CN109447490A - A kind of family change relationship anomalous discrimination method based on station address - Google Patents

A kind of family change relationship anomalous discrimination method based on station address Download PDF

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CN109447490A
CN109447490A CN201811307364.9A CN201811307364A CN109447490A CN 109447490 A CN109447490 A CN 109447490A CN 201811307364 A CN201811307364 A CN 201811307364A CN 109447490 A CN109447490 A CN 109447490A
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CN109447490B (en
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曾嵘
邵航建
傅航天
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Hangzhou Zhicheng Electronic Technology Co Ltd
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Abstract

Present invention mainly discloses a kind of, and the family based on station address becomes relationship anomalous discrimination method, comprising the following steps: input subscriber data set T selects validated user data acquisition system T, with selected k1A address code constructs k as big address1Layer decision tree, every layer of decision tree contain k2A subscriber data set traverses k1Each subscriber data set in layer decision tree, if the number of users in subscriber data set is less than l1, then the user in subscriber data set is judged as that family becomes suspicion user, to remaining TUA subscriber data set handles to obtain P cluster set { Q1,Q2,...,Qp, traverse p cluster set QkIf clustering set QkInterior number of users is less than l2, then cluster set Q is exportedkInterior all users are that family becomes suspicion user.The present invention has the characteristics of saving time, manpower and fund cost, improving input-output ratio.

