With the rapid development of computer technology, it is possible to process multi-type and mass data in real-time. In order to overcome the problems of distributed streaming computing in processing a large number of pig video streaming data when the delay was high and with poor scalability problems, a node resource scheduler algorithm was proposed, and a pluggable distributed real-time flow computation model was constructed. A system of video monitoring and analysis for pig breeding based on distributed flow calculation was developed. The system implemented the functions of pig video stream data acquisition, task scheduling, real-time calculation, pluggable expansion and result display. The test cluster consisted of a master node and three slave nodes. Under the cluster, the background refreshing method of improved hybrid Gaussian model was adopted to realize the multi-camera and multi-target detection in real-time. The average processing rate was 29.00% higher than the traditional mixed Gaussian model, the average detection rate was 79.00%, and the average false detection rate was 70.96% lower than that of the traditional mixed Gaussian model. The results showed that the pluggable distributed streaming real-time computing model had good scalability and low latency. The improved hybrid Gaussian model algorithm had high detection rate and low false detection rate.