Siebel Maintenance Release Guide: 8.1.1.5 Rev C

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"MR_GUIDE_8115_REV_C.pdf" 是Siebel Maintenance Release Guide的版本8.1.1.5,修订版C,发布于2011年9月,由Oracle公司及其关联公司版权所有。该文档主要针对Oracle BI(Business Intelligence)平台中的Siebel集成,提供错误代码解析和BIP(Business Intelligence Publisher)的配置指南。 在SiebelMaintenance Release Guide中,用户可以学习到如何处理和解决与Oracle BI Siebel组件相关的各种问题。文档的关键部分是通过错误代码类型来查找问题的解决方案。这通常涉及到对系统日志的分析,识别特定的错误代码,然后根据文档提供的指导找到对应的解决步骤。这种故障排查方法对于系统管理员和IT专业人员来说至关重要,因为它能帮助他们快速定位并修复系统中出现的异常。 BIP(Business Intelligence Publisher)是Oracle BI套件的一部分,用于创建、管理和分发企业报告。在文档中,你将了解到如何配置BIP,包括设置数据源、设计报告模板、安排报告的自动分发以及定制用户访问权限等。BIP支持多种数据源,如Oracle数据库、Siebel CRM数据,甚至其他非Oracle系统的数据,使得企业能够生成全面、个性化的报告,满足不同业务部门的需求。 此外,文档还可能涵盖了如何更新和维护Siebel系统,以确保系统的稳定性和性能。这可能包括补丁应用、系统优化、性能监控以及最佳实践的建议。对于大型企业来说,定期的系统维护和更新是保持BI系统高效运行的关键。 对于美国政府或为其代理的任何一方收到的软件或相关文档,文档中也包含了特定的权益声明。根据美国法律,这些交付可能包含受限制使用的机密和专有信息,除非获得许可或法律规定,否则禁止对其进行反向工程、拆解或反编译。 "MR_GUIDE_8115_REV_C.pdf" 是一个详尽的参考资料,为处理Oracle BI Siebel系统的错误和配置BIP提供了清晰的指引,是IT专业人士进行系统维护和故障排除的重要工具。

select distinct a.EMPI_ID, a.PATIENT_NO, a.MR_NO, a.PAT_NAME, a.PAT_SEX, a.PAT_AGE, a.PAT_PHONE_NO, b.DIAG_RESULT, a.ADMIT_DATE, a.DISCHARGE_DEPT_NAME, a.ATTEND_DR from BASIC_INFORMATION a join PA_DIAG b on a.MZZY_SERIES_NO=b.MZZY_SERIES_NO join EXAM_DESC_RESULT_CODE c on a.MZZY_SERIES_NO=c.MZZY_SERIES_NO join DRUG_INFO d on a.MZZY_SERIES_NO=d.MZZY_SERIES_NO join EMR_CONTENT e on a.MZZY_SERIES_NO=e.MZZY_SERIES_NO JOIN TEST_INFO A17 ON a.MZZY_SERIES_NO = A17.MZZY_SERIES_NO where a.PAT_AGE>='18' and (to_char(a.ADMIT_DATE,'YYYY-MM-DD') >= '2021-01-01') AND (b.DIAG_RESULT LIKE '%鼻咽癌%' or b.DIAG_RESULT LIKE '%鼻咽恶性肿瘤%' or b.DIAG_CODE LIKE '%C11/900%') and d.DRUG_NAME not in (select DRUG_NAME FROM DRUG_INFO WHERE DRUG_NAME like '卡培他滨') and b.DIAG_RESULT NOT IN (SELECT DIAG_RESULT FROM PA_DIAG WHERE DIAG_RESULT LIKE '%HIV阳性%') and b.DIAG_RESULT NOT IN (SELECT DIAG_RESULT FROM PA_DIAG WHERE DIAG_RESULT LIKE '%充血性心力衰竭%') AND to_char(( A17.TEST_DETAIL_ITEM_NAME = '中性粒细胞' AND A17.TEST_RESULT >= 1.5 ) OR ( A17.TEST_DETAIL_ITEM_NAME = '血小板' AND A17.TEST_RESULT >= 100 ) OR ( A17.TEST_DETAIL_ITEM_NAME = '血红蛋白' AND A17.TEST_RESULT >= 9 ) OR ( A17.TEST_DETAIL_ITEM_NAME = '丙氨酸氨基转移酶' AND A17.TEST_RESULT <= 2.5 ) OR ( A17.TEST_DETAIL_ITEM_NAME = '天门冬氨酸氨基转移酶' AND A17.TEST_RESULT <= 2.5 ) OR ( A17.TEST_DETAIL_ITEM_NAME = '肌酐清除率' AND A17.TEST_RESULT > 51 ) OR ( A17.TEST_DETAIL_ITEM_NAME = '肌酐' AND A17.TEST_RESULT <=1.5 ) OR ( A17.TEST_DETAIL_ITEM_NAME = '凝血酶原时间' AND A17.TEST_RESULT <= 1.5 ))语句哪里有问题

2023-06-07 上传

class AbstractGreedyAndPrune(): def __init__(self, aoi: AoI, uavs_tours: dict, max_rounds: int, debug: bool = True): self.aoi = aoi self.max_rounds = max_rounds self.debug = debug self.graph = aoi.graph self.nnodes = self.aoi.n_targets self.uavs = list(uavs_tours.keys()) self.nuavs = len(self.uavs) self.uavs_tours = {i: uavs_tours[self.uavs[i]] for i in range(self.nuavs)} self.__check_depots() self.reachable_points = self.__reachable_points() def __pruning(self, mr_solution: MultiRoundSolution) -> MultiRoundSolution: return utility.pruning_multiroundsolution(mr_solution) def solution(self) -> MultiRoundSolution: mrs_builder = MultiRoundSolutionBuilder(self.aoi) for uav in self.uavs: mrs_builder.add_drone(uav) residual_ntours_to_assign = {i : self.max_rounds for i in range(self.nuavs)} tour_to_assign = self.max_rounds * self.nuavs visited_points = set() while not self.greedy_stop_condition(visited_points, tour_to_assign): itd_uav, ind_tour = self.local_optimal_choice(visited_points, residual_ntours_to_assign) residual_ntours_to_assign[itd_uav] -= 1 tour_to_assign -= 1 opt_tour = self.uavs_tours[itd_uav][ind_tour] visited_points |= set(opt_tour.targets_indexes) # update visited points mrs_builder.append_tour(self.uavs[itd_uav], opt_tour) return self.__pruning(mrs_builder.build()) class CumulativeGreedyCoverage(AbstractGreedyAndPrune): choice_dict = {} for ind_uav in range(self.nuavs): uav_residual_rounds = residual_ntours_to_assign[ind_uav] if uav_residual_rounds > 0: uav_tours = self.uavs_tours[ind_uav] for ind_tour in range(len(uav_tours)): tour = uav_tours[ind_tour] quality_tour = self.evaluate_tour(tour, uav_residual_rounds, visited_points) choice_dict[quality_tour] = (ind_uav, ind_tour) best_value = max(choice_dict, key=int) return choice_dict[best_value] def evaluate_tour(self, tour : Tour, round_count : int, visited_points : set): new_points = (set(tour.targets_indexes) - visited_points) return round_count * len(new_points) 如何改写上述程序,使其能返回所有已经探索过的目标点visited_points的数量,请用代码表示

2023-06-10 上传