55 results about "Gaussian plume" patented technology

The invention discloses an ecological environment pollution source tracing method based on data analysis, and relates to the field of ecological environment, and the method comprises the steps: carrying out the type selection of equipment, obtaining original data, carrying out the arrangement of the equipment after the type selection of the equipment, carrying out the multi-source data access after the arrangement of the equipment, and carrying out the data format standardization based on the obtained original data. And performing data anomaly detection based on the original data after data format standardization, and performing vacancy value filling based on the original data after data anomaly detection. According to the method, through multi-source data fusion, data cleaning, pollution diffusion modeling, pollution source identification and contribution rate calculation and visual early warning, the precision and reliability of pollution tracing are improved, firstly, format standardization, time synchronization and anomaly detection are carried out on pollution monitoring data of different sources, and data consistency and integrity are ensured; and then combining a Gaussian plume model, a fluid dynamic model and a Fick diffusion model.

The invention discloses a pollution source dynamic tracing method and equipment for industrial park hidden gas leakage. Comprising the following steps: acquiring meteorological monitoring data at each observation point through fixed monitoring equipment and / or mobile monitoring equipment; correcting the coordinates of the observation points; predicting a first gas pollutant predicted concentration corresponding to each observation point after coordinate correction according to a Gaussian plume diffusion model; inputting the first gas pollutant predicted concentration and meteorological monitoring data into a CatBoost model to obtain a second gas pollutant predicted concentration; taking the difference between the actual gas pollutant concentration at each observation point and the second gas pollutant predicted concentration as a target function, and solving the target function to obtain the pollutant source intensity and the source position; completing one iteration; and setting the observation point near the current predicted pollutant source position, and so on, and obtaining the final pollutant source intensity and source position after n iterations.

The invention discloses an unmanned aerial vehicle cluster dynamic path planning method and system based on a Gaussian plume model. The method comprises the steps that chemical plant leakage gas concentration, wind speed data and thermal imaging data are collected; dynamically correcting the diffusion coefficient of the Gaussian plume model according to the wind speed data, and generating a leakage source probability distribution diagram in combination with the leakage gas concentration and the thermal imaging data so as to determine the concentration gradient value and pheromone field intensity of each position of the chemical plant, thereby planning the search path of the unmanned aerial vehicle cluster; and performing grid division on the leakage source probability distribution diagram to determine a search task, performing search task distribution according to an auction algorithm and the real-time state of the unmanned aerial vehicle, performing leakage source search positioning in combination with a search path, and finally marking a leakage point and returning the leakage point to a command center of the unmanned aerial vehicle cluster in real time. Dynamic path planning of the unmanned aerial vehicle cluster is realized in combination with multi-source data fusion, the unmanned aerial vehicles are guided to preferentially search high-probability regions through task allocation, and the positioning precision of leakage sources and the resource utilization rate of the unmanned aerial vehicles are improved.

The invention discloses an urban particulate matter migration path identification method based on data fusion of a dynamic diffusion model and multi-source sensing. According to the method, firstly, multi-source pollution related data of a fixed monitoring station, a mobile monitoring device, a meteorological observation node and a traffic sensing system are collected, and standardized input is constructed after time synchronization, coordinate mapping and exception handling. And then establishing a hybrid dynamic diffusion model fusing a two-dimensional Gaussian plume model and a Lagrange particle tracking mechanism, and realizing assimilation of monitoring data and model output in combination with an improved particle filtering algorithm to obtain a correction concentration field. Based on concentration gradient analysis and particle trajectory superposition, a pollution migration path is extracted, then indexes such as a migration intensity index (MII), a path stability factor (PSF) and path average correlation are calculated, and recognition and sorting of a migration direction, a pollution source position and path stability are achieved. Meanwhile, an alpha dynamic weight factor is introduced, adaptive weighting is realized between the Gaussian model and the LPDM model, and the path strength and consistency are considered. Experimental results show that the method is superior to a traditional method in average absolute deviation (MAD) and hot spot overlap ratio (IoU) indexes, diffusion and migration rules of particulate matters under complex urban conditions can be more accurately revealed, and the method has important environmental governance and emergency management application value.

