CN110569248A - Improved SPC (selective pressure control) residential water supply leakage monitoring and early warning method - Google Patents
Improved SPC (selective pressure control) residential water supply leakage monitoring and early warning method Download PDFInfo
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Abstract
The invention discloses a method for monitoring and early warning water supply leakage of a community, which improves SPC. The invention determines the MNF threshold value through data mining, and takes the advantages of the conventional SPC method for detecting the gradual change abnormity, thereby perfecting the leakage monitoring means, well solving the practical application problems that the MNF threshold value is difficult to determine, the single threshold value is difficult to consider different seasons and the like, and improving the accuracy of automatic monitoring and early warning of the water supply leakage of the cell.
Description
Technical Field
The invention belongs to the field of water supply leakage monitoring, and particularly relates to a district water supply leakage monitoring and early warning method for improving SPC.
Technical Field
With the maturity of large meter remote transmission technology and the establishment of a district secondary water supply monitoring platform, the automatic acquisition and transmission of district water inlet examination table data are gradually realized, and the water department carries out the DMA management of residential districts. Considering that the current night minimum flow (MNF) is an important index for evaluating the actual leakage level of the independent metering area[1]. Therefore, the management personnel of the department of hostage often adopt the MNF method to automatically monitor the leakage, and simultaneously utilize the flow data to carry out the leakage situation of the cellAnd condition evaluation, routing inspection and leakage detection are arranged in time, water loss is reduced, and a positive effect is achieved.
In practical application, the nighttime minimum flow method has higher requirements on experience, the nighttime minimum flow times of all regions are different, the determination method of the reference value (threshold value) is limited by objective conditions, and the universality is poor[2]. Document [2]]in contrast, the method for statistically determining the legal night water consumption and then obtaining the reference value of the minimum night flow is considered to be highly applicable. In fact, the legal water consumption at night requires enough and representative sample amount according to statistical sampling investigation, which brings difficulty to practical application.
In addition to the versatility problem, the difficulty in determining the night minimum flow threshold is: (1) if the threshold value is lower, the alarm sensitivity is high, and false alarms are increased; if the threshold value is higher, the alarm sensitivity is low, the false alarm can be reduced, but the false alarm can be missed. (2) The water consumption changes with factors such as seasons, temperature, holidays and the like, and the minimum flow at night is influenced. Instability such as drift, layering, trend and the like exist in the minimum flow at night, and if a single threshold value is adopted, the instability is considered in the aspects of false alarm and missed alarm.
The current alarm method adopted by the water supply company in England is a flat line method, and 20 percent of set threshold values are respectively added or subtracted according to the average value of the height of 12 months in the past[3]. This flat wire method does enable the sensitive detection of catastrophic pipe burst events, but presents a number of false alarm problems. Under uncertain conditions, the MNF threshold determined by the water department is conservative. A certain water department receives system alarm 4554 times in the year 2018 in an accumulated manner, and false alarm accounts for 88.5% of the total alarm number after distinguishing true alarm 525 times, so that water department management personnel are seriously troubled.
Reference to the literature
[1] The method is characterized in that the production and marketing differential rate control [ J ] of a large-area water supply system is realized by adopting a night minimum flow method in the forest rising sun, 2011,37(9) is 101-104, DOI is 10.3969/j.issn.1002-8471.2011.09.026.
[2] Lilan, Wushan, Couchura et al.development of nighttime minimum flow based on independent metering zones [ J ]. Water supply and drainage, 2018,44(6):135-141.
[3] Wu Zhengyi, Zhang Qing Zhongji, China building industry Press, 2017.11, for reducing water supply leakage.
Disclosure of Invention
Aiming at the problems that MNF threshold values are difficult to determine, single threshold values are difficult to take different seasons and the like into consideration, the invention provides a residential water supply leakage monitoring and early warning method for improving SPC by using a statistical process control SPC method and mining historical data, and the method comprises the following steps:
Step 1, establishing a district water supply leakage monitoring database
And aiming at a certain cell, establishing a cell water supply leakage monitoring database. Wherein the data items include: sampling time, interval water supply, flow, leakage marks, repair marks, etc. Relevant data collected by an SCADA (Supervisory Control and DataAcquisition) system and a water supply first-aid repair system are imported into a database through necessary matching and format conversion. Wherein the 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.
