Please select the most similar patent number from A, B, C and D. Which number is?
The present invention relates to the field of precipitation forecast, and specifically relates to a multi-mode integrated precipitation forecast method and apparatus. In the method and the apparatus, the forecast performance of each forecast mode is evaluated, and the forecast effects, at different precipitation intensities, of each mode, and a traditional integrated forecast effect are analyzed; and then, an integration method is improved, namely, a sliding-window method is used to screen out a forecast mode having, within a preset time duration, the best forecast effect at a certain precipitation intensity, the various forecast modes are integrated to generate an integrated forecast mode, and integrated forecast is calculated using probability match set average optimization. In comparison with the differences of forecast effects, at each precipitation intensity, of traditional integrated forecast, a precipitation value, corresponding to an intensity using a mode having the effect which is better than that of the traditional integrated forecast, is converted and provided, intensity by intensity, for the integrated forecast, such that the phenomenon of the forecast effects, at certain intensities, of the traditional integrated forecast being poor can be alleviated, thereby forming optimal integrated forecast that integrates each individual optimal forecast mode.
A: A tropical cyclone intensity forecast information generation method and system. The method comprises: obtaining data to be processed according to different data dimensions (S101); preprocessing said data to obtain initial data (S102); and predicting the initial data by using a preset forecast model to obtain tropical cyclone intensity forecast information (S103). Because the initial data comprises a climate persistence characteristic factor, an environment characteristic factor, and a brainstorming characteristic factor, factors affecting tropical cyclones can be fully utilized to predict the tropical cyclone intensity forecast information in combination with the preset forecast model, so that prediction processing is more intelligent and objective, a tropical cyclone forecast system is perfected, and the forecast accuracy is improved.
B: An adaptive deep learning-based intelligent forecasting method，apparatus and device for a complex industrial system, and a storage medium. The intelligent forecasting method for a complex industrial system comprises: establishing a dynamic model of a complex industrial system (S1); establishing an offline deep-learning forecasting model by using the dynamic model (S2); establishing an online deep-learning forecasting model by using the offline deep-learning forecasting model (S3); establishing a deep-learning correction model by using the same structure as the online deep-learning forecasting model (S4); and correcting the online deep-learning forecasting model by using the deep-learning correction model (S5), wherein the online deep-learning forecasting model is used for forecasting the parameters of the complex industrial system in real time. To address the problem of a complex industrial system having low forecasting precision and poor real-time forecasting performance, an offline deep-learning forecasting model, an online deep-learning forecasting model, a deep-learning correction model and a self-correcting mechanism are established, thereby realizing accurate real-time forecasting of a complex industrial system.
C: A real-time weather forecast system and forecast method by using a rain-sensing device (1). Rain falling information in a specific region is collected by using the rain-sensing device (1), and is sent to a system for analysis, so as to forecast the weather in real time. The system comprises the rain sensing device (1), a vehicle-mounted control module (2), a vehicle-mounted display screen (3), a system data center (4), and a mobile terminal device (5), a signal input end of the vehicle-mounted control module (2) being connected to a signal output end of the rain sensing device (1), a rain falling signal sensed by the rain sensing device (1) being sent to the vehicle-mounted control module (2), the vehicle-mounted control module (2) performing data communication with the system data center (4) through a communication channel, and the system data center (4) sending, through the communication channel, data information obtained through processing to the mobile terminal device (5) or the vehicle-mounted display screen (3) connected to the system, thereby timely monitoring and releasing the weather condition.
D: The present invention relates to the technical field of aluminum oxide comprehensive production decision-making, and provides an aluminum oxide comprehensive production index decision-making method based on a multi-scale deep convolutional network. The method mainly consists of several sub-models: a multi-scale deep splicing convolutional network forecasting sub-model for reflecting the influence of a bottom-layer production process index on an aluminum oxide comprehensive production index; a fully-connected neural network forecasting sub-model for reflecting the influence of an aluminum oxide upper-layer scheduling index on the comprehensive aluminum oxide production index; a fully-connected neural network forecasting sub-model for reflecting the influence of the aluminum oxide comprehensive production index at the past moment on a current aluminum oxide comprehensive production index; and a multi-scale information neural network integration model for cooperatively optimizing sub-model parameters. According to the method, by means of the integrated forecasting model structure, the memory ability of a shallow network, and the feature extraction ability of a deep network, precise decision-making of aluminum oxide production indexes is realized.
Answer: C