and the fire position is further accurate through fuzzy analysis of data of the front and back adjacent equipment; performing risk assessment on the fire according to the fire position and the abnormal pixel point proportion; intelligent detection, precise positioning and risk analysis of the tunnel fire are realized, and the tunnel safety management level is effectively improved.

The invention relates to the technical field of mine safety, and discloses a coal mine underground gas monitoring method and system. The method comprises the following steps: acquiring methane absorption intensity data by adopting a tunable diode laser absorption spectrum, converting the methane absorption intensity data into gas concentration based on a Lambert-Beer law, performing temperature and humidity compensation, calculating a transmission priority according to concentration deviation to form a data packet, and establishing a spatial interpolation model by utilizing multipoint data to predict gas concentration distribution. Calculating a risk assessment index based on the predicted distribution, automatically adjusting the rotating speed of the ventilator, and controlling the electrical equipment to be powered off. According to the method, the technical problems of low detection precision, incomplete monitoring coverage, low data transmission efficiency and lack of prediction and early warning capabilities in the existing underground coal mine gas monitoring method are solved. And the accuracy of gas concentration detection and the intelligent level of gas monitoring are improved.

The invention discloses a gas concentration prediction method based on micro-seismic monitoring data, and belongs to the technical field of coal mine safety monitoring. Comprising the steps that micro-seismic sensors and gas sensors which are distributed in a net shape are arranged in a mining area, and data are collected in real time and subjected to denoising and standardization processing; the method comprises the following steps of: constructing a gas concentration prediction model fusing a long-short-term memory network and a convolutional neural network by extracting multi-dimensional characteristic parameters such as a microseismic energy gradient, a seismic source aggregation degree and a gas concentration change rate, and dynamically distributing spatial-temporal characteristic weights by utilizing an attention mechanism; a safety threshold value is dynamically adjusted based on coal seam permeability, mining depth and geological parameters, score calculation of comprehensive danger evaluation indexes is combined, and three-level early warning of underground sound-light alarm, ventilation regulation and control and remote expert cooperation is triggered; and meanwhile, through periodic model calibration and parameter retraining, the prediction precision is improved. According to the invention, space-time collaborative perception and self-adaptive early warning of the gas risk are realized, and the real-time performance and reliability of coal mine safety prevention and control are obviously improved.