CN113917905A - Comprehensive analysis method for booster station auxiliary control system - Google Patents
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- G05B23/00—Testing or monitoring of control systems or parts thereof
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- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
The invention discloses a comprehensive analysis method of a booster station auxiliary control system, which comprises the steps of discretization based on entropy; scanning the transaction database, sequencing after obtaining the support count of each item set, and generating a tree according to each scanned transaction; and mining strong association rules according to the generated trees. The invention mainly researches a multi-index association analysis and prediction algorithm and application, provides a new parallel mining algorithm and an increment mining algorithm based on a dictionary tree, and improves the existing common data discretization method so as to discretize each continuous index data by clustering based on an entropy distribution function.
Description
Technical Field
The invention relates to the technical field of booster station auxiliary control systems, in particular to a comprehensive analysis method of a booster station auxiliary control system.
Background
The key of the comprehensive analysis of the booster station auxiliary control system is the correlation analysis and early warning of various indexes: because new business data are continuously added into the original data set, the result obtained by mining the original data set must be considered; the existing method needs to rescan the original data set under the worst condition, and the efficiency is low.
In order to perform multi-index correlation analysis and prediction, continuous data needs to be discretized, and the most common continuous data discretization method at present is complex and difficult to perform on a single machine.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the technical problem solved by the invention is as follows: how to carry out effective comprehensive analysis to booster station auxiliary control system.
In order to solve the technical problems, the invention provides the following technical scheme: including, entropy-based discretization; scanning the transaction database, sequencing after obtaining the support count of each item set, and generating a tree according to each scanned transaction; and mining strong association rules according to the generated trees.
As a preferable scheme of the comprehensive analysis method of the booster station auxiliary control system, the method comprises the following steps: the information entropy gain calculation formula comprises,
wherein S isiThe entropy function Ent is calculated according to the data distribution conditions of different classes in the given data set, and T is a potential boundary of the S set.
As a preferable scheme of the comprehensive analysis method of the booster station auxiliary control system, the method comprises the following steps: given n different classes, the entropy of S includes,
wherein p is obtained by dividing the number of rows of I in the S category by the total number of data rows in S, and each obtained boundary value can be divided until a stop condition Ent (S) -I (S,1) > o is satisfied, so that the division of the boundary is more favorable for improving the precision of the classification mining result.
As a preferable scheme of the comprehensive analysis method of the booster station auxiliary control system, the method comprises the following steps: scanning the transaction database to obtain the support count of each item set; the counts of all of the sets include { I1,6}, { I2,7} { I3,6}, { I4,2}, { I5,2 }.
As a preferable scheme of the comprehensive analysis method of the booster station auxiliary control system, the method comprises the following steps: further comprising, sorting by support count; each transaction of the transaction database is scanned to generate a tree.
As a preferable scheme of the comprehensive analysis method of the booster station auxiliary control system, the method comprises the following steps: the mining strong association rule comprises the steps of starting from each frequent pattern with the length of 1, and constructing a conditional pattern base of the frequent pattern; and constructing the FP tree and recursively mining the FP tree.
As a preferable scheme of the comprehensive analysis method of the booster station auxiliary control system, the method comprises the following steps: also included is that pattern growth is achieved through frequent pattern concatenation of postfix patterns with conditional FP-trees.
As a preferable scheme of the comprehensive analysis method of the booster station auxiliary control system, the method comprises the following steps: including that for a given set of data rows S, each value in attribute A can be considered as a potential boundary of an interval or a value T, and if a value v in attribute A divides data S into two parts and satisfies A < vLA ≧ v, respectively, then for data S, a threshold will be selected based on the maximum informative entropy gain obtained for the divided subset.
The invention has the beneficial effects that: the invention mainly researches a multi-index association analysis and prediction algorithm and application, provides a new parallel mining algorithm and an increment mining algorithm based on a dictionary tree, and improves the existing common data discretization method so as to discretize each continuous index data by clustering based on an entropy distribution function.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic sequence diagram of a comprehensive analysis method of a booster station auxiliary control system according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a spanning tree of each transaction of the comprehensive analysis method of the booster station auxiliary control system according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
The invention aims to provide a certain amount of data for supporting scientific management decision, people cannot express potential rules contained in the data by using explicit methods such as mathematical formulas and the like, and some rules become more and more strongly associated rules along with the increase of business data, so that the potential rules are effectively mined, and the method has important significance for scientific decision and production and management guidance.
Because the auxiliary control indexes usually comprise a plurality of indexes, and some potential association rules exist among the indexes, multi-index association analysis is required to be carried out, and meanwhile, according to the known multi-index association rules, a group company can make an effective index plan by adjusting the combination of input indexes, so that the optimization of output indexes (target indexes), namely index prediction, is achieved.
Referring to fig. 1 and fig. 2, a first embodiment of the present invention provides a comprehensive analysis method for a booster station auxiliary control system, which specifically includes:
s1: entropy-based discretization.
S2: and scanning the transaction database, sequencing after obtaining the support count of each item set, and generating a tree according to each scanned transaction.
S3: and mining strong association rules according to the generated trees.
