CN111859292A - Water supply leakage monitoring method for night water use active cell - Google Patents
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Abstract
The invention discloses a method for monitoring water supply leakage of a night water use active community. The method firstly collects data, calculates the MNF value, and counts the average value and standard deviation of each season according to the seasons. And secondly, calculating the MNF season change threshold, and updating the MNF season change threshold by using the latest almanac history data. MNF seasonal thresholds are then calculated and checked for accuracy. And finally, monitoring and early warning the water supply leakage of the active water consumption cell at night. The invention determines the MNF season change threshold and the season threshold through data mining, can automatically detect the change point and the season change threshold during season change, well solves the actual problem that the conventional MNF method is interfered by night water consumption, and improves the accuracy of automatic monitoring and early warning of water supply leakage of the night water consumption active cell.
Description
Technical Field
The invention belongs to the field of water supply leakage monitoring, and particularly relates to a water supply leakage monitoring method for a night water use active cell.
Background
Along with the abundance of urban life, the night activities of people are obviously increased, and the water consumption at night is increased, particularly in a district with more shops and tenants or a rural residence district (rural area). Since the water is active at Night, it directly affects the interpretation of the conventional Minimum Night Flow Method (MNF). The MNF threshold value is set to be small, and false alarms are increased; if the setting is large, the report may be missed. Although subdivided measurement and assessment management can be adopted, the hardware investment of remote transmission large meters, intelligent sub meters and the like is involved.
According to incomplete investigation, the MNF value of the cell which is activated by using water at night accounts for more than 1% of the water consumption of the whole day and is far higher than the level of < 0.7% of other cells, and the MNF data has large annual change range and is closely related to seasons. If the leakage monitoring is carried out by adopting the conventional MNF method with a fixed threshold value all the year around, the leakage monitoring is inevitable.
Disclosure of Invention
Aiming at the problem that the conventional MNF method is interfered by night water, the invention provides a method for monitoring water supply leakage of a night water active cell by mining historical data, which comprises the following steps:
step 1, collecting data, calculating MNF value, and counting the average value and standard deviation of each season according to the seasons
And collecting the data of the examination table of the active night water use cell, wherein the data comprises sampling time, interval water supply quantity and flow, recent historical data exceeds 12 months, and the sampling interval is less than or equal to 15 minutes.
And performing data preprocessing, performing integral elimination on abnormal day data, interpolating a few missing data on normal days, and filtering individual abnormity.
For the last year calendar history data, 0:00-5:00 per day, the MNF value is calculated as one hour with the least water usage at night. And according to the season change time of the local weather talent, the average value u of the four seasons of spring, summer, autumn and winter is counted 1、u2、u3、u4And standard deviation σ1、σ2、σ3、σ4。
Step 2, calculating MNF season change threshold
The season change threshold from spring to summer is TS1＝u1+(u2-u1)*σ1/(σ1+σ2)；
The threshold value of the season change from summer to autumn is TS2＝u2+(u3-u2)*σ2/(σ2+σ3)；
The threshold value of changing seasons from autumn to winter is TS3＝u3+(u4-u3)*σ3/(σ3+σ4)；
The season change threshold from winter to spring is TS4＝u4+(u1-u4)*σ4/(σ4+σ1)；
Step 3, utilizing the latest calendar history data to update MNF season change threshold
Setting the minimum night flow of the jth day in the latest calendar history data as MNFj，1≤j≤365；
MNF for 5 continuous days when winter is transited to springj-4～MNFjIs greater than the season-change threshold TS4If yes, the spring season is judged to be changed, and the 5 th day is marked as j1；
MNF for 5 continuous days when spring is transited to summerj-4～MNFjIs greater than the season-change threshold TS1If yes, the system is judged to be in summer and changed into season, and the 5 th day is marked as j2；
MNF for 5 continuous days when summer transits to autumnj-4～MNFjIs less than the season-change threshold TS2If the season changes, the season changes to autumn, and the 5 th day is marked as j3；
MNF for 5 continuous days when the autumn is transited to the winterj-4～MNFjIs less than the season-change threshold TS3If yes, the season is judged to be changed from winter, and the 5 th day is marked as j4。
According to j1、j2、j3、j4Recalculating the average value u1、u2、u3、u4And standard deviation σ1、σ2、σ3、σ4. Such as relative error of the mean value of the MNF of the previous round<And if 1%, updating and calculating the MNF season change threshold, and entering the step 4. Otherwise, returning to the step 2.
