CN113065721A - Method, device, equipment and medium for graded early warning of leakage events of community water supply network - Google Patents

Method, device, equipment and medium for graded early warning of leakage events of community water supply network Download PDF

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CN113065721A
CN113065721A CN202110492568.XA CN202110492568A CN113065721A CN 113065721 A CN113065721 A CN 113065721A CN 202110492568 A CN202110492568 A CN 202110492568A CN 113065721 A CN113065721 A CN 113065721A
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data
water supply
supply network
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abnormal
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刘书明
周啸
吴雪
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Tsinghua University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
    • F17D—PIPE-LINE SYSTEMS; PIPE-LINES
    • F17D5/00—Protection or supervision of installations
    • F17D5/02—Preventing, monitoring, or locating loss
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply

Abstract

The invention provides a method, a device, equipment and a medium for graded early warning of a community water supply network leakage event based on abnormal state estimation, wherein the method comprises the following steps: acquiring inlet flow data of a water supply network of a community, preprocessing the acquired data, and repairing a missing value and an abnormal value; judging whether the data at the moment to be detected is an outlier or not, mapping the detection result to be the input state of a Kalman filter KF, and estimating and judging the possibility of leakage events through a self-adaptive abnormal state; and outputting different early warning levels according to the possibility of the leakage event. Compared with the prior art, the method, the device, the equipment and the medium for the graded early warning of the community water supply network leakage events based on abnormal state estimation can specifically identify the community water supply network leakage events according to the flow change rule of the community water supply network leakage events, reduce the false alarm caused by factors such as random user water consumption and monitoring errors, and realize more accurate early warning of the leakage events.

Description

Method, device, equipment and medium for graded early warning of leakage events of community water supply network
Technical Field
The disclosure relates to the technical field of water supply network safety early warning, in particular to a method, a device, equipment and a medium for grading early warning of a community water supply network leakage event based on abnormal state estimation.
Background
Due to corrosion, uneven stress, internal pressure and other reasons, water pipes in a community water supply network can have local pipe looseness, damage, fracture and the like, and leakage events are formed. The leakage of the pipeline not only causes a large amount of water resource waste, but also can cause the invasion of pollutants, the scouring and sedimentation of the foundation and the like, thereby threatening the physical health and the safety of lives and property of residents. Because partial pipeline leakage occurs in night or underground pipelines, the leakage is difficult to find in time through modes of manual investigation, user complaints and the like, and even if the pipeline leakage is found through modes of regular inspection and the like, the problems of high labor cost investment, difficult guarantee of timeliness and the like exist.
With the development of meter manufacturing technology and data transmission technology in recent years, a large number of cells are provided with inlet flow meters for collecting data in real time. Monitoring value changes caused by leakage events are identified by analyzing the monitoring data changes of the flow meter, early warning is timely given out to the leakage events, and the safety and the economical efficiency of water supply of a community can be remarkably improved. At present, a great deal of research is carried out at home and abroad on detecting leakage events by analyzing the change of pipe network flow monitoring data, and the main used methods can be classified into a prediction-classification method, a clustering method and the like. The prediction-classification method comprises the steps of firstly obtaining a state prediction value at the current moment from historical data, and then comparing the state prediction value with an actual monitoring value at the current moment. If the difference between the monitoring value and the predicted value is larger than a certain threshold value, the current moment is considered to be abnormal, and whether a leakage event occurs or not is judged; the clustering method compares and analyzes the monitoring data (or data change) at the current moment with the historical data, and judges whether the data at the current moment is an abnormal value compared with the historical data, so as to judge whether a leakage event occurs.
However, the above method has a disadvantage in that the leakage event cannot be effectively distinguished from the data change caused by normal water consumption of a large user, data monitoring error, and the like. Especially for the cell, due to the relatively small number of users, the randomness of water consumption is more obvious, and the water consumption is easier to be confused with the flow change generated by the leakage event to cause false alarm. How to quantitatively judge the possibility of occurrence of a leakage event according to the flow change characteristics caused by the leakage event and specifically eliminate the influence caused by monitoring errors, water consumption of a large user and the like still remains to be solved in the existing research.
Disclosure of Invention
Technical problem to be solved
Aiming at the technical problems existing at present, the disclosure provides a method, a device, equipment and a medium for graded early warning of a community water supply network leakage event based on abnormal state estimation.
(II) technical scheme
The purpose of the present disclosure can be achieved by the following technical solutions:
the invention provides a district water supply network leakage event grading early warning method based on abnormal state estimation, which comprises the following steps: acquiring inlet flow data of a water supply network of a community, preprocessing the acquired data, and repairing a missing value and an abnormal value; judging whether the data at the moment to be detected is an outlier or not, mapping the detection result to be the input state of a Kalman filter KF, and estimating and judging the possibility of leakage events through a self-adaptive abnormal state; and outputting different early warning levels according to the possibility of the leakage event.
