WO2018214060A1 - Procédé et système de prédiction d'indice de qualité d'air à petite échelle pour une ville - Google Patents

Procédé et système de prédiction d'indice de qualité d'air à petite échelle pour une ville Download PDF

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WO2018214060A1
WO2018214060A1 PCT/CN2017/085715 CN2017085715W WO2018214060A1 WO 2018214060 A1 WO2018214060 A1 WO 2018214060A1 CN 2017085715 W CN2017085715 W CN 2017085715W WO 2018214060 A1 WO2018214060 A1 WO 2018214060A1
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data
air quality
prediction
predicted
time
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PCT/CN2017/085715
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Chinese (zh)
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王绍鑫
陈矿
吴建东
曹袭亚
林爱德华·罗伯特
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北京质享科技有限公司
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Priority to PCT/CN2017/085715 priority Critical patent/WO2018214060A1/fr
Priority to CN201780005024.8A priority patent/CN108701274B/zh
Publication of WO2018214060A1 publication Critical patent/WO2018214060A1/fr

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  • the invention relates to the technical field of air quality index prediction, in particular to a city small-scale air quality index prediction method and system based on machine learning algorithm.
  • AQI air quality index
  • SO 2 sulfur dioxide
  • NO 2 nitrogen dioxide
  • NO nitrogen monoxide
  • CO carbon monoxide
  • O 3 ozone
  • PM2.5 suspended particulate matter
  • Atmospheric dispersion modeling This model simulates the transport, diffusion and migration processes of atmospheric pollutants, and predicts the temporal and spatial distribution of a certain pollutant concentration under different pollution source conditions, meteorological conditions and underlying surface conditions.
  • the mathematical model is a simplified mathematical description of the migration and diffusion of pollutants in the lower atmosphere. The form of the model varies according to different modeling theory systems, contaminant migration, diffusion processes, and different description objects.
  • the SPRINTARS method Spectral Radiation-Transport Model for Aerosol Species
  • Kyushu University Japan is a typical representative. It is a numerical model developed on a global scale to simulate the effects of atmospheric suspended particulate matter on the climate system and atmospheric pollution.
  • the atmospheric aerosols present in the troposphere are studied.
  • Such methods are scientific in nature, but have the following disadvantages: the diffusion form of pollutants is mainly considered from the macroscopic atmospheric circulation, and it is difficult to distinguish the specific climate conditions of key areas (such as cities). Due to the specific climatic conditions in the same area, it will change due to seasons, time periods, and even human factors. For example, before and after a new chemical plant in a certain area, the emission and accumulation of pollutants are significantly different. Therefore, it is difficult to accurately target specific areas.
  • the method has a large amount of data collection and a huge amount of data calculation, and at least a large amount of pollution source specific information needs to be collected. And satellite meteorological information, while configuring high-performance hardware devices to provide data processing functions, high cost, professional, and not suitable for ordinary users.
  • the technical problem to be solved by the present invention is to use a cooperative training algorithm that combines various prediction methods to perform air quality index prediction for each geographical point in a city range not limited to an air quality monitoring base station, while keeping the calculation low. Improve the accuracy of prediction while increasing complexity.
  • the technical solution adopted by the invention is: a city small-scale air quality index prediction method, comprising:
  • the time prediction model corresponding to the current time prediction represents the relationship between the historical monitoring data and the current monitoring data, and corresponds to the temporal prediction model predicted in the future, and the historical monitoring data and the current monitoring number are represented. According to the relationship between the monitoring data at various moments in the future, according to the specified time span of the future period, including a plurality of time prediction models corresponding to each moment;
  • the spatial prediction model characterizes the relationship between the real-time monitoring data of each known location or base station and the air quality index data of the to-be-predicted point whose real-time monitoring data is unknown.
  • the invention realizes the air quality prediction of the location outside the base station through the establishment of each model and the fusion of the prediction results of each model at the time of prediction, and the prediction result integrates the influence of various related factors, and the accuracy is higher.
  • the present invention also includes:
  • the monitoring data monitored by the air quality monitoring base station in the present invention includes date and time, base station name, and base station Latitude, AQI data, temperature, air pressure, wind, humidity, and weather type data.
  • AQI data for the case of missing historical data, interpolation of local time series can be performed.
  • step S3 a multivariate linear regression method is used to establish a temporal prediction model corresponding to the current time prediction and the future time prediction.
