2254results about "Particle suspension analysis" patented technology

The invention relates to the technical field of coal mine safety monitoring, in particular to an underground coal mine dust concentration monitoring method and system based on multi-modal data fusion, and the method comprises the steps: synchronously collecting dust concentration time sequence data, dust image data, sound wave signal data and environmental parameters through a multi-modal sensor array deployed in an underground coal mine; carrying out preprocessing and feature extraction on the collected data; predicting the decomposed high-frequency and low-frequency component signals by adopting a long short-term memory neural network and a grey Markov model; when the environment humidity is greater than 80%, carrying out light scattering compensation on the predicted value; inputting various predicted values into an improved D-S evidence theory fusion device, and outputting a fusion dust concentration monitoring value; and when the threshold value is exceeded or the temperature and humidity composite condition is reached, an acousto-optic alarm is triggered and a spraying dust-settling device is started. According to the method, the problems of low precision of a single sensor, multi-source data conflict, high-humidity environment measurement deviation and insufficient time sequence and fusion precision in underground coal mine dust concentration monitoring can be solved.

The invention relates to the technical field of urban road construction, in particular to an intelligent flying dust monitoring and dust falling control system for urban road construction, which comprises a flying dust monitoring module, a dust falling control module, a dust falling control module and a dust falling control module, the data fusion processing center is used for processing the monitoring data and generating a dust fall control instruction; the intelligent dust falling execution module is used for triggering a dust falling device according to the instruction; the cloud platform and the mobile terminal are used for storing the monitoring data, visually displaying the flying dust thermodynamic diagram and pushing standard exceeding early warning information to management personnel; data are collected in real time through the flying dust monitoring module, a flying dust pollution source and the diffusion trend of the flying dust pollution source are recognized in combination with the dynamic analysis and prediction capacity of the data fusion processing center, the intelligent dust falling execution module is used for triggering dust falling measures, and meanwhile remote monitoring and early warning are achieved through the cloud platform and the mobile terminal. The problem of response lag in a traditional method is solved, and the control efficiency is improved.

A method for extracting a wear particle feature signal based on segmentation entropy is provided, including obtaining a raw signal to be processed by performing real-time data acquisition using a lubricating oil wear particle monitoring system; obtaining a preprocessed signal by performing low-pass filtering and harmonic interference suppression on the raw signal to be processed; dividing the preprocessed signal into a plurality of time domain sequence segments with a sliding window; calculating segmentation entropy corresponding to each time domain sequence segment, normalizing a segmentation entropy set to obtain normalized segmentation entropy; obtaining an adaptive threshold through curve fitting based on empirical cumulative distribution of normalized segmentation entropy, obtaining a plurality of non-zero discrete time domain signal segments by segmenting the preprocessed signal by the adaptive threshold; and obtaining final extraction results of the wear particle feature signal by excluding residual noise interference through target signal feature recognition indices.

The invention relates to the technical field of mine environment control, and discloses an intelligent environment monitoring and control method in a mine laneway. The method comprises the steps that a real-time environment parameter set of a mine laneway is collected, the set contains gas concentration data, dust concentration data and temperature and humidity data, and real-time capture of laneway environment multi-dimensional parameters is achieved; then, based on the collected real-time environment parameter set, calculating an environment safety evaluation index so as to scientifically and objectively reflect the roadway environment safety condition; a dynamic control strategy is generated according to the environmental safety evaluation indexes, and the strategy comprises ventilation equipment regulation and control parameters and early warning level parameters and can be flexibly adjusted according to environmental changes; and finally, the ventilation equipment regulation and control parameters are sent to a mine ventilation system, and roadway environment regulation and control are executed. According to the method, environmental parameters can be comprehensively monitored, safety risks can be accurately evaluated, ventilation equipment can be flexibly regulated and controlled, the early warning function can be perfected, energy waste can be reduced, labor intensity can be reduced, and underground operation safety and mining efficiency can be guaranteed.

