CN113568937A - Electric quantity fitting method based on VEE process - Google Patents
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Abstract
The invention discloses an electric quantity fitting method based on a VEE process, which comprises the following steps: step 1, a master station establishes a VEE flow and a rule base to check and verify electric energy data; step 2, verifying and judging the electric energy data according to the VEE rule base, fitting the data, and storing, releasing and inquiring the fitted data; the technical problems that the collected electric quantity data is inaccurate due to abnormal conditions such as data loss, data turnover, data sudden decrease and data sudden increase easily occurring during electric quantity collection in the prior art are solved.
Description
Technical Field
The invention belongs to the technical field of electric quantity processing, and particularly relates to an electric quantity fitting method based on a VEE (vector electric quantity) process.
Background
The electric quantity is an important index for measuring the continuous operation capacity of a power supply enterprise, and is comprehensive reflection of quality and management level in aspects of power grid planning, construction, production operation, marketing service and the like. In the process of collecting, transmitting and storing the electric quantity information, abnormal conditions such as data missing, data overturning, data sudden decrease and data sudden increase often occur, so that the collected electric quantity data is inaccurate.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: the electric quantity fitting method based on the VEE process is provided to solve the technical problems that collected electric quantity data is inaccurate and the like due to the fact that abnormal conditions such as data missing, data overturning, data abrupt decrease and data abrupt increase are easy to occur during electric quantity collection in the prior art.
The technical scheme of the invention is as follows:
an electric quantity fitting method based on a VEE process comprises the following steps:
step 1, a master station establishes a VEE flow and a rule base to check and verify electric energy data;
step 2, verifying and judging the electric energy data according to the VEE rule base, and fitting the data;
and 3, storing, releasing and inquiring the fitted data.
The method for the master station to establish the VEE flow and the rule base to check and verify the electric energy data comprises the following steps:
step 1.1, the master station calculates electric quantity and performs data screening according to the collected electric energy data to realize abnormal checking of the inconsistency of the meter codes when the meter codes lack numbers, stop running of the meter codes, reverse running of the meter codes, jump of the meter codes, sum and total inequality of peak-valley level and freezing and zero time; and marking the abnormal table code as 'check failed', and identifying the name of the abnormal check rule.
The method for fitting the data in the step 2 comprises the following steps:
and 2.1, when the electricity cannot be calculated due to the 'check failure' data, judging that the data acquisition is failed by the master station, and taking the arithmetic mean of the interval electricity quantity at the front time point and the rear time point in the defect interval of the electric energy meter as an electricity fitting value when the number of the missing points in the continuous time point is less than or equal to 3 hours.
The method for fitting data in step 2 further comprises:
step 2.2, when the number of the missing points in the continuous time points is more than 3 hours and less than 72 hours, carrying out classification fitting according to time types; when the number of the missing points is more than 3 hours and less than 72 hours and more than one event type exists, entering the step 2.3; entering step 2.4 when the number of defects in the continuous time points is more than 72 hours;
step 2.3, when more than one time type exists in the defect time period, carrying out slicing calculation according to the time type, and finally obtaining final fitting electric quantity data through superposition calculation;
step 2.4, when the number of the missing points in the continuous time points is larger than 72 hours, if the difference between the front and rear meter codes in the defective interval is smaller than or equal to 1, taking the interval electric quantity arithmetic mean value of the front and rear time periods in the defective interval of the electric energy meter as an electric quantity fitting value; if the difference between the front and rear codes of the defect interval is larger than 1, entering step 2.5;
and 2.5, if the difference between the front table code and the rear table code of the defect interval is greater than 1, the master station does not perform fitting calculation any more.
The method for classifying and fitting according to time types comprises the following steps: the time types are divided into working days, double holidays and national legal holidays, and if the time interval with the defects is in the working days, the data are fitted according to the average value of the working days of the previous month; if the interval of the defect time period is in the double holidays, fitting according to the average value of the data of the double holiday interval of the previous month; and if the defect time interval is in the legal holiday, fitting and processing the data according to the latest holiday interval of the same type.
