CN103646351A - Detection method for discriminating stealing event based on metering variations in electricity, water and gas - Google Patents
Detection method for discriminating stealing event based on metering variations in electricity, water and gas Download PDFInfo
- Publication number
- CN103646351A CN103646351A CN201310552660.6A CN201310552660A CN103646351A CN 103646351 A CN103646351 A CN 103646351A CN 201310552660 A CN201310552660 A CN 201310552660A CN 103646351 A CN103646351 A CN 103646351A
- Authority
- CN
- China
- Prior art keywords
- node
- user
- average daily
- day
- metering
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 20
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 17
- 230000005611 electricity Effects 0.000 title claims abstract description 16
- 230000002159 abnormal effect Effects 0.000 claims abstract description 3
- 230000000052 comparative effect Effects 0.000 claims description 12
- 235000013305 food Nutrition 0.000 claims description 6
- 238000000034 method Methods 0.000 claims description 5
- 230000003442 weekly effect Effects 0.000 claims description 4
- 238000012216 screening Methods 0.000 claims description 2
- 238000012163 sequencing technique Methods 0.000 claims description 2
- 239000007789 gas Substances 0.000 abstract description 11
- 238000007405 data analysis Methods 0.000 abstract description 4
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 abstract 2
- 239000003345 natural gas Substances 0.000 abstract 1
- 239000008239 natural water Substances 0.000 abstract 1
- 230000005540 biological transmission Effects 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 5
- 239000003034 coal gas Substances 0.000 description 3
- 239000011435 rock Substances 0.000 description 3
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 2
- -1 electricity Substances 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- 239000008399 tap water Substances 0.000 description 2
- 235000020679 tap water Nutrition 0.000 description 2
- 241000208340 Araliaceae Species 0.000 description 1
- 241001269238 Data Species 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 206010063900 Steal syndrome Diseases 0.000 description 1
- 230000002547 anomalous effect Effects 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000007639 printing Methods 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 238000009418 renovation Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 239000002912 waste gas Substances 0.000 description 1
- 239000002351 wastewater Substances 0.000 description 1
Landscapes
- Arrangements For Transmission Of Measured Signals (AREA)
Abstract
The invention relates to the technical field of anti-stealing in the energy metering field of electric energy, natural gas, water and the like, and particularly relates to a detection method for discriminating a stealing event based on metering variations in electricity, water and gas. The detection method comprises the following steps of metering an average daily measuring value of a user; calculating a deviation percentage among average daily measuring values of different users before and after a node day; discriminating and alarming, that is, suppose that an operator set an alarm threshold of abnormal deviation percentage to be beta under the premise of a metering allowable error in the industry, comparing acquired values of Jnm and Kpm with the beta, and alarming information is given out when one of the Jnm and the Kpm is less than the beta. The detection method provided by the invention not only effectively ensures the accuracy and the rapidity of data analysis and solve a problem that metering stealing events in which internal workers take part are difficult to discover, but also can judge the time when the stealing event occurs reliably, thereby providing powerful technical support for the operator to carry out anti-stealing operations.
Description
Technical field
The present invention relates to the anti-usurp technology field for electric energy and coal gas, rock gas, water equal energy source metering field, be specifically related to a kind ofly based on electricity, water, gas metering, change and screen the detection method of the event of stealing.
Background technology
For electric power, coal gas, rock gas, tap water, blowdown and other energy measurement fields, how minority energy user considers to be reduced energy consumption or to be reduced waste gas, discharge of wastewater by technical renovation, scrap build, lifting management level and the mode such as optimize the structure of production; But secure personal gain all illegal means, to reduce, enterprise produces, operating cost, obtains income.