The invention relates to the field of pollution source analysis, and discloses a formaldehyde pollution source analysis and ozone influence quantification method and system for coupling a receptor model and a box-type model, and the method comprises the steps: collecting the concentration of formaldehyde and other pollutants in urban atmosphere in real time through online monitoring equipment, and carrying out abnormal value marking and data standardization processing; performing source analysis on the processed data based on a positive definite factor matrix method to generate a pollution source contribution matrix; converting the source contribution matrix into a molecular concentration input file which can be recognized by the box-type model; embedding a free radical circulation mechanism into the box-type model, and executing photochemical simulation under different pollution source scenes; and generating a pollution source contribution heat map based on a simulation result, and outputting management and control suggestions through a deep learning model. The method has the following effects: by coupling the receptor model and the box type model, not only can different pollution sources of formaldehyde be determined, but also the influence of each pollution source on ozone generation through photochemical reaction can be analyzed.

The invention relates to the technical field of environmental science, in particular to a box-type model-based inversion method for methane sulfonic acid particles in marine droplet aerosol, which comprises the following steps of: 1, acquiring satellite remote sensing data, ground observation data and laboratory simulation data, and carrying out data standardization processing; 2, dividing the target sea area into a plurality of sub-areas as independent box bodies; 3, calculating the discharge flux of the marine droplet aerosol based on the dimethyl sulfide concentration and the wind speed, and updating the spatial and temporal distribution of the discharge source; 4, embedding a chemical reaction path for oxidizing dimethyl sulfide on the surface of the aerosol into the box-type model to generate methanesulfonic acid, and introducing the specific surface area of the aerosol to correct the reaction rate; and step 5, outputting spatio-temporal distribution of the concentration of the methane sulfonic acid, and comparing measured data for verification. The accuracy of aerosol source item estimation is improved, the fusion efficiency of multi-source data is improved, and the adaptive capacity of the model in a complex environment is enhanced.

The invention discloses an intelligent monitoring method for an argon recovery system, particularly relates to the technical field of gas purification adsorbent state monitoring, and is used for solving the problem of adsorbent recessive failure early warning lag caused by dependence on tail gas average concentration monitoring of an existing argon recovery system. The method comprises the following steps: collecting regeneration tail gas concentration time sequence data in real time, and extracting a slope change rate of a desorption rate rising section and an area integral value of a desorption peak attenuation section; constructing an area integral value sequence and calculating a symmetric deviation degree; identifying a pressure pulse disturbance time period to obtain effective time period data; calculating the fluctuation abnormality of the slope change rate offset and the symmetry deviation degree based on the effective data; when the two regeneration periods simultaneously exceed the threshold value and three continuous regeneration periods meet the condition, an adsorbent replacement early warning signal is generated, and early-stage accurate early warning of adsorbent performance degradation is achieved.

The invention relates to the technical field of sewage detection, provides an intelligent detection method for suspended particles in coking sewage based on image deep learning, and aims to solve the problems of detection lag and low precision in the prior art. According to the technical scheme, the method comprises the following steps: synchronously acquiring sound wave and image data through a high-frequency ultrasonic transducer and an industrial camera, and extracting acoustic parameters and optical characteristic parameters; using a dual-channel convolutional neural network to fuse features, and combining a cross-modal attention mechanism to perform dynamic weighting; and outputting a particle concentration classification result and a particle size distribution prediction value through a multi-task learning model, and performing online learning to compensate environmental interference factors. The device is mainly used for realizing real-time and high-precision suspended particle monitoring and automatic process adjustment of coking sewage treatment.

