Method and system for predicting water quality of water supply network based on water age entropy and storage medium

Abstract

The invention provides a method for predicting water quality of a water supply pipe network based on water age entropy. The method comprises the following steps: S1, establishing a water supply pipe network model based on an actual water supply pipe network; s2, simulating water supply of an actual water supply pipe network by using the water supply pipe network model, and counting the flow of each pipeline and the water age of each pipeline node according to a certain sampling time interval; s3, calculating the water age entropy of each pipeline node of different sampling points; s4, acquiring water quality influence factors of each pipeline node of different sampling points; s5, calculating a correlation coefficient of the water age entropy-water quality influence factor of each pipeline node of different sampling points; s6, fitting a water age entropy-water quality influence factor relation curve of each pipeline node; and S7, predicting the water quality influence factor of each pipeline node at a certain time by using the fitted water age entropy-water quality influence factor relation curve of each pipeline node. The water age entropy concept is introduced into the pipe network water quality evaluation, the residual chlorine value of the pipe network is predicted through the water age entropy, and the water supply quality safety is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of water quality prediction of water supply network, and in particular to a method, system and storage medium for predicting water quality of water supply network based on water age entropy. Background Art

[0002] With the rapid development of social economy and the improvement of people's living standards, water resources have gradually become a limiting factor for urban progress and development. As an important urban infrastructure, the safe and reliable operation of the water supply network is the most important guarantee for people's normal production and life.

[0003] The service life of water supply networks (such as pipe sections and components) and the performance of many water facilities related to urban safety are not only affected by hydraulic conditions, but also closely related to the water quality of the water supply network. In the study of water supply network performance evaluation, scholars mostly focus on the hydraulic safety performance of water supply networks, and often ignore the water quality reliability of water supply networks. When hydraulic accidents such as pipe bursts and leakages occur in water supply networks, the impact range is limited to the accident section and surrounding sections. When the water quality is abnormal at a certain point in the water supply network, all users of the downstream water supply network will be affected. It can be seen that compared with hydraulics, water quality problems in water supply networks have a wider impact range and are more difficult to deal with after an accident.

[0004] The longer the water stays in the water supply network, the more opportunities there are for physical, chemical and biological reactions between the water and the microorganisms on the pipe wall, and the greater the risk of corrosion of pipe fittings and components, which increases the risk of pipe bursts and leakage. Water age is the retention time of water in the pipe network and is an important indicator reflecting changes in water quality conditions.

[0005] The existing analysis methods of water supply network reliability can be mainly divided into three categories: analytical method, simulation method, and proxy index method. The analytical method mainly includes fault analysis method, hierarchical analysis method, and topological reliability analysis method. Usually, the state is selected by enumeration method first, and then the reliability is calculated by mathematical deduction. The simulation method is represented by Monte Carlo method and quasi-Monte Carlo method. It uses static or dynamic hydraulic simulation methods to simulate different working conditions of the pipe network system, analyzes the probability of the pipe network supplying the required water volume of users at a certain service water pressure within a specified time, and calculates the reliability of the water supply network according to the prior definition of reliability. The proxy index method uses a simplified mathematical model to replace the complex simulation or analytical model, and solves the results similar to the original simulation analysis model, thereby reducing the amount of calculation and calculation time, and greatly improving the efficiency of evaluation and analysis. This method mainly includes energy entropy, flow entropy and resilience index method.

[0006] The above reliability analysis methods are mostly used to study pressurized multi-loop municipal water supply networks, and mainly focus on hydraulic reliability research. In addition, analytical methods and simulation methods are often used in the study of water supply networks, while proxy index methods are rarely reported. In addition, the existing literature on the hydraulic reliability of water supply networks mostly focuses on static hydraulic conditions, while less research is done on dynamic hydraulic conditions and water quality conditions. The existing water supply network reliability analysis methods are difficult to effectively predict the water quality of water supply networks. Summary of the invention

[0007] The present invention proposes a method, system and storage medium for predicting water quality of a water supply network based on water age entropy, so as to solve the technical problem that the existing water supply network reliability analysis method is difficult to effectively predict the water quality of the water supply network.

