CN108319573B - Energy statistical data based abnormity judgment and restoration method - Google Patents
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Abstract
The invention particularly relates to a method for judging and repairing abnormity based on energy statistical data, which comprises a judging process and a repairing process, wherein the judging process comprises the following steps: judging whether the acquired data loss with the granularity N is required to be repaired according to whether the original data aN of the granularity N is acquired or not; and judging whether the granularity N needs to be repaired for sudden reduction of the acquired data or not according to whether the adjacent granularity statistic value of the granularity N is less than 0 or not. And judging whether the acquired data is suddenly enlarged and needs to be repaired according to whether the adjacent granularity statistic value of the granularity N is that the value N is more than or equal to k × A or not. The repairing process comprises the steps of directly adding the historical average value, and if the collected data is restored to the original base number, repairing according to the proportion of the adjacent granularity statistics value. The invention can intelligently judge the abnormality of the original collected data caused by equipment problems in the energy related management system and carry out the statistical data restoration of abnormal scenes.
Description
Technical Field
The invention relates to the field of smart power grids and energy metering, in particular to a method for judging and repairing abnormity based on energy statistical data.
Background
The energy management statistical system comprises various energy classification and itemized devices including an electric meter, a water meter, a gas meter, a heat meter, an oxygen meter and the like. In an energy-related management system, the remote pulse data of an energy metering device is generally input into an associated energy platform or system, and the platform or system processes and stores the data. The problem that the quality of the value data is unified in the processing process is always difficult to solve, and is mainly shown in the following steps: the data of the equipment layer is unstable, and the ratio that the equipment data is not reported according to the actual value caused by equipment failure is quite high; the data reporting value is misplaced due to network environment factors; the environment measured by the device is unstable, resulting in abnormal environmental data, etc. Under the condition of sudden change and loss of partial data, the data statistics value of the energy management related system becomes worthless, and in order to effectively solve the abnormal condition of the statistical data, a method for providing the data to each energy management platform and system so as to carry out abnormal judgment on the collected data and repair the abnormal condition is needed.
Disclosure of Invention
The technical problem to be solved is as follows:
in order to achieve the purpose, the invention provides a method for judging and repairing the abnormality based on the energy statistical data. The method judges and repairs the collected data by adopting a method of carrying out comparative analysis processing on the calculated statistic value of each time granularity. The time granularity is calculated as statistics, and in practical application, the year statistics, the month statistics, the day statistics, the hour statistics and the like are common.
The technical scheme is as follows:
1. a method for abnormality judgment and restoration based on energy statistic data comprises the following steps: a judgment process and a repair process, wherein the judgment process comprises the following steps:
the method comprises the following steps: judging whether the original data aN of the current granularity N is obtained or not, and if so, performing a second step; if the acquired data is not acquired, judging that the acquired data with the granularity N is lost and needs to be repaired, marking the abnormal attribute flag =1 of the granularity N, and entering a data loss repairing process.
Step two: judging whether the adjacent granularity statistic value of the granularity N is less than 0: if not less than 0, entering a third step; if the value N is smaller than 0, judging that the granularity N is the sudden reduction of the acquired data, and simultaneously setting the abnormal attribute flag =1 of the granularity N; and entering a recovery process of suddenly reducing the collected data.
Step three: judging whether the adjacent granularity statistic value of the granularity N meets the condition that the value N is more than or equal to k × A, wherein A is the historical average value of the adjacent granularity statistic value, and k is a preset safety coefficient; if the value N is more than or equal to k × A, judging that the acquired data is suddenly increased, and marking the abnormal attribute flag =1 of the granularity; entering a recovery process of suddenly enlarging acquired data; and if the condition that the value N is not more than or equal to k × A is not met, the original data of the granularity N is put into a warehouse.
