Abstract: Disclosed is an autonomous mining method of industrial big data based on model sets, which comprises the following steps: S1, building model sets and a mining engine based on domain knowledge and structural characteristics of multi-source heterogeneous data; S2, carrying out data sampling on the multi-source heterogeneous data, and counting the fault-tolerant estimation of random error variance; S3, mining data sets by using the mining engine, and determining the optimal fault-tolerant model of each sampled data sequence and the optimal fault-tolerant estimation of model parameters; S4, performing goodness-of-fit statistics calculation and VV&A test by using the optimal fault-tolerant model; S5, acquiring data model representation and connotation knowledge based on model clustering. The method can realize the automation of the mining process of big data, the integration of associated knowledge, the expansion of model sets, the integration of mining and modeling and the optimization of mining results.

Abstract: Disclosed is an autonomous mining method of industrial big data based on model sets, which comprises the following steps: S1, building model sets and a mining engine based on domain knowledge and structural characteristics of multi-source heterogeneous data; S2, carrying out data sampling on the multi-source heterogeneous data, and counting the fault-tolerant estimation of random error variance; S3, mining data sets by using the mining engine, and determining the optimal fault-tolerant model of each sampled data sequence and the optimal fault-tolerant estimation of model parameters; S4, performing goodness-of-fit statistics calculation and VV&A test by using the optimal fault-tolerant model; S5, acquiring data model representation and connotation knowledge based on model clustering. The method can realize the automation of the mining process of big data, the integration of associated knowledge, the expansion of model sets, the integration of mining and modeling and the optimization of mining results.

Abstract: Techniques for generating dynamic dust emission risk index values via construction and use of a dynamic dust emission risk index model are provided. In one example, a computer-implemented method comprises generating, by a system operatively coupled to a processor, a dynamic dust emission risk index value based on a dynamic dust emission risk index model. The computer implemented method also includes supplying, by the system, the dynamic dust emission risk index value to an air quality model. Additionally, the computer implemented method further comprises generating, by the system, a dust emission forecast based on the air quality model.

Abstract: Post-processing corrections can be applied to operational numerical model forecasts of weather variables to reduce forecast model errors. The post-processing technique is based on an analog post-processing correction scheme. In the analog scheme, the system searches for previous model forecasts that are similar to the current forecast, and modifies the current forecast based on errors in such previous predictions. An “abnormal (or abnormal) index” can be generated for each analog as a function of several meteorology variables (i.e., humidity, pressure and temperature) and two experience coefficients. The abnormal index can be used to exclude some errors. Furthermore, dynamic weights based on the “abnormal index” can be applied to the errors. Using the “abnormal index” weights in a post-processing technique may generate a more accurate air quality forecast.

Abstract: Post-processing corrections can be applied to operational numerical model forecasts of weather variables to reduce forecast model errors. The post-processing technique is based on an analog post-processing correction scheme. In the analog scheme, the system searches for previous model forecasts that are similar to the current forecast, and modifies the current forecast based on errors in such previous predictions. An “abnormal (or abnormal) index” can be generated for each analog as a function of several meteorology variables (i.e., humidity, pressure and temperature) and two experience coefficients. The abnormal index can be used to exclude some errors. Furthermore, dynamic weights based on the “abnormal index” can be applied to the errors. Using the “abnormal index” weights in a post-processing technique may generate a more accurate air quality forecast.

Abstract: A method for establishing and using a pollutant emission scavenging forecasting (PESF) model to calculate a purgeable pollutant emission to dynamically control an emitted pollutant at any target concentration. The dynamic control considers the initial pollutant concentration and pollutant scavenging ability of atmosphere. The method further takes into account the constraint conditions specified by a user, and employs a dynamic emission correction system to quickly calculate an optimal pollutant emission scheme. If the emission is lower than minimum acceptable value for a current time instance (a moment), the method corrects the emission intensity before (prior to) this moment by changing the initial concentration at this time. This initial concentration is the final pollutant concentration of a prior moment. Since the method makes full use of the atmospheric pollutants scavenging ability, the dynamic emission control scheme can provide the most effective and lowest economic losses solution.