Step 4, calculating MNF seasonal threshold and checking accuracy of MNF seasonal threshold
According to seasonsCalculating respective MNF season threshold TMi＝ui+3kσiWherein i is 1,2,3, 4; k is more than or equal to 0.8 and less than or equal to 1.2;
and (4) checking the false alarm rate and the false missing rate of the current calendar history data. K is adjusted, and the false alarm rate is reduced as much as possible on the premise of no false alarm.
Step 5, monitoring and early warning of water supply leakage of active night water use community
Acquiring actually measured data of a cell examination and check table, and calculating to obtain the MNF (minimum night flow) of the dayjThe value is obtained. Such as continuous 5 days MNFj-4～MNFjIf the average value of (1) exceeds the season-changing threshold value, the season-changing threshold value is switched first. And judging whether the seasonal threshold value of the season is exceeded or not, and if the seasonal threshold value is exceeded, early warning is carried out.
The invention determines the MNF season change threshold and the season threshold through data mining, can automatically detect the change point and the season change threshold during season change, well solves the actual problem that the conventional MNF method is interfered by night water consumption, and improves the accuracy of automatic monitoring and early warning of water supply leakage of the night water consumption active cell.
Drawings
FIG. 1: the method of the invention is a schematic flow chart.
FIG. 2: the QG cell MNF season threshold is adopted in the embodiment of the invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings, comprising the steps of:
step 1, collecting data, calculating MNF value, and counting the average value and standard deviation of each season according to the seasons
And collecting the data of the examination table of the active night water use cell, wherein the data comprises sampling time, interval water supply quantity, flow and the like, the recent historical data exceeds 12 months, and the sampling interval is less than or equal to 15 minutes. And performing data preprocessing, performing integral elimination on abnormal day data, interpolating a few missing data on normal days, and filtering individual abnormity.
For the last year calendar history data, 0:00-5:00 per day, the MNF value is calculated as one hour with the least water usage at night. And according to the season change time of the local weather talent, the average value u of the four seasons of spring, summer, autumn and winter is countediAnd standard deviation σi(i＝1,2,3,4)。
Step 2, calculating MNF season change threshold
As shown in Table 1, a season change threshold TS is calculatedi(i＝1,2,3,4)。
TABLE 1 season change threshold calculation
| Season change point | Season change threshold calculation |
| Spring → summer | TS1＝u1+(u2-u1)*σ1/(σ1+σ2) |
| Summer → autumn | TS2＝u2+(u3-u2)*σ2/(σ2+σ3) |
| Autumn → winter | TS3＝u3+(u4-u3)*σ3/(σ3+σ4) |
| Winter → spring | TS4＝u4+(u1-u4)*σ4/(σ4+σ1) |
Step 3, utilizing the latest calendar history data to update MNF season change threshold
Setting the minimum night flow of the jth day in the latest calendar history data as MNFj，1≤j≤365。
MNF for 5 consecutive days when winter → spring transitionsj-4～MNFjIs greater than the season-change threshold TS4If yes, the spring season is judged to be changed, and the 5 th day is marked as j1(ii) a MNF for 5 consecutive days when spring → summer transitionj-4～MNFjIs greater than the season-change threshold TS1If yes, the system is judged to be in summer and changed into season, and the 5 th day is marked as j 2(ii) a MNF for 5 consecutive days when summer → autumn is in transitionj-4～MNFjIs less than the season-change threshold TS2If the season changes, the season changes to autumn, and the 5 th day is marked as j3(ii) a MNF for 5 consecutive days when autumn → winter is in transitionj-4～MNFjIs less than the season-change threshold TS3If yes, the season is judged to be changed from winter, and the 5 th day is marked as j4。
According to j1、j2、j3、j4Recalculating the MNF average value u of the four seasonsiAnd standard deviation σi(i ═ 1,2,3, 4). Such as relative error of MNF mean value of previous round<1%, the MNF season change threshold TS is updated and calculatediProceed to step 4. Otherwise, returning to the step 2.
Step 4, calculating MNF seasonal threshold and checking accuracy of MNF seasonal threshold
Calculating respective MNF season thresholds TM by seasonsi＝ui+3kσi(i ═ 1,2,3,4), where k is 0.8 ≦ k ≦ 1.2, and k is taken to be 1.0 by default. And (4) checking the false alarm rate and the false missing rate of the current calendar history data. K is adjusted, and the false alarm rate is reduced as much as possible on the premise of no false alarm.
