摘要

蛋鸡的亚健康状态是指蛋鸡介于健康与疾病间的过渡状态，其监测与识别对于蛋鸡健康预警和养殖收益均具有重要意义。养殖蛋鸡群在健康状况下可获得最大生产收益，但实际生产中其疾病常发现于亚临床状态，于临床状态才开始药物治疗，既不利于疫病防控，也不利于禽肉和蛋品质的安全。而有效地在亚健康到亚临床之间介入，通过饲养管理、营养调控等方法使其恢复到健康状态，将有巨大的效益与价值。因此及时辨别蛋鸡亚健康状态对早期疾病预警意义重大。本文以产蛋期蛋鸡为研究对象，构建亚健康评价指标体系和评价标准，以形成量化蛋鸡健康状况的评价量表，并对亚健康评价量表进行试验验证；结合团队通过图片、视频和音频采集并分析动物行为、粪便和咳嗽等的智能模型与设备，构建无损、无接触的蛋鸡亚健康评估方法，并根据蛋鸡场的实际情况构建智能化监测平台，以期为蛋鸡亚健康状态的监测和评估奠定基础，为蛋鸡生产的疾病预警和精准养殖提供新思路和新工具。

试验一 产蛋期蛋鸡亚健康评价指标体系和评价标准的构建

产蛋期是蛋鸡养殖的关键时期，构建产蛋期蛋鸡亚健康评价指标体系和评价标准，不仅有利于准确评估鸡群健康状况和精准饲养与管理的调整，也可为无损、无接触监测产蛋期蛋鸡健康状况提供参考指标和科学依据。本研究基于蛋鸡场调研、文献研究和专家会议，从鸡群的健康状况、行为表现、生产性能和鸡群信息与环境4个方面初步构建了产蛋期蛋鸡亚健康评价指标集和指标评分标准，运用专家咨询法筛选指标确立评价指标体系并修改指标评分标准，通过层次分析法确定指标权重。共邀请了14位专家进行咨询，专家咨询问卷有效回收为100%，专家权威系数为0.875，肯德尔协调系数为0.386，表明专家积极性、权威程度、意见协调程度均较好，最终确立的评价指标体系和其评价标准，包括4个一级指标，27个二级指标，初步形成评价量表。健康状况、行为表现、生产性能和鸡舍环境4个一级指标的权重分别为0.254、0.245、0.253、0.248。健康状况包括死淘率、鸡冠颜色和声音异常等9个二级指标，行为表现包括采食量、饮水量和冷热扎堆等6个二级指标，生产性能包括产蛋率、畸形蛋以及破损蛋率和平均蛋重等5个二级指标，鸡舍环境包括温度、湿度和光照强度等7个二级指标。初步构建产蛋期蛋鸡亚健康评价指标体系和评价标准，专家咨询可靠性较好，评价指标覆盖较全面。

