2802 results about "Gas concentration" patented technology

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 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 tank equipment sealing micro-leakage intelligent early warning and positioning method, which comprises the following steps of: deploying acoustic sensors, vibration sensors, gas concentration sensors, temperature and humidity sensors and the like aiming at different positions of a tank to realize multi-mode original signal acquisition; various signals are standardized and corrected through denoising, normalization and structural difference mapping, and the influence of the environment and the material structure on the signals is compensated; extracting multi-dimensional feature parameters, and carrying out space-time weighted fusion by adopting an attention mechanism to generate feature vectors marked by confidence; according to the method, a self-learning anomaly recognition algorithm is combined, a judgment model is continuously optimized through transfer learning and online sample accumulation, micro-leakage anomaly judgment is achieved, accurate positioning of a tank leakage source point is executed based on a physical propagation model, and the sensitivity and the self-adaptive capacity of micro-leakage detection are improved; and leakage identification and positioning requirements in a complex environment and a dynamic working condition can be effectively met.

The invention discloses a petrochemical explosion-proof humanoid robot trajectory planning method, device and equipment, and the method comprises the steps: collecting petrochemical environment gas concentration data, robot joint motion parameters and target position information, carrying out the dangerous region calibration of the gas concentration data, generating a forbidden map, and determining safety nodes and joint motion ranges; constructing an initial path skeleton based on target position information and safety node distribution, identifying a local dangerous section and a major dangerous section, and generating a motion constraint condition and a poison avoidance path; generating a motion primitive library based on the motion constraint condition and the joint motion range, performing stability filtering to generate stable motion primitives, and forming an advancing scheme set; acquiring a joint load peak value to construct a dynamic envelope, and judging execution fitness to form an execution probability field; and constructing a three-dimensional decision space based on the poison avoidance path, the advancing scheme set and the execution probability field, searching an optimal convergence point to generate a final planning trajectory, and providing a safe and efficient trajectory planning scheme for the petrochemical explosion-proof humanoid robot.

The invention discloses an intelligent induction and early warning system for poisonous gas in a limited space, and relates to the technical field of gas induction and early warning. A three-dimensional structure model of the limited space is constructed, and non-uniform grid division is carried out based on barrier characteristics; multiple types of toxic gas sensing units are arranged in the grids, and gas concentration data are collected in real time; constructing a toxic gas three-dimensional cloud picture, and generating a dynamic weight field based on the concentration gradient; fusing operator positioning and trajectory prediction information, and establishing an individual risk exposure model; calculating a space-time intersection of the toxic gas cloud picture and the high-risk track area to form a fusion risk matrix; realizing linkage early warning control of a region level and an individual level through multi-level dynamic threshold judgment and thermal block clustering identification; the system can realize accurate perception and active response of the toxic gas gathering trend and personnel exposure risk in the complex limited space, has the technical advantages of high sensitivity, high intelligence and low false alarm, and is suitable for various limited space scenes such as chemical engineering, tunnels and mines.

The invention provides a partial discharge on-line monitoring method and system based on multi-modal fusion and adaptive noise reduction, and the method comprises the steps: employing an ultrahigh frequency UHF sensor, a miniature ultrasonic sensor, and a miniature detector for gas dissolved in oil, which are disposed on a transformer; synchronously acquiring electric signals, sound signals and characteristic gas concentration data in oil generated in the partial discharge process; performing format unification, abnormal value elimination and time alignment processing on the electric signal, the sound signal and the gas concentration data in an edge calculation unit to obtain aligned multi-source original data; performing adaptive wavelet noise reduction processing on the electric signal to obtain a de-noised UHF signal; respectively extracting time domain, frequency domain and chemical features from the de-noised UHF signal, the aligned sound signal and the gas concentration data to form a multi-dimensional feature vector; and inputting the multi-dimensional feature vector into a pre-trained defect traceability model, and outputting a partial discharge defect type, severity level and development trend prediction result.

