CN111737239B - Meter value correction method, device and storage medium - Google Patents
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Abstract
The invention discloses a meter value correcting method, a meter value correcting device and a storage medium. By adopting the technical scheme of the invention, cumulant errors caused by meter interruption data and boundary value increment can be repaired, and the calculation amount of subsequent data statistics is reduced.
Description
Technical Field
The invention relates to the technical field of measurement, in particular to a meter value correction method, a meter value correction device and a storage medium.
Background
During the statistics of energy consumption, meters are often needed for data acquisition, such as a yield meter, a meter, a water meter, a yield meter, a water gas meter, a water-coal-slurry meter, a natural gas meter, a steam meter, a hot oil meter, a diesel meter, a carbon dioxide meter, an argon meter and the like. The method for calculating the energy consumption per hour by using the current calculation table specifically comprises the following steps: assuming one meter reading is taken every minute, the value taken at 59 minutes of the hour is subtracted from the value taken at 00 minutes of the hour to obtain the energy consumption value for the hour. Since 00 minutes of the next hour belongs to the next hour, the energy consumption value obtained from the hour group can be only 59 minutes of the hour. For example, year 2020, 4, 21, 7: the table count collected by 59 was 4300, for 7 points, 8: 00 does not belong to the 7 point statistic, so 8 cannot be taken: the tabulated value of 00. The meter value of the meter collected on 21/4/2020 is 3200, and then the energy consumption value at 7/4/21/2020 is 4300-.
However, the existing energy consumption statistical method depends on the accuracy of the materials collected by the meter, and when the meter jumps or the collection is interrupted due to a fault, calculation errors or calculation failure can be caused. Furthermore, when grouped by hour, there is a loss of incremental value at the hour boundary. For example, assume 7: the gauge value collected at 00 is 3200, 7: the table counts collected by 59 were 4300, 8: the table counts collected at 00 are 4320, 8: 59 the value of the table collected is 5320. Then according to the existing statistical method, the energy consumption values at 7 and 8 are 1100 and 1000, respectively, and the sum of the two hours is 2100. However, if the energy consumption of two hours, i.e. 7 point and 8 point, is calculated simultaneously, the energy consumption value is 5320-: 59 to 8: 00 this minute has a meter increment and this minute is also a boundary point. Although the error is small, the accumulated error will be large.
Disclosure of Invention
The embodiment of the invention provides a meter value correction method, a meter value correction device and a storage medium, which can be used for correcting cumulant errors caused by meter interruption data and boundary value increment and reducing the calculation amount of subsequent data statistics.
The invention provides a meter value correction method, which comprises the following steps:
acquiring original acquisition values of a meter, grouping the original acquisition values according to hours, and cleaning data of each group of data according to a preset cleaning rule to obtain extreme value column data corresponding to each hour; wherein the extremum column data comprises: time, a maximum X and a minimum Y of the table in the set of data;
sequencing all the extreme value column data according to the first-to-last acquisition time to obtain matrix data of M rows and 3 columns; m is a positive integer greater than 2;
and assigning the two extreme value column data of the adjacent rows to correct each extreme value column data in the matrix data, thereby realizing the correction of the original acquisition value of the meter.
Further, the assigning process is performed on the two extremum column data of the adjacent rows, specifically:
calculating the minimum value Y in the extreme value column data of the (N + 1) th rowN+1And the maximum value X in the N row extreme value column dataNA difference of (d); wherein N is more than 0 and less than M;
judging whether the difference value is larger than a preset value or not;
if yes, reassigning the maximum value in the N row of extreme value column data as: xN+(XN-YN+1) 2; and reassigning the minimum value in the extreme value column data of the (N + 1) th row as: y isN+1-(XN-YN+1)/2；
Otherwise, the maximum value in the N-th row of extreme value column data and the minimum value in the N + 1-th row of extreme value column data are not processed.
Further, after the correction of the original acquisition value of the meter is implemented, the method further includes:
and calculating the energy consumption of each hour or the total energy consumption in a preset time interval according to the corrected table value.
Accordingly, the present invention provides a meter value correction device, comprising: the device comprises a data cleaning module, a sorting module and a correcting module;
the data cleaning module is used for acquiring original acquisition values of the meter, grouping the original acquisition values according to hours, and cleaning data of each group of data according to a preset cleaning rule to acquire extreme value line data corresponding to each hour; wherein the extremum column data comprises: time, a maximum X and a minimum Y of the table in the set of data;
the sorting module is used for sorting all the extreme value line data according to the first-come and last-come acquisition time to obtain matrix data of M rows and 3 lines; m is a positive integer greater than 2;
and the correction module is used for carrying out assignment processing on the two extreme value line data of the adjacent rows so as to correct each extreme value line data in the matrix data, thereby realizing the correction of the original acquisition value of the meter.