Step 5, monitoring and early warning of water supply leakage of active night water use community
Acquiring actually measured data of a cell examination and check table, and calculating to obtain the MNF (minimum night flow) of the dayjThe value is obtained. Such as continuous 5 days MNFj-4～MNFjAnd if the average value exceeds the season change threshold value, switching the season threshold value. And judging whether the current season threshold value is exceeded or not, and if the current season threshold value is exceeded, early warning is carried out.
Example (b):
the embodiment is a QG rural residential community in a certain city, and the annual daily water consumption is approximately 380 tons1620 ton interval, MNF value of 5m3/h～20m3The variation range is large in the/h interval and is closely related to daily water consumption and seasons.
The technical scheme of the invention is further specifically described by the following embodiments and with reference to the attached drawings (such as fig. 1), and the technical scheme comprises the following steps:
step 1, collecting data, calculating MNF value, and counting the average value and standard deviation of each season according to the seasons
And collecting the data of the examination and check table of the QG cell, wherein the data comprise sampling time, interval water supply, flow and the like, wherein the historical data which is latest up to now are 2018-2019, and the sampling interval is 15 minutes. Preprocessing the data, eliminating abnormal data and slightly interpolating missing data.
For 2019 historical data, the MNF value is calculated according to one hour with minimum water consumption at night, wherein the MNF value is 0:00-5:00 every day. And according to the season change time of the local weather talent propaganda (according to the local weather station 2019 Yangtze: 3.11 spring, 5.22 summer, 10.12 autumn and 11.29 winter), calculating the average value u of spring, summer, autumn and winter in four seasonsiAnd standard deviation σi(i ═ 1,2,3,4), see table 2.
TABLE 2 QG initial values of the mean value and standard deviation of the four seasons MNF in the residential area
| Computing item | Spring season | Summer season | Autumn | Winter season |
| Mean value u | u1＝11.59 | u2＝15.09 | u3＝12.42 | u4＝9.94 |
| Standard deviation sigma | σ1＝1.49 | σ2＝2.51 | σ3＝2.09 | σ4＝2.05 |
Step 2, calculating MNF season change threshold value
As shown in Table 3, a season change threshold TS is calculatedi(i＝1,2,3,4)。
TABLE 3 QG CELL QUALITY-CHANGE THRESHOLD
| Season change point | Season change threshold calculation |
| Spring → summer | TS1＝u1+(u2-u1)*σ1/(σ1+σ2)＝12.90 |
| Summer → autumn | TS2＝u2+(u3-u2)*σ2/(σ2+σ3)＝13.63 |
| Autumn → winter | TS3＝u3+(u4-u3)*σ3/(σ3+σ4)＝11.17 |
| Winter → spring | TS4＝u4+(u1-u4)*σ4/(σ4+σ1)＝10.89 |
Step 3, utilizing the latest calendar history data to update MNF season change threshold
The minimum night flow of the j day in the history data of 2019 is MNFj，1≤j≤365。
MNF for 5 consecutive days before day 3 and 18 when winter → spring transitionsj-4～MNFjIs greater than the season-change threshold TS by an average value of 10.904When the season changed from spring to spring, the number of days 5 (3 months and 18 days) was j1＝77；
MNF for 5 consecutive days before 13 months of 5 when spring → summer transitionj-4～MNFjIs greater than the season-change threshold TS, and an average value of 13.081When the measured value is 12.90, it is judged to fall into summer, and the 5 th day (5 th and 13 th days) is denoted as j2＝133；
MNF for 5 consecutive days before 5 days of 10 months when summer → autumn is in transitionj-4～MNFjIs less than the season change threshold TS, is 13.602When the season changes to autumn, the season was judged to be 13.63, and the 5 th day (10 months and 5 days) was denoted as j3＝278；
MNF for 5 consecutive days before 12 and 6 in autumn → winter transitionj-4～MNFjIs less than the season change threshold TS, is 11.153When the season changed to winter, the season was judged to be changed, and the 5 th day (12 th and 6 th days) was denoted as j4＝340。
According to j1＝77、j2＝133、j3＝278、j4340, the average value u of the MNF in four seasons of spring, summer, autumn and winter is recalculated iAnd standard deviation σi(i ═ 1,2,3,4), see table 4.
TABLE 4 QG CELL FOUR-SEASON MNF mean and Standard deviation update values
| Computing item | Spring season | Summer season | Autumn | Winter season |
| Mean value u | u1＝11.66 | u2＝15.08 | u3＝12.45 | u4＝9.89 |
| Standard deviation sigma | σ1＝1.19 | σ2＝2.88 | σ3＝1.85 | σ4＝2.27 |
Relative error of MNF mean of previous round<1%, the MNF season change threshold TS is updated and calculatedi(see Table 5), proceed to step 4. Otherwise, returning to the step 2.