The invention discloses a construction site big data raise dust monitoring system based on big data, which relates to the technical field of construction site big data raise dust monitoring and comprises a data acquisition module, a feature extraction module, a dynamic evaluation module, a trend prediction module, an anomaly identification module, a traceability modeling module and a response control module. The data acquisition module is used for acquiring multi-source heterogeneous data in a construction site environment, and the multi-source heterogeneous data comprises particulate matter concentration, meteorological parameters, construction activity records and image information; the feature extraction module is used for carrying out standardization processing on the multi-source heterogeneous data and extracting time sequence features and space features reflecting the flying dust state; the dynamic evaluation module is used for performing fusion modeling on the extracted features by adopting a graph neural network and a dynamic weight adjustment algorithm to generate a real-time flying dust quality index DQI; and the trend prediction module is used for training a historical flying dust quality index (DQI) sequence by adopting an LSTM-Attention model.

The invention relates to the technical field of waste gas treatment, and particularly discloses a dynamic management method and system for industrial waste gas treatment, and the system comprises a region division module which is used for dividing an adsorption bed layer in adsorption equipment into a plurality of monitoring regions with similar sizes in a waste gas treatment process, by combining waste gas data at a waste gas outlet for coupling analysis, high-quality real-time waste gas concentration data can be obtained after coupling treatment on the basis of diversified data, and then the concentration of the waste gas is determined based on the data and state data of adsorption equipment in the waste gas treatment process. The real-time adsorption saturation of the adsorption equipment can be accurately analyzed, and the data reflects the adsorption saturation of the current activated carbon adsorption bed layer, so that the waste gas collection air volume can be optimized by dynamically adjusting the waste gas collection air volume through the air speed adjusting module subsequently based on the data, and the desorption of attachments is accelerated; the waste gas treatment effect is improved.

The invention provides a sand and dust scene atmosphere pollution source identification method and system, computer equipment and a medium, and belongs to the field of environmental pollution analys.The method comprises the steps that the types of volatile organic compounds and the types of fine particulate matter PM2.5 in the atmosphere of a sand and dust scene to be identified in a target area are identified, and the concentration of the volatile organic compounds and the concentration of the fine particulate matter PM2.5 are measured; carrying out mineral dust proportion calculation on the types of the fine particulate matters PM2.5 and the concentration of the fine particulate matters PM2.5 to obtain a mineral dust quantification result, and determining a dust source contribution weight according to the active metal increment in the mineral dust quantification result; and performing positive matrix decomposition prediction on the types of the volatile organic compounds, the concentration of the volatile organic compounds, the types of the fine particulate matters PM2.5 and the concentration of the fine particulate matters PM2.5 based on the dust source contribution weight, and determining the pollution source contribution proportion of the atmosphere of the sand and dust scene to be identified. According to the method, the accuracy of pollution source identification is guaranteed, and a basic guarantee is provided for subsequent pollution source treatment.

The invention discloses a clean room particulate matter monitoring system and method based on temperature field information, and relates to the field of intelligent monitoring. The system comprises an environment detection module used for detecting real-time air parameters of a detected environment; the environment purification module is used for purifying the air of the detected environment; the motion module is used for moving the environment detection module and the environment purification module to a designated position according to an inspection instruction, and the inspection instruction comprises an inspection path or / and a temperature gradient change instruction; the control module is used for controlling the environment detection module, the environment purification module and the movement module; according to the invention, the newly found abnormal area can be intelligently planned and automatically incorporated into the existing inspection route, so that the robot can maintain the high efficiency of fixed path inspection, the autonomous and intelligent identification and dynamic path optimization capabilities are remarkably improved, the overall workload and detection time are greatly reduced, and the inspection efficiency is improved. And the monitoring efficiency and accuracy of the particulate matters in the clean room and the intelligent level of long-term maintenance are obviously improved.

