In recent years, the poultry breeding industry has been converted into a large-scale, intensive, and intelligent production mode. The Internet of Things (IoT) is under rapid development, which promotes the development of precision livestock farming. In this study, we developed an intelligent service platform for a facility environment based on the IoT structure, utilizing the capabilities of Platform as a Service (PaaS). The platform consists of four layers, including an information perception layer, network layer, management service layer, and application layer. By using the cloud service model with a distributed network architecture, asynchronous data transmission, and a distributed file system, the platform can centrally manage multiple farm’s data. The intelligent service platform includes the following functions: displaying environmental data, water and electricity consumption, data analysis, and managing production data. Over a 500-day trial period in a live poultry house, the platform demonstrated high data integrity (>87%) and resilience against network disruptions and power outages. The data validity of each environmental element exceeded 94%, among which the validity of the temperature, humidity, and carbon dioxide concentration exceeded 99%. The overall accuracy of the dataset remained relatively high, providing a robust data foundation for further research. Key features included audio analysis, environmental monitoring, and production data management. The platform’s operational status was efficiently communicated via data statistics and email alerts, facilitating timely system recovery. The demonstrated modules included sound recognition, psychrometric charts for visual alerts, and financial analysis tools, offering versatile solutions for integrating PLF models and advanced analytics.

The scale and intensification of poultry production continue to increase, and Internet of Things (IoT) technology has been widely adopted in poultry farming. Along with the increasing IoT adoption comes the need to deal with the issues of real-time processing, analysis and application of massive field data. Time series data in large-scale egg production have the characteristics of multiple collection equipment or sensors, large datasets, and high-speed throughput. This research describes a monitoring and alarm system based on time series database and alarm model. The system uses distributed stream computing technology to process various time series data at high speed. The time series database “OpenTSDB” is used in the system to storage and query the time series data efficiently. The write performance of OpenTSDB is scalable, which can reach a write performance of one million qps, and the query delay is less than 100ms in 99% of the cases. Multiple alarm strategies were developed, allowing the system to alarm as single-indicator or indicator segment and enabling application in more scenarios. The monitoring and alarm system is expected to significantly advance the ability of modern poultry producers in their efficient monitoring and processing of big data and real-time alarm of the flock performance and housing conditions. Keywords: Time Series Data; OpenTSDB; Distributed Stream Computing; Laying-hens