CN104216985A - Method and system for discriminating abnormal data - Google Patents
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Abstract
The embodiment of the invention discloses a method for discriminating abnormal data, which is realized on a plurality of sample data sets, and comprises the following steps: acquiring a first sample data set and a plurality of corresponding service types; setting a screening rule in each corresponding service type, and obtaining screening data of each service type according to the set screening rule; judging whether the screening data of each service type exist in a comparison data set screened by a plurality of sample data sets except the first sample data set; if yes, determining that the screening data are abnormal data. According to the embodiment of the invention, the problem that the analysis result has larger error due to the deviation in the sampling process can be corrected, and the method can be used for sampling a plurality of sub-categories, so that the error rate of the sampling result is reduced; meanwhile, all abnormal data can be locked quickly and accurately in complex large data (data totality, not just a sample set).
Description
Technical Field
The invention relates to the technical field of marketing inspection of power systems, in particular to a method and a system for discriminating abnormal data.
Background
The marketing inspection of the power system is to carry out internal professional inspection and supervision on the construction and execution of the marketing system, the specification of marketing behaviors, the marketing work quality and the like according to relevant policies, regulations and regulations.
The existing normalized marketing inspection work system is based on a scientific sampling and evaluation model of a statistical principle, firstly introduces service data into statistical software, then samples the service data through a sampling module of general statistical software, and finally introduces investigation result data into the statistical software for statistical inference, so that under the condition that all data cannot be collected or analyzed, high-precision inference is made with less cost by collecting random samples, and the defect is that: once any deviation exists in the sampling process, the analysis result has a large error, and when random sampling is used for sampling of a plurality of sub-categories, the error rate of the random sampling result is greatly increased.
Meanwhile, when the service data is increased in quantity, the method for finding out the abnormal data by the sampling survey method has the problems that all the abnormal data cannot be found and the finding efficiency is low, namely the abnormal data cannot be locked quickly in the complex big data.
When the data is rich and complex 'big data', different from the 'small data' era of random sampling analysis and obtaining the most information with the least data, all data (at least as much data as possible), namely 'sample = total', needs to be collected and utilized to carry out deep analysis and mining on the whole data, so that higher accuracy is brought.
Disclosure of Invention
The embodiment of the invention aims to provide a method and a system for discriminating abnormal data, which can correct the problem that a deviation exists in the sampling process and a larger error occurs in an analysis result, can be used for sampling a plurality of sub-categories, reduce the error rate of the sampling result, and can quickly lock all abnormal data in complex large data.
In order to solve the above technical problem, an embodiment of the present invention provides a method for discriminating abnormal data, which is implemented on a plurality of sample data sets, where the method includes:
acquiring a first sample data set and a plurality of corresponding service types in the first sample data set;
setting a screening rule in each service type corresponding to the acquired first sample data set, and obtaining screening data of each service type in the first sample data set according to the set screening rule;
judging whether the obtained screening data of each service type in the first sample data set exists in a comparison data set screened by the plurality of sample data sets except the first sample data set;
if so, determining that the screened data in the compared data set screened by the plurality of sample data sets except the first sample data set is abnormal data under the condition of the same service type.
Wherein, the specific steps of setting a screening rule in each service type corresponding to the obtained first sample data set, and obtaining the screening data of each service type in the first sample data set according to the set screening rule include:
in the acquired first sample data set, setting one or more screening attributes contained in a screening rule corresponding to each service type according to each service type corresponding to the first sample data set;
obtaining screening data of each service type in the first sample data set according to one or more screening attributes contained in the screening rule corresponding to each set service type; the screening data is a data set formed by screening each service type in the first sample data set through one or more corresponding screening attributes.
Wherein, the specific step of judging whether the obtained screening data of each service type in the first sample data set exists in the comparison data screened by the plurality of sample data sets except the first sample data set comprises:
obtaining a screening rule correspondingly set for each service type in the first sample data set;
respectively setting the screening rules correspondingly set for each acquired service type in the plurality of sample data sets except the first sample data set to obtain a comparison data set of each service type;
and judging whether the screening data of each service type obtained in the first sample data set is contained in a comparison data set corresponding to the same service type.
