KBF：基于知识的预测

“Knowledge Based Forecasting”通常缩写为KBF，这种简写方式在书写和使用上都更加便捷高效。该术语在商业领域中尤为常见，广泛应用于战略规划、市场分析和风险预测等场景。其中文含义是“基于知识的预测”，强调通过整合行业经验、历史数据和专业知识来支持决策过程。

Due to the lack of service parts knowledge management, majority commercial software often rely on purely historical data based forecast models, such as linear regression and moving average, etc. Its forecasting capability is quite limited when facing so complicated causes of service parts requirement.
由于缺乏知识管理，面对复杂的维修备件的需求诱因，现行大多数的商用软件主要是根据历史需求数据采用线性回归分析或移动平均等单纯基于历史需求数量变化的预测模型，其预测能力十分有限。
By digging into history knowledge ( uncertainty reasoning rules ), cloud decision-making model for remaining capacity is established, and cloud decision-making method is given according to inflow which is forecasted based on forecasting period and remaining period.
通过挖掘历史沉积的知识（不确定推理规则），构建并给出了基于预报期和余留期预报来水量的水库余留库容云模型决策模型及云模型决策方法。

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