In an embodiment of the present disclosure, the acquiring data of the inlet flow rate of the water supply network of the cell, preprocessing the acquired data, and repairing the missing value and the abnormal value includes: acquiring inlet flow data of a district water supply network by using a flowmeter, rearranging the flow data according to a time sequence, identifying missing values in the data, and repairing the missing values; defining data outlier detection limit NsigmaCalculating the mean Qmean and standard deviation Q of all the monitored datastdDefinition of
Figure BDA0003051587380000021
Figure BDA0003051587380000022
Data of (2)And repairing the abnormal value.
In an embodiment of the present disclosure, the repairing the missing value includes: for the missing value, the mean value of the flow data at the corresponding time of the previous day and the next day is used for repairing the missing value; and if the data of the previous day and the next day are still missing, using the non-missing data of the corresponding time of the most adjacent day to repair the data.
In an embodiment of the present disclosure, the repairing the outlier includes: replacing the abnormal value by using the average value of the flow data at the corresponding time of the previous day and the next day; and if the data of the previous day and the data of the next day are still abnormal values, replacing the abnormal values by using the non-abnormal value data of the time corresponding to the most adjacent day.
In the embodiment of the present disclosure, after the repairing the abnormal value, the method further includes: and d, defining the days nd of historical data to be compared, and acquiring historical flow monitoring data values of the inlet flow meter of the water supply network of the community at the current time and in the previous nd days.
In this disclosure, the determining whether the data at the time to be detected is an outlier, mapping the detection result to an input state of a kalman filter KF, and estimating and determining the possibility of the occurrence of the leakage event through the adaptive abnormal state includes: and (3) setting the current moment as D day and t moment, extracting historical data at the same moment every day on nd days before the history as a data set D to be detected, and expressing the historical data as follows: d ═ Qd-nd，t，…，Qd-1，t，Qd，t) Wherein Q isd，tThe monitoring value of the cell entrance flow at d day and t moment is represented; inputting a data set D to be detected into a DBSCAN clustering algorithm, and identifying outliers in the data set; judging the cell entrance flow monitoring value Q at the current momentd，tWhether it is an outlier; setting initial state information i of Kalman filter KF 00 corresponds to a variance P0State prediction process variance P ═ 1Q1 is ═ 1; mapping the DBSCAN outlier detection result into input state information of a Kalman filter KF; and calculating the optimal state estimation of the current moment by using a Kalman filter KF according to the state information of the current moment and the state prediction of the previous moment, wherein the optimal state estimation is the possibility of the leakage event at the current moment.
In this disclosure, the inputting the data set D to be detected into the DBSCAN clustering algorithm to identify an outlier in the data set includes:
step 221: setting the minimum clustering example number gamma of the DBSCAN clustering algorithm to be 2;
step 222: regarding each datum in the data set D to be detected as a point on a coordinate axis, respectively calculating Euclidean distances between every two points, and obtaining nd (nd-1)/2 pairs of Euclidean distances in total; all the calculated distances are arranged from large to small to obtain a vector
Figure BDA0003051587380000031
Step 223: setting distance threshold xi of DBSCAN algorithm to be 0.15 and enabling
Figure BDA0003051587380000032
Wherein round is an operation function and represents rounding for rounding; epsilon is the clustering neighborhood radius of DBSCAN;
step 224: randomly selecting one data in the D as a current point, and enabling the clustering mark C to be 1;
step 225: all points with Euclidean distance less than epsilon with the current point are taken as adjacent points of the current point; if the number of the adjacent points is less than gamma, marking the current point as an outlier, and going to step 227; otherwise, marking the current point as a core point of the cluster C;
step 226: repeating step 225 for each neighboring point of the current point in turn until no new neighboring points appear; all discovered neighboring points belong to cluster C;
step 227: the next point that has not been marked is selected as the current point, let the cluster mark C ═ C +1, and repeat steps 225 and 226 until all points are marked.
In this disclosure, the mapping the DBSCAN outlier detection result to the input state information of the kalman filter KF includes: when the detection result of the current moment is an outlier, settingInput state information i of Kalman filter KF k1, input process variance P R_k1 is ═ 1; when the detection result of the current moment is a normal value, setting input state information i of a Kalman filter KF k0, input process variance PR_k＝10。
In this disclosure, the calculating an optimal state estimation of the current time by using a kalman filter KF according to the state information of the current time and the state prediction of the previous time includes:
step 261: according to the state p at the previous momentk-1And its variance Pk-1Estimating the state prediction value P at the current timek|k-1And its variance Pk|k-1Expressed as:
Pk|k-1＝FkPk-1
Pk|k-1＝FkPk-1Fk T+PQ
wherein FkThe operation is a state predictor, and superscript T represents transposition operation;
step 262: calculating Kalman gain G of the current momentkExpressed as:
Gk＝Pk|k-1Hk(HkPk|k-1Hk T+PR)-1
wherein HkInputting an operator for the state;
step 263: computing an optimal state estimate p for the current timekAnd variance PkExpressed as:
pk＝Pk|k-1+Gk(ik-HkPk|k-1)
Pk＝(I-GkHk)Pk|k-1(I-GkHk)T+GkPRGk T
wherein I represents an identity matrix;
step 264: estimating p the optimal state of the current momentkThe leak event probability at the current time is considered.