The invention discloses a linearity closed-loop optimization method for a semiconductor gas sensor with adjustable voltage dynamic compensation, which relates to the technical field of gas detection and comprises the following steps: controlling the semiconductor gas sensor to start in a clean air environment, outputting an initial test voltage sequence through a voltage regulator, the method comprises the steps of collecting sensor reference resistance under different voltages, screening out initial working voltage with the minimum resistance fluctuation quantity, synchronously collecting initial environment temperature and humidity, and calibrating the sensor reference resistance based on preset temperature and resistance coefficients and humidity and resistance coefficients to obtain initial reference resistance. By dynamically adjusting the working voltage and combining real-time compensation on the environment temperature and humidity, the interference of environment factor fluctuation on the resistance characteristic of the sensitive material can be continuously counteracted, so that the linear relation between the output signal of the sensor and the target gas concentration is kept stable, the dynamic optimization of the linearity in a complex environment is realized, and the sensitivity of the sensor is improved. And more reliable basic data is provided for gas concentration detection in an industrial scene.

The invention relates to a gas sensor environment anti-interference drift compensation method, system and equipment, and the method comprises the steps: collecting a mixed gas response signal in an environment, and obtaining an electromagnetic interference signal under the same timestamp; threshold truncation is carried out on spike pulses in the mixed gas response signals to obtain trend waveform signals caused by gas concentration changes, and denoising processing is carried out on the electromagnetic interference signals to obtain effective electromagnetic interference parameters; performing feature recognition on the effective electromagnetic interference parameters through a preset interference-response cooperative processing model to determine an interference mode and an interference intensity grade, synchronously analyzing the trend waveform signal to obtain a response feature curve of the target gas and the interference gas, and obtaining a drift compensation coefficient of the target gas; and carrying out collaborative optimization on the drift compensation coefficient through a preset interaction influence model to obtain a final compensation result so as to achieve the purpose of outputting a high-precision target gas concentration detection result.

The invention belongs to the technical field of sensor error correction, and discloses a data synchronization and error correction method based on multi-source gas detection, and the method comprises the steps: firstly achieving the time-space synchronization of a multi-source sensor through the combination of GPS / Beidou hardware time service and a gas diffusion transmission model; non-negative matrix factorization (NMF) is adopted to decouple sensor cross interference, and an LSTM neural network is utilized to predict a sensor drift amount caused by environmental temperature and humidity changes; and finally, carrying out dynamic weight fusion based on the real-time confidence of each sensor, and outputting a high-precision gas concentration value. The problem of data distortion caused by time sequence dislocation, cross sensitivity and environment drift in a multi-sensor system is effectively solved, the accuracy of detection data and the reliability of the system are remarkably improved, and the method is particularly suitable for multi-component gas detection scenes in the fields of chemical engineering, environment monitoring and the like.

The invention discloses an automatic calibration method and system for a mining laser gas detector, and relates to the technical field of instrument calibration. The method comprises the following steps: dividing detection beam parameters into measurement beam parameters and reference beam parameters; enabling the measurement light beam parameter to penetrate through the to-be-measured gas area to obtain a measurement signal, and enabling the reference light beam parameter to penetrate through the reference gas chamber to obtain a reference signal; performing photoelectric conversion calculation on the reference signal to obtain a gas concentration detection value; the gas concentration detection value is compared and analyzed with a reference data value according to the central wavelength position of the mining laser gas detector, and a calibration unit is activated according to a comparison result; and automatically correcting the mining laser gas detector, generating a correction parameter set, calculating a measurement signal, and generating a concentration value of the gas to be detected. The technical problems of low calibration efficiency and unstable calibration precision of the mining laser gas detector in the prior art are solved, and the technical effects of realizing automatic calibration of the mining laser gas detector and improving the calibration efficiency and precision are achieved.

The invention relates to the field of coal mine exhaust control, in particular to a coal mine exhaust control method and device based on environmental perception. The method comprises the following steps: acquiring multi-dimensional monitoring signals based on environment sensing sensors at multiple positions of a mine, performing distributed sensing marking, and constructing a global gas concentration sensing graph; the method comprises the following steps: acquiring position information of operating personnel in real time based on a miner positioning sensor, performing short-time movement prediction on the operating personnel, and generating a miner movement prediction path; obtaining a coal mine internal structure chart, and carrying out average oxygen consumption rate budget and instantaneous oxygen supply demand calculation based on the miner movement prediction path to obtain oxygen supply demand indexes of each unit; and according to the oxygen supply demand index of each unit, intelligent exhaust power calculation adjustment is carried out, and a conventional exhaust control strategy is constructed. Ventilation efficiency of the coal mine area is improved, emergency blast treatment is carried out under the risk condition, the risk problem response capacity is greatly improved, and safety of the coal mine area is enhanced.