Further, the modification module is configured to assign values to two extreme value line data of adjacent rows, and specifically includes:
the correction module calculates a minimum value Y in the extreme value line data of the (N + 1) th rowN+1And the maximum value X in the N row extreme value column dataNA difference of (d); wherein N is more than 0 and less than M;
judging whether the difference value is larger than a preset value or not;
if yes, reassigning the maximum value in the N row of extreme value column data as: xN+(XN-YN+1) 2; and reassigning the minimum value in the extreme value column data of the (N + 1) th row as: y isN+1-(XN-YN+1)/2；
Otherwise, the maximum value in the N-th row of extreme value column data and the minimum value in the N + 1-th row of extreme value column data are not processed.
Further, the apparatus further comprises: an energy consumption calculation module;
and the energy consumption calculation module is used for calculating the energy consumption of each hour or the total energy consumption in a preset time interval according to the corrected table value.
Accordingly, the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program runs, the apparatus in which the computer-readable storage medium is located is controlled to execute the table value correction method according to the present invention.
Therefore, according to the meter value correction method, the meter value correction device and the storage medium provided by the invention, the original collected values are grouped and data-cleaned to obtain the extreme value column data corresponding to each hour, all the extreme value column data are sorted according to the collection time from first to last to obtain the matrix data of M rows and 3 columns, and finally, the two extreme value column data of adjacent rows are assigned to correct each extreme value column data in the matrix data, so that the original collected values of the meter are corrected. Compared with the prior art that the original acquisition value is directly used for subsequent energy consumption statistics, the method provided by the invention has the advantages that the original acquisition value of the meter is modified, the cumulant error caused by meter-break data and boundary value increment can be repaired, and the calculation amount of subsequent data statistics is reduced. In addition, the invention replaces the last value and the most previous value in the time node with the maximum value and the minimum value, thereby reducing the sequencing consumption.
Drawings
FIG. 1 is a schematic flow chart diagram illustrating a method for correcting a meter value according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart diagram illustrating an embodiment of a data cleansing method provided by the present invention;
FIG. 3 is a schematic diagram of a meter acquisition waveform provided by the present invention;
fig. 4 is a schematic structural diagram of an embodiment of a meter value correction apparatus provided in the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of an embodiment of a meter value correction method according to the present invention. The method shown in fig. 1 includes steps 101 to 103, and each step is as follows:
step 101: acquiring original acquisition values of a meter, grouping the original acquisition values according to hours, and cleaning data of each group of data according to a preset cleaning rule to obtain extreme value column data corresponding to each hour; wherein the extremum column data comprises: time, maximum X and minimum Y of the table in the set of data.
In this embodiment, in step 101, data cleaning is performed on each set of data, mainly to clean up interference caused by the jump value and the reset value. The jump value is a value at a jump point, which is a point at which the magnitude of upward increase and downward decrease of data from the previous point exceeds a set threshold value compared to the data. The jump is a jump towards the downward direction and is called a down-jump point, and conversely, the jump is an up-jump point. The reset value is a numerical value at a reset point, and the meter reset generally means that the meter is normally clear 0. When the data of the meter is accumulated to be large, a worker needs to read a plurality of bits during meter reading, and in order to solve the problem, a factory can carry out the operation of cleaning the meter count value after the meter runs for a period of time. The statistical data can cause the meter data to jump downwards suddenly, but then the meter data can be accumulated normally.
In this embodiment, after the data is cleaned, extremum line data corresponding to each hour is obtained, where the extremum line data includes: time, maximum X and minimum Y of the table in the set of data. For example, after data washing at 11 days 21/4/2020, the maximum value of the meter is 4300 and the minimum value of the meter is 3200, which is recorded as [2020042111,4300,3200 ].
In this embodiment, the method for data cleaning may be a conventional cleaning method, or may be the cleaning method provided by the present invention. The cleaning method comprises the following steps: judging whether the nth original data point in the original data curve is the increment of the nth-1 fitting data point of the fitting data curve; judging whether the (n + 1) th original data point is the increment of the nth original data point; if yes, drawing the nth original data point to the fitted data curve; if not, judging whether the nth original data point is the increment of n-1 original data points in the original data curve or not; judging whether the n +1 th original data point is an increment or a decrement exceeding a preset threshold compared with the nth original data point; if yes, calculating to obtain the value of the nth fitting data point of the fitting data curve; if not, taking the value of the n-1 th fitting data point of the fitting data curve as the value of the nth fitting data point; and obtaining a complete fitted data curve according to all the fitted data points. Where n is the number of origin points, the specific cleaning process may be, but is not limited to, the description with reference to fig. 2.