Abstract: A system, method and computer program product for establishing and using a pollutant emission scavenging forecasting (PESF) model to calculate a purgeable pollutant emission to dynamically control an emitted pollutant at any target concentration. The dynamic control considers the initial pollutant concentration and pollutant scavenging ability of atmosphere. The method further takes into account the constraint conditions specified by a user, and employs a dynamic emission correction system to quickly calculate an optimal pollutant emission scheme. If the emission is lower than minimum acceptable value for a current time instance (a moment), the method corrects the emission intensity before (prior to) this moment by changing the initial concentration at this time. This initial concentration is the final pollutant concentration of a prior moment.

Abstract: A method and a device for predicting insulator pollution grade includes acquiring prediction data affecting the insulator pollution grade; acquiring current pollution status of the insulator; predicting the insulator pollution grade based on the prediction data, the current pollution status and an insulator pollution grade calculating model, wherein the insulator pollution grade calculating model at least comprises an initial pollution status variable of the insulator, and a pollutant accumulation prediction and a pollutant reduction prediction based on the initial pollution status variable, at least one of the accumulation prediction and the reduction prediction being associated with the prediction data, and the initial pollution status variable being associated with the current pollution status.

Abstract: According to one or more embodiments of the present invention, a method of forecasting air quality is provided. The method includes determining weather pattern classifications based on global atmospheric information from a global weather model and determining a synoptic scale correction factor in response to the determination of the weather pattern classifications. The method also includes blending the global atmospheric information the synoptic scale correction factor to produce a data set and blending the data set with regional atmospheric information from a regional weather model to generate weather fields. The method further includes blending chemical information from a global chemical model and the synoptic scale correction factor to produce a second data set and blending the second data set into a regional chemical model based on the weather fields to forecast the air quality.

Abstract: The present invention proposes a lightning forecast method, comprising: identifying a Targeted Weather of SDA carrier (TWLC) based on radar reflectivity data; forecasting a future TWLC state based on the identified TWLC; building or updating an SDA model based on SDA observation data and detected TWLC state-related data; and calculating the probability of producing SDA in the forecasted future TWLC according to the SDA model.

Abstract: The present invention proposes a lightning forecast method, comprising: identifying a Targeted Weather of SDA carrier (TWLC) based on radar reflectivity data; forecasting a future TWLC state based on the identified TWLC; building or updating an SDA model based on SDA observation data and detected TWLC state-related data; and calculating the probability of producing SDA in the forecasted future TWLC according to the SDA model.

Abstract: Post-processing corrections can be applied to operational numerical model forecasts of weather variables to reduce forecast model errors. The post-processing technique is based on an analog post-processing correction scheme. In the analog scheme, the system searches for previous model forecasts that are similar to the current forecast, and modifies the current forecast based on errors in such previous predictions. An “abnormal (or abnormal) index” can be generated for each analog as a function of several meteorology variables (i.e., humidity, pressure and temperature) and two experience coefficients. The abnormal index can be used to exclude some errors. Furthermore, dynamic weights based on the “abnormal index” can be applied to the errors. Using the “abnormal index” weights in a post-processing technique may generate a more accurate air quality forecast.

Abstract: Post-processing corrections can be applied to operational numerical model forecasts of weather variables to reduce forecast model errors. The post-processing technique is based on an analog post-processing correction scheme. In the analog scheme, the system searches for previous model forecasts that are similar to the current forecast, and modifies the current forecast based on errors in such previous predictions. An “abnormal (or abnormal) index” can be generated for each analog as a function of several meteorology variables (i.e., humidity, pressure and temperature) and two experience coefficients. The abnormal index can be used to exclude some errors. Furthermore, dynamic weights based on the “abnormal index” can be applied to the errors. Using the “abnormal index” weights in a post-processing technique may generate a more accurate air quality forecast.

