139 results about "Air pollutant concentrations" patented technology

The invention discloses an automotive exhaust emission data fusion system. The automotive exhaust emission data fusion system comprises a roadside air pollutant concentration estimation module, a roadside air pollutant concentration prediction module, a city global atmospheric environment prediction module, an automotive exhaust emission factor estimation module and an automotive exhaust emission feather analysis module, wherein the five modules are used for respectively realizing different data analysis functions, and the different functions can be realized by virtue of the different modules; the modules can be independently used, or two or more modules can be combined for use, so as to realize the storage, analysis and fusion of automotive exhaust telemetering data, automotive attributes, driving working stations, detection time and meteorological condition data; and by combining with a vehicle-mounted diagnosis system database, a portable emission test system database, a vehicle inspection station offline database, a traffic information database and a geographic information database, automotive exhaust telemetering data is analyzed, and the highest discriminatory key indexes and statistical data are acquired, so that effective supports are provided for the formulation of relevant decisions of government departments.

The invention relates to an LSTM (Long Short-Term Memory)-RNN model-based air pollutant concentration forecast method. The method comprises the steps of monitoring and collecting air pollutant concentration data in a relatively long time; preprocessing historical data to construct training, verification and test sample data of a to-be-trained LSTM-RNN model; obtaining a pre-trained LSTM-RNN model through the training sample data, then performing fine adjustment and training through the constructed verification and test sample data to obtain LSTM-RNN model parameters, improving model precision by further correcting model parameters, and taking a corrected LSTM-RNN model as an air pollutant concentration forecast model; and finally taking the preprocessed air pollutant concentration data of a target city in the relatively long time as input data of the LSTM-RNN model, and performing model output to obtain a forecast result of air pollutant concentration at a current moment or at a moment in the future.

The embodiment of the invention provides a regional air pollutant concentration prediction method, terminal and computer readable storage medium. The method includes using a calculated daily average historical pollutant concentration data set and a preprocessed daily historical meteorological data set as sample data sets, utilizing a random forest model to perform training, wherein the random forest model includes a plurality of decision trees, each decision tree being implemented by use of a multilayer feedforwad neural network; determining predicted meteorological data of a preset number ofdays in the future predicted on the same day at current time; preprocessing the predicted meteorological data; and according to the preprocessed predicted meteorological data and pollutant concentration data monitored on the same day at current time, utilizing the trained random forest model to predict pollutant concentration data of the preset number of days in the future of a region to be predicted. The regional air pollutant concentration prediction method provided by the embodiment of the invention improves regional air pollutant concentration prediction precision, and has relatively a high generalization capability.

The invention discloses an air pollutant concentration forecast method. The method comprises the following steps of determining a plurality of undetermined models and setting that a number of nodes of an output layer of each undetermined model is m; from historical concentration data, extracting a training data set and a verification data set; training each undetermined model till that the undetermined model is converged; determining the undetermined model corresponding to a minimum integration error to be a forecast model; and inputting a forecast data set into the forecast model, and taking an output result of the forecast model as a forecast result of forecast time delay r. By using the air pollutant concentration forecast method, based on a STDL model, forecast of air pollutant concentration is performed; implicit space-time correlation in air pollutant concentration data can be extracted; forecast data of a plurality of monitoring sites can be synchronously acquired and forecast precision is high; and precision of extreme air pollutant concentration forecast can be improved.

The invention provides air purification equipment and an air purification method. The air purification equipment comprises a shell, a mobile unit and a gas concentration detection unit, a central control unit, a gas inlet unit and an air purification unit which are arranged in the shell; the gas concentration detection unit is used for detecting the gas integral concentration of an optical path in a monitored area; the central control unit is used for determining the varieties of air pollutants and concentration reconstruction graphs of different varieties of air pollutants and acquiring geographic position information of the area where the concentration of the air pollutants exceeds the standard most seriously in the monitored area and generating a driving signal when the concentration of the air pollutants exceeds the standard; the mobile unit is used for moving to the area where the concentration of the air pollutants exceeds the standard most seriously in the monitored area under control of the driving signal; the gas inlet unit controls the gas inlet volume under control of the driving signal; the air purification unit comprises multiple layers of filter screens used for purifying air entering the air purification equipment. Accordingly, fixed-point air purification can be achieved.

