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    • 9. 发明授权
    • Self-optimizing algorithm for real-time problem resolution using historical data
    • 使用历史数据实时解决问题的自优化算法
    • US07962472B2
    • 2011-06-14
    • US12240442
    • 2008-09-29
    • Steven C. EricksonIvory Wellman KnipferJeffrey George Komatsu
    • Steven C. EricksonIvory Wellman KnipferJeffrey George Komatsu
    • G06F7/00G06F17/30
    • G06F11/079G05B23/0275G06F11/0709G06F11/0793
    • A self-optimizing algorithm for real-time problem resolution using historical data. Upon receiving failure symptom characteristics for a product or process failure, the algorithm queries historical failure data to locate historical failure symptoms and corrective actions matching the failure symptom characteristics. If a total number of the historical corrective actions identified meets a minimum match threshold, the algorithm selectively prunes a failure symptom characteristic having the lowest priority level to form an adjusted search query. The algorithm may repeat the querying, identifying, and determining steps using the adjusted search query until the total number of historical corrective actions identified meets the minimum match threshold. Once the threshold is met, the algorithm sorts the historical corrective actions to form a list of recommended corrective actions for the failure symptom characteristics and provides the list of recommended corrective actions to an end user.
    • 一种使用历史数据实时解决问题的自优化算法。 在收到产品或过程故障的故障症状特征时,该算法查询历史故障数据,以查找与故障症状特征匹配的历史故障症状和纠正措施。 如果识别的历史纠正措施的总数达到最小匹配阈值,则该算法选择性地修改具有最低优先级的故障症状特征以形成经调整的搜索查询。 该算法可以使用经调整的搜索查询重复查询,识别和确定步骤,直到所识别的历史校正动作的总数达到最小匹配阈值。 一旦满足阈值,算法对历史纠正措施进行排序,以形成针对故障症状特征的建议纠正措施的列表,并向最终用户提供推荐的纠正措施列表。