Description

A kind of family change relationship anomalous discrimination method based on station address
Technical field
The present invention relates to station address information acquiring technology field, it is different that especially a kind of family based on station address becomes relationship Normal method of discrimination.
Background technique
With the fast development of power grid, administration of power networks is changed into lean from original rough formula.In recent years due to city The problem of fast development and history administration of power networks are left, there are still need improvements for power distribution network management.Wherein, family, which becomes, closes System is affected to power distribution network management, influences to include that power off notifying is a large amount of to family, platform area same period line loss, equipment management, industry expansion etc. Sales service.Family becomes the belonging relation that relationship refers to Electricity customers and distribution transformer.The recognition methods of family change relationship mistake Predominantly artificial scene differentiates or installs hardware additional by batch and differentiate, is both needed to devote a tremendous amount of time and fund cost.Currently, each Power supply enterprise, which has been carried out, repeatedly concentrates artificial investigation work, and family becomes relationship accuracy and has reached higher level.In view of remaining Family become that relationship abnormal user quantity is relatively few and electricity consumption behavior is more covert, reuse artificial or arranged by additional equipment Look into, need to devote a tremendous amount of time, manpower and fund cost, input-output ratio it is extremely low.
Summary of the invention
In view of the deficienciess of the prior art, the present invention provides a kind of family change relationship anomalous discrimination side based on station address The characteristics of method has and saves time, manpower and fund cost, improves input-output ratio.
In order to achieve the above object, the present invention is achieved through the following technical solutions: a kind of family change based on station address Relationship anomalous discrimination method, comprising the following steps:
1) user's set T is inputted, user's set T includes n user { H1,H2,...,Hn, each user includes 7 addresses Code；
2) validated user set T is selected, to the user H of address missing and address exceptioniIt is handled；
3) with selected k1A address code constructs k as big address1Layer decision tree, wherein 1≤k1≤7；Then every layer of decision tree Contain k2A user gathers { T1,T2,...,Tk2, each user's set TjIn include k3A user Tj={ H1,H2,...,Hk3, Wherein 1≤j≤k2, 1≤k3≤n；
4) with user's set TjAs the node set of decision tree, definition node threshold value is l1, traverse k1It is every in layer decision tree Number of users k in a node set3If k3< l1, then user's set T is exportedjIn all users be all family become suspicion user And delete user；Otherwise, user's set TjIn all users be all normal users, be not processed；
5) it is left TuA user's set, each user contain 7-k1A address code carries out Z-score normalized；
6) defining minimum classification number is p, and cluster set threshold value is l2；
7) selection weighted minimum distance carries out Hierarchical clustering analysis, obtains P cluster set { Q1,Q2,...,Qp}；
8) p cluster set Q is traversedkIf clustering set QkInterior number of users is less than l2, then cluster set Q is exportedkIt is interior All users be family become suspicion user；Otherwise, set Q is clusteredkInterior all users are normal users, are not processed；
9) step 4 and step 8 are combined, all families is exported and becomes suspicion user.
The present invention is further arranged to: in the step 1,7 address codes include province code, city code, district code, street code, Neighbourhood committee's code, road code and cell code.
The present invention is further arranged to: in shown step 2, selecting the specific processing method of validated user set T are as follows: if with Amount amount n is greater than 10000, then directly rejects to the user of address code information missing or address code exception in user's set T；If Number of users n is less than 10000, the user of address code information missing, if as neighbouring two users sector address code, With sector address code supplement, the user of address code exception is rejected；If neighbouring two users sector address code is different, If seeing in the same electricity box user code, perhaps user code electricity box or access point only should in the same access point User then rejects, if there are also other users for electricity box or access point, with the user of these identical electricity box or access point Location is supplemented.
The present invention is further arranged to: in the step 3, every layer of decision tree contains the quantity k of user's set2It is different.
The present invention is further arranged to: in the step 5, the calculation formula of Z-score normalized are as follows:Wherein σ is that data standard is poor, and μ is sample mean, and x refers to user code, and normalized makes difference The data of magnitude are uniformly converted into the same magnitude.
The present invention is further arranged to: in the step 7, P cluster gathers [Q1,Q2,...,Qp] specific algorithm are as follows: Weighted input vector is [W1,W2,….,Wm‐k1], Hierarchical clustering analysis is carried out, each user is one kind, every time to any two Class carries out minimum distance calculation, and two nearest classes of distance are classified as one kind, and termination when clustering number and being reduced to P obtains P A cluster gathers [Q1,Q2,...,Qp]。
The present invention is further arranged to: the calculation of any two classes minimum range uses Euclidean distance calculation.
The present invention have the beneficial effect that because power supply station fill transformer, a series of power supply units of electricity box etc. be all by It is allocated management according to station address, so station address includes certain regularity.Utilize areal station address Individual abnormal users are accurately identified, thus judge that family becomes relationship, it is rapid for big address (such as cities and counties' code) abnormal response, simultaneously Data acquisition is simple, and algorithm frame is simple, and state modulator is less, effectively improves efficiency of algorithm, accelerates inquiry velocity, reduces error Rate saves time and fund cost, improves input-output ratio.
Detailed description of the invention
Fig. 1 is flow chart of the invention.
Specific embodiment
In conjunction with attached drawing, present pre-ferred embodiments are described in further details.
As shown in Figure 1, a kind of family based on station address becomes relationship anomalous discrimination method, comprising the following steps:
1) user's set T is inputted, user's set T includes n user { H1,H2,...,Hn, each user includes 7 addresses Code；7 address codes include province's code, city's code, district code, street code, neighbourhood committee's code, road code and cell code；
2) validated user set T is selected, to the user H of address missing and address exceptioniIt is handled；Select validated user The specific processing method of set T are as follows: if number of users n be greater than 10000, in user's set T address code information lack or The user of address code exception directly rejects；If number of users n is less than 10000, the user of address code information missing, if phase up and down As adjacent two users sector address code, then with sector address code supplement, the user of address code exception is rejected；If neighbouring Two user sector address codes are different, then with seeing in the same electricity box in user code or the same access point user Location code, if perhaps rejecting electricity box or access point if access point only has the user if electricity box, there are also other users, with this The station address of a little identical electricity box or access point is supplemented；
3) with selected k1A address code constructs k as big address1Layer decision tree, wherein 1≤k1≤7；Then every layer of decision tree Contain k2A user gathers { T1,T2,...,Tk2, each user's set TjIn include k3A user Tj={ H1,H2,...,Hk3, Wherein 1≤j≤k2, 1≤k3≤n；Every layer of decision tree contains the quantity k of user's set2It is different；For example, with selected province's code, City code, street code three address codes as big address construct three layers of decision tree, first layer save code have 3301,3302,3303, then for Three classes, the user that first layer decision tree contains gather sum k2It is 3, three user's set are respectively T1, T2, T3, each user's collection Close the number of users that contains all with l1Compare.
4) with user's set TjAs the node set of decision tree, definition node threshold value is l1, (l1According to live practical hair Raw situation selection) traversal k1Number of users k in layer decision tree in each node set3If k3< l1, then user's set T is exportedj In all users be all family become suspicion user and delete user；Otherwise, user's set TjIn all users be all positive it is common Family is not processed；
5) it is left TuA user's set, each user contain 7-k1A address code carries out Z-score normalized, calculates Formula are as follows:Wherein σ is that data standard is poor, and μ is sample mean, and x refers to user code, normalized So that different magnitude of data are uniformly converted into the same magnitude；
6) defining minimum classification number is p, and cluster set threshold value is l2；(l2Situation selection is actually occurred according to scene with p)
7) selection weighted minimum distance carries out Hierarchical clustering analysis, and weighted input vector is [W1,W2,….,Wm‐k1], (add Weight vector selection is determined by live actual state, such as [9,7,5,3,1]) Hierarchical clustering analysis is carried out, each user is one Class, carries out minimum distance calculation to any two class every time, and calculation uses Euclidean distance calculation, such as two class Q1 (x11,x12,…,x1n) and Q2(x21,x22,…,x2n) between Euclidean distance, calculation formula are as follows:Wherein X indicates user code；Two nearest classes of distance are classified as one kind, termination when clustering number and being reduced to P obtains P Cluster set { Q1,Q2,...,Qp}；
8) p cluster set Q is traversedkIf clustering set QkInterior number of users is less than l2, then cluster set Q is exportedkIt is interior All users be family become suspicion user；Otherwise, set Q is clusteredkInterior all users are normal users, are not processed；
9) step 4 and step 8 are combined, all families is exported and becomes suspicion user.
Above-described embodiment is only used for illustrating inventive concept of the invention, rather than the restriction to rights protection of the present invention, It is all to be made a non-material change to the present invention using this design, protection scope of the present invention should all be fallen into.