In an embodiment of the present disclosure, the outputting different warning levels according to the possibility of the occurrence of the leakage event includes: setting low-grade early warning when the possibility of the leakage event is 0.01-0.4, medium-grade early warning when the possibility of the leakage event is 0.4-0.7, and high-grade early warning when the possibility of the leakage event is more than 0.7; determining a leak event probability p at the current timekAnd outputting the corresponding early warning grade within the threshold range.
The present disclosure in another aspect provides a device for early warning in a classified manner of a leakage event of a district water supply network based on abnormal state estimation, the device comprising:
the data preprocessing module is used for acquiring inlet flow data of a water supply network of a community, preprocessing the acquired data and repairing a missing value and an abnormal value;
the DBSCAN-KF coupling state estimation module is used for judging whether the data at the moment to be detected is an outlier, mapping the detection result to the input state of a Kalman filter KF, and estimating and judging the possibility of leakage events through a self-adaptive abnormal state;
and the grading early warning module is used for outputting different early warning grades according to the possibility of the leakage event.
Yet another aspect of the present disclosure provides an electronic device including: a processor; a memory storing a computer executable program that, when executed by the processor, causes the processor to execute the method for hierarchical pre-warning of a residential water supply network leakage event based on abnormal state estimation.
In yet another aspect, the present disclosure provides a storage medium containing computer-executable instructions, where the computer-executable instructions are executed to implement the method for hierarchical early warning of a leakage event of a district water supply network based on abnormal state estimation.
Yet another aspect of the present disclosure provides a computer program comprising: computer-executable instructions that when executed perform the method for hierarchical pre-warning of a cell water supply network leakage event based on abnormal state estimation.
(III) advantageous effects
According to the method, the device, the equipment and the medium for the graded early warning of the community water supply network leakage event based on abnormal state estimation, the community water supply network leakage event can be specifically identified according to the flow change rule of the community water supply network leakage event, the false alarm caused by factors such as random user water consumption and monitoring errors is reduced, and more accurate early warning of the leakage event is realized. Compared with the prior art, the method has the following advantages:
1. in the prior art, a leakage event is generally judged based on a single-moment data outlier, and false alarm is easily caused by factors such as random water consumption of a large user, monitoring errors and the like. The DBSCAN (sensitivity-Based Clustering of Applications with Noise) is a Clustering algorithm Based on Density, the DBSCAN Clustering algorithm further analyzes the distribution condition of outliers in a period of time according to the time persistence of leakage events through a state estimation algorithm, the possibility of leakage is judged, false alarm caused by the conditions of water consumption of large users, monitoring errors and the like is effectively avoided, and the accuracy of leakage event detection is improved;
2. most methods determine a leak event only including "occurred" and "not occurred". Compared with the existing method, the method can provide more comprehensive and accurate information for operating personnel and provide decision basis and guidance for maintenance and repair of the pipe network.
Drawings
For a more complete understanding of the present disclosure and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:
fig. 1 is a flowchart of a method for hierarchical early warning of a leakage event of a residential water supply network based on abnormal state estimation according to an embodiment of the present disclosure.
Fig. 2 is a block diagram of a neighborhood water supply network leakage event classification early warning device based on abnormal state estimation according to an embodiment of the disclosure.
Fig. 3 is an identification of outliers, leak event probability, early warning level, spanning 1 month of data, in accordance with an embodiment of the present disclosure.
FIG. 4 is a block diagram of an electronic device for hierarchical early warning of a cell water supply network leakage event based on abnormal state estimation, in accordance with an embodiment of the present disclosure.
[ reference numerals ]:
s1, S2, S3: step (ii) of
200: district water supply network leakage event grading early warning device based on abnormal state estimation
201: data preprocessing module
202: DBSCAN-KF coupling state estimation module
203: grading early warning module
400: electronic device
410: processor with a memory having a plurality of memory cells
420: memory device
421: computer program
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The embodiment of the disclosure provides a method, a device, equipment and a medium for graded early warning of a community water supply network leakage event based on abnormal state estimation. As shown in fig. 1, fig. 1 is a flowchart of a method for hierarchical early warning of a leakage event of a residential water supply network based on abnormal state estimation according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of an application scenario in which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, but does not mean that the embodiments of the present disclosure may not be used in other environments or scenarios.
As shown in fig. 1, the method for early warning in a stage of a leakage event of a district water supply network based on abnormal state estimation in the embodiment of the present disclosure includes the following steps:
step S1: acquiring inlet flow data of a water supply network of a community, preprocessing the acquired data, and repairing a missing value and an abnormal value;
step S2: judging whether the data at the moment to be detected is an outlier or not, mapping the detection result to be the input state of a Kalman filter KF, and estimating and judging the possibility of leakage events through a self-adaptive abnormal state;
step S3: and outputting different early warning levels according to the possibility of the leakage event.
