河北省唐山市玉田县白菜种植环境分析数据

采集白菜种植的空气温湿度、光照强度、PM10、PM2.5 等数据，全面评估不同环境条件对白菜生长的具体影响。如确定适宜的空气温度和湿度水平，以利于白菜种子萌发和幼苗生长，维持植株的水分平衡。明确合适的光照强度和时长，保证白菜进行充分的光合作用，促进叶片的生长和营养物质的积累。同时，监测空气中的 PM10 和 PM2.5 含量，避免空气污染对白菜生长造成不良影响，确保白菜的品质和安全。利用这些数据构建白菜品质与环境参数关联的深度网络模型，深入探索何种环境条件组合可以使白菜具有更高的维生素含量、更优的口感和更好的外观品质等，为显著提升白菜品质奠定基础。基于数据驱动的方式，为白菜种植环境的智能调控提供有效的支持，依据实时数据自动调整通风强度、遮阳程度以及采取适当的空气净化措施等，为白菜创造适宜的生长环境，提高种植效率和产量。将优化后的环境调控经验推广应用至不同地区的白菜种植中，形成多维度细粒度的感知与控制模式，有力推动白菜种植产业朝着科学、高效、模式可移植的方向发展，全面提升整个产业的竞争力。1.数据采集：本系统通过空气湿温度传感器、PM传感器、光照传感器等物联网设备，结合4G/5G、Wi-Fi与有线网络，实时采集种植环境中的空气湿温度、PM10、PM2.5、光照等多维数据。 2.算法规则：系统采用环境参数评分算法，对环境数据进行评分。基于作物生长理想条件（如温度、湿度、光照、PM值等），并通过以下公式计算：环境参数评分=100-Σ(w_i×|当前值_i-理想值_i|/容差_i)其中，Σ表示对所有参数的累加，w_i是第i个参数的权重。当前值_i是第i个参数的实际测量值，理想值_i是第i个参数的理想值。容差_i是第i个参数的允许波动范围。权重、理想值和容差范围设定基于历史数据分析以及实际种植经验的确定。对作物生长影响较大的参数获得较高的权重。容差范围则考虑到环境因素的波动性，针对作物对不同环境变化的耐受性设定进行适当设定，环境参数偏离理想值越多，扣分越大，以空气湿度为例，其权重为3，理想值设定为60%，容差范围为±3%，扣分计算如下：空气湿度扣分=3×|95.2-60|/3=3×11.7=35.1。根据这些评分生成具体的环境优化方案。

This dataset collects data including air temperature and humidity, light intensity, PM10, PM2.5, and other relevant parameters from Chinese cabbage cultivation, aiming to comprehensively evaluate the specific impacts of various environmental conditions on Chinese cabbage growth. Specifically, it aims to determine suitable air temperature and humidity levels to facilitate Chinese cabbage seed germination and seedling growth, and maintain plant water balance; clarify appropriate light intensity and duration to ensure sufficient photosynthesis of Chinese cabbage, promote leaf growth and nutrient accumulation; meanwhile, monitor PM10 and PM2.5 concentrations in the air to avoid adverse effects of air pollution on Chinese cabbage growth, and ensure the quality and safety of the produce. Using these collected data, a deep learning network model correlating Chinese cabbage quality and environmental parameters is developed to thoroughly investigate which combination of environmental conditions can yield Chinese cabbage with higher vitamin content, better sensory taste, and more desirable appearance quality, laying a solid foundation for substantially improving Chinese cabbage quality. This data-driven approach provides effective support for intelligent regulation of Chinese cabbage cultivation environments: it can automatically adjust ventilation intensity, shading degree, and implement appropriate air purification measures based on real-time monitoring data, thereby creating optimal growth conditions for Chinese cabbage and enhancing cultivation efficiency and yield. The optimized environmental regulation experience will be promoted and applied to Chinese cabbage cultivation across different regions, forming a multi-dimensional and fine-grained perception and control model, which effectively drives the Chinese cabbage cultivation industry toward a scientific, efficient, and highly transplantable development path, and comprehensively enhances the overall competitiveness of the industry.

1. Data Collection: This system collects multi-dimensional real-time data including air temperature and humidity, PM10, PM2.5, and light intensity from the cultivation environment via IoT devices such as air temperature and humidity sensors, PM sensors, and light sensors, combined with 4G/5G, Wi-Fi, and wired network connections.

2. Algorithm Rules: The system employs an environmental parameter scoring algorithm to evaluate collected environmental data. The scoring is calculated based on the ideal growth conditions for crops (such as temperature, humidity, light intensity, PM concentrations, etc.) using the following formula:
$$ ext{Environmental Parameter Score} = 100 - sumleft(w_i imes frac{| ext{Current Value}_i - ext{Ideal Value}_i|}{ ext{Tolerance}_i}
ight)$$
Here, $sum$ denotes the summation over all parameters, $w_i$ is the weight of the $i$-th parameter, $ ext{Current Value}_i$ is the actual measured value of the $i$-th parameter, $ ext{Ideal Value}_i$ is the ideal target value of the $i$-th parameter, and $ ext{Tolerance}_i$ is the allowable fluctuation range of the $i$-th parameter.
The weights, ideal values, and tolerance ranges are determined based on historical data analysis and practical cultivation experience. Parameters exerting greater impacts on crop growth are assigned higher weights. The tolerance ranges are appropriately set considering the volatility of environmental factors and the crop's tolerance to varying environmental changes: the greater the deviation of environmental parameters from the ideal values, the more points will be deducted. Taking air humidity as an example, its weight is set to 3, the ideal value is 60%, and the tolerance range is ±3%. The deduction calculation is as follows:
$$ ext{Air Humidity Deduction} = 3 imes frac{|95.2 - 60|}{3} = 3 imes 11.7 = 35.1$$
Specific environmental optimization schemes are then generated based on these calculated scores.