月统计分摊给日统计的问题

LocoDaiblo 2008-02-27 09:05:42
问题描述:
现有1个月份(30天)的数据,内容有 月总时间、合格时间、超上限时间、超下限时间、合格率、超上限率、超下限率等内容。时间单位为:分钟。 30天*60分钟*24小时 = 43200 分钟。1天*60分钟*24小时 = 1440分钟。 这是满时间的情况,而实际情况下一个月的总时间和日总时间并不是满意,同样合格率也有高低。 所以有如下1个月的具体例值。

月总时间 月合格率 月超上限率 月超下限率 月合格时间 月超上限时间 月超下限时间
41050 98.25% 0.75% 1% 月总时间-超上限时间-超下限时间 取整(41050*0.0075) 取整(41050*0.01)

日统计 1日 1380 95.21% 2.79% 2% ...................................

...
...

30日 1420 99.15% 0.35% 0.5% .........


现在我对 月总时间、月合格率、月超上限率、月超下限率 人为更改为

43200 99.10% 0.45% 0.45%

要求系统程序对日统计值进行自动调整,并最大限度地保留一点“真实性”。 请问怎么做?
并且调整时,日总时间不能超过 1440,合格率+超上限率+超下限率=100% 日总时间=日合格时间+日超上限时间+日超下限时间,时间都为整数。

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不讨论道德,只讨论技术。

日总时间=日合格时间+日超上限时间+日超下限时间=1440分钟
日合格率=日合格时间/1440
日超上限率=日超上限时间/1440
日超下限率=日超下限时间/1440
是这样吗?

那好办,大致上还是根据原始比例来分摊。
假设真实数据是:月合格率=96%,
现在将月合格率上调到98%,这意味着一个月之内总的合格时间增加了864分钟。
假设原来每天的日合格时间为T[i],那么调整之后的日合格时间为:
T[i]=T[i]+864*(1440-T[i])/[43200*(1-96%)]
日合格时间该变之后,记得把那天的超上限时间和超下限时间也按比例下调,相应日统计数值也得改变。
每一天都调整完之后,回过头来再求和计算月超上限、月超下限时间,并调整对应的月统计比例。
dobear_0922 2008-02-28
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做假帐不就是忽悠人吗? 这个涉及到道德问题,,,
LocoDaiblo 2008-02-27
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太对了,就是要做假帐。

无论日统计还是月统计,最初真实值都是通过采集设备从终端硬件设备上读取到。

读取到的内容有 总时间、合格时间、超上限时间、超下限时间、合格率、超上限率、超下限率。

需求: 人为对 月统计值内容进行更改, 要求软件程序上自动对 日统计值内容进行更改。 并且 最好让修改的结果看起来有点像‘真实的’,也就是最好让人看不出来结果是被改过的。

现在有些客户就是老喜欢有这个所谓的‘调整’功能,没办法只能做,明明是正的,他们一定要弄点歪。
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听不明白,感觉楼主想要做假帐。

合格时间、超上限、超下限时间是怎么回事,日统计值和月统计值怎么来的,先把这些说清才行。
jmulxg 2008-02-27
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需求没分析透彻
LocoDaiblo 2008-02-27
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不是忽悠,是诚心求教!