The invention relates to the technical field of risk assessment, and particularly discloses an artificial intelligence-based hydrogen leakage risk assessment model, which comprises a dynamic monitoring data module, a risk feature extraction module, a leakage rate prediction and source point inversion module and a leakage evolution module, the dynamic monitoring data module collects real-time parameters and preprocesses the real-time parameters; the risk feature extraction module introduces a 1D-CNN and CNN-LSTM fusion neural network to generate a leakage risk factor sequence; the leakage rate prediction and source point inversion module predicts the leakage rate by using a sliding window and a dynamic correction mechanism, and inverts the position of a hydrogen leakage source in combination with a Gaussian plume model and sensor data; and the leakage evolution module displays a high-risk area heat map and a leakage source position. According to the method, the multi-source sensing data and the physical model are fused, and the defect that the leakage risk cannot be dynamically predicted through a traditional method is overcome.

The invention discloses a mining area methane emission inversion method based on satellite observation and a Gaussian plume model. The method comprises the following steps: S1, acquiring a satellite inversion XCH4 data set, meteorological data, emission list data and global energy monitoring data; s2, determining a methane emission source and a research area in a geographic map by using the emission list data and the global energy monitoring data; s3, obtaining methane background concentration and XCH4 enhancement in the geographic map by using an atmospheric background concentration calculation method; s4, combining XCH4 enhanced spatial distribution and meteorological data in a geographic map to identify a methane enhanced plume of a methane emission source in the research area; and S5, performing Gaussian plume flow field simulation inversion of the methane emission source through the Gaussian plume model, and outputting the methane emission rate of the methane emission source at the current time through inversion of the Gaussian plume model. According to the method, the methane emission and the time sequence data of the methane emission can be obtained through accurate inversion, the frequency of obtaining the methane emission is improved, and the methane emission can be obtained according to time scale accumulation.

The invention relates to the field of cooperative control of atmospheric pollutant and carbon dioxide emission of a power system, and discloses a power system pollution and carbon reduction method based on source-network interaction, which relates to an analysis method and specifically comprises four steps. The method comprises the following steps: 1, establishing an atmospheric pollutant and CO2 emission model; 2, space-time diffusion characteristics of atmospheric pollutants are described through a Gaussian plume diffusion model, and the influence of the space-time diffusion characteristics on a typical load area is analyzed; 3, establishing a power plant coal blending and power grid dispatching combined optimization model containing pollution reduction and carbon reduction constraints; and 4, establishing a collaborative optimization framework and dividing responsibilities of a source network. Compared with a power coal blending and power dispatching technology adopted by a current power plant and a power grid, the method has the advantages that on the basis of daily load prediction, collaborative interaction and joint response between the power grid and the power plant are enhanced through refined decision making, and emission of atmospheric pollutants and CO2 in power generation and transmission links is reduced to the maximum extent.

The invention relates to a pollutant monitoring method, device and equipment based on multi-point source superposition diffusion, and belongs to the technical field of atmospheric pollutant diffusion analysis. The latitude and longitude coordinates of the emission source, the evaluation point and the evaluation area are represented by a geographic coordinate system, and the latitude and longitude coordinates are converted into plane coordinates under a projection coordinate system; a two-dimensional rectangular grid is adopted to cover an evaluation area, and a continuous space is discretized into a plurality of grid units; establishing a reference coordinate system and a diffusion coordinate system; the constructed Gaussian plume diffusion model is adopted to calculate pollutant concentration data of each emission source to each grid point, concentration contributions of the multiple emission sources are superposed, and total concentration data of the grid points are obtained; and enabling the pollutant concentration data of the evaluation point to be equivalent to the function output of the total concentration data of one or more grid points in the grid unit where the evaluation point is located, so as to obtain the pollutant concentration data of the target position. The invention provides a refined technical scheme for pollution diffusion simulation of complex multi-point sources.

