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 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 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 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 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 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.