In the monitoring data of the community water supply network, due to instrument faults, transmission signal interference and the like, missing values, obvious abnormal values and the like are often contained in the data. Therefore, the monitoring data should first be preprocessed before analyzing the monitoring data and identifying the leak event. In an embodiment of the present disclosure, the acquiring data of the inlet flow rate of the water supply network of the residential area, preprocessing the acquired data, and repairing the missing value and the abnormal value in step S1 specifically includes:
step S11: acquiring inlet flow data of a district water supply network by using a flowmeter, rearranging the flow data according to a time sequence, identifying missing values in the data, and repairing the missing values; wherein the repairing the missing value comprises: for the missing value, the mean value of the flow data at the corresponding time of the previous day and the next day is used for repairing the missing value; and if the data of the previous day and the next day are still missing, using the non-missing data of the corresponding time of the most adjacent day to repair the data.
Step S12: defining data outlier detection limit NsigmaCalculating the average Q of all the monitored datameanStandard deviation QstdDefinition of
Figure BDA0003051587380000081
Figure BDA0003051587380000082
The data of (1) is an abnormal value, and the abnormal value is repaired;
wherein the repair outliers comprise: replacing the abnormal value by using the average value of the flow data at the corresponding time of the previous day and the next day; and if the data of the previous day and the data of the next day are still abnormal values, replacing the abnormal values by using the non-abnormal value data of the time corresponding to the most adjacent day.
Step S13: and d, defining the days nd of historical data to be compared, and acquiring historical flow monitoring data values of the inlet flow meter of the water supply network of the community at the current time and in the previous nd days.
DBSCAN (sensitivity-Based Spatial Clustering of Applications with Noise) is a Density-Based Clustering algorithm, and the DBSCAN Clustering algorithm is proved to be a data Clustering and outlier detection method with good performance in the engineering field. However, the outlier in the data cannot completely correspond to the occurrence of the leakage event, and therefore, it cannot be finally determined whether the leakage event really occurs simply by the outlier detection result of the DBSCAN. Aiming at the problem, the method is based on the persistence characteristic of the leakage event, coupling analysis is further carried out on the outlier detection result of the DBSCAN by adopting a Kalman Filter (KF) state estimation algorithm, and a DBSCAN-KF coupling state estimation method is provided, namely step S2.
In the embodiment of the present disclosure, the determining in step S2 whether the data at the time to be detected is an outlier, mapping the detection result to an input state of a kalman filter KF, and determining the possibility of the occurrence of the leakage event by adaptive abnormal state estimation specifically includes:
step S21: setting the current time as d days and t times, extracting n before the calendardThe historical data of the same moment every day is a data set D to be detected, and is represented as: d ═ Qd-nd，t，…，Qd-1，t，Qd，t) Wherein Q isd，tThe monitoring value of the cell entrance flow at d day and t moment is represented;
step S22: inputting a data set D to be detected into a DBSCAN clustering algorithm, and identifying outliers in the data set;
step S23: judging the cell at the current momentInlet flow monitoring value Qd，tWhether it is an outlier;
step S24: setting initial state information i of Kalman filter KF 00 corresponds to a variance P0State prediction process variance P ═ 1Q＝1；
Step S25: mapping the DBSCAN outlier detection result into input state information of a Kalman filter KF;
step S26: and calculating the optimal state estimation of the current moment by using a Kalman filter KF according to the state information of the current moment and the state prediction of the previous moment, wherein the optimal state estimation is the possibility of the leakage event at the current moment.
In the embodiment of the present disclosure, the step S22 of inputting the data set D to be detected into the DBSCAN clustering algorithm, and identifying outliers in the data set includes:
step 221: setting the minimum clustering example number gamma of the DBSCAN clustering algorithm to be 2;
step 222: regarding each datum in the data set D to be detected as a point on a coordinate axis, respectively calculating the Euclidean distance between every two points, and obtaining n in totald(nd-1)/2 pairs of euclidean distances; all the calculated distances are arranged from large to small to obtain a vector
Figure BDA0003051587380000091
Step 223: setting distance threshold xi of DBSCAN algorithm to be 0.15 and enabling
Figure BDA0003051587380000092
Wherein round is an operation function and represents rounding for rounding; epsilon is the clustering neighborhood radius of DBSCAN;
step 224: randomly selecting one data in the D as a current point, and enabling the clustering mark C to be 1;
step 225: all points with Euclidean distance less than epsilon with the current point are taken as adjacent points of the current point; if the number of the adjacent points is less than gamma, marking the current point as an outlier, and going to step 227; otherwise, marking the current point as a core point of the cluster C;
step 226: repeating step 225 for each neighboring point of the current point in turn until no new neighboring points appear; all discovered neighboring points belong to cluster C;
step 227: the next point that has not been marked is selected as the current point, let the cluster mark C ═ C +1, and repeat steps 225 and 226 until all points are marked.