Step 2, analyzing the traffic statistical characteristics of the 0:00-6:00 night time period in the stationary phase, determining the MNF night time period, and counting the night minimum traffic value
For stationary phase (at least 1 month, at most 12 months), the mean value u of the flow at each sampling instant of the 0:00-6:00 night period is calculatedtStandard deviation σtFrom thirteen options of 0:00-3:00, 0:15-3:15, … … 3:00-6:00, one 3-hour period with the smallest mean and standard deviation was selected and determined as the MNF nighttime period.
For the stationary phase, the minimum flow value is found from the MNF night time period every day, and the average value u thereof is calculatedMNFAnd standard deviation σMNF. The stable period refers to a period in which the number of users and water usage habits in a cell are substantially stable.
Step 3, initially determining a standard deviation coefficient k and a control upper limit UCL
and calculating the probability P alpha of leakage as the leakage frequency/detection frequency according to the leakage first-aid repair record of the past 12 months of the cell. And the leakage times are calculated according to the first-aid repair records, and the leakage times are not repeatedly calculated before the same leakage event is not repaired.
According to the central limit theorem, UCL ═ uMNF+k*σMNFWhere k, satisfies P α ≈ PObserved value>UCL. Such as P alpha<0.00135, k is 3. Here, UCL is the upper control limit, and k is the standard deviation coefficient.
if the cell has not been lost in the past 12 months, the cell of the same type (the cell of the same type refers to a cell whose water consumption scale, pipe, service year, and house type (high-rise, multi-story, or mixed) are similar) may be referred to, or simply regarded as the case where P α < 0.00135.
Step 4 improvement of SPC method and verification of its accuracy
Statistical process control SPC (left side of table 1) can detect abrupt and gradual anomalies. According to the actual situation of the water supply leakage monitoring of the cell, the SPC method is Improved, as shown in the right side of the table 1, and when k is more than or equal to 2 and less than or equal to 3, Improved-SPC detection rules #1 and #2 are selected; Improved-SPC detection rules #1 and #3 are selected when 1 ≦ k < 2.
TABLE 1 statistical Process control SPC and improvements
using historical data from the past 12 months, the accuracy of the improved SPC method can be verified. If all leakage events can be detected according to the current k, slightly increasing the k, and searching the maximum k value meeting all detected leakage events. If all leakage events cannot be detected at the current k, slightly reducing the k, and searching for the minimum k value meeting all detected leakage events. Then, the upper control limit UCL ═ u is correctedMNF+k*σMNF。
Step 5, monitoring and early warning of water supply leakage of residential area
acquiring SCADA measured data, finding out the minimum flow value of the MNF at night on the same day, and performing overrun judgment and early warning by using an improved SPC detection rule # 1; and finding out the latest continuous 3 hour (or 5 hour) flow data of MNF at night, and carrying out overrun judgment and early warning by using the improved SPC detection rule #2 (or # 3).
And (3) continuously updating the cell leakage monitoring database, if the missing report is found or the false report rate is more than 5%, returning to the step (2), and re-determining the threshold value by using the recent stable-period sample data as much as possible to ensure the monitoring and early warning accuracy.
The invention determines the MNF threshold value through data mining, and takes the advantages of the conventional SPC method for detecting the gradual change abnormity, thereby perfecting the leakage monitoring means, well solving the practical application problems that the MNF threshold value is difficult to determine, the single threshold value is difficult to consider different seasons and the like, and improving the accuracy of automatic monitoring and early warning of the water supply leakage of the cell.
Drawings
FIG. 1: the method of the invention is a schematic flow chart.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
Example (b): the method for monitoring and early warning of water leakage in a cell for improving SPC in the present embodiment, as shown in fig. 1, includes the following steps:
Step 1, establishing a district water supply leakage monitoring database
And aiming at a certain cell, establishing a cell water supply leakage monitoring database. The data items include: sampling time, interval water supply, flow, leakage marks, repair marks, etc. And (4) importing the relevant data collected by the SCADA system and the water supply first-aid repair system into a database through necessary matching and format conversion. Wherein, the historical data is from 2018.3 to 2019.3, the sampling interval is 15 minutes, and the sampling period exceeds 12 months. In the historical data, even data is missing or abnormal, and the data is preprocessed according to conventional methods such as interpolation filling, individual abnormal filtering, abnormal date removing and the like.