According to the embodiment of the invention, the nth original data point is respectively compared with the (n-1) th fitted data point of the fitted data curve, the (n-1) th original data point of the original data curve and the (n + 1) th original data point, the original data curve can be depicted as the fitted data curve capable of accurately representing data without depending on the whole original data curve, the calculation complexity is effectively reduced, the data points to be fitted are compared through a hyperbolic mechanism, the accuracy of the data fitted data curve can be effectively improved, and the data cleaning efficiency can be effectively improved.
Step 102: and sequencing all the extreme value column data according to the acquisition time from first to last to obtain matrix data of M rows and 3 columns.
In this embodiment, M is a positive integer greater than 2, and the matrix data formed by sorting the positive integers according to the time sequence is as follows:
[2020042101,2300,1200]
[2020042102,4300,3200]
[2020042103,6300,5200]
[2020042104,8300,7200]
[2020042105,10300,9200]
[2020042106,12300,11200]
……
step 103: and assigning the two extreme value column data of the adjacent rows to correct each extreme value column data in the matrix data, thereby realizing the correction of the original acquisition value of the meter.
In this embodiment, step 103 specifically includes:
calculating the minimum value Y in the extreme value column data of the (N + 1) th rowN+1And the maximum value X in the N row extreme value column dataNA difference of (d); wherein N is more than 0 and less than M;
judging whether the difference value is larger than a preset value or not;
if yes, reassigning the maximum value in the N row of extreme value column data as: xN+(XN-YN+1) 2; and reassigning the minimum value in the extreme value column data of the (N + 1) th row as: y isN+1-(XN-YN+1)/2；
Otherwise, the maximum value in the N-th row of extreme value column data and the minimum value in the N + 1-th row of extreme value column data are not processed.
As an example of this embodiment, after step 103, the method may further include: and calculating the energy consumption of each hour or the total energy consumption in a preset time interval according to the corrected table value. The energy consumption value is calculated through the corrected table calculation value, cumulant loss can be repaired, and the calculation accuracy is improved.
For better explaining the principle and the process of the invention, refer to fig. 3, and fig. 3 is a schematic diagram of a meter acquisition waveform provided by the invention. Assuming that fig. 3 is a graph of 4, 21 and 2020, the vertical line is the hour boundary, point a is 1: 45, point B is 2: 15. the left side of the vertical line is a 1 o 'clock curve and the right side is a 2 o' clock curve. Point C is the minimum (and minimum) value of the curve AC, and point D is the maximum (maximum) value of the curve BD. As can be seen from fig. 3, there is half an hour of broken data between a and B.
Assume that the value of the table at point C is 0, the value of the table at point A is 2000, the value of the table at point B is 3000, the value of the table at point D is 5000, and the set value is 500. The table values of the four points are all values after data cleaning.
If the total energy consumption values of 1 o 'clock and 2 o' clock are calculated according to the conventional calculation method, the calculated energy consumption value is 1000 different from the value obtained by subtracting the C point table value (5000-0-5000) from the D point table value (2000-0) + (D point table value-B point table value) — (2000-0) + (5000-3000) — 4000), and the accumulation amount of BA is obviously neglected due to data interruption.
With the correction method of the present invention, the minimum value of 2 points at 4/21/2020-the maximum value of 1 point at 4/21/2020-the table count value of B-the table count value of a-1000 is larger than the set threshold value of 500, and therefore, the points a and B are reassigned.
1 o' clock maximum value new assignment: maximum at point 1 + (minimum at point 2-maximum at point 1)/2 ═ 2000+ (3000-;
new minimum value of 2 o' clock is assigned: minimum of 2 points- (minimum of 2 points-maximum of 1 point)/2 ═ 2500.
At this time, the total energy consumption of 1 point and 2 points is (the maximum value of 1 point is newly assigned-C point table value) + (the minimum value of D point table value-2 point is newly assigned) (2500-0) + (5000-. The accumulated quantity of the AB section data is obviously supplemented back when the accumulated quantity is consistent with the quantity obtained by subtracting the C point table value from the D point table value (5000-0 is 5000). The principle is to divide the accumulated amount of the segment of AB data into 1 o 'clock and 2 o' clock on average.