  • step S3 includes the steps of:
  • ⁇ i is the regression coefficient
  • X i is the model input data
  • Y 1 is the air quality index of the time to be predicted
  • the model input data is the 1 hour historical AQI data and the temperature, air pressure, wind, humidity and weather type data of the hour at the current time;
  • the model input data is the current time AQI data, l 1 hour historical AQI data, and current temperature, air pressure, wind, humidity and weather type data.
  • each regression coefficient in each initial multiple linear regression model can be obtained, thereby obtaining each initial multiple linear regression model, that is, an initial time prediction model.
  • the present invention may use a numerical number for the weather type data, such as 0 for sunny days, 1 for cloudy cloudy days, 2 for rainy days, and the like. Other existing data processing and presentation methods can also be employed.
  • a two-dimensional linear interpolation method is used to establish a spatial prediction model for air quality prediction at a specified coordinate, including:
  • the input of the model is S 2
  • the output of the model is the air quality index of the location to be predicted
  • the griddata () represents the two-dimensional interpolation function.
  • the initial training data set of the spatial prediction model contains only the air quality index at the base station.
  • the traffic data includes length data of the smooth path segment, the slow road segment, and the congestion road segment in each of the to-be-predicted locations and the air quality monitoring base station.
  • the geographic point of interest data includes distribution data of geographic object entities within a set radius area around the base station and the air quality monitoring base station; the geographic object entity types include schools, banks, restaurants, and gas stations. Other geographic object entities may also be included, and the exhaustion is not described.
  • step S5 of the present invention establishes a dynamic prediction model by using a multiple linear regression method, including the steps of:
  • S51 Obtain traffic data and geographic interest point data within a given radius of each base station corresponding to each time point in the historical database, and the traffic data includes the proportion data of the length of the smooth path segment, the slow road segment, and the congestion segment length, and is defined as T 1 .
  • T 2 , T 3 geographic interest point data includes the number of geographical interest points within a given radius of the base station, defined as T 4 , T 5 ,..., T q , and the air quality index monitoring of the corresponding base station at the corresponding time.
  • Data establish an initial training set S 3 ;
  • the model input quantity is the traffic data and geographic interest point data within a given radius of the to-be-predicted location at the specified time
  • the model output quantity Y 3 is the air quality index of the point to be predicted.
  • Dynamic prediction model initial training data in S 3 only contains the data at the base station in the history database.
  • the values of the regression coefficients in the dynamic prediction model can be obtained through the training of the training set before each prediction, so that the corresponding dynamic prediction model is obtained, and the current and future moments of the air quality index data are obtained by using the dynamic prediction model.
  • the prior art can predict the future time of the traffic data. Therefore, when the present invention performs dynamic prediction for the future time, the input data can directly use the traffic prediction data that has been predicted by the prior art.
  • indoor air quality and outdoor air quality have various types of numerical relationships. This depends on a variety of conditions: the type of building environment, the floor, the distance from the main road, whether to open the central air conditioning, whether to open the window ventilation, whether to open the air purifier, etc. will affect the relationship between indoor and outdoor air quality index.
  • the regression tree algorithm is used to establish an indoor and outdoor prediction model, including the steps:
  • the model input quantity is the indoor air quality index M shared by the user acquired at the time to be predicted, and the indoor air quality index related data, and the model output quantity Y 4 is the air quality index data of the to-be-predicted place at the time to be predicted.
  • the model coefficients of the regression tree RT() are also different, and the training of the present invention is adopted.
  • the input and output data are trained, and the regression tree model and its coefficient which characterize the relationship between indoor and outdoor air quality index under each condition are obtained, which is applied to the subsequent prediction of the air quality index of the predicted location under the same conditions.
  • the input data may be data of a model input data acquired by using the prior art at a corresponding time in the future.
  • the indoor and outdoor prediction model is established as follows:
  • indoor air quality is about 60% of outdoor air quality.
  • step S7 of the present invention performs collaborative training on the established time prediction model, spatial prediction model, dynamic prediction model and indoor and outdoor prediction model, including:
  • the time prediction model, the spatial prediction model, the dynamic prediction model, and the indoor and outdoor prediction models are predictors F 1 , F 2 , F 3 , and F 4 , respectively, and the training sets of each predictor are respectively recorded as L 1 , L 2 , L 3 , L 4 , initialize the training set to:
  • the weight vector for initializing each predictor prediction result is [w 1 , w 2 , w 3 , w 4 ], and the sum of the four weighting factors is equal to 1.