Step 2, analyzing the traffic statistical characteristics of the 0:00-6:00 night time period in the stationary phase, determining the MNF night time period, and counting the night minimum traffic value
For the 2018.3-2019.2 stationary phase, the mean value u of the flow at each sampling instant of the 0:00-6:00 night period is calculatedtstandard deviation σtThirteen from 0:00 to 3:00, 0:15 to 3:15, … … 3:00 to 6:00In one option, the 3-hour period of 2:00-5:00 with the smallest mean and standard deviation is selected and determined as the MNF nighttime period.
During the stationary phase at 2018.3-2019.2, the minimum flow value is found daily from the MNF nighttime hours and calculated as: average value u thereofMNF3.194 ton/h, standard deviation σMNF1.091 tons/hour.
Step 3, initially determining a standard deviation coefficient k and a control upper limit UCL
The 12-month leakage repair record of the cell 2018.3-2019.2 has 2 leakage accidents, and the probability P alpha of the leakage of the cell is 2/365 to 0.55%.
According to the central limit theorem, UCL ═ uMNF+k*σMNFWhen k is 2.55, P α is 0.55% ≈ PObserved value>UCL. Then UCL is 5.976 tons/hour.
Step 4 improvement of SPC method and verification of its accuracy
Statistical process control SPC (left side of table 1) can detect abrupt and gradual anomalies. The SPC is modified according to the actual situation of the cell water supply leakage monitoring as shown in table 2, and when k is 2 ≦ k <3, SPC detection rules #1 and #2 are selected.
TABLE 2 improved statistical Process control
The accuracy of the improved SPC protocol was examined using the 12 month historical data of 2018.3-2019.2. If all leakage events can be detected if the current k is 2.55, the k is slightly increased, the maximum k value satisfying all detected leakage events is searched for to be 2.7, and the upper limit of the correction control UCL is uMNF+2.7*σMNF6.140 tons/hr.
If fixed upper limit UCL +3 sigma is 6.467 tons/hour, the false alarm rate is lower, but one time of false alarm is missed.
Step 5, monitoring and early warning of water supply leakage of residential area
Acquiring SCADA measured data, finding out the minimum flow value of the MNF at night on the same day, and performing overrun judgment and early warning by using an improved SPC detection rule # 1; and finding out the latest continuous 3-hour flow data at the night of the MNF, and performing overrun judgment and early warning by using an improved SPC detection rule # 2.
The leakage monitoring database of the district is continuously updated, 2019.3 data is utilized, the false alarm rate is less than 1%, and no missing report exists, which shows that the method is effective, improves the accuracy of prediction and early warning, and greatly lightens the working pressure of water department managers.
if the subsequent missing report is found, or the false report rate is more than 5%, returning to the step 2, and re-determining the threshold value by using the recent sample data in the stabilization period as much as possible to ensure the accuracy of monitoring and early warning.
the example shows that compared with a flat wire method or a method adopting a fixed control upper limit UCL (u +2 sigma), the method reduces a large amount of false alarms; compared with a method of adopting a fixed control upper limit UCL (u +3 sigma), the method avoids missing report. Therefore, the method of the invention not only reduces false alarm, but also keeps enough sensitivity to detect leakage abnormity.