The method for storing the fitted data in the step 3 comprises the following steps:
and 3.1, managing VEE results, firstly storing the acquired data in an acquisition database, storing the data processed by the VEE process in a snapshot database, and writing the effective version data in the release database after T +3 days.
Step 3, the method for releasing the fitted data comprises the following steps:
step 3.2, releasing VEE data, writing the final version data in the snapshot database into a release database after T +3 days, providing data service for applications with high requirements on data integrity, and pushing data to all external system interfaces; adding the collected data, the estimated data and the edited data to all the provided data.
Step 3, the method for inquiring the fitted data comprises
Step 3.3, snapshot data query, wherein data processed by the VEE process is stored in a snapshot database, data service is provided for applications with high real-time requirements, and current version data query and historical version backtracking query of the snapshot database are supported; the current version query result is effective version data of all current data, and the historical version data query result is a change log of a certain record;
and 3.4, issuing data query, writing the effective version data after T +3 days into an issuing database, providing data service for applications with high integrity requirements, and supporting the final version data query.
The invention has the beneficial effects that:
according to the method, abnormal data or data missing during collection collected by the master station are judged, the abnormal data occurring in the data collection process of the master station is identified by a data fitting method, complete data missing and time-sharing data missing estimation and correction are performed on the abnormal data according to a verification and estimation method, the influence of data abnormality on the measurement of electric power enterprise operation indexes is reduced, complete and reliable data are provided for electric energy data value mining, and the accuracy of collected electric quantity data is improved; the technical problems that the collected electric quantity data is inaccurate due to abnormal conditions such as data loss, data turnover, data sudden decrease and data sudden increase easily occurring during electric quantity collection in the prior art are solved.
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FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
A method for fitting electric quantity based on VEE process includes:
step 1, a master station establishes a VEE (verification, estimation and editing) process and a rule base to check and verify electric energy data;
and 2, verifying and judging the electric energy data according to the VEE rule base, and fitting the data which accord with the rules.
And 3, storing, releasing and inquiring the fitted data.
The VEE flow and rule base flow judgment method in the step 1 comprises the following steps:
step 1.1: and the master station calculates the electric quantity and performs data screening according to the collected electric energy data to realize abnormal checking of the meter codes, such as meter code shortage, meter code stop, meter code backward walking, meter code reverse walking, meter code jumping, sum and total inequality of peak-valley level, inconsistency of the frozen meter codes with zero time and the like. And marking the abnormal table code as 'check failed', and identifying a specific abnormal check rule name. Step 1.2 is entered for "check not passed" data.
Step 1.2: and when the electricity quantity cannot be calculated due to the 'check failure' data, the master station considers that the data acquisition fails, and when the number of the deficiency points in the continuous time points is less than or equal to 3 hours, the arithmetic mean value of the electricity quantity in the interval of the previous time point and the next time point in the defect interval of the electric energy meter is taken as an electricity quantity fitting value, and the process is ended. And when the number of the defects in the continuous time points is more than 3 hours and less than 72 hours, entering the step 1.3.
Step 1.3: and when the number of the missing points in the continuous time points is more than 3 hours and less than 72 hours, classifying and fitting according to time types. The time types are divided into working days, double holidays and national legal holidays (the holidays are divided into two types of small holidays and large holidays). If the interval of the defect time period is in the working day, fitting according to the average value of the working day data of the previous month; if the interval of the defect time period is in the double holidays, fitting according to the average value of the data of the double holiday interval of the previous month; and if the defect time interval is in the legal holiday, fitting the data according to the latest holiday interval of the same type, and ending the process. When the number of missing points is greater than 3 hours and less than 72 hours and there are multiple event types, step 1.4 is entered. And when the number of the defects in the continuous time points is more than 72 hours, the step 1.5 is carried out.
Step 1.4: and when the plurality of time types exist in the defect time period, performing fragmentation calculation according to the time types, finally obtaining the final fitting electric quantity data through superposition calculation, and ending the process.
