A Gaussian distribution-based timed task abnormality monitoring method, an electronic device, and a readable storage medium. The method comprises: acquiring preset parameter data of a monitored current timed task (S10); calculating a probability superposition value of the preset parameter data of the current timed task according to a preset calculation formula (S20), the probability superposition value being a superposition value of probability values of the preset parameter data of the current timed task occurring in a Gaussian distribution probability space of a preset normal timed task; and analyzing, on the basis of the probability superposition value, whether the monitored current timed task is abnormal (S30). According to the present method, whether the timed task is abnormal is intelligently determined, without manual analysis and determination by an operator, thereby saving labor and time costs, and improving efficiency.
GAUSSIAN DISTRIBUTION-BASED TIMED TASK ABNORMALITY MONITORING METHOD, ELECTRONIC DEVICE, AND MEDIUM