30 results about "Background concentrations" patented technology

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 technical field of environment monitoring and atmospheric component source analysis, in particular to a method and system for quantitatively evaluating greenhouse gas source contribution. The method comprises the following steps: extracting background concentration and calculating abnormal quantity through robust local weighted regression based on station hourly greenhouse gas concentration data; combining the abnormal quantity with meteorological data, calculating a comprehensive event score, performing event layering, and identifying a strong event window; a backward trajectory is calculated for the strong event hours, and normalized spatial weight distribution is generated; according to the distribution, the observation abnormal quantity is segmented into a local contribution part and an external contribution part according to a mass conservation principle; and the source industry influencing the observation concentration can be judged according to the diagnosis ratio of the characteristic species in the sampling data. According to the method, the quantitative and auditing separation of a concentration increment source can be realized under the condition of not depending on a complex model and strong prior, and the precision, the robustness, the interpretability and the cross-site comparability of a traceability result are effectively improved.

The invention discloses an environment gas leakage intelligent detection method based on big data analysis. The method comprises the following steps: collecting monitoring data and constructing a sensor network space-time diagram fused with meteorological conditions; predicting the background concentration of each node by using a sequence decomposition attention model; calculating a concentration residual error and constructing a space-time residual error map; extracting an abnormal feature vector through a physical information enhanced graph attention network; calculating an abnormal score in combination with the normal mode memory bank and generating a leakage source probability distribution diagram; when the conditions are met, calling a computational fluid dynamics model to simulate a theoretical concentration field; and determining a leakage event through spatial correlation analysis. According to the invention, the false alarm rate of gas leakage detection in an open environment is obviously reduced, and the leakage source positioning precision is improved.

This invention relates to a geochemical prospecting method based on graph neural networks and spatial features, enabling geochemical prospecting under different geological environments based on accurately predicted background concentrations of different chemical elements. The method includes: preprocessing raw geochemical multi-element data and separating it from background data to obtain background area data; constructing spatial context statistical features for each sampling point of the geochemical multi-element data according to a preset spatial scale, and fusing them with the original environmental features of that sampling point to generate an enhanced environmental feature vector for each sampling point; constructing a graph topology based on the statistical correlation between chemical elements in the background area data using the maximum spanning tree algorithm; constructing a graph neural network based on the graph topology, inputting the enhanced environmental feature vector of each sampling point into the graph neural network, outputting the predicted background concentration of each chemical element at each sampling point, and calculating a mineral prediction map.

The utility model discloses a kind of formaldehyde environmental chamber equipment with background concentration self-checking and automatic cleaning function, the formaldehyde environmental chamber equipment includes cabin, the sample holder for placing sample is installed in the cabin, the temperature and humidity sensor for real-time monitoring environmental parameter in cabin and the air circulation device for maintaining air flow uniformity in cabin are used, the formaldehyde environmental chamber equipment also includes sampling detection module, spray cleaning module and main control system. The formaldehyde environmental chamber equipment can effectively replace the conventional means of existing dependence on artificial judgment and operation, realize the rapid judgment and cleaning response of test chamber state, greatly shorten test preparation time, reduce artificial operation frequency and detection time cost.

The impurity acquisition system acquires a plurality of impurities in a liquid to be tested, and includes a plurality of branch paths (30-1) to (30-3) branching in parallel from a flow path through which the liquid to be tested passes, and a control device (100) that controls the conditions for passing the liquid to be tested through each of the plurality of branch paths (30-1) to (30-3) based on background concentrations measured in advance for each of the plurality of impurities.