The service types comprise business expansion installation, power utilization change, reading, checking, metering, power utilization inspection, customer service and line loss management.
The embodiment of the invention also provides a system for discriminating abnormal data, which comprises: the device comprises an acquisition unit, a screening unit, a judgment unit and a determination unit; wherein,
the acquiring unit is configured to acquire a first sample data set and a plurality of corresponding service types in the first sample data set;
the screening unit is configured to set a screening rule in each service type corresponding to the acquired first sample data set, and obtain screening data of each service type in the first sample data set according to the set screening rule;
the judging unit is configured to judge whether the filtered data of each service type in the obtained first sample data set is present in a contrast data set filtered by the plurality of sample data sets except the first sample data set;
the determining unit is configured to, under the same service type, filter data in comparison data sets filtered by the plurality of sample data sets except the first sample data set is abnormal data.
Wherein the screening unit includes:
a setting module, configured to set, in the obtained first sample data set, one or more screening attributes included in a screening rule corresponding to each service type according to each service type corresponding to the first sample data set;
the screening module is used for obtaining screening data of each service type in the first sample data set according to one or more screening attributes contained in the screening rule corresponding to each set service type; the screening data is a data set formed by screening each service type in the first sample data set through one or more corresponding screening attributes.
Wherein the judging unit includes:
the first obtaining module is used for obtaining a screening rule which is correspondingly set by each service type in the first sample data set;
a second obtaining module, configured to set the obtained screening rule corresponding to each service type in the plurality of sample data sets except the first sample data set, respectively, to obtain a comparison data set of each service type;
and the judging module is used for judging whether the screening data of each service type obtained in the first sample data set is contained in the comparison data set corresponding to the same service type.
The service types comprise business expansion installation, power utilization change, reading, checking, metering, power utilization inspection, customer service and line loss management.
The embodiment of the invention has the following beneficial effects:
in the embodiment of the invention, because each service type in a sample data set is provided with the screening rule, the method can be simultaneously used for sampling a plurality of sub-categories, the error rate of the sampling result is reduced, and the screening data obtained corresponding to each service type is compared with other data sets except the sample data set, so that abnormal data is quickly locked, the problem that the analysis result has larger error due to deviation in the sampling process is solved. Meanwhile, when the data is rich and complex 'big data', all data of the service population (namely 'sample = population') can be screened for abnormal data according to the set screening rule, all abnormal data can be locked quickly and accurately, deep analysis and mining of the whole data are achieved, and higher accuracy is brought.
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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 some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.
Fig. 1 is a flowchart of a method for screening abnormal data according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a system for discriminating abnormal data according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, in the embodiment of the present invention, a method for screening abnormal data is provided, where the method is implemented on a plurality of sample data sets, and the method includes:
step S101, a first sample data set and a plurality of corresponding service types in the first sample data set are obtained; the service types include but are not limited to business expansion installation, electricity utilization change, checking and accepting, metering, electricity utilization inspection, customer service and line loss management.
Step S102, setting a screening rule in each service type corresponding to the acquired first sample data set, and obtaining screening data of each service type in the first sample data set according to the set screening rule;
setting one or more screening attributes contained in a screening rule corresponding to each service type in a first sample data set according to each service type corresponding to the first sample data set;
obtaining screening data of each service type in a first sample data set according to one or more screening attributes contained in a screening rule corresponding to each set service type; the screening data is a data set formed by screening each service type in the first sample data set through one or more corresponding screening attributes.
Step S103, judging whether the screening data of each service type in the obtained first sample data set exists in the contrast data screened by the plurality of sample data sets except the first sample data set; if yes, executing the next step S104; if not, the process is ended.
The specific process is that a screening rule set corresponding to each service type in a first sample data set is obtained;
respectively setting the obtained screening rules correspondingly set by each service type in the plurality of sample data sets except the first sample data set to obtain a comparison data set of each service type;
and judging whether the screening data of each service type obtained in the first sample data set is contained in the comparison data set corresponding to the same service type.
Step S104, determining that the screened data in the compared data set screened by the plurality of sample data sets except the first sample data set is abnormal data under the condition of the same service type.
