Along with the development of economy and information technology, a large amount of data are produced in many applications. However, due to the influence of some factors, such as hardware equipments, manual operations, and multi-source data integration, serious data quality issues sunch as data inconsistency arise, which makes it more challenging to manage data effectively. Hence, it is crucial to develop new data cleaning technology to improve data quality to better support data management and analysis. Existing work in this area mainly focuses on the situation where functional dependencies are used to describe data inconsistency. Once some violations are detected, some tuples must be changed to suit for the functional dependency set via update (instead of insert or delete). Besides functional dependency, there also exist other types of constraints, such as the hard constraint, quantity constraint, equivalent constraint, and non-equivalent constraint. However, it becomes more difficult when more inconsistent conditions are involved. This paper addresses the general scenario where functional dependencies and other constraints co-exist. Corresponding data repair algorithm is designed to improve the data quality effectively. Experimental results show that the proposed method performs effectively and efficiently.