CN116628914A - Inflow and infiltration analysis method for drainage pipe network, computer equipment and medium - Google Patents

Inflow and infiltration analysis method for drainage pipe network, computer equipment and medium Download PDF

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Publication number
CN116628914A
CN116628914A CN202310906318.5A CN202310906318A CN116628914A CN 116628914 A CN116628914 A CN 116628914A CN 202310906318 A CN202310906318 A CN 202310906318A CN 116628914 A CN116628914 A CN 116628914A
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inflow
infiltration
information
pipe network
drainage
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CN116628914B (en
Inventor
赵云鹏
陈亚松
王殿常
朱雅婷
陈俊润
聂中林
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Beijing Gezhouba Electric Power Rest House
China Three Gorges Corp
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Beijing Gezhouba Electric Power Rest House
China Three Gorges Corp
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/18—Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G06T17/05—Geographic models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/08—Fluids
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A20/00—Water conservation; Efficient water supply; Efficient water use
    • Y02A20/152—Water filtration

Abstract

The invention relates to the technical field of water environment information processing, and provides a drainage pipe network inflow and infiltration analysis method, computer equipment and a medium. The inflow and infiltration analysis method of the drainage pipe network comprises the following steps: acquiring a topological structure diagram and point cloud topographic data of a drainage pipe network; dividing a drainage pipe network according to the topological structure diagram and the point cloud topographic data to obtain a plurality of drainage partitions; determining key monitoring nodes of the drainage pipe network according to inflow and seepage data in each drainage partition and the drainage pipe network; and at the key monitoring node, monitoring information of inflow pollutants and infiltration groundwater in the drainage pipe network. By the method, the drainage partition is accurately divided, and inflow and infiltration analysis efficiency is improved.

Description

Inflow and infiltration analysis method for drainage pipe network, computer equipment and medium
Technical Field
The invention relates to the technical field of water environment information processing, in particular to a drainage pipe network inflow and infiltration analysis method, computer equipment and a medium.
Background
In the prior art, when pollutant inflow and infiltration analysis is carried out on a drainage pipe network, the drainage pipe network is divided only by depending on pipe network information or GIS data. When the pipe network information is utilized for dividing, the information such as topography, elevation and the like is not combined, the trend of the rain sewage cannot be truly reflected, and the water drainage division is inaccurate. When drainage areas are carried out based on GIS data, only drainage partition ranges after all surface low-lying spaces are filled (namely, the most unfavorable scenes) can be expressed, the influence of accumulated water easy waterlogging points such as urban recessed bridges on drainage partitions under different rainfall conditions cannot be reflected, and the defect of inaccurate drainage partition is also caused. When the drainage partition is inaccurate, the inflow and infiltration analysis result of the subsequent drainage pipe network can be influenced.
In addition, in the process of carrying out pollutant inflow and infiltration analysis on the drainage pipe network, key monitoring nodes are also required to be determined, and in the related art, the main pipe, the secondary main pipe and the connection place of the main pipe and the secondary main pipe are confirmed as the key monitoring nodes only according to experience. Meanwhile, the current related technology only judges the inflow and infiltration position of the pipe network according to the liquid level and conductivity data of the drainage pipe network, so that the information such as inflow and infiltration intensity, time and the like are difficult to judge in an omnibearing manner, and the effect of pollutant inflow and infiltration prediction analysis is not achieved.
Disclosure of Invention
The invention provides a drainage pipe network inflow infiltration analysis method, computer equipment and a medium for accurately dividing drainage subareas and improving inflow infiltration analysis efficiency.
In a first aspect, the present invention provides a method for analyzing inflow and infiltration of a drain pipe network, the method comprising:
acquiring a topological structure diagram and point cloud topographic data of a drainage pipe network;
dividing a drainage pipe network according to the topological structure diagram and the point cloud topographic data to obtain a plurality of drainage partitions;
determining key monitoring nodes of the drainage pipe network according to the drainage subareas and inflow and infiltration data in the drainage pipe network;
And at the key monitoring node, monitoring information of inflow pollutants or infiltration groundwater in the drainage pipe network.
In consideration of the fact that in the related technology, when a drainage area is divided, only drainage pipe network information or GIS data is relied on, and the information such as the topography of the drainage pipe network is not combined, the trend of rainwater and sewage cannot be truly reflected, so that drainage partition division is inaccurate, when the drainage partition is inaccurate, key monitoring nodes obtained according to the drainage partition are affected, and further the inaccuracy of inflow and infiltration information of the drainage pipe network is caused.
In some alternative embodiments, the step of obtaining a topological structure of the drainage network comprises:
acquiring a vector diagram of pipe network information of a drainage pipe network;
from the vector diagram, a topology is determined.
According to the embodiment, the vector diagram comprises the drainage system, the pipeline connection relation and other information of the drainage pipe network, and the topological structure of the pipeline can be obtained through conversion of the pipe network information vector diagram comprising the drainage pipe network.
In some alternative embodiments, the step of obtaining point cloud topographic data for the drainage network comprises:
acquiring a digital elevation model and a digital earth surface model of the position of a drainage pipe network;
and acquiring point cloud terrain data of the drainage pipe network according to the digital elevation model and the digital surface model.
Through the embodiment, the digital elevation model describes the elevation information of the drainage pipe network, the digital surface model describes the topography information of the drainage pipe network, the gravity flow direction of sewage can be quickly identified according to the elevation information and the topography information, point cloud data are formed according to the elevation information and the topography information, and compared with traditional mapping topography data, the accuracy is higher.
In some alternative embodiments, the method for dividing the drainage pipe network according to the topological structure diagram and the point cloud topographic data to obtain a plurality of drainage partitions includes:
simulating a rain sewage self-flowing process of a drainage pipe network according to a topological structure diagram, point cloud topographic data and a two-dimensional hydraulic model formed based on a digital elevation model and a digital earth surface model;
Determining the trend of the rain sewage in the drainage pipe network according to the self-flowing process of the rain sewage;
and dividing the drainage pipe network according to the trend of the rain sewage to obtain a plurality of drainage subareas.
Through the embodiment, the topological structure diagram and the point cloud topographic data are input into the two-dimensional hydraulics model, the rain sewage gravity flow process is obtained through simulation, the trend of the rain sewage is determined according to the rain sewage gravity flow process, and the obtained drainage partition is more accurate.
In some alternative embodiments, the inflow infiltration data includes first inflow infiltration information and a posterior probability density corresponding to the first inflow infiltration information, the first inflow infiltration information includes an inflow infiltration position, and the key monitoring node of the drainage pipe network is determined according to the inflow infiltration data in each drainage partition and the drainage pipe network, including:
acquiring first inflow infiltration information in a drainage pipe network and posterior probability density corresponding to the first inflow infiltration information;
screening the inflow and infiltration positions in the first inflow and infiltration information according to a preset confidence interval and a posterior probability density to obtain screened inflow and infiltration positions;
determining upstream nodes and downstream nodes of the screened inflow infiltration positions in the drainage partition to which the inflow infiltration positions belong;
And taking the upstream node and the downstream node as key monitoring nodes of the drainage pipe network.