The invention relates to the technical field of blow-off gas recovery, and particularly discloses a natural gas well blow-off gas recovery device and a recovery technology.The natural gas well blow-off gas recovery device comprises a sand removing mechanism, sand-containing natural gas can form vortex after entering the sand removing mechanism, a sand discharging opening is formed in the bottom of the sand removing mechanism, and the natural gas well blow-off gas recovery device further comprises a guiding mechanism which comprises a guiding piece and a driving piece; the guide part comprises a hollow rotating shaft and a spiral blade, the hollow rotating shaft is rotationally connected into the sand removing mechanism, the spiral blade is connected into the hollow rotating shaft, a flowing channel for guiding a vortex track can be formed between the spiral blade and the sand removing mechanism, and the driving part is installed in the sand removing mechanism and connected with the hollow rotating shaft; the driving piece can drive the hollow rotating shaft to rotate to drive the spiral blade to rotate, so that the track of the vortex is changed; according to the natural gas well blow-off gas recovery device and process, the flowing track of the vortex can be continuously changed, and a low-speed vortex area is prevented from being formed in the sand discharging opening.

The invention provides an abnormal value screening method based on particulate matter component reconstruction, and relates to the technical field of environment monitoring. The method comprises the following steps: obtaining standard ion component data, standard carbon component data and standard inorganic element component data; obtaining a plurality of reconstruction component data; target PM2.5 data are determined; determining a doubt reconstruction component data judgment result of the plurality of reconstruction component data at each moment; determining a univariate time sequence abnormal point judgment result of each kind of reconstruction component data at each moment; determining a multivariable time sequence abnormal point judgment result of the multiple reconstruction component data at each moment; and determining whether the reconstruction component data at each moment is abnormal data. According to the method, the component reconstruction characteristics under different pollution levels can be evaluated, and the understanding of the model on the relationship among the particulate matter components is enhanced, so that the stability and accuracy of identifying whether the reconstructed component data is abnormal data are improved, the calculation efficiency is optimized, and environment monitoring auditing personnel are assisted to make decisions.

The invention relates to the related technical field of dust detection, and provides a mining induced charge and light scattering fusion dust detection device and method.The dust detection device comprises a workbench, a sampling assembly and a detection assembly, and the sampling assembly comprises a sampling air pump, a conical airflow homogenizer and a closed sampling cavity; the detection assembly comprises an annular sliding rail, a light scattering detection module and an electrostatic induction detection module; the dust detection method comprises the following steps: S1, device initialization and zero calibration; s2, sampling preparation of dust-containing airflow; s3, dual-module synchronous signal acquisition; s4, data processing and temperature and humidity compensation; s5, carrying out anti-interference protection assembly; and S6, data fusion and result output. S7, device cleaning and standby; through cooperative work of the double detection modules, dust detection signals in different concentration intervals are accurately captured, wide-range coverage from low concentration to high concentration is achieved, and synchronous monitoring of dust concentration and particle size characteristics is achieved.

The invention discloses a wind power generation equipment gearbox oil state detection method and system, and the method comprises the steps: obtaining an oil sample, carrying out the quantitative analysis of solid, liquid and gas pollutants through an oil pollution degree analysis model, and processing the parameters such as kinematic viscosity through an oil physicochemical property analysis model; and after the tensors of the two results are fused, feature decoupling and deep feature mining are performed, and finally, the oil state is judged by a state evaluation decision model. The system is correspondingly provided with six units such as a sample acquisition unit and a pollution analysis unit to realize the functions. According to the scheme, the defects that traditional detection is single and correlation analysis is lacked are overcome, a certain index is not independently detected any more, pollution and physicochemical property information is comprehensively integrated, the relation between the pollution and the physicochemical property information is deeply analyzed through tensor fusion, feature decoupling and other technical means, accurate evaluation of the oil state of the gearbox is achieved, and the detection accuracy is improved. And misjudgment caused by one-sided detection or no consideration of mutual relations is effectively avoided.

The invention provides a dust explosion-proof early warning method and system, and relates to the technical field of safety monitoring and early warning, and the method comprises the steps: collecting and standardizing the multi-source data of a dust movement track, an airflow field and electrostatic potential distribution, calculating a vortex intensity index and a dust gathering trend coefficient, and constructing a multilayer neural network electrostatic coupling effect model, and nonlinear coupling mapping of dust concentration, electrostatic field intensity and vortex intensity is realized. And when the danger increment output by the model and the growth rate of the dust gathering trend coefficient exceed a preset threshold value, triggering dust explosion-proof early warning. Therefore, the risk change under the action of the electrostatic field can be detected, the recognition capability of abnormal working conditions and a coupling mechanism is remarkably improved, and the safety control level is greatly improved through early-stage accurate early warning and intelligent disposal of dust explosion hidden dangers.

