CN113392523B - Sewage pipe network health condition diagnosis method based on long-duration multi-measuring-point - Google Patents
Sewage pipe network health condition diagnosis method based on long-duration multi-measuring-point Download PDFInfo
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Abstract
The invention discloses a method for diagnosing the health condition of a sewage pipe network based on long-duration multi-measuring points, which comprises the following parts: the method comprises the following steps of learning a drainage rule of a single-measuring point, analyzing inflow and infiltration of single-measuring point sewage based on the drainage rule, analyzing inflow and infiltration of block sewage based on multiple measuring points, analyzing mixed flow of rainwater of a sewage pipe network based on the single-measuring point, and deducing and early warning of rainfall well risk based on the single-measuring point. The model can effectively diagnose the health condition of the sewage pipe network in the parcel, improve the pertinence of the pipe network field investigation and reduce the field work load.
Description
Technical Field
The invention relates to a method for diagnosing the monitoring condition of a sewage drainage pipe network, in particular to a method for diagnosing the health condition of a single-point and multi-point sewage flow and liquid level data pipe network based on a probability model.
Background
1. Related research status is established by a sewage drainage system investigation and evaluation system
The investigation and evaluation of the sewage drainage system are the precondition of analyzing the topological relation of the current drainage pipe network, defining the monitoring units and arranging the distributed online flow monitoring points, and are the basic conditions for intelligently diagnosing the health condition of the sewage drainage system.
At present, the research on the independent application of detection means such as QV, CCTV and the like in the pipeline inspection is relatively mature. The special research and practice work is carried out on the CCTV, QV and other pipeline detection technologies in the plum field, the Baiding, the Liu Jipeng and the like, and the values of the QV and CCTV technologies in the pipe network health condition evaluation are determined by Boufergene A, J.C.P.Cheng, S.S.Kumar, W.Wu, M.D.Yang and the like abroad.
In the aspect of water quality and water quantity monitoring, a quantitative determination method is provided for problems such as underground water infiltration and rain and sewage mixed connection of a pipe network based on water quality and water quantity monitoring data such as Xuzu, Shelton, Houhou and Kracht.
The method is characterized in that related research aiming at the joint application of the QV, CCTV and other pipeline detection technologies and the water quality and water quantity monitoring means to the sewage drainage system diagnosis and evaluation is less, Wuwenjun analyzes and judges rain and sewage mixed connection and external water leakage existing in a drainage pipe network through water quality and water quantity monitoring data in the drainage pipe network, and meanwhile, the CCTV technology is used for field verification, so that a set of drainage pipe network collection efficiency diagnosis technology system is extracted.
In summary, application research for pipeline detection and water quality and quantity monitoring in sewage drainage system evaluation at home and abroad has a certain foundation, and due to the fact that a drainage pipe network has the characteristics of complexity and concealment at present, the implementation difficulty of investigation and evaluation work is high. The method is carried out only by single means such as pipeline detection, water quality and water quantity monitoring and the like, and is lack of systematicness, unclear and undefined in technical system and less in economic and technical consideration.
2. Intelligent diagnosis method for drainage system and software application research
In developed countries such as foreign Europe and America, beginning in the eighties of the twentieth century, problems such as analysis and positioning of a pipe network system, pipeline accidents, paths and the like are managed and decided on the basis of a GIS system, and an intelligent drainage informatization management system is formed; in the aspect of pipe network operation and maintenance, a sewage pipeline system standardized performance evaluation model based on a performance index system is provided by using a monitoring technology, and pipeline repair and cost are managed. An intelligent water network is proposed in Zhang Meiling in 2009, water resource management facilities are fully utilized on the basis of metering, a platform for detecting water quality and water quantity is established, and a high-efficiency water resource circulation management system is constructed. Countries such as israel, australia, france and spain are also continuously promoting relevant work of intelligent water affairs.