The invention relates to the technical field of ship emission monitoring, in particular to a ship emission telemetering method based on multiband spectrum inversion, which comprises the following steps of: acquiring O4 differential inclined column concentration along the horizontal direction by utilizing shore-based multiband telemetering, calculating effective light path lengths of different bands, dividing the NO2 horizontal column concentration subjected to synchronous inversion by the effective light path lengths, and calculating the ship emission telemetering result. The average concentration on the horizontal path is obtained, and NO2 concentrations of different horizontal layers are separated based on multiband inversion; the method comprises the following steps: constructing a ship plume model in which a ship navigation trajectory is coupled with a Gaussian plume model on the basis of ship AIS (Automatic Identification System) data in combination with wind direction and wind speed information, matching plume trajectories of different ships with passed concentration layers, and quantifying the concentration of NO2 discharged by a single ship in combination with plume width information calculated by the ship plume model. According to the method, the emission condition of the in-flight ship is evaluated based on a telemetering means, monitoring can be carried out under the condition that normal navigation of the ship is not interfered, and powerful technical means and data support are provided for ship emission supervision.

The invention relates to the field of data processing, in particular to a smart city data processing method and system based on big data, and the method comprises the steps: dividing a city region into a plurality of sub-regions, and setting a plurality of monitoring points in the city region; the diffusion fluctuation degree in each sub-region is calculated, wherein the diffusion fluctuation degree represents the influence degree of the sub-region buildings on pollutant diffusion; adjusting diffusion parameters in the Gaussian plume model by using the influence degree to obtain an optimal diffusion coefficient, and obtaining the monitoring concentration of each monitoring point; setting the initial position of a pollution source, calculating the estimated concentration of pollutants at each monitoring point by using a Gaussian plume model, and calculating the difference between the estimated concentration and the monitored concentration; and updating the initial position of the pollution source, and taking the position of the pollution source when the difference is minimum as the final position of the pollution source. According to the invention, precise positioning of the gas pollution source is realized.

The invention discloses a ship high-sulfur fuel oil monitoring method based on a Gaussian plume model, and the method aims at ship flue gas remote sensing monitoring, based on the diffusion concentration of sulfur dioxide discharged by a ship at a monitoring point, an optimization algorithm is utilized, an emission source intensity inversion problem is resolved into an objective function unconstrained multi-dimensional extreme value problem, and a ship high-sulfur fuel oil monitoring result is obtained. According to the method, the emission source intensity of the ship is calculated, and the ship fuel oil consumption amount calculated by the basic information of the ship is combined, so that an underway ship fuel oil sulfur content back calculation method without the help of the carbon dioxide concentration is established, and the ship with the sulfur content exceeding the standard is effectively identified. Meanwhile, in consideration of the fact that the wind direction and the line source direction are not completely consistent in the actual navigation environment, a Gaussian plume model corrected by combining the included angle between the wind direction and the line source, the effective height of the ship flue gas emission source, the effective wind speed and the diffusion coefficient is constructed, and the accuracy of the ship high-sulfur fuel oil monitoring result is further improved.

The invention relates to the technical field of gas leakage detection and optical remote sensing, particularly discloses a laser natural gas leakage point positioning and imaging processing method based on an unmanned aerial vehicle, and aims to solve the problems of multiple coverage blind areas, low positioning precision and easiness in environmental interference in the prior art. The method comprises the following steps: acquiring a methane concentration integral value through a tunable diode laser absorption spectrum system carried by an unmanned aerial vehicle; constructing an initial concentration point cloud in combination with the high-precision pose and scanning angle data; performing space-time filtering and interpolation enhancement by adopting Kalman filtering and a Gaussian plume model to generate a high-confidence three-dimensional concentration field; and accurately positioning a leakage source by using a gradient rising tracking algorithm in combination with curvature and continuity constraints, and matching pipe network geographic information to perform verification alarm. According to the technical scheme, large-range, high-precision and full-automatic natural gas leakage detection and visual positioning are realized, and the signal-to-noise ratio, the anti-interference capability and the response efficiency are remarkably improved.

