营造与作物需求相适应的小气候，促进光合作用和物质积累是设施园艺高效生产的基础。针对作物生长过程中多环境因子相互作用对光合速率影响规律复杂，难以融合调控效率精准获取设施环境调控目标值的难题，拟面向设施番茄，研究光合速率时序模型与设施环境调控目标寻优方法。设计作物生长全程细粒度多因子嵌套试验，研究基于遗传支持向量机的全程光合速率时序模型建模方法；利用遗传算法建立温度与最优光合速率映射函数，基于曲率拐点理论研究生长适宜温度区间获取方法，探寻融合调控效率的光合速率次优目标值优化算法，建立温度关联的光合速率目标值模型；基于光合速率时序动态模型，以光合速率次优目标值为约束条件，研究调控成本最优的环境调控目标值寻优方法；以光合速率模型和目标值寻优方法为核心构建数据融合中心，设计多传感器信息融合试验平台，进行方法验证与完善。研究成果对揭示设施环境调控规律具有重要意义，将大力推动现代设施园艺的快速发展。

An appropriate and efficiently microclimate construction for plants to promote their photosynthesis and nutrient accumulation is the key problem of modern facility horticulture production. The difficulty is, under the influence of plant-environment interaction, to obtain regulation objective values accurately which integrated with regulation efficiency. In this project, take tomato growth environment regulation as an illustration, we propose a photosynthetic rate time-series model in the whole process based on Genetic Algorithms-Support Vector Machine (GA-SVM) algorithm and design several whole crop growth fine-grained nested factor tests to prove it. Based on the function of temperature and optimistic photosynthetic rate from previous step, we seek the photosynthetic rate sub-optimization map combined with regulation efficiency using curvature inflection point theory to obtain the appropriate temperature range for tomato growth and create desired photosynthetic rate value model. Further step, with the desired value model from previous step and the most optimized regulation financial cost as constraint conditions, we research on tomato growth environment regulation values optimal seeking method. And based on the tomato photosynthetic rate model and the previous optimal seeking method, we construct tomato growth converged data center and development multi-sensor information fashioned test platform to do method validation and improvement. This research will be of great importance in clarifying facility high-efficiency regulation principle, and will also lay the foundation for rapid development of modern facility horticulture.