A method for drawing a real-time dynamic three-dimensional power grid contamination area distribution diagram includes the steps of S1, establishing a corresponding relation between air pollutant concentration and the gradation for surface pollution, and S2, drawing the contamination area distribution diagram. The S2 further includes the steps of S2-1, conducting square mesh generation and rasterization processing on an object region, S2-2, reading real-time air pollutant concentration, S2-3, calculating the air pollutant concentration, S2-4, reading the position, the discharging type and the discharging amount of pollution sources, S2-5, reading the data of wind directions, wind speeds and rainfalls, S2-6, checking and amending the air pollutant concentration by the aid of the data obtained in the steps (4) and (5), S2-7, dividing the gradation for surface pollution and coloring according to the corresponding relation in the S1, S2-8, making a clustering analysis and denoising, S2-9, conducting vectorization and smoothing, and loading DOM, DEMs, power grid device information and the pollution sources to a three-dimensional GIS platform, S2-10, repeating the steps from (2) to (9) for 5-60 minutes to form the dynamic contamination area distribution diagram.

The invention puts forward a locking method for an urban air pollution hot spot region, and belongs to the field of air pollution monitoring. The method comprises the following steps that: S1: establishing and grid and carrying out numbering on a monitored region; S2: on the basis of a Gaussian multi-point-source air pollutant diffusion model, carrying out diffusion fitting on air pollutants in the region, and meanwhile, calculating the concentration value of each grid pollutant in the region; S3: selecting the grid with the highest air pollutant concentration as a starting point, and lockingan air pollution hot spot region; S4: selecting the grid with the highest air pollutant concentration value in a grid set around the starting point grid; S5: taking the grid selected in S4 as a new starting point grid; and S6: judging whether the starting point is a boundary grid or not, if the starting point is the boundary grid, ending the method, and otherwise, continuously executing S4. By useof the method, the air pollution hot spot region can be accurately locked, and the dynamic process of the pollutant diffusion in the region can be more effectively mastered.

The invention relates to a roadside air pollutant concentration prediction method based on reconstruction deep learning. On the basis of a reconstruction deep learning method, a depth reconstruction Elman model is provided according to the time-space distribution characteristics of roadside air pollutants; according to the characteristics of a restricted Boltzmann machine, initialization of the depth reconstruction Elman model is completed by utilization of part input data of a roadside air pollutant concentration data set; the depth reconstruction Elman model is trained by adoption of a gradient descent algorithm; and, due to the characteristic mapping function of the model, a real-time roadside air pollutant concentration prediction method based on the factors, such as road network information, weather information and traffic information, can be obtained.

The invention discloses an air pollutant concentration prediction method based on a graph attention mechanism. The method comprises steps of constructing a spatial pollutant concentration prediction model based on a graph attention mechanism; and taking the meteorological data, the air monitoring data and the environmental factor data as model input data, constructing a graph adjacency matrix through a graph attention mechanism, extracting graph information characteristics by utilizing a graph convolutional neural network layer and a multi-layer perceptron network layer, and outputting a predicted air pollutant concentration value. According to the method, the air pollutant concentration prediction is more accurate, and the process is more efficient.

The invention relates to fine granularity area estimation of air pollutant, in particular to a fine granularity air pollutant concentration area estimation method based on spatial characteristics. The method comprises a preprocessing stage, a predicting stage and an estimating stage. In the preprocessing stage, mesh generation is carried out to obtain a train sample. In the predicting stage, a distance measure learning method is used for learning the mahalanobis distance function to find k areas in the shortest distance, and multiple linear regression training is carried out on characteristic vectors formed by the k areas. In the estimating stage, a multiple linear regression model obtained through training is used for estimating the air pollutant concentration in the areas. The method has the advantages that the air pollutant concentration of some small areas without air quality monitoring stations can be estimated, adjacent air quality monitoring stations are fully utilized, the k adjacent areas are introduced, the fine granularity change of the air pollutant concentration in space can be better captured, and the pollutant concentration of the current area can be estimated more accurately.