Abstract: A system, method and computer program product for establishing and using a pollutant emission scavenging forecasting (PESF) model to calculate a purgeable pollutant emission to dynamically control an emitted pollutant at any target concentration. The dynamic control considers the initial pollutant concentration and pollutant scavenging ability of atmosphere. The method further takes into account the constraint conditions specified by a user, and employs a dynamic emission correction system to quickly calculate an optimal pollutant emission scheme. If the emission is lower than minimum acceptable value for a current time instance (a moment), the method corrects the emission intensity before (prior to) this moment by changing the initial concentration at this time. This initial concentration is the final pollutant concentration of a prior moment.

Abstract: A method for establishing and using a pollutant emission scavenging forecasting (PESF) model to calculate a purgeable pollutant emission to dynamically control an emitted pollutant at any target concentration. The dynamic control considers the initial pollutant concentration and pollutant scavenging ability of atmosphere. The method further takes into account the constraint conditions specified by a user, and employs a dynamic emission correction system to quickly calculate an optimal pollutant emission scheme. If the emission is lower than minimum acceptable value for a current time instance (a moment), the method corrects the emission intensity before (prior to) this moment by changing the initial concentration at this time. This initial concentration is the final pollutant concentration of a prior moment. Since the method makes full use of the atmospheric pollutants scavenging ability, the dynamic emission control scheme can provide the most effective and lowest economic losses solution.

Abstract: According to one or more embodiments of the present invention, a method of forecasting air quality is provided. The method includes determining weather pattern classifications based on global atmospheric information from a global weather model and determining a synoptic scale correction factor in response to the determination of the weather pattern classifications. The method also includes blending the global atmospheric information the synoptic scale correction factor to produce a data set and blending the data set with regional atmospheric information from a regional weather model to generate weather fields. The method further includes blending chemical information from a global chemical model and the synoptic scale correction factor to produce a second data set and blending the second data set into a regional chemical model based on the weather fields to forecast the air quality.

Abstract: Techniques for generating dynamic dust emission risk index values via construction and use of a dynamic dust emission risk index model are provided. In one example, a computer-implemented method comprises generating, by a system operatively coupled to a processor, a dynamic dust emission risk index value based on a dynamic dust emission risk index model. The computer implemented method also includes supplying, by the system, the dynamic dust emission risk index value to an air quality model. Additionally, the computer implemented method further comprises generating, by the system, a dust emission forecast based on the air quality model.

Abstract: The present invention proposes a lightning forecast method, comprising: identifying a Targeted Weather of SDA carrier (TWLC) based on radar reflectivity data; forecasting a future TWLC state based on the identified TWLC; building or updating an SDA model based on SDA observation data and detected TWLC state-related data; and calculating the probability of producing SDA in the forecasted future TWLC according to the SDA model.

Abstract: The present invention proposes a lightning forecast method, comprising: identifying a Targeted Weather of SDA carrier (TWLC) based on radar reflectivity data; forecasting a future TWLC state based on the identified TWLC; building or updating an SDA model based on SDA observation data and detected TWLC state-related data; and calculating the probability of producing SDA in the forecasted future TWLC according to the SDA model.

Abstract: A method and a device for predicting insulator pollution grade includes acquiring prediction data affecting the insulator pollution grade; acquiring current pollution status of the insulator; predicting the insulator pollution grade based on the prediction data, the current pollution status and an insulator pollution grade calculating model, wherein the insulator pollution grade calculating model at least comprises an initial pollution status variable of the insulator, and a pollutant accumulation prediction and a pollutant reduction prediction based on the initial pollution status variable, at least one of the accumulation prediction and the reduction prediction being associated with the prediction data, and the initial pollution status variable being associated with the current pollution status.