CN112801812B - Rural water supply operation detection method based on Internet of things and time series analysis - Google Patents

Rural water supply operation detection method based on Internet of things and time series analysis Download PDF

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CN112801812B
CN112801812B CN202011597570.5A CN202011597570A CN112801812B CN 112801812 B CN112801812 B CN 112801812B CN 202011597570 A CN202011597570 A CN 202011597570A CN 112801812 B CN112801812 B CN 112801812B
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张恒飞
成雪夫
田昊
梅林辉
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Changjiang Xinda Software Technology Wuhan Co ltd
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Abstract

The invention relates to the technical field of rural water supply, and discloses a rural water supply operation detection method based on Internet of things and time sequence analysistLet yt＝xt(ii) a Calculating predicted value x 'by utilizing moving average method't(ii) a Calculating the confidence interval upper limit Lup(ii) a If xt>LupAnd xt‑1<LupThen let yt＝x’t(ii) a Calculating predicted value y 'by means of Holt-Winters method't(ii) a Calculating confidence interval upper limit L 'of water consumption'upIf y ist>L’upThen an anomaly is diagnosed and an alarm is given. The rural water supply operation detection method based on the Internet of things and time sequence analysis fully considers the characteristics of multi-line length of rural water supply points and unstable network state, and effectively monitors the operation of a rural water supply network.

