400results about How to "Reduce training difficulty" patented technology

The invention discloses a power load short-term prediction method, model, device and system, and the method comprises the steps of receiving the load data, and complementing the missing data of the load data; receiving the influence factor data which comprises the air temperature data, the holiday data and the industry category data to which the power consumers belong, and quantifying the influence factors by adopting a quantification method corresponding to the load data; processing the historical load data by adopting the wavelet decomposition, carrying out the multi-scale decomposition to obtain four historical load reconstruction data sequences, and respectively carrying out correlation measurement on the four historical load reconstruction data sequences and the influence factor datato obtain a correlation characteristic data set of each reconstruction load characteristic and the influence factor; and carrying out preliminary prediction on the four sequences obtained according tothe load data by adopting a cubic exponential smoothing algorithm, further optimizing a preliminary prediction result, and finally obtaining a power short-term load prediction value as the power loadscheduling reference data.

Embodiments of the invention provide an object recommendation method and device, a storage medium and computer equipment, and the method comprises the steps: obtaining various attribute information ofa target object, constructing a feature vector of the target object, and inputting the feature vector into a prediction module according to an operation sequence of each target object to obtain a prediction feature vector.Thus, the expression content of the feature vectors is enriched, the operation sequence of all the target objects is considered in the model calculation process, and the accuracy of the prediction result is improved; moreover, since the plurality of objects output by the application platform share one piece of attribute information, the number of parameters for training theprediction model is greatly reduced, the model training difficulty is reduced, and the method can be suitable for a large number of application scenarios.

The invention discloses a multi-sensor attitude data fusion method and system based on a neural network. The method comprises the following steps: generating original attitude data through a pluralityof sensors; Taking the original attitude data as the input of the convolutional neural network, and taking the attitude data output after the convolution layer, the pooling layer, the full connectionlayer and the first activation function as the output of the convolutional neural network for output; Taking the output of the convolutional neural network as the input of the artificial neural network; and according to a preset general kernel structure, not outputting input of a preset node corresponding to any hidden layer of the artificial neural network through a second activation function, and outputting the input of the remaining nodes corresponding to any hidden layer through a second activation function, and outputting the attitude angle data output by the neuron node of the hidden layer at the tail end as the output of the artificial neural network. According to the fusion method, the convolutional neural network and the optimized artificial neural network are effectively combined, so that the measurement precision of the attitude angle data is improved.

The embodiment of the invention provides a generation method and device for a dynamic picture. The method comprises the steps that a target vector is generated, and the target vector is predicted by adopting a prediction network to obtain a foreground dynamic picture, a foreground masked dynamic picture and a background static picture; the foreground dynamic picture and the foreground masked dynamic picture are split into at least one foreground static picture frame and corresponding foreground masked static picture frames; the first foreground static picture frame, the first foreground maskedstatic picture frame and the background static picture are input to a first cell of a long short-term memory network, and a first picture frame of the dynamic picture is generated predictively; and the n foreground static picture frame, the corresponding foreground masked static picture frame and an n-1 picture frame of the dynamic picture are input to an n cell of the long short-termmemory network, and an n picture frame of the dynamic picture is generated predictively. Accordingly, the problem that the generation accuracy rate of the dynamic picture is low is solved, and the generation accuracy rate of the dynamic picture can be increased.

The invention relates to the agricultural field and the environment-friendly field, in particular to nano microorganism organic and inorganic compound fertilizer and a production method thereof. The fertilizer comprises the following components in parts by weight: 5 to 40 parts of layered silicate, 0.1 to 0.25 part of bacillus megaterium, 0.1 to 0.25 part of bacillus mucilaginosus, 0.1 to 0.25 part of bacillus subtilis, and 0.1 to 0.25 part of base fertilizer containing nitrogen element, potassium element and phosphor element, wherein the nitrogen element, the potassium element and the phosphor element are 6-18% of total weight of the base fertilizer. The production method comprises the following steps: strains of the bacillus megaterium, the bacillus mucilaginosus and the bacillus subtilis are cultivated respectively to obtain strains in a spore state; the base fertilizer is mixed and smashed; and the smashed base fertilizer, the strains in the spore state and the layered silicate are mixed, pelletized and dried. The fertilizer can provide the nutrition needed by crops, and can decompose nitrogen fertilizer, potassium fertilizer and phosphor fertilizer adsorbed and fixed in the soil, so that soil damage is avoided.

The invention discloses an image classification method based on time domain coding and a pulse neural network. The method comprises the following steps: S1, constructing a sample set based on time domain coding and category marking of an image; s2, constructing a pulse neural network as a classification model; s3, training the spiking neural network by using the constructed sample set to obtain a maturely trained spiking neural network; and S4, carrying out time domain coding on an image to be identified, and inputting the image to the maturely trained pulse neural network to obtain a classification result of the image. According to the invention, through a direct training framework which does not need to calculate neuron membrane potential, the training difficulty of the spiking neural network is reduced, and then real-time low-power-consumption image recognition and classification are effectively realized.