The invention relates to a prediction method for urban PM10 concentration based on a characteristic expansion fusion neural network, which comprises the following steps of: step S1: constructing a model of the concentration prediction of the urban PM10 based on the feature-expanded stack self-encoder and LSTM network; step S2: selecting training data and test data from monitoring data of pollutants and meteorology; step S3: carrying out the training on the feature-expanded stack self-encoder by using the training data; step S4: processing the feature vectors of the output of the stack self-encoder based on the Gaussian function, calculating the corresponding influence weights for the feature vectors of different cities, and weighting and summing to obtain a new feature vector; step S5: inputting the new feature vector into the LSTM to carry out integral training of the model; step S6: inputting test data into a trained model, and measuring the error of the prediction result generated by the test data; step S7: adopting the training and fine-tuning models for air pollutant concentration prediction. Compared with the prior art, the invention has the advantages of accurate predictionand the like.

The invention relates to a real-time monitoring system of temporal and spatial distribution of air pollutant concentration on urban road network, including a data center and a sensor network. The sensor network includes a plurality of sensors distributed on urban road network. Formed by a plurality of sensors, the real-time monitoring data are transmitted to the data center which combines with the data from external databases. The external databases consist of a vehicle information database, a vehicle annual inspection database, a geographic information database of the urban road network, and an urban air pollutant monitoring database. The data are fused by a self-tuning asynchronous complementary filtering algorithm. The precise real-time monitoring of temporal and spatial distribution of air pollutant concentration is finally achieved. The real-time monitoring system of temporal and spatial distribution of air pollutant concentration on urban road network has the advantages of using all kinds of existing traffic sensors on roads without adding extra equipment, achieving a precise real-time monitoring of temporal and spatial distribution of air pollutant concentration by means of an atmospheric modeling and the data fusion technology, and providing data support for legislating pollution control policies.

The invention relates to a controlling system for controlling an air handling system that is arranged to handle air in an enclosure such as a cabin of a vehicle. The controlling system comprises a first sensing unit, a second sensing unit, and a controller. The first sensing unit is arranged to generate a first output signal based on a measurement of the 5 concentration of certain airborne pollutants at the upstream side of an air cleaning unit that is comprised in the air handling system. The second sensing unit is arranged to generate a second output signal based on a measurement of the concentration of certain airborne pollutants at the downstream side of the air cleaning unit. The first output signal and the second output signal are used for specific purposes in a decision-making process that can be 10 performed by the controller. The controller is arranged to switch the air handling system into recirculation mode when the first output signal exceeds a predetermined threshold value. Furthermore, the controller is arranged to fine-tune settings of the air handling system, such as the mixing ratio of air admitted from outside the enclosure and recirculating air from the enclosure in the total air flow passing through the air handling system when the air handling 15 system operates in an outside-air-inlet mode. The controlling system according to the invent ion has an increased functionality and can maintain an acceptable air quality inside an enclosed space under a large variety of circumstances.

The invention discloses respiratory disease patient person-time dynamic prediction method based on a neural network. According to the method, the distribution of outpatients and inpatients of respiratory diseases is extracted from medical records, and regional air pollutant concentration, regional meteorological data, holidays, day of the week effect and regional socio-economic level and other information are integrated. An LSTM-based neural network model is applied to implement the method. The method comprises the follow steps: step 1: integrating multi-source data; step 2: data preprocessing; step 3: constructing the LSTM neural network model; step 4: model verification and optimization; and step 5: incremental learning and dynamic prediction. The scientific basis for solving the problems of optimizing the allocation of regional medical and health resources and studying the impact of air pollution on the burden of disease can be provided through dynamic prediction of the respiratorydisease patient person-time with high accuracy.

The invention discloses a method for evaluating short-term exposure concentration of air pollutants of people. The method mainly comprises the following steps: 1, acquiring the following data: (1) land utilization variable data; (2) traffic road distribution variable data; (3) meteorological factor data; (4) daily air pollutant concentration data of the monitoring station; and 2, constructing a model: according to the data in the step 1, fitting the relationship among the land utilization variable, the traffic road distribution variable, the meteorological factor and the daily air pollutant concentration data of the monitoring station. According to the method, the space-time regression model is utilized to popularize a traditional land utilization regression model from a single consideration space dimension to a time and space dimension; meteorological change grid data with high temporal-spatial resolution is obtained by utilizing satellite remote sensing data; and the short-term exposure level of the air pollutant concentration of the crowd is effectively estimated under the condition of considering the short-term trend and the long-term trend of the air pollutant concentration change.