Description

Rural water supply operation detection method based on Internet of things and time series analysis
Technical Field
The invention relates to the technical field of rural water supply, in particular to a rural water supply operation detection method based on Internet of things and time series analysis.
Background
The rural population proportion of China is large, and the drinking water safety guarantee of vast rural areas has a plurality of difficulties. In recent years, through rural drinking water life engineering, sleepiness relieving engineering and consolidated promotion engineering construction, more than 1100 million water supply engineering is built up to the end of 2018, 9.4 hundred million rural population is served, living sanitary conditions of rural masses are improved, and health level and quality of life are improved. However, rural water supply projects are multi-point, wide in line length, complex in network conditions, difficult to implement in traditional automatic monitoring means, high in water supply operation and maintenance difficulty, difficult to find problems, serious in leakage, high in management cost, poor in water supply guarantee rate and the like, and still universal.
The rural water supply system mainly comprises a water source area, a water plant, a pump station, a reservoir, pipelines, various gate valves, household metering and the like. The rural water supply operation detection is mainly realized by monitoring and analyzing the system components, wherein the main content is to detect whether the pipelines have burst leakage loss. The technical problems of rural water supply operation detection comprise: 1) building a monitoring system covering the whole rural water supply chain; 2) and judging the running state of the water supply system based on the monitoring data, and particularly detecting and alarming the burst leakage which is difficult to find.
Wherein, the prior art mainly has the following disadvantages:
from the research object, the technical achievement mainly aims at urban water supply, needs to monitor the precondition that the coverage is high, the data stability is good, and the like, and is not suitable for the actual situation of rural water supply. From the practical aspect, the algorithm complexity is high, and real-time detection and alarm are not easy to realize in the rural scene with more points and wide lines.
Disclosure of Invention
The invention aims to provide a rural water supply operation detection method based on the Internet of things and time sequence analysis, fully considers the characteristics of multi-line length of rural water supply points and unstable network state, and effectively monitors the operation of a rural water supply network.
In order to achieve the purpose, the rural water supply operation detection method based on the Internet of things and time series analysis comprises the following steps:
A) method for obtaining monitoring value x of running state of pipe network node from equipment of Internet of thingstT is a time node, and stores the time node into a database, and adds a data sequence ytLet yt＝xt；
B) Taking out N monitoring values from the database, performing prediction calculation with a moving average window as N, and obtaining a current prediction value x't，x’t＝(xt-1+xt-2+xt-3+xt-4+…+xt-n)/n；
C) Calculating the upper limit L of the confidence interval by using a method of combining the moving average with the standard deviationup，Lup＝x’t+ (mae + a σ), wherein mae represents the median loss of absolute value of residual error in the sliding window, σ represents the standard deviation of residual error in the sliding window, and a is the weight of standard deviation;
D) if xt>LupAnd xt-1<LupThen let yt＝x’t；
E) Calculating predicted value y 'by means of Holt-Winters method'tThe calculation steps are as follows:
lt＝α(yt-st-T)+(1-α)(lt-1+bt-1)
bt＝β(lt-lt-1)+(1-β)bt-1
st＝γ(yt-lt)+(1-γ)st-T
y’t＝lt-m+mbt-m+st-T-m+1+(m-1)modT
wherein, ytThe sequence length of (2) is N, ltRepresents the intercept, btRepresents the variation trend within the period, stRepresents a periodic component, T is the period length, m is the length of T from the start node of the period, alpha, betaAnd gamma are respectively the accumulated weight of intercept, trend and periodic component on the time sequence, and the values of alpha, beta and gamma are calculated by a truncated Newton conjugate gradient method;
F) at ytOn the basis, a water consumption confidence interval is established by utilizing a Brutlag method, and an upper limit value L 'of the confidence interval is taken'up，
L’up＝lt-1+bt-1+st-T+mdt-T
dt＝γ│yt-y’t│+(1-γ)dt-T
G) If yt>L’upThen an anomaly is diagnosed and an alarm is given.
Preferably, in the step a), if the internet of things device is not online and the current monitoring value is missing, the predicted value x 'in the step B) is taken'tLet yt＝x’t；
Preferably, in the step D) and the step E), the time span of N is 1-3 days.
Preferably, in the step C), the value range of n is 3-6.
Compared with the prior art, the invention has the following advantages:
1. the characteristics of the multi-line length of the rural water supply points are fully considered, and the operation monitoring of the rural water supply network is realized by utilizing the front-end equipment of the Internet of things;
2. the unstable situation of the network state of the rural area is fully considered, and the problems of the loss and the noise of the monitoring value are processed by using a moving average method;
3. the method for detecting and alarming based on the medium-short term monitoring data can effectively cope with seasonal changes of rural water use;
4. the method has the advantages of low calculation complexity, short consumed time and higher practical value.
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Fig. 1 is a flow chart of the rural water supply operation detection method based on internet of things and time series analysis.
Detailed Description
The invention is described in further detail below with reference to the figures and the specific embodiments.
(1) Firstly, a rural water supply automatic measurement and control system with full chain coverage is established
The rural water supply automatic measurement and control system utilizes the wireless Internet of things technology, combines metering equipment, pipeline pressure sensing equipment, a water level meter, a water quality sensor, video monitoring and the like, realizes the automatic safe operation of the whole life cycle of water supply from a water source to a faucet, realizes automatic scheduling and unattended operation of all levels of pump stations, monitors pipe network automation and measures water in real time. The automatic measurement and control system comprises a water source area monitoring part, a water plant monitoring part, a pump station monitoring part, a storage water tank monitoring part, a pipe network monitoring part, a household monitoring part and the like.
Monitoring a water source: building water quality monitoring and video monitoring equipment in various water source places such as reservoirs, interception and the like, wherein the water quality monitoring comprises indexes such as residual chlorine, turbidity, PH value, water temperature, conductivity and the like;
monitoring a water plant: the water plant monitoring realizes the intelligent monitoring of a process and water treatment system, a power supply and distribution system, a water pump and electromechanical equipment, and monitoring indexes comprise water taking flow, pipeline pressure, pipeline flow, valve opening, water tank level, water quality monitoring, unit running state, video monitoring and the like. The indexes of residual chlorine, turbidity, PH value, water temperature and conductivity of factory water of a water plant are monitored on line;
monitoring a pump station: the pump station monitoring realizes the monitoring control of equipment such as a pump station unit, a high-low level water pool, a water supply valve and the like, realizes the intelligent combined dispatching operation of the pump station, and achieves the aim of unattended operation;
fourthly, monitoring the storage water tank: the regulation and storage water tank monitoring indexes comprise water tank water level, water supply valve opening and closing state, battery electric quantity, signal intensity, video monitoring and the like;