After ninety years, China carries out related research on GIS, and researchers establish a municipal drainage pipe network model and a GIS spatial data model in 2000; kuang Nuo et al (2011) adopt GIS/GPS/SCADA and other technical means to realize pipe network facility management, data monitoring, patrol scheduling, water drainage user management and customer service; zhao Bao kang and so on (2012) take cities such as Zhenjiang, Wuxi, Changzhou and so on as examples, and combines the business management division work as the water supply and drainage field to realize business such as industry supervision, asset management, production scheduling, online monitoring, pipe network information digitization and so on. With the continuous improvement of the internet of things, a real-time acquisition system and the like, a management platform established by combining the internet of things, cloud computing, big data, mobile internet and other new-generation information technologies in Hangzhou city is introduced in Yangzhou super (2019) and the like, and the platform carries out full-coverage monitoring on a drainage system. Similar systems are primarily focused on drainage facility operation and management.
The research of domestic 'intelligent drainage' focuses on the aspects of optimization of the running efficiency of a pipe network, decision assistance and resource integration, such as the stone dragon; luhanqing; cheiramine; jinlijiao, etc. carries out corresponding research to rainwater system. Zhang Mingkai establishes a pollutant process line model for inflow infiltration evaluation for a sewage system, and performs dynamic simulation and prediction on the flow and water quality change of a sewage pipe network based on a probability statistical method. The basic change characteristics of liquid level, flow and water quality in a sewage pipe network under the drought flow condition are researched. The flourishing and the like are mainly researched aiming at the problems of rainwater infiltration and the like of a drainage system.
According to the mastered foreign related documents, the major intelligent water affair system is continuously improved and promoted in various countries, the development direction of the municipal industry is seen from the trend, a certain system of similar intelligent management system is formed in China, but more data of main nodes such as sewage plants, tap water plants and the like are brought into a comprehensive management and financial evaluation system of a drainage system; aiming at the problems that more researches on drainage pipelines focus on rainwater drainage system pipeline jacking, pipeline overflow risk and the like, the researches on a sewage system are less, and basically, the researches are performed under limited data conditions in a small amount and in a short time, the matching degree and the application value of the research results and the practice are influenced to a certain degree, and the researches on the quantitative researches and the analyses of multiple data such as flow speed, liquid level, water quantity, water quality, rainfall and the like are less.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for diagnosing the health condition of a sewage pipe network based on multiple measuring points with long duration, which comprises the following parts:
the method comprises the following steps of learning a drainage rule of a single-measuring point, analyzing inflow and infiltration of single-measuring point sewage based on the drainage rule, analyzing inflow and infiltration of block sewage based on multiple measuring points, analyzing mixed flow of rainwater of a sewage pipe network based on the single-measuring point, and deducing and early warning based on rainfall-well risk of the single-measuring point;
the working steps are as follows:
and Step-1, acquiring confidence interval parameters of the drainage law, inflow infiltration diagnosis parameters and rainwater inflow infiltration parameters.
And Step-2, acquiring the drainage parameters of each measuring point and calculating the drainage rule. The outlier data are removed, an empirical distribution model of the drainage parameters is calculated, and finally the drainage parameter distribution range under the specified parameters is calculated according to the drainage rule confidence interval parameters.
And Step-3, performing inflow infiltration analysis based on drainage rule parameters, rain and drought season parameters and rainfall parameters.
And Step-3.1, acquiring drainage parameters meeting the distribution condition of a drainage rule in a specified time period of inflow infiltration, and dividing the drainage parameters according to full pass, partial pass and full no pass. And eliminating the data with the qualification rate less than the tolerance in the all-pass data and the partial-pass data.
And Step-3.2, calculating the water drainage flow rate of each node per day according to the data in Step-3.1, and calculating the clear water flow rate and the corresponding inflow infiltration rate per day.
And Step-3.3, counting all effective inflow infiltration data in the zone, carrying out ks verification, and determining unbiased estimated quantity according to a data accumulation distribution function.
And Step-3.4, finally, counting and calculating estimated values of all the areas, analyzing and comparing the estimated values of inflow infiltration rate of the areas in a specified time period, and determining the health condition of the pipe network by combining engineering field investigation.