Step 1.5: and when the number of the missing points in the continuous time points is more than 72 hours, and if the difference between the table codes before and after the defective interval (the ending table code-the starting table code) is less than or equal to 1, taking the interval electric quantity arithmetic mean value of the time intervals before and after the defective interval of the electric energy meter as an electric quantity fitting value. If the difference between the table codes before and after the defect interval (stop table code-start table code) is greater than 1, go to step 1.6.
Step 1.6: if the difference between the table codes before and after the defect interval (stop table code-start table code) is greater than 1, the fitting may have a large deviation due to too many defect points and too large difference between the table codes before and after, and the master station does not perform fitting calculation any more.
Step 2, the data management and execution process based on the VEE check processing is as follows:
step 2.1, VEE result management, wherein collected data are firstly stored in a collection database, data processed by a VEE process are stored in a snapshot database, effective version data in T +3 days later are written into a release database, and T represents the current day; go to step 2.2.
And 2.2, releasing VEE data, writing the final version data in the snapshot database into a release database after T +3 days, providing data service for applications with high requirements on data integrity, and pushing data to all external system interfaces. And adding data labels such as collected data (normal and abnormal), estimated data (missing and abnormal), edited data (collected and estimated) and the like to all the provided data, and entering the step 2.3 after the data are published.
And 2.3, snapshot data query, wherein data processed by the VEE process is stored in a snapshot database, data service is provided for applications with high real-time requirements, and current version data query and historical version backtracking query of the snapshot database are supported. And (4) the current version query result is effective version data of all current data, the historical version data query result is a change log of a certain record, and the step 2.4 is carried out.
And 2.4, issuing data query, writing effective version data after T +3 days (data is not additionally acquired) into the issuing database, providing data service for applications with high integrity requirements, supporting the final version data query, and ending the process.
Claims (8)
1. An electric quantity fitting method based on a VEE process comprises the following steps:
step 1, a master station establishes a VEE flow and a rule base to check and verify electric energy data;
step 2, verifying and judging the electric energy data according to the VEE rule base, and fitting the data;
and 3, storing, releasing and inquiring the fitted data.
2. The VEE process-based electric quantity fitting method according to claim 1, wherein: the method for the master station to establish the VEE flow and the rule base to check and verify the electric energy data comprises the following steps:
step 1.1, the master station calculates electric quantity and performs data screening according to the collected electric energy data to realize abnormal checking of the inconsistency of the meter codes when the meter codes lack numbers, stop running of the meter codes, reverse running of the meter codes, jump of the meter codes, sum and total inequality of peak-valley level and freezing and zero time; and marking the abnormal table code as 'check failed', and identifying the name of the abnormal check rule.
3. The VEE process-based electric quantity fitting method according to claim 1, wherein: the method for fitting the data in the step 2 comprises the following steps:
and 2.1, when the electricity cannot be calculated due to the 'check failure' data, judging that the data acquisition is failed by the master station, and taking the arithmetic mean of the interval electricity quantity at the front time point and the rear time point in the defect interval of the electric energy meter as an electricity fitting value when the number of the missing points in the continuous time point is less than or equal to 3 hours.
4. The VEE process-based electric quantity fitting method according to claim 3, wherein: the method for fitting data in step 2 further comprises:
step 2.2, when the number of the missing points in the continuous time points is more than 3 hours and less than 72 hours, carrying out classification fitting according to time types; when the number of the missing points is more than 3 hours and less than 72 hours and more than one event type exists, entering the step 2.3; entering step 2.4 when the number of defects in the continuous time points is more than 72 hours;
step 2.3, when more than one time type exists in the defect time period, carrying out slicing calculation according to the time type, and finally obtaining final fitting electric quantity data through superposition calculation;
step 2.4, when the number of the missing points in the continuous time points is larger than 72 hours, if the difference between the front and rear meter codes in the defective interval is smaller than or equal to 1, taking the interval electric quantity arithmetic mean value of the front and rear time periods in the defective interval of the electric energy meter as an electric quantity fitting value; if the difference between the front and rear codes of the defect interval is larger than 1, entering step 2.5;
and 2.5, if the difference between the front table code and the rear table code of the defect interval is greater than 1, the master station does not perform fitting calculation any more.