The method and apparatus of eliminate air contaminant with powder belong to the field of air purifying technology. The method can change gaseous air contaminant into granular contaminant and eliminate the granular contaminant in a conventional purifying device without resulting in pipeline jamming. The air contaminant eliminating process has proper powder amount selected, properly controlled rotation speed of the fan and powder concentration. The powder and air contaminant react physically and chemically in the mixing reactor, and the treated air contaminant is then eliminated with one dry dedusting device. The present invention has simple process, low cost and other advantages.

The invention relates to the technical field of environment monitoring, in particular to an air pollutant concentration prediction method and device and electronic equipment. The air pollutant concentration prediction method comprises the steps of obtaining first position information of a target prediction station and a prediction time period of air pollutant concentration; Acquiring second position information of a plurality of air detection stations adjacent to the target prediction station, historical air pollutant concentration and acquisition time of the historical air pollutant concentration according to the first position information; And predicting the air pollutant concentration of the target prediction station in the prediction time period according to the second position information, the historical air pollutant concentration and the collection time. The scheme provided by the invention relates to a neural network algorithm for establishing a prediction model, and the schemeis beneficial to improving the accuracy of data prediction and reducing prediction errors.

The invention provides a method for monitoring indoor air pollution and controlling air exchange. The method is applicable to a ventilation system comprising at least two sensors, a signal processing module and an air exchange control module, and takes a time T as a detection period to circularly detect the ventilation system. The method comprises the following steps: acquiring sensing data in the first detection period, transmitting the acquired sensing data to the signal processing module to process the sensing data, obtaining a first air pollution scale value, judging the indoor air condition, obtaining a first air exchange amount to exchange air at the first air exchange volume; acquiring sensing data in the Nth detection period, transmitting the acquired sensing data to the signal processing module to process sensing data, obtaining the Nth air pollution scale value, judging whether the air pollutant concentration is reduced or not, obtaining Nth air exchange volume to exchange air at the Nth air exchange volume, and circularly executing the Nth+1 detection period. The method can monitor the indoor air pollution condition comprehensively, automatically adjust the air exchange volume in time, and reduce the air pollutant diffusion.

The invention discloses a space-time prediction method for the tail gas concentration of urban motor vehicles. Time-space data of the tail gas concentration of motor vehicles in a target city within ayear is collected and preprocessed, training, verification and test data sets based on Encoder-Decoder based lSTM neural network model are constructed, the training data set is sent the model for training to obtain a pre-training model parameter, the pre-training model parameter is tuned finely via the verification and test data sets, the model is further corrected to improve the model precision,the corrected model serves as a time-space prediction model for the tail gas concentration of urban motor vehicles, preprocessed concentration data of air pollutants within relatively long time of the target city service as input data of the model, the tail gas concentration of motor vehicles in certain area in the city at certain time can be output from the model, and thus, time-space predictionof the tail gas concentration is realized.

The invention relates to full-intelligent air purification equipment, and belongs to the technical field of air purifiers. The full-intelligent air purification equipment comprises an outdoor air inlet, an indoor air inlet, a two-way flow heat exchanger, an air purification unit, an outdoor detection sensor, an indoor detection sensor, a display controller and an indoor subunit. The display controller judges the pollution degree difference and temperature difference of air according to an air pollutant concentration value and a temperature value sent back by the indoor detection sensor and the outdoor detection sensor, opens or closes the outdoor air inlet or the air return port and sends an instruction to make the indoor subunit work and purify the indoor air. The full-intelligent air purification equipment integrates the functions of a fresh air system and the functions of an indoor air purifier well, the indoor air is purified in a targeted mode through different control modes, and indoor air quality and outdoor air quality can be visually known through automatic detection. The equipment has multiple adjustable modes and is provided with the indoor subunit with multiple functions, and then the concepts of health, comfort, intelligence, convenience and the like of home life are achieved.