The invention discloses a spectrum aliasing decoupling and concentration inversion method under the cross influence of multi-source environmental factors, belongs to the field of industrial process control and environment monitoring, and constructs an environment-spectrum collaborative fusion decoupling model for concentration prediction. The method specifically comprises the following steps: respectively collecting absorption spectrum signals of specified mixed gas at different temperatures, pressures and known concentrations, meanwhile, collecting environmental parameter data, constructing a multi-source data set, and carrying out denoising, dimension reduction and preprocessing on the multi-source data set; constructing a self-supervised feature extraction network for adaptive modulation of environmental parameters to realize deep fusion of spectrum and environmental information; the feature expression capability and generalization performance of the self-supervised feature extraction network are improved by using a self-supervised learning mechanism; and constructing a BPBO-GRNN self-adaptive concentration inversion optimization model for realizing inversion of mixed gas concentration and self-adaptive optimization of model parameters. According to the invention, high-precision concentration inversion and stable detection of the aliasing gas can be realized.

The invention provides a method and a system for detecting gas concentration in an epitaxial growth reaction cavity, computer equipment and a storage medium. The method comprises the following steps: acquiring a reflected light signal, a reference signal and a reaction cavity environment signal; carrying out wavelength calibration and intensity baseline calibration on the reflected light signal according to the reference signal, and generating an experimental absorption spectral line according to the calibrated reflected light signal; according to the reaction cavity environment signal, fitting the experimental absorption spectral line and a target absorption spectral line obtained by querying a preset database, and performing inversion to obtain concentration information of the gas to be detected. According to the invention, through double calibration of a reference channel, an experimental spectral line and a database target spectrum are strictly aligned on a frequency scale and an amplitude scale, interference of linear displacement and baseline fluctuation on fitting is reduced, and quantitative precision is improved; and meanwhile, an environmental condition is introduced as a physical constraint, so that parameter coupling is weakened, the identifiability and robustness of inversion are improved, the dependence on pure empirical correction is reduced, and the stability of detection data is improved.

The invention discloses a coal mine goaf fire monitoring method and system based on multi-source data fusion, and relates to the technical field of coal mine goaf fire monitoring, and the method comprises the steps: collecting the concentration, concentration and air pressure of each wireless sensing node in a coal mine goaf in real time; according to the concentration of each node, a dual-parameter dynamic coupling gradient field traceability algorithm of fusion / gas expansion and chemical reaction mechanism is adopted to calculate a fire source coordinate; performing anomaly detection according to the concentration of each node, and positioning an air leakage point by adopting a three-dimensional oxygen concentration gradient field positioning algorithm and combining air pressure compensation for an oxygen concentration anomaly point; based on goaf point cloud data, a goaf three-dimensional grid model is constructed, and a fire source spreading path, an air leakage channel and gas concentration distribution are dynamically and visually displayed in combination with multi-source monitoring data and fire source and air leakage point positioning results. The goaf fire prevention and control system can achieve all-around prevention and control of goaf fire.

The invention provides a method for solving signal drift of an oxygen sensor in a high-temperature and high-pressure environment, which comprises the following steps: collecting acid-base gas concentration data and zirconium oxide material surface state information in the high-temperature and high-pressure environment, and classifying erosion sensitivity degrees by adopting a support vector machine algorithm to obtain an initial response characteristic deviation value; a neural network model is adopted to process the determined potential signal drift trend and oxygen content detection historical data, a simulation response characteristic curve is generated, and a quantitative mapping relation of the pH value change to erosion sensitivity is obtained; refining the correlation between the determined long-term monitoring reliability index and the pH value change by adopting a neural network model, generating a dynamic response characteristic prediction sequence, and obtaining a signal drift suppression threshold set; and through fusion of the obtained signal drift suppression threshold set and the oxygen content detection real-time signal, if it is judged that erosion sensitivity is aggravated in a prediction sequence, a compensation model is activated to update and circulate, and optimized sensor stable output is obtained.

