Patent ID: 11959900
Assignee: CENTRAL SOUTH UNIVERSITY
Field: Measurement (Instruments)
Classification: CPC G  B | IPC B  G

Claim 0:
1. A health early warning method for passengers on a train in an outdoor air polluted environment, which is implemented by a health early warning system for the passengers on the train in the outdoor air polluted environment the health early warning system for the passengers on the train in the outdoor air polluted environment comprises an air quality monitoring station data acquisition module, a train data acquisition module, a train air pollution prediction module and a train air environment early warning module, wherein an output end of the air quality monitoring station data acquisition module and an output end of the train data acquisition module are both connected with an input end of the train air pollution prediction module; an output end of the train air pollution prediction module is connected with the train air environment early warning module; the air quality monitoring station data acquisition module is configured to acquire data information of air quality monitoring stations and upload the data information of air quality monitoring stations to the train air pollution prediction module; the train data acquisition module is configured to acquire data information of the train and upload the data information of the train to the train air pollution prediction module; the train air pollution prediction module is configured to perform a short-term prediction on air pollution of the train according to the uploaded data information and upload a predicted result to the train air environment early warning module; and the train air environment early warning module is configured to perform an air environment early warning according to the predicted result, thereby performing an early warning on an air environment of the train and on health of the passengers on the train; and
the health early warning method for the passengers on the train in the outdoor air polluted environment comprises the following steps:
S1. acquire the data information of each air quality monitoring station and corresponding air quality monitoring data of the each air quality monitoring station;
S2. acquire pollutant data information of a position where the train is located and position information of the train;
S3. perform a train-air quality monitoring station coupling analysis according to data acquired in the steps S1 and S2, thereby screening out a plurality of associated air quality monitoring stations; wherein the step S3 is, specifically, to perform a coupling analysis and screen out the associated air quality monitoring stations in the following steps:
A. calculate a predicted position of the train after future T minutes by using the following equation, wherein T is a positive integer:

LOCTC=[LOTC+T×v×{right arrow over (r)}LOT,LATC+T×v×{right arrow over (r)}LAT]

wherein, LOCTC is longitude and latitude information of the predicted position of the train after the future T minutes; LOTC is current longitude information of the train, LATC is current latitude information of the train, v is the information of average speed per minute of the train in past several minutes, {right arrow over (r)}LOT is a longitude unit vector of the directed path of the remaining running route of the train, and {right arrow over (r)}LAT is a latitude unit vector of the directed path of the remaining running route of the train;
B. calculate a distance between the train and the each air quality monitoring station after the future T minutes;
C. calculate, by using the following equation, mutual information between the pollutant data information on a train side and the air quality monitoring data of the each air quality monitoring station at a plurality of continuous moments, thereby obtaining a deterministic association index set between the train side and the each air quality monitoring station:, MI
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wherein, A200C is an AQI sequence on the train side in 200 continuous moments, A200S,i is an AQI value of the i-th air quality monitoring station; P(a,b) is a joint probability distribution function of random variables A200C and A200S,i, P(a) is a marginal probability distribution function of A200C, P(b) is a marginal probability distribution function of A200S,i H(A200C) is a marginal entropy, and H(A200C|A200S,i) is a conditional entropy; and
D. analyze and select the plurality of associated air quality monitoring stations with a greatest environmental impact on the train side after the T minutes according to the distance between the train and the each air quality monitoring station;

S4. establish a spatial mixture model for multi-pollutant concentration indexes according to the plurality of associated air quality monitoring stations screened out in the step S3;
S5. perform the short-term prediction on air pollutant concentrations of the position where the train is located by using the spatial mixture model for multi-pollutant concentration indexes established in the step S4; and
S6. perform the early warning on the air environment of the train according to a result of the short-term prediction in the step S5.