Using the above method, 7: 59-8: the error caused by statistics of the meter increment of the boundary second of 00 can be eliminated.
Correspondingly, referring to fig. 4, fig. 4 is a schematic structural diagram of an embodiment of the meter value correction device provided in the present invention. As shown in fig. 4, the apparatus includes: a data cleansing module 401, a sorting module 402, and a correction module 403.
The data cleaning module 401 is configured to obtain original acquisition values of the meter, group the original acquisition values by hour, and perform data cleaning on each group of data according to a preset cleaning rule to obtain extreme value line data corresponding to each hour; wherein, the extremum column data comprises: time, maximum X and minimum Y of the table in the set of data.
The sorting module 402 is configured to sort all extremum line data according to the first-come-last acquisition time, and obtain matrix data of M rows and 3 lines; m is a positive integer greater than 2.
The correcting module 403 is configured to assign two extremum line data of adjacent rows to correct each extremum line data in the matrix data, so as to correct the original collected value of the meter.
In this embodiment, the modification module 403 is configured to assign two extremum column data of adjacent rows, specifically:
the correction module 403 calculates the minimum value Y in the extreme value row data of the (N + 1) th rowN+1And the maximum value X in the N row extreme value column dataNA difference of (d); wherein N is more than 0 and less than M;
judging whether the difference value is larger than a preset value or not;
if yes, reassigning the maximum value in the N row of extreme value column data as: xN+(XN-YN+1) 2; and reassigning the minimum value in the extreme value column data of the (N + 1) th row as: y isN+1-(XN-YN+1)/2；
Otherwise, the maximum value in the N-th row of extreme value column data and the minimum value in the N + 1-th row of extreme value column data are not processed.
As an example of this embodiment, the meter value correction apparatus further includes: and an energy consumption calculation module. And the energy consumption calculation module is used for calculating the energy consumption of each hour or calculating the total energy consumption in a preset time interval according to the corrected table value.
Correspondingly, the embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, and when the computer program runs, the apparatus where the computer-readable storage medium is located is controlled to execute the table value correction method according to the present invention.
In summary, the present invention has the following improvements and advantages:
therefore, according to the meter value correction method, the meter value correction device and the storage medium provided by the invention, the original collected values are grouped and data-cleaned to obtain the extreme value column data corresponding to each hour, all the extreme value column data are sorted according to the collection time from first to last to obtain the matrix data of M rows and 3 columns, and finally, the two extreme value column data of adjacent rows are assigned to correct each extreme value column data in the matrix data, so that the original collected values of the meter are corrected. Compared with the prior art that the original acquisition value is directly used for subsequent energy consumption statistics, the method provided by the invention has the advantages that the original acquisition value of the meter is modified, the cumulant error caused by meter-break data and boundary value increment can be repaired, and the calculation amount of subsequent data statistics is reduced.
In addition, the invention uses the maximum value and the minimum value of each hour to correct the last value and the first value of the original acquisition value, and does not depend on the meter value of 00 minutes and the meter value of 59 minutes. And moreover, maximum value and minimum value calculation are introduced, so that the sorting consumption can be reduced, and the calculation amount is small.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (5)
1. A method of meter value correction, comprising:
acquiring original acquisition values of a meter, grouping the original acquisition values according to hours, and cleaning data of each group of data according to a preset cleaning rule to obtain extreme value column data corresponding to each hour; wherein the extremum column data comprises: time, a maximum X and a minimum Y of the table in the set of data;
sequencing all the extreme value column data according to the first-to-last acquisition time to obtain matrix data of M rows and 3 columns; m is a positive integer greater than 2;
assigning two extreme value column data of adjacent rows to correct each extreme value column data in the matrix data, thereby realizing the correction of the original acquisition value of the meter; wherein, the assigning process is performed on the two extreme value column data of the adjacent rows, specifically: calculating the minimum value Y in the extreme value column data of the (N + 1) th rowN+1And the maximum value X in the N row extreme value column dataNA difference of (d); wherein N is more than 0 and less than M; judging whether the difference value is larger than a preset value or not; if yes, reassigning the maximum value in the N row of extreme value column data as: xN+(XN-YN+1) 2; and reassigning the minimum value in the extreme value column data of the (N + 1) th row as: y isN+1-(XN-YN+1) 2; otherwise, the maximum value in the N row extreme value column data and the (N + 1) th row extreme value column are not comparedThe minimum value in the data is processed.
