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.