For a given set of data rows S, each value in attribute A can be considered as an interval potential boundary or value T, and if one value v in attribute A bisects data S and satisfies A < vLA ≧ v, respectively, then for data S, a threshold will be chosen based on the maximum informative entropy obtained for the divided subset.
Specifically, the information entropy gain calculation formula includes:
wherein S isiThe entropy function Ent is calculated according to the data distribution conditions of different classes in the given data set, and T is a potential boundary of the S set.
Further, given n different classes, the entropy of S includes:
wherein p is obtained by dividing the number of rows of I in the S category by the total number of data rows in S, and each obtained boundary value can be divided until a stop condition Ent (S) -I (S,1) > o is satisfied, so that the division of the boundary is more favorable for improving the precision of the classification mining result.
Scanning the transaction database to obtain the support count of each item set;
the counts of all sets include, { I1,6}, { I2,7}, { I3,6}, { I4,2}, { I5,2 };
sorting according to the support count;
each transaction of the transaction database is scanned to generate a tree.
Preferably, mining the strong association rule comprises:
constructing a conditional mode base of each frequent mode with the length of 1;
constructing an FP tree and recursively excavating the FP tree;
pattern growth is achieved by frequent pattern concatenation of postfix patterns with conditional FP-trees.
Example 2
The present embodiment is a second embodiment of the present invention, and is different from the first embodiment in that the present embodiment provides an association rule mining verification, which specifically includes:
before excavation, the following explanation and definition are carried out: starting with each frequent pattern of length 1 (the initial suffix pattern), its conditional pattern base (a "sub-database" consisting of a set of prefix paths where FP-trees appear together with suffixes) is constructed, then its (conditional) FP-tree is constructed and mined recursively, pattern growing being achieved by the suffix pattern concatenation with the frequent pattern concatenation produced by the conditional FP-tree.
Considering I5 — the last item in L, but not the first, I5 appears at the leaf node at the deepest level of the tree, and there are two leaf nodes, listing these two paths { I2, I1, I5: 1}, { I2, I1, I3, I5: 1, it is clear that the prefix path of I5 is { I2, I1: 1}, { I2, I1, I3: 1, forming the conditional pattern base of I5, and its conditional FP tree apparently contains only { I2, I1:2} (I3 only occurs once, less than the minimum support count of 2).
Thus, this path produces all combinations of frequent patterns: { I2, I5:2}, { I1, I5:2}, { I2, I1, I5:2} then all the frequent patterns generated based on the conditional patterns of I5 were found, and the same method was used for the iterations of I4, I3, I1, resulting in the following table:
table 1: and (5) a relation table.
The association rules are as follows:
in practice, association rule mining is carried out on a plurality of index data of the information system of the large north mountain auxiliary control system, and practice shows that the multi-index association analysis and prediction of the information system of the large north mountain auxiliary control system is an effective and feasible algorithm.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (8)
1. A comprehensive analysis method of a booster station auxiliary control system is characterized by comprising the following steps: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
entropy-based discretization;
scanning the transaction database, sequencing after obtaining the support count of each item set, and generating a tree according to each scanned transaction;
and mining strong association rules according to the generated trees.
2. The comprehensive analysis method of the booster station auxiliary control system according to claim 1, characterized in that: the information entropy gain calculation formula comprises,
wherein S isiThe entropy function Ent is calculated according to the data distribution conditions of different classes in the given data set, and T is a potential boundary of the S set.
3. The comprehensive analysis method of the booster station auxiliary control system according to claim 2, characterized in that: given n different classes, the entropy of S includes,
wherein p is obtained by dividing the number of rows of I in the S category by the total number of data rows in S, and each obtained boundary value can be divided until a stop condition Ent (S) -I (S,1) > o is satisfied, so that the division of the boundary is more favorable for improving the precision of the classification mining result.
4. The comprehensive analysis method of the booster station auxiliary control system according to claim 2 or 3, characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
scanning the transaction database to obtain the support count of each item set;
the counts of all of the sets include { I1,6}, { I2,7} { I3,6}, { I4,2}, { I5,2 }.
5. The comprehensive analysis method of the booster station auxiliary control system according to claim 4, characterized in that: also comprises the following steps of (1) preparing,
sorting according to the support count;
each transaction of the transaction database is scanned to generate a tree.
6. The comprehensive analysis method of the booster station auxiliary control system according to claim 5, characterized in that: the mining strong association rule comprises that,
constructing a conditional mode base of each frequent mode with the length of 1;
and constructing the FP tree and recursively mining the FP tree.
7. The comprehensive analysis method of the booster station auxiliary control system according to claim 6, characterized in that: also included is that pattern growth is achieved through frequent pattern concatenation of postfix patterns with conditional FP-trees.
8. The comprehensive analysis method of the booster station auxiliary control system according to claim 7, characterized in that: including that for a given set of data rows S, each value in attribute A can be considered as a potential boundary of an interval or a value T, and if a value v in attribute A divides data S into two parts and satisfies A < vLA ≧ v, respectively, then for data S, a threshold will be selected based on the maximum informative entropy gain obtained for the divided subset.
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