CN114493077A - Effectiveness evaluation method for metering standard device in power industry - Google Patents
Effectiveness evaluation method for metering standard device in power industry Download PDFInfo
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- 238000007689 inspection Methods 0.000 claims description 6
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Abstract
The invention discloses an effectiveness evaluation method for a metering standard device in the power industry, which relates to the field of metering in the power industry and aims at providing a conformity judgment for a calibrated metering standard device according to the requirement of a user of the metering standard device; the calibration result has the problem of influence factors of measurement uncertainty on judgment reliability, and the like, and the following scheme is proposed, wherein the method comprises the following steps of S1, constructing an effectiveness evaluation index system of the metering standard device in the power industry; s2, determining an evaluation index; s3: and determining the weight of the index system. The method comprises the determination of an effectiveness evaluation index system of the power industry metering standard device, the determination method of the weight of the index system and the effectiveness evaluation method of the power industry metering standard device, determines an evaluation model according to an evaluation object, and solves the problem that the effectiveness of the power industry metering standard device is difficult to evaluate.
Description
Technical Field
The invention relates to the field of electric power industry metering, in particular to an effectiveness evaluation method for a metering standard device in the electric power industry.
Background
Two main problems that exist of validity of present electric power industry measurement standard device: the user of the metering standard device requests to give a conformity judgment to the calibrated metering standard device; the calibration result has influence factors of measurement uncertainty on judgment reliability, an evaluation technology for determining the effectiveness of the metering standard device based on theoretical research results is researched and compared with the existing evaluation and evaluation technologies, a metering device calibration result evaluation system which accords with the characteristics of the southern power grid is established, the effectiveness of the calibration result of various metering devices can be properly evaluated from multiple angles and multiple dimensions, and the effectiveness evaluation method of the metering standard device in the power industry is provided aiming at the classification of science and technology projects of the southern power grid and each stage of the whole research process by combining the characteristics of the power industry.
Disclosure of Invention
Objects of the invention
In order to solve the technical problems in the background art, the invention provides an effectiveness evaluation method of a metering standard device in the power industry, which comprises the steps of determining an effectiveness evaluation index system of the metering standard device in the power industry, determining a weight of the index system and determining the effectiveness evaluation method of the metering standard device in the power industry, wherein the effectiveness of the calibrated metering standard device is accurately judged, the influence of measurement uncertainty on the judgment of the effectiveness of the metering standard device is avoided, an evaluation model is determined according to an evaluation object, and the problem that the effectiveness of the metering standard device in the power industry is difficult to evaluate is solved.
(II) technical scheme
The invention provides an effectiveness evaluation method for a metering standard device in the power industry, which comprises the following steps:
s1, constructing an effectiveness evaluation index system of the electric power industry metering standard device;
s2, determining an evaluation index;
s3: and determining the weight of the index system.
As a preferable scheme of the invention, the effectiveness evaluation index system of the electric power industry metering standard device is divided into a target layer, a criterion layer and an index layer, wherein Kij is a subdivision index under the criterion layer, and Kij, n is a subdivision index under the index layer.
As a preferred scheme of the present invention, the determination of the evaluation index is divided into a quantity value tracing and calibration result, and the quantity value tracing and calibration result is further divided into stability, repeatability, device inspection qualification rate and spot inspection record qualification rate.
As a preferable aspect of the present invention, the establishing of the index system weight includes an association rule mining algorithm, where the association rule mining algorithm includes a concept of an association rule, a classification of an association rule, an idea of an Apriori algorithm, and a flow of the Apriori algorithm.
As a preferred scheme of the present invention, the association rule mining is divided into the following steps:
s1: finding frequent item sets and needing to satisfy Supportcount (A)
minsup；
S2: generating strong association rules from frequent item sets, required to satisfy Supportcount (A)
Minsup, and Confidence (A)
mincon。
As a preferred aspect of the present invention, the association rule is classified into: boolean association rule and Numerical association rule; the association rules are classified into Single dimension association rules and Multidimensional association rules according to the dimensionality of the data; the association rules are classified according to the abstract level of the data as: the Single-Level association rule is associated with a Multilevel association rule.