The invention discloses an urban atmospheric pollution real-time monitoring and tracing method based on reinforcement learning, and relates to the technical field of atmospheric environment monitoring, and the method comprises the following steps: S1, collecting a pollution source data set; s2, constructing a Gaussian plume fidelity simulation model; s3, constructing a CFD fidelity simulation model; s4, constructing a multi-fidelity simulation model based on a Gaussian process proxy model; s5, constructing a DPPO reinforcement learning model; s6, using an improved multi-fidelity Bayesian optimization algorithm to continuously train and iterate the DPPO reinforcement learning model; and S7, generating a standardized pollution source real-time traceability analysis report. According to the method, the limitations of much manual intervention, low efficiency and poor real-time performance in a traditional urban pollution monitoring and tracing method are overcome, and an efficient and accurate solution is provided for intelligent real-time monitoring of urban environmental pollution and accurate and rapid tracing of pollution sources.

The invention belongs to the technical field of detection and measurement, and discloses a gas inspection robot multi-source harmful gas monitoring method based on image recognition, and the method comprises the steps: obtaining an inspection area image through a visible light camera and an infrared camera, extracting features, and generating a leakage probability distribution diagram with a coordinate index; combining the probability graph with laser ranging data to form a target area set; self-adaptive sampling tracks of three modes of grid, spiral and jumping are generated in a grading manner according to the confidence degree; synchronously starting infrared, electrochemical and photoacoustic spectrum sensors along the track to collect concentration data, and carrying out zero cross calibration between adjacent sampling points; combining the calibrated multi-source data with environmental parameters, dynamically updating the weight through Kalman filtering, and outputting a fusion concentration result; on the basis, a Gaussian plume diffusion model is adopted to reconstruct a concentration field, the leakage level is judged, sound-light alarm is triggered when the leakage level exceeds the limit, and meanwhile the concentration gradient serves as feedback to correct the path of the next stage.

The invention discloses a calculation method and device of an atmospheric dispersion factor and a site selection method of a nuclear power plant. The method comprises the following steps: acquiring reanalysis meteorological data; determining the central position of the target area, wherein the central position comprises latitude and longitude coordinates; and according to a Gaussian plume model, and / or a Gaussian puff model, and the reanalysis meteorological data, calculating an atmospheric dispersion factor at a preset distance from the center position. According to the method, the problem that effective evaluation of the atmospheric dispersion factor cannot be carried out for a target area in which meteorological station construction and meteorological element observation are not carried out temporarily in the prior art can be solved.

The invention relates to the technical field of monitoring control, and discloses a method and a system for monitoring new pollutants in a chemical industry park. The method comprises the following steps: acquiring spectral data of a chemical industry park through a multispectral fusion sensor array to obtain a three-dimensional spectral matrix; calculating a new pollutant risk level by using a CIPCI algorithm, and generating a hierarchical control instruction; using a deep neural network DNN to identify pollutant types and quantify the concentration; a PID-fuzzy control fusion algorithm is adopted to adjust control parameters; and multi-node cooperative control is realized in combination with a Gaussian plume diffusion model. The technical problem that the new pollutants in the chemical industry park cannot be subjected to differential real-time monitoring control according to the risk level is solved, and the intelligent level of new pollutant monitoring and the pollution risk prevention and control effect are improved.

The invention discloses a fire source positioning method and terminal based on an Internet of Things smoke detector, and relates to the technical field of fire source positioning, and the method comprises the steps: 1, collecting the detection data in a monitoring region in real time, and when the detection data meets a preset triggering condition, executing the step 2: starting a time-limited data collection window, step 2, verifying the rationality of the detection data by adopting an RANSAC algorithm, and filtering abnormal detection data; step 3, carrying out multi-modal wind direction estimation, and carrying out wind direction fusion calculation; 4, establishing a three-dimensional Gaussian plume model, simulating spatial distribution of smoke under the action of wind direction and turbulence diffusion, and obtaining a plurality of candidate fire source positioning points; multi-source data mutual verification and compensation are carried out through a multi-modal data fusion mechanism, system errors of a single data source are eliminated, data reliability is improved by matching with an enhanced RANSAC anomaly detection algorithm and an environment adaptive Gaussian plume model, and accurate positioning of a fire source when an indoor fire occurs is realized by matching with time control and confidence evaluation.