Through the embodiment, the pollutant inflow and infiltration position and the corresponding posterior probability density of the drainage pipe network are utilized to screen the inflow and infiltration position, and the key monitoring nodes are determined according to the obtained upstream and downstream nodes of the drainage partition where the inflow and infiltration position is located, so that the problems that the node establishment is inaccurate and the inflow and infiltration analysis effect is influenced due to the fact that the key monitoring nodes are established in the prior art depending on experience and the like are overcome, the problem of monitoring resource waste caused by the fact that a large number of key monitoring nodes are established in the prior art is also solved, and the inflow and infiltration analysis efficiency is improved.
In some alternative embodiments, at a critical monitoring node, monitoring information of inflow contaminants or infiltration groundwater in a drainage network, comprising:
and at key monitoring nodes, calculating and analyzing information of inflow pollutants or infiltration groundwater in the drainage pipe network by using a chemical balance method.
In some alternative embodiments, obtaining first inflow infiltration information and a first posterior probability density corresponding to the first inflow infiltration information in a drainage pipe network includes:
determining second inflow and infiltration information of inflow and infiltration water quality characteristic factors of the drainage pipe network;
Collecting a first actual concentration of an inflow infiltration water quality characteristic factor at the tail end position of a drainage pipe network;
and taking the second inflow infiltration information as a priori distribution parameter, and determining the first inflow infiltration information and the posterior probability density corresponding to the first inflow infiltration information in the drainage pipe network according to the first actual concentration.
In consideration of the related technology, traversing all pipe network nodes in the water quality monitoring process, performing large-scale water quality monitoring, and further causing excessive monitoring investment cost, through the implementation mode, the second inflow and infiltration information is used as a priori distribution parameter by utilizing the Bayesian theory and expressed as a probability density function, and the actual concentration acquired at the tail end of the drainage pipe network is combined, so that inflow and infiltration information and corresponding posterior probability density are obtained.
In some alternative embodiments, determining second inflow infiltration information for an inflow infiltration water quality characterization factor of a drainage network comprises:
Collecting samples of sewage or groundwater in a plurality of preset areas at different moments;
acquiring the inflow and infiltration water quality characteristic factor concentration of each sample;
determining third inflow and infiltration information according to the concentrations of the inflow and infiltration water quality characteristic factors;
collecting a second actual concentration of the inflow infiltration water quality characteristic factors at the tail end position of the drainage pipe network;
and screening the third inflow and infiltration information according to the second actual concentration and the one-dimensional water quality simulation model to obtain second inflow and infiltration information.
Through the embodiment, the sewage or groundwater samples at different positions at different times are collected to obtain the third inflow and infiltration information, and meanwhile, the third inflow and infiltration information is screened by utilizing the actual concentration at the tail end position of the drainage pipe network and the one-dimensional water quality simulation model, so that the obtained second inflow and infiltration information is more accurate and more accords with the actual condition of a water collecting area of the pipe network, and when the second inflow and infiltration information is calculated as a priori distribution parameter, the obtained first inflow and infiltration information and the corresponding posterior probability density are more accurate.
In some alternative embodiments, the inflow and infiltration information includes a plurality of inflow and infiltration concentrations of the inflow and infiltration water quality characteristic factors, and inflow and infiltration time and inflow and infiltration positions corresponding to the inflow and infiltration concentrations, and the method includes screening third inflow and infiltration information according to the second actual concentration and the one-dimensional water quality simulation model to obtain second inflow and infiltration information, including:
Uniformly sampling each inflow and infiltration concentration in the third inflow and infiltration information to obtain a plurality of sampled inflow and infiltration concentrations;
according to the inflow and infiltration concentration after sampling and the one-dimensional water quality simulation model, calculating each simulation concentration of the tail end position of the drainage pipe network;
and screening the third inflow infiltration information according to the simulated concentration and the second actual concentration to obtain second inflow infiltration information.
In some alternative embodiments, the third influent infiltration information is filtered based on each simulated concentration and the second actual concentration to obtain second influent infiltration information, including:
calculating the fitting degree of each simulated concentration and the second actual concentration;
using each inflow and infiltration concentration corresponding to which the fitting degree is larger than a preset threshold value as the inflow and infiltration concentration after screening;
and taking the filtered inflow infiltration concentration, inflow infiltration time and inflow infiltration position corresponding to the filtered inflow infiltration concentration as second inflow infiltration information.
In some alternative embodiments, the inflow and infiltration information includes a plurality of inflow and infiltration concentrations of the inflow and infiltration water quality characteristic factors, inflow and infiltration time and inflow and infiltration positions corresponding to the inflow and infiltration concentrations, the second inflow and infiltration information is used as a priori distribution parameter, and the first inflow and infiltration information and the posterior probability density corresponding to the first inflow and infiltration information in the drainage pipe network are determined according to the first actual concentration, including:
Inputting each inflow and infiltration concentration in the second inflow and infiltration information into a one-dimensional water quality simulation model, and determining the first inflow and infiltration information and the simulation concentration of the end position of the drainage pipe network corresponding to the first inflow and infiltration information;
and taking the second inflow infiltration information as a priori distribution parameter, and inputting the simulated concentration and the first actual concentration of the tail end position of the drainage pipe network corresponding to the first inflow infiltration information into a pre-constructed posterior probability density function to obtain the posterior probability density corresponding to the first inflow infiltration information.
In some alternative embodiments, the influent water quality profile comprises an influent contaminant water quality profile, the pollution source of the drainage network comprises a household pollution source, and/or an industrial pollution source;
the inflow pollutant water quality characteristic factors of the living pollution sources comprise escherichia coli and/or total nitrogen;
the influent contaminant water quality characteristic factor of the industrial source includes conductivity, and/or fluoride.
In some alternative embodiments, the influent infiltration water quality characteristic factor comprises an infiltration groundwater water quality characteristic factor, the infiltration groundwater water quality characteristic factor comprises E.faecalis, and/or total nitrogen.
According to the embodiment, the inflow and infiltration water quality characteristic factors are selected according to the type of pollution sources in the drainage partition, the domestic pollution sources take fecal coliform indexes, total nitrogen and the like as the inflow pollution source water quality characteristic factors, the industrial pollution sources take conductivity, fluoride and the like as the inflow pollution source water quality characteristic factors, and the infiltration groundwater takes fecal coliform indexes and total nitrogen as the water quality characteristic factors. Considering that the escherichia coli faecalis index, total nitrogen, conductivity and fluoride are characteristic factors with relatively stability and relatively good designability, the characteristic factors are used as inflow and infiltration water quality characteristic factors, so that the influence of degradation, oxidation, reduction and other reactions in the pollutant conveying process on the inflow and infiltration analysis of pollutants can be effectively avoided, pollution sources in different types of drainage pipelines can be effectively identified, and larger errors of results are avoided.
In a second aspect, the present invention also provides a computer device, including a memory and a processor, where the memory and the processor are communicatively connected to each other, and the memory stores computer instructions, and the processor executes the computer instructions, so as to execute the steps of the drainage pipe network inflow and infiltration analysis method according to the first aspect or any embodiment of the first aspect.