[0008] One aspect of the present invention is to provide a method for predicting water quality of a water supply network based on water age entropy, the method comprising the following steps:

[0009] S1. Establishing a water supply network model based on the actual water supply network, wherein the water supply network model at least includes a topological structure, a pipe length, and pipe nodes corresponding to the actual water supply network;

[0010] S2. Using the water supply network model to simulate the water supply of the actual water supply network, and counting the flow rate of each pipeline and the water age of each pipeline node at a certain sampling time interval;

[0011] S3, calculating the water age entropy of each pipeline node at different sampling points according to the flow rate of each pipeline and the water age of each pipeline node counted by the water supply network model;

[0012] S4. Detect the water quality influencing factors of each pipeline node of the actual water supply network at a certain sampling time interval, and obtain the water quality influencing factors of each pipeline node at different sampling points;

[0013] S5, using the water age entropy of each pipeline node at different sampling points obtained in step S3 and the water quality influencing factor of each pipeline node at different sampling points obtained in step S4, calculate the correlation coefficient between the water age entropy and the water quality influencing factor of each pipeline node at different sampling points;

[0014] S6, using the water age entropy of each pipeline node at different sampling points obtained in step S3 and the water quality influencing factors of each pipeline node at different sampling points obtained in step S4, fitting a water age entropy-water quality influencing factor relationship curve of each pipeline node;

[0015] S7, using the water supply network model to simulate the water supply of the actual water supply network, and counting the flow of each pipeline at a certain time, the water age of each pipeline node, and calculating the water age entropy of each pipeline node at a certain time;

[0016] The water quality influencing factors of each pipeline node at a certain time are predicted by using the fitted water age entropy-water quality influencing factor relationship curve of each pipeline node and the calculated water age entropy of each pipeline node at a certain time.

[0017] In a preferred embodiment, in step S3, the water age entropy of each pipeline node at different sampling points is calculated by the following method:

[0018]

[0019] Among them, S i represents the water age entropy of a sampling point pipeline node i; D i represents the upstream adjacent pipeline node set of pipeline node i; Q xi represents the flow rate from the upstream pipeline node x of a sampling point pipeline node i to pipeline node i, m 3 / h; T xi represents the water age of pipeline xi between a sampling point pipeline node i and pipeline node x; Q i represents the total flow rate of a sampling point into the pipeline node i, m 3 / h; T i represents the water age of a sampling point pipeline node i; S x Represents the water age entropy of a sampling point pipeline node x.

[0020] In a preferred embodiment, in step S3, the method further comprises introducing a maximum water age entropy index to perform reliability evaluation on the calculated water age entropy of each pipeline node at different sampling points.

[0021] In a preferred embodiment, the water age entropy of each pipeline node is evaluated for reliability by the following method:

[0022]

[0023] Among them, De i represents the reliability of the water age entropy of pipeline node i; S i represents the calculated water age entropy of a sampling point pipeline node i; S i，max Represents the maximum water age entropy index of pipeline node i.

[0024] In a preferred embodiment, in step S3, the method further comprises, using the reliability of the water age entropy of each pipeline node to perform reliability evaluation on the established water supply network model.

[0025] In a preferred embodiment, the established water supply network model is evaluated for reliability by the following method:

[0026]

[0027] in, represents the reliability of the established water supply network model; I represents the number of pipeline nodes in the established water supply network model; De i represents the reliability of the water age entropy of pipeline node i; w i Represents the weight coefficient of pipeline node i.

[0028] In a preferred embodiment, in step S5, the correlation coefficient between the water age entropy and the water quality influencing factor of each pipeline node at different sampling points is calculated by the following method:

[0029]

[0030] Among them, Correl (S i , Y i ) represents the correlation coefficient between the water age entropy and water quality influencing factors of a sampling point pipeline node i; S i represents the water age entropy of a sampling point pipeline node i; Y i represents the water quality influencing factors of a sampling point pipeline node i; represents the mean water age entropy of pipeline node i at all sampling points; Represents the mean of water quality influencing factors at all sampling points and pipeline node i.

[0031] In a preferred embodiment, in step S7, a risk warning model is established, the fitted water age entropy-water quality influencing factor relationship curve of each pipeline node is embedded into the established risk warning model, and a risk warning value is set in the risk warning model;

[0032] When it is predicted that the water quality influencing factors of a certain pipeline node at a certain time exceed the risk warning value, the risk warning model will issue a water quality risk warning.

[0033] Another aspect of the present invention is to provide a system for predicting water quality of a water supply network based on water age entropy, wherein the system is used to execute a method for predicting water quality of a water supply network based on water age entropy provided by the present invention.

