One of the long-standing concerns of many energy companies around the world is how to supply energy produced by companies to users. However, many companies have difficulties in supplying energy stably due to failures in these equipments and are still investing enormous time and money in responding to failures. Distributing digitized measurement equipment to collect and analyze data is one of the efforts to reduce these costs. The questions is whether you are ready to respond quickly if any one piece of equipment fails in the large-scale infrastructure needed to supply energy to hundreds of thousands of users. In particular, if there is no way to quickly identify the failed equipment layer and the equipment in that layer that has a problem, the company's facility management costs increase and it is difficult to accurately measure users' energy consumption.
'Energy Company D,' for example, struggled to manage failures in the opration of installed digital meeting equipment. THerea re serveral tiers of equipment between the home meter and the "Company A" computer room. When you press the microwave oven button at home, the power usage information is sent to the modem layer that transmits/receives data in the residential complex, the data control unit layer installed in public facilities or underground to control the flow of data, and the enterprise it is collected to the corporate server through the "front-end processor" layer that exists in the problem is that this process takes too long to determine exactly which piece of equipment has failed.
If a failure occurs in the data control unit that manages the data of 10 regions, the repair personnel can be dispatched to the nearest branch from where this unit is installed. However, if it is not possible to quickly determine whether the failed equipment is A or a modem is one of the 10 regions under A, then it is inevitable to invest more human and material resources to respond to the failure.
Company D had a large amount of log data but did not have a big data system to store and manage it.
Since all log data containing equipment network information was stored in a relational database, the performance to process huge network data was very poor. As a result, many unnecessary costs were incurred, such as delays in repairs duet to not knowing exactly which equipment had dailed, and energy usage not being properly measured at the time of failure.
In these circumstances, there are four solutions that AGEDB
… What's AGEDB again?
AGEDB is a product that use graph query modeling along with the existing relational model simultaneously. AGEDB is a solution to query and explore RDB with its SQL syntax support.
Graph Data storage ecosystem AGEDB stores all log data in the Hadoop ecosystem (Apache Hadoop) and builds a system that manages the data in graph form using AGEDB graph database. Each device, in reality is represented as a single "vertex" in the graph model, and the relationship between devices is expressed as an "edge," forming a "graph" structure, then the equipment data can be viewed and modified on the graph.
Network Visualization: AGEDB provides a platform to visualize equipment networks. This platform provides visualization that allows you to understand the overall structure and status of your infrastructure at a glance, as well as check the status of your equipment. Administrators can monitor the equipment status by region and section, failure response status, statistical values, etc. while viewing the dashboard, and can also directly view log data by selecting each device on the graph.
Pattern analysis: AGEDB has built a system to analyze failures in detail based on these platforms. With this scheme, you can immediately find and analyze devices that are exceeding acceptable temperatures or slowing your network too much. In the end, it is possible to organize the status and causes of failures and inquire when necessary, and to quickly respond to failures by analyzing what types of failures exist, which failures must be dealt with first, and what are the repeated failure patterns. In addition, by learning and using failure patterns in an artificial intelligence model, it provides a basis for "preventing" failures rather than simply "managing" them.
History management system : AGEDB has established a system to manage equipment history. This system manages and records all activities required to maintain and manage a large number of equipment. In addition, it provides a function to view the repair history, repair details, and parts replacement history of each equipment in graph by linking with the graph model. All equipment data is coded to support the systematic management of equipment by the person in charge of equipment operation.
The core of the solution provided by AGEDB is a platform that manages and visualizes the complex, physically connected equipment as it is in the real world using a graph model.
ACompanies can find equipment with problems in a large equipment network and quickly query the equipment's status and repair history. GDB models are easy to visualize and easy to understand. The company's equipment manager can understand the overall structure of the infrastructre at a glance and grasp the status intuitively through the visualization platform. In addition, since the status of individual equipment can be inquired with one-click, there is no longer a need to go through a table join process to retrieve data after a long time when curious about the status of a specific equipment.
AGEDB 's failure analysis system provides a cornerstone for more advanced analysis beyond cost reduction through efficient failure response.
If you are interested in knowing more about AGEDB and its solutions, here's your next step!