ABSTRACT
Along with the striding of the Chinese medicine(CM) manufacturing toward the Industry 4.0, some digital factories have accumulated lightweight industrial big data, which become part of the enterprise assets. These digital assets possess the possibility of solving the problems within the CM production system, like the Sigma gap and the poverty of manufacturing knowledge. From the holistic perspective, a three-tiered architecture of CM industrial big data is put forward, and it consists of the data integration layer, the data analysis layer and the application scenarios layer. In data integration layer, sensing of CM critical quality attributes is the key technology for big data collection. In data analysis and mining layer, the self-developed iTCM algorithm library and model library are introduced to facilitate the implementation of the model lifecycle methodologies, including process model development, model validation, model configuration and model maintenance. The CM quality transfer structure is closely related with the connection mode of multiple production units. The system modeling technologies, such as the partition-integration modeling method, the expanding modeling method and path modeling method, are key to mapping the structure of real manufacturing system. It is pointed out that advance modeling approaches that combine the first-principles driven and data driven technologies are promising in the future. At last, real-world applications of CM industrial big data in manufacturing of injections, oral solid dosages, and formula particles are presented. It is shown that the industrial big data can help process diagnosis, quality forming mechanism interpretations, real time release testing method development and intelligent product formulation design. As renewable resources, the CM industrial big data enable the manufacturing knowledge accumulation and product quality improvement, laying the foundation of intelligent manufacturing.
Subject(s)
Algorithms , Big Data , Commerce , Data Mining , Medicine, Chinese Traditional , Quality Control , Technology, PharmaceuticalABSTRACT
Objective: The mobile medical collaborative service platform can realize mobile nursing informatization, which will bring new changes to the nursing work, and work flow is optimized. The construction of mobile nursing system is the trend of development in informatization, in order to better service to patients, realize full nursing work informatization. Methods: Based on wireless dynamic environment, and three subsystems including nursing collaboration portal, intelligent mobile terminal application system, the message engine service system, it adopts hierarchical architecture design, based on SOA structure mode, whose modules are loosely coupled, and platform core includes: the message engine subsystem, the medical task analysis engine subsystem, WEB middleware subsystem and the client subsystem,in order to realize the service management of mobile medical collaboration. Results: According to the actual situation of nursing work in hospitals, construction of mobile nursing system by the stage of application, to meet the core work of nursing, and then realize the basic nursing work, the construction of digital implementation of nursing information, to realize the informatization construction of digital care. Conclusion: Application of the system to achieve the whole informatization of the nursing management, through access to health services, cloud services center, to share resources, to achieve full coverage of all aspects of smart care management for people's health to provide effective protection.
ABSTRACT
Obiective To expand the functions of the original data integration platform combined business process to achieve cross-system integration in hospital, Methods On the basis of efficient data interaction for every medical information system in hospital, more related function modules for the DIP were added. Results The new function modules can satisfy DIP multiple needs in the hospital daily work and make the DIP more powerful. Conclusion The applicability and rationality of DIP are improved by extended function.