This invention relates to the field of data processing technology, and more specifically, to an intelligent processing method for multi-source monitoring data of high-risk chemical products. The method includes: acquiring the concentration sequence of high-risk chemical gases and synchronous micrometeorological parameters within the target monitoring area; constructing an environmental non-stationarity intensity based on the characteristics of the micrometeorological parameters; calculating a baseline drift tendency coefficient by combining historical trends to assess the probability that concentration changes are attributable to environmental drift; dynamically correcting the background baseline using the baseline drift tendency coefficient to generate net leakage characteristics; and introducing the net leakage characteristics into a cumulative sum control chart algorithm to construct an adaptive damping cumulative quantity to achieve leakage monitoring of high-risk chemical products. This invention accurately assesses the impact of micrometeorology on background concentrations, scientifically distinguishes between environmental drift and actual leakage, effectively eliminates interference and reduces false alarms, dynamically adjusts the cumulative intensity to ensure stable detection under different environments, improves monitoring accuracy and reliability, and provides efficient protection for chemical safety.

The invention relates to the technical field of environment monitoring, in particular to a point source plume influence point identification method based on concentration distribution. According to the technical scheme, the method comprises the following steps: carrying out underway observation around an emission point source, collecting solar spectrum and position information at the same time, carrying out inversion to obtain the concentration of a target gas column, and calculating the average mole fraction of a dry air column; generating a concentration distribution diagram by combining the position information, identifying a concentration background area according to the distribution characteristics, and determining a background concentration baseline and a variation amplitude; based on the background change amplitude and the observation uncertainty, setting a plume influence point judgment threshold value, calculating a concentration enhancement value of each point relative to the background, and if the enhancement value exceeds the threshold value, judging that the point is a plume influence point; and determining the range of the plume influence zone based on the spatial distribution of the plume influence points. According to the method, the plume influence area can be accurately identified under the condition that wind field data cannot be obtained or errors are obvious, and the reliability of emission flux estimation is effectively improved.

This invention relates to the field of environmental monitoring and atmospheric composition source apportionment technology, specifically a method and system for quantitatively assessing the contribution of greenhouse gas sources. The method includes: extracting background concentrations and calculating anomalies based on hourly greenhouse gas concentration data from various stations using robust local weighted regression; combining anomalies with meteorological data to calculate a comprehensive event score and perform event stratification to identify strong event windows; calculating backward trajectories for strong event hours to generate a normalized spatial weight distribution; dividing observed anomalies into local and external contribution components according to the mass conservation principle based on this distribution; and further determining the source industries affecting observed concentrations based on the diagnostic ratios of characteristic species in the sampled data. This invention enables quantitative and auditable separation of the sources of concentration increments without relying on complex models and strong priors, effectively improving the accuracy, robustness, interpretability, and cross-site comparability of source tracing results.

This invention relates to a ship sewage discharge control system and method based on a water quality sensing and diffusion model. The system includes an AIS data acquisition module, a water quality sensing module, a discharge equipment monitoring module, a discharge impact module, a decision command generation module, and a discharge data storage module. The AIS data acquisition module is used to acquire the ship's position, ground speed, heading, and timestamp information in real time. The water quality sensing module is used to detect the background concentration of pollutants in the receiving water around the ship in real time. The discharge equipment monitoring module is used to monitor the operating status of the discharge equipment. The discharge impact module has a built-in two-dimensional advection-diffusion model. The decision command generation module is used to generate discharge decision commands that allow or suspend discharge. This system uses dynamic strategies to precisely regulate ship sewage discharge, providing environmentally friendly intelligent control for ship sewage discharge. The discharge data storage module replaces log recording, providing a data foundation for subsequent pollution research.

The application discloses a methane point source remote sensing detection inversion method based on physical guidance deep learning, which comprises the following steps: obtaining a sample data set of a research area; inputting the sample data set into a radiation transmission combination model to obtain a synthetic methane remote sensing image radiance sample set; a multi-task prediction head of a multi-task deep learning model comprises a background concentration prediction head, a concentration enhancement prediction head and a plume mask prediction head; the multi-task deep learning model is trained by using the synthetic methane remote sensing image radiance sample set; and the synthetic methane remote sensing image radiance data of the research area is input into the multi-task deep learning model to obtain concentration enhancement distribution data and a plume pixel mask. The application realizes plume injection to generate a synthetic training sample through radiation transmission simulation, realizes joint optimization of plume mask prediction, concentration enhancement regression and background concentration estimation through a multi-task deep learning model, and improves detection precision and emission inversion reliability under complex background conditions.

