Due to the antagonism and synergy among environmental factors in the poultry house, the influence process becomes extremely complex. As a result, it is difficult to predict and evaluate the degree of such influence accurately. In this paper, we study the poultry house environment factor and its relationship with poultry production performance, using the gray relation analysis (
GRA
) to filtrate the main factors that influence the evaluation of the poultry house environment. Put forward using the gray relation degree (
GRD
) to improve the method for structuring the judgment matrix, and weights are more objective and reasonable. The evaluation index system and evaluation model are constructed through the analytic hierarchy process (
AHP
). It is expected that the comprehensive evaluation of the indoor environment status of the poultry house can guide the optimization of the environmental control in the poultry house and obtain better production indicators of the poultry. In this study, the experimental broiler house was enclosed in autumn. Because of the ventilation system, the indoor environment is still affected by the outdoor environment. The top 3 in the calculation of weights were outdoor environment (0.4315), indoor temperature (0.2384), and indoor air quality (0.1687), which were consistent with experience. From October 24 to 27, the environmental evaluation values of the experimental broiler house were {2.4367, 2.8149, 2.3857, 2.5669}, that is, the evaluation results were {good, good, good, good}; consistent with the expert manual judgment. The correctness and practicability of the proposed method were verified. This paper provides a scientific basis for environmental evaluation and environmental control in the poultry house.
The arrangement of temperature sensors in most existing large-scale laying hen houses is random or placed according to the experience of breeders. However, this can't achieve accurate monitoring of the henhouse. The temperature in the laying hen houses cannot be uniformly controlled, leads to the reduction of production efficiency of laying hens. In this paper, aiming at the placement of temperature sensors in laying hens' houses, a placement optimization method was proposed. Firstly, the correlation coefficient of sensors is calculated to eliminate redundant sensors. Then, all the remaining sensors are arranged and combined. Finally, taking the grey correlation degree of each combination as the objective function, the dual-structure coding genetic algorithm is used to optimize the sensor combination. The strategy was tested in a large hen house. When the initial deployment of 81 sensors is reduced to at least 3, the established target can still be achieved, and the position of the target sensor can be calculated. This strategy not only meets the goal of accurately monitoring the hen house temperature, but also saves the hardware cost, which has important application value.INDEX TERMS dual-structure coding; genetic algorithm; grey correlation degree; large laying hen house; optimal sensor placement
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