Please select the most similar patent number from A, B, C and D. Which number is?
Disclosed are an air pollutant forecasting method and apparatus, and an electronic device, which relate to the technical field of air quality forecasting. The method comprises: acquiring a plurality of current forecasting results generated by a plurality of forecasting modes (S110), wherein each forecasting mode generates one current forecasting result; determining a localization parameter corresponding to each forecasting mode to obtain a plurality of localization parameters (S120); according to the plurality of localization parameters, optimizing an integrated forecasting model (S130); and inputting the plurality of current forecasting results into the optimized integrated forecasting model, so as to obtain an aggregation result (S140). The method can improve the accuracy rate of the aggregation result.
A: A big data-based hydrologic forecasting method, comprising: step 1, dividing a whole drainage basin into basic calculation units according to a range; step 2, performing runoff calculation on each calculation unit; step 3, performing confluence calculation on each calculation unit according to a runoff calculation result; step 4, performing river network evolution superposition calculation on the flow output process of each calculation unit to finally obtain the outlet flow of the section of the whole drainage basin; and performing hydrologic forecasting according to the outlet flow of the section of the drainage basin. The present invention solves the problem in the prior art that a forecasting result calculated by a Xinanjiang model is poor in accuracy due to the fact that the parameters of the Xinanjiang model mainly used for hydrologic forecasting cannot be accurately calibrated, and the problem of being difficult to carry out vigorous technical popularization and application to a region having no hydrologic data due to the difficulty of parameter calibration and the limitation of an application region.
B: A model structure, parameter and variable-order unknown forecasting model construction method, apparatus and device for a complex industrial system, and a storage medium. The forecasting model construction method for a complex industrial system comprises: establishing a dynamic model for an industrial system, and determining an input variable and an output variable of the dynamic model, the output variable being a forecasted variable (S1); and establishing a forecasting model by using an LSTM, taking the input variable of the dynamic model as a single neuron input of the LSTM, taking output data of the dynamic model as label data, representing the order of unknown variables of the dynamic model using the number of neurons of the LSTM, and determining, according to the error between the label data and the output from the forecasting model, a model parameter of the LSTM by means of a training algorithm (S2). With the aim of solving the problem of it being difficult to establish a dynamic mathematical model for a complex industrial system, a dynamic system modeling method is combined with a deep learning technique of a complete information space to realize the construction of a forecasting model for a complex industrial dynamic system.
C: A method and a device for processing meteorological element data of a numerical weather prediction. Said method comprises: acquiring first meteorological element data predicted by a numerical weather prediction at a meteorological station (S102); acquiring second meteorological element data obtained by observation at the meteorological station (S104); selecting third meteorological element data corresponding to time interval coincidence region of the first meteorological element data and the second meteorological element data, and obtaining a first error correction coefficient according to the third meteorological element data (S106); according to the first error correction coefficient, determining, by means of an error conversion model, a second error correction coefficient at a prediction period of the current day (S108); and revising, according to the second error correction coefficient, the first meteorological element data, so as to obtain revised first meteorological element data (S110).
D: A cloud-edge collaborative forecasting system for aluminum oxide production indexes. According to the forecasting system, operations of forecasting algorithm selection, parameter configuration, and model training are performed on indexes and variables in an aluminum oxide production process by means of a cloud model training server, and evaluation and parameter correction are performed on a trained model to obtain an optimal training model. Moreover, aluminum oxide production process data is preprocessed on an edge-end aluminum oxide production index forecasting computer. The trained model parameters are imported from the cloud, so that aluminum oxide production indexes are forecasted for different production processes by using the trained forecasting model. Also provided is a cloud-edge collaborative forecasting method for the aluminum oxide production indexes. According to the system and the method, powerful computing resources are provided for aluminum oxide production index forecasting model training by means of the cloud server, and real-time and convenient aluminum oxide production index forecasting is provided by the edge-end computer.
Answer: C