User Case | GreptimeDB helps an airport in Guizhou with its smart energy IoT system

The open source China community team made its first live broadcast, telling the story behind the open source China community in the name of sharing."

In recent years, the rapid development of cloud computing and Internet of Things technology has prompted the electricity and energy systems of many traditional units to move toward digitalization, informatization, and intelligence, aiming to achieve real-time intelligent collaboration throughout the entire process and improve production efficiency. With the continuous enhancement of power collection and monitoring data functions, the amount of data is also increasing , which requires a more efficient database system to store and analyze data, and then mine greater value.

GreptimeDB is a time series database that is distributed, open source, cloud native and highly compatible. Since its open source, it has strongly supported the application of energy IoT platforms, financial observability, new energy vehicle data storage and analysis and other business scenarios. .

During the implementation of the weak current project for the third phase expansion of an international airport in Guizhou, after comparative research on domestic and foreign products such as GreptimeDB, Apache IoTDB and InfluxDB, GreptimeDB was finally selected as the time series database solution for the project. The solution based on GreptimeDB realizes efficient and reliable power distribution timing data writing, storage and query operations, ensuring efficient and stable operation of the system.

Background of the project

After the third phase expansion of an international airport in Guizhou Province, it is necessary to build a smart energy Internet of Things platform project based on the current status of the first and second phase power distribution systems, and optimize and improve automatic data collection and intelligent analysis of the power distribution system.

This project involves the following applications:

Internet of Things data collection platform : complete the collection of power meter data across the site, realize remote meter reading functions, and push the data to the airport big data exchange platform in real time;
Airport big data platform : Through the integration of multi-source data, functions such as statistical analysis of power consumption data and energy consumption prediction are realized.

When building the power distribution data collection platform in the second step, it is necessary to complete the collection of power meter data in the entire site and implement the remote meter reading function. At the same time, this platform also needs to push data to the airport big data exchange platform in real time. The time series database plays a central role in this link because it can efficiently process and store power meter data that changes over time, providing support for remote meter reading and real-time data push. In addition, the application of time series database also lays the data foundation for subsequent data statistical analysis, energy consumption prediction and other functions.

Project Challenges

Large number of devices and indicators : The airport IoT platform is connected to thousands of different types of devices, including electricity meters, water meters, etc., and nearly 10,000 other devices to be connected. Each type of equipment has many physical model indicators, all involving time series data, including sampling indicators, equipment status, etc. The collection frequency of each indicator is relatively high, and data sampling of a single indicator is performed every few minutes on average. It also faces a large number of physical problems. Device data model storage;
Large amount of data : The sampled data are all real-time data streams, which require storage and query capabilities to handle large-scale data volumes;
Long data storage cycle : data needs to be compressed and stored optimized to effectively reduce storage space usage and reduce storage and maintenance costs;
Time series data query is complex : a large number of time window based queries and aggregation operations require statistical analysis, trend prediction and other operations on time series data.

In the Internet of Things scenario, choosing a time series database has more advantages than traditional databases because time series databases can better cope with challenges. When the team selects a time series database, in addition to considering the above challenges, it also pays attention to multiple indicators such as underlying security, easy integration, convenient operation and maintenance, and open source. Among multiple time series database vendors, after comparing vendors such as GreptimeDB, Apache IoTDB, and InfluxDB, the project team finally chose the domestic, open source time series database GreptimeDB as the preferred solution.

During the project development process, the team paid special attention to the security of the underlying runtime, and GreptimeDB met the basic selection indicators. At the same time, GreptimeDB has the advantages of domestic open source software and fully meets the needs of our domestic IoT business scenario projects. After nearly ten months of comprehensive running test comparisons, GreptimeDB has been fully qualified for the challenges faced by the project.

Solutions and Architecture

The implementation architecture of GreptimeDB in the overall solution is as follows:

This project involves complex IoT business scenarios. In the figure, you can see two places where GreptimeDB is used, one is the Internet of Things platform and the other is the business application platform. They are located in different scenarios.

The IoT platform is responsible for collecting the raw data of the device and storing it in real time, and at the same time pushing the data to the big data platform for processing. The processed data is then pushed to the business application platform for use. The business application platform also uses GreptimeDB to store time series data processed by the big data platform, and uses its convenient query and statistical functions to visually display business scenarios.

final result

GreptimeDB time series database not only provides long-lasting, stable, efficient and agile integration capabilities, but also includes rich application functions. For example, it supports query and aggregation operations based on time windows, as well as practical functions such as time series data statistics and analysis. GreptimeDB improves efficiency in project advancement and greatly reduces complexity in real-time data collection for the Internet of Things.

Partner

Misu Technology Company has incorporated GreptimeDB into the development/use system of smart IoT, and has greatly explored the value of GreptimeDB in the smart IoT scenario of an airport in Guizhou.

As an IoT infrastructure software/hardware supplier and AI digital solution provider, Misu Technology adheres to the concepts of independent innovation, neutrality, reliability, flexibility and openness, and is committed to building an advanced cornerstone platform for the digital world. With excellent technical strength and independent research and development capabilities, we provide advanced MQTT message servers, edge ubiquitous operating systems and related edge collection equipment, and provide customers with powerful ecological capabilities and values such as the Internet of Things and digital twins. Through continuous innovation, we are committed to providing customers with high-quality and efficient IoT infrastructure and AI digital solutions.

As an open source project, GreptimeDB welcomes students who are interested in time series databases, Rust language, etc. to participate in contributions and discussions. For students who are participating in a project for the first time, it is recommended to start with the issue with the good first issue tag. We look forward to meeting you in the open source community! Star us on GitHub Now: https://github.com/GreptimeTeam/greptimedb Search GreptimeDB on WeChat and follow the official account to not miss more technical information and benefits~

About Greptime:

Greptime Greptime Technology is committed to providing real-time and efficient data storage and analysis services for fields that generate large amounts of time series data, such as smart cars, the Internet of Things, and observability, helping customers mine the deep value of data. Currently there are three main products:

GreptimeDB is a time series database written in Rust language. It is distributed, open source, cloud native and highly compatible. It helps enterprises read, write, process and analyze time series data in real time while reducing long-term storage costs.
GreptimeCloud can provide users with fully managed DBaaS services, which can be highly integrated with observability, Internet of Things and other fields.
GreptimeAI is an observability solution tailored for LLM applications.
The vehicle-cloud integrated solution is a time-series database solution that goes deep into the actual business scenarios of car companies, and solves the actual business pain points after the company's vehicle data grows exponentially.

GreptimeCloud and GreptimeAI have been officially tested. Welcome to follow the official account or official website for the latest developments! If you are interested in the enterprise version of GreptimDB, you are welcome to contact the assistant (search greptime on WeChat to add the assistant).

Official website: https://greptime.cn/ GitHub: https://github.com/GreptimeTeam/greptimedb Documents: https://docs.greptime.cn/ Twitter: https://twitter.com/Greptime Slack: https: //www.greptime.com/slack LinkedIn: https://www.linkedin.com/company/greptime