7 results about "Air pollutant concentrations" patented technology

A graph attention network-bidirectional recurrent interpolation network (GAI-BRI)-based air pollutant concentration interpolation prediction method, a device, and a medium. The method comprises: on the basis of the extracted air pollutant concentrations, meteorological data and satellite data from multiple distributed sites within a given time range and region, constructing an air pollutant concentration map for describing pollutant conditions within the given time range and region; and inputting numerical values in the air pollutant concentration map into a GAT-BRI prediction model to obtain an air pollutant concentration interpolation prediction result for a future time period, wherein the GAT-BRI prediction model comprises a GAT that is arranged at the bottom layer and used for extracting spatial features from the air pollutant concentration map, a BRI that is arranged at the middle layer and used for extracting temporal features from the spatial features outputted by the GAT, and a feedforward network that is arranged at the top layer and used for outputting an interpolation prediction result. The method fully exploits the spatial and temporal dependencies among multiple cities, achieving high prediction accuracy.

The present disclosure relates to a method, device and electronic equipment for determining a monitoring station position, the method comprising: obtaining first meteorological information and first concentration information of an air pollutant of N target sub-regions at T time, and second meteorological information and second concentration information of an air pollutant of M preset sub-regions at T time; determining the concentration information of the air pollutant of a second target sub-region at a first time according to the first meteorological information and the first concentration information; determining the concentration information of the air pollutant of a first target sub-region at the first time according to the second meteorological information and the second concentration information; and determining a monitoring station of the air pollutant from the N target sub-regions according to the concentration information of the air pollutant of the N target sub-regions at the first time. Through the above technical solution, the influence of the air pollutant outside the target region on the boundary is considered, and the accuracy and rationality of the determined monitoring station are improved.

The application discloses a range hood purification control method and device and a range hood, and belongs to the technical field of kitchen appliances. The method comprises the following steps: acquiring the working state of the range hood; in the case that the working state is a start-up state, acquiring the air pollutant concentration at the current time and predicting the change trend of the air pollutant concentration at a future target time; and controlling the operation mode of the auxiliary air purification device according to the air pollutant concentration at the current time and the change trend. The application further acquires the air pollutant concentration at the current time and predicts the change trend of the pollutant concentration at the future target time in the start-up state, realizes dynamic perception and prediction of the kitchen air quality, makes the purification function no longer dependent on manual operation or fixed timing of the user, but matches the generation rule and diffusion trend of pollutants in the actual cooking process, thereby reducing the technical problem that the purification function is disconnected from the real demand, and improving the air purification effect of the kitchen environment.

The invention relates to a method for determining individual air pollutant levels in specific regions using an electronic device (30) with a processor (32). The method comprises the following steps: - Calculating an individual respiratory volume of a person taking into account the duration of stay and mode of movement of the person in a measurement region (22, 24), - Obtaining a prevailing air pollutant concentration in the measurement region (22, 24) via a network, - Determining the individual air pollutant exposure of the person, taking into account the calculated individual breathing volume and the obtained air pollutant concentration, - Output of a signal based on the individual air pollution level.

The present application relates to the technical field of intelligent transportation and Internet of Vehicles, in particular to a traffic and atmospheric pollution two-way coupling flow prediction method based on double-path dynamic fusion. The method comprises the following steps: collecting traffic flow and air pollutant concentration data, and performing spatio-temporal alignment and reversible instance normalization; dividing the data into two branches, extracting time-dependent features through gated convolution and probabilistic sparse self-attention in the first branch, and obtaining variable interaction features through dimension reshaping, context extraction and reversible coupling transformation in the second branch; performing two-way feature interaction through cross-attention, dynamically generating weights based on channel attention, and performing residual connection after weighted fusion; linearly mapping the fused features and performing inverse normalization to obtain traffic flow prediction values. The present application significantly improves the prediction accuracy and robustness in pollution-sensitive scenarios.

Embodiments of the present disclosure provide a machine learning correction method based on low-cost atmospheric particulate matter sensor measurement, applied to the technical field of meteorological data processing. The method comprises obtaining a CSV file of low-cost atmospheric particulate matter sensor measurement data, and performing data preprocessing and feature engineering to construct an input feature set; dividing the input feature set into a training set and a test set according to the site identification in the CSV file; based on the preset model parameters, using the DART-LightGBM model, performing training according to the training set and early stopping with the early stopping set, evaluating on the test set, and saving the trained air pollutant concentration correction model, performing feature importance analysis and multi-dimensional evaluation on the model; and outputting deployment support files including a site mapping file and a feature list file. Based on this, the DART-LightGBM model can be trained and output, the site mapping and the feature list can be formed, and a closed-loop process from preprocessing to deployment is formed, which is convenient for consistent deployment of multiple sites.