As large-scale beef cattle facilities become more and more common in China, sophisticated and even intelligent management of cattle has gained prominence. In the meantime, the Internet of Things, big data, artificial intelligence (AI) and even large models are developing at a rapid pace and are constantly permeating in all industries, making the intelligent management, including traditional breeding, possible. In this study, Yangxin Yi Liyuan 5G digital farm was used as the research object, which integrated application of intelligent electronic ear tags and intelligent collars, as well as a variety of environmental sensors for temperature and humidity, ammonia, carbon dioxide, wind direction and speed, light and air quality (H₂S, PM2.5, PM10, TSP). Simultaneously, a comprehensive dynamic perception of individual physiological indicators, such as the degree of exercise and rumination, was utilized to determine the estrus period of breeding cattle and forecast the ideal breeding period, and was employed to ascertain the estrus period of breeding cattle, forecast the ideal mating period, and determine whether the feed adjustment was necessary by monitoring variations in rumination time. Comprehensively monitoring the environmental indicators, such as temperature, humidity and air quality, is necessary to accurately manage and ventilate the cattle house. The pertinent data was analyzed using the MY SQL database technology and DELPHI language technology. In order to create a digital control platform for the breeding environment, health status, epidemic prevention, and feed management of beef cattle, this research built a digital control platform that integrated automatic collection of key data in breeding links, automatic conversion and calculation of data, wired and wireless transmission of data, remote storage and processing control of data, and so on. This study also shown that the more the system was used and improved, the more data was collected and extended in both size and type during the breeding process, and the more fundamental and derived data could be mined and appreciated. This paved way for the eventual development of a large-scale model for breeding beef cattle in the future.