In recent years, fast development along with robotization reading technique, in electric power, tap water, coal gas, rock gas, blowdown etc., use the field of measuring apparatus, increasing operator selects, by remote meter reading technology, the reading of gauging table is uploaded to data center of operator in many ways.Remote meter reading technology, has no doubt solved at present to a certain extent due to the problems such as difficulty of checking meter that number of users is huge and the factor such as very extensive that distributes causes, yet the intellectuality that himself exists is low and cannot select voluntarily the defects such as the event of stealing and still exist; This is because traditional data analysis software is the data of identifying ANOMALOUS VARIATIONS by variable quantity and the historical variations trend of unit of account time period data, and for large-scale energy operator, the data that data center receives are but magnanimity, the reason that causes continuous data to reduce is various especially, as the factors such as measuring apparatus fault, data transmission disorder, the objective underproduction, energy efficiency all may cause measuring the metering numerical value change before and after the date, and be difficult to directly qualitatively think that it exists and illegally steal phenomenon; Particularly for the internal staff of operator and user, make secret deals with situation about illegally stealing, this class taking and carring away disguise is high, investigation difficulty is large, the amount of stealing is large, and operator is hard to guard against especially.How to develop a kind of detection method that event is illegally stolen in its existence of practical examination that is suitable for, with in lengthy and tedious and complicated data traffic, being different from measuring apparatus fault, data transmission disorder, the objective underproduction, energy efficiency etc. and may causing measuring the metering numerical value changing factor before and after the date and select out illegal taking and carring away targetedly, is those skilled in the art's technical barrier urgently to be resolved hurrily in recent years.
Summary of the invention
Object of the present invention is for overcoming above-mentioned the deficiencies in the prior art, providing a kind of changes and screens the detection method of the event of stealing based on electricity, water, gas metering, this detection method has not only guaranteed accuracy, the rapidity of data analysis effectively, solved and be difficult to find that the metering that internal work personnel participate in steals an event difficult problem, and can provide reliable judgement to the time of taking and carring away generation, for operator's counterplot amount work of stealing provides strong technical support.
For achieving the above object, the technical solution adopted in the present invention is as follows:
Based on electricity, water, gas metering, change and screen the detection method of the event of stealing, comprising the steps:
1), the average daily variable of metering user
The day that the behavior of legal event occurs the user of take is node day, under the mandate of Ye Jiyi operator, staff be day for measuring apparatus operation of making of the task of operator for node day, under the prerequisite of rejecting node day, by instrument, obtain following metering respectively during in the average daily variable of user:
A, node be the average daily variable of user in m week a few days ago, and wherein m is integer, 1≤m≤4;
B, node be the average daily variable of user in each week in the future;
C, node be the average daily variable of fortnightly user in the future;
2), the deviation percent between the average daily variable of different user before and after computing node day:
A, node average daily variable weekly is in the future with respect to node deviation percent a few days ago,
Be J
nm=(S
n-Q
m)/Q
m* 100%,
B, node fortnightly average daily variable is in the future with respect to node deviation percent a few days ago,
Be K
pm=(D
p-Q
m)/Q
m* 100%;
Described Q
mfor the node average daily variable of user in m week a few days ago;
Described S
nfor according to the average daily variable of user in the node of sequence of event n week in the future, and n is positive integer;
Described D
pfor according to the node of the sequence of event p average daily variable of user of two weeks in the future, described p is positive integer;
3), screen and report to the police
If the alarm threshold value of the abnormal variation number percent that operator sets under the prerequisite of industry metering permissible error is β, by the J obtaining
nmand K
pmvalue compare with β value respectively, J
nmand K
pmwhen the value of one of them is less than β value, send warning message.
The present invention can also be able to further realization by following technical measures:
In step 1), in calculating the process of the average daily variable of user, the data of the S day of actual metered data minimum in during operator measures according to the working condition rejecting of its user place industry, and according to time sequencing by calculating as the continuous data of S day with adjacent, identical number of days, the actual metered data that do not counted of described S day, described S is integer, S≤3;
Further, when user is the enterprise of public institution or non-service class and non-food class, and national legal festivals and holidays in during metering while being National Day or the Spring Festival, S=3; If the national legal festivals and holidays in during metering are not while being National Day or the Spring Festival, S=1;
When user is the enterprise of service class and food and drink class, and national legal festivals and holidays in during metering while being the Spring Festival, S=3; If the national legal festivals and holidays in during metering are not while being not the Spring Festival, S=1.
Preferably, the value of described m is 1 or 2 or 4.
