The invention discloses a data flow
abnormality detection method based on a parallel
Kalman algorithm. The data flow
abnormality detection method comprises the following steps that 1, measurement data of a sensor in a period of time is acquired; 2, the measurement data is compared with a measurement value in a previous period of time, once a change is generated, an
estimation value is calculated through the
Kalman algorithm according to the measurement value, an absolute value of a difference between the
estimation value and the measurement value is compared with a specified threshold value, and if the absolute value is not smaller than the threshold value, the absolute value is judged to be an abnormal value, and the next step is conducted; 3, the generation reasons of the abnormal value are judged by considering a time
influence factor, a space
influence factor and other factors such as the flood period, the weather and the human factors which influence
abnormality detection and recorded, and information is stored in a
database. According to the data flow
abnormality detection method, the time
influence factor, the space influence factor and the other
provenance information influence factor are taken into account; an
algorithm task is decomposed and processed in parallel in order to improve the
algorithm efficiency, and the detection precision is improved.