And Step-4, performing rainwater mixed flow analysis based on the drainage rule parameters, the rain and drought season parameters and the rainfall parameters.
And Step-4.1, triggering rainfall data to trigger rainfall field initial judgment at any measuring point, and defining the rainwater mixed flow ending time of rainfall in the field by combining a drainage rule and the actually measured flow data.
And Step-4.2, screening abnormal data according to the time period from the beginning of rainfall to the end of mixed flow.
And Step-4.3, measuring flow data according to the drainage rule parameters and the defined rainfall starting and stopping time period, calculating the flow increment caused by rainfall, and calculating the rainwater mixed flow data of each rainfall.
Step-4.4, statistically analyzing mixed flow data of each area according to different rainfall scales with Step3, and determining the health condition of the pipe network.
And Step-5, carrying out liquid level well overflow estimation and early warning based on drainage rule parameters, rainfall parameters and the like.
And Step-5.1, calculating the liquid level rising amount caused by rainfall according to the drainage liquid level rule, the actually measured liquid level and the actually measured rainfall.
And Step-5.2, performing interval estimation calculation of the liquid level rising amount under a specified confidence level based on the actually measured rainfall amount and the liquid level rising amount.
And Step-5.3, calculating the confidence coefficient of the liquid level change triggering early warning under the condition of forecasting rainfall, and carrying out well overflow probability early warning.
Compared with the prior art, the invention has the following beneficial effects:
1. the diagnostic analysis to sewage drainage pipe network has filled the blank of current wisdom drainage field in the sewage field practice.
2. The long-time sewage drainage parameter monitoring data are utilized to respectively construct a sewage drainage rule, the characteristics of different measuring points are considered when clear water and rainwater are divided, analysis and calculation errors caused by intermittent water use and drainage fluctuation are eliminated, and the precision and pertinence of a probability distribution model are improved.
3. The statistical analysis is carried out by utilizing the diagnostic data of the multiple nodes, a diagnostic data probability distribution model of the monitoring region is constructed, a better estimator of the diagnostic data of the region is provided, and the accuracy and the stability of the estimator are improved. Further, the relative severity of the measured location health within the home zone may be reliably determined when making fine measurements.
4. Based on the estimators of different diagnostic data of different regions, the urgency degree of further repairing and perfecting is determined in a targeted and stable manner, and a reliable judgment basis is provided for pipe network repairing and perfecting projects.
Detailed Description
In the past, the sewage drainage monitoring project is limited in monitoring range or duration and limited in data volume, so that data analysis can be generally carried out by professional technicians by adopting related professional technical knowledge and mathematical tools to diagnose the problems of the pipe network one by one, and the research application is suitable for distinguishing the problems which are small in range, short in time and obvious. For drainage basin and city-level pipe network systems, few nodes and single instantaneous data analysis cannot meet the requirements for obtaining more effective and accurate data.
Therefore, aiming at the problems, the invention extends to district level and city level drainage parameter probability models and discrimination from single-point drainage parameter rule learning and probability model construction, and fully utilizes the monitoring data.
The invention is further illustrated with reference to specific embodiments below.
And (4) dividing the rain season and the dry season of the plot area according to the rainfall characteristics of the project plot area.
The drainage law learning module is used for independently constructing drainage laws for all monitoring points. According to data obtained by a sewage drainage system, drainage parameters have considerable complexity and uncertainty, and the randomness of numerical distribution of the drainage parameters in time and space cannot be reflected by methods such as simple linear fitting or trend extrapolation prediction and the like. If the distribution rules of the drainage flow in all time periods are different, the overall distribution rules are two: a) the early morning low ebb period distribution is concentrated, and the statistical value standard deviation sigma is small; b) in a conventional period of water, the standard deviation sigma is normal, and the statistical distribution presents the characteristic of conventional normal distribution. Therefore, the research steps in this section are: a. performing distribution statistics on the drainage parameters Xi, and constructing accumulated empirical distribution ECDCFi of the drainage parameters in each time period;
b. and according to the statistical distribution, performing mathematical description on the data of each monitoring period of the node.