The repair process comprises the following steps:
step A: the granular data loss repairing process comprises the following steps: when the original data aN of the granularity N is not obtained, obtaining the last sampling data aK of the granularity K which can be obtained recently; acquiring the proportion of the adjacent granularity data statistic values of granularity N 'and K' of corresponding time in the last time period; if the proportion of the adjacent granularity data statistic of the last time period is successfully obtained, the data statistic of the granularity N of the repair time period is brought in according to the proportion of the adjacent granularity statistic of the granularity N 'and the granularity K' of the last time period, and then repair data of the lost granularity N are calculated; if the data statistics value of the last period is not obtained successfully, calculating the average value of the adjacent granularity statistics values from the granularity K to the granularity (N-1), directly repairing the data of the granularity N by using a method of taking the average values of the adjacent granularity statistics values from the granularity K to the granularity (N-1), and warehousing the repaired data of the granularity N.
And B: and (3) a repairing process that the collected data suddenly becomes smaller or larger: acquiring a historical average value A of adjacent granularity statistics, setting the adjacent granularity statistics value of the granularity N as the historical average value A, namely restoring the data of the granularity N to a (N-1) + A, and marking the abnormal attribute flag =1 of the granularity N; acquiring original data a (N + M) of granularity (N + M) after preset M granularities, and calculating an adjacent statistic value (N + M) corresponding to the granularity (N + M); judging whether value (N + M) is larger than or equal to a preset safety system value k A or not; if the particle size is larger than or equal to the safety system value k × A, normally warehousing the repaired particle size N statistical value; and if the granularity is not more than or equal to the safety system value k × A, performing step C to perform secondary repair on the granularity N.
And C: and (3) secondary repairing: acquiring the proportion of the adjacent granularity data statistic values of granularity N 'and (M + N)' corresponding to time in the last period; if the adjacent granularity data statistic of the last time period is successfully obtained, the data statistic of the granularity N of the repair time period is brought in according to the proportion of each adjacent granularity statistic between the granularity N 'and (M + N)' of the last time period, and then secondary repair data of the granularity N are calculated; if the data statistics value of the last time period is not successfully acquired, calculating the average value of the adjacent granularity statistics values from the granularity (N-1) to the granularity (M + N), and directly restoring the data of the granularity N by using a method of averaging the adjacent granularity statistics values from the granularity (N-1) to the granularity (M + N); and finally, warehousing the data with the granularity N repaired for the second time.
The aN is the collected original data of the granularity N, a (N-1) is the original data of the granularity N adjacent to the previous granularity, a (N + 1) is the original data of the granularity N adjacent to the next granularity, and value N is the adjacent granularity statistic of the granularity N, wherein value N = aN-a (N-1).
Further, still include: when the granularity N does not exist, the neighbor granularity statistic value of the preset granularity N =0 and the neighbor granularity statistic value (N + 1) =0 of the granularity N + 1. .
Further, the granularity raw data includes a value in granularity units of one year, a value in granularity units of one month, a value in granularity units of one day, and a value in granularity units of one hour.
The granularity in the present invention adopts the concept of time granularity. The expression form in practical application may be daily statistics, monthly statistics, yearly statistics, and the like. Of course, the period of the particle size may be 1 minute, 15 minutes, or the like. The historical average value A of the adjacent particle size statistics referred to in step B of the present invention is: averaging the average value of the year statistics according to the historical value of the year statistics, and storing; averaging the month statistical average values according to the corresponding months of the previous 5 years, and storing data of 12 months, wherein the average value of 1 month is the average value of 1 month in 2013, 2014, 2015, 2016 and 2017; the day statistics are averaged according to the week number corresponding to the previous 4 weeks, and the data of 7 days in one week are stored; the hour statistical average value is taken by averaging the corresponding hours of each day of the previous week, and the data is stored for 24 hours in 1 day.
Thirdly, the beneficial effects are that:
the invention classifies the data abnormity and carries out targeted repair. In the method, the anomalies are divided into the following types by calculating the statistic value of each granularity: 1) and abnormal statistics caused by the loss of the granularity of the collected data. 2) The collected data is suddenly reduced and then is recovered to be normal, so that the statistical value is abnormal. 3) The collected data does not recover to be normal after suddenly reducing, so that the statistical value is abnormal. 4) The collected data are restored to be normal after suddenly becoming large, and the statistical value is abnormal. 5) The collected data are not recovered to be normal after suddenly increasing, so that the statistical value is abnormal. And performing targeted repair according to different abnormalities. Through the process, the abnormity of the statistical value caused by the abnormity of the original collected data caused by the equipment problem in the energy related management system can be intelligently judged; and through the statistical data restoration of abnormal scenes, the occurrence of statistical data loss, high and low and frequent data accumulation caused by equipment problems in the system is avoided, the manual restoration of subsequent system reports is reduced, and the data quality of the energy related management system is improved.