As an example, the first sample data set is a data set of a business and power distribution integrated system, and the other sample data sets include, but are not limited to, a data set of a marketing management system, a data set of a metering automation management system, a data set of a marketing decision support system, and a data set of a customer service information system, where the multiple data sets form a data interaction warehouse and divide multiple service types in the first sample data set, where the service types include: business expansion installation, electricity utilization change, reading, checking, metering, electricity utilization inspection, customer service, line loss management and the like.
In the first sample data set, one or more screening attributes contained in the screening rules corresponding to each service type are set according to each service type corresponding to the first sample data set, so that a screening data list of all service type sets is obtained.
Assuming that the service data set includes a plurality of data sets E1 to En, etc., where the first sample data set is E1, a screening rule (condition) R is constructed in the first sample data set E1, and under the condition of the screening rule R, the data in the first sample data set E1 is classified and screened to obtain a required abnormal service data set C.
The function expression: y = f (x), where f is the filtering rule R, x is the attributes a1, a2 … … An, and y is the abnormal traffic data set C. R rule includes attributes A1, A2 … … An, rule R may be a set of attributes A1, A2 … … An, i.e., R ∈ { A1, A2 … … An }. The process of constructing R is also to continuously search the attributes A1 and A2 … … An to obtain all the sets under the conditions of A1 and A2 … … An.
Specifically, in a data set of which the service type is business expansion in the first sample data set E1, the screening rule R1 is set as: the power scheme replies with a timeout. At this point, the filter rule R1 may be a set of several attributes A1, A2, namely R1 ∈ { A1, A2 … … }. Under the condition of "power scheme reply timeout": as long as two attributes are satisfied, abnormal data of power supply scheme overtime can be found. These two attributes are respectively: a1= single power supply client over 15 working days from the date of accepting customer power application, and a2= double power supply client over 30 working days from the date of accepting customer power application, so abnormal data of power supply scheme timeout in business expansion installation data is screened out.
By analogy, with filtering rule R2: the customer is overtime by the verification of the electrical engineering design data; rule R3: after the setting of the screening rules such as installation form power-on timeout is completed, the business expansion installation screening data set C1 can be determined in the data set of the expansion installation.
Meanwhile, the screening rules R1, R2, and R3 are respectively set in other service data sets (e.g., E2 to En) except the first service data set E1, to obtain a comparison data set Cm with service type of business expansion in the other service data sets (e.g., E2 to En), wherein the comparison data set is a big data set Cm formed by the other service data sets (e.g., E2 to En) according to the screening rules R1, R2, and R3, respectively.
When the business extension type screening data set C1 in the first sample data set E1 includes the business extension type big data set Cm in other business data sets (e.g., E2 to En), i.e., C1 ⊆ Cm, the business extension type screening data set C1 in the first sample data set E1 is determined to be a business extension type exception data set.
The embodiment of the invention has the following beneficial effects:
in the embodiment of the invention, because each service type in a sample data set is provided with the screening rule, the method can be simultaneously used for sampling a plurality of sub-categories, the error rate of the sampling result is reduced, and the screening data obtained corresponding to each service type is compared with other data sets except the sample data set, so that abnormal data is quickly locked, the problem that the analysis result has larger error due to deviation in the sampling process is solved. Meanwhile, when the data is rich and complex 'big data', all data of the service population (namely 'sample = population') can be screened for abnormal data according to the set screening rule, all abnormal data can be locked quickly and accurately, deep analysis and mining of the whole data are achieved, and higher accuracy is brought.
As shown in fig. 2, in an embodiment of the present invention, a system for screening abnormal data is further provided, where the system includes: an acquisition unit 210, a screening unit 220, a judgment unit 230, and a determination unit 240; wherein,
the obtaining unit 210 is configured to obtain a first sample data set and a plurality of corresponding service types in the first sample data set;
the screening unit 220 is configured to set a screening rule in each service type corresponding to the obtained first sample data set, and obtain screening data of each service type in the first sample data set according to the set screening rule;
the determining unit 230 is configured to determine whether the filtered data of each service type in the obtained first sample data set all exist in a contrast data set filtered by the plurality of sample data sets except the first sample data set;
the determining unit 240 is configured to determine that, under the same service type, screened data existing in a contrast data set screened by the plurality of sample data sets except the first sample data set is abnormal data.
