Research on Sea Area Data Management Based on a Uniform Code Assignment Method

2019 ◽  
Vol 53 (3) ◽  
pp. 39-45
Author(s):  
Derui Song ◽  
Daoyan Xu ◽  
Jianli Zhang ◽  
Houjun Wang ◽  
Jianhua Zhao
Keyword(s):  
Data Management ◽  
Process Management ◽  
Hierarchical Structures ◽  
Dynamic Monitoring ◽  
Assignment Method ◽  
Uniform Code ◽  
Sea Use ◽  
Management Method ◽  
Sea Area ◽  
Allocation Methods

AbstractBased on a data management method that utilizes unified coding and allocation numbers, this paper proposes an online, direct reporting, full-process management method, from ownership information generation to allocation number printing. In the unified coding and allocation, our method includes mainly top-level nodes (national nodes), secondary nodes (provincial and municipal nodes), and base-level nodes (county-level nodes) of multiple hierarchical structures with upper and lower affiliation. Data audit and registration approval authority is implemented through different levels of branch nodes, which focuses on the development and optimization of coding and allocation methods. It also uses the national sea area dynamic monitoring and management system (NSADMMS) as a platform to achieve the application of coding and allocation methods. After 4 years of operation, the practicality of the research content is verified. At the same time, the sea space resource utilization data managed by this method show that there is a larger fluctuation of the sea area type in the industry, and open sea use is dominant in the sea use area.

scholarly journals Characterizing Big Data Management

10.28945/2192 ◽  
2015 ◽  
Author(s):  
Rogério Rossi ◽  
Kechi Hirama
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Big Data ◽  
Data Management ◽  
Process Management ◽  
Three Dimensions ◽  
Storage And Retrieval ◽  
Human Aspects ◽  
Business Approach ◽  
Support Decision Making

[The final form of this paper was published in the journal Issues in Informing Science and Information Technology.] Considering that big data is a reality for an increasing number of organizations in many areas, its management represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources, people and processes are crucial dimensions to facilitate the management of big data in any organization, allowing information and knowledge from a large volume of data to support decision-making. Big data management must be supported by technology, people and processes; hence, this article discusses these three dimensions: the technologies for storage, analysis and visualization of big data; the human aspects of big data; and, in addition, the process management involved in a technological and business approach for big data management.

Interoperable Process Engineering System for Collaborative Optimization of Product Life-Cycle

2014 ◽  
pp. 166-191 ◽  
Author(s):  
Alberto Armijo ◽  
Mikel Sorli
Keyword(s):  
Life Cycle ◽  
Information Systems ◽  
Data Management ◽  
Product Life Cycle ◽  
Process Management ◽  
Business Processes ◽  
Collaborative Optimization ◽  
Market Conditions ◽  
Product Life ◽  
Business Agility

Most of the industrial organizations, including SMEs, need to quickly react and adapt to the changing market conditions imposed by globalization, such as new sustainability directives or new type of customers. The fulfillment of these requirements on time is a must so as to remain competitive in the global markets. Since data management information systems are already present in almost all the corpus of industrial enterprises as custom developments or standard PLM solutions, the natural technical evolution that aims to provide an effective answer to these changing market conditions comprises the shifting from a data management perspective towards a process management view. Hence, the challenge is how to manage business processes that build upon existing information systems so as to encourage business agility, efficiency, and interoperability. The proposed approach roots on the Business Process Management (BPM) discipline and leverages process optimization through the systematic modeling and reengineering of business processes accompanied by supporting interoperable and configurable eco-services, which are conceived as sustainability-aware services designed to optimize some aspects of the product life-cycle through eco-constraints management.

CAE Data Management Using Traditional PDM Systems

2004 ◽  
Author(s):  
Alhad A. Joshi
Keyword(s):  
Data Management ◽  
Product Design ◽  
Data Model ◽  
Process Management ◽  
Low Cost ◽  
Data Types ◽  
Physical Test ◽  
Design Alternatives ◽  
Physical Testing ◽  
Warranty Costs