The method can perform modular processing on the logic service judgment of each data abnormal type, reduce the re-writing of related codes under the condition of increasing the data types subsequently, and can perform effective multiplexing.
Drawings
FIG. 1 is a flow chart of data repair when granular data is lost;
FIG. 2 is a flow chart of data abnormality and restoration after the granularity acquisition data suddenly becomes smaller, wherein the flow chart includes restoration flow for restoring normal and restoring abnormal;
fig. 3 is a flowchart of data exception and repair after the granularity acquisition data suddenly becomes large, wherein the flowchart includes a repair process of recovering normal and not recovering normal.
Detailed Description
The method is described below with reference to the accompanying drawings. As shown in fig. 1, first, it is determined whether the original data aN of the current granularity N is acquired, and if so, the original data is analyzed; if the data is not acquired, judging that the acquired data loss of the granularity N needs to be repaired, and marking the abnormal attribute flag =1 of the granularity N, wherein the adjacent granularity statistic value of the granularity N =0 and the adjacent granularity statistic value (N + 1) =0 of the granularity (N + 1) are preset when the adjacent granularity N statistic value is calculated, and the preset purpose is to confirm that the data of the granularity is empty to repair when the system detects that the adjacent granularity statistic value is 0. When the data is not acquired, acquiring the last sampling data aK of the granularity K which can be acquired recently; acquiring the proportion of adjacent granularity statistics values of granularity K 'and N' corresponding to time in the last time period; if the adjacent granularity statistic of the last time period is successfully obtained, the data statistic of the granularity N of the repair time period is brought in according to the proportion of the adjacent granularity statistic of the granularity N 'and the granularity K' of the last time period, and then the repair data of the lost granularity N is calculated; if the adjacent granularity statistics of the last period is not successfully acquired, calculating the average value of the adjacent granularity statistics from the granularity K to the granularity N-1, directly restoring the data of the granularity N by using a method of averaging the adjacent granularity statistics from the granularity K to the granularity N-1, and warehousing the restored data of the granularity N. If the historical data of the last period is not acquired in the above process, the values of the (K-N + 1) granularity periods are calculated according to the average distribution, namely, each adjacent granularity statistic value from the granularity K to the granularity N-1 is value/(N-1-K), wherein value = a (N-1) -a K.
For example, the following steps are carried out: when the acquired data of the granularity N is lost, the adjacent granularity statistic value which can cause the intermediate loss is 0. If the collected data of the 1 point is a1, the collected data a2 of the 2 points is not collected to be null, and the statistic value of the adjacent granularity data of the 2 points is 0, namely value2=0, the data is judged to be abnormal data and needs to be repaired; after the granularity of the data collected at the point 3 is recovered, a3 is collected, and since a2 is empty, the statistic value of the adjacent granularity of the point 3 is also 0, and the data is judged to be abnormal data and needs to be repaired; data granularity a4 was collected at 4 points, and the data statistics at those 4 points were a4-a 3. The method for restoring the 2-point and 3-point statistical values comprises the following steps: and after calculating value = a3-a1, carrying out proportional distribution according to the proportion of the adjacent statistic value in the same time period yesterday. If the ratio of the 2-point neighborhood statistic to the 3-point neighborhood statistic is 6:4 yesterday, then today the 2-point neighborhood granularity statistic modification value is value 6/(6+4), and the 3-point neighborhood granularity statistic modification value is value 4/(6+ 4). The annual statistics value, the monthly statistics value and the daily statistics value are similar, and the repairing mode is reasonably distributed and repaired according to the historical proportion. If the historical data of the previous period is not acquired, the repairing method of the 2-point and 3-point statistical values comprises the following steps: value = a3-a1 is calculated, the average value of the neighborhood statistics from 1 point to 3 points is calculated as (a 3-a 1)/(3-1), and the repair data of 2 points is calculated as a1+ (a 3-a 1)/(3-1).