The invention belongs to the field of dust production characteristic research of coal mining machines in coal mines, and relates to a device and method for researching coal breaking dust production characteristics of cutting picks, the device comprises a dustproof shell, a coal sample restraint device, the cutting picks, a counterweight and lifting system, a pressurization system and the like, and the dust production amount and particle size distribution are accurately measured by simulating the coal breaking process of the cutting picks under different working conditions. The experiment method comprises the steps of sample preparation, device arrangement, experiment operation, data acquisition and processing and the like, and the experiment accuracy is ensured by utilizing the secondary impact prevention lock. According to the method, dust production influence factors can be effectively analyzed, a dust generation mechanism is disclosed, a basis is provided for coal cutter design and operation parameter optimization, an effective dust control strategy is formulated, and an operation environment is improved.

In order to allow an analyzer to be calibrated or a sampling channel to be purged without removing a sampling probe from a sampling location, provided are a sampling channel through which an exhaust gas from an internal combustion engine flows, an analyzer that is connected to the sampling channel and analyzes the exhaust gas, a diluter provided upstream of the analyzer in the sampling channel, a recirculated channel that branches from a branch point set between the analyzer and the diluter in the sampling channel and joins the diluter, a pump that is provided in the recirculated channel and guides part of the exhaust gas from the branch point to the diluter, an additional channel that is connected to the recirculated channel or the sampling channel and through which a calibration gas or a purge gas flows, and an opening and closing mechanism provided in the additional channel.

A method and apparatus used to determine atmospheric extinction coefficients in conjunction with an unmanned aerial vehicle are provided. The unmanned aerial vehicle may include a central body, at least one motor operatively connected to the central body, a condensation particle detection payload, and a data acquisition board including memory operatively connected to the data acquisition board, and a processor configured to generate extinction information.

The invention discloses a ventilation method and device based on precise monitoring of the environment in a farm and a storage medium, relates to the technical field of analysis control, and constructs a three-dimensional pollution concentration field model by collecting data such as the pollution gas concentration, suspended particles, temperature and humidity, airflow velocity and the like of a manure ditch in real time and combining the spatial layout of a breeding house. On the basis of the model and ventilation constraints, a virtual ventilation strategy composed of multiple sets of air supply speeds, air directions and air opening degrees is automatically generated, simulation is conducted, disturbance scores are calculated, and the optimal strategy is selected to be applied to ventilation control. Environmental parameters are continuously monitored in the ventilation process, if it is detected that pollutants rise or temperature and humidity are abnormal, the strategy generation and screening process is automatically restarted, and dynamic closed-loop optimization adjustment of a ventilation scheme is achieved; dynamic ventilation control can be carried out according to the actual manure environment, the influence of gas backflow and pollution disturbance is reduced, and the influence on the farm environment is reduced; the influence on the growth health of livestock and poultry is reduced.

The invention belongs to the field of sensors and signal processing, and particularly relates to a wavelet kernel scale sensitivity oriented abrasive particle induced voltage signal noise reduction method, which comprises the following steps: acquiring an abrasive particle induced voltage signal, and carrying out harmonic elimination on the abrasive particle induced voltage signal to obtain a preprocessed signal; constructing a wavelet kernel function, and calculating the wavelet kernel function and the preprocessed signal to obtain a kernel scale guiding spectrum; constructing a sparse joint noise reduction model based on the kernel scale guiding spectrum; processing the sparse joint noise reduction model to obtain a convex optimization objective function; solving the convex optimization objective function by combining a self-adaptive step gradient descent method and a self-adaptive iterative shrinkage threshold method to obtain a weight vector representing the distribution of the abrasive particle characteristic signals; carrying out binarization processing on the weight vector representing the distribution of the abrasive particle characteristic signals to obtain a characteristic indication vector; carrying out Hadamard product on the feature indication vector and the preprocessed signal, and then carrying out low-pass filtering to obtain a noise reduction signal; according to the method, the abrasive particle characteristic signals can be self-adaptively subjected to non-destructive enhancement and noise reduction processing in a strong interference environment.