X iu ＝percentile(X i ,α)
X i50 ＝percentile(X i ,50)
X id ＝percentile(X i ,1-α)
Wherein, X i 、X iu 、X i50 、X id The drainage parameters, alpha quantiles, binary quantiles and 1-alpha quantiles in the ith time period in the historical monitoring data of the monitoring point.
c. And selecting quantiles under the distribution domain of the acceptable drainage parameters according to engineering requirements and division of rainy and dry seasons, and providing drainage rule description of rainy and dry seasons.
And the clear water inflow infiltration analysis module is used for diagnosing and analyzing the condition of clear water inflow infiltration in the pipe network in dry seasons for each measuring point. Theoretically, a night minimum flow method can be used for calculating the clear water quantity, however, according to measured data, drainage parameter rules of all measuring points are different, the drainage off-peak period often does not appear at night, and meanwhile, under the action of various influences such as night drainage, farmer pumping, clogging of monitoring probes and the like, simple night minimum flow calculation of the clear water quantity is not suitable.
The inflow infiltration analysis module firstly reads the allowable upper and lower limits of the total day time interval accumulated flow rule of the measuring point calculated in the drainage rule module
And (5) carrying out drainage rule verification on all time periods of the day of the j.
For a predetermined tolerance T * Tolerance calculation T on day j j ≥T * And if the day data of the measuring point passes the verification, entering inflow infiltration calculation, otherwise, not carrying out the inflow infiltration calculation on the day data.
The monitoring data according with the drainage rule is subjected to the next inflow infiltration calculation, and the basic value of the amount of the clean water every day
Total day clear water flow Q * The total daily cumulative flow Q and the inflow infiltration rate P of the clear water
For a simple example, if the monitoring frequency of a certain station is 5min, the number of segments n is 24 × 60/5 is 288 throughout the day. According to historical data, the allowable upper limit and the allowable lower limit of the drainage rule are respectively 2.51m 3 /h，0.89m 3 The specified tolerance is 50%. Of these, 31 pieces of data in the j-th day were in the range [0,0.89 ], and 152 pieces of data were in the range (2.51, + ∞)]Then the tolerance calculation value of the j th day
Thus, day j data did not enter the in-flow infiltration calculation.
The rainwater mixed flow module is used for diagnosing and analyzing the rainwater mixed flow condition of each rainfall in rainy season for each measuring point. The rainwater mixing module is defined according to the rainfall time and the rainwater part in the total flow. When the area of the sheet area monitored by the monitoring point is large in space, sometimes, rainfall is not found at the position of the rainfall monitoring equipment, but rainfall is generated at the upstream of the catchment subarea to cause rainwater to mix in, so that the flow of the measuring point is increased, and at the moment, the calculation of the collected rainwater is insufficient simply according to the reported data of the rainfall monitoring equipment. On the other hand, when rainfall is over, surface runoff influx caused by rainfall may be completed quickly, and groundwater infiltration due to the rise of the groundwater level caused by rainfall is often long in duration. In a third aspect, the general rise of the groundwater level in the monitoring area in rainy season causes an increase in the amount of clean water mixed in by non-rainfall. And moreover, the confluence subareas of different measuring points are not consistent, and the rainwater mixed flow is independently analyzed by considering different monitoring point arrangements.
Therefore, the rainwater mixed flow module firstly carries out rainfall time redefinition on the rainfall data. The rainwater mixed flow module firstly reads the allowable upper limit of the flow of each time period all day in the season calculated in the drainage rule module
Reporting rainfall moment t on rainfall monitoring equipment 0 Triggering backtracking, checking the measured flow and the allowable upper limit of the flow
When the measured flow rate is
Triggering and correcting the initial time t of rainfall s ＝t i . When the backtracking period does not meet the verification condition, the rainfall starting time is not corrected, and the rainfall starting time t s ＝t 0 。
After rainfall begins, when rainfall monitoring data is firstly returned to 0 time t e And triggering rainfall ending time verification. After the rainfall is reset to zero, the t th is calculated e+n The time discrimination coefficient is u e+n ，
From the moment of rainfall zero e At first, according to the time interval of the drainage flow monitoring as a unit, calculating t e+i Flow data discrimination factor F (u) for each specified stabilization period l at each time i )，
When F (u) i ) First time not 0 or t e+i -t e When the current time is more than or equal to 2days, stopping the calculation trigger and corresponding t e+i The moment is the corrected rainfall ending moment.