In an embodiment of the present disclosure, the mapping the DBSCAN outlier detection result to input state information of a kalman filter KF in step S25 includes:
step S251: when the detection result of the current moment is an outlier, setting input state information i of a Kalman filter KF k1, input process variance PR_k＝1；
Step S252: when the detection result of the current moment is a normal value, setting input state information i of a Kalman filter KF k0, input process variance PR_k＝10。
In the embodiment of the present disclosure, the calculating, in step S26, an optimal state estimation of the current time by using a kalman filter KF according to the state information of the current time and the state prediction of the previous time includes:
step 261: according to the state (leak event probability) p at the previous momentk-1And its variance Pk-1Estimating the state prediction value p at the current momentk|k-1And its variance Pk|k-1Expressed as:
pk|k-1＝Fkpk-1
Pk|k-1＝FkPk-1Fk T+PQ
wherein FkThe operation is a state predictor, and superscript T represents transposition operation;
step 262: calculating Kalman gain G of the current momentkExpressed as:
Gk＝Pk1k-1Hk(HkPk|k-1Hk T+PR)-1
wherein HkInputting an operator for the state;
step 263: computing an optimal state estimate p for the current timekAnd variance PkExpressed as:
pk＝pk|k-1+Gk(ik-Hkpk|k-1)
Pk＝(I-GkHk)Pk|k-1(I-GkHk)T+GkPRGk T
wherein I represents an identity matrix;
step 264: estimating p the optimal state of the current momentkThe leak event probability at the current time is considered.
For the operation management of the actual pipe network, the possibility value of the leakage event cannot play an intuitive guiding role, and the step S3 of the present disclosure further corresponds the possibility of the leakage event to different early warning levels, specifically including:
step S31: setting low-grade early warning when the possibility of the leakage event is 0.01-0.4, medium-grade early warning when the possibility of the leakage event is 0.4-0.7, and high-grade early warning when the possibility of the leakage event is more than 0.7;
step S32: determining a leak event probability p at the current timekAnd outputting the corresponding early warning grade within the threshold range.
Based on a flow chart of a method for graded early warning of a leakage event of a district water supply network based on abnormal state estimation according to an embodiment of the disclosure shown in fig. 1, fig. 2 shows a block diagram of a graded early warning device of a leakage event of a district water supply network based on abnormal state estimation according to an embodiment of the disclosure.
As shown in fig. 2, the hierarchical early warning apparatus 200 for a leakage event of a residential water supply network based on abnormal state estimation provided by the embodiment of the present disclosure includes a data preprocessing module 201, a DBSCAN-KF coupling state estimation module 202, and a hierarchical early warning module 203. Wherein: the data preprocessing module 201 is configured to acquire inlet flow data of a water supply network in a cell, preprocess the acquired data, and repair a missing value and an abnormal value; the DBSCAN-KF coupling state estimation module 202 is configured to determine whether data at a time to be detected is an outlier, map a detection result to an input state of the kalman filter KF, and estimate and determine a possibility of a leakage event through a self-adaptive abnormal state. The grading pre-warning module 203 is used for outputting different pre-warning grades according to the possibility of the leakage event.
It should be understood that the data preprocessing module 201, the DBSCAN-KF coupling state estimation module 202, and the classification pre-warning module 203 may be combined and implemented in one module, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module.
According to an embodiment of the present disclosure, at least one of the data preprocessing module 201, the DBSCAN-KF coupling state estimation module 202, and the rank warning module 203 may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or in a suitable combination of three implementations of software, hardware, and firmware. Alternatively, at least one of the data preprocessing module 201, the DBSCAN-KF coupling state estimating module 202, and the hierarchical early warning module 203 may be at least partially implemented as a computer program module, which may perform the functions of the respective modules when executed by a computer.
The embodiment of the disclosure takes the monitoring data of an inlet flow meter of a certain living cell as an example, and further explains the implementation process of the classified early warning method for the leakage event of the water supply network of the cell based on abnormal state estimation.
(1) Monitoring data is collected, and the data is preprocessed.
In the embodiment, the monitoring data of the flow meter at the entrance of the target cell from 5 months in 2018 to 2 months in 2021 are extracted, the data monitoring time interval is 15 minutes, 99600 data are extracted, and the flow rate range is 0.2-4 cubic meters/15 minutes. Data preprocessing analysis shows that data loss occurs in 3 time periods in total, and each loss lasts for 4 hours; data abnormality occurs in 7 time periods in total, and the abnormality is represented by that the flow rate is obviously larger (rising by tens of times in a short time) or obviously smaller (a 0 value or a negative value occurs). And replacing the identified missing value and the abnormal value by using the average values of the monitoring data at the corresponding time of the previous day and the next day respectively.
(2) Detecting outliers in data
For each preprocessed data point, historical data of the same time 30 days before the history of the data point is extracted first, and a detection data set is established. The detection data set is input into the DBSCAN algorithm to obtain an outlier detection recognition result, as shown by a circle mark in fig. 3, fig. 3 is a recognition result of the outlier, the possibility of a leakage event, and the early warning level according to the embodiment of the present disclosure, and the data span is 1 month.