As shown in fig. 2 and fig. 3, when the original data of the granularity N is successfully acquired, the abnormal data is classified into two types, i.e., the acquired data is suddenly reduced and the acquired data is increased by determining whether the adjacent statistical data of the granularity N is greater than zero. When the data suddenly becomes small, the collected data is certain to have an abnormality because the meter data of the energy source should be cumulatively increased. Acquiring the average value of the adjacent granularity statistics, setting the adjacent granularity statistics value of the granularity N as the historical average value A for repairing, and after repairing, checking the data acquired after the granularity N to see whether the granularity N needs secondary repair. The detection method comprises the following steps: the method comprises the following steps of dividing the abnormity into two types by carrying out comparative analysis on data with preset M granularities: one is that the data is restored to normal later, and the other is that the data can not be restored to normal; specifically, after preset M granularities, acquiring original data a (N + M) of granularity (N + M), and calculating an adjacent statistic value (N + M) corresponding to the granularity (N + M); judging whether value (N + M) is larger than or equal to a preset safety system value k A or not; if the data is not more than or equal to the safety system value k × A, the data is restored to the former base number later, and secondary repair is needed; and if the safety system value k is larger than or equal to the safety system value k A, the original data of the granularity (N + M) is not restored to the original base number, and the repair data of the granularity N is normally put in storage.
When the adjacent statistic data of the granularity N is larger than zero, the collected data can be normal and suddenly enlarged, and whether the data is normal or suddenly enlarged is judged by judging whether the adjacent statistic value is larger than or equal to a preset safety system value k A. And if the adjacent granularity statistic value of the granularity N is larger than or equal to a preset safety system value k A, the acquired data of the granularity suddenly becomes larger and needs to be repaired. When the data of the granularity N suddenly increases, acquiring a historical average value A of adjacent granularity statistics, restoring the adjacent granularity statistics value of the granularity N to the historical average value A, namely restoring the data of the granularity N to a (N-1) + A, and marking the abnormal attribute flag =1 of the granularity N; acquiring original data a (N + M) of granularity (N + M) after preset M granularities, and calculating an adjacent statistic value (N + M) corresponding to the granularity (N + M); judging whether value (N + M) is larger than or equal to a preset safety system value k A or not; if the data is not more than or equal to the safety system value k × A, the data is restored to the original base number, and the corrected statistic value of the granularity N needs to be corrected for the second time; and if the particle size is larger than or equal to the safety system value k × A, normally warehousing the repaired value of the particle size N.
The above mentioned secondary repair process is: acquiring the proportion of the adjacent granularity data statistic values of granularity N 'and (M + N)' corresponding to time in the last period; if the adjacent granularity data statistic of the last time period is successfully obtained, the data statistic of the granularity N of the repair time period is brought in according to the proportion of each adjacent granularity statistic between the granularity N 'and (M + N)' of the last time period, and then secondary repair data of the granularity N are calculated; if the data statistics value of the last time period is not successfully acquired, calculating the average value of the adjacent granularity statistics values from the granularity (N-1) to the granularity (M + N), directly restoring the data with the granularity N by using a method of averaging the adjacent granularity statistics values from the granularity (N-1) to the granularity (M + N), and finally warehousing the secondarily restored data.
It can be seen from the above description of the method that the above method for repairing the loss of the collected original data is the same as the method adopted for the secondary repair, so that the method has the advantages of performing modular processing of the program for the logic service judgment of each data abnormal type, reducing the re-writing of the related codes under the condition of increasing the data types in the future, and performing effective multiplexing.