TABLE 5 QG CELL MNF Quaternary threshold update values
| Season change point | Spring → summer | Summer → autumn | Autumn → winter | Winter → spring |
| Threshold value of season change | TS1＝12.66 | TS2＝13.48 | TS3＝11.30 | TS4＝11.05 |
Step 4, calculating MNF seasonal threshold and checking accuracy of MNF seasonal threshold
Calculating respective MNF season thresholds TM by seasonsi＝ui+3kσi(i ═ 1,2,3,4), where k is 0.8 ≦ k ≦ 1.2, and k is taken to be 1.0 by default.
TABLE 6 QG cell MNF season threshold
| Computing item | Spring season | Summer season | Autumn | Winter season |
| Seasonal threshold | TM1＝15.22 | TM2＝23.71 | TM3＝17.99 | TM4＝16.69 |
Using the history data of 2019, two alarms of 3.18 and 7.10 (as shown in fig. 2) are detected, and the alarms are matched with the actual situation through investigation. And false alarm are not generated, and k does not need to be adjusted.
Step 5, monitoring and early warning of water supply leakage of active night water use community
The season change threshold and the season threshold are used for monitoring actual leakage of the QG cell, actual measurement data of a cell examination and check table are obtained, and MNF (minimum night flow) of the day is obtained through calculationjThe value is obtained. Such as continuous 5 days MNFj-4～MNFjAnd if the average value exceeds the season change threshold value, switching the season threshold value. And judging whether the current season threshold value is exceeded or not, and if the current season threshold value is exceeded, early warning is carried out.
In this example, if the threshold is calculated as the MNF mean and standard deviation for the year 2019, then 3.18 missing accidents are missed; if the detected 3.18 leakage accident is taken as a reference, the false alarm is carried out for 33 times. Therefore, the method of the invention sets the threshold value according to seasons, can well solve the practical problem that the conventional MNF method is interfered by the water used at night, and improves the accuracy of automatic monitoring and early warning of water supply leakage of the active community using water at night. The method of the invention can also be applied to a primary or secondary independent metering area, if the real-time inlet and outlet flow can be mastered.
The specific embodiments described herein are merely illustrative of the methods of the present invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (1)
1. A method for monitoring water supply leakage of a night active water cell is characterized by comprising the following steps:
step 1, collecting data, calculating MNF value, and counting the average value and standard deviation of each season according to the seasons
Collecting data of an examination table of a night active water consumption cell, wherein the data comprises sampling time, interval period water supply quantity and flow, recent historical data exceeds 12 months, and the sampling interval is less than or equal to 15 minutes;
Performing data preprocessing, performing integral elimination on abnormal day data, interpolating a few missing data in a normal day, and filtering individual abnormity;
calculating the MNF value of the last calendar history data at 0:00-5:00 every day according to the minimum water consumption at night; and according to the season change time of the local weather talent, the average value u of the four seasons of spring, summer, autumn and winter is counted1、u2、u3、u4And standard deviation σ1、σ2、σ3、σ4；
Step 2, calculating MNF season change threshold
The season change threshold from spring to summer is TS1=u1+(u2-u1)* σ1/(σ1+σ2)；
The threshold value of the season change from summer to autumn is TS2=u2+(u3-u2)* σ2/(σ2+σ3)；
The threshold value of changing seasons from autumn to winter is TS3=u3+(u4-u3)* σ3/(σ3+σ4)；
The season change threshold from winter to spring is TS4=u4+(u1-u4)* σ4/(σ4+σ1)；
Step 3, utilizing the latest calendar history data to update MNF season change threshold
Setting the minimum night flow of the jth day in the latest calendar history data as MNFj，1≤j≤365；
MNF for 5 continuous days when winter is transited to springj-4～MNFjIs greater than the season-change threshold TS4If yes, the spring season is judged to be changed, and the 5 th day is marked as j1；
MNF for 5 continuous days when spring is transited to summerj-4～MNFjIs greater than the season-change threshold TS1If yes, the system is judged to be in summer and changed into season, and the 5 th day is marked as j2；