Claims (7)

1. a kind of family based on station address becomes relationship anomalous discrimination method, it is characterised in that: the following steps are included:
1) user's set T is inputted, user's set T includes n user { H1,H2,...,Hn, each user includes 7 address codes；
2) effective user's set T is selected, to the user H of address missing and address exceptioniIt is handled；
3) with selected k1A address code constructs k as big address1Layer decision tree, wherein 1≤k1≤7；Then every layer of decision tree contains k2A user gathers { T1,T2,...,Tk2, each user's set TjIn include k3A user Tj={ H1,H2,...,Hk3, wherein 1 ≤j≤k2, 1≤k3≤n；
4) with user's set TjAs the node set of decision tree, definition node threshold value is l1, traverse k1Each section in layer decision tree Number of users k in point set3If k3< l1, then user's set T is exportedjIn all users be all that family becomes and suspicion user and deletes Except user；Otherwise, user's set TjIn all users be all normal users, be not processed；
5) it is left TuA user's set, each user contain 7-k1A address code, by TuA user, which gathers, carries out Z-score normalizing Change processing；
6) defining minimum classification number is p, and cluster set threshold value is l2；
7) selection weighted minimum distance carries out Hierarchical clustering analysis, obtains P cluster set { Q1,Q2,...,Qp}；
8) p cluster set Q is traversedkIf clustering set QkInterior number of users is less than l2, then cluster set Q is exportedkInterior is all User is that family becomes suspicion user；Otherwise, set Q is clusteredkInterior all users are normal users, are not processed；
9) step 4 and step 8 are combined, all families is exported and becomes suspicion user.
2. a kind of family based on station address according to claim 1 becomes relationship anomalous discrimination method, it is characterised in that: institute It states in step 1,7 address codes include province's code, city's code, district code, street code, neighbourhood committee's code, road code and cell code.
3. a kind of family based on station address according to claim 1 becomes relationship anomalous discrimination method, it is characterised in that: institute Show in step 2, select the specific processing method of validated user set T are as follows: if number of users n is greater than 10000, user is gathered The user of address code information missing or address code exception directly rejects in T；If number of users n is less than 10000, address code information The user of missing, if supplemented as neighbouring two users sector address code with the sector address code, address code exception User rejects；If neighbouring two users sector address code is different, user code in the same electricity box is seen, or User code in the same access point, if perhaps rejecting electricity box or access if access point only has the user if electricity box There are also other users for point, then are supplemented with the station address of these identical electricity box or access point.
4. a kind of family based on station address according to claim 1 becomes relationship anomalous discrimination method, it is characterised in that: institute It states in step 3, every layer of decision tree contains the quantity k of user's set2It is different.
5. a kind of family based on station address according to claim 1 becomes relationship anomalous discrimination method, it is characterised in that: institute It states in step 5, the calculation formula of Z-score normalized are as follows:Wherein σ is that data standard is poor, and μ is sample This average value, x refer to user code, and normalized makes different magnitude of data uniformly be converted into the same magnitude.
6. a kind of family based on station address according to claim 1 becomes relationship anomalous discrimination method, it is characterised in that: institute It states in step 7, P cluster gathers [Q1,Q2,...,Qp] specific algorithm are as follows: weighted input vector be [W1,W2,….,Wm‐k1], Hierarchical clustering analysis is carried out, each user is one kind, carries out minimum distance calculation to any two class every time, distance is nearest Two classes are classified as one kind, termination when clustering number and being reduced to P, obtain P cluster set [Q1,Q2,...,Qp]。
7. a kind of family based on station address according to claim 6 becomes relationship anomalous discrimination method, it is characterised in that: appoint The calculation for two class minimum ranges of anticipating uses Euclidean distance calculation.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110231528A (en) * 2019-06-17 2019-09-13 国网重庆市电力公司电力科学研究院 Transformer family based on load characteristic model library becomes anomalous identification method and device

