Domestic Service Groups Smart Matching Technology

With the accelerating pace of urban life and the development of specialized division of labor, household service has become an emerging service industry in a modern city. It plays a significant role in meeting the needs of modern families' lives, promoting the division of industries, and solving the problem of employment as well. Meanwhile, it has been ranked as one of the focal points of the development of community-based service.Looking forward to the future development and demand, household service is a complex process that combines individualized skill services with environmental, cultural and psychological adjustment and adaptation. The satisfaction with household service supply-demand matching will be affected by many-sided, complex factors with different requirements of people. The traditional supply-demand matching mode of household service only emphasizes employer's demands for household service staff's basic information such as skills, working hours, payment, health status, age, gender and working experiences etc., which cannot take into account the differences and conflicts of the two sides in the cultural backgrounds, behaviors, habits and psychological characteristics. Therefore, the matching rate is not satisfied.After doing some exploratory research on the above problems, the paper provides a group intelligent matching mode and analyzes as well as discusses the related technology and its achievement. Through the two processes-clustering and matching, the mode meets the supply-demand matching of employers and household service staff. Clustering, which is to meet some objective conditions, is the process mainly achieving simple matching; matching, which is to meet some subjective conditions, is the process mainly achieving complex matching. For clustering process, the paper quoted Optimizing Ant Colony Clustering Algorithm based on group intelligent, which is proposed by Dai Weihui, Liu shouji, Wang Chunshi from Fudan University. The algorithm is proposed both in reference to Denueubourg's basic model and on the basis of LF algorithm. Through the introduction of the adjustment process and short-term memory, it makes ant colony can consult historical information while transporting objects and makes an iterative adjustment of ant colony cluster. Thereby, it enhances the convergence velocity of algorithm and the validity of clustering. For matching process, the paper adopted a model based on human soft matching technology. It firstly constructs element model for household service staff and employers and takes the wishes of household service staff and the expectations of employers into account. Moreover, it greatly improves the matching between household service staff and employers. The research of the paper provides a reference to satisfy the needs of supply-demand matching in future household service and solve the similar complex problems.The paper is the research of the National Science and Technology Plan topic "Digital Community Services Demonstration Project" (No: 2006BAH02A30) undertook by the author's advisor.