Further, J
nmin m value be 1 or 4, K
pmin m value be 2 or 4.
Further, whether the legal event of decision node day is to measure while stealing event, first want each week comparative result under W pattern after decision node, then each week after decision node the comparative result under G pattern, the last comparative result of every two weeks after decision node under X pattern and Y mode respectively again, its every comparative result is and is less than β value, is also all to detect while there is stealing suspicion under W, G, X and Y mode, can determine that the legal event of this node day possesses the suspicion of stealing;
If the be directed to node average daily variable of user in each week and the average daily variable of user of the last week node day and the comparison pattern set is designated as W pattern in the future;
Be directed to node in the future the average daily variable of user in each week and node a few days ago surrounding the average daily variable of user and the comparison pattern set is designated as G pattern;
Be directed to node in the future the average daily variable of fortnightly user and node day the last fortnight the average daily variable of user and the comparison pattern set is designated as X pattern;
Be directed to node in the future the average daily variable of fortnightly user and node a few days ago surrounding the average daily variable of user and the comparison pattern set is designated as Y mode.
Preferably, the value of described β is-20%.
Concrete, the principle of following when calculating the average daily variable of user is as follows:
1) continuous data of node day must be rejected, if because the legal event of this node day possesses the suspicion of illegally stealing, because understanding the electric weight amount of the stealing size on the same day of illegally stealing the same day after event generation, thus the same day continuous data size nonsensical;
2) continuous data before and after country's legal festivals and holidays must be treated with a certain discrimination.
China's legal festivals and holidays are: New Year's Day, a holiday; The Spring Festival, three day holiday; The Ching Ming Festival, a holiday; International Labour Day, a holiday; The Dragon Boat Festival, a holiday; The Mid-autumn Festival, a holiday; National Day, three day holiday.
For different metering users, because industry is different, the consumption habit of the media such as electricity, gas, water and laws of use are also different, and therefore, the continuous data disposal route recording during country's legal festivals and holidays must be treated with a certain discrimination, as:
A, for the enterprise of public institution and non-service, food and drink class, do not comprising under the condition in National Day and the Spring Festival, a minimum continuous data in country's place week legal festivals and holidays is rejected, the continuous data of the next day of disallowable data day is replaced to the continuous data of disallowable data day automatically, each continuous data is passed front calculating (being also that each continuous data is all only calculated once) successively afterwards simultaneously; At National Day and Spring Festival, place festivals or holidays three minimum continuous datas of two weeks are rejected, sort by date with the continuous data filling of adjacent day of postponing by the time of disallowable data day simultaneously and replace; If occur disallowable data day 2 or 3 dates adjacent, the continuous data of corresponding latter 2 days or 3 days is replaced to the continuous data of disallowable data day automatically, each continuous data is all only calculated once equally.
B, for service, food and drink class enterprise, the operation rule during legal festivals and holidays is different from general enterprises and institutions in country for the user of this type of industry, do not comprising under the condition in the Spring Festival, need not reject the minimum data in place week, but at Spring Festival, reject the minimum continuous data of three days in place week, and use the continuous data automatic filling of adjacent day of postponing by the time, then calculate the average daily variable of user of corresponding seven days.
The explanation of above-mentioned a, b part, the average daily variable of computing node user in the future of only take is example, when needing the average daily variable of user a few days ago of computing node, the continuous data of adjacent day above pushing away in chronological order of disallowable data day is replaced to the continuous data of disallowable data day automatically, should guarantee that equally each continuous data is all only calculated once.
3), J
nmin m value be 1 or 4, K
pmin m value be 2 or 4, also now calculate the average daily variable of gained user as follows respectively:
The average daily variable of user of the last week node day, is designated as Q
1;
Node day the last fortnight the average daily variable of user, be designated as Q
2;
Node is the average daily variable of user of surrounding a few days ago, is designated as Q
4.