As a preferable aspect of the present invention, 7, the Apriori algorithm includes:
s1: let I be a set I of m items I { I1, I2, …, im }, and let the task-related data set D be a set of database transactions, where each transaction T is a set of items, such that each transaction has a unique identifier TID;
s2: if a is an item set, a transaction T contains a, and if and only if the association rule is an implication in the form of a ═ B [ s, c ], the rule a ═ B stands in a transaction set D, s represents the Support (Support) of the association rule, represents that the data set D contains the intersection of a and B at the same time, and is min sup that the association rule specified by the user must satisfy; c represents the Confidence (Confidence) of the Association Rule, represents the percentage of the data set D containing the transaction A and B, and is min con which the Association Rule specified by the user must satisfy.
As a preferred scheme of the invention, the formulas of min sup and min con are as follows:
Support(A＝＞B)＝P(A∪B)
the rule meeting the min sup threshold and the min con threshold at the same time is called a strong rule, the Item set is called an Item set (Item set), the set containing k items is called a k Item set, the frequency of the Item set is the number of transactions containing the Item set, the frequency, the support count or the count of the Item set are short, for I and D, all Item sets meeting the min sup specified by the user in T, namely the non-empty subset of I of Configence (A) not less than min con, is called a frequent Item set or a large Item set, and all frequent Item sets not contained by other elements are selected from the frequent Item set and called a maximum frequent Item set.
Compared with the prior art, the technical scheme of the invention has the following beneficial technical effects:
the method comprises the determination of an effectiveness evaluation index system of the electric power industry metering standard device, the determination method of the weight of the index system and the effectiveness evaluation method of the electric power industry metering standard device, the accurate judgment of effectiveness is given to the calibrated metering standard device, the influence of measurement uncertainty on the judgment of the effectiveness of the metering standard device is avoided, an evaluation model is determined according to an evaluation object, and the problem that the effectiveness of the electric power industry metering standard device is difficult to evaluate is solved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic structural diagram of an analysis model of an effectiveness evaluation process of a metering standard device in the power industry according to the effectiveness evaluation method of the metering standard device in the power industry provided by the invention.
Fig. 2 is a schematic structural diagram of an effectiveness verification index of the electric power industry metering standard device in the effectiveness evaluation method of the electric power industry metering standard device provided by the invention.
Detailed Description
The following description is merely exemplary in nature and is not intended to limit the present disclosure, application, or uses. It should be understood that throughout the drawings, identical or similar reference numerals indicate identical or similar parts and features. The drawings are only schematic representations of the concepts and principles of the embodiments of the present disclosure, and do not necessarily show specific dimensions and proportions of the various embodiments of the disclosure. Certain features that are in certain figures may be used in an exaggerated manner to illustrate relevant details or structures of embodiments of the present disclosure.
The first embodiment is as follows: referring to fig. 1-2, the method for evaluating the effectiveness of the metering standard device in the power industry comprises the following steps: s1, constructing an effectiveness evaluation index system of the electric power industry metering standard device; s2, determining an evaluation index; s3: and determining the weight of an index system, wherein the effectiveness evaluation index system of the electric power industry metering standard device is divided into a target layer, a standard layer and an index layer, Kij is a subdivision index under the standard layer, Kij, n is a subdivision index under the index layer, the determination of the evaluation index is divided into a quantity value traceability and calibration result, and the quantity value traceability and calibration result is subdivided into stability, repeatability, device inspection qualification rate and sampling inspection record qualification rate.
The second embodiment: referring to fig. 1-2, on the basis of the first embodiment, the method for evaluating the effectiveness of the power industry metering standard device includes an association rule mining algorithm, where the association rule mining algorithm includes concepts of association rules, classifications of association rules, ideas of Apriori algorithm, and flows of Apriori algorithm, and the association rule mining is divided into the following steps:
s1: finding frequent item sets and needing to satisfy Supportcount (A)
minsup；
S2: generating strong association rules from frequent item sets, required to satisfy Supportcount (A)
minsup, and Confidence (A)
mincon。
The association rules are classified according to their variable types as: boolean association rule and Numerical association rule; the association rules are classified into Single dimension association rules and Multidimensional association rules according to the dimensionality of the data; the association rules are classified according to the abstraction level of the data into: Single-Level association rules are associated with Multilevel association rules.