The invention provides control method and device for an air treatment device. The method comprises the steps of monitoring air pollutant concentration; and according to the monitored air pollutant concentration and a running duration of an air purification module in the air treatment device, determining a pollutant accumulation amount of the air purification module, comparing to determine that the pollutant accumulation amount is higher than a threshold value accumulation amount, and determining to replace purification liquid of the air treatment device. According to the method, the pollutant accumulation amount of the air purification module is monitored in real time, and the purification liquid of the air treatment device is determined to be replaced when the pollutant accumulation amount is higher than the threshold value accumulation amount, so that the technical problems that the water quality of the purification liquid is poor and indoor air is polluted for the second time are solved, and meanwhile, the technical problem that the use experience of a user is poor since the user needs to frequently check the water quality of the purification liquid in a water tank in the prior art is further solved.

The invention discloses an air pollutant concentration prediction method. The method includes the steps that multi-scale wavelet decomposition is performed on air pollutant data through a Mallat algorithm, a final-level-scale low-frequency approximating sequence uses a support vector regression (SVR) model for modeling prediction, other high-frequency detailed sequences use an auto-regression movement average (ARMA) model for modeling prediction, various levels of coefficient sequences are reconstructed through the Mallat algorithm, and an air pollutant concentration prediction result is obtained. Different models are used for sequences of different levels of scales obtained after wavelet decomposition according to the application characteristics of different models, and SVR modeling prediction is used for the final-level-scale relatively-unstable low-frequency approximating sequence and ARMR modeling prediction is used for other stable high-frequency detailed sequences on the basis of the characteristic that ARMA is more suitable for prediction of stable sequences and SVR is more suitable for prediction of unstable sequences. The method can achieve high prediction precision.

The invention discloses a traffic air pollutant concentration prediction method based on a meteorological condition. The method comprises the following steps: S1, calculating an integrated emission factor based on equivalent traffic volume; S2, calculating the pollutant emission rate of a link, preprocessing a pollutant diffusion source based on a meteorological condition, and calculating the source strength of the pollutant diffusion source; S3, determining the pollution range and concentration of each pollutant according to the diffusion pattern of each pollutant under the meteorological condition; and S4, calculating the pollutant residual concentration in a current time period and the pollutant concentration in a future time period in an observation area, and predicting the predicted pollutant concentration in the future time period in the observation area. The invention further discloses a traffic air pollutant concentration prediction system based on a meteorological condition. Real-time traffic data and meteorological data are considered in the estimation of traffic air pollutant concentration. The concentration residual of pollutants is also considered. The practicability and accuracy of traffic air pollutant concentration prediction are enhanced.

The invention discloses a group count determining and stationing method of a pollutant purification ventilation system for a road tunnel. In the group count determining and stationing method, a group count N and an interval Sj of the pollutant purification ventilation system set as required are obtained by calculating through a formula so that tunnel air pollutant concentration can meet a control standard of the tunnel air pollutant concentration or air externally exhausted from the tunnel achieves an environmental standard of an air pollutant.

The invention discloses an air quality anomaly detection method based on distributed online principal component analysis. The method comprises acquiring known air pollutant concentration data of a city, designing a distributed online principal component analysis model, and processing air pollutant concentration data through the air quality anomaly detection method based on distributed online principal component analysis to obtain a judgment result so that the air quality anomaly is detected. The method can conveniently predict the overall air quality in a certain range, can suppress the influence caused by the abnormal training data, has the function of denoising, and has a fast processing speed and shorter time.

The invention relates to an intelligent air purifying device with a sub machine considering indoor temperature, and belongs to the technical field of air purifiers. The intelligent air purifying device comprises an air inlet arranged outdoors, an air returning opening arranged indoors, an air purifying unit, indoor and outdoor detection sensors, a display controller and the indoor sub machine. The display controller judges the air pollution degree and the temperature difference according to an air pollutant concentration value and a temperature value which are sent back by the indoor and outdoor detection sensors, then the air return opening is opened or closed, and meanwhile an instruction is sent to enable the indoor sub machine to work. According to the intelligent air purifying device, the functions of a primary air system and an indoor air purifier are well combined, the indoor air is purified in a targeted mode through different control ways, and adjustment is carried out according to the indoor and outdoor temperature difference; meanwhile, the indoor air purifying sub machine is provided, the sub machine has various functions, and the intelligent air purifying devices realizes household life concepts of health, energy conservation, comfort, intelligence, convenience and the like.