MNF for 5 continuous days when summer transits to autumnj-4～MNFjIs less than the season-change threshold TS2If the season changes, the season changes to autumn, and the 5 th day is marked as j3；
MNF for 5 continuous days when the autumn is transited to the winter j-4～MNFjIs less than the season-change threshold TS3If yes, the season is judged to be changed from winter, and the 5 th day is marked as j4；
According to j1、j2、j3、j4Recalculating the average value u1、u2、u3、u4And standard deviation σ1、σ2、σ3、σ4E.g. relative error of mean value of MNF of previous round<If 1%, the MNF season change threshold value is updated and calculated, and the step 4 is entered; otherwise, returning to the step 2;
step 4, calculating MNF seasonal threshold and checking accuracy of MNF seasonal threshold
Calculating respective MNF season thresholds TM by seasonsi=ui+3kσiWherein i =1,2,3, 4; k is more than or equal to 0.8 and less than or equal to 1.2;
checking the false alarm rate and the missing report rate of the latest calendar history data; k is adjusted, and the false alarm rate is reduced as much as possible on the premise of no false alarm;
step 5, monitoring and early warning of water supply leakage of active night water use community
Acquiring actually measured data of a cell examination and check table, and calculating to obtain the MNF (minimum night flow) of the dayjA value; such as continuous 5 days MNFj-4～MNFjIf the average value of the time difference exceeds the season-changing threshold value, the season threshold value is switched first, then whether the time difference exceeds the season threshold value of the current season is judged, and if the time difference exceeds the season threshold value of the current season, early warning is given.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113864664A (en) * | 2021-09-29 | 2021-12-31 | 广东粤海水务投资有限公司 | Pipe network leakage early warning method and system based on flow distribution probability calculation |
| CN114219295A (en) * | 2021-12-15 | 2022-03-22 | 深圳市润泽鑫技术有限责任公司 | Intelligent night water flow monitoring method, system and device and storage medium |
| CN116642138A (en) * | 2023-05-25 | 2023-08-25 | 大连智水慧成科技有限责任公司 | New leakage detection method for water supply network |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107038502A (en) * | 2017-04-18 | 2017-08-11 | 国网安徽省电力公司芜湖供电公司 | Consider the improvement wavelet packet electricity demand forecasting method of Seasonal Characteristics |
| CN109670670A (en) * | 2018-11-13 | 2019-04-23 | 上海电器科学研究所（集团）有限公司 | A kind of electric load method for splitting dividing season attribute |
| CN110173627A (en) * | 2019-06-03 | 2019-08-27 | 山东建筑大学 | A kind of solar energy system |
| CN111353127A (en) * | 2018-12-24 | 2020-06-30 | 顺丰科技有限公司 | Single variable point detection method, system, equipment and storage medium |
-
2020
- 2020-07-07 CN CN202010646073.3A patent/CN111859292B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107038502A (en) * | 2017-04-18 | 2017-08-11 | 国网安徽省电力公司芜湖供电公司 | Consider the improvement wavelet packet electricity demand forecasting method of Seasonal Characteristics |
| CN109670670A (en) * | 2018-11-13 | 2019-04-23 | 上海电器科学研究所（集团）有限公司 | A kind of electric load method for splitting dividing season attribute |
| CN111353127A (en) * | 2018-12-24 | 2020-06-30 | 顺丰科技有限公司 | Single variable point detection method, system, equipment and storage medium |
| CN110173627A (en) * | 2019-06-03 | 2019-08-27 | 山东建筑大学 | A kind of solar energy system |
Non-Patent Citations (2)
| Title |
|---|
| 李露: "夜间合法用水量预测与DMA漏损分析方法研究", 中国硕士学位论文全文数据库, 28 February 2017 (2017-02-28) * |
| 江永伟;杨奇;王海涛;应志豪;何必仕;徐哲;: "小区漏损预警中最小夜间流量法的改进与应用", 浙江水利水电学院学报, no. 01, 25 February 2020 (2020-02-25) * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113864664A (en) * | 2021-09-29 | 2021-12-31 | 广东粤海水务投资有限公司 | Pipe network leakage early warning method and system based on flow distribution probability calculation |
| CN113864664B (en) * | 2021-09-29 | 2023-08-15 | 广东粤海水务投资有限公司 | Pipe network leakage early warning method and system based on flow distribution probability calculation |
| CN114219295A (en) * | 2021-12-15 | 2022-03-22 | 深圳市润泽鑫技术有限责任公司 | Intelligent night water flow monitoring method, system and device and storage medium |
| CN116642138A (en) * | 2023-05-25 | 2023-08-25 | 大连智水慧成科技有限责任公司 | New leakage detection method for water supply network |
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