The invention provides a gas detection method based on galvanometer scanning, and the method comprises the following steps: obtaining a visible light image of a to-be-detected region collected by a visible light imaging module, processing the visible light image based on a pre-trained semantic segmentation model, and obtaining a background category matrix; acquiring gas concentration information of each scanning point obtained by scanning the to-be-detected area by the galvanometer laser telemetering assembly, wherein the galvanometer laser telemetering assembly is registered with the visible light imaging module in advance; and acquiring a background category corresponding to each scanning point according to the background category matrix, and correcting a gas concentration signal of the point based on a background correction coefficient associated with the background category. According to the gas detection method, the problems of false detection and the like caused by factors such as unknown background reflectors and unknown detection object types in the existing galvanometer scanning gas detection are effectively solved, and the accuracy of gas concentration detection is improved.

The invention provides a power transformer free gas detection method and system for material screening based on machine learning, and the method comprises the steps: carrying out the sampling of a compound space, and calculating a material characteristic data set of different doping configurations for the adsorption performance of a target gas through a high-throughput first principle; based on the material characteristic data set, a CGCNN convolution kernel is fused through a GNN framework, and a pre-trained material screening model is obtained and used for predicting the adsorption performance of different doping configurations to target gas; screening by utilizing a pre-trained material screening model to obtain an optimal doping configuration meeting a preset performance threshold value; screening based on the optimal doping configuration to obtain an optimal gas sensitive material; acquiring a response signal of the sensor array to the dissolved gas removed from the transformer oil, identifying the type of the target gas, and estimating the corresponding gas concentration; wherein the sensor array is prepared from an optimal gas sensitive material. According to the invention, rapid, accurate and on-line detection of various key free gases in transformer oil can be realized.

The invention relates to the technical field of on-line monitoring, in particular to a flue gas cooler leakage monitoring system and method.The method comprises the steps that a multi-source real-time sensing layer module continuously collects tube plate weld joint area temperature data, cooling water flow characteristics and flue gas sulfur-containing gas concentration, and the sampling period is 250 milliseconds; the intelligent feature fusion module performs noise reduction processing and dynamic weight fusion on the multi-source data and outputs a leakage probability vector; the dual-system collaborative early warning module maps the probability vectors into confidence levels, a 65% confidence threshold triggers an acoustic verification system to start, an acoustic emission sensor array captures fractured sound wave features and then compares the fractured sound wave features with a voiceprint library through a convolutional neural network, and an early warning signal is output when the fractured sound wave features exceed a 0.85 similarity threshold; and the dynamic knowledge base updates the case characteristics online according to the early warning result. According to the system, second-level identification and continuously optimized closed-loop monitoring at the initial stage of leakage germination are realized, and the problem of response delay caused by intermittent detection in the prior art is solved.

The invention relates to the technical field of gas detection, and provides a doped gas concentration detection method and system based on a gas analyzer. The method comprises the following steps: constructing a standard spectrum database under the same pressure and temperature as a to-be-measured site; the method comprises the following steps: introducing sample gas into an optical absorption cell, carrying out wavelength scanning through a tunable diode laser in a plurality of adjacent absorption spectral line intervals containing target gas and interference gas, superposing a high-frequency sinusoidal modulation signal on a driving current, and synchronously detecting a second harmonic component of transmission laser to generate an actual measurement spectrum; fitting the actually measured spectrum with a synthetic spectrum generated on the basis of a standard spectrum database, and outputting a target gas concentration ratio as a detection result when a residual error meets a threshold value, so as to solve the technical problem that the detection precision is reduced due to spectrum overlapping caused by interference gas, and achieve the purpose that the detection precision is reduced by constructing the standard spectrum database and second harmonic spectrum fitting analysis. And the accuracy and the anti-interference capability of gas detection are improved.

The invention discloses a switch equipment performance aging intelligent diagnosis method and device based on multi-parameter fusion, relates to the technical field of power equipment abnormal defect diagnosis, and solves the problem of low precision of aging judgment of switch equipment depending on a single temperature and humidity threshold value or a single gas parameter in the prior art. The method comprises the following steps: acquiring internal and external humiture and internal gas characteristic parameters of equipment; constructing multi-channel standardized monitoring data through time synchronization, filtering and de-noising and exception elimination processing; calculating an absolute humidity difference inside and outside the cabinet, constructing a humidity expectation model, and obtaining a standardized residual error; performing offset detection and trend smoothing processing on the standardized residual error to obtain a cumulant and a smoothing trend quantity, and fusing the cumulant and the smoothing trend quantity to calculate an offset index; outputting a moisture seepage risk score, and triggering early warning when the moisture seepage risk score and the characteristic gas concentration exceed a threshold value. According to the invention, real-time high-precision evaluation of the aging state of the equipment is realized, early hidden dangers are effectively identified, and the safety and reliability of a power system are improved.

