5results about How to "Predict failure" patented technology

The present invention relates to the field of solid-state drive fault prediction, and in particular to a solid-state drive fault prediction method and system based on artificial intelligence. The method comprises the following steps: fine-grained division of the solid-state drive into storage units, and serialization of the storage units to construct a storage unit sequence for each partition; obtaining a solid-state drive operation log; performing multi-time point state evolution on the storage unit sequence for each partition based on the solid-state drive operation log, and performing deep dynamic evolution learning to generate a state evolution law for each storage unit; performing periodic access load peak calculation on the solid-state drive operation log, thereby generating a simulated access data packet for each storage unit; performing dynamic pressure access simulation based on the state evolution law of each storage unit and the simulated access data packet of each storage unit to obtain response state evaluation data for each storage unit. The present invention realizes efficient and accurate solid-state drive fault prediction.

The invention relates to an automobile driving condition multi-scale identification method. The method comprises the following steps: S1, acquiring and preprocessing driving condition data; s2, short-term working condition sample division and long-term working condition sample division are carried out, and long-term working condition categories are divided according to long-term working condition samples; s3, performing characteristic parameter calculation on the divided working condition data to obtain characteristic parameter data of each section of working condition sample; s4, performing principal component analysis and dimension reduction; s5, performing short-term working condition clustering to obtain a short-term working condition category; and S6, performing model training based on the feature parameter data after dimension reduction, the short-term working condition category and the long-term working condition category to obtain a multi-time-scale working condition recognition model, acquiring actual working condition data by the multi-time-scale working condition recognition model, and outputting a recognition result. Compared with the prior art, the method has the advantages of improving the accuracy of identifying the vehicle driving working condition, especially the sudden change working condition, and the like.

The invention provides an LSTM model generation method, a fault prediction method and a fault prediction device. The generation method comprises the following steps: acquiring a plurality of historical times of an energy storage power station and historical feature information corresponding to each historical time; and generating an input sequence and an output sequence, creating an LSTM model, and training the LSTM model based on the input sequence and the output sequence. Faults of the energy storage power station can be predicted based on the LSTM model and the SVM model.

The invention discloses a data protection method and system based on a USB flash disk, and relates to the technical field of USB flash disk security, and the method comprises the steps: obtaining the working environment data of a storage medium, collecting the number of interference sources in a signal interference region, and obtaining an environment interference degree evaluation value through processing; dynamically adjusting a preset storage medium state risk assessment threshold according to the environment interference degree assessment value; monitoring the state data of the storage medium, processing the state data to obtain a storage medium position anomaly evaluation value, and performing monitoring period division according to the storage medium position anomaly evaluation value to obtain a first monitoring period and a second monitoring period; performing primary data protection according to the state data of the storage medium in the first monitoring period; and according to the state data of the storage medium in the second monitoring period and the environment interference degree assessment value, performing comprehensive analysis to obtain a storage medium state risk assessment value, and performing secondary data protection according to the storage medium state risk assessment value.

This invention presents a data-driven method for assessing the condition and predicting the lifespan of nuclear power plant transformers. The method includes: collecting actual test, maintenance, and operation data of nuclear power plant transformers; combining this data with multiphysics simulation data to construct a dataset for assessing the condition of nuclear power plant transformers; using a large dataset of actual and simulated data, employing data mining methods to calculate the Mahalanobis distance between the aging data and the health baseline model of the nuclear power plant transformers, and classifying the equipment into different health levels accordingly to achieve accurate assessment of equipment condition; and through intelligent analysis of the obtained current health status and operational data of the nuclear power plant transformers, predicting faults and remaining lifespan. This invention effectively overcomes the shortcomings of traditional equipment condition monitoring and operation and maintenance decision-making processes that rely on manual experience, improving the accuracy and real-time performance of equipment condition assessment, and enabling the prediction of remaining lifespan and faults.