The invention relates to an intelligent air purifying device considering indoor temperature and belongs to the technical field of air purifiers. The intelligent air purifying device comprises an air inlet arranged outdoors, an air return opening arranged indoors, an air purifying unit, an outdoor detection sensor, an indoor detection sensor and a display controller. The display controller judges the air pollution degree of indoor air and outdoor air and the indoor and outdoor temperature difference according to an air pollutant concentration value and a temperature value which are sent back by the outdoor detection sensor and the indoor detection sensor, and therefore the air inlet and / or the air return opening are / is selectively opened and closed. According to the intelligent air purifying device, the functions of a primary air system and an indoor air purifier are well combined, the indoor air is purified in a targeted mode through different control ways, the current indoor and outdoor air quality is visually known through automatic detection, adjustment can be carried out according to the indoor and outdoor temperature difference, and the intelligent air purifying device realizes household life concepts of health, energy conservation, comfort, intelligence, convenience and the like.

The invention provides an intelligent observation system for the concentration of urban air pollutants. The intelligent observation system comprises an information acquisition subsystem, a data processing platform and a visual display subsystem which are arranged on an urban bus, wherein the information acquisition subsystem comprises a Beidou satellite navigation system, a pollutant concentrationacquisition module and a data transmission module; the information acquisition subsystem acquires observation position information by the Beidou satellite navigation system, carries out acquisition and preprocessing on the pollutant concentration data of the observation position by the pollutant concentration acquisition module and transmits the pollutant concentration data to the data processingplatform by the data transmission module, wherein the pollutant concentration acquisition module comprises a sensing and monitoring device for acquiring the pollutant concentration data of the observation position; the data processing platform carries out data abnormal detection and abnormal type analysis on pollutant concentration data, carries out corresponding processing on the detected abnormal data according to the abnormal type and uploads the processed pollutant concentration data and observation position information to the visual display subsystem.

The invention discloses a movable GNSS-based urban bus-mounted air pollutant concentration monitoring system. The movable GNSS-based urban bus-mounted air pollutant concentration monitoring system comprises a vehicle-mounted system, a data processing and management system and a visualized display system; the vehicle-mounted system is installed on the top of a bus, comprises an air pollutant concentration monitoring sensor, a GNSS receiver, a power supply apparatus and a data transmission device, the data processing and management system receives the data transmitted by from the data transmission device, and processes and stores the data, and uploads the processed standard data to the visualized display system; and the visualized display system utilizes the standard data processed by the data processing and management system to display a real-time distribution diagram of the urban air pollutant concentration and a tendency diagram of the pollutant concentration on a fixed point along with the time and provides a reported product of the air pollutant concentration. The movable GNSS-based urban bus-mounted air pollutant concentration monitoring system can automatically monitor the concentration of the urban air pollutants with high time-space resolution ratio, and has the advantages of low cost, ease operation and high practicability.

The invention provides a PM2.5 and ozone collaborative management and control area identification method and device, and the method comprises the steps of dividing a to-be-monitored place into at least one unit monitoring area, and determining an air quality collection point in the unit monitoring area; collecting the air substance concentration of the unit monitoring area at the air quality collection point; drawing an ozone EKMA curve graph and / or a PM2.5 concentration distribution graph corresponding to the unit monitoring area based on the air substance concentration; and according to the ozone EKMA curve graph and / or the PM2.5 concentration release graph, determining the type of the air management and control area of the unit monitoring area. According to the invention, the supervision area of the to-be-monitored place can be quickly identified, and the factors causing the increase of the air pollutant concentration of the to-be-monitored place can be accurately positioned, so that the problem of ozone and PM2.5 is solved, and a management and control direction is provided for improving the air quality.