At a defined onset of rainfall time t s End of rainfall time t e+i Reading the corresponding upper limit of flow allowance in the drainage rule module
Measured flow rate q (t) i ). Rainwater flow q in sewage at any moment r In order to realize the purpose,
the total flow Q and the total amount Q of mixed rainwater in the rainfall of the field r In order to realize the purpose of the method,
Q＝∑q(t i )
Q r ＝∑q r (t i )
the mixed flow coefficient R of the rainwater is,
after all the rainwater mixed flow parameters are obtained through calculation, according to the total rainfall H of each rainfall r And (4) carrying out classified statistics according to rainfall scale. It can be classified into light rain, medium rain, heavy rain and heavy rain.
And the multi-node analysis module is used for counting clear water inflow infiltration and rainwater mixed flow parameters of each single node obtained by system analysis, constructing a probability distribution model, and analyzing and determining engineering analysis adopted statistics.
The multi-node analysis module reads the number of the monitoring points of the region, determines the sample capacity n, and determines the significance level alpha according to the requirement, thereby determining the critical value D nα 。
The analysis parameter statistics X calculated by the other modules are read.
The ks check is performed assuming that the statistic X conforms to a particular cumulative probability distribution.
When D is present α <D nα And (4) receiving an assumption, and determining a statistical estimator as an engineering analysis reference according to the statistical distribution.
As a simple example, a certain area monitoring point corresponds to the degree of freedom 149, the significance level is 5%, and D is determined nα 0.114. The inflow infiltration parameter P is read, which accumulates the empirical distribution function F (P). Assuming that it meets the mean of
Normal distribution with standard deviation sigma, then cumulative probability distribution function F n (p) calculating D n Value of
Therefore, the inflow infiltration analysis satisfies normal distribution, and the mean value and the variance are adopted to diagnose and analyze parameters of the system health condition.
And the liquid level well overflowing estimation module is used for carrying out statistical analysis on rainfall and liquid level change of each measuring point and calculating and forecasting the well overflowing condition of the measuring points under the specified confidence level.
And the liquid level overflow estimation and early warning module performs regression analysis on the liquid level variation of each measuring point under different historical rainfall quantities, and simultaneously calculates a prediction interval of liquid level prediction under 90% confidence level.
Firstly, reading the liquid level mean value of the corresponding season of the measuring points in the drainage rule module
Reading the liquid level h of all time periods from 0 th moment to nth moment in all raining occasions in historical data ri The actual measurement of the rainfall x in the field indicates the liquid level rise
Y＝max(h ri -h i )
At the same time, the prediction interval at a given confidence level 1- α is calculated from the sample capacity n.