The invention relates to the field of gas concentration detection, and particularly discloses a real-time feedback and adaptive learning method for natural gas infrared spectrum measurement, which comprises the following steps: S1, acquiring infrared spectrum information of a natural gas body by using an infrared spectrometer, and constructing a historical sample set; s2, preprocessing the spectral data of the historical sample set; s3, selecting an optimal algorithm and a hyper-parameter by adopting XGBoost and Bayesian optimization; s4, constructing a qualitative model to identify gas types and match data; s5, calculating the similarity between a field sample and a historical sample through a Siamese network, and setting a threshold value to screen local data; s6, improving the KNN to construct a local dynamic quantitative model to predict the concentration; s7, processing low-similarity abnormal data by the global dynamic model, and improving the reliability by combining moving average and abnormal calibration; and S8, introducing reinforcement learning and online gradient descent to adjust parameters in real time to optimize the precision. According to the technical scheme, high-accuracy natural gas detection can be carried out in a complex environment.

The invention relates to the technical field of fire-fighting early warning, and provides a fire-fighting gas alarm method. The method comprises the following steps: acquiring response time signal stability and detection precision data through a sensor to obtain a real-time monitoring sequence; calculating a correlation coefficient between response time and signal stability according to the real-time monitoring sequence, and determining a change trend index; inputting the change trend index into a preset regression model to obtain a performance reduction predicted value; if the performance reduction predicted value exceeds a preset threshold value, judging that a fault is a potential fault and generating an alarm signal; after an alarm signal is obtained, a detection precision parameter is adjusted through a threshold mechanism to optimize sensor configuration; recollecting gas concentration and concentration fluctuation data according to the optimized sensor configuration to determine an updated monitoring sequence; the updated monitoring sequence is matched with the abnormal smoke characteristics to judge the fuel gas leakage risk level; and a fault prediction result is obtained by inputting risk levels into a classification model, and a maintenance system is activated to respond, so that high-efficiency early warning is provided for catering kitchen gas monitoring, and safety is ensured.

The invention relates to the technical field of fuel cells, in particular to a method and system for accurately controlling the hydrogen stoichiometric ratio of a fuel cell, and the method comprises the steps: firstly, obtaining a quantitative anode health state value through a fusion calculation model by collecting load current, anode inlet and outlet pressure, temperature, high-frequency resistance and other multi-source signals in real time; a feed-forward model based on a load current change rate predicts sudden change of hydrogen demand, and an optimal hydrogen flow instruction is jointly generated in combination with output of a PID controller which adaptively adjusts parameters according to a health state value. According to the method, the integrated diagnosis parameter, namely the anode health state value, is created, so that the internal state of the fuel cell can be accurately perceived and prospectively judged. Through deep fusion of multi-source heterogeneous information such as high-frequency resistance, anode pressure difference, current and temperature, a comprehensive index capable of comprehensively and quantitatively reflecting anode water content, gas concentration and runner smoothness is generated.

The invention discloses a transformer insulating oil chromatographic data abnormity identification method, and relates to the technical field of data identification, and the method comprises the following steps: carrying out dissolved gas analysis on an insulating oil sample through a gas chromatograph to obtain a chromatographic original response curve; performing baseline correction and de-noising processing on the chromatographic original response curve, positioning each gas peak based on a first-order derivative method and a second-order derivative method, and extracting chromatographic peak height and chromatographic peak area parameters; according to the extracted chromatographic peak height and chromatographic peak area, three Duval fault ratios are calculated, and the ratio of the total soluble gas concentration to each gas concentration threshold value is calculated; combining into a multi-dimensional diagnosis feature vector; according to the method, the chromatographic peak shape of the insulating oil sample is subjected to multi-stage baseline correction and de-noising treatment, and the gas peak position is accurately positioned based on the first-order and second-order derivative methods, so that the high precision of chromatographic peak height and chromatographic peak area extraction is ensured, the accuracy of dissolved gas concentration calculation is improved, and the misjudgment risk caused by noise or baseline drift is reduced.

