Logistics park supply chain management platform based on the Internet of Things and cloud computing architecture
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The logistics park supply chain management framework proposed in this paper is based on the Internet of Things and cloud computing technologies. It can not only support the full dynamic tracking of logistics resources and related items, realize timely and appropriate information classification and push services, but also support the platform with SaaS , PaaS and IaaS provide various IT resource application services for enterprises in the supply chain of the park……
1 Introduction
As the same area of logistics gradually becomes a space area where a large number of logistics companies gather, how to accelerate the construction of a public platform for logistics park supply chain management and support various logistics companies to rely on the logistics park to carry out logistics supply chain services has become a hot spot for domestic and foreign scholars. . However, compared with the existing supply chain management platform, the construction of the logistics park supply chain management platform faces the following problems: ①Mobile work tasks, the various logistics enterprises gathered in the park often show their business activities as frequent movement within a larger space. The service process requires higher timeliness and accuracy of business data collection in the supply chain of the park; ②Highly specialized division of labor, logistics parks have typical characteristics of industrial clusters, and related companies have a higher degree of professional division. Therefore, for collaboration between companies The reliability and timeliness of information transmission are more demanding; ③The demand for massive data services. There are a large number of logistics companies gathered in the logistics park. The acceptance of SaaS and PaaS service models is also high. Therefore, the types and quantity of information on the park supply chain management platform have doubled. It is required to have efficient mass data processing capabilities; ④Intelligent information service needs, as the mass characteristics of park data become more prominent, how to intelligently mine and process mass logistics data to support enterprises to obtain timely and accurate information at the right place and time Appropriate information or knowledge services are also a major challenge facing the current logistics park supply chain management platform.
The rapid development of the Internet of Things technology provides new ideas for solving the above problems. The Internet of Things refers to the use of radio frequency identification (RFID), infrared sensors, global positioning systems, laser scanners and other information sensing equipment to connect any item to the Internet according to an agreed protocol for information exchange and communication to achieve intelligence A network of intelligent identification, positioning, tracking, monitoring and management, with the characteristics of comprehensive perception, reliable transmission and intelligent processing. However, the existing research on the Internet of Things mainly focuses on the research of common basic key technologies in the field of Internet of Things. Such as the Internet of Things coding technology, RFID radio frequency identification, sensors, wireless network transmission, high-performance computing, intelligent control, etc., but how to make efficient use of massive item information in the later stage. Integrate various services. And provide more humanized services to enterprises or individuals. It has not received enough attention. at the same time. Existing applied research is also found in intelligent transportation, electric meter reading and smart home, etc., and there are few applied researches in the field of logistics supply chain management. Therefore, this article intends to analyze the characteristics of supply chain management in the same area of logistics. Deepen the research on the application of the Internet of Things and cloud computing technology in the supply chain management of logistics parks, and establish a park supply chain management platform based on the Internet of Things technology to improve the integration and utilization of social logistics resources in the logistics area and optimize the allocation efficiency.
2. Platform technical architecture
The logistics weekly supply chain management platform based on the Internet of Things technology refers to the application of various Internet of Things technology such as terminal data collection equipment such as sensors and wireless sensor networks. Realize the whole-process monitoring of the status of logistics resources such as vehicles, goods, containers, and warehousing. Establish a unified middleware for multi-data integration in the park. On this basis, the SOA platform architecture is adopted to establish an integrated management platform for the supply chain in the weekly area. The support platform uses SaaS software to provide application software system services for various logistics service entities in the surrounding area and abroad, and provides various services for users inside and outside the park in the form of PaaS platform services. Class web service. And then establish the park supply chain “cloud computing” public service center. Through the data mining of the supply chain in the park, the optimal allocation of logistics resources in the same area is realized. The conceptual model of the platform includes 7 layers, including logistics resource layer, data collection layer, network communication layer, supply chain data layer, supply chain application layer, supply chain service layer, and supply chain decision-making layer, as shown in Figure 1.
(1) Logistics resource layer
This layer depicts the view description of the logistics resource object in the park supply chain management.
(2) Data collection layer
This layer uses the key technology of the IoT month to realize the monitoring and tracking of the status of various logistics resources during the banquet. According to the data collection time period, it is divided into three types: real-time data collection based on terminal data equipment such as RFlD; regular data collection based on a dedicated enterprise interface system; emergency data collection based on specific situations, such as road traffic when a special natural disaster occurs Information collection.
(3) Network communication layer
This layer is based on the integration of the wired/wireless network and sensor network of the logistics park, and establishes an IoT network communication system with adaptive self-organization characteristics, focusing on the construction of a wireless sensor network based on the convergence point of mixed platforms.
(4) Supply chain scattered data layer
This layer provides four types of data management components such as data definition, data integration, data exchange and data distribution, and establishes a unified description of multiple logistics data view models and special integrated data middleware that supports independent and unified access to park logistics resources and business data .
(5) Supply chain application layer
With the SaaS application model, it provides users with multiple software system leasing services including cargo transportation management system, warehouse management system, driver’s mobile phone service system, freight forwarding management system, LCD/LED information release system, etc.
(6) Supply chain service layer
This layer defines four general supply chain service units such as resource location services, information delivery services, and resource scheduling services, and supports the provision of the above four types of Web services for users in the form of PaaS platform services.
(7) Supply chain decision-making layer
Focus on establishing and relying on the “cloud computing” public service center in the park, according to user requirements and resources in the same area to optimize the allocation target, dispatch related computing resources, and carry out distributed massive data mining; through data analysis and mining results, support the business collaboration and cooperation of the supply chain in the park Management optimization.
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