(3) The detection result of the DBSCAN at each time is mapped to input state information of the KF, and the KF is used to estimate the optimal state at the current time in combination with the historical state information, and the obtained result is shown as a "leakage possibility" curve in fig. 3.
(4) And inputting the estimation result of the possibility of the leakage event into the grading early warning module. And setting the possibility of the leakage event to be 0.01-0.4, namely low-level early warning, 0.4-0.7, namely medium-level early warning, and more than 0.7, namely high-level early warning, and respectively outputting the early warning result at each moment, as shown by a dotted line in fig. 3.
As can be seen from the analysis of the results shown in fig. 3, for monitoring data fluctuation caused by water consumption of a large user, monitoring errors and the like, the possibility of leakage events is judged to be low, and the early warning level is low, as shown by the results in the period from 12 months 9 days to 12 months 13 days; and for monitoring data fluctuation caused by the leakage event, the possibility of the leakage event is judged to be high, and the early warning level is high, as shown by results in the period from 12 months 17 days to 12 months 19 days. It can be seen from the results that the DBSCAN-KF coupling state estimation module provided by the present disclosure effectively identifies pipe network leakage events and avoids false alarms caused by other situations.
The embodiment of the present disclosure further provides a device for performing a hierarchical early warning on a leakage event of a district water supply network based on abnormal state estimation, as shown in fig. 4, fig. 4 is a block diagram of an electronic device 400 for performing a hierarchical early warning on a leakage event of a district water supply network based on abnormal state estimation according to an embodiment of the present disclosure. The electronic device 400 includes: one or more processors 410; a memory 420 storing a computer executable program that, when executed by the processor 410, causes the processor 410 to implement the method for hierarchical pre-warning of a district water supply network leakage event based on abnormal state estimation shown in fig. 1.
In particular, processor 410 may include, for example, a general purpose microprocessor, an instruction set processor and/or related chip set and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), and/or the like. The processor 410 may also include onboard memory for caching purposes. Processor 410 may be a single processing unit or a plurality of processing units for performing different actions of a method flow according to embodiments of the disclosure.
The memory 420, for example, can be any medium that can contain, store, communicate, propagate, or transport the instructions. For example, a readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. Specific examples of the readable storage medium include: magnetic storage devices, such as magnetic tape or Hard Disk Drives (HDDs); optical storage devices, such as compact disks (CD-ROMs); a memory, such as a Random Access Memory (RAM) or a flash memory; and/or wired/wireless communication links.
The memory 420 may include a computer program 421, which computer program 421 may include code/computer-executable instructions that, when executed by the processor 410, cause the processor 410 to perform a method according to an embodiment of the disclosure, or any variation thereof.
The computer program 421 may be configured with, for example, computer program code comprising computer program modules. For example, in an example embodiment, code in computer program 421 may include at least one program module, including, for example, module 421A, module 421B, … …. It should be noted that the division and number of the modules are not fixed, and those skilled in the art may use suitable program modules or program module combinations according to actual situations, so that the processor 410 may execute the method according to the embodiment of the present disclosure or any variation thereof when the program modules are executed by the processor 410.
The embodiments of the present disclosure also provide a computer-readable medium, which may be included in the device/apparatus/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer readable medium carries one or more programs which, when executed, implement a method for hierarchical pre-warning of a district water supply network leakage event based on abnormal state estimation according to an embodiment of the present disclosure.
According to embodiments of the present disclosure, a computer readable medium may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, optical fiber cable, radio frequency signals, etc., or any suitable combination of the foregoing.
The present disclosure also provides a computer program comprising: computer-executable instructions that when executed perform a method for hierarchical early warning of a cell water supply network leakage event based on abnormal state estimation in accordance with an embodiment of the present disclosure.
The present disclosure has been described in detail so far with reference to the accompanying drawings. From the above description, those skilled in the art should clearly recognize the present disclosure.
It is to be noted that, in the attached drawings or in the description, the implementation modes not shown or described are all the modes known by the ordinary skilled person in the field of technology, and are not described in detail. In addition, the above definitions of the respective elements are not limited to the specific structures, shapes or modes mentioned in the embodiments, and those skilled in the art may easily modify or replace them.
Of course, the present disclosure may also include other parts according to actual needs, and since the parts are not related to the innovation of the present disclosure, the details are not described herein.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the disclosure, various features of the disclosure are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various disclosed aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that is, the claimed disclosure requires more features than are expressly recited in each claim. Rather, as the following claims reflect, disclosed aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this disclosure.
Further, in the drawings or description, the same drawing reference numerals are used for similar or identical parts. Features in various embodiments illustrated in the description may be freely combined to form a new scheme without conflict, and in addition, each claim may be taken alone as an embodiment or the features in various claims may be combined to form a new embodiment. Further, elements or implementations not shown or described in the drawings are of a form known to those of ordinary skill in the art. Additionally, while exemplifications of parameters including particular values may be provided herein, it is to be understood that the parameters need not be exactly equal to the respective values, but may be approximated to the respective values within acceptable error margins or design constraints.