[0034] Another aspect of the present invention is to provide a computer storage medium, wherein the computer storage medium is used to store computer execution instructions, and the computer execution instructions are used to execute a method provided by the present invention for predicting water quality of a water supply network based on water age entropy.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention proposes a method, system and storage medium for predicting water quality of a water supply network based on water age entropy. Aiming at the characteristics of dynamic changes in water use in a water supply network, a simulation method and a proxy index method are combined. Based on the basic idea of ​​information entropy and a hydraulic water quality dynamic simulation method, a water age entropy evaluation index is introduced on the basis of path flow, and water quality influencing factors (residual chlorine value) of each pipeline node are detected in the water supply network. A water age entropy-water quality influencing factor relationship curve of the pipeline node is fitted, so that the water quality influencing factors of the pipeline node are predicted through the fitted water age entropy-water quality influencing factor relationship curve of the pipeline node, so as to provide an early warning for water quality risks in the pipeline network, reduce the occurrence of water quality complaints, and ensure the safety of water supply quality.

[0037] The present invention proposes a method, system and storage medium for predicting water quality of a water supply network based on water age entropy, which introduces the concept of water age entropy into water quality evaluation of the network, and predicts residual chlorine value of the network by water age entropy, which has guiding significance for water quality management and safety assurance of the network, provides early warning for water quality risks of the network, reduces the occurrence of water quality complaints, and ensures the safety of water supply quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the specific implementation of the present invention or the technical solution in the prior art, the following briefly introduces the drawings required for use in the specific implementation or the prior art description. Obviously, the drawings described below are some implementations of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 The present invention is a flow chart of a method for predicting water quality of a water supply network based on water age entropy.

[0040] Figure 2 It is a schematic diagram of a water supply network model established in one embodiment of the present invention.

[0041] Figure 3 It is a schematic diagram of the water age entropy data of the No. J11 pipeline node of the 9 sampling points calculated in one embodiment of the present invention, and the water quality influencing factor data of the No. J11 pipeline node of the 9 sampling points detected.

[0042] Figure 4 It is a relationship curve between water age entropy and water quality influencing factors of the J11th pipeline node fitted in one embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the above and other features and advantages of the present invention more clear, the present invention is further described below in conjunction with the accompanying drawings. It should be understood that the specific embodiments given herein are for the purpose of explaining to those skilled in the art and are only exemplary and not restrictive.

[0044] Combination Figure 1 According to an embodiment of the present invention, a method for predicting water quality of a water supply network based on water age entropy is provided, comprising the following method steps:

[0045] Step S1: establishing a water supply network model based on the actual water supply network, wherein the water supply network model at least includes a topological structure, a pipe length, and pipe nodes corresponding to the actual water supply network.

[0046] The actual water supply network of the present invention refers to the actual water supply network in use, and the water supply network model refers to the water supply network model established according to the actual water supply network and used to calculate the water age entropy of the water supply network pipeline nodes.

[0047] like Figure 2 As shown, in this embodiment, a water supply network model is established according to an actual water supply network. The water supply network model is set according to the actual water supply network, and corresponds to the actual water supply network topology structure, including a water tank 1, a water pump 2, 30 pipes 3, 10 valves 4, and 20 pipe nodes. The topology structure, pipe length, and pipe nodes of the water supply network model correspond to the actual water supply network.

[0048] In this embodiment, the 20 pipeline nodes are pipeline node No. J1, pipeline node No. J2, pipeline node No. J3, pipeline node No. J4, pipeline node No. J5, pipeline node No. J6, pipeline node No. J7, pipeline node No. J8, pipeline node No. J9, pipeline node No. J10, pipeline node No. J10, pipeline node No. J12, pipeline node No. J13, pipeline node No. J14, pipeline node No. J15, pipeline node No. J16, pipeline node No. J17, pipeline node No. J18, pipeline node No. J19 and pipeline node No. J20, respectively. Figure 2 shown.

[0049] Step S2: Use the water supply network model to simulate the water supply of the actual water supply network, and count the flow of each pipeline 3 and the water age of each pipeline node at a certain sampling time interval.

[0050] In the process of simulating the water supply of the actual water supply network using the established water supply network model of the present invention, it should be ensured that the flow and pressure calibration of the water supply network model meet the relevant accuracy requirements and are consistent with the water supply flow, pressure and other parameters of the actual water supply network to meet the operating conditions of the actual water supply network.