The invention discloses a trace detection method and device for trace elements in metro leakage water, and relates to the technical field of metro leakage detection.The method comprises the steps that the stratum structure, the underground water burial depth and background water quality parameters of a target area are obtained, and a set number of leakage suspicious areas are delineated in combination with a tunnel design drawing; and selecting rare earth elements of which the background concentration is less than a set concentration threshold and a fluorescent compound to form a tracer agent. The rare earth elements are combined with the fluorescent compound to realize full-dimensional coverage of deep and shallow layers, and the problem of leak detection of hidden leakage in a traditional single method is solved. Through technology fusion of dual-mode tracing, three-dimensional monitoring and intelligent modeling, comprehensive breakthrough in detection depth, precision, efficiency and risk early warning capability is realized, and a systematic solution is provided for accurate prevention and control of subway deep leakage under complex geological conditions.

The invention discloses a high-emission ship screening method based on a spectral remote sensing technology, and the method comprises the steps: carrying out the multi-azimuth angle and multi-elevation angle observation of ship plume based on shore-based spectral remote sensing equipment, and carrying out the inversion to obtain the optical path integral concentration of SO2 and NO2 at different positions of smoke plume, the mass concentration result of SO2 and NO2 in the plume and the ratio RSN of SO2 and NO2 are obtained through background concentration deduction and in combination with optical path information; the method comprises the following steps: pre-processing RSN data and ship characteristic data, then performing data grouping, performing statistics on the RSN data in each group, determining a calculation key percentile, taking the calculation key percentile as an RSN upper limit of compliant emission to calculate a fuel oil sulfur content calculation factor corresponding to the group, and sorting fuel oil sulfur content calculation factor results subjected to grouping calculation into a structured table to form a lookup table. Based on the lookup table scheme, the fuel sulfur content of the newly observed ship is further calculated, whether the ship meets the standard or not is judged, early warning information is sent out for the high-emission ship, and ship supervision is assisted. The method is used for solving the problem of high-emission ship screening.

This invention relates to the field of coal mine safety monitoring and environmental sensing, and discloses a method and system for detecting and correcting harmful gases in underground mines based on vehicle emission monitoring. The method first acquires multi-source data on vehicle emission sources, spatial location, airflow field, and sensor observations, and performs time synchronization alignment. Next, it calculates the vehicle's flow field coupling factor to the sensor and calculates the exhaust gas transmission lag time based on the airflow field and location data. Based on emission source data, lag time, and diffusion parameters, it constructs an interference prediction model to quantitatively calculate the theoretical interference concentration generated by the vehicle. Furthermore, it calculates the dynamic confidence weight coefficient and combines the observed data with the theoretical value to calculate the corrected real environmental gas concentration. Finally, it compares the real concentration with a safety threshold and executes alarm triggering or suppression control based on the result. This invention can effectively isolate vehicle exhaust gas interference, restore the true background concentration in the roadway, reduce the false alarm rate, and improve the accuracy of coal mine monitoring and production efficiency.

The application discloses an air quality prediction and source tracing method and system based on multi-scale model coupling, and belongs to the technical field of air quality prediction and evaluation. The method comprises the following steps: obtaining a pollution source emission inventory and meteorological field data, driving a regional scale model to perform regional air quality prediction, and obtaining a regional scale concentration field. The regional scale concentration field is taken as a background concentration field, and a local scale model and a small scale model are synchronously driven to perform pollution source contribution concentration prediction, and a local scale contribution concentration field and a small scale contribution concentration field are obtained respectively. Each scale concentration field is fused to generate a comprehensive concentration distribution map and perform source tracing analysis, and air quality influence is dynamically evaluated according to the source tracing analysis result. The application optimizes the accuracy of the regional scale model in small scale range simulation and solves the problem of the lack of a local scale concentration background field, and improves the accuracy of prediction and source tracing.