试验二 产蛋期蛋鸡亚健康评价量表的验证

本试验旨在验证初步构建的蛋鸡亚健康评价量表对蛋鸡健康状况的反映程度。基于试验一构建的评价指标体系和评价标准，形成针对本试验养殖环境和设备条件的亚健康评价量表。试验选取27周龄的白来航蛋鸡72只，对鸡群进行亚健康评分（记为初始评分），随后分为注射生理盐水组(NS，n=36)和注射脂多糖组(LPS，n=36)，NS组连续7天腹腔注射生理盐水(注射量为1 mg/kg BW)，LPS组连续7 d腹腔注射LPS(注射量为1 mg/kg BW)。连续注射7 d后再对NS和LPS两组分别进行亚健康评分，评分后每组取18只采样，测定蛋鸡生产性能、蛋品质、血清炎症因子、血浆和肝脏抗氧化等指标。结果如下：1) 初始评分为80.04分，NS组评分为76.97分，二者评分变化反映连续7天腹腔注射引起的鸡群应激对蛋鸡健康状况的影响。2) LPS组评分为70.38分，相较初始评分下降9.66分，二者评分变化反映连续7天腹腔注射LPS造成的免疫应激对蛋鸡健康状况的影响。3) LPS组在注射的3 d、4 d、5 d产蛋率较NS组显著降低(P<0.05)，同时降低了平均蛋重(P<0.05)，导致料蛋比极显著升高(P<0.001)。4) LPS组的蛋白高度、哈氏单位、蛋黄重量、蛋壳厚度均较NS组极显著下降(P<0.01)。5) LPS组的脾脏重量和脾脏指数较NS组极显著增大(P<0.001)。6) LPS组蛋鸡血浆的MDA含量较NS组显著升高(P<0.05)，GSH-Px活性显著降低(P<0.05)，T-SOD活性极显著降低(P<0.001)；肝脏CAT活性和T-AOC活性极显著降低(P<0.01)，GSH-Px活性极显著升高(P<0.01)。7) LPS组蛋鸡血清的IL-6含量较NS组显著升高(P<0.001)。综上所述，本试验通过腹腔注射LPS引起的蛋鸡免疫应激和炎症反应，改变了鸡群的健康状况，通过量表获得的评分变化、蛋鸡氧化还原状态指标和炎症因子水平变化，验证了亚健康评分量表对鸡群健康的反映力，为依据亚健康评价指标体系构建蛋鸡亚健康监测平台提供了理论参考。

试验三 蛋鸡亚健康监测系统的搭建

当前多数规模化蛋鸡场都大量使用了自动化养殖设施，包括了自动环控设备、自动料线和水线、自动集蛋系统等，部分企业也形成了基于物联网技术的数据管理平台，但平台目前仅实现了可视化功能，展示设备采集的生产和环境信息等数据，不具备综合分析数据和辅助决策功能。对蛋鸡亚健康指标及评价标准系统研究的缺乏，制约了现有大数据平台对多源数据的融合和分析。因此，针对蛋鸡群体健康的评估和疾病预警需求，试验三基于蛋鸡亚健康评价指标体系以及评价标准的构建研究，结合团队研发的基于图像和音频分析的异常粪便和声音监测系统，和规模化蛋鸡场自动化环控、水料和集蛋系统的数据采集能力，在大数据平台中构建了蛋鸡亚健康监测系统。蛋鸡亚健康监测软件平台采用IntelliJ IDEA进行编写，选用MySQL数据库进行运行数据的储存与管理，中间件的实现使用了SpringBoot和MyBatis框架，中间件与数据库之间建立连接，通过MyBatis提供的数据访问层来操作数据库，执行相应的SQL语句。系统集成了多种健康评估模型和异常特征检测模型，形成了可视化大数据平台展示界面，且具备回调历史数据、调取异常声音音频等实用功能，具有对鸡群健康状况有监测和评分功能，能协助管理者及时发现鸡群异常原因，辅助生产管理的调整。

综上，本研究通过专家咨询法和层次分析法构建了蛋鸡亚健康评价指标体系和评价方法，初步形成评价量表。并通过腹腔注射LPS的动物试验，初步验证量表评分能一定程度上反映蛋鸡群的健康状况。再基于量表，综合应用鸡舍的实际情况和当前规模化蛋鸡场的养殖模式，构建了蛋鸡亚健康监测系统，是实现了无损、无接触监测蛋鸡健康状况的积极探索。