补充: 一个月的日统计值 要和 月统计值统计结果对应。
dobear_0922 2008-02-27
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晕,楼主想干嘛?忽悠谁呀?
中文名: 算法导论 原名: Introduction to Algorithms 作者: Thomas H.Cormen, 达特茅斯学院计算机科学系副教授 Charles E.Leiserson, 麻省理工学院计算机科学与电气工程系教授 Ronald L.Rivest, 麻省理工学院计算机科学系Andrew与Erna Viterbi具名教授 Clifford Stein, 哥伦比亚大学工业工程与运筹学副教授 资源格式: PDF(完整书签目录) 出版社: The MIT Press ISBN 978-0-262-03384-8 (hardcover : alk. paper)—ISBN 978-0-262-53305-8 (pbk. : alk. paper) 发行时间: 2009年0930 地区: 美国 语言: 英文 1 The Role of Algorithms in Computing 5 1.1 Algorithms 5 1.2 Algorithms as a technology 11 2 Getting Started 16 2.1 Insertion sort 16 2.2 Analyzing algorithms 23 2.3 Designing algorithms 29 3 Growth of Functions 43 3.1 Asymptotic notation 43 3.2 Standard notations and common functions 53 4 Divide-and-Conquer 65 4.1 The maximum-subarray problem 68 4.2 Strassen's algorithm for matrix multiplication 75 4.3 The substitution method for solving recurrences 83 4.4 The recursion-tree method for solving recurrences 88 4.5 The master method for solving recurrences 93 4.6 Proof of the master theorem 97 5 Probabilistic Analysis and Randomized Algorithms 114 5.1 The hiring problem 114 5.2 Indicator random variables 118 5.3 Randomized algorithms 122 5.4 Probabilistic analysis and further uses of indicator random variables 130 II Sorting and Order Statistics Introduction 147 6 Heapsort 151 6.1 Heaps 151 6.2 Maintaining the heap property 154 6.3 Building a heap 156 6.4 The heapsort algorithm 159 6.5 Priority queues 162 7 Quicksort 170 7.1 Description of quicksort 170 7.2 Performance of quicksort 174 7.3 A randomized version of quicksort 179 7.4 Analysis of quicksort 180 8 Sorting in Linear Time 191 8.1 Lower bounds for sorting 191 8.2 Counting sort 194 8.3 Radix sort 197 8.4 Bucket sort 200 9 Medians and Order Statistics 213 9.1 Minimum and maximum 214 9.2 Selection in expected linear time 215 9.3 Selection in worst-case linear time 220 III Data Structures Introduction 229 10 Elementary Data Structures 232 10.1 Stacks and queues 232 10.2 Linked lists 236 10.3 Implementing pointers and objects 241 10.4 Representing rooted trees 246 11 Hash Tables 253 11.1 Direct-address tables 254 11.2 Hash tables 256 11.3 Hash functions 262 11.4 Open addressing 269 11.5 Perfect hashing 277 12 Binary Search Trees 286 12.1 What is a binary search tree? 286 12.2 Querying a binary search tree 289 12.3 Insertion and deletion 294 12.4 Randomly built binary search trees 299 13 Red-Black Trees 308 13.1 Properties of red-black trees 308 13.2 Rotations 312 13.3 Insertion 315 13.4 Deletion 323 14 Augmenting Data Structures 339 14.1 Dynamic order statistics 339 14.2 How to augment a data structure 345 14.3 Interval trees 348 IV Advanced Design and Analysis Techniques Introduction 357 15 Dynamic Programming 359 15.1 Rod cutting 360 15.2 Matrix-chain multiplication 370 15.3 Elements of dynamic programming 378 15.4 Longest common subsequence 390 15.5 Optimal binary search trees 397 16 Greedy Algorithms 414 16.1 An activity-selection problem 415 16.2 Elements of the greedy strategy 423 16.3 Huffman codes 428 16.4 Matroids and greedy methods 437 16.5 A task-scheduling problem as a matroid 443 17 Amortized Analysis 451 17.1 Aggregate analysis 452 17.2 The accounting method 456 17.3 The potential method 459 17.4 Dynamic tables 463 V Advanced Data Structures Introduction 481 18 B-Trees 484 18.1 Definition of B-trees 488 18.2 Basic operations on B-trees 491 18.3 Deleting a key from a B-tree 499 19 Fibonacci Heaps 505 19.1 Structure of Fibonacci heaps 507 19.2 Mergeable-heap operations 510 19.3 Decreasing a key and deleting a node 518 19.4 Bounding the maximum degree 523 20 van Emde Boas Trees 531 20.1 Preliminary approaches 532 20.2 A recursive