5. The VEE process-based electric quantity fitting method according to claim 4, wherein: the method for classifying and fitting according to time types comprises the following steps: the time types are divided into working days, double holidays and national legal holidays, and if the time interval with the defects is in the working days, the data are fitted according to the average value of the working days of the previous month; if the interval of the defect time period is in the double holidays, fitting according to the average value of the data of the double holiday interval of the previous month; and if the defect time interval is in the legal holiday, fitting and processing the data according to the latest holiday interval of the same type.
6. The VEE process-based electric quantity fitting method according to claim 1, wherein: the method for storing the fitted data in the step 3 comprises the following steps:
and 3.1, managing VEE results, firstly storing the acquired data in an acquisition database, storing the data processed by the VEE process in a snapshot database, and writing the effective version data in the release database after T +3 days.
7. The VEE process-based electric quantity fitting method according to claim 1, wherein: step 3, the method for releasing the fitted data comprises the following steps:
step 3.2, releasing VEE data, writing the final version data in the snapshot database into a release database after T +3 days, providing data service for applications with high requirements on data integrity, and pushing data to all external system interfaces; adding the collected data, the estimated data and the edited data to all the provided data.
8. The VEE process-based electric quantity fitting method according to claim 1, wherein: step 3, the method for inquiring the fitted data comprises
Step 3.3, snapshot data query, wherein data processed by the VEE process is stored in a snapshot database, data service is provided for applications with high real-time requirements, and current version data query and historical version backtracking query of the snapshot database are supported; the current version query result is effective version data of all current data, and the historical version data query result is a change log of a certain record;
and 3.4, issuing data query, writing the effective version data after T +3 days into an issuing database, providing data service for applications with high integrity requirements, and supporting the final version data query.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108898246A (en) * | 2018-06-19 | 2018-11-27 | 深圳供电局有限公司 | Load prediction management system based on power system |
| CN109063125A (en) * | 2018-08-02 | 2018-12-21 | 国网安徽省电力有限公司 | A kind of electric energy data abnormality detection and processing method based on electrical energy measurement item |
| CN110913032A (en) * | 2019-11-18 | 2020-03-24 | 国家电网有限公司 | Method and platform for realizing DNS (Domain name System) domain name request analysis by using power grid threat information |
| CN111141950A (en) * | 2019-12-03 | 2020-05-12 | 深圳供电局有限公司 | Method for judging and repairing time-sharing code abnormality of electric meter of metering automation system |
| CN111177651A (en) * | 2019-12-03 | 2020-05-19 | 深圳供电局有限公司 | Time-sharing missing code fitting method for electric meter of metering automation system |
-
2021
- 2021-08-03 CN CN202110886412.XA patent/CN113568937A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108898246A (en) * | 2018-06-19 | 2018-11-27 | 深圳供电局有限公司 | Load prediction management system based on power system |
| CN109063125A (en) * | 2018-08-02 | 2018-12-21 | 国网安徽省电力有限公司 | A kind of electric energy data abnormality detection and processing method based on electrical energy measurement item |
| CN110913032A (en) * | 2019-11-18 | 2020-03-24 | 国家电网有限公司 | Method and platform for realizing DNS (Domain name System) domain name request analysis by using power grid threat information |
| CN111141950A (en) * | 2019-12-03 | 2020-05-12 | 深圳供电局有限公司 | Method for judging and repairing time-sharing code abnormality of electric meter of metering automation system |
| CN111177651A (en) * | 2019-12-03 | 2020-05-19 | 深圳供电局有限公司 | Time-sharing missing code fitting method for electric meter of metering automation system |
Non-Patent Citations (2)
| Title |
|---|
| 万崇华，许传志: "调查研究方法与分析 新编", 中国统计出版社, pages: 262 - 263 * |
| 李洋;蔡志远;刘海涛;苏剑;李蕊;: "广域分布式电源运营管控系统", 电力自动化设备, no. 03, pages 108 - 114 * |
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