吕恩利, 何欣源, 罗毅智, 等. 哺乳母猪智能饲喂物联网系统设计[J]. 华南农业大学学报, 2023, 44(1): 57-64. DOI: 10.7671/j.issn.1001-411X.202203003
    引用本文: 吕恩利, 何欣源, 罗毅智, 等. 哺乳母猪智能饲喂物联网系统设计[J]. 华南农业大学学报, 2023, 44(1): 57-64. DOI: 10.7671/j.issn.1001-411X.202203003
    LÜ Enli, HE Xinyuan, LUO Yizhi, et al. Design of intelligent feeding IoT system for lactating sows[J]. Journal of South China Agricultural University, 2023, 44(1): 57-64. DOI: 10.7671/j.issn.1001-411X.202203003
    Citation: LÜ Enli, HE Xinyuan, LUO Yizhi, et al. Design of intelligent feeding IoT system for lactating sows[J]. Journal of South China Agricultural University, 2023, 44(1): 57-64. DOI: 10.7671/j.issn.1001-411X.202203003

    哺乳母猪智能饲喂物联网系统设计

    Design of intelligent feeding IoT system for lactating sows

    • 摘要:
      目的  设计哺乳母猪智能饲喂物联网系统,以实现哺乳母猪饲喂状况远程监控。
      方法  系统通过Netty传输自定义TCP通信协议,实现与终端设备的数据传输和指令应答功能。采用SpringBoot与Vue前后端分离架构,开展人机交互界面设计,包括猪场生产情况详情界面、母猪饲喂信息查询界面和统计数据下载界面。
      结果  试验结果表明,在3000个连接数下,系统平均响应时间为0.33 s,单位时间内处理数据量范围在750~1180条,系统在加入自定义业务线程池后,单位时间处理数据量增加了250条,处理量提高了31%。
      结论  该系统满足了对哺乳舍智能饲喂设备的连接管理和数据处理的实际应用需求。

       

      Abstract:
      Objective  In order to design an intelligent feeding Internet of Things system for lactating sows, and realize the remote monitoring of feeding status of lactating sows.
      Method  The system transmited the custom TCP communication protocol through Netty to realize the function of data transmission and instruction reply with the terminal equipment. The design of human-computer interaction interface was carried out by using SpringBoot and Vue front-end and back-end separation architecture, including pig farm production detail interface, sow feeding information query interface and statistical data download interface.
      Result  The test results showed that the average response time of the system was 0.33 s with 3000 connections, and the amount of data processed per unit time ranged from 750 to 1180. After the system was added into the custom business thread pool, the number of data processed per unit time increased by 250, and the processing capacity increased by 31%.
      Conclusion  The system meets the practical application requirements of connection management and data processing for intelligent feeding equipment in nursing house.

       

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