[0051] In this embodiment, the water supply network model simulates the water supply process of the actual water supply network, and sets the water quality simulation time step to 1 hour and the simulation period to 72 hours.

[0052] In this embodiment, the water consumption and water use mode of the No. J11 pipeline node are added to the water supply network model. The water flow rate of the No. J11 pipeline node is set to 0.167L / S from 9:00 to 16:00, and the water flow rate of the No. J11 pipeline node is set to 0.08L / S from 16:00 to 9:00 the next day. At the same time, according to the valve opening / closing mode of the actual water supply network, the valve of the water supply network model is opened / closed to simulate the water age of the No. J11 pipeline node in the actual water supply network.

[0053] The present invention utilizes the established water supply network model to simulate the water supply process of the actual water supply network, and counts the flow rate of each pipeline 3 at different sampling points and the water age of each pipeline node according to a certain sampling time interval.

[0054] In this embodiment, the No. J11 pipeline node is taken as an example, and the flow rate of each pipeline 3 and the water age of the No. J11 pipeline node are counted at a sampling time interval of 3 hours during the day and 6 hours at night. In this embodiment, a total of 9 sampling (statistics) are performed to complete the sampling of the water age of the No. J11 pipeline node at 9 sampling points, as shown in Table 1, which is a table of 9 sampling points of the water age of the No. J11 pipeline node.

[0055] Table 1: 9 sampling points of water age of pipeline node J11

[0056]

[0057] Step S3, calculating the water age entropy of each pipeline node at different sampling points according to the flow of each pipeline 3 at different sampling points and the water age of each pipeline node statistically obtained by the water supply network model.

[0058] Specifically, according to an embodiment of the present invention, the WNTR data package in the python software is called to calculate the water age entropy of each pipeline node at different sampling points by the following method:

[0059]

[0060] Among them, S i represents the water age entropy of a sampling point pipeline node i; D i represents the upstream adjacent pipeline node set of pipeline node i; Q xi represents the flow rate from the upstream pipeline node x of a sampling point pipeline node i to pipeline node i, m 3 / h; T xi represents the water age of pipeline xi between a sampling point pipeline node i and pipeline node x; Q i represents the total flow rate of a sampling point into the pipeline node i, m 3 / h; T irepresents the water age of a sampling point pipeline node i; S x Represents the water age entropy of a sampling point pipeline node x.

[0061] This embodiment takes the J11th pipeline node as an example. In step S2, the water age sampling (statistics) of the J11th pipeline node at 9 sampling points is completed. According to the flow rate of each pipeline 3 at the 9 sampling points and the water age of the J11th pipeline node counted by the water supply network model, the water age entropy of the J11th pipeline node at the 9 sampling points is calculated.

[0062] like Figure 3 The blue point data shown are the calculated water age entropy of the J11 pipeline node at the 9 sampling points. Figure 3 The horizontal axis is the 9 sampling points of the water age of the J11 pipeline node, and the vertical axis is the water age entropy value of the pipeline node (left side) and the water quality influencing factor value (right side) (the water quality influencing factors are explained in detail below).

[0063] According to an embodiment of the present invention, the maximum water age entropy index is introduced to evaluate the reliability of the water age entropy of each pipeline node calculated at different sampling points. Specifically, the reliability of the water age entropy of each pipeline node is evaluated by the following method:

[0064]

[0065] Among them, De i represents the reliability of the water age entropy of pipeline node i; S i represents the calculated water age entropy of a sampling point pipeline node i; S i，max Represents the maximum water age entropy index of pipeline node i.

[0066] Furthermore, the present invention uses the reliability of the water age entropy of each pipeline node to evaluate the reliability of the established water supply network model. Specifically, the reliability of the established water supply network model is evaluated by the following method:

[0067]

[0068] in, represents the reliability of the established water supply network model; I represents the number of pipeline nodes in the established water supply network model; De i represents the reliability of the water age entropy of pipeline node i; w i Represents the weight coefficient of pipeline node i.

[0069] When the reliability of the water supply network model is established The reliability De of the water age entropy of pipeline node i iThe closer it is to 1, the stronger the reliability of the water supply network model and pipeline node i. Conversely, the weaker the reliability of the water supply network model and pipeline node i. The water supply network model is adjusted and corrected, and the actual water supply network is focused on inspection and maintenance.

[0070] In some embodiments, the reliability evaluation of the established water supply network model may not set the weight w i .