The above description is only a part of the embodiments of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (5)
1. A method for diagnosing the health condition of a sewage pipe network based on multiple measuring points with long duration comprises the following steps:
the method comprises the following steps of learning a drainage rule of a single-measuring point, analyzing inflow and infiltration of single-measuring point sewage based on the drainage rule, analyzing inflow and infiltration of block sewage based on multiple measuring points, analyzing mixed flow of rainwater of a sewage pipe network based on the single-measuring point, and deducing and early warning based on rainfall-well risk of the single-measuring point;
the working steps are as follows:
step-1, acquiring a confidence interval parameter of a drainage rule, an inflow infiltration diagnosis parameter and a rainwater inflow infiltration parameter;
step-2, acquiring the drainage parameters of each measuring point, calculating a drainage rule, removing outlier data, calculating an empirical distribution model of the drainage parameters, and calculating a drainage parameter distribution range under specified parameters according to the drainage rule confidence interval parameters;
step-3, performing inflow infiltration analysis based on drainage rule parameters, rain and drought season parameters and rainfall parameters;
step-3.1, acquiring drainage parameters meeting the drainage rule distribution condition in the inflow infiltration specified time period, and eliminating data with the percent of pass less than the tolerance in all-pass data and part-pass data according to the division of all-pass, part-pass and all-no-pass;
step-3.2, calculating the water discharge flow rate of each node per day according with the data in Step-3.1, counting, and calculating the clear water flow rate per day and the corresponding inflow infiltration rate;
step-3.3, counting all effective inflow infiltration data in the zone, carrying out ks verification, and determining unbiased estimated quantity according to a data accumulation distribution function;
step-3.4, finally, counting and calculating the estimated value of each block, analyzing and comparing the estimated value of the inflow infiltration rate of each block in a specified time period, and determining the health condition of the pipe network by combining engineering field investigation;
step-4, performing rainwater mixed flow analysis based on the drainage rule parameters, the rainy season parameters and the dry season parameters;
step-4.1, triggering rainfall data to trigger rainfall field initial judgment for any measuring point, and defining the rainwater mixed flow ending time of rainfall in the field by combining a drainage rule and actually measured flow data;
step-4.2, screening abnormal data according to the time period from the beginning of the rainfall to the end of the mixed flow;
step-4.3, measuring flow data in a period of starting and stopping rainfall according to drainage rule parameters and the defined period of stopping rainfall, calculating the flow increment caused by rainfall, and calculating the rainwater mixed flow data of each rainfall;
step-4.4, statistically analyzing mixed flow data of each area according to different rainfall scales with Step3 to determine the health condition of the pipe network;
step-5, performing liquid level well overflow estimation and early warning based on the drainage rule parameters and the rainfall parameters;
step-5.1, calculating the liquid level rising amount caused by rainfall according to the drainage liquid level rule, the actually measured liquid level and the actually measured rainfall;
step-5.2, performing interval estimation calculation of the liquid level rising amount under a specified confidence level based on the actually measured rainfall amount and the liquid level rising amount;
and Step-5.3, calculating the confidence coefficient of the liquid level change triggering early warning under the condition of forecasting rainfall, and carrying out well overflow probability early warning.
2. The method of claim 1, wherein the method comprises the steps of: the health condition diagnosis needs to be based on multi-point and long-time drainage information monitoring, and the drainage information comprises flow, flow velocity, liquid level, rainfall, pump station flow, sewage plant treatment capacity and sewage quality.
3. The method of claim 1, wherein the method comprises the steps of: the health condition diagnosis is that a drainage rule calculation part calculates an accumulative distribution function model which is counted and constructed based on a large amount of drainage data.
4. The method of claim 1, wherein the method comprises the steps of: and the inflow infiltration analysis is to select data under a specified confidence level as an inflow infiltration background value based on the accumulated distribution function model of the drainage data, to judge whether the drainage behavior on the day conforms to the conventional characteristics of the monitoring points, to perform data cleaning, to calculate the minimum flow at night under specified parameters for effective data, and to determine the inflow infiltration amount and inflow infiltration rate.
5. The method of claim 1, wherein the method comprises the steps of: and the rainwater mixed flow analysis is based on the accumulated distribution function model of the drainage data, selects data under a specified confidence level as a flow background value for calculating a flow increase value caused by rainfall and calculating the rainwater mixed flow quantity and the rainwater mixed flow rate.
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| CN114370611B (en) * | 2022-01-17 | 2024-04-12 | 四创科技有限公司 | Pipe network inflow and infiltration monitoring method and terminal |
| CN116126963A (en) * | 2022-12-30 | 2023-05-16 | 哈尔滨航天恒星数据系统科技有限公司 | Inflow infiltration diagnosis method for nonlinear multi-source heterogeneous drainage model |
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