Wherein, the screening unit 220 includes:
a setting module 2201, configured to set, in the obtained first sample data set, one or more filtering attributes included in a filtering rule corresponding to each service type according to each service type corresponding to the first sample data set;
a screening module 2202, configured to obtain screening data of each service type in the first sample data set according to one or more screening attributes included in the screening rule corresponding to each set service type; the screening data is a data set formed by screening each service type in the first sample data set through one or more corresponding screening attributes.
The determining unit 230 includes:
a first obtaining module 2301, configured to obtain a filtering rule set corresponding to each service type in the first sample data set;
a second obtaining module 2302, configured to set the obtained screening rule corresponding to each service type in the plurality of sample data sets except the first sample data set, respectively, to obtain a comparison data set of each service type;
a determining module 2303, configured to determine whether the screening data of each service type obtained in the first sample data set is included in a comparison data set corresponding to the same service type.
The service types comprise business expansion installation, power utilization change, checking and accepting, metering, power utilization inspection, customer service and line loss management.
In the embodiment of the present invention, the system for screening abnormal data first obtains the first sample data set and the corresponding multiple service types in the first sample data set through the obtaining unit 210, and by setting a filtering rule in each service type corresponding to the first sample data set in the filtering unit 220, and obtaining screening data of each service type in the first sample data set according to the set screening rule, it is determined in the determining unit 230 whether the filtered data of each service type in the first sample data set exists in the contrast data set filtered by the plurality of sample data sets except the first sample data set, if yes, it is determined by the determining unit 240 that the filtered data existing in the compared data set filtered by the plurality of sample data sets except the first sample data set is abnormal data under the same service type.
It should be noted that, in the foregoing system embodiment, each included system unit is only divided according to functional logic, but is not limited to the above division as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by relevant hardware instructed by a program, and the program may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (8)
1. A method of screening for anomalous data implemented on a plurality of sample data sets, the method comprising:
acquiring a first sample data set and a plurality of corresponding service types in the first sample data set;
setting a screening rule in each service type corresponding to the acquired first sample data set, and obtaining screening data of each service type in the first sample data set according to the set screening rule;
judging whether the obtained screening data of each service type in the first sample data set exists in a comparison data set screened by the plurality of sample data sets except the first sample data set;
if so, determining that the screened data in the compared data set screened by the plurality of sample data sets except the first sample data set is abnormal data under the condition of the same service type.
2. The method according to claim 1, wherein the step of setting a filtering rule in each service type corresponding to the obtained first sample data set, and obtaining the filtering data of each service type in the first sample data set according to the set filtering rule comprises:
in the acquired first sample data set, setting one or more screening attributes contained in a screening rule corresponding to each service type according to each service type corresponding to the first sample data set;
obtaining screening data of each service type in the first sample data set according to one or more screening attributes contained in the screening rule corresponding to each set service type; the screening data is a data set formed by screening each service type in the first sample data set through one or more corresponding screening attributes.
3. The method according to claim 1, wherein the step of determining whether the filtered data of each service type in the obtained first sample data set exists in the compared data set filtered by the plurality of sample data sets except the first sample data set comprises:
obtaining a screening rule correspondingly set for each service type in the first sample data set;
respectively setting the screening rules correspondingly set for each acquired service type in the plurality of sample data sets except the first sample data set to obtain a comparison data set of each service type;
and judging whether the screening data of each service type obtained in the first sample data set is contained in a comparison data set corresponding to the same service type.
4. The method of any one of claims 1 to 3, wherein the traffic types include business expansion installation, electricity usage change, meter reading, metering, electricity usage check, customer service, line loss management.
5. A system for screening anomaly data, the system comprising: the device comprises an acquisition unit, a screening unit, a judgment unit and a determination unit; wherein,
the acquiring unit is configured to acquire a first sample data set and a plurality of corresponding service types in the first sample data set;
the screening unit is configured to set a screening rule in each service type corresponding to the acquired first sample data set, and obtain screening data of each service type in the first sample data set according to the set screening rule;
the judging unit is configured to judge whether the filtered data of each service type in the obtained first sample data set is present in a contrast data set filtered by the plurality of sample data sets except the first sample data set;
the determining unit is configured to determine that, under the same service type, screened data existing in a contrast data set screened by the plurality of sample data sets except the first sample data set is abnormal data.