[0071] Step S4: Detect the water quality influencing factors of each pipeline node of the actual water supply network at a certain sampling time interval, and obtain the water quality influencing factors of each pipeline node at different sampling points.

[0072] In a specific embodiment, the water quality influencing factors of each pipeline node of the actual water supply network are detected at a certain sampling time interval using an online water quality detection device for pipeline nodes, and the water quality influencing factors of each pipeline node at different sampling points are obtained. In this embodiment, the water quality influencing factor of the pipeline node is residual chlorine.

[0073] The sampling time interval and sampling point for detecting water quality influencing factors at each pipeline node of the actual water supply network in the present invention should correspond one-to-one with the sampling time interval for counting the flow rate of each pipeline and the water age of each pipeline node in step S2.

[0074] In this embodiment, taking the J11 pipeline node as an example, the faucet at the J11 pipeline node is opened to open the valve to a certain extent, the current flow rate is determined, and the position of the valve opening is recorded. At this time, the flow rate is called a small flow rate, and the value is 0.08L / s; the faucet is opened to the maximum, and the flow rate is determined. At this time, the flow rate becomes a large flow rate, and the value is 0.16L / s.

[0075] The water quality influencing factors (residual chlorine) of the No. J11 pipeline node are detected at a sampling time interval of 3 hours during the day and at a sampling time interval of 6 hours at night. In this embodiment, a total of 9 sampling (detection) is performed to complete the sampling of the water quality influencing factors (residual chlorine) of the No. J11 pipeline node at 9 sampling points, as shown in Table 2. Table of 9 sampling points of the water quality influencing factors (residual chlorine) of the No. J11 pipeline node.

[0076] Table 2 Water quality influencing factors (residual chlorine) at 9 sampling points of pipeline node J11

[0077]

[0078] like Figure 3 The yellow point data shown are the water quality influencing factors (residual chlorine) of the J11 pipeline node among the 9 sampling points tested. Figure 3The horizontal axis in the middle is the 9 sampling points of the water age of the J11 pipeline node, and the vertical axis is the water age entropy value of the pipeline node (left side) and the water quality influencing factor value (right side).

[0079] Step S5, using the water age entropy of each pipeline node at different sampling points obtained in step S3 and the water quality influencing factor of each pipeline node at different sampling points obtained in step S4, calculate the correlation coefficient between the water age entropy and the water quality influencing factor of each pipeline node at different sampling points.

[0080] Specifically, according to an embodiment of the present invention, the correlation coefficient between the water age entropy and the water quality influencing factor of each pipeline node at different sampling points is calculated by the following method:

[0081]

[0082] Among them, Correl (S i , Y i ) represents the correlation coefficient between the water age entropy and water quality influencing factors of a sampling point pipeline node i; S i represents the water age entropy of a sampling point pipeline node i; Y i represents the water quality influencing factors of a sampling point pipeline node i; represents the mean water age entropy of pipeline node i at all sampling points; Represents the mean of water quality influencing factors at all sampling points and pipeline node i.

[0083] The present invention evaluates the correlation between the water age entropy of each pipeline node and the water quality influencing factor (residual chlorine) through the correlation coefficient of the water age entropy-water quality influencing factor of each pipeline node at different sampling points.

[0084] Specifically, when the correlation coefficients of the water age entropy and water quality influencing factors (residual chlorine) of a pipeline node at all calculated sampling points are close to +1 or -1, the water age entropy of the pipeline node is positively correlated or negatively correlated (strongly correlated) with the water quality influencing factors (residual chlorine). If the correlation coefficient of the water age entropy and water quality influencing factors (residual chlorine) of the pipeline node at a certain sampling point is close to 0, the water age entropy of the pipeline node is weakly correlated or uncorrelated with the water quality influencing factors (residual chlorine).

[0085] In this embodiment, the pipeline node No. J11 is taken as an example. In step S3, the water age entropy of the pipeline node No. J11 among the nine sampling points is obtained ( Figure 3 The water quality influencing factor (residual chlorine) of the pipeline node No. J11 of the 9 sampling points is obtained in step S4 ( Figure 3Yellow dot in the middle). Using the water age entropy of the No. J11 pipeline node of the 9 sampling points obtained in step S3 and the water quality influencing factor (residual chlorine) of the No. J11 pipeline node of the 9 sampling points obtained in step S4, the correlation coefficients (Correl1, Correl2, Correl3, ... Correl9) of the water age entropy of the No. J11 pipeline node of the 9 sampling points and the water quality influencing factor (residual chlorine) calculated are close to +1 or -1, and there is a positive or negative correlation between the water age entropy of the No. J11 pipeline node and the water quality influencing factor, and there is a strong correlation between the water age entropy of the No. J11 pipeline node and the water quality influencing factor.