The invention relates to the technical field of sensor networks and Internet of Things, in particular to a data center air quality intelligent early warning method and system based on a sensor network. The method comprises the following steps: constructing a self-organizing cooperative network by deploying multiple types of sensor nodes in a key area of a data center, and collecting and correcting multi-dimensional air data in real time; the data reliability is improved through inter-node dynamic weight fusion and local anomaly recognition; establishing an air parameter and machine room structure correlation model by utilizing space mapping and time sequence analysis, and identifying a micro-scale diffusion trend; a self-adaptive dynamic threshold mechanism is constructed, and threshold rolling optimization is realized in combination with historical statistics and real-time feedback; and generating a graded alarm strategy based on multi-stage early warning judgment and triggering conditions, and continuously self-optimizing early warning precision and response efficiency through closed-loop feedback. According to the invention, all-around, intelligent and high-reliability early warning and regulation and control of the air quality of the data center are realized.

The invention relates to a respiratory dust concentration online monitoring device and method for a coal mine, and belongs to the technical field of respiratory dust concentration monitoring. The device comprises a virtual impact separation module, a light scattering detection module, a flow control module and a data processing module. The virtual impact separation module is used for separating respirable dust and non-respirable dust; the respirable dust enters the light scattering detection module, the respirable dust is irradiated by a laser beam which is incident in the direction perpendicular to the flowing direction of the respirable dust, a scattered light signal obtained after dust scattering is transmitted to the data processing module, and the concentration of the respirable dust is calculated in real time; the flow control module is used for controlling the rotating speed of the sucking pump in real time. Through multi-technology fusion and intelligent algorithm compensation, the accuracy and environmental adaptability of respiratory dust concentration measurement are improved, the problems that a traditional separation device is prone to blockage and needs frequent maintenance are solved, and long-term continuous separation monitoring of respiratory dust can be achieved.

The invention discloses a PM2.5 concentration prediction method and device, a medium and a product, and relates to the technical field of atmospheric environment monitoring and deep learning prediction.The method comprises the steps that laser radar vertical profile data containing extinction coefficients and depolarization ratio vertical profiles and a scalar time series data sequence are obtained; converting the laser radar vertical profile data sequence into a vertical sequence containing position information at any moment; adopting a multi-channel spatial feature extraction model, and obtaining a multi-channel spatial feature vector based on the vertical sequence; and after the scalar time sequence data sequence and the multi-channel spatial feature vector are spliced to obtain a fused feature vector, a PM2.5 concentration prediction value is obtained by adopting a time sequence prediction model. According to the method, the technical pain point caused by the fact that aerosol types cannot be distinguished and'high altitude-ground 'cross-layer physical association cannot be captured in the prior art is solved, so that accurate and robust prediction of the PM2.5 concentration on the ground is realized under complex weather conditions such as sand and dust transportation and boundary layer evolution.

The invention provides a space particulate matter concentration detection method and device in an industrial and mining environment, relates to the technical field of industrial and mining environment detection, and solves the technical problem of low detection accuracy in the prior art. The method comprises the following steps: sensing a charge signal of the suspended particulate matter through a charge acquisition probe; the charge acquisition probe is used for generating induction current according to electrostatic induction charge characteristics of the suspended particulate matters; carrying out amplification processing and noise processing on the charge signal, and adopting a differential signal processing mechanism to eliminate environmental noise in the noise processing; the processed charge signals are input into a particulate matter characteristic model, and the spatial particulate matter concentration is obtained through calculation; the particulate matter characteristic model is used for representing an association relationship between electrostatic induction charge characteristics and concentrations of particulate matters with different particle sizes. The method is used in the detection process of the space particulate matter concentration.