In a third aspect, the present invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the drainage network inflow infiltration analysis method of the first aspect or any of the embodiments of the first aspect.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for inflow infiltration analysis of a drainage network according to an exemplary embodiment;
FIG. 2 is a schematic diagram of an inflow and infiltration analysis device for a drain pipe network according to an exemplary embodiment;
fig. 3 is a schematic diagram of a hardware structure of a computer device according to an exemplary embodiment.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In addition, the technical features of the different embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
The invention provides a drainage pipe network inflow infiltration analysis method, computer equipment and a medium for accurately dividing drainage subareas and improving inflow infiltration analysis efficiency.
Fig. 1 is a flow chart of a method for inflow and infiltration analysis of a drain network according to an exemplary embodiment, the method comprising the following steps.
Step S101: and obtaining a topological structure diagram of the drainage pipe network and point cloud topographic data.
In an alternative embodiment, the topology map may be obtained from network information of the drainage network.
In an alternative embodiment, the point cloud terrain data may be obtained from high-definition radar on board the drone.
Step S102: and dividing the drainage pipe network according to the topological structure diagram and the point cloud topographic data to obtain a plurality of drainage partitions.
In an alternative embodiment, the topology structure comprises a pipeline structure of the drainage pipe network, the point cloud terrain data comprises elevation information and surface information of the drainage pipe network, and the drainage pipe network is divided by combining the pipeline structure, the elevation information and the surface information of the drainage pipe network, so that the obtained drainage subareas are more fit with the actual sewage flow direction.
Step S103: and determining key monitoring nodes of the drainage pipe network according to the drainage subareas and inflow and infiltration data in the drainage pipe network.
In an alternative embodiment, the inflow and infiltration data in the drainage pipe network comprises inflow and infiltration information and posterior probability densities corresponding to the inflow and infiltration information, wherein the inflow and infiltration information comprises inflow pollutant information and infiltration groundwater information. The influent infiltration information includes, but is not limited to, influent infiltration location, influent infiltration time, and influent infiltration concentration.
In an alternative embodiment, when the inflow infiltration information is an inflow infiltration position, the key monitoring node of the drainage network is obtained by determining an upstream node and a downstream node of the inflow infiltration position in the drainage partition.
Step S104: and at the key monitoring node, monitoring information of inflow pollutants or infiltration groundwater in the drainage pipe network.
In an alternative embodiment, chemical equilibrium monitoring may be used to obtain accurate information of inflow contaminants or infiltration groundwater from the drainage network.
In consideration of the fact that in the related technology, when a drainage area is divided, only drainage pipe network information or GIS data is relied on, and the information such as the topography of the drainage pipe network is not combined, the trend of rainwater and sewage cannot be truly reflected, so that drainage partition division is inaccurate, when the drainage partition is inaccurate, key monitoring nodes obtained according to the drainage partition are affected, and further the information of inflow pollutants or infiltration groundwater of the drainage pipe network is inaccurate.
In an example, in step S101, a topology structure diagram of a drain pipe network is obtained by:
first, a vector diagram of pipe network information of a drainage pipe network is acquired.
In an alternative embodiment, the pipe network information includes, but is not limited to, drainage system, pipe type, pipe connection relationship, rain water trend, pipe diameter, pump station size, etc.
Then, from the vector diagram, a topology is determined.
In an alternative embodiment, the information of the drainage system, the pipeline connection relationship and the like of the drainage pipe network in the vector diagram can be converted into the topological structure of the drainage pipe network through software such as a storm flood management model (storm water management model, SWMM) and the like.
In an example, in step S101, the step of acquiring the point cloud topographic data of the drainage pipe network is specifically:
firstly, a digital elevation model and a digital surface model of the position of a drainage pipe network are obtained.
In an alternative embodiment, the digital elevation model (Digital Elevation Model, DEM) is a solid ground model that implements a digital simulation of the ground topography (i.e., a digital representation of the topography surface morphology) with limited topography elevation data, and is represented as an array of ordered values.
In an alternative embodiment, the digital surface model (Digital Surface Model, DSM) refers to a ground elevation model that includes the surface slope, height of buildings, bridges, trees, and the like. Compared with the DEM, the DEM only comprises the elevation information of the terrain, does not comprise other surface information, and the DSM covers the elevations of other surface information except the ground to express the ground fluctuation condition.
And then, according to the digital elevation model and the digital surface model, acquiring the point cloud terrain data of the drainage pipe network.
The digital elevation model describes elevation information of the drainage pipe network, the digital surface model describes ground information of the drainage pipe network, the gravity flow direction of sewage can be recognized quickly according to the elevation information and the topographic information, point cloud data are formed according to the elevation information and the topographic information, and compared with traditional topographic data for surveying and mapping, accuracy is higher.
In an example, in step S102, according to the topology structure diagram and the point cloud terrain data, the specific implementation manner of obtaining the plurality of drainage partitions is:
firstly, simulating a rain sewage self-flowing process of a drainage pipe network according to a topological structure diagram, point cloud topographic data and a two-dimensional hydraulic model formed based on a digital elevation model and a digital earth surface model.
Then, according to the rain sewage self-flowing process, determining the trend of the rain sewage in the drainage pipe network.
Finally, dividing the drainage pipe network according to the trend of the rain sewage to obtain a plurality of drainage subareas.
In the above embodiment, the two-dimensional hydraulic model may simulate the water level and water flow changes generated by the action of various forces, and input the topological structure diagram and the point cloud topographic data to the two-dimensional hydraulic model to simulate and obtain the rain sewage gravity flow process, and determine the trend of the rain sewage according to the rain sewage gravity flow process, so that the obtained drainage partition is more accurate.
In an example, the inflow infiltration data includes first inflow infiltration information and a posterior probability density corresponding to the first inflow infiltration information, the first inflow infiltration information includes an inflow infiltration position, and in the step S103, the specific step of determining the key monitoring node of the drainage pipe network includes:
step a1: and acquiring first inflow infiltration information in the drainage pipe network and posterior probability density corresponding to the first inflow infiltration information.
Step a2: and screening the inflow and infiltration positions in the first inflow and infiltration information according to the preset confidence interval and the posterior probability density to obtain screened inflow and infiltration positions.
Step a3: and determining upstream nodes and downstream nodes of the filtered inflow infiltration positions in the associated drainage partition.
In an alternative embodiment, the nodes refer to service nodes in the drainage partition, and the service nodes may be service wells, pump stations, and the like, which are not particularly limited herein. The drainage subareas are determined according to the topological characteristics of the pipe network, the self-flowing direction of the rain sewage and other information, and after the screened inflow and infiltration positions are obtained, the upstream nodes and the downstream nodes in the drainage subareas where the inflow and infiltration positions are located can be used as key monitoring nodes.
Step a4: and taking the upstream node and the downstream node as key monitoring nodes of the drainage pipe network.
Through the embodiment, the pollutant inflow or groundwater infiltration position and the corresponding posterior probability density of the drainage pipe network are utilized to screen the inflow infiltration position, and the key monitoring nodes are determined according to the obtained upstream and downstream nodes of the drainage partition where the inflow infiltration is located, so that the problem of monitoring resource waste caused by setting up the key monitoring nodes in a large number in the prior art is solved, and the monitoring efficiency is improved.