The invention relates to the technical field of toxic gas risk prediction of electrochemical energy storage boxes, in particular to an electrochemical energy storage box toxic gas risk prediction method based on multi-model fusion, which comprises the following steps: searching a preset number of similar transportation scenes in a preset weight scene library, obtaining preset optimal weights of the box-type model, the Gaussian plume model and the machine learning model in a preset number of similar transportation scenes; performing interpolation calculation based on a preset optimal weight and the similarity corresponding to the similar transportation scene to obtain target weights of the box-type model, the Gaussian smoke plume model and the machine learning model, and performing weighted summation based on the target weights to obtain target diffusion data; and obtaining a toxic gas risk prediction result of the electrochemical energy storage box at least based on the target diffusion data. Therefore, by fusing the box type model, the Gaussian plume model and the machine learning model, the accuracy and reliability of toxic gas risk prediction of the electrochemical energy storage box are improved.

The invention discloses a gas leakage concentration prediction method and system based on an improved Gaussian plume model, belongs to the technical field of data processing, and aims to solve the technical problem of how to improve the accuracy and reliability of gas leakage concentration prediction. Comprising the following steps: selecting an adaptive grading calculation strategy from a multi-modal aperture adaptive leakage speed grading calculation model based on a leakage aperture, and predicting and calculating the leakage intensity of a pipeline based on the selected grading calculation strategy; an improved intelligent Gaussian plume model is constructed by introducing a logistic time growth function, a wind speed segmented intelligent compensation mechanism, diffusion coefficient lower limit constraint and concentration physical amplitude limiting, and for a gas leakage pipeline, the wind speed and the leakage intensity corresponding to the pipeline are used as input; and the diffusion concentration and the diffusion distance of the output leaked gas are predicted through the improved intelligent Gaussian plume model.

The invention discloses a natural gas leakage point accurate positioning method fusing multi-source parameter dynamic correction. The method comprises the steps that S1, a distributed monitoring network is constructed (N is larger than or equal to 3 non-collinear integrated detection modules, a methane sensor with the detection limit smaller than or equal to 1 ppm is included, and environment parameters such as concentration and diffusion waves and the leakage rate are synchronously collected according to the frequency larger than or equal to 0 Hz); s2, establishing a double-stage diffusion coupling model (wherein the diffusion coefficient is corrected based on the Fick's law in the soil stage of Z < 0, the wind direction influence is corrected based on a dynamic Gaussian plume model in the atmosphere stage of Z > = 0, and coupling is realized through soil porosity correlation transfer coefficient); s3, constructing a multi-dimensional equation set, optimizing a diffusion wave distance equation through a calibration experiment, and solving a leakage point coordinate by adopting a particle swarm algorithm; and S4, verifying the calibration precision through residual errors, and adapting to low-concentration, high-wind-speed and complex soil scenes. The positioning error is smaller than or equal to 5 cm, the environment adaptation range is enlarged by three times, the false alarm rate is smaller than or equal to 0.1%, and the precise positioning requirement under the complex working condition can be met.

This invention relates to the field of environmental science and technology, and discloses a simulation system for atmospheric pollution diffusion, including a data acquisition module, a data preprocessing module, a dynamic simulation model module, a simulation calculation module, a visualization module, and a decision support module. The data acquisition module collects data from multiple sources; the data preprocessing module cleans, fuses, and optimizes the collected data; the dynamic simulation model module employs a dynamic hybrid diffusion model and combines deep learning to optimize model parameters; the simulation calculation module performs efficient simulation of pollutant diffusion; the visualization module dynamically displays the pollutant concentration distribution and diffusion path in three dimensions; and the decision support module generates pollution control suggestions and risk warning information based on the simulation results. In this invention, by introducing a dynamic hybrid diffusion model, intelligent switching between Gaussian plume models and CFD models is achieved, enabling adaptation to various scenarios ranging from simple to complex terrains.