The sub-healthy state of laying hens refers to the transitional state between health and disease, and its monitoring and identification are of great significance for both the health warning and farming income of laying hens. Laying hen flocks can achieve maximum production benefits when they are in good health, but in actual production, diseases are often found in subclinical states, with drug treatment starting only when clinical symptoms appear. This is not conducive to disease prevention and control, nor to the safety of poultry meat and egg products. Effective intervention between sub-health and subclinical states, through feeding management, nutritional regulation, and other methods to restore them to a healthy state, will bring enormous benefits and value. Therefore, timely identification of the sub-healthy state of laying hens is of great significance for early disease warning. This article takes laying hens during the laying period as the research object, constructs a sub-healthy evaluation index system and evaluation criteria to form a quantitative evaluation scale of laying hen health status, and verifies the sub-healthy evaluation scale through experiments. Combined with the team's intelligent models and devices for collecting and analyzing animal behaviors, feces, coughs, etc., through pictures, videos, and audio, a non-destructive, non-contact assessment method for the sub-healthy evaluation of laying hens is constructed. Based on the actual situation of laying hen farms, an intelligent monitoring platform is built to lay the foundation for the monitoring and evaluation of laying hen sub-healthy states and provide new ideas and tools for disease warning and precision farming in laying hen production.

1． Construction of Evaluation Index System and Evaluation Standards for Sub-healthy Assessment of Laying Hens during the Egg-laying Period

The laying period is a critical stage in the breeding of laying hens. Constructing an evaluation index system and standards for the sub-healthy state of laying hens during this period is not only conducive to accurately assessing the health status of the flock and adjusting precise feeding and management but also provides reference indicators and scientific basis for non-destructive, non-contact monitoring of laying hen health during the laying period. This study, based on field surveys, literature research, and expert meetings, preliminarily constructed an evaluation index set and scoring criteria for the sub-healthy state of laying hens during the laying period from four aspects: health status of the flock, behavioral performance, production performance, and flock information and environmental factors. The expert consultation method was used to select indicators to establish the evaluation index system and modify the scoring criteria, and the weights of the indicators were determined using the analytic hierarchy process. Fourteen experts were invited for consultation, with a 100% effective response rate for the expert consultation questionnaire. The expert authority coefficient was 0.875, and the Kendall coordination coefficient was 0.386, indicating a good level of expert enthusiasm, authority, and opinion coordination. The final evaluation index system and its criteria were established, including four primary indicators, 27 secondary indicators, forming a preliminary evaluation scale. The weights of the four primary indicators, namely health status, behavioral performance, production performance, and henhouse environment, were 0.254, 0.245, 0.253, and 0.248, respectively. Health status includes nine secondary indicators such as mortality rate, comb color, and abnormal vocalization. Behavioral performance includes seven secondary indicators such as feed intake, water consumption, and clustering behavior. Production performance includes five secondary indicators such as egg production rate, abnormal eggs, egg breakage rate, and average egg weight. Henhouse environment includes seven secondary indicators such as temperature, humidity, and light intensity. The preliminary construction of the evaluation index system and standards for the sub-healthy state of laying hens during the laying period showed good reliability in expert consultation, and the evaluation indicators covered a comprehensive range.

2． Validation of the Sub-healthy Assessment Scale for Laying Hens during the Egg-laying Period.