In an example, in the step S104, at the key monitoring node, the information of the inflow pollutants or the infiltration groundwater in the drainage pipe network is monitored, and then the information of the inflow and infiltration position, time, intensity, etc. is accurately determined by using a chemical balance method. The method comprises the steps of arranging an on-line water quality and quantity monitor at a key monitoring node, continuously monitoring for 10-15 days, obtaining long-period monitoring data, and analyzing the monitoring data by a chemical balance method to obtain information of inflow pollutants or infiltration groundwater in a drainage pipe network.
In an example, in the step a1, the first inflow infiltration information in the drainage pipe network and the posterior probability density corresponding to the first inflow infiltration information are obtained by the following steps:
step b1: and determining second inflow and infiltration information of the inflow and infiltration water quality characteristic factors of the drainage pipe network.
In an alternative embodiment, the inflow and infiltration information includes the location, time, and water quality characteristic factor concentration of the drain network inflow and infiltration.
In an alternative embodiment, the inflow and infiltration information can be determined by collecting the concentrations of inflow and infiltration water quality characteristic factors at different positions of the drainage network at different times.
In an alternative embodiment, the inflow and infiltration information is different for different types of inflow pollution source water quality characterization factors.
Step b2: a first actual concentration of an influent infiltration water quality characterization factor at a terminal location of a drainage network is collected.
In an alternative embodiment, the influent infiltration water quality characteristic factor comprises an influent contaminant water quality characteristic factor, an infiltration groundwater water quality characteristic factor. The collecting position of the inflow pollutant water quality characteristic factors is positioned at the tail end discharge port of the rainwater pipe network; the infiltration groundwater quality characteristic factor collecting position is positioned at the last intercepting well of the sewage pipe network entering the sewage treatment plant.
Step b3: and taking the second inflow infiltration information as a priori distribution parameter, and determining the first inflow infiltration information and the posterior probability density corresponding to the first inflow infiltration information in the drainage pipe network according to the first actual concentration.
In an alternative embodiment, the inflow and infiltration information refers to the inflow and infiltration water quality characteristic factor, and when the inflow and infiltration is x and the inflow and infiltration time is y, the corresponding inflow and infiltration concentration is z. The corresponding posterior probability density is that the inflow infiltration is x, the inflow infiltration time is y, and the inflow infiltration concentration is z.
In one example, step b1 above determines the second inflow and infiltration information of the inflow and infiltration water quality characteristic factor of the drainage network by:
step c1: and collecting sewage or underground water samples of a plurality of preset areas at different moments.
In an alternative embodiment, the different time instants may be a plurality of different time instants within a preset time period. For example, the sampling of sewage or groundwater in different areas of the drain network at 0, 6, 12, 18 may be performed for 10 consecutive days.
Step c2: and obtaining the water quality characteristic factor concentration of each sample.
Step c3: and determining third inflow and infiltration information according to the water quality characteristic factor concentration of each sample.
Step c4: and collecting and testing a second actual concentration of the corresponding sample water quality characteristic factor at the tail end position of the drainage pipe network.
Step c5: and screening the third inflow and infiltration information according to the second actual concentration and the one-dimensional water quality simulation model to obtain second inflow and infiltration information.
Through the embodiment, the sewage or groundwater samples at different positions at different times are collected to obtain the third inflow and infiltration information, and meanwhile, the third inflow and infiltration information is screened by utilizing the actual concentration at the tail end position of the drainage pipe network and the one-dimensional water quality simulation model, so that the obtained second inflow and infiltration information is more accurate and more accords with the actual condition of the water collecting area of the pipe network, and when the second inflow and infiltration information is calculated as the prior distribution parameter, the obtained first inflow and infiltration information and the corresponding posterior probability density are more accurate.
In an alternative embodiment, a one-dimensional water quality simulation model may be constructed by a SWMM model.
In an alternative embodiment, the influent penetration information includes a plurality of influent penetration concentrations of the influent penetration water quality characteristic factor, and an influent penetration time and an influent penetration position corresponding to each of the influent penetration concentrations.
In step c5, the third inflow infiltration is filtered by:
Firstly, uniformly sampling each inflow and infiltration concentration in the third inflow and infiltration information to obtain a plurality of sampled inflow and infiltration concentrations. Illustratively, the MATLAB toolkit is used to invoke the monte carlo method, set the step size to 1000, and uniformly sample the first concentration to obtain a plurality of concentration values. For example, the first concentration range is 1 mg/L-10 mg/L, and the first concentration range is uniformly and randomly sampled from 1.0, 1.1, 1.2 … 9.7.9.7, 9.8, 9.9 and 10.0 according to the Monte Carlo method to obtain a plurality of concentration values, and the Monte Carlo method can be used for rapidly traversing all the concentration values, so that the water quality and water quantity detection is reduced, the pollutant tracing efficiency is greatly improved, and the pollutant tracing cost is reduced.
And then, according to the inflow and infiltration concentration after sampling and the one-dimensional water quality simulation model, calculating each simulation concentration of the tail end position of the drainage pipe network.
In an alternative embodiment, the one-dimensional water quality simulation model is formulated as:
wherein A is the cross-sectional area of water, (m) 2 ) The method comprises the steps of carrying out a first treatment on the surface of the Q is flow, (m) 3 /h); in the embodiment of the invention, C is the concentration of the water quality characteristic factor of the inflow and infiltration sample after sampling, (mg/L or mu s/cm); t is time,(s); x is the flow path from the end position of the drainage pipe network, (m); e (E) x Is a longitudinal discrete coefficient, (m 2 /s）；K 1 Is the first order degradation coefficient of the pollutant,(s) -1 ）；Indicating that the inflow infiltration information is X, i.e. the inflow infiltration position is +.>The inflow infiltration time is T, and the inflow infiltration concentration is M.
And finally, screening the third inflow infiltration information according to the simulated concentration and the second actual concentration to obtain the second inflow infiltration information.
In the embodiment of the invention, the third inflow infiltration information can be screened through the fitting degree of the simulated concentration and the second actual concentration, and the method specifically comprises the following steps:
first, a fitting degree of each of the simulated concentrations and the second actual concentration is calculated. Illustratively, the fitness test method may be a residual sum of squares test, chi-square (c 2) test, and linear regression test, etc., without specific limitation herein.
And then, taking each inflow and infiltration concentration corresponding to which the fitting degree is larger than a preset threshold value as the inflow and infiltration concentration after screening. The setting of the preset threshold value may be set according to actual needs, and is not particularly limited herein.
And finally, taking the filtered inflow infiltration concentration, inflow infiltration time and inflow infiltration position corresponding to the filtered inflow infiltration concentration as second inflow infiltration information.
Through the embodiment, posterior sampling is carried out on the inflow and infiltration water quality characteristic factors by using a Monte Carlo method, so that the simulated concentration of the tail end position of the drainage pipe network is obtained, the second inflow and infiltration information is further optimized according to the actual concentration and the simulated concentration by combining a likelihood function, the concentration value similar to the actual concentration is reserved, the concentration value with larger phase difference with the actual concentration is abandoned, the prior distribution parameter is more accurate, and the obtained pollution source information and posterior probability density are more accurate.