Over the past decade, Computer Aided Engineering (Simulation) has experienced explosive growth being a significant enabler for: 1. Validating product design; 2. Providing low-cost methods for exploring a variety of product design alternatives; 3. Optimizing parts for better service performance; 4. Reducing dependence on physical testing; 5. Reducing warranty costs; 6. Achieving faster time to market. This rapid growth in the number of simulations performed and the amount of data generated in the absence of any significant data and process management initiatives has led to considerable inefficiencies in the CAE domain. Many companies now recognize the need to manage their CAE process and data as well as their desire to leverage their existing PDM systems as the primary repositories of CAE data. Some major issues are: 1. There is a need for a PDM data model to support CAE; 2. The CAE data model can be very complex; 3. There is an immense variety of CAE applications and data types; 4. Many CAE simulations require access to physical test data for input and correlation; 5. Data management discipline is not typically part of the CAE culture today. Despite the unique challenges posed by bringing PDM into the CAE world, the transition could occur faster than it has in the CAD world. This presentation will showcase an approach for managing CAE data in traditional PDM systems. Two working examples of CAE process automation software solutions integrated with CAD and PDM will be discussed. In particular, these applications will show how CAE users can leverage established PDM infrastructure and interact with EDS’ Teamcenter/Enterprise, Teamcenter/Engineering and Dassault Systeme’s SmarTeam through seamless integrations with their CAE systems.

scholarly journals Hazard Analysis of Traditional Post-Harvest Operation Methods and the Loss Reduction Effect Based on Five Time (5T) Management: The Case of Rice in Jilin Province, China

Agriculture ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 877
Author(s):  
Na Zhang ◽  
Wenfu Wu ◽  
Yujia Wang ◽  
Shuyao Li
Keyword(s):  
Research Team ◽  
Process Management ◽  
Loss Rate ◽  
Jilin Province ◽  
Time Interval ◽  
Drying Methods ◽  
Rice Grain ◽  
Post Harvest ◽  
Management Method ◽  
Whole Life Cycle

Traditional post-harvest operation methods applied in rice fields lack advanced management knowledge and technology, which has led to post-harvest losses. We proposed the concept of Five Time (5T) management for the first time. 5T management divides the whole life cycle of rice into different growth time interval to complete process management. This paper mainly introduces the management of rice grain period, that is, the post-harvest management period, including the operation process management of harvesting, field stacking, drying, warehousing, and storing. In 2019, our research team formulated the 5T management method, which considers the entire post-harvest process, and carried out a pilot application of this method at the Jilin Rice Industry Alliance of Jilin Province. Moreover, to promote the 5T management method, our research team carried out follow-up experiments in rice production enterprises and found severe post-harvest rice losses. This paper combined a large number of literature and the basic theory research of rice post-harvest to analyze the traditional methods for post-harvest processing and the associated rice losses. By implementing the 5T management method, 4.33% of losses incurred during the T1 harvesting period could be recovered. In the T2 field period, drying rice within 48 h after harvesting could reduce losses by 2.5%. In the T3 drying period, the loss rate could be reduced by 1.6% if traditional drying methods were replaced by mechanical drying and by 0.6% if cyclic drying was implemented to prevent over-drying. In the T5 storage period, the loss rate of 7% could be reduced by adopting advanced grain storage technologies such as low-temperature storage. Overall, the rice loss rate could be reduced by 15.43%, which is equivalent to a yield of 32.68 million tonnes of rice. The important factors in each period are strictly controlled in the 5T management method to prevent the post-harvest losses caused by flawed concepts and improper management and to increase the amount of usable fertile land.

scholarly journals The Research on Wetland Ecological Engineering Database Based on 3S Technology

2015 ◽  
Vol 9 (1) ◽  
pp. 979-983
Author(s):  
Liwei Liu ◽  
Yinlong Zhang
Keyword(s):  
Data Management ◽  
Systems Engineering ◽  
Ecological Engineering ◽  
Regional Governance ◽  
Dynamic Monitoring ◽  
Management Decision ◽  
Ecological Management ◽  
Ecological Data ◽  
3S Technology ◽  
Engineering Database

Wetlands are important to the survival of the earth environment and ecosystems, along with the extent of the ecological study of regional governance continues to accelerate, the regulators have implemented various ecological management project management process kinds of data and information related to the increasing range. The traditional data management model is difficult to meet the current needs of engineering data management; On the other hand, the study area of wetland ecological management is a very large and complex systems engineering, the development of ecological planning for the study area, researchers need to master the ecological evolution and resource development data for the study area have a comprehensive understanding of the historical evolution. Therefore, the establishment of research and development based on 3S technology which research regional ecological engineering design data management needs of ecological engineering database is very important for the planning of the regional ecological engineering, management, decision-making, assessment, analysis and dynamic monitoring to provide technical support to achieve the dynamic management of ecological engineering, comparative study of regional environmental and ecological data at different times to seek more rational governance.

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