The present application belongs to the technical field of agricultural environmental science, and particularly relates to a transgenic corn gene drift prediction method based on wind speed dominance, which collects environmental parameters of wind speed, wind direction, humidity, atmospheric stability, positioning, range and altitude, corn variety specificity parameters and historical gene drift observation data in a target area; constructs a Gaussian plume diffusion model based on the preprocessed data to determine a gene drift frequency atlas; under a Bayesian neural field framework, the gene drift frequency atlas is taken as input, the actually measured gene drift data is taken as model observation values, and the preprocessed environmental parameters and corn variety specificity parameters are taken as covariates to optimize the model, so as to output a predicted gene drift flow threshold distance and give a risk prevention and control report. The present application combines the advantages of physical models and empirical models, overcomes the defects that the prior art does not fully consider the differences in varieties and environmental factors, and realizes accurate prediction of gene drift.

The invention provides an urban environmental sanitation building monitoring method and system based on a big data technology, and a storage medium. Comprising the steps of inputting pollutant emission concentration, humidity of an area where the environmental sanitation building is located, and real-time wind speed and real-time wind direction in the area where the environmental sanitation building is located into an improved Gaussian plume model to obtain pollution degrees of various pollutants to the area where the environmental sanitation building is located; determining the average influence degree of various pollutants emitted by the environmental sanitation building on the regional population by combining the population number and the pollution degree of the region where the environmental sanitation building is located; according to the real-time weights corresponding to the various pollutants in the attributes of the region where the environmental sanitation building is located and the average influence degree of the various pollutants on the population of the region, the environmental influence index of the environmental sanitation building is determined; and if the environmental influence index of the environmental sanitation building is higher than a set threshold value, generating a maintenance work order, and sending the maintenance work order to garbage transfer equipment or maintenance equipment in the environmental sanitation building.

A system includes a gas leak instrument configured to monitor for gas leaks from an emission source, wherein the gas leak instrument includes a gas sensor, a wind sensor, and a controller coupled to the gas sensor and the least one wind sensor. The controller has a processor, a memory, and instructions stored on the memory and executable by the processor to cause operations including determining a baseline gas concentration in an environment based on first gas measurements from the gas sensor and first wind measurements from the wind sensor and determining an emission gas rate based on the baseline gas concentration, second gas measurements from the gas sensor second wind measurements from the wind sensor, and the Gaussian plume model.

The invention belongs to the technical field of gas detection, and particularly relates to a sulfur hexafluoride (SF) concentration detection method and device based on a dual-band tunable diode laser absorption spectrum (TDLAS) and electronic equipment. In order to solve the problems that in an existing SFdetection technology, quantification and positioning functions are separated, the anti-interference capability is poor, and high sensitivity and rapid imaging cannot be synchronously achieved, the invention provides a method combining dual-band differential absorption and TDLAS. According to the scheme, a detection device in which an open type backward reflection path is combined with a built-in White cell (a multi-reflection cell) is adopted, 10.55 microns are selected as a measurement wave band, and 9 microns are selected as a reference wave band; through synchronous or alternate acquisition of dual-band signals, wavelet denoising preprocessing, differential absorption concentration inversion, Kalman filtering gas cloud tracking and Gaussian plume model leakage source inversion, rapid imaging positioning and accurate concentration quantification of an SFleakage point are realized. The system has the advantages of high detection sensitivity, strong anti-interference capability and integrated quantitative positioning function, and is suitable for efficient inspection and accurate diagnosis of the SFleakage of the power equipment.