[0086] It should be understood that in this embodiment, the 20 pipeline nodes all calculate the correlation coefficient of the water age entropy-water quality influencing factor (residual chlorine) corresponding to different sampling points (9 sampling points), and evaluate the correlation between the water age entropy and the water quality influencing factor of the 20 pipeline nodes.

[0087] Step S6, using the water age entropy of each pipeline node at different sampling points obtained in step S3 and the water quality influencing factors of each pipeline node at different sampling points obtained in step S4, fitting the water age entropy-water quality influencing factor (residual chlorine) relationship curve of each pipeline node.

[0088] In step S5, the correlation between the water age entropy and the water quality influencing factors of each pipeline node is evaluated by the correlation coefficient of the water age entropy of each pipeline node at different sampling points and the water quality influencing factors. When the correlation between the water age entropy and the water quality influencing factors of a certain pipeline node is positively correlated or negatively correlated (strongly correlated), the water age entropy-water quality influencing factor (residual chlorine) relationship curve of the pipeline node is fitted according to step S6, that is, the water age entropy of the pipeline node at different sampling points obtained in step S3 and the water quality influencing factors of the pipeline node at different sampling points obtained in step S4 are used to fit the water age entropy-water quality influencing factor (residual chlorine) relationship curve of the pipeline node.

[0089] like Figure 4 As shown, taking the No. J11 pipeline node as an example, through the correlation coefficient of the water age entropy-water quality influencing factor (residual chlorine) of the No. J11 pipeline node at 9 sampling points, it is clear that the water age entropy of the No. J11 pipeline node has a strong correlation with the water quality influencing factor (residual chlorine). Using the water age entropy of the No. J11 pipeline node at 9 sampling points and the water quality influencing factor (residual chlorine) of the No. J11 pipeline node at 9 sampling points, the water age entropy-water quality influencing factor (residual chlorine) relationship curve of the No. J11 pipeline node is fitted. For example, the water age entropy data of the No. J11 pipeline node and the water quality influencing factor (residual chlorine) data of the No. J11 pipeline node are fitted using the least squares method.

[0090] It should be understood that among the 20 pipeline nodes in this embodiment, when each pipeline node satisfies the positive correlation or negative correlation (strong correlation) between the water age entropy and the water quality influencing factor, the water age entropy-water quality influencing factor (residual chlorine) relationship curve of each pipeline node is fitted.

[0091] Step S7: Use the water supply network model to simulate the water supply of the actual water supply network, and count the flow of each pipeline and the water age of each pipeline node at a certain time, and calculate the water age entropy of each pipeline node at a certain time.

[0092] The water quality influencing factors (residual chlorine) of each pipeline node at a certain time are predicted by using the fitted water age entropy-water quality influencing factors (residual chlorine) relationship curve of each pipeline node and the calculated water age entropy of each pipeline node at a certain time.

[0093] When the water quality influencing factor (residual chlorine) of each pipeline node at a certain time needs to be predicted, the water supply of the actual water supply network is simulated by using the water supply network model, and the flow rate of each pipeline and the water age of each pipeline node at a certain time are counted, and the water age entropy of each pipeline node at a certain time is calculated. The water quality influencing factor (residual chlorine) of each pipeline node is predicted by using the fitted water age entropy-water quality influencing factor (residual chlorine) relationship curve of each pipeline node.

[0094] Further, according to an embodiment of the present invention, a risk warning model is established, the fitted water age entropy-water quality influencing factor (residual chlorine) relationship curve of each pipeline node is embedded into the established risk warning model, and a risk warning value is set in the risk warning model.

[0095] When it is predicted that the water quality influencing factor (residual chlorine) of a certain pipeline node at a certain time exceeds the risk warning value, the risk warning model issues a water quality risk warning.

[0096] According to an embodiment of the present invention, a system for predicting water quality of a water supply network based on water age entropy is provided, which is used to execute a method for predicting water quality of a water supply network based on water age entropy of the present invention.

[0097] According to an embodiment of the present invention, a computing storage medium is provided for storing computer-executable instructions for executing a method for predicting water quality of a water supply network based on water age entropy according to the present invention.

[0098] The present