Abstract: In one embodiment, a Kalman filter based capacity forecasting method includes acquiring a capacity time sequence of an object to be forecasted; establishing a dynamical model for the capacity time sequence, and extracting a state transition parameter and a process noise parameter of the dynamical model; performing Kalman filter estimation on the capacity time sequence by using the state transition parameter and the process noise parameter to generate at least one state characteristic signal; segmenting the capacity time sequence according to the at least one state characteristic signal, and determining at least one corresponding segmentation point; and forecasting the capacity at future time according to the at least one segmentation point determined in the capacity time sequence. By adopting the technical solutions of the invention, accurate forecasting of the capacity growth is achieved, to facilitate operation and maintenance personnel making a reasonable capacity expansion plan.

Abstract: Embodiments of the present disclosure provide a disk capacity predicting method, apparatus, equipment and non-volatile computer storage medium. On the one hand, the change data of the disk capacity is obtained according to the historical capacity data of the disk; then the target inflection point in the historical capacity data is obtained according to the change data of the disk capacity; and then the linear relationship between the time and disk capacity is obtained according to the historical capacity data after the target inflection point. Therefore, technical solutions provided by embodiments of the present disclosure may achieve improvement of accuracy of prediction of disk capacity trend and reduce the cost needed in disk capacity prediction.

Abstract: A system monitoring method and apparatus comprises: collecting periodically status indicator data of a monitored system to generate a status indicator data sequence; selecting predetermined pieces of status indicator data according to data collecting time in a reverse chronological order; determining a category from predetermined categories, the predetermined pieces of status indicator data belonging to the determined category; selecting, from the historical status indicator data, status indicator data belonging to the determined category and obtained in a collection period as characteristic data of the determined category; calculating a predicted value of a status indicator of the system at a predicting moment using the characteristic data; and determining whether the system is abnormal, based on a difference between the calculated predicted value and a true value of the status indicator of the system collected at the predicting moment.

Abstract: In one embodiment, a Kalman filter based capacity forecasting method includes acquiring a capacity time sequence of an object to be forecasted; establishing a dynamical model for the capacity time sequence, and extracting a state transition parameter and a process noise parameter of the dynamical model; performing Kalman filter estimation on the capacity time sequence by using the state transition parameter and the process noise parameter to generate at least one state characteristic signal; segmenting the capacity time sequence according to the at least one state characteristic signal, and determining at least one corresponding segmentation point; and forecasting the capacity at future time according to the at least one segmentation point determined in the capacity time sequence. By adopting the technical solutions of the invention, accurate forecasting of the capacity growth is achieved, to facilitate operation and maintenance personnel making a reasonable capacity expansion plan.

Abstract: The present disclosure provides a data predicting method and apparatus. The method comprises: acquiring at least one time factor of a prediction moment; predicting a desired value of data of the prediction moment according to a preset data model and said at least one time factor of the prediction moment. As compared with a method of predicting the desired value on a year-on-year basis or a month-on-month basis in the prior art, the above technical solution of the present disclosure may effectively improve the prediction accuracy of the desired value of the data of the prediction moment and thereby improve a monitoring effect of monitoring abnormality according to the desired value of the predicted data.

Abstract: A system monitoring method and apparatus comprises: collecting periodically status indicator data of a monitored system to generate a status indicator data sequence; selecting predetermined pieces of status indicator data according to data collecting time in a reverse chronological order; determining a category from predetermined categories, the predetermined pieces of status indicator data belonging to the determined category; selecting, from the historical status indicator data, status indicator data belonging to the determined category and obtained in a collection period as characteristic data of the determined category; calculating a predicted value of a status indicator of the system at a predicting moment using the characteristic data; and determining whether the system is abnormal, based on a difference between the calculated predicted value and a true value of the status indicator of the system collected at the predicting moment.

Abstract: Embodiments of the present disclosure provide a disk capacity predicting method, apparatus, equipment and non-volatile computer storage medium. On the one hand, the change data of the disk capacity is obtained according to the historical capacity data of the disk; then the target inflection point in the historical capacity data is obtained according to the change data of the disk capacity; and then the linear relationship between the time and disk capacity is obtained according to the historical capacity data after the target inflection point. Therefore, technical solutions provided by embodiments of the present disclosure may achieve improvement of accuracy of prediction of disk capacity trend and reduce the cost needed in disk capacity prediction.