In one example, the influent infiltration information includes a plurality of influent infiltration concentrations of the influent infiltration water quality characteristic factor, and an influent infiltration time and an influent infiltration location corresponding to each influent infiltration concentration.
In the step b3, the posterior probability density corresponding to the first inflow and infiltration information in the drainage pipe network is determined by the following method:
firstly, inputting each inflow and infiltration concentration in the second inflow and infiltration information into a one-dimensional water quality simulation model, and determining the first inflow and infiltration information and the simulation concentration of the drainage pipe network end drainage position corresponding to the first inflow and infiltration information.
In an alternative embodiment, the one-dimensional water quality simulation model formula is also expressed as:
Wherein A is the cross-sectional area of water, (m) 2 ) The method comprises the steps of carrying out a first treatment on the surface of the Q is flow, (m) 3 /h); in the embodiment of the invention, C is the concentration (mg/L) or the conductivity (mu s/cm) of each water quality characteristic factor in the second inflow infiltration information; t is time,(s); x is the flow path from the end position of the drainage pipe network, (m); e (E) x Is a longitudinal discrete coefficient, (m 2 /s）；K 1 Is the first order degradation coefficient of the pollutant,(s) -1 ）；Indicating that the end position of the drainage pipe network is X (inflow infiltration is +.>The influent infiltration time was T and the influent infiltration concentration was M).
And then, taking the second inflow infiltration information as a priori distribution parameter, and inputting the simulated concentration and the first actual concentration of the tail end position of the drainage pipe network corresponding to the first inflow infiltration information into a pre-constructed posterior probability density function to obtain the posterior probability density corresponding to the first inflow infiltration information.
In an alternative embodiment, the posterior probability density function is expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,a posterior probability density function for the inflow infiltration water quality characteristic factor; />Is a priori distribution parameter, namely second inflow infiltration information; />Is the standard deviation; />Is the first actual concentration; m is the inflow and infiltration concentration of a pollution source; / >An inflow infiltration location for a source of contamination; t is inflow infiltration time of a pollution source; />Is the simulated concentration; n is the total number of simulations.
According to the embodiment of the invention, a Bayesian theory is utilized to establish a posterior probability density function containing the inflow and infiltration concentration and inflow and infiltration time of a pollution source, the second inflow and infiltration information is used as a priori distribution parameter, and according to the actual concentration of the drainage position at the tail end of the drainage pipe network, the water quality characteristic factor concentration of inflow and infiltration at different inflow and infiltration positions and different inflow and infiltration times can be obtained, and compared with a chemical balance method which only can obtain the inflow position and proportion of the pollutant, the pollution tracing result is greatly enriched.
In one example, the influent infiltration water quality characteristic factor comprises an influent contaminant water quality characteristic factor, the pollution source of the drainage network comprises a household pollution source, and/or an industrial pollution source; the inflow pollutant water quality characteristic factors of the living pollution sources comprise escherichia coli and/or total nitrogen; the influent contaminant water quality characteristic factor of the industrial source includes conductivity, and/or fluoride.
In one example, the influent infiltration water quality characteristic factor comprises an infiltration groundwater water quality characteristic factor, the infiltration groundwater water quality characteristic factor comprises E.faecalis, and/or total nitrogen.
The inflow and infiltration water quality characteristic factors are selected according to the pollution sources or the types of underground water in the drainage partition, the living pollution sources take fecal escherichia coli indexes, total nitrogen and the like as inflow pollution source characteristic factors, and the industrial pollution sources take electric conductivity, fluoride and the like as inflow pollution source water quality characteristic factors; the infiltration groundwater takes the escherichia coli index, total nitrogen and the like as characteristic factors. Considering that the escherichia coli faecalis index, total nitrogen, conductivity and fluoride are characteristic factors with relatively stability and relatively good designability, the characteristic factors are used as inflow and infiltration water quality characteristic factors, so that the influence of degradation, oxidation, reduction and other reactions in the pollutant conveying process on the inflow and infiltration analysis of pollutants can be effectively avoided, pollution sources in different types of drainage pipelines can be effectively identified, and larger errors of results are avoided.
The inflow and infiltration analysis of the drainage network is described below by way of a specific example.
Example 1
The sewage pipe network pollution source inflow and underground water infiltration analysis steps are as follows:
step d1: the SWMM software is used for converting a vector diagram containing pipe network information such as drainage system, pipeline type, pipeline connection relation, rain sewage trend, pipe diameter, elevation, pump station scale and the like into a drainage pipe network topological structure diagram;
Step d2: the method comprises the steps of obtaining a high-precision digital elevation model and a digital earth surface model based on an unmanned aerial vehicle-mounted high-precision radar, combining software such as Inoworks and MIKE Flood, and rapidly simulating a ground runoff channel and a rain sewage self-flowing process under the condition of urban complex topography, so that a drainage pipe network topological structure diagram is utilized to accurately divide drainage subareas;
step d3: constructing prior distribution functions of pollution source and groundwater quality characteristic factor concentration by using a Bayesian theory, performing posterior sampling on the pollution source and groundwater quality characteristic factor concentration by using a Monte Carlo method, analyzing a theoretical calculation value obtained by simulation and a likelihood function of a discharge real-time measurement water quality characteristic factor concentration or a sewage treatment plant inflow water quality characteristic factor concentration, thereby obtaining posterior probability densities of positions, concentrations and time of a pollution source inflow rainwater pipeline and a groundwater infiltration sewage pipeline;
step d4: selecting a position corresponding to a confidence interval with the probability of 80% of the inflow or groundwater infiltration position of the pollution source based on the probability density of the inflow or groundwater infiltration position of the pollution source according to a back calculation result, and taking an adjacent upstream and downstream manhole as a key monitoring node by combining a pipe network topological structure and a drainage partition;
Step d5: and (3) arranging water quality and water quantity on-line monitors at key pipe network monitoring nodes, continuously monitoring for 10-15 days, acquiring long-period monitoring data, analyzing abnormal conditions of upstream and downstream water quality and water quantity by combining a chemical balance method, verifying a back calculation result, and accurately acquiring the position, strength, time and other information of a pollution source inflow rainwater pipeline and a groundwater infiltration sewage pipeline.
The process of obtaining posterior probability densities of pipe inflow and infiltration locations, inflow and infiltration concentrations, inflow and infiltration times for a drainage pipe network is described below with a specific example.