The utility model discloses a coastal wetland shallow underground environment monitoring device, which belongs to the field of coastal wetland environment research and comprises an outer shell, a sealing cover A is arranged at the upper end of the outer shell, a sealing flange is arranged at the lower end of the outer shell, and a sensor mounting seat is arranged on the sealing flange. A plurality of water quality sensors for collecting water quality data are mounted on the sensor mounting seat, a battery and a control circuit board are arranged in the outer shell, and the battery and the plurality of water quality sensors are electrically connected with the control circuit board. According to the utility model, the sensor is buried underground by using the monitoring vertical pipe structure, and the influence of tidal water on a monitored target water source can be effectively isolated by means of the floating ventilation structure, so that the monitoring result is more accurate.

The invention specifically relates to an inspection image enhancement method for safety production, and relates to the technical field of image processing, and the method comprises the steps: collecting and preprocessing multi-source data; mie scattering characteristic analysis and transmissivity theoretical calculation; a transmissivity correction model driven by on-site concentration; carrying out image enhancement and noise reduction processing; and performing model optimization and safety production scene verification. According to the method, multi-source data acquisition and preprocessing comprehensively cover a safety production scene, and the influence of particulate matters on light propagation is accurately described by combining a Mie scattering theory and a transmissivity correction model driven by field concentration; actual interferences such as non-spherical particles, agglomeration effect and airflow unevenness are fully considered, and a pixel-level accurate transmissivity matrix is generated; differentiated noise processing is carried out for scenes with different concentrations, and key information such as equipment defects is effectively restored in combination with a detail enhancement strategy; the method provides a more reliable clear image for safety production inspection, assists inspection personnel and algorithms in accurately identifying hidden dangers, reduces the risks of missing inspection and false inspection, and guarantees the production safety.

The invention discloses an atmospheric greenhouse gas delta13C isotope observation construction method, which belongs to the technical field of atmospheric environment monitoring and is realized through three steps of monitoring data acquisition, traceability data acquisition and data processing standardization. According to the invention, through a double-site cooperative monitoring network and in combination with data acquisition of background sites and urban sites, the atmospheric CH4 concentration can be monitored in real time, and the delta 13C isotope value of CH4 can be accurately analyzed. And the source of CH4 can be qualitatively identified by utilizing a Kearing plot model, and high precision and high credibility of source analysis are ensured. According to the method, the Bayesian mixture model and the machine learning technology are adopted, the CH4 emission contributions of different sources are quantitatively estimated, and the emission sources and driving factors of CH4 are comprehensively analyzed in combination with multi-source tracer data and social economic data.

The invention discloses a bridge underwater safety detection method based on opto-acoustic dual modes, and relates to the technical field of underwater engineering detection, and the method comprises the following steps: obtaining water area particle size distribution and concentration data by using a multi-frequency acoustic suspended sediment concentration profiler, establishing an environmental parameter database, configuring a shipborne detection device according to the initial environmental parameter database, and detecting the underwater safety of a bridge according to the shipborne detection device. The method comprises the following steps: setting a three-axis stabilized platform carrying a polarization imaging camera and a multispectral LED array, deploying a multi-beam detection module and ADCP equipment, and establishing a unified coordinate system; multi-angle original intensity images are collected through a polarized optical imaging module, and multi-exposure fusion is carried out after processing is carried out through a mutual information minimization algorithm. An optical image and an acoustic point cloud are deeply fused by adopting a graph neural network, the detection precision is improved through dynamic weight optimization of an attention mechanism and a space-time registration algorithm, a multi-index fusion model is constructed based on fuzzy logic and a D-S evidence theory for adaptive early warning decision, upgrading from single-parameter alarm to multi-dimensional decision is realized, and the false alarm rate is reduced.