6. The system of claim 5, wherein the screening unit comprises:
a setting module, configured to set, in the obtained first sample data set, one or more screening attributes included in a screening rule corresponding to each service type according to each service type corresponding to the first sample data set;
the screening module is used for obtaining screening data of each service type in the first sample data set according to one or more screening attributes contained in the screening rule corresponding to each set service type; the screening data is a data set formed by screening each service type in the first sample data set through one or more corresponding screening attributes.
7. The system of claim 5, wherein the determining unit comprises:
the first obtaining module is used for obtaining a screening rule which is correspondingly set by each service type in the first sample data set;
a second obtaining module, configured to set the obtained screening rule corresponding to each service type in the plurality of sample data sets except the first sample data set, respectively, to obtain a comparison data set of each service type;
and the judging module is used for judging whether the screening data of each service type obtained in the first sample data set is contained in the comparison data set corresponding to the same service type.
8. The system of any one of claims 5 to 7, wherein the traffic types include business expansion installation, electricity usage change, carbon copy, metering, electricity usage checking, customer service, line loss management.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104809676A (en) * | 2015-05-11 | 2015-07-29 | 成都准星云学科技有限公司 | Method and device for analyzing mistake type of answer |
| CN106156791A (en) * | 2016-06-15 | 2016-11-23 | 北京京东尚科信息技术有限公司 | Business datum sorting technique and device |
| CN106251094A (en) * | 2016-08-26 | 2016-12-21 | 国网江西省电力公司电力科学研究院 | A kind of 10kV Business Process System work order unusual fluctuation analytical equipment and the method for analysis |
| CN106296399A (en) * | 2015-06-11 | 2017-01-04 | 交通银行股份有限公司 | The data processing method of business rule formulation and system |
| CN108363680A (en) * | 2018-01-04 | 2018-08-03 | 国网福建省电力有限公司 | A kind of power consumer electrical feature rendering method and user terminal |
| CN108833188A (en) * | 2018-07-17 | 2018-11-16 | 顺丰科技有限公司 | A kind of warning message management method, device, equipment and storage medium |
| CN109598525A (en) * | 2017-09-30 | 2019-04-09 | 北京国双科技有限公司 | Data processing method and device |
| CN110782550A (en) * | 2019-09-20 | 2020-02-11 | 腾讯科技（深圳）有限公司 | Data acquisition method, device and equipment |
| CN113496440A (en) * | 2021-06-28 | 2021-10-12 | 国网上海市电力公司 | User abnormal electricity utilization detection method and system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007257283A (en) * | 2006-03-23 | 2007-10-04 | Tdk Corp | Memory controller and flash memory system |
| CN101557111A (en) * | 2009-05-20 | 2009-10-14 | 重庆市电力公司 | Intelligent acquired electricity consumption data screening processing system |
| CN103514514A (en) * | 2013-09-23 | 2014-01-15 | 广州供电局有限公司 | On-line monitoring method for electricity marketing business data |
-
2014
- 2014-09-04 CN CN201410446368.0A patent/CN104216985B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007257283A (en) * | 2006-03-23 | 2007-10-04 | Tdk Corp | Memory controller and flash memory system |
| CN101557111A (en) * | 2009-05-20 | 2009-10-14 | 重庆市电力公司 | Intelligent acquired electricity consumption data screening processing system |
| CN103514514A (en) * | 2013-09-23 | 2014-01-15 | 广州供电局有限公司 | On-line monitoring method for electricity marketing business data |
Non-Patent Citations (4)
| Title |
|---|
| 叶永乐: "多系统数据分析对比在营销稽查工作中的探索及应用", 《电子世界》 * |
| 李红梅等: "青海电力营销稽查监控系统架构设计与应用", 《电力信息化》 * |
| 深圳供电局有限公司: "《电力营销在线信息化稽查》", 28 February 2014 * |
| 王海燕等: "营销稽查监控系统的建设与应用", 《电力信息化》 * |
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