The invention discloses a gas pipeline leakage judgment method and a storage medium. The method comprises the steps that S1, adaptive background concentration extraction is carried out; for the data points one by one, taking the median in the sub-window as an initial background concentration value, and further correcting the background concentration by adopting the change condition of the initial background concentration in the main window to obtain a background concentration value adapted to the data points one by one; s2, judging a methane source; the methane source is judged by comprehensively considering the ratio of methane to ethane enhancement value, methane and ethane change synchronism and the ethane change condition at the methane peak value. The storage medium is realized based on the method. The method has the advantages of simple principle, wide application range, easiness in implementation, high detection precision and the like.

The application discloses a kind of fine particulate matter space-time distribution diagram construction method and device, including obtaining the background environment fine particulate matter concentration dataset and fine particulate matter concentration dataset corresponding to the preset research area;Based on the background environment fine particulate matter concentration dataset, concentration correction operation is carried out to fine particulate matter concentration dataset, to determine the background concentration correction dataset;Through social science statistical software, average fine particulate matter concentration value and each road corresponding vehicle data are subjected to pearson correlation detection operation, to generate correlation scatter diagram;In the background concentration correction dataset, the background concentration correction data corresponding to the road where all abnormal points in correlation scatter diagram are located is excluded, to generate traffic correction dataset;Through geographic information system software, a plurality of preset variation function models are used to carry out regional interpolation analysis on traffic correction dataset, to determine fine particulate matter space-time distribution diagram.The technical problem that the accuracy of the obtained PM 2.5 concentration spatial distribution is low is solved.

The application provides a gas emission flux determination method, device, equipment and readable storage medium, and the method comprises the following steps: obtaining preprocessed satellite data corresponding to an observation point; simulating a gas concentration based on an Euler atmospheric transmission model to obtain a gas background concentration; determining a transmission operator according to the preprocessed satellite data and a Lagrangian particle diffusion model; constructing an objective function according to the transmission operator, and determining the gas emission flux corresponding to the observation point according to the objective function. The application solves the problem of inaccurate estimation of the gas emission flux in the existing gas emission flux determination method.

The present disclosure provides a method for analyzing atmospheric fine particulate matter based on nitrogen oxide emissions, which can be applied to the fields of atmospheric chemistry and atmospheric pollution prevention and control technology. The method comprises: calling nitrogen oxide emissions of a target region in a unit time period from a pollution source monitoring system by using an electronic device; inputting the nitrogen oxide emissions in the unit time period into a fine particulate matter load function to output a fine particulate matter load in the unit time period, wherein the fine particulate matter load function is constructed based on a target data set of the target region; generating a fine particulate matter treatment system with nitrogen oxide emission limit value as an index by using the fine particulate matter load in the unit time period and an environmental capacity of the fine particulate matter, wherein the environmental capacity of the fine particulate matter is generated based on a spatial grid model of the target region according to a preset concentration compliance value of the fine particulate matter of the target region, an initial background concentration and an integral function between the material concentration and the total amount of the material.

The application provides an FTIR ship exhaust remote sensing detection method based on YOLOv8 and SORT target tracking, comprising the following steps: collecting ship video stream data in real time; inputting the ship video stream frame by frame into a YOLOv8 target detection model for feature extraction, obtaining an optimal target detection frame, and obtaining a ship target position; synchronizing the ship target position information and the detection frame information detected by the YOLOv8 to a SORT target tracking algorithm, predicting the ship target motion trajectory through a Kalman filter, realizing cross-frame target association in combination with a Hungarian algorithm, and obtaining a tracking result; based on the tracking result, inferring a ship exhaust area plume area, controlling an FTIR holder to scan the plume area, and obtaining an interference graph; performing Fourier transform and quantitative calculation on the interference graph, outputting exhaust concentration after removing background concentration; and according to the sulfur-carbon ratio in the exhaust concentration, whether the sulfur content of the crude oil of the ship exceeds the standard is inversely deduced.