Example 2
The step of obtaining the posterior probability density of the position, the concentration and the time of the pipeline according to the Bayesian theory and the Monte Carlo method comprises the following steps:
step e1: carrying out investigation on a drainage partition and a sewage treatment plant, finding out main pollution source types in the sewage direct drainage water receiving range, including domestic sewage, industrial wastewater and the water quality of the inlet water of the sewage treatment plant, and primarily judging the illegal discharge number of the domestic sewage and the industrial wastewater and whether underground water infiltration exists;
step e2: collecting sewage directly discharged during 0, 6, 12 and 18 days, connecting 10 cells in a sheet area into municipal dry pipes, connecting 10 industrial enterprise production workshop tail water in the sheet area, water inlet of a sewage treatment plant in the sheet area and groundwater samples at different points, monitoring fecal coliform indexes, total nitrogen, conductivity and fluoride concentration of 48 samples for the sewage directly discharged water sample, monitoring fecal coliform indexes and total nitrogen concentration for domestic sewage and sewage treatment plant water samples, monitoring conductivity and fluoride concentration for industrial wastewater, determining respective concentration ranges, and taking water quality characteristic factor concentration ranges of domestic sewage and industrial wastewater discharge sources and groundwater as input values of prior functions;
Step e3: according to Bayesian theory, the prior distribution parameters, likelihood functions and posterior probability density functions are combined, and the pollution source inflow rainwater pipe network tracing process and the groundwater infiltration sewage pipe network analysis process are converted into posterior probability density functions for solving the unknown parameters X.
Wherein, P (X|y), the pollutant infiltrates or the posterior probability density that the groundwater inflow pollution information corresponds to;
p(X i ) The prior distribution parameters are the concentration ranges of the water quality characteristic factors of the inflow pollution source and the infiltrated groundwater, and are generally considered to be uniformly distributed in the value interval;
sigma, standard deviation;
Y i the actual concentration of the direct-discharge sewage fecal coliform, total nitrogen, conductivity and fluoride, or the actual concentration of the sewage fecal coliform and total nitrogen of a sewage treatment plant;
m, the concentration of the inflow of a pollution source or the infiltration of groundwater;
a location where a source of contamination is in-flowed or groundwater is in-permeated;
and T, the inflow time of a pollution source or the infiltration time of groundwater.
The inflow and infiltration information of the end position of the drainage pipe network is represented as X, namely the inflow and infiltration position isThe inflow infiltration time is T, and the inflow infiltration concentration is M.
Wherein the method comprises the steps ofThe method is characterized by comprising the following steps of:
wherein A is the cross-sectional area of water, (m) 2 ) The method comprises the steps of carrying out a first treatment on the surface of the Q is flow, (m) 3 /h); c is the concentration of each water quality characteristic factor in inflow and infiltration information (mg/L); t is time,(s); x is the flow path from the end position of the drainage pipe network, (m); e (E) x Is a longitudinal discrete coefficient [ (]m 2 /s）；K 1 Is the first order degradation coefficient of the pollutant,(s) -1 ）；Indicating that the inflow infiltration information is X, i.e. the inflow infiltration position is +.>The inflow infiltration time is T, and the end position of the drainage pipe network is simulated to be the concentration when the inflow infiltration concentration is M.
The prior distribution parameters p (X) are respectively based on the concentration range X of the pollution source and the groundwater quality characteristic factors i ) The pipe network topological structure is combined, and the actual concentration Y of the water quality characteristic factors corresponding to the directly discharged sewage or the inlet water of a sewage treatment plant is combined i The posterior probability density of the unknown parameter X, namely the inflow of the pollution source or the infiltration position, concentration and time of groundwater can be obtained.
Step e4: and (3) calling a Monte Carlo method by using a MATLAB kit, uniformly sampling in a concentration range X of a living pollution source water quality characteristic factor (escherichia coli and total nitrogen), and setting the step length to be 1000. And calculating probability density according to the simulation calculated value and the actual concentration Y of the sewage fecal coliform and the total nitrogen, discarding the unknown parameter X corresponding to the lower probability according to the unknown parameter X corresponding to the higher retention probability of the preset confidence interval, and finally obtaining the probability density of the discharge position, the discharge concentration and the discharge time of the domestic pollution source of the sheet region.
Step e5: the Monte Carlo method is called by using MATLAB tool package, uniform sampling is carried out in the concentration range X of the characteristic factors (conductivity and fluoride) of the industrial pollution source water quality, and the step size is set to be 1000. And calculating probability density according to the simulation calculated value, the direct-discharge sewage conductivity and the fluoride actual concentration Y, discarding the unknown parameter X corresponding to the lower probability according to the unknown parameter X corresponding to the higher retention probability of the preset confidence interval, and finally obtaining the probability density of the discharge position, the discharge concentration and the discharge time of the industrial pollution source of the patch area.
Step e6: the Monte Carlo method is called by using MATLAB kit, uniform sampling is carried out in the concentration range X of groundwater quality characteristic factors (fecal coliform and total nitrogen), and the step length is set to be 1000. Calculating the fitting degree according to the simulation calculated value and the actual concentration Y of the fecal coliform and the total nitrogen of the influent water of the sewage treatment plant, reserving prior distribution parameters corresponding to the higher fitting degree, discarding prior distribution parameters corresponding to the lower fitting degree, and finally obtaining the probability density of the position, the intensity and the time of the underground water infiltration sewage pipe network of the sheet area.
Based on the same inventive concept, the embodiment of the invention also provides an inflow and infiltration analysis device of a drainage pipe network, as shown in fig. 2, the device comprises:
An acquisition module 201, configured to acquire a topology structure diagram of a drainage pipe network and point cloud topographic data; the details are described in step S101 in the above embodiments, and are not described herein.
The dividing module 202 is configured to divide the drainage pipe network according to the topology structure diagram and the point cloud topographic data to obtain a plurality of drainage partitions; the details refer to the description of step S102 in the above embodiment, and are not repeated here.
The determining module 203 is configured to determine key monitoring nodes of the drainage pipe network according to the drainage partitions and inflow and seepage data in the drainage pipe network; the details are described in step S103 in the above embodiments, and are not described herein.
And the monitoring module 204 is used for monitoring inflow and infiltration information in the drainage pipe network at the key monitoring nodes. The details are referred to the description of step S104 in the above embodiment, and will not be repeated here.
In one example, the acquisition module 201 includes:
the first acquisition submodule is used for acquiring a vector diagram of pipe network information of the drainage pipe network; the details are described in the above embodiments, and are not repeated here.
And the first determination submodule is used for determining the topological structure according to the vector diagram. The details are described in the above embodiments, and are not repeated here.
In an example, the acquisition module 201 further includes:
the second acquisition submodule is used for acquiring a digital elevation model and a digital earth surface model of the position of the drainage pipe network; the details are described in the above embodiments, and are not repeated here.
And the third acquisition submodule is used for acquiring the point cloud terrain data of the drainage pipe network according to the digital elevation model and the digital earth surface model. The details are described in the above embodiments, and are not repeated here.
In one example, the partitioning module 202 includes:
the simulation sub-module is used for simulating the rain sewage self-flowing process of the drainage pipe network according to the topological structure diagram, the point cloud topographic data and a two-dimensional hydraulic model formed based on the digital elevation model and the digital earth surface model; the details are described in the above embodiments, and are not repeated here.
The second determining submodule is used for determining the trend of the rain sewage in the drainage pipe network according to the rain sewage self-flowing process; the details are described in the above embodiments, and are not repeated here.
And the dividing sub-module is used for dividing the drainage pipe network according to the trend of the rain sewage to obtain a plurality of drainage partitions. The details are described in the above embodiments, and are not repeated here.