pipe network monitoring: the pipe network monitoring system monitors data such as pipe network pressure, flow and the like, analyzes the acquired data to support business functions such as pipe network pipe burst monitoring, daily pipeline inspection and maintenance and the like, and monitoring indexes of the pipe network comprise pipe pressure, accumulated flow, battery capacity, signal intensity and the like;
sixthly, monitoring by users: the on-home monitoring of the human-drinking engineering adopts a wireless remote transmission water meter, realizes water consumption recording and storage, electronic display and remote meter reading, and carries out on-off control according to the appointed water consumption, and the on-home monitoring indexes comprise the current reading of the water meter, the state of a valve of the water meter, the electric quantity of a battery, the signal intensity and the like.
(2) Interpolating and denoising monitored data
As shown in fig. 1, A) obtaining a monitoring value x of the running state of a pipe network node from equipment of the Internet of thingstT is a time node, and stores the time node into a database, and adds a data sequence ytLet yt＝xtObjects of the whole compilation of the operation monitoring data of the pipe network mainly comprise accumulated flow of the pipe network, pipeline pressure, water level of a water storage tank and the like, the data of the monitoring equipment of the Internet of things is easy to be lost due to unstable signals in vast rural areas, the operation monitoring data of the pipe network is typical time sequence data, and the lost data can be well interpolated by using a sliding average method with a small order number;
B) the moving average method comprises calculating moving average by sequentially increasing and decreasing new and old data period by period to obtain variation trend of time sequence data, predicting according to the variation trend, complementing missing data in the monitoring process, taking N monitoring values from the database, performing prediction calculation with a moving average window of N to obtain the predicted value x't，x’t＝(xt-1+xt-2+xt-3+xt-4+…+xt-n) N; in formula (II), x'tDenotes a predicted value for the current time, n denotes a window size of a moving average, xt-1Representing the previous actual value, xt-nRepresenting the actual value of the previous n period;
C) on the basis of interpolation of the whole encoding data, the upper limit of a normal value is determined by combining the median of absolute value loss of residual errors in a sliding window and the standard deviation by using a sliding average method, so that noise removal is realized, and the upper limit L of a confidence interval is calculated by using the method of combining the sliding average with the standard deviationup，Lup＝x’t+ (mae + a σ), where mae represents a sliding windowThe absolute value of the inner residual error loses the median, sigma represents the standard deviation of the residual error in the sliding window, and a is the standard deviation weight; comparing and finding that a smaller sliding window is more sensitive to noise, and the selected window is estimated to be in 1-4 hours;
D) during the noise removal, a smoothing operation cannot be performed for values exceeding the upper limit twice in succession, i.e. if xt>LupAnd xt-1<LupThen let yt＝x’t；
(3) Pipe network running state detection by using medium and short term data
E) On the basis of the monitoring data after the reorganization and interpolation, medium-short term data are utilized, a triple exponential method (a specific algorithm is a Holt-Winters method) is adopted to predict the water consumption, and the formula is as follows:
lt＝α(yt-st-T)+(1-α)(lt-1+bt-1)
bt＝β(lt-lt-1)+(1-β)bt-1
st＝γ(yt-lt)+(1-γ)st-T
y’t＝lt-m+mbt-m+st-T-m+1+(m-1)modT
wherein, ytThe sequence length of (1) is N, ltRepresents the intercept, btRepresents the variation trend within the period, stExpressing a periodic component, wherein T is the period length, m is the length of a T distance period starting node, alpha, beta and gamma are respectively intercept, trend and the accumulated weight of the periodic component on a time sequence, and the values of the alpha, the beta and the gamma are calculated by adopting a truncated Newton conjugate gradient method;
F) after obtaining the water consumption curve calculated by the algorithm, establishing a water consumption confidence interval by using a Brutlag method:
L’up＝lx-1+bx-1+sx-T+mdt-T
dt＝γ│yt-y’t│+(1-γ)dt-T
G) if yt>L’upThen diagnoseIs abnormal and alarms.
In the step A), if the Internet of things equipment is not on line and the monitoring value is lost, the predicted value x 'in the step B) is taken'tLet yt＝x’t(ii) a And in the step D) and the step E), the time span of N is 1-3 days, and in the step C), the value range of N is 3-6.
In one embodiment, the acquisition frequency is 15 minutes/time and the sliding average window is set to 4 (i.e., 1 hour). A rural water supply operation detection method based on Internet of things and time series analysis comprises the following steps:
A) obtaining pipe network node running state monitoring value x from Internet of things equipmenttT is a time node, and stores the time node into a database, and adds a data sequence ytLet yt＝xt；
B) 96 monitoring values within 24 hours are taken out from the database, prediction calculation with a sliding average window of 4 is carried out, and the current prediction value x 'is obtained't，x’t＝(xt-1+xt-2+xt-3+xt-4) And 4, if the Internet of things equipment is not on line and the monitoring value is lost, making yt＝x’t；
C) Calculating the upper limit L of the confidence interval by using a method of combining the moving average with the standard deviationup，Lup＝x’t(mae + a σ), wherein mae represents the median of absolute value loss of the residual error in the sliding window, σ represents the standard deviation of the residual error in the sliding window, and a is the standard deviation weight and is 1.5;
D) if xt>LupAnd xt-1<LupThen let yt＝x’t；
E) At ytOn the basis, a Holt-Winters method is utilized to calculate a predicted value y'tThe calculation steps are as follows:
lt＝α(yt-st-T)+(1-α)(lt-1+bt-1)
bt＝β(lt-lt-1)+(1-β)bt-1
st＝γ(yt-lt)+(1-γ)st-T
y’t＝lt-m+mbt-m+st-T-m+1+(m-1)modT
wherein, ytThe sequence length of (2) is 96 values in 24 hours, the reproduction period is selected to be 4 hours,/tRepresents the intercept, btRepresents the variation trend within the period, stThe values of α, β, and γ are calculated by a truncated newton conjugate gradient method, and the results obtained in this example are α ═ 0.009, β ═ 0.018, and γ ═ 0.124;
F) establishing a water consumption confidence interval by using a Brutlag method, and taking an upper limit value L'up，
L’up＝lt-1+bt-1+st-T+mdt-T
dt＝γ│yt-y’t│+(1-γ)dt-T
G) If yt > L' up, diagnosing as abnormal and alarming
In this embodiment, after the internet of things monitoring system is established, denoising and interpolation preprocessing of the monitoring data is performed by using the monitoring data once in 15 minutes, the moving average algorithm with a window of 4 and a sequence length of 96, and a corresponding confidence upper limit calculation method thereof. And then, the preprocessed data sequence is used as the input of a Holt-Winters method, a truncated Newton conjugate gradient method is used as a parameter optimization calculation method to obtain a predicted value, and the predicted value is compared with the confidence interval upper limit calculated by a Brutlag method to make diagnosis and alarm.
The rural water supply operation detection method based on the Internet of things and time sequence analysis comprises the steps of firstly adopting an Internet of things monitoring means to realize data acquisition of a rural water supply network, then preprocessing a monitoring data sequence by considering the factor of unstable network state, generating seasonal problems by adopting long-sequence data, only considering interplanetary changes in medium-short monitoring sequences, predicting the influence of digestive interplanetary changes by using triple indexes, and realizing prediction and detection. The method has the advantages of low calculation complexity, strong practicability and wide applicability, and can be applied and popularized in vast rural water supply scenes.