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040006502A1 (en) * 2002-05-03 2004-01-08 Masiello Ralph D. Valuing and optimizing scheduling of generation assets for a group of facilities
CN105160416A (en) * 2015-07-31 2015-12-16 国家电网公司 Transformer area reasonable line loss prediction method based on principal component analysis and neural network
CN107292765A (en) * 2017-06-21 2017-10-24 国网辽宁省电力有限公司 A kind of user power utilization behavior monitoring method based on clustering algorithm
CN107958395A (en) * 2017-12-13 2018-04-24 美林数据技术股份有限公司 A kind of recognition methods of electric system abnormal user
CN207637296U (en) * 2017-12-14 2018-07-20 国网北京市电力公司 Family becomes the test device of relationship identification system
CN108318759A (en) * 2018-01-25 2018-07-24 国网浙江海宁市供电有限公司 A kind of various dimensions taiwan area family becomes relation recognition method

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040006502A1 (en) * 2002-05-03 2004-01-08 Masiello Ralph D. Valuing and optimizing scheduling of generation assets for a group of facilities
CN105160416A (en) * 2015-07-31 2015-12-16 国家电网公司 Transformer area reasonable line loss prediction method based on principal component analysis and neural network
CN107292765A (en) * 2017-06-21 2017-10-24 国网辽宁省电力有限公司 A kind of user power utilization behavior monitoring method based on clustering algorithm
CN107958395A (en) * 2017-12-13 2018-04-24 美林数据技术股份有限公司 A kind of recognition methods of electric system abnormal user
CN207637296U (en) * 2017-12-14 2018-07-20 国网北京市电力公司 Family becomes the test device of relationship identification system
CN108318759A (en) * 2018-01-25 2018-07-24 国网浙江海宁市供电有限公司 A kind of various dimensions taiwan area family becomes relation recognition method

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
HONGTAO XIE，FUHUA SHANG: "The study of methods for post-pruning decision trees based on comprehensive evaluation standard", 《 2014 11TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD)》, 11 November 2014 (2014-11-11), pages 903 - 908 *
丁晓等: "基于配用电大数据的短期负荷预测", 《电力工程技术》, no. 03, 28 May 2018 (2018-05-28), pages 27 - 33 *
王守相等: "基于随机森林算法的台区合理线损率估计方法", 《电力自动化设备》, no. 11, 18 November 2017 (2017-11-18), pages 45 - 51 *
蔡晓燕: "基于智能用电大数据分析的台区线损管理初探", 《科技创新与应用》 *
蔡晓燕: "基于智能用电大数据分析的台区线损管理初探", 《科技创新与应用》, no. 17, 18 June 2016 (2016-06-18), pages 181 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110231528A (en) * 2019-06-17 2019-09-13 国网重庆市电力公司电力科学研究院 Transformer family based on load characteristic model library becomes anomalous identification method and device

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