The invention provides a method and
system for synchronously detecting the number of cases infected with multi-
drug-
resistant bacteria based on MapReduce and
big data management. Based on the MapReduce framework, the
parallel computing capability of a
machine in a distributed
system is utilized, the task of calculating the number of cases infected with multi-
drug-
resistant bacteria in a hospital in millions and tens of millions of inpatients beyond the memory and storage limit of a
server is divided into tens of millions and billions of small tasks, the small tasks are simultaneously executedon a plurality of machines, and intermediate output results of the small tasks are summarized to generate a final result. According to the invention, massive
parallel computing can be carried out on
big data of millions, tens of millions and hundred millions of inpatients according to various calibers such as provincial and municipal areas, hospital levels, hospital beds, comprehensive and specialized departments, publicity and camp, all types of infections related to the multi-
drug-
resistant bacteria are managed, accurate statistics of the number of cases infected with the multi-drug-resistant
bacteria in the hospital is achieved, hospital infection is integrally evaluated, and overall prevention, control and management of hospital infection of the multi-drug-resistant
bacteria are achieved.