A blade fault diagnosis method, comprising: acquiring a blade rotation audio collected by an audio collection device during operation of a wind turbine generator system; preprocessing the blade rotation audio based on a wind noise filtering algorithm to obtain a blade rotation audio filtered out of wind noise; dividing the blade rotation audio filtered out of wind noise to obtain audio segments corresponding to blades of the wind turbine generator system respectively; diagnosing, based on the audio segments, whether the blades each corresponding to one of the audio segments are faulty, wherein diagnosing, based on the audio segments, whether the blades each corresponding to one of the audio segments are faulty comprises: processing each of the audio segments by a Fourier transform to obtain a second feature value of each of the audio segments, wherein the second feature value is configured to characterize a frequency domain feature of each of the audio segments; inputting the second feature value of each of the audio segments to a blade fault diagnosis model to obtain a first fault diagnosis result of each of the blades, wherein the blade fault diagnosis model is a pre-trained model configured to identify whether a blade corresponding to the audio segment is faulty according to the second feature value of the audio segment; counting durations of the audio segments corresponding to the blades respectively; obtaining a second fault diagnosis result by judging whether there is a faulty blade according to whether a difference between durations of every two audio segments exceeds a preset threshold value; and judging, by combining the first fault diagnosis result of each of the blades and the second fault diagnosis result, whether there is a faulty blade. acquiring a blade rotation audio collected by an audio collection device during operation of a wind turbine generator system; preprocessing the blade rotation audio based on a wind noise filtering algorithm to obtain a blade rotation audio filtered out of wind noise; dividing the blade rotation audio filtered out of wind noise to obtain audio segments corresponding to blades of the wind turbine generator system respectively; diagnosing, based on the audio segments, whether the blades each corresponding to one of the audio segments are faulty, wherein diagnosing, based on the audio segments, whether the blades each corresponding to one of the audio segments are faulty comprises: processing each of the audio segments by a Fourier transform to obtain a second feature value of each of the audio segments, wherein the second feature value is configured to characterize a frequency domain feature of each of the audio segments; inputting the second feature value of each of the audio segments to a blade fault diagnosis model to obtain a first fault diagnosis result of each of the blades, wherein the blade fault diagnosis model is a pre-trained model configured to identify whether a blade corresponding to the audio segment is faulty according to the second feature value of the audio segment; counting durations of the audio segments corresponding to the blades respectively; obtaining a second fault diagnosis result by judging whether there is a faulty blade according to whether a difference between durations of every two audio segments exceeds a preset threshold value; and judging, by combining the first fault diagnosis result of each of the blades and the second fault diagnosis result, whether there is a faulty blade. processing each of the audio segments by a Fourier transform to obtain a second feature value of each of the audio segments, wherein the second feature value is configured to characterize a frequency domain feature of each of the audio segments; inputting the second feature value of each of the audio segments to a blade fault diagnosis model to obtain a first fault diagnosis result of each of the blades, wherein the blade fault diagnosis model is a pre-trained model configured to identify whether a blade corresponding to the audio segment is faulty according to the second feature value of the audio segment; counting durations of the audio segments corresponding to the blades respectively; obtaining a second fault diagnosis result by judging whether there is a faulty blade according to whether a difference between durations of every two audio segments exceeds a preset threshold value; and judging, by combining the first fault diagnosis result of each of the blades and the second fault diagnosis result, whether there is a faulty blade.