To enhance the real-time performance, the number of detections per day is increased. If 3 whole-point flow data in 2:00-5:00 night time are used for detection, trend abnormity detection can be accelerated. The method of the invention can also be applied to independent metering areas where industrial water and resident water are mixed, if the real-time industrial water consumption can be mastered.
the specific embodiments described herein are merely illustrative of the spirit of the 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 (2)
1. A district water supply leakage monitoring and early warning method for improving SPC is characterized by comprising the following steps:
step 1, establishing a district water supply leakage monitoring database
Aiming at a certain cell, establishing a cell water supply leakage monitoring database; wherein the data items include: sampling time, interval water supply quantity, flow, leakage mark and repair mark;
Importing the data collected by the SCADA system and the water supply first-aid repair system into a database through necessary matching and format conversion; wherein, the 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;
Step 2, analyzing the traffic statistical characteristics of the 0:00-6:00 night time period in the stationary phase, determining the MNF night time period, and counting the night minimum traffic value
calculating the mean value u of the flow at each sampling moment in the night period of 0:00-6:00 aiming at the stationary phasetStandard deviation σtSelecting a 3-hour period with the minimum mean value and standard deviation from thirteen options of 0:00-3:00, 0:15-3:15 and … … 3:00-6:00, and determining the period as the MNF night period;
For the stationary phase, the minimum flow value is found from the MNF night time period every day, and the average value u thereof is calculatedMNFAnd standard deviation σMNF；
Step 3, initially determining a standard deviation coefficient k and a control upper limit UCL
Calculating the probability P alpha of leakage = leakage times/detection times according to the leakage first-aid repair record of the cell in the past 12 months;
According to the central limit theorem, the upper limit UCL = u is controlledMNF +k*σMNFWhere k is the coefficient of standard deviation, P α ≈ PObserved value>UCL；
And 4, improving statistical process control SPC, specifically:
When k is more than or equal to 2 and less than 3, the SPC detection rules #1 and #2 are selected for improved statistical process control;
When k is more than or equal to 1 and less than 2, selecting the SPC detection rules #1 and # 3;
Wherein rule #1 is an observed value ≧ u + k σ; the detection requirement is single-point detection, and the abnormality type is obvious abnormality; rule #2 is observed value ≧ u +2 σ; the detection requirement is 2 points of the continuous 3 points, and the abnormal type is dark leakage; rule #3 is that the observed value is greater than or equal to u + sigma; the detection requirement is detection of 4 points in the continuous 5 points, and the abnormal type is microleakage or drift;
Step 5, monitoring and early warning of water supply leakage of residential area
Acquiring SCADA measured data, finding out the minimum flow value of the MNF at night on the same day, and performing overrun judgment and early warning by using an improved SPC detection rule # 1; finding out the latest continuous 3 hour or 5 hour flow data at the night time of MNF, and carrying out overrun judgment and early warning by using the improved SPC detection rule #2 or # 3;
And (3) continuously updating the cell leakage monitoring database, if the missing report is found or the false report rate is more than 5%, repeating the step (2), and re-determining the upper control limit UCL by using the recent stable-period sample data to ensure the monitoring and early warning accuracy.
2. The method as claimed in claim 1, wherein the monitoring and early warning method for water leakage in the water supply cell is implemented by the following steps: using historical data of past 12 months to check the accuracy of SPC; if all leakage events can be detected according to the current k, slightly increasing the k, and searching the maximum k value meeting all detected leakage events; if all leakage events cannot be detected at the current k, slightly reducing the k, and searching for the minimum k value meeting all detected leakage events; then, the control upper limit UCL is corrected.
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| CN111259334A (en) * | 2020-01-14 | 2020-06-09 | 杭州电子科技大学 | Monitoring and early warning method for water use abnormity of large users of industrial enterprises |
| CN111720753A (en) * | 2020-04-09 | 2020-09-29 | 苏州市自来水有限公司 | Cell DMA (direct memory access) leakage detection control method based on noise monitoring technology |
| CN113775939A (en) * | 2021-07-29 | 2021-12-10 | 河海大学 | Online identification and positioning method for newly increased leakage of water supply pipe network |
| 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 |
| CN114323412A (en) * | 2021-12-29 | 2022-04-12 | 杭州电子科技大学 | Method for detecting pressure disturbance event of water supply pipe network |
| CN114323412B (en) * | 2021-12-29 | 2024-04-30 | 杭州电子科技大学 | Water supply pipe network pressure disturbance event detection method |
| CN116432863A (en) * | 2023-05-18 | 2023-07-14 | 安徽舜禹水务股份有限公司 | Integral peak-shifting scheduling method for secondary water supply based on mathematical programming |
| 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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