Claims (4)

1. A rural water supply operation detection method based on Internet of things and time series analysis is characterized by comprising the following steps: the method comprises the following steps:
A) obtaining pipe network node running state monitoring value x from Internet of things equipmenttT is a time node, and stores the time node into a database, and adds a data sequence ytLet yt＝xt；
B) Taking out N monitoring values from the database, performing prediction calculation with a moving average window as N, and obtaining a current prediction value x't，x’t＝(xt-1+xt-2+xt-3+xt-4+…+xt-n)/n；
C) Calculating the upper limit L of the confidence interval by using a method of combining the moving average with the standard deviationup，Lup＝x’t(mae + a σ), where mae represents the median absolute loss of the residual within the sliding window, σ represents the standard deviation of the residual within the sliding window, and a is the standard deviation weight;
D) if xt>LupAnd xt-1<LupThen let yt＝x’t；
E) At ytOn the basis, a Holt-Winters method is utilized to calculate a predicted value y'tThe calculation steps are as follows:
lt＝α(yt-st-T)+(1-α)(lt-1+bt-1)
bt＝β(lt-lt-1)+(1-β)bt-1
st＝γ(yt-lt)+(1-γ)st-T
y’t＝lt-m+mbt-m+st-T-m+1+(m-1)modT
wherein, ytThe sequence length of (1) is N, ltDenotes intercept, btShowing the weekTrend of change over time, stExpressing a periodic component, wherein T is the period length, m is the length of a T distance period starting node, alpha, beta and gamma are respectively intercept, trend and the accumulated weight of the periodic component on a time sequence, and the values of the alpha, the beta and the gamma are calculated by adopting a truncated Newton conjugate gradient method;
F) establishing a water consumption confidence interval by using a Brutlag method, and taking an upper limit value L'up，
L’up＝lt-1+bt-1+st-T+mdt-T
dt＝γ│yt-y’t│+(1-γ)dt-T
G) If yt>L’upThen an anomaly is diagnosed and an alarm is given.
2. The rural water supply operation detection method based on the internet of things and time series analysis of claim 1, wherein: in the step A), if the monitoring value is lost due to the fact that the Internet of things equipment is not on line, the predicted value x 'in the step B) is taken'tLet yt＝x’t。
3. The rural water supply operation detection method based on the internet of things and time series analysis of claim 1, wherein: in the step D) and the step E), the time span of N is 1-3 days.
4. The rural water supply operation detection method based on the internet of things and time series analysis of claim 1, wherein: in the step C), the value range of n is 3-6.
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