A method for monitoring formation of ice on a wind turbine blade, comprising: capturing an image of the blade through a camera; detecting a region of the blade from the captured image; clearing image information of a background region from the captured image, which is in the captured image except for the region of the blade, to obtain a blade image; and inputting the obtained blade image into a recognition model of ice on a blade obtained by training on a sample set, to determine whether ice is on the captured blade, wherein the sample set comprises a plurality of blade images indicating that ice is on blades; wherein the recognition model of ice on the blade is obtained by training on the sample set using a convolution neural network; wherein the recognition model of ice on the blade comprises an input layer, a first predetermined quantity of convolutional layers, a second predetermined quantity of pooling layers, a vector layer for vectorizing a feature of the blade image processed by the convolutional layers and the pooling layers, a classifier for classifying vectors outputted from the vector layer, and an output layer; wherein the input layer comprises a plurality of input neurons for describing pixels of the blade image, the output layer comprises an output neuron for indicating whether ice is on the blade, the convolutional layers each performs feature extraction on an output of a previous layer by using at least one trained convolution kernel to obtain an output of the convolutional layer itself, and the pooling layers each performs aggregation statistical processing on an output of a previous convolutional layer by using at least one trained pooling kernel to obtain an output of the pooling layer itself; wherein in the recognition model of ice on the blade, the convolutional layers and the pooling layers are arranged alternately after the input layer, and a quantity of feature mappings for each layer of the convolutional layers and the pooling layers is greater than a quantity of feature mappings for a previous layer. capturing an image of the blade through a camera; detecting a region of the blade from the captured image; clearing image information of a background region from the captured image, which is in the captured image except for the region of the blade, to obtain a blade image; and inputting the obtained blade image into a recognition model of ice on a blade obtained by training on a sample set, to determine whether ice is on the captured blade, wherein the sample set comprises a plurality of blade images indicating that ice is on blades; wherein the recognition model of ice on the blade is obtained by training on the sample set using a convolution neural network; wherein the recognition model of ice on the blade comprises an input layer, a first predetermined quantity of convolutional layers, a second predetermined quantity of pooling layers, a vector layer for vectorizing a feature of the blade image processed by the convolutional layers and the pooling layers, a classifier for classifying vectors outputted from the vector layer, and an output layer; wherein the input layer comprises a plurality of input neurons for describing pixels of the blade image, the output layer comprises an output neuron for indicating whether ice is on the blade, the convolutional layers each performs feature extraction on an output of a previous layer by using at least one trained convolution kernel to obtain an output of the convolutional layer itself, and the pooling layers each performs aggregation statistical processing on an output of a previous convolutional layer by using at least one trained pooling kernel to obtain an output of the pooling layer itself; wherein in the recognition model of ice on the blade, the convolutional layers and the pooling layers are arranged alternately after the input layer, and a quantity of feature mappings for each layer of the convolutional layers and the pooling layers is greater than a quantity of feature mappings for a previous layer.