Unless a technical obstacle or contradiction exists, the above-described various embodiments of the present disclosure may be freely combined to form further embodiments, which are all within the scope of protection of the present disclosure.
While the present disclosure has been described in connection with the accompanying drawings, the embodiments disclosed in the drawings are intended to be illustrative of the preferred embodiments of the disclosure, and should not be construed as limiting the disclosure. The dimensional proportions in the drawings are merely schematic and are not to be understood as limiting the disclosure.
Although a few embodiments of the present general inventive concept have been shown and described, it would be appreciated by those skilled in the art that changes may be made in these embodiments without departing from the principles and spirit of the general inventive concept, the scope of which is defined in the claims and their equivalents.
The above-mentioned embodiments are intended to illustrate the objects, aspects and advantages of the present disclosure in further detail, and it should be understood that the above-mentioned embodiments are only illustrative of the present disclosure and are not intended to limit the present disclosure, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.

Claims (14)

1. A classified early warning method for a community water supply network leakage event based on abnormal state estimation is characterized by comprising the following steps:
acquiring inlet flow data of a water supply network of a community, preprocessing the acquired data, and repairing a missing value and an abnormal value;
judging whether the data at the moment to be detected is an outlier or not, mapping the detection result to be the input state of a Kalman filter KF, and estimating and judging the possibility of leakage events through a self-adaptive abnormal state;
and outputting different early warning levels according to the possibility of the leakage event.
2. The graded early warning method for the district water supply network leakage event based on abnormal state estimation as claimed in claim 1, wherein the steps of obtaining inlet flow data of the district water supply network, preprocessing the obtained data, and repairing the missing value and the abnormal value comprise:
acquiring inlet flow data of a district water supply network by using a flowmeter, rearranging the flow data according to a time sequence, identifying missing values in the data, and repairing the missing values;
defining data outlier detection limit NsigmaCalculating the average Q of all the monitored datameanStandard deviation QstdDefinition of
Figure FDA0003051587370000011
The data of (a) is an abnormal value, and the abnormal value is fixed.
3. The abnormal state estimation-based graded pre-warning method for the leakage event of the district water supply network according to claim 2, wherein the repairing the missing value comprises:
for the missing value, the mean value of the flow data at the corresponding time of the previous day and the next day is used for repairing the missing value; and if the data of the previous day and the next day are still missing, using the non-missing data of the corresponding time of the most adjacent day to repair the data.
4. The staged pre-warning method for the leakage event of the district water supply network based on abnormal state estimation as claimed in claim 2, wherein the repairing the abnormal value comprises:
replacing the abnormal value by using the average value of the flow data at the corresponding time of the previous day and the next day; and if the data of the previous day and the data of the next day are still abnormal values, replacing the abnormal values by using the non-abnormal value data of the time corresponding to the most adjacent day.
5. The staged pre-warning method for a district water supply network leakage event based on abnormal state estimation as claimed in claim 2, further comprising, after said repairing abnormal values:
defining historical data days n to be compareddObtaining the current time and the previous n of the inlet flowmeter of the water supply network of the communitydDay historical flow monitoring data values.
6. The abnormal state estimation-based graded early warning method for the leakage event of the district water supply network according to claim 1, wherein the step of judging whether the data of the time to be detected is an outlier or not, mapping the detection result to the input state of a Kalman filter KF, and judging the possibility of the leakage event through adaptive abnormal state estimation comprises the following steps:
setting the current time as d days and t times, extracting n before the calendardThe historical data of the same moment every day is a data set D to be detected, and is represented as: d ═ Qd-nd，t，…，Qd-1，t，Qd，t) Wherein Q isd，tThe monitoring value of the cell entrance flow at d day and t moment is represented;
inputting a data set D to be detected into a DBSCAN clustering algorithm, and identifying outliers in the data set;
judging the cell entrance flow monitoring value Q at the current momentd，tWhether it is an outlier;
setting initial state information of Kalman filter KFMessage i00 corresponds to a variance P0State prediction process variance P ═ 1Q＝1；
Mapping the DBSCAN outlier detection result into input state information of a Kalman filter KF;
and calculating the optimal state estimation of the current moment by using a Kalman filter KF according to the state information of the current moment and the state prediction of the previous moment, wherein the optimal state estimation is the possibility of the leakage event at the current moment.