The deviation percent formula now calculating between the average daily variable of different user is as follows:
J
n1＝（S
n－Q
1）/Q
1×100%；
J
n4＝（S
n－Q
4）/Q
4×100%；
K
p2＝（D
p－Q
2）/Q
2×100%；
K
p4＝（D
p－Q
4）/Q
4×100%。
The computation schema of four kinds of deviation percents that also i.e. now the present invention uses is specific as follows:
W pattern: one week/mono-week pattern, be the directed to node average daily variable of user in each week and the average daily variable of user of the last week node day and the comparison pattern set in the future;
G pattern: one week/surrounding pattern, be the directed to node average daily variable of user in each week and the node average daily variable of user of surrounding and the comparison pattern set a few days ago in the future;
X pattern: two weeks/two weeks patterns, be directed to node in the future the average daily variable of fortnightly user and node day the last fortnight the average daily variable of user and the comparison pattern set;
Y mode: two weeks/surrounding pattern, is directed to the node average daily variable of fortnightly user and the node average daily variable of user of surrounding and the comparison pattern set a few days ago in the future.
Whether the legal event of decision node day is to measure while stealing event, first want each week comparative result under W pattern after decision node, then each week after decision node the comparative result under G pattern, the last comparative result of every two weeks after decision node under X pattern and Y mode respectively again, its every comparative result is and is less than β value, also be all to detect while there is stealing suspicion under W, G, X and Y mode, can determine that the legal event of this node day possesses the suspicion of stealing.
Compared to the prior art the present invention has following beneficial effect:
1) the present invention is applicable to all fields with measuring apparatus, not only from data, add up, and to take the legal event that user occurs be node, the metering situation of assessing the measuring apparatus of legal event between the emergence period of simultaneously also take is basis, the comprehensive suspicion that judges whether legal event has metering to steal, and then fundamentally solved the existing problem of existing anti-usurp technology.
2) the present invention has not only guaranteed accuracy, the rapidity of data analysis effectively, solved and be difficult to find that the metering that internal work personnel participate in steals an event difficult problem, and the time that can occur taking and carring away provide reliable judgement; While there is stealing operation in the staff who authorizes when operator when carrying out a certain legal event, Electro-metering after its object client must produce low quantification trend immediately, this detection method is obtained by the measuring and calculating under W, G, X and tetra-kinds of patterns of Y, thereby to it, whether during in legal event, exist stealing operation to carry out comprehensive suspicion judgement with the net result of four kinds of patterns, so that it is selected out from numerous electric weight influence factors of lengthy and tedious complexity, finally stop internal system operation phenomenon, for operator's counterplot amount work of stealing provides strong technical support.
3) the present invention is not only applicable to weekly the identical enterprise of rest number of days, and is applicable to weekly the time of having a rest and divides the enterprise of large alternate Sunday which is a working day.
4) the present invention can use metering instrument long-distance automatic meter reading system to be realized rapidly by family, and process is as follows:
A, first by the various gauging tables such as water, electricity, gas, heat, measure user's energy consumption used, and send metering to collector;
B, collector convert the variable of various gauging tables to the digital signal corresponding with it, and send concentrator to;
C, concentrator, by after the data centralization of various gauging tables, transfer to system main website by digital transmission modes such as Optical Fiber Transmission, wireless transmission, telephone line transmission and low-voltage power line carrier transmissions;
The data that d, system main website gather whole system are classified and are stored and calculate, node to be determined in the future, system main website utilizes the detection method in the present invention to realize the statistical study of all kinds of numerical value such as water, electricity, gas, heat, when the legal event existence of predicate node day is stolen after suspicion, give the alarm, and the functions such as inquiry, form, importing, derivation, printing are provided.
Embodiment
Yi Mou restaurant power consumption is example below, and the present invention will be further described.
For there is the n week after node time in n, n is positive integer, and the second day from the legal event of each generation starts timing, take seven as a metering cycle, and first seven days, n=1; After this every mistake is seven days, n+1;
When n is odd number, P value cannot round, and therefore directly calculates J
n1, J
n4value, and carry out corresponding data comparison and screen and report to the police;
When n is even number, p=n/2, calculates J
n1, J
n4, K
p2, K
p4value, analyzes suspicion grade, and carries out corresponding data comparison and screen and report to the police.
Yi Mou restaurant power consumption is example below, and the present invention will be further described.