Although the present invention has been described with reference to the preferred embodiments, it should be understood that various changes and modifications can be made therein by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (3)
1. A method for abnormality judgment and restoration based on energy statistic data comprises the following steps: a judgment process and a repair process, wherein the judgment process comprises the following steps:
the method comprises the following steps: judging whether the original data aN of the current granularity N is obtained or not, and if so, performing a second step; if the acquired data is not acquired, judging that the acquired data with the granularity N is lost and needs to be repaired, marking the abnormal attribute flag =1 of the granularity N, and entering a data loss repairing process;
step two: judging whether the adjacent granularity statistic value of the granularity N is less than 0: if not less than 0, entering a third step; if the value N is smaller than 0, judging that the granularity N is the sudden reduction of the acquired data, and simultaneously setting the abnormal attribute flag =1 of the granularity N; and entering a recovery process of suddenly reducing acquired data;
step three: judging whether the adjacent granularity statistic value of the granularity N meets the condition that the value N is more than or equal to k × A, wherein A is the historical average value of the adjacent granularity statistic value, and k is a preset safety coefficient; if the value N is more than or equal to k × A, judging that the acquired data is suddenly increased, and marking the abnormal attribute flag =1 of the granularity; entering a recovery process of suddenly enlarging acquired data; if the condition that the value N is not more than or equal to k × A is not met, the original data of the granularity N are put into a warehouse;
the repair process comprises the following steps:
step A: the granular data loss repairing process comprises the following steps: when the original data aN of the granularity N is not obtained, obtaining the last sampling data aK of the granularity K which can be obtained recently; acquiring the proportion of the adjacent granularity data statistic values of granularity N 'and K' of corresponding time in the last time period; if the proportion of the adjacent granularity data statistic of the last time period is successfully obtained, the data statistic of the granularity N of the repair time period is brought in according to the proportion of the adjacent granularity statistic of the granularity N 'and the granularity K' of the last time period, and then repair data of the lost granularity N are calculated; if the data statistics value of the last time period is not obtained successfully, calculating the average value of adjacent granularity statistics values from the granularity K to the granularity (N-1), directly repairing the data of the granularity N by using the average value of the adjacent granularity statistics values from the granularity K to the granularity (N-1), and warehousing the repaired data of the granularity N;
and B: and (3) a repairing process that the collected data suddenly becomes smaller or larger: acquiring a historical average value A of adjacent granularity statistics, setting the adjacent granularity statistics value of the granularity N as the historical average value A, namely restoring the data of the granularity N to a (N-1) + A, and marking the abnormal attribute flag =1 of the granularity N; acquiring original data a (N + M) of granularity (N + M) after preset M granularities, and calculating an adjacent statistic value (N + M) corresponding to the granularity (N + M); judging whether value (N + M) is larger than or equal to a preset safety system value k A or not; if the particle size is larger than or equal to the safety system value k × A, normally warehousing the repaired particle size N statistical value; if the particle size is not larger than or equal to the safety system value k × A, performing step C to perform secondary repair on the particle size N;
and C: and (3) secondary repairing: acquiring the proportion of the adjacent granularity data statistic values of granularity N 'and (M + N)' corresponding to time in the last period; if the adjacent granularity data statistic of the last time period is successfully obtained, the data statistic of the granularity N of the repair time period is brought in according to the proportion of each adjacent granularity statistic between the granularity N 'and (M + N)' of the last time period, and then secondary repair data of the granularity N are calculated; if the data statistics value of the last time period is not successfully acquired, calculating the average value of the adjacent granularity statistics values from the granularity (N-1) to the granularity (M + N), and directly restoring the data to the granularity N by using the average value of the adjacent granularity statistics values from the granularity (N-1) to the granularity (M + N); finally, warehousing the data with the granularity of N for secondary restoration;
the aN is the collected original data of the granularity N, a (N-1) is the original data of the granularity N adjacent to the previous granularity, a (N + 1) is the original data of the granularity N adjacent to the next granularity, and value is the adjacent granularity statistic of the granularity N.
2. The method according to claim 1, wherein the energy statistics based anomaly determination and restoration method comprises: further comprising: when the granularity N does not exist, the neighbor granularity statistic value of the preset granularity N =0 and the neighbor granularity statistic value (N + 1) =0 of the granularity N + 1.
3. The method according to claim 1, wherein the energy statistics based anomaly determination and restoration method comprises: the granularity raw data includes a value in granularity units of one year, a value in granularity units of one month, a value in granularity units of one day, and a value in granularity units of one hour.
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