  • the AQI fusion value at the time to be predicted is:
  • the present invention performs at least one round of training for each time prediction.
  • each round of the training process is completed, and the next round of training, the data of the training data sets of each model will be Updated to provide more accurate predictions in subsequent training.
  • the newly added data in each training data set is the relevant data at the predicted position where the sum of the prediction results of the predictors and the deviation of the cooperative training results is the smallest in the previous round of training.
  • the newly added training data is The coordinates and AQI data at the predicted location obtained from the previous round of training; for the dynamic prediction model, the newly added training data is the historical air quality index data and traffic data and geographic interest point data at the predicted location, and so on.
  • the AQI fusion value is calculated in the following formula using the following formula:
  • the invention also provides a city small-scale air quality index prediction system, comprising:
  • the area dividing module divides the urban area into a grid-shaped advancing area, and the grid intersection points correspond to the location of the air quality index to be predicted;
  • the historical monitoring data acquisition module acquires historical monitoring data of the air quality monitoring base station and establishes a historical database; the historical monitoring data includes AQI data, weather data, and weather type data;
  • a time prediction model building module which establishes a time prediction model based on a historical database
  • the spatial prediction model building module acquires real-time monitoring data of each air quality monitoring base station and establishes a spatial prediction model
  • the dynamic prediction model establishing module acquires traffic data and geographic interest point data of each to-be-predicted location and air quality monitoring base station, and establishes a dynamic prediction model
  • the indoor and outdoor prediction model building module acquires the indoor air quality index shared by the user and establishes an indoor and outdoor prediction model
  • the collaborative training module cooperatively trains the established time prediction model, spatial prediction model, dynamic prediction model and indoor and outdoor prediction model to fuse the prediction results of all models to obtain all the predicted locations at the current time and for a period of time in the future. Air quality index forecast.
  • the urban small-scale air quality prediction method provided by the invention has the following advantages:
  • the invention combines multiple data sources and multiple prediction models, avoiding the limitations of a single prediction model and ensuring the accuracy of the model;
  • the invention separates multiple prediction models and then finally cooperates with each other, which reduces the overall computational complexity and shortens the calculation time.
  • Figure 1 is a schematic flow chart of the method of the present invention.
  • the urban small-scale air quality index prediction method of the invention comprises the steps of:
  • the time prediction model corresponding to the current time prediction represents the relationship between the historical monitoring data and the current monitoring data, and corresponds to the temporal prediction model predicted in the future, and represents the historical monitoring data and the current monitoring data and the future. Monitoring the relationship between the data at each moment in time, according to the specified time span of the future period, including a plurality of temporal prediction models corresponding to each moment;
  • the spatial prediction model characterizes the relationship between the real-time monitoring data of each known location or base station and the air quality index data of the to-be-predicted point whose real-time monitoring data is unknown.
  • the invention realizes the air quality prediction of the location outside the base station through the establishment of each model and the fusion of the prediction results of each model at the time of prediction, and the prediction result integrates the influence of various related factors, and the accuracy is higher.
  • Figure 1 is a flow chart of the present invention. As shown in FIG. 1, the present invention uses a cooperative training algorithm with multiple prediction models to predict the null. Gas quality index. The various prediction models, cooperative training algorithms, and final evaluation accuracy for predicting air quality are described in detail below.
  • a square grid system is built in the area to be predicted.
  • the to-be-predicted area is the inner five-ring area of Beijing, and a square grid system is established, and the grid size is one square kilometer.
  • the grid intersection is the location where the air quality index is to be predicted.
  • the number of air quality monitoring base stations is recorded as N. In this embodiment, there are 36 air quality monitoring base stations in Beijing.
  • the sampling time interval for the historical data is preferably 1 hour.
  • the local time series interpolation is completed for the case of missing historical data.
  • a unified time series prediction model is established for each air quality monitoring base station, and is used to predict the air quality index of the specified predicted location at a certain point in time in the future. This step further includes the following substeps:
  • the length of the historical sequence used and the forecast period Specifies the length of the historical sequence used and the forecast period.
  • the data at the current time is recorded as x n
  • the length of the historical sequence is L 1
  • the history sequence is recorded as
  • the future sequence length is L 2
  • the future sequence is recorded as
  • the length of the historical sequence is selected to be 6
  • the length of the prediction period is selected to be 6. That is, at any time, the corresponding 6-hour historical data is used to predict the most recent 6-hour air quality index.