Statistics ginseng for certain restaurant's power consumption is shown in Table 1
This restaurant's power consumption statistical form of table 1
As seen from the above table, legal event has occurred on April 11st, 2012, node day is on April 11st, 2012, and data analysis is as follows:
First 1,2,4 week of node, average daily variable is respectively:
Node first week in the future, n=1, has:
According to threshold value comparison, the node data results of first week is in the future: J
11and J
14value is all less than β value;
Node is second week in the future, n=2, and p=1 has:
According to threshold value comparison, the node in the future data results of second week is: J
21, J
24, K
12and K
14value is all less than β value;
Node the 3rd week in the future, n=3, has:
According to threshold value comparison, the node in the future data results of the 3rd week is: J
31and J
34value be all less than β value;
Node is 4th week in the future, n=4, and p=2, has:
According to threshold value comparison, the node in the future data results of the 3rd week is: J
41, J
44, K
22and K
24value be all less than β value;
To sum up, the Electro-metering in this restaurant is via screening after statistics and detection, and the testing result under its W, G, X and tetra-kinds of patterns of Y all reaches system alarm line, and this legal event exists stealing suspicion, and system is sent warning message reported result, waits for that operator processes.
Claims (7)
1. based on electricity, water, gas metering, change and screen the detection method of the event of stealing, it is characterized in that comprising the steps:
1), the average daily variable of metering user
The day that the behavior of legal event occurs the user of take is node day, under the mandate of Ye Jiyi operator, staff be day for measuring apparatus operation of making of the task of operator for node day, under the prerequisite of rejecting node day, by surveying instrument, obtain following metering respectively during in the average daily variable of user:
A, node be the average daily variable of user in m week a few days ago, and wherein m is integer, 1≤m≤4;
B, node be the average daily variable of user in each week in the future;
C, node be the average daily variable of fortnightly user in the future;
2), the deviation percent between the average daily variable of different user before and after computing node day:
A, node average daily variable weekly is in the future with respect to node deviation percent a few days ago,
Be J
nm=(S
n-Q
m)/Q
m* 100%,
B, node fortnightly average daily variable is in the future with respect to node deviation percent a few days ago,
Be K
pm=(D
p-Q
m)/Q
m* 100%;
Described Q
mfor the node average daily variable of user in m week a few days ago;
Described S
nfor according to the average daily variable of user in the node of sequence of event n week in the future, and n is positive integer;
Described D
pfor according to the node of the sequence of event p average daily variable of user of two weeks in the future, described p is positive integer;
3), screen and report to the police
If the alarm threshold value of the abnormal variation number percent that operator sets under the prerequisite of industry metering permissible error is β, by the J obtaining
nmand K
pmvalue compare with β value respectively, J
nmand K
pmwhen the value of one of them is less than β value, send warning message.
2. according to claim 1ly based on electricity, water, gas metering, change and screen the detection method of the event of stealing, it is characterized in that: in described step 1), in calculating the process of the average daily variable of user, the data of the S day of actual metered data minimum in during operator measures according to the working condition rejecting of its user place industry, and according to time sequencing by calculating as the continuous data of S day with adjacent, identical number of days, the actual metered data that do not counted of described S day, described S is integer, S≤3.
3. according to claim 2ly based on electricity, water, gas metering, change and screen the detection method of the event of stealing, it is characterized in that:
When user is the enterprise of public institution or non-service class and non-food class, and national legal festivals and holidays in during metering while being National Day or the Spring Festival, S=3; If the national legal festivals and holidays in during metering are not while being National Day or the Spring Festival, S=1;
When user is the enterprise of service class and food and drink class, and national legal festivals and holidays in during metering while being the Spring Festival, S=3; If the national legal festivals and holidays in during metering are not while being not the Spring Festival, S=1.
4. according to claim 1ly based on electricity, water, gas metering, change and screen the detection method of the event of stealing, it is characterized in that: the value of described m is 1 or 2 or 4.
5. according to claim 4ly based on electricity, water, gas metering, change and screen the detection method of the event of stealing, it is characterized in that: described step 2), J
nmin m value be 1 or 4, K
pmin m value be 2 or 4.