2. The meter value correction method of claim 1, wherein after said performing the meter raw acquisition value correction, further comprising:
and calculating the energy consumption of each hour or the total energy consumption in a preset time interval according to the corrected table value.
3. A meter value correction device, characterized by comprising: the device comprises a data cleaning module, a sorting module and a correcting module;
the data cleaning module is used for acquiring original acquisition values of the meter, grouping the original acquisition values according to hours, and cleaning data of each group of data according to a preset cleaning rule to acquire extreme value line data corresponding to each hour; wherein the extremum column data comprises: time, a maximum X and a minimum Y of the table in the set of data;
the sorting module is used for sorting all the extreme value line data according to the first-come and last-come acquisition time to obtain matrix data of M rows and 3 lines; m is a positive integer greater than 2;
the correction module is used for carrying out assignment processing on two extreme value line data of adjacent rows so as to correct each extreme value line data in the matrix data, thereby realizing correction of an original acquisition value of the meter; the correction module is used for assigning the two extreme value line data of the adjacent rows, and specifically comprises the following steps: the correction module calculates a minimum value Y in the extreme value line data of the (N + 1) th rowN+1And the maximum value X in the N row extreme value column dataNA difference of (d); wherein N is more than 0 and less than M; judging whether the difference value is larger than a preset value or not; if yes, reassigning the maximum value in the N row of extreme value column data as: xN+(XN-YN+1) 2; and reassigning the minimum value in the extreme value column data of the (N + 1) th row as: y isN+1-(XN-YN+1) 2; otherwise, the maximum value in the N-th row of extreme value column data and the minimum value in the N + 1-th row of extreme value column data are not processed.
4. The meter value correction apparatus according to claim 3, characterized in that the apparatus further comprises: an energy consumption calculation module;
and the energy consumption calculation module is used for calculating the energy consumption of each hour or the total energy consumption in a preset time interval according to the corrected table value.
5. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the table count value correction method according to any one of claims 1 to 2.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6094622A (en) * | 1996-10-22 | 2000-07-25 | Abb Power T&D Company Inc. | System and method for automatically determining the electrical energy service type to which an energy meter is connected |
| CN107621621A (en) * | 2017-09-05 | 2018-01-23 | 天津市电力科技发展有限公司 | A kind of intelligent electric energy meter is in Line synthesis error calibration method |
| CN107655548A (en) * | 2017-09-05 | 2018-02-02 | 天津市电力科技发展有限公司 | Check method during a kind of on-line operation intellectual water meter |
| CN107741578A (en) * | 2017-09-05 | 2018-02-27 | 天津市电力科技发展有限公司 | A kind of original meter reading data processing method for intelligent electric energy meter kinematic error remote calibration |
| CN107782421A (en) * | 2017-10-12 | 2018-03-09 | 三川智慧科技股份有限公司 | Based on the calibration method without magnetic NB IoT Internet of Things water meter measurement threshold values |
| CN109975738A (en) * | 2019-03-21 | 2019-07-05 | 深圳市敏泰智能科技有限公司 | A kind of electric water meter calibration method and calibrating installation |
-
2020
- 2020-05-25 CN CN202010449663.7A patent/CN111737239B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6094622A (en) * | 1996-10-22 | 2000-07-25 | Abb Power T&D Company Inc. | System and method for automatically determining the electrical energy service type to which an energy meter is connected |
| CN107621621A (en) * | 2017-09-05 | 2018-01-23 | 天津市电力科技发展有限公司 | A kind of intelligent electric energy meter is in Line synthesis error calibration method |
| CN107655548A (en) * | 2017-09-05 | 2018-02-02 | 天津市电力科技发展有限公司 | Check method during a kind of on-line operation intellectual water meter |
| CN107741578A (en) * | 2017-09-05 | 2018-02-27 | 天津市电力科技发展有限公司 | A kind of original meter reading data processing method for intelligent electric energy meter kinematic error remote calibration |
| CN107782421A (en) * | 2017-10-12 | 2018-03-09 | 三川智慧科技股份有限公司 | Based on the calibration method without magnetic NB IoT Internet of Things water meter measurement threshold values |
| CN109975738A (en) * | 2019-03-21 | 2019-07-05 | 深圳市敏泰智能科技有限公司 | A kind of electric water meter calibration method and calibrating installation |
Non-Patent Citations (1)
| Title |
|---|
| 基于实时补偿的智能电能表日计时误差修正方法研究;杨鹏龙等;《电气工程与自动化》;20191025(第30期);第20-21页 * |
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