  • all consecutive L 1 +1+L 2 hour sequences in the extraction history database constitute the training data set S 1 .
  • the multivariate linear regression model was used to predict the current time and the next 6 hours.
  • a multivariate linear regression model is established for each predicted time point, that is, there are a total of seven time prediction models.
  • the input data S 1 is the 6-hour historical data of the AQI and the temperature, air pressure, wind, humidity, and weather type of the previous hour.
  • the input data S 1 is the current time AQI and 6-hour historical AQI data, and the current time temperature, air pressure, wind, humidity, weather type.
  • the output of the multiple linear regression model is the AQI data at the time point that needs to be predicted.
  • Multiple linear regression models can be written in the following form,
  • ⁇ i is the regression coefficient
  • X i is the input data
  • Y 1 is the air quality index of the point to be predicted.
  • the spatial prediction model uses a two-dimensional linear interpolation algorithm.
  • the input data S 2 is the latitude and longitude, AQI of the base station or grid point of the known AQI value.
  • the spatial prediction model can be expressed as:
  • x, y are the coordinates of the point to be predicted
  • S 2 is the input data, that is, the training set
  • Y 2 is the air quality index of the point to be predicted.
  • the initial training data S 2 of the spatial prediction model contains only relevant data at the base station.
  • the griddata function is an existing interpolation function.
  • the initial training data of S 2 is only the base station related data. After the training set is updated, the updated data is the previous round prediction result value of the to-be-predicted location with the smallest deviation of the prediction result in the previous round of training.
  • the traffic data includes unblocked, slow, and congested road lengths, and converted into proportional data;
  • the geographic interest point data includes distribution data of various types of geographic object entities within a given radius of the designated location, such as schools, banks, The number of restaurants, gas stations, etc.;
  • the dynamic prediction model is established by using multiple linear regression models.
  • the input data is traffic data and geographic interest point data, and the output data is AQI data.
  • the model form is as follows,
  • T 1 , T 2 , T 3 are the ratio of smooth, slow, and congested road segments
  • T 4 , T 5 ,..., T q are the number of geographic interest points of various types
  • Y 3 is to be The air quality index of the predicted point.
  • the initial dynamic prediction model training data S 3 only contains the data at the base station.
  • the indoor air quality index is measured by an air quality sensor placed on an air purifier that is compatible with the software system.
  • the indoor air quality index is measured by an air quality sensor placed on an air purifier that is compatible with the software system.
  • indoor air quality and outdoor air quality have various types of numerical relationships. This depends on a variety of conditions: the type of building environment, the floor, the distance from the main road, whether the central air conditioning is turned on, whether the window is ventilated, whether the air purifier is turned on, etc.
  • the regression tree algorithm was used to fit the indoor and outdoor air quality index relationships under each category.
  • indoor and outdoor prediction models can be expressed as
  • RT is the regression tree algorithm
  • M is the indoor air quality index measured by the sensor
  • Y 4 is the outdoor air quality index to be predicted.
  • indoor and outdoor prediction models are obtained using the following method. According to the statistical relationship between indoor and outdoor air quality published by the US Environmental Protection Agency [1], indoor air quality is about 60% of outdoor air quality, namely:
  • M is the indoor air quality index measured by the sensor and Y 4 is the outdoor air quality index to be predicted.
  • the cooperative training algorithm is a semi-supervised learning algorithm whose main purpose is to efficiently use a small amount of marker data and a large amount of unlabeled data to train the predictor.
  • This embodiment uses a simplified version of the collaborative training algorithm. The specific implementation steps are as follows:
  • the time prediction model, the spatial prediction model, the dynamic prediction model, and the indoor and outdoor prediction models are predictors F 1 , F 2 , F 3 , and F 4 , respectively, and the training sets of each predictor are respectively recorded as L 1 , L 2 , L 3 , L 4 , initialize the training set to:
  • the weight vector for initializing each predictor prediction result is [w 1 , w 2 , w 3 , w 4 ], and the sum of the four weighting factors is equal to 1.
  • the AQI fusion value at the time to be predicted is:
  • the present invention performs at least one round of training for each time prediction.
  • each round of the training process is completed, and the next round of training, the data of the training data sets of each model will be Updated to provide more accurate predictions in subsequent training.