Example three: referring to fig. 1-2, on the basis of the second embodiment, the method for evaluating the effectiveness of the electric power industry metering standard device includes:
s1: let I be a set of m items I ═ I1, I2, …, im, and let the task-related dataset D be a set of database transactions, where each transaction T is a set of items, with each transaction having a unique identifier TID;
s2: if a is an item set, a transaction T contains a, and if and only if the association rule is an implication in the form of a ═ B [ s, c ], the rule a ═ B stands in a transaction set D, s represents the Support (Support) of the association rule, represents that the data set D contains the intersection of a and B at the same time, and is min sup that the association rule specified by the user must satisfy; c represents the Confidence (Confidence) of the Association Rule, represents the percentage of the data set D containing the transaction A and B, and is min con which the Association Rule specified by the user must satisfy. The formulas of min sup and min con are as follows:
Support(A＝＞B)＝P(A∪B)
the rule meeting the min sup threshold and the min con threshold at the same time is called a strong rule, the Item set is called an Item set (Item set), the set containing k items is called a k Item set, the frequency of the Item set is the number of transactions containing the Item set, the frequency, the support count or the count of the Item set are short, for I and D, all Item sets meeting the min sup specified by the user in T, namely the non-empty subset of I of Confi (A) being greater than or equal to min con, is called a frequent Item set or a large Item set, and all frequent Item sets not contained by other elements are selected from the frequent Item set and called a maximum frequent Item set.
The working principle and the concrete explanation of the invention are as follows:
firstly, evaluating an index system architecture model according to the effectiveness of a scientific standard device, starting from a measuring standard device effectiveness evaluation method, preliminarily determining an evaluation index set by taking the effectiveness of the measuring standard device as an index of a target layer, evaluating the index system architecture model according to the effectiveness of the index quantity standard device, wherein the whole index system consists of the target layer, a standard layer and an index layer (shown in figure 1), the target layer is a benefit index investigation factor, the standard layer comprises two evaluation factors of economic benefit and social benefit, the index layer comprises 21 specific evaluation indexes, and the scientific and technological achievement benefit evaluation index system (shown in figure 1);
based on the concept of association rules, setting a rule which simultaneously meets a min sup threshold and a min con threshold as a strong rule, setting a set of items as an Item set (Item set), setting a set containing k items as a k Item set, setting the frequency of the Item set as the number of transactions containing the Item set, namely the frequency, support count or count of the Item set, and selecting all frequent Item sets which are not contained by other elements in the frequent Item sets as a maximum frequent Item set for all Item sets which meet the min sup specified by a user, namely a non-empty subset of I of Confit (A) which is not less than min con, namely a frequent Item set or a large Item set for I and D;
and the design quantity event is I, and set I as a set I consisting of m items { I1, I2, …, im }, and set a data set D related to the task as a set of database transactions, wherein each transaction T is a set of items, each transaction is enabled to have a unique identifier TID, A is an item set, the transaction T contains A, and if and only if the association rule is an implication in the form of A ═ B [ s, c ], the rule A ═ B is established in the transaction set D, s represents the Support degree (Support) of the association rule, represents that the data set D simultaneously contains the intersection of A and B, and is min sup which must be satisfied by the association rule specified by the user; c represents the Confidence (Confidence) of the Association Rule, represents the percentage of the data set D containing the transaction A and B, is min con which the Association Rule specified by the user must satisfy, and is based on the formula
Support(A＝＞B)＝P(A∪B)
Calculating a min sup threshold and a min con threshold, then acquiring a frequent item set by using an iteration mode of priori knowledge layer-by-layer search, calculating a (k +1) -th item set from a k-th item set, firstly finding out a frequent 1-item set, marking as L1, then generating a candidate item set C2 by using L1, then calculating a 2-item set L2, and so on, repeating and iterating for multiple times until no more frequent k-item sets Lk can be found, and secondly, satisfying the supportcount (A) for the frequent item set
Minsup, a strong association rule is generated from the frequent item set. Need to satisfy Supportcount (A)
Minsup, and Confidence (A)
mincon。
Finally, it should be noted that: the above examples are only used to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. The effectiveness evaluation method of the metering standard device in the power industry is characterized by comprising the following steps:
s1, constructing an effectiveness evaluation index system of the electric power industry metering standard device;
s2, determining an evaluation index;
s3: and determining the weight of the index system.