In an example, the inflow infiltration data in the determining module 203 includes the first inflow infiltration information and a posterior probability density corresponding to the first inflow infiltration information, and the determining module 203 includes:
the fourth acquisition submodule is used for acquiring the first inflow infiltration information and the posterior probability density corresponding to the first inflow infiltration information in the drainage pipe network; the details are described in the above embodiments, and are not repeated here.
The screening submodule is used for screening the inflow and infiltration positions in the first inflow and infiltration information according to the preset confidence interval and the posterior probability density to obtain screened inflow and infiltration positions; the details are described in the above embodiments, and are not repeated here.
A third determining submodule, configured to determine an upstream node and a downstream node of the screened inflow and infiltration position in the associated drainage partition; the details are described in the above embodiments, and are not repeated here.
And the fourth determining submodule is used for taking the upstream node and the downstream node as key monitoring nodes of the drainage pipe network. The details are described in the above embodiments, and are not repeated here.
In one example, the monitoring module 204 includes:
And the judging sub-module is used for judging the information of inflow pollutants or infiltration groundwater in the drainage pipe network by using a chemical balance method at the key monitoring nodes. The details are described in the above embodiments, and are not repeated here.
In an example, the fourth acquisition submodule includes:
the determining unit is used for determining second inflow and infiltration information of the drainage pipe network; the details are described in the above embodiments, and are not repeated here.
The collecting unit is used for collecting the first actual concentration of the inflow infiltration water quality characteristic factors at the drainage position of the tail end of the drainage pipe network; the details are described in the above embodiments, and are not repeated here.
The calculation unit is used for taking the second inflow infiltration information as a priori distribution parameter, and determining the first inflow infiltration information and the posterior probability density corresponding to the first inflow infiltration information in the drainage pipe network according to the first actual concentration. The details are described in the above embodiments, and are not repeated here.
In an example, the determining unit includes:
the first collecting subunit is used for collecting sewage or underground water samples of a plurality of preset areas at different moments; the details are described in the above embodiments, and are not repeated here.
The acquisition subunit is used for acquiring the pollution source or the groundwater quality characteristic factor concentration of each sewage or groundwater sample; the details are described in the above embodiments, and are not repeated here.
A first determining subunit, configured to determine third inflow and infiltration information according to the concentration of each inflow and infiltration water quality feature factor; the details are described in the above embodiments, and are not repeated here.
The first collecting subunit is used for collecting the second actual concentration of the inflow infiltration water quality characteristic factors at the tail end position of the drainage pipe network; the details are described in the above embodiments, and are not repeated here.
And the screening subunit is used for screening the third inflow infiltration information according to the second actual concentration and the one-dimensional water quality simulation model to obtain second inflow infiltration information. The details are described in the above embodiments, and are not repeated here.
In an example, the inflow and infiltration information includes a plurality of inflow and infiltration concentrations of the inflow and infiltration water quality characteristic factors, inflow and infiltration time and inflow and infiltration positions corresponding to the inflow and infiltration concentrations, and the screening subunit is configured to uniformly sample the inflow and infiltration concentrations in the third inflow and infiltration information to obtain a plurality of sampled inflow and infiltration concentrations; according to the inflow and infiltration concentration after sampling and the one-dimensional water quality simulation model, calculating each simulation concentration of the tail end drainage position of the drainage pipe network; and screening the third inflow infiltration information according to the simulated concentration and the second actual concentration to obtain second inflow infiltration information. The details are described in the above embodiments, and are not repeated here.
In an example, the screening subunit is further configured to calculate a fitness of each of the simulated concentration and the second actual concentration; using each inflow and infiltration concentration corresponding to which the fitting degree is larger than a preset threshold value as the inflow and infiltration concentration after screening; and taking the filtered inflow infiltration concentration, inflow infiltration time and inflow infiltration position corresponding to the filtered inflow infiltration concentration as second inflow infiltration information. The details are described in the above embodiments, and are not repeated here.
In an example, the inflow infiltration information includes a plurality of inflow infiltration concentrations of the inflow infiltration water quality feature factor, and inflow infiltration times and inflow infiltration positions corresponding to the respective inflow infiltration concentrations, and the calculation unit includes:
the second determining subunit is used for inputting each inflow and infiltration concentration in the second inflow and infiltration information into the one-dimensional water quality simulation model, and determining the first inflow and infiltration information and the simulation concentration of the drainage pipe network tail end drainage position corresponding to the first inflow and infiltration information; the details are described in the above embodiments, and are not repeated here.
And the third determining subunit is used for taking the second inflow and infiltration information as a priori distribution parameter, inputting the simulated concentration and the first actual concentration of the tail end discharge position of the drainage pipe network corresponding to the first inflow and infiltration information into a pre-constructed posterior probability density function, and obtaining the posterior probability density corresponding to the first inflow and infiltration information. The details are described in the above embodiments, and are not repeated here.
The specific limitations and beneficial effects of the device described above can be found in the limitations of the drainage network inflow and infiltration analysis methods described above, and will not be described in detail herein. The various modules described above may be implemented in whole or in part by software, hardware, or a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
Fig. 3 is a schematic diagram of a hardware structure of a computer device according to an exemplary embodiment. As shown in fig. 3, the device includes one or more processors 310 and a memory 320, the memory 320 including persistent memory, volatile memory and a hard disk, one processor 310 being illustrated in fig. 3. The apparatus may further include: an input device 330 and an output device 340.
The processor 310, memory 320, input device 330, and output device 340 may be connected by a bus or other means, for example in fig. 3.
The processor 310 may be a central processing unit (Central Processing Unit, CPU). The processor 310 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSPs), application specific integrated circuits (Application Specific Integrated Circuit, ASICs), field programmable gate arrays (Field-Programmable Gate Array, FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 320 is used as a non-transitory computer readable storage medium, and includes a persistent memory, a volatile memory, and a hard disk, and may be used to store a non-transitory software program, a non-transitory computer executable program, and a module, such as a program instruction/module corresponding to a drainage network inflow and infiltration analysis method in an embodiment of the present application. The processor 310 executes various functional applications of the server and data processing, i.e., implements any of the drainage network inflow and infiltration analysis methods described above, by running non-transitory software programs, instructions, and modules stored in the memory 320.
Memory 320 may include a storage program area that may store an operating system, at least one application program required for functionality, and a storage data area; the storage data area may store data, etc., as needed, used as desired. In addition, memory 320 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 320 may optionally include memory located remotely from processor 310, which may be connected to the data processing device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 330 may receive input numeric or character information and generate signal inputs related to user settings and function control. The output device 340 may include a display device such as a display screen.
One or more modules are stored in the memory 320 that, when executed by the one or more processors 310, perform the method as shown in fig. 1.
The product can execute the method provided by the embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. Technical details which are not described in detail in the present embodiment can be found in the embodiment shown in fig. 1.