  • the newly added data in each training data set is the relevant data at the predicted position where the sum of the prediction results of the predictors and the deviation of the cooperative training results is the smallest in the previous round of training.
  • the newly added training data is The coordinates and AQI data at the predicted location obtained from the previous round of training; for the dynamic prediction model, the newly added training data is the historical air quality index data and traffic data and geographic interest point data at the predicted location, and so on.
  • the AQI fusion value is calculated in the following formula using the following formula:
  • Step S81 evaluates the accuracy of the current time prediction system
  • step 7 to obtain an AQI prediction value of each base station in the k-th base station that is separately isolated, at the current time, and record
  • Obtaining the measured AQI value of the kth base station is y 1 , y 2 , . . . , y c , and the accuracy of the current time prediction by the prediction system when removing the kth base station may be described by the following indicator ⁇ k :
  • Step S82 evaluates the accuracy of AQI prediction in future time
  • the predicted value of the grid in which all base stations are located at a specified future time after performing the step S7 is recorded.
  • the actual measured values of the base station are z 1 , z 2 ,..., z N , and the accuracy of the prediction system for future predictions is:
  • the present invention utilizes a cooperative training algorithm that combines various prediction methods to perform air quality index prediction for each geographical point in a city range not limited to an air quality monitoring base station, while maintaining a low computational complexity while improving The accuracy of the forecast.
  • the invention also provides a city small-scale air quality index prediction system, comprising:
  • the area dividing module divides the urban area into a grid-shaped advancing area, and the grid intersection points correspond to the location of the air quality index to be predicted;
  • the historical monitoring data acquisition module acquires historical monitoring data of the air quality monitoring base station and establishes a historical database; Historical monitoring data includes AQI data, meteorological data, and weather type data;
  • a time prediction model building module which establishes a time prediction model based on a historical database
  • the spatial prediction model building module acquires real-time monitoring data of each air quality monitoring base station and establishes a spatial prediction model
  • the dynamic prediction model establishing module acquires traffic data and geographic interest point data of each to-be-predicted location and air quality monitoring base station, and establishes a dynamic prediction model
  • the indoor and outdoor prediction model building module acquires the indoor air quality index shared by the user and establishes an indoor and outdoor prediction model
  • the collaborative training module cooperatively trains the established time prediction model, spatial prediction model, dynamic prediction model and indoor and outdoor prediction model to fuse the prediction results of all models to obtain all the predicted locations at the current time and for a period of time in the future. Air quality index forecast.
  • embodiments of the present application can be provided as a method, system, or computer program product.
  • the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment in combination of software and hardware.
  • the application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.
  • the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
  • the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
  • These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
  • the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.

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Abstract

La présente invention concerne un procédé et un système de prédiction d'indice de qualité d'air à petite échelle pour une ville. Le procédé comprend les étapes qui consistent : à diviser une zone urbaine en grille à emplacements multiples devant faire l'objet d'une prédiction ; à acquérir des données historiques relatives à chaque modèle, et à créer des modèles sur la base des données historiques, à savoir un modèle de prédiction temporelle correspondant respectivement à une prédiction concernant un moment courant et à des prédictions concernant chaque moment d'une période future, un modèle de prédiction spatiale permettant de réaliser des prédictions de qualité d'air quant à des emplacements à des coordonnées spécifiées, un modèle de prédiction dynamique permettant de caractériser la relation de données de trafic et de données d'un emplacement géographique d'intérêt avec un indice de qualité d'air, et un modèle de prédiction d'intérieur et d'extérieur destiné à caractériser la relation d'un indice de qualité d'air intérieur avec un indice de qualité d'air extérieur ; lors de la réalisation d'une prédiction, à effectuer un apprentissage coordonné sur le modèle de prédiction temporelle, le modèle de prédiction spatiale, le modèle de prédiction dynamique et le modèle de prédiction d'intérieur et d'extérieur créés par rapport à l'un quelconque des emplacements devant faire l'objet d'une prédiction à tout moment en temps réel, de façon à faire fusionner les résultats de prédiction de tous les modèles et à obtenir des valeurs prédites d'indice de qualité d'air de chacun des emplacements devant faire l'objet d'une prédiction à un moment courant correspondant et à chaque moment de la période future.
PCT/CN2017/085715 2017-05-24 2017-05-24 Procédé et système de prédiction d'indice de qualité d'air à petite échelle pour une ville WO2018214060A1 (fr)

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