6. according to claim 5ly based on electricity, water, gas metering, change and screen the detection method of the event of stealing, it is characterized in that:
Whether the legal event of decision node day is to measure while stealing event, first want each week comparative result under W pattern after decision node, then each week after decision node the comparative result under G pattern, the last comparative result of every two weeks after decision node under X pattern and Y mode respectively again, its every comparative result is and is less than β value, also be all to detect while there is stealing suspicion under W, G, X and Y mode, determine that the legal event of this node day possesses the suspicion of stealing;
Wherein:
If the be directed to node average daily variable of user in each week and the average daily variable of user of the last week node day and the comparison pattern set is designated as W pattern in the future;
Be directed to node in the future the average daily variable of user in each week and node a few days ago surrounding the average daily variable of user and the comparison pattern set is designated as G pattern;
Be directed to node in the future the average daily variable of fortnightly user and node day the last fortnight the average daily variable of user and the comparison pattern set is designated as X pattern;
Be directed to node in the future the average daily variable of fortnightly user and node a few days ago surrounding the average daily variable of user and the comparison pattern set is designated as Y mode.
7. according to measuring and changing the detection method of screening the event of stealing based on electricity, water, gas described in claim 1～6 any one, it is characterized in that: in described step 3), the value of described β is-20%.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310552660.6A CN103646351A (en) | 2013-11-08 | 2013-11-08 | Detection method for discriminating stealing event based on metering variations in electricity, water and gas |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310552660.6A CN103646351A (en) | 2013-11-08 | 2013-11-08 | Detection method for discriminating stealing event based on metering variations in electricity, water and gas |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN103646351A true CN103646351A (en) | 2014-03-19 |
Family
ID=50251561
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310552660.6A Pending CN103646351A (en) | 2013-11-08 | 2013-11-08 | Detection method for discriminating stealing event based on metering variations in electricity, water and gas |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103646351A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106104282A (en) * | 2014-08-28 | 2016-11-09 | 单立辉 | An Electrical Measurement Method Based on Reference Energy |
| CN107688958A (en) * | 2017-07-14 | 2018-02-13 | 国网浙江省电力公司 | A kind of user that data are copied based on multilist collection uses energy exception analysis method |
| CN109146710A (en) * | 2018-09-18 | 2019-01-04 | 中国电力科学研究院有限公司 | A kind of method and system for the energy prices abnormal parameters state judging measuring equipment |
| CN112001621A (en) * | 2020-08-21 | 2020-11-27 | 广州云徙科技有限公司 | Intelligent early warning method for key indexes |
| CN114049033A (en) * | 2021-11-22 | 2022-02-15 | 国网江苏省电力有限公司连云港供电分公司 | A monitoring method for pollutant discharge enterprises based on the distribution of electricity consumption data |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030083788A1 (en) * | 2001-10-30 | 2003-05-01 | Yasushi Harada | Operation support system and method |
| CN101477163A (en) * | 2009-01-04 | 2009-07-08 | 保定市三川电气有限责任公司 | Method for monitoring electric consumption |
| CN101551884A (en) * | 2009-05-08 | 2009-10-07 | 华北电力大学 | A fast CVR electric load forecast method for large samples |
| CN103187804A (en) * | 2012-12-31 | 2013-07-03 | 萧山供电局 | Station area electricity utilization monitoring method based on bad electric quantity data identification |
-
2013
- 2013-11-08 CN CN201310552660.6A patent/CN103646351A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030083788A1 (en) * | 2001-10-30 | 2003-05-01 | Yasushi Harada | Operation support system and method |
| CN101477163A (en) * | 2009-01-04 | 2009-07-08 | 保定市三川电气有限责任公司 | Method for monitoring electric consumption |
| CN101551884A (en) * | 2009-05-08 | 2009-10-07 | 华北电力大学 | A fast CVR electric load forecast method for large samples |
| CN103187804A (en) * | 2012-12-31 | 2013-07-03 | 萧山供电局 | Station area electricity utilization monitoring method based on bad electric quantity data identification |