The invention relates to the field of forest fire monitoring, and discloses a forest fire monitoring method and system based on cooperation of an unmanned aerial vehicle and an AI, the unmanned aerial vehicle flies according to an AI planning path, a forest environment image is collected and divided into a plurality of target monitoring sub-regions, and the monitoring key point of each sub-region is flexibly adjusted according to multi-source environment data; for an under-forest area, heat source abnormity is detected by analyzing temperature and gas concentration, an under-forest fire source is accurately identified and positioned, and the risk of fire triggering is evaluated; for the on-forest area, heat source abnormity is analyzed based on temperature and gas concentration, the sub-area where the on-forest fire source is located is positioned, and the risk is evaluated. Finally, the system performs early warning and feedback on a risk identification result in time, thereby realizing accurate capture and early warning of forest hidden fire sources, remarkably enhancing the initiative and systematicness of forest fire monitoring, and effectively improving the prevention and control efficiency of forest fire.

The invention relates to the field of gas leakage identification, and discloses a gas leakage identification method and system based on sound localization, a medium and equipment, and the method comprises the steps: collecting a leakage sound wave signal through a microphone array, and extracting acoustic features in a complex background; simulating a diffusion process of gas in a turbulence environment after gas leakage through an established leakage gas convection-diffusion model, and combining gas concentration distribution in the simulated diffusion process with acoustic characteristics to predict sound source localization at a leakage position; modeling a sound source localization search process as POMDP, and iteratively updating a confidence state of a sound source position through a particle filter; spatial distribution features are extracted from the confidence state through DBSCAN clustering to serve as input of the LSTM-DQN network, spatial and temporal features are fused through the constructed LSTM-DQN network, cross-scene generalization is achieved through transfer learning, and sound source coordinates are dynamically optimized and output through the Q network. According to the invention, the positioning accuracy and real-time performance in a complex environment are improved.

The invention relates to the field of thermal fault early warning of machine room inspection robots, in particular to a multi-modal sensing thermal fault early warning method of a machine room inspection robot, which comprises the following steps: acquiring infrared thermal image, acoustics, vibration and gas concentration data through a multi-modal sensor array, and generating voxelization temperature distribution; according to the method, temperature features are extracted through wavelet transform and a fractional Brownian motion algorithm and are fused into an equipment global temperature feature set; constructing noise, vibration and gas concentration tower extraction auxiliary features, and combining non-extensive entropy and a graph attention model to be fused into a high-dimensional fault feature pool; building a fiber bundle model by taking the machine room topology as a base space, and locating a thermal fault after optimizing parameters by a simulated annealing algorithm; and finally, a dynamic thermodynamic diagram and the like are generated through digital twinning, so that accurate early warning is realized.

The invention relates to the technical field of laser spectrum gas detection, and particularly discloses a TDLAS (tunable diode laser absorption spectroscopy) double-gas concentration synchronous calibration and decoupling method in the presence of spectrum aliasing interference, and the method comprises the following steps: acquiring a multi-band harmonic signal subjected to adaptive optimization while acquiring environmental parameters; an optimal frequency band signal is determined based on the signal quality index; and calculating a spectral aliasing coefficient according to the characteristics of the optimal frequency band signal, dynamically adjusting operation parameters of a search algorithm based on a numerical value interval in which the spectral aliasing coefficient is located, and determining an initial range of dual-gas concentration in a search space. According to the method, the improved genetic algorithm based on the spectral aliasing coefficient dynamic adjustment search strategy is combined with the incremental support vector regression model, stage processing from coarse calibration to fine decoupling is achieved, and the calculation efficiency and the search precision can be automatically balanced when the spectral aliasing degree changes; and online correction is carried out on the model in combination with aging characteristics.

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 provides an intelligent isotope gas concentration regulation and control method based on concentration prediction, which comprises the following steps: collecting multi-dimensional data in a closed box subjected to emptying treatment to construct a standardized control-response data sequence; predicting the isotope gas concentration through a neural network model to obtain the predicted isotope gas concentration and a long-term concentration drift trend estimation item; inputting the current standardized control-response data sequence into Transform to obtain current state representation, and generating a control instruction of valve opening and closing time and sucking pump power on the basis of the current state representation in combination with predicted concentration and target concentration; executing the control instruction, comparing the actual concentration with the predicted concentration, and coding the deviation into a deviation risk code through a multi-layer perceptron in combination with the control instruction; receiving the current state representation and the deviation risk code to establish a state-deviation mapping model, fitting deviation distribution parameters of the control deviation, and accumulating control experience to optimize a long-term strategy.