7. The abnormal state estimation-based hierarchical early warning method for the leakage event of the community water supply network according to claim 6, wherein the step of inputting the data set D to be detected into a DBSCAN clustering algorithm to identify the outliers in the data set comprises the following steps:
step 221: setting the minimum clustering example number gamma of the DBSCAN clustering algorithm to be 2;
step 222: regarding each datum in the data set D to be detected as a point on a coordinate axis, respectively calculating the Euclidean distance between every two points, and obtaining n in totald(nd-1)/2 pairs of euclidean distances; all the calculated distances are arranged from large to small to obtain a vector
Figure FDA0003051587370000021
Step 223: setting distance threshold xi of DBSCAN algorithm to be 0.15 and enabling
Figure FDA0003051587370000031
Wherein round is an operation function and represents rounding for rounding; epsilon is the clustering neighborhood radius of DBSCAN;
step 224: randomly selecting one data in the D as a current point, and enabling the clustering mark C to be 1;
step 225: all points with Euclidean distance less than epsilon with the current point are taken as adjacent points of the current point; if the number of the adjacent points is less than gamma, marking the current point as an outlier, and going to step 227; otherwise, marking the current point as a core point of the cluster C;
step 226: repeating step 225 for each neighboring point of the current point in turn until no new neighboring points appear; all discovered neighboring points belong to cluster C;
step 227: the next point that has not been marked is selected as the current point, let the cluster mark C ═ C +1, and repeat steps 225 and 226 until all points are marked.
8. The abnormal state estimation-based hierarchical early warning method for the leakage event of the district water supply network according to claim 6, wherein the mapping of the DBSCAN outlier detection result to the input state information of a Kalman filter KF comprises:
when the detection result of the current moment is an outlier, setting input state information i of a Kalman filter KFk1, input process variance PR_k＝1；
When the detection result of the current moment is a normal value, setting input state information i of a Kalman filter KFk0, input process variance PR_k＝10。
9. The abnormal state estimation-based graded pre-warning method for the leakage event of the district water supply network according to claim 6, wherein the calculating of the optimal state estimation of the current time by using a Kalman filter KF according to the state information of the current time and the state prediction of the last time comprises:
step 261: according to the state p at the previous momentk-1And its variance Pk-1Estimating the state prediction value P at the current timek|k-1And its variance Pk|k-1Expressed as:
Pk|k-1＝FkPk-1
Pk|k-1＝FkPk-1Fk T+PQ
wherein FkThe operation is a state predictor, and superscript T represents transposition operation;
step 262: calculating the current timeMoment kalman gain GkExpressed as:
Gk＝Pk|k-1Hk(HkPk|k-1Hk T+PR)-1
wherein HkInputting an operator for the state;
step 263: computing an optimal state estimate p for the current timekAnd variance PkExpressed as:
pk＝Pk|k-1+Gk(ik-Hkpk|k-1)
Pk＝(I-GkHk)Pk|k-1(I-GkHk)T+GkPRGk T
wherein I represents an identity matrix;
step 264: estimating p the optimal state of the current momentkThe leak event probability at the current time is considered.
10. The classified pre-warning method for the leakage event of the district water supply network based on abnormal state estimation as claimed in claim 1, wherein the outputting different pre-warning levels according to the possibility of the leakage event comprises:
setting low-grade early warning when the possibility of the leakage event is 0.01-0.4, medium-grade early warning when the possibility of the leakage event is 0.4-0.7, and high-grade early warning when the possibility of the leakage event is more than 0.7;
determining a leak event probability p at the current timekAnd outputting the corresponding early warning grade within the threshold range.
11. A staged pre-warning device for a leakage event of a district water supply network based on abnormal state estimation, the device comprising:
the data preprocessing module is used for acquiring inlet flow data of a water supply network of a community, preprocessing the acquired data and repairing a missing value and an abnormal value;
the DBSCAN-KF coupling state estimation module is used for judging whether the data at the moment to be detected is an outlier, mapping the detection result to the input state of a Kalman filter KF, and estimating and judging the possibility of leakage events through a self-adaptive abnormal state;
and the grading early warning module is used for outputting different early warning grades according to the possibility of the leakage event.
12. An electronic device, comprising:
a processor;
a memory storing a computer executable program which, when executed by the processor, causes the processor to perform the method of hierarchical pre-warning of district water supply network leakage events based on abnormal state estimation according to any one of claims 1 to 10.
13. A storage medium containing computer executable instructions which when executed implement the method of hierarchical pre-warning of district water supply network leakage events based on abnormal state estimation of any one of claims 1 to 10.
14. A computer program, comprising: computer executable instructions for implementing the method for hierarchical early warning of a leakage event of a district water supply network based on abnormal state estimation of any one of claims 1 to 10 when executed.
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Cited By (3)

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CN113789829A (en) * 2021-09-15 2021-12-14 王达 Big data-based community water supply method and system
CN115931055A (en) * 2023-01-06 2023-04-07 长江信达软件技术（武汉）有限责任公司 Rural water supply operation diagnosis method and system based on big data analysis
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

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113789829A (en) * 2021-09-15 2021-12-14 王达 Big data-based community water supply method and system
CN113789829B (en) * 2021-09-15 2024-03-19 广东科力水务技术股份有限公司 District water supply method and system based on big data
CN115931055A (en) * 2023-01-06 2023-04-07 长江信达软件技术（武汉）有限责任公司 Rural water supply operation diagnosis method and system based on big data analysis
CN115931055B (en) * 2023-01-06 2023-06-16 长江信达软件技术（武汉）有限责任公司 Rural water supply operation diagnosis method and system based on big data analysis
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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