Non-Patent Citations (2)
| Title |
|---|
| 兰孝兵: "获取窃电信息的方法和对策", 《中华民居》 * |
| 胡琛: "数据挖掘技术在电量管理与反窃电系统中的应用与研究", 《中国优秀硕士学位论文全文数据库社会科学Ⅰ辑》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106104282A (en) * | 2014-08-28 | 2016-11-09 | 单立辉 | An Electrical Measurement Method Based on Reference Energy |
| CN107688958A (en) * | 2017-07-14 | 2018-02-13 | 国网浙江省电力公司 | A kind of user that data are copied based on multilist collection uses energy exception analysis method |
| CN107688958B (en) * | 2017-07-14 | 2020-09-18 | 国网浙江省电力公司 | User energy anomaly analysis method based on multi-table centralized reading data |
| CN109146710A (en) * | 2018-09-18 | 2019-01-04 | 中国电力科学研究院有限公司 | A kind of method and system for the energy prices abnormal parameters state judging measuring equipment |
| CN109146710B (en) * | 2018-09-18 | 2023-09-22 | 中国电力科学研究院有限公司 | Method and system for judging abnormal state of energy price parameter of metering equipment |
| CN112001621A (en) * | 2020-08-21 | 2020-11-27 | 广州云徙科技有限公司 | Intelligent early warning method for key indexes |
| CN114049033A (en) * | 2021-11-22 | 2022-02-15 | 国网江苏省电力有限公司连云港供电分公司 | A monitoring method for pollutant discharge enterprises based on the distribution of electricity consumption data |
| CN114049033B (en) * | 2021-11-22 | 2024-06-07 | 国网江苏省电力有限公司连云港供电分公司 | A monitoring method for pollutant-discharging enterprises based on electricity consumption data distribution |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103455855B (en) | A kind of intermittent electricity stealing prevention detection method based on power information data analysis | |
| CN103646351A (en) | Detection method for discriminating stealing event based on metering variations in electricity, water and gas | |
| CN104779699A (en) | Multifunctional station line loss management system | |
| CN113221931B (en) | Electricity stealing prevention intelligent identification method based on electricity utilization information acquisition big data analysis | |
| CN104391202A (en) | Abnormal electricity consumption judging method based on analysis of abnormal electric quantity | |
| CN106295985A (en) | A kind of platform district line loss management system of local electric network and management method | |
| CN111539604A (en) | A method for calculating and monitoring the index of resumption of work and production of enterprises based on power data | |
| CN106443556A (en) | Method for intelligently diagnosing electric energy meter | |
| CN108595687B (en) | Water consumption abnormity detection method and database server | |
| CN105160864A (en) | Operating data monitoring system and method for intelligent ammeters | |
| CN113922412A (en) | Panorama evaluation method and system for new energy multi-station short circuit ratio, storage medium and computing device | |
| CN114169424B (en) | Pollution discharge prediction method based on k-nearest neighbor regression algorithm and electricity consumption data | |
| CN106019087A (en) | Intermittent electricity stealing monitoring system | |
| CN103837777A (en) | Method and system for power quality assessment of power supply system | |
| CN105118108B (en) | Long-distance oil and gas pipeline inspection method and system | |
| CN110837532A (en) | A method for detecting electricity stealing behavior of charging piles based on big data platform | |
| CN104502692B (en) | The detection method and central processor equipment of electricity unusual fluctuation | |
| CN114154871A (en) | Building carbon emission analysis system based on big data processing | |
| CN117674402A (en) | Up and down through-type line loss active analysis and management method, device and computer equipment | |
| CN114049033A (en) | A monitoring method for pollutant discharge enterprises based on the distribution of electricity consumption data | |
| CN105139150A (en) | User electricity charge risk evaluating system of charge big data | |
| CN111612054A (en) | A method for identifying electricity stealing behavior of users based on non-negative matrix factorization and density clustering | |
| CN115841398A (en) | Water supply pipe network leakage analysis method and system based on Internet of things | |
| CN103049994A (en) | Production quality detection system of acquisition unit | |
| CN115375520A (en) | Monitoring method and system for realizing pollutant emission through electric power information |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20140319 |
|
| RJ01 | Rejection of invention patent application after publication |