Animal diseases seriously affect the healthy development of animal husbandry and public health safety. The purification of animal diseases in large-scale farms is an extremely effective measure to eliminate and control the occurrence of diseases. Master the key measures of animal disease purification, and continue to promote the purification work. To achieve the goal of preventing and controlling animal diseases by standardizing and institutionalizing. In this paper, an intelligent animal husbandry breeding monitoring and early warning system is designed according to the characteristics of the livestock growth environment. The system is controlled by STM32 and adopts GSM wireless communication module to monitor the livestock environment in real time and collect data through sensors. Epidemic disease is monitored. Research data shows that once an infectious disease occurs on a large-scale pig farm, it will cause very serious consequences. Based on laboratory test data analysis and learning from the relatively good pig farm experience of biosafety, the original immunization program was adjusted and optimized, and the intelligent monitoring safety system was improved. The experimental results showed that the positive rate of blue ear antibodies in sows was 76%, and the average value of antibodies was 1.13; the positive rate of blue ear antibodies in boars was 99%, and the average value of antibodies was 1.22; the positive rate of blue ear antibodies in finishing pigs was 26%., The average antibody is 0.488; the positive rate of blue-ear antibody in nursery pigs is 18%, and the average antibody is 0.366. Therefore, the establishment of an animal disease monitoring and early warning information platform, combining animal disease monitoring information, laboratory testing services, quality system management, statistical analysis of monitoring results, animal disease early warning and predictive information management, can realize the informatization of animal disease monitoring and early warning Management, comprehensively improve the standardization level and early warning efficiency of major animal diseases.

Huimin Yang. The Establishment of an Intelligent Monitoring and Early Warning System for Large-scale Breeding Animal Epidemics in Urban Planning Areas. Zoology and Animal Physiology (2021), Vol. 2, Issue 1: 34-47. https://doi.org/10.38007/ZAP.2021.020104.

[1] Hairong, L., Yahui, M., Xiaochen, Z., & Yongbo, D. (2018). “Research on Monitoring and Early-Warning System of Marine Organisms for the Intake of Nuclear Power Plants”, Animal Husbandry and Feed ence,10(04), pp.26-30.

[2] Jarius, S., Eichhorn, P., Franciotta, D., Petereit, H. F., Akman-Demir, G., & Wick, M., et al. (2017). “The Mrz Reaction As A Highly Specific Marker of Multiple Sclerosis: Re-Evaluation and Structured Review of the Literature”, Journal of Neurology, 264(3), pp.453-466.

[3] Caragliu, & Andrea. (2017). “Handbook of Regional & Urban Economics, Vol. 5. Gilles Duranton, J. Vernon Henderson & William C. Strange, Eds. Amsterdam 2915: Elsevier. 2064 Pp. Isbn: 978‐0‐444‐59533‐1”, Tijdschrift voor Economische en Sociale Geografie, 108(1), pp.137-140.

[4] Manda, T., Samant, S., Pendhe, K., Naphade, R., & Yadav, S. (2019). “Claims and Settlement in Road Project”, Journal of Civil Engineering, Science and Technology, 10(1), pp.1-11.

[5] Luongo, J. C., Fennelly, K. P., Keen, J. A., Zhai, Z. J., Jones, B. W., & Miller, S. L. . (2016). “Role of Mechanical Ventilation in the Airborne Transmission of Infectious Agents in Buildings”, Indoor Air, 26(5), pp.666-678.

[6] Hashem, I. A. T., Chang, V., Anuar, N. B., Adewole, K., Yaqoob, I., & Gani, A., et al. (2016). “The Role of Big Data in Smart City”, International Journal of Information Management, 36(5), pp.748-758.

[7] Li, Y., Dai, W., Ming, Z., & Qiu, M. (2016). “Privacy Protection for Preventing Data Over-Collection in Smart City”, IEEE Transactions on Computers, 65(5), pp.1339-1350.

[8] Centenaro, M., Vangelista, L., Zanella, A., & Zorzi, M. (2016). “Long-Range Communications in Unlicensed Bands: The Rising Stars in the Iot and Smart City Scenarios”, IEEE Wireless Communications, 23(5), pp.60-67.

[9] Schleicher, J. M., Vogler, M., Dustdar, S., & Inzinger, C. (2016). “Enabling A Smart City Application Ecosystem: Requirements and Architectural Aspects”, IEEE Internet Computing, 20(2), pp.58-65.

[10] Zhang, K., Ni, J., Yang, K., Liang, X., Ren, J., & Shen, X. S. (2017). “Security and Privacy in Smart City Applications: Challenges and Solutions”, IEEE Communications Magazine, 55(1), pp.122-129.

[11] Jiang, D., Zhang, P., Lv, Z., & Song, H. (2016). “Energy-Efficient Multi-Constraint Routing Algorithm with Load Balancing for Smart City Applications”, IEEE Internet of Things Journal, 3(6), pp.1437-1447.

[12] Paganelli, F., Turchi, S., & Giuli, D. (2017). “A Web of Things Framework for Restful Applications and Its Experimentation in A Smart City”, IEEE Systems Journal, 10(4), pp.1412-1423.