The embodiment of the invention also provides a non-transitory computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions can execute the analysis method in any of the method embodiments. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a Flash Memory (Flash Memory), a Hard Disk (HDD), or a Solid State Drive (SSD); the storage medium may also comprise a combination of memories of the kind described above.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
The foregoing is merely exemplary of embodiments of the present invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (15)

1. A method for inflow infiltration analysis of a drainage network, the method comprising:
acquiring a topological structure diagram and point cloud topographic data of a drainage pipe network;
dividing the drainage pipe network according to the topological structure diagram and the point cloud topographic data to obtain a plurality of drainage partitions;
determining key monitoring nodes of the drainage pipe network according to the drainage subareas and inflow and seepage data in the drainage pipe network;
and at the key monitoring node, monitoring information of inflow pollutants or infiltration groundwater in the drainage pipe network.
2. The method of claim 1, wherein the step of obtaining a topology map of the drainage network comprises:
acquiring a vector diagram of pipe network information of the drainage pipe network;
and determining the topological structure according to the vector diagram.
3. The method of claim 1 or 2, wherein the step of obtaining point cloud topography data of the drainage network comprises:
acquiring a digital elevation model and a digital earth surface model of the position of the drainage pipe network;
and acquiring point cloud terrain data of the drainage pipe network according to the digital elevation model and the digital surface model.
4. The method of claim 3, wherein partitioning the drainage network according to the topology map and the point cloud topography data to obtain a plurality of drainage partitions comprises:
Simulating a rain sewage self-flowing process of the drainage pipe network according to the topological structure diagram, the point cloud topographic data and a two-dimensional hydraulic model formed based on the digital elevation model and the digital earth surface model;
determining the trend of the rain sewage in the drainage pipe network according to the rain sewage self-flowing process;
and dividing the drainage pipe network according to the trend of the rain sewage to obtain a plurality of drainage subareas.
5. The method of claim 1, wherein the influent penetration data comprises first influent penetration information and a posterior probability density corresponding to the first influent penetration information, the first influent penetration information comprises influent penetration locations, and determining key monitoring nodes of the drainage network based on the influent penetration data in each of the drainage partitions and the drainage network comprises:
acquiring first inflow infiltration information and posterior probability density corresponding to the first inflow infiltration information in the drainage pipe network;
screening the inflow and infiltration positions in the first inflow and infiltration information according to a preset confidence interval and the posterior probability density to obtain screened inflow and infiltration positions;
determining upstream nodes and downstream nodes of the screened inflow infiltration positions in the drainage partition to which the inflow infiltration positions belong;
And taking the upstream node and the downstream node as key monitoring nodes of the drainage pipe network.
6. The method of claim 5, wherein monitoring, at the critical monitoring node, information of inflow contaminants or infiltration groundwater in the drainage network comprises:
and judging information of inflow pollutants or infiltration groundwater in the drainage pipe network by using a chemical balance method at the key monitoring nodes.
7. The method of claim 5, wherein obtaining the first inflow infiltration information and the posterior probability density corresponding to the first inflow infiltration information in the drainage network comprises:
determining second inflow and infiltration information of inflow and infiltration water quality characteristic factors of the drainage pipe network;
collecting a first actual concentration of the inflow infiltration water quality characteristic factor at the tail end position of the drainage pipe network;
and taking the second inflow infiltration information as a priori distribution parameter, and determining the first inflow infiltration information and the posterior probability density corresponding to the first inflow infiltration information in the drainage pipe network according to the first actual concentration.
8. The method of claim 7, wherein determining second inflow infiltration information for an inflow infiltration water quality characterization of the drainage network comprises:
Collecting samples of sewage or groundwater in a plurality of preset areas at different moments;
acquiring the inflow and infiltration water quality characteristic factor concentration of each sample;
determining third inflow and infiltration information according to the concentrations of the inflow and infiltration water quality characteristic factors;
collecting a second actual concentration of the inflow infiltration water quality characteristic factor at the tail end position of the drainage pipe network;
and screening the third inflow and infiltration information according to the second actual concentration and the one-dimensional water quality simulation model to obtain the second inflow and infiltration information.
9. The method of claim 8, wherein the influent penetration information includes a plurality of influent penetration concentrations of an influent penetration water quality characteristic factor, and an influent penetration time and an influent penetration position corresponding to each of the influent penetration concentrations, and wherein the screening the third influent penetration information based on the second actual concentration and the one-dimensional water quality simulation model to obtain the second influent penetration information includes:
uniformly sampling each inflow and infiltration concentration in the third inflow and infiltration information to obtain a plurality of sampled inflow and infiltration concentrations;
according to the inflow and infiltration concentration after sampling and the one-dimensional water quality simulation model, calculating each simulation concentration of the tail end position of the drainage pipe network;
And screening the third inflow infiltration information according to the simulated concentration and the second actual concentration to obtain the second inflow infiltration information.
10. The method of claim 9, wherein screening the third influent infiltration information based on each of the simulated concentration and the second actual concentration to obtain the second influent infiltration information comprises:
calculating the fitting degree of each simulated concentration and the second actual concentration;
using each inflow and infiltration concentration corresponding to which the fitting degree is larger than a preset threshold value as the inflow and infiltration concentration after screening;
and taking the filtered inflow and infiltration concentration, inflow and infiltration time and inflow and infiltration position corresponding to the filtered inflow and infiltration concentration as the second inflow and infiltration information.
11. The method of claim 7, wherein the influent penetration information includes a plurality of influent penetration concentrations of an influent penetration water quality characteristic factor, and an influent penetration time and an influent penetration position corresponding to each of the influent penetration concentrations, wherein determining the first influent penetration information and a posterior probability density corresponding to the first influent penetration information in the drain pipe network based on the first actual concentration using the second influent penetration information as a priori distribution parameter includes:
Inputting each inflow and infiltration concentration in the second inflow and infiltration information into a one-dimensional water quality simulation model, and determining the first inflow and infiltration information and the simulation concentration of the tail end position of the drainage pipe network corresponding to the first inflow and infiltration information;
and taking the second inflow infiltration information as a priori distribution parameter, and inputting the simulated concentration and the first actual concentration of the tail end position of the drainage pipe network corresponding to the first inflow infiltration information into a pre-constructed posterior probability density function to obtain the posterior probability density corresponding to the first inflow infiltration information.
12. The method of claim 7, wherein the influent infiltration water quality characterization factor comprises an influent contaminant water quality characterization factor, and the pollution source of the drainage network comprises a household pollution source, and/or an industrial pollution source;
the inflow pollutant water quality characteristic factors of the living pollution source comprise escherichia coli and/or total nitrogen;
the influent contaminant water quality characteristic factor of the industrial source includes conductivity, and/or fluoride.
13. The method of claim 7 or 12, wherein the influent penetration water quality characteristic factor comprises an penetration groundwater quality characteristic factor comprising escherichia coli faecalis, and/or total nitrogen.
14. A computer device comprising a memory and a processor, said memory and said processor being communicatively coupled to each other, said memory having stored therein computer instructions, said processor executing said computer instructions to perform the steps of the drainage network inflow and infiltration analysis method of any of claims 1-13.
15. A computer readable storage medium having stored thereon a computer program, which when executed by a processor performs the steps of the drain pipe network inflow infiltration analysis method according to any of claims 1-13.
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