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The present invention belongs to the field of navigation technology, and specifically relates to a Doppler radar assisted strapdown inertial navigation motion base rough alignment method under large azimuth misalignment angle. Step 1: define gyro constant drift and accelerometer constant error, establish the coordinate transformation matrix from ideal navigation coordinate system n to actual navigation coordinate system n′ and the mathematical platform attitude error equation and velocity error equation of strapdown inertial navigation under large azimuth misalignment angle; Step 2: establish the state equation of strapdown inertial navigation motion base rough alignment under large azimuth misalignment angle, and obtain the state matrix; Step 3: construct the measurement of Doppler velocity radar assisted strapdown inertial navigation rough alignment, and then combine it with the coordinate transformation matrix of step 1 to obtain the measurement equation and measurement matrix of strapdown inertial navigation motion base rough alignment under large azimuth misalignment angle; Step 4: use t k Measurement of time Z k The Kalman filter is used to realize the rough alignment filtering solution of the strapdown inertial navigation system moving base under large azimuth misalignment angle, thereby realizing the rough alignment of the strapdown inertial navigation system.

The application provides a power distribution network insulation defect early warning method and device based on a transient grounding event sequence, comprising: obtaining transient grounding data based on distributed synchronous recording, using a transient method to judge and output the grounding event of the power distribution network, and taking it as a current event; judging whether the transient grounding times of the related grounding section and grounding phase within a preset time limit exceed a threshold value, and outputting an alarm if yes; taking the grounding section and grounding phase of the current event as a keyword to construct a transient grounding event sequence in combination with the current event and four previous transient grounding history events; extracting multi-dimensional features to construct a feature vector of each event for the waveform data of each event in the sequence; inputting the feature vector sequence formed by the transient grounding event sequence into a trained hidden Markov model group, and outputting the insulation defect state of each feature dimension of the current event; and using a decision tree to infer, determine the insulation defect state corresponding to the current event, and output an alarm according to the state.