The application discloses a Toci full-spectrum anion true value monitoring method, system, device and medium, and relates to the technical field of chemical analysis. The method comprises the following steps: acquiring a response signal in a target water sample; taking time as a horizontal coordinate axis and the response signal as a vertical coordinate axis; drawing a background response peak and a target response peak; acquiring a peak shape parameter of the background response peak; calculating a difference value between the peak shape parameter and a preset reference peak shape parameter; converting the difference value into a feature correction instruction; compensating the target response peak based on the feature correction instruction to obtain a true response peak; calculating a first peak area of the true response peak; obtaining an initial concentration of a target anion according to the first peak area; acquiring a background concentration of a background anion; calculating a concentration adjustment coefficient according to the background concentration; multiplying the initial concentration and the concentration adjustment coefficient to obtain a target concentration of the target anion. The technical scheme provided by the application can improve the accuracy of monitoring the target anion.

The invention discloses an in-situ photocatalytic oxidation effect evaluation method and system for VOCs discharged by plants, and relates to the technical field of biological substance analys.The method comprises the steps that living branches are placed into a first dynamic flux box and a second dynamic flux box respectively, and gaps of penetrating-out parts are sealed; environment air is introduced, the air inlet flow is set, continuous air exchange is maintained, and air in the box is evenly mixed; a to-be-detected photocatalyst is applied to leaf surfaces of the first flux box, and the catalyst is not applied to the second flux box as a contrast; sampling is switched under illumination, and the target VOCs concentration is obtained; firstly, baseline flux is measured, a natural difference coefficient is obtained through calculation, and after contrast flux is corrected, the VOCs removal efficiency is calculated. According to the technical scheme, the background concentration and the outlet concentration are synchronously obtained under the same sampling gas path, fluctuation and individual difference interference are reduced, in-situ quantitative evaluation is achieved, and result accuracy, comparability and repeatability are improved.

The invention discloses a vehicle-mounted underway high-precision greenhouse gas two-dimensional distribution monitoring system and method, and relates to the technical field of environment monitoring. According to the vehicle-mounted underway high-precision greenhouse gas two-dimensional distribution monitoring method, gas is alternately collected through double-path sampling probes arranged at different heights of a vehicle roof, and instantaneous interference of exhaust emission of motor vehicles on a road is removed by combining an underway position and real-time meteorological data which are synchronously obtained and based on concentration difference and meteorological information by utilizing a specific algorithm, so that the real-time distribution of the greenhouse gas is realized. Therefore, the real environment background greenhouse gas concentration is extracted. According to the method, the high-precision two-dimensional greenhouse gas concentration distribution diagram of the underway area is inverted through spatial interpolation processing according to the purified background concentration data, credible quantification of regional scale greenhouse gas spatial distribution is realized, a key technical means is provided for accurate measurement and traceability of carbon emission, and the method is suitable for popularization and application. The core problem that the existing mobile monitoring technology is difficult to accurately reflect regional background distribution due to self-path pollution is effectively solved.

The application discloses a firework discharge amount inversion, influence evaluation and prediction method and a related device, belongs to the technical field of atmospheric pollutant evaluation, and the inversion method comprises the following steps: obtaining pollutant measured concentrations of target area environmental monitoring stations in a firework discharge period and before and after the period; calculating the background concentration in the discharge period by using a machine learning model trained based on the data before and after the period, comparing the measured concentration, separating out the firework contribution concentration, and performing interpolation processing; using the gridized firework contribution concentration obtained through the interpolation processing to iteratively optimize the firework contribution concentration simulated by an air quality model, and obtaining a firework discharge iterative optimization emission inventory. The application does not need to rely on prior data such as firework discharge types, quantities, emission factors and space-time information, and can realize accurate quantitative inversion of the firework discharge amount only by using conventional environmental monitoring station data, and the calculation is simple and has strong applicability.

The application provides a dam downstream river section river pollution discharge amount dynamic regulation method and system, the method comprises the following steps: collecting research river section hydrology and water quality monitoring data; according to the collected hydrology and water quality monitoring data, the monthly flow condition and water quality index concentration condition of the research river section are determined, and the flow condition and concentration condition range are expanded; a two-dimensional hydrodynamic water quality model is established to simulate hydrology and water quality, the flow condition and water quality index concentration condition of a certain month of the research river section are selected, the pollution carrying capacity of each group of conditions of the research river section is calculated; the influence factors are quantified, the comprehensive influence value of flow change and water quality change is calculated, and the dominant interval is delimited; the flow and pollutant background concentration of the upstream control section of the target river section and the river pollution discharge amount of the target river section are monitored in real time, the real-time data are compared with the delimited dominant interval, and the dynamic regulation strategy is developed and executed. The application can realize the scientific development of the river pollution discharge amount dynamic regulation strategy based on the upstream hydrology and water quality conditions.