This experiment aimed to validate the preliminary constructed sub-healthy evaluation scale for laying hens regarding the degree to which it reflects the health status of the hens. Based on the evaluation index system and criteria established in Experiment One, a sub-healthy evaluation scale tailored to the breeding environment and equipment conditions of this experiment was developed. The experiment selected 72 White Leghorn laying hens at 27 weeks of age. The flock was initially scored for sub-healthy conditions (referred to as initial scores). Subsequently, they were divided into the Normal Saline group (NS, n=36) and the Lipopolysaccharide group (LPS, n=36). The NS group received continuous intraperitoneal injections of normal saline for 7 days (at a dose of 1 mg/kg body weight), while the LPS group received continuous intraperitoneal injections of LPS for 7 days (at a dose of 1 mg/kg body weight). After 7 days of continuous injections, sub-healthy scores were reassessed for both NS and LPS groups. Subsequently, 18 samples were taken from each group for the determination of laying hen production performance, egg quality, serum inflammatory factors, plasma, and liver antioxidant indicators. The results are as follows: (1) The initial score was 80.04 points, with the NS group scoring 76.97 points, reflecting the impact of stress caused by continuous intraperitoneal injections on the health status of the flock. (2) The LPS group scored 70.38 points, showing a decrease of 9.66 points compared to the initial score, indicating the effect of immune stress caused by continuous intraperitoneal injections of LPS on the health status of the laying hens. (3) The LPS group showed a significant decrease in egg production rate on the 3rd, 4th, and 5th days of injection compared to the NS group (P<0.05), along with a decrease in average egg weight (P<0.05), resulting in a significantly increased feed-to-egg ratio (P<0.001). (4) The LPS group exhibited significant decreases in albumen height, Haugh unit, yolk weight, and eggshell thickness compared to the NS group (P<0.01). (5) The spleen weight and spleen index of the LPS group were significantly increased compared to the NS group (P<0.001). (6) The LPS group showed a significant increase in malondialdehyde (MDA) content in plasma compared to the NS group (P<0.05), significant decreases in glutathione peroxidase (GSH-Px) activity and total superoxide dismutase (T-SOD) activity (P<0.05), significant decreases in catalase (CAT) activity and total antioxidant capacity (T-AOC) activity in the liver (P<0.01), and a significant increase in GSH-Px activity in the liver (P<0.01). (7) The interleukin-6 (IL-6) content in serum was significantly increased in the LPS group compared to the NS group (P<0.001). In summary, this experiment, through intraperitoneal injection of LPS, induced immune stress and inflammatory reactions in laying hens, altering the health status of the flock. Changes in scores obtained from the evaluation scale, indicators of oxidative-reductive status in laying hens, and levels of inflammatory factors validated the effectiveness of the sub-healthy evaluation scale in reflecting the health of the flock, providing theoretical reference for constructing a sub-healthy monitoring platform for laying hens based on the sub-healthy evaluation index system.

3． Construction of a subhealth monitoring system for laying hens

Most of the current large-scale egg farms use a large number of automated breeding facilities, including automatic environmental control equipment, automatic material and water lines, automatic egg collection systems, etc. Some enterprises have also formed a data management platform based on IoT technology, but the platform currently only realises visualisation functions and displays data collected by the equipment, such as production and environmental information, and does not have the function of comprehensive analysis of the data and assisted decision-making. The lack of systematic research on subhealth indicators and evaluation standards for laying hens restricts the fusion and analysis of multi-source data by the existing big data platform. Therefore, in response to the assessment of the health of the laying hen population and the demand for disease early warning, Experiment 3 constructed a laying hen subhealth monitoring system in the big data platform based on the research on the evaluation index system of laying hen subhealth as well as the construction of the evaluation standard, combined with the abnormal faeces and sound monitoring system based on image and audio analysis developed by the team, and the data collection capability of the automated ring control, water and egg collection system of the scaled-up laying hen farms. Laying hens' subhealth monitoring software platform is written in IntelliJ IDEA, and MySQL database is selected for the storage and management of operational data, and SpringBoot and MyBatis framework are used for the implementation of the middleware, which establishes a connection between the middleware and the database, and operates the database through the data access layer provided by MyBatis, executing the corresponding SQL statements. The system integrates a variety of health assessment models and abnormal feature detection models, forming a visual big data platform display interface, and has practical functions such as calling back historical data, accessing abnormal sound and audio, etc. It has the function of monitoring and scoring the health status of chickens, which can assist managers in discovering the causes of abnormalities of chickens in time and assist in the adjustment of production management.

In summary, this study constructed the evaluation index system and evaluation method of subhealth of laying hens through expert consultation method and hierarchical analysis method, and initially formed the evaluation scale. And through the animal test of intraperitoneal injection of LPS, it was preliminarily verified that the scale score could reflect the health status of laying hens to a certain extent. Then based on the scale, comprehensively applying the actual situation of chicken houses and the current breeding mode of large-scale egg farms, a subhealth monitoring system for laying hens was constructed, which is a positive exploration to achieve non-destructive and non-contact monitoring of the health status of laying hens.

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