structure 536 20.3 The van Emde Boas tree 545 21 Data Structures for Disjoint Sets 561 21.1 Disjoint-set operations 561 21.2 Linked-list representation of disjoint sets 564 21.3 Disjoint-set forests 568 21.4 Analysis of union by rank with path compression 573 VI Graph Algorithms Introduction 587 22 Elementary Graph Algorithms 589 22.1 Representations of graphs 589 22.2 Breadth-first search 594 22.3 Depth-first search 603 22.4 Topological sort 612 22.5 Strongly connected components 615 23 Minimum Spanning Trees 624 23.1 Growing a minimum spanning tree 625 23.2 The algorithms of Kruskal and Prim 631 24 Single-Source Shortest Paths 643 24.1 The Bellman-Ford algorithm 651 24.2 Single-source shortest paths in directed acyclic graphs 655 24.3 Dijkstra's algorithm 658 24.4 Difference constraints and shortest paths 664 24.5 Proofs of shortest-paths properties 671 25 All-Pairs Shortest Paths 684 25.1 Shortest paths and matrix multiplication 686 25.2 The Floyd-Warshall algorithm 693 25.3 Johnson's algorithm for sparse graphs 700 26 Maximum Flow 708 26.1 Flow networks 709 26.2 The Ford-Fulkerson method 714 26.3 Maximum bipartite matching 732 26.4 Push-relabel algorithms 736 26.5 The relabel-to-front algorithm 748 VII Selected Topics Introduction 769 27 Multithreaded Algorithms Sample Chapter - Download PDF (317 KB) 772 27.1 The basics of dynamic multithreading 774 27.2 Multithreaded matrix multiplication 792 27.3 Multithreaded merge sort 797 28 Matrix Operations 813 28.1 Solving systems of linear equations 813 28.2 Inverting matrices 827 28.3 Symmetric positive-definite matrices and least-squares approximation 832 29 Linear Programming 843 29.1 Standard and slack forms 850 29.2 Formulating problems as linear programs 859 29.3 The simplex algorithm 864 29.4 Duality 879 29.5 The initial basic feasible solution 886 30 Polynomials and the FFT 898 30.1 Representing polynomials 900 30.2 The DFT and FFT 906 30.3 Efficient FFT implementations 915 31 Number-Theoretic Algorithms 926 31.1 Elementary number-theoretic notions 927 31.2 Greatest common divisor 933 31.3 Modular arithmetic 939 31.4 Solving modular linear equations 946 31.5 The Chinese remainder theorem 950 31.6 Powers of an element 954 31.7 The RSA public-key cryptosystem 958 31.8 Primality testing 965 31.9 Integer factorization 975 32 String Matching 985 32.1 The naive string-matching algorithm 988 32.2 The Rabin-Karp algorithm 990 32.3 String matching with finite automata 995 32.4 The Knuth-Morris-Pratt algorithm 1002 33 Computational Geometry 1014 33.1 Line-segment properties 1015 33.2 Determining whether any pair of segments intersects 1021 33.3 Finding the convex hull 1029 33.4 Finding the closest pair of points 1039 34 NP-Completeness 1048 34.1 Polynomial time 1053 34.2 Polynomial-time verification 1061 34.3 NP-completeness and reducibility 1067 34.4 NP-completeness proofs 1078 34.5 NP-complete problems 1086 35 Approximation Algorithms 1106 35.1 The vertex-cover problem 1108 35.2 The traveling-salesman problem 1111 35.3 The set-covering problem 1117 35.4 Randomization and linear programming 1123 35.5 The subset-sum problem 1128 VIII Appendix: Mathematical Background Introduction 1143 A Summations 1145 A.1 Summation formulas and properties 1145 A.2 Bounding summations 1149 B Sets, Etc. 1158 B.1 Sets 1158 B.2 Relations 1163 B.3 Functions 1166 B.4 Graphs 1168 B.5 Trees 1173 C Counting and Probability 1183 C.1 Counting 1183 C.2 Probability 1189 C.3 Discrete random variables 1196 C.4 The geometric and binomial distributions 1201 C.5 The tails of the binomial distribution 1208 D Matrices 1217 D.1 Matrices and matrix operations 1217 D.2 Basic matrix properties 122

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