This application discloses an agricultural machinery PM based on drone monitoring data. 2.5 This invention addresses the technical challenges of non-overlapping monitoring points and emission sources, as well as poor local adaptability of emission factors in existing agricultural machinery emission monitoring methods and systems. It first utilizes unmanned aerial vehicles (UAVs) to carry PM2.5 data. 2.5 The monitoring system collects pollutant concentration data downwind of the agricultural machinery operation area and preprocesses the data by performing missing data completion, anomaly removal, and duplicate correction. Secondly, based on a Gaussian plume diffusion model, combined with on-site meteorological parameters and agricultural machinery operating parameters, it inversely calculates the emission source intensity of agricultural machinery. Finally, it uses the analytic hierarchy process (AHP) to perform weighted analysis on multiple factors such as agricultural machinery age, operating mode, environmental humidity, and temperature, achieving localized and accurate calibration of emission factors. This application effectively solves the problem of spatial misalignment between monitoring points and emission sources, significantly improving the PM2.5 concentration of agricultural machinery. 2.5 The accuracy and reliability of emission estimates provide a quantitative basis for the compilation of regional emission inventories and the formulation of agricultural machinery renewal policies.

The present application relates to the technical field of ship emission monitoring, and particularly relates to a ship emission remote sensing method based on multi-band spectral inversion, which uses shore-based multi-band remote sensing to obtain O4 differential slant column density in the horizontal direction, calculates the effective optical path length of different bands, divides the synchronously inverted NO2 horizontal column density by the effective optical path length to obtain the average concentration on the horizontal path, separates the NO2 concentration of different horizontal layers based on multi-band inversion; constructs a ship plume model coupled with the ship sailing track and Gaussian plume model based on ship AIS data combined with wind direction and wind speed information, matches the plume track of different ships with the concentration layer passed, and quantifies the NO2 concentration of single ship emission combined with the plume width information calculated by the ship plume model. The present application evaluates the emission of ships at sea based on remote sensing means, can carry out monitoring without interfering with the normal sailing of ships, and provides strong technical means and data support for ship emission supervision.

The present invention belongs to the technical field of alkane gas leakage detection, and specifically relates to a gas leakage detection method based on deep learning and optical flow method. The present invention comprises: using a Gaussian plume model to simulate the image data of real gas leakage and produce a data set; using a deep learning network YOLOv8‑seg model for training; using a mid-infrared camera to monitor the area where leakage may occur; when a leakage occurs, detecting and segmenting the leakage gas area to generate a binary segmentation mask of the leakage gas; using the optical flow method to analyze the movement direction of the gas plume in the binary segmentation mask. To solve the problem that it is difficult to find the leakage point in the existing gas detection means, the present invention can more accurately track the diffusion trajectory of the leaked gas and achieve rapid tracing.

The application discloses a kind of methane emission rate measurement method, device, electronic equipment and storage medium, it is related to methane emission measurement technical field.The method comprises: obtaining unmanned aerial vehicle measurement data and vehicle measurement data obtained by measuring target emission source respectively;According to the first position data in unmanned aerial vehicle measurement data, flight trajectory is constructed and is layered according to altitude, according to the first emission attribute data corresponding to each layer flight trajectory, the line flux integral value corresponding to each layer flight trajectory is determined, and the first methane emission rate is determined according to line flux integral value;Based on vehicle measurement data, the initial grid flux of each grid in emission section is calculated using mass balance method, and the initial grid flux is integrated after being expanded by Gaussian plume model, to obtain the second methane emission rate;The first methane emission rate and the second methane emission rate are added to determine the final methane emission rate.The above technical solution improves the accuracy of methane emission rate measurement.

The present application relates to the technical field of ship emission monitoring, and particularly relates to a method and system for measuring ship exhaust emission rate in real time based on ground-based DOAS. The technical scheme comprises installation and calibration of the instrument, real-time data acquisition and processing, and optimal estimation method based on Monte Carlo radiation transfer model simulation. The present application collects spectral information and distribution information of ship emission plume in real time, establishes a discrete Gaussian plume model and inputs it into the Monte Carlo radiation transfer model, reconstructs the radiation transfer process of the plume in the atmosphere, simulates the radiance data in the field of view of the spectrometer, compares it with the actually collected spectral information, and infers the ship exhaust emission rate through the optimal estimation method, so as to fully exert the characteristics of passive DOAS instrument measurement, such as rapidness and flexibility.