The present application relates to carbon emission detection technology field, specifically for carbon source positioning monitoring method and system based on multi-source data fusion, the present application constructs park flow field characteristic library and trains fluid dynamics reduced order model, uses satellite remote sensing data to carry out background concentration correction to ground sensor, agent outputs guess source parameter under the constraint of geographic fence, inputs model to generate full field theoretical concentration and extracts virtual reading, constructs reward function based on observation and virtual reading residual, drives agent to carry out iterative optimization through maximization of reward, until the convergence of bayesian probability distribution to lock target, the present application effectively eliminates environmental interference by fusing physical model and multi-source data, realizes real-time, automation and facility-level accurate positioning of unorganized emission source in complex park.

The invention relates to a method for monitoring the influence of a sand and dust weather process on the air quality of an urban environment, and the method comprises the steps: calculating the average daily observation concentration of target particles of each non-sand and dust influence day, and the average daily prediction concentration of the target particles of each sand and dust influence day under the assumption that the sand and dust influence day is free of sand and dust; determining the reference background concentration of the target particulate matter in the monitoring time period; and determining the contribution of the sand and dust weather process in the monitoring time period to the concentration of the target particulate matter according to the average value of the daily average observation concentration of the target particulate matter in the day set and the reference background concentration of the target particulate matter in the monitoring time period.

The application provides a satellite remote sensing-based ship NO2 emission calculation method, and comprises the following steps: obtaining satellite remote sensing images and ship AIS data, identifying ship exhaust plume bands and background pixels, calculating ship exhaust plume band and background NO2 concentration, and calculating the NO2 emission of the ship per unit time. The application uses the ground NO2 concentration monitoring data of the atmospheric pollution remote sensing satellite, combines the real-time AIS data of the ship identity and positioning, and realizes the top-down ship atmospheric pollutant emission monitoring.

This invention belongs to the field of safety measurement technology and discloses a laser detection method and system for gas leaks in sealed environments. The method includes: collecting operational data, environmental data, and monitoring data; using the operational data, environmental data, and a pre-set digital geographic grid of the monitoring area as input to a pre-trained gas migration model to obtain a dynamic background concentration field distribution map of the entire monitoring area; combining the spatial coordinates of each detection node to obtain the local background prediction value of each detection node; performing inversion analysis on the monitoring data to obtain the original gas volume fraction measurement value of each detection node, then subtracting the corresponding local background prediction value to obtain the compensated gas concentration residual value and analyzing it to obtain anomaly node clusters; and analyzing the anomaly node clusters with potential leak sources to obtain the location of the leak source. This invention enables the system to separate the weak residual signal characterizing the actual leak with high confidence from data submerged by strong background noise.

The invention discloses a pollutant and greenhouse gas dynamic traceability method and system based on machine learning, relates to the technical field of environment monitoring and data processing, and improves the accuracy and reliability of traceability analysis by constructing a collaborative mutual verification and dynamic feedback mechanism of pollutant and greenhouse gas traceability analysis. According to the method, cross validation is carried out by using two different types of homologous substance data, and contradictions occurring in independent analysis are intelligently solved through a deep fusion model, so that the analysis uncertainty caused by factors such as atmospheric chemical reaction, background concentration noise or multi-source mixing in a single tracing method is effectively overcome; according to the method and the system, a traceability result which is closer to a real physical process and high in credibility is obtained, quantitative evaluation on the reliability of a traceability conclusion is realized by introducing mutual identification confidence evaluation and comprehensive credibility evaluation links, and subsequent environment supervision, law enforcement and governance actions are enabled to be more targeted and efficient.