2. The effectiveness evaluation method of the power industry metering standard device according to claim 1, wherein the effectiveness evaluation index system of the power industry metering standard device is divided into a target layer, a criterion layer and an index layer, wherein Kij is a subdivision index under the criterion layer, and Kij, n is a subdivision index under the index layer.
3. The electric power industry measurement standard device effectiveness evaluation method according to claim 1, wherein the determination of the evaluation index is divided into a magnitude tracing and a calibration result;
preferably, the value tracing and calibration result is further subdivided into stability, repeatability, device inspection qualification rate and sampling inspection record qualification rate.
4. The electric power industry metering standard device effectiveness evaluation method of claim 1 wherein the establishment of the index system weights includes an association rule mining algorithm,
the association rule mining algorithm comprises concepts of association rules, classification of association rules, ideas of an Apriori algorithm and a process of the Apriori algorithm.
5. The electric power industry metering standard device effectiveness evaluation method according to claim 4, wherein the association rule mining is divided into the following steps:
s1: finding out a frequent item set which needs to meet the requirement of supportcount (A) </sup;
s2: a strong association rule is generated by a frequent item set, and needs to meet the Supportcount (A) < minsup > and Confidence (A) < mincon >.
6. The electric power industry metering standard device effectiveness evaluation method according to claim 4, wherein the association rules are classified according to their variable types as: boolean association rule and Numerical association rule; the association rules are classified into Single dimension association rules and Multidimensional association rules according to the dimensionality of the data; the association rules are classified according to the abstract level of the data as: Single-Level association rules are associated with Multilevel association rules.
7. The electric power industry metering standard device effectiveness evaluation method according to claim 4, wherein the flow of the Apriori algorithm comprises:
s1: let I be a set of m items I ═ I1, I2, …, im, and let the task-related dataset D be a set of database transactions, where each transaction T is a set of items, with each transaction having a unique identifier TID;
s2: if a is an item set, a transaction T contains a, and if and only if the association rule is an implication in the form of a ═ B [ s, c ], the rule a ═ B stands in a transaction set D, s represents the Support (Support) of the association rule, represents that the data set D contains the intersection of a and B at the same time, and is min sup that the association rule specified by the user must satisfy; c represents the Confidence (Confidence) of the Association Rule, represents the percentage of the data set D containing the transaction A and B, and is min con which the Association Rule specified by the user must satisfy.
8. The electric power industry metering standard device effectiveness evaluation method according to claim 7, wherein the formulas of min sup and min con are as follows:
Support(A＝＞B)＝P(A∪B)
preferably, the rule satisfying both the min sup threshold and the min con threshold is called a strong rule, the set of items is called an Item set (Item set), the set containing k items is called a k Item set, the frequency of occurrence of an Item set refers to the number of transactions including an Item set, which is referred to as the frequency, support count or count of an Item set for short, for all Item sets satisfying the min sup specified by the user in I and D, i.e., the non-empty subset of I of confidence (a) ≧ min con is called a frequent Item set or a large Item set, and all frequent Item sets not included by other elements are selected in the frequent Item set and called a maximum frequent Item set.
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| CN111639237A (en) * | 2020-04-07 | 2020-09-08 | 安徽理工大学 | Electric power communication network risk assessment system based on clustering and association rule mining |
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- 2021-11-05 CN CN202111304846.0A patent/CN114493077A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102479351A (en) * | 2010-11-25 | 2012-05-30 | 西安计量技术研究院 | Method for intelligently managing newly established measurement standard and system thereof |
| CN106408436A (en) * | 2016-09-18 | 2017-02-15 | 国网福建省电力有限公司 | Fuzzy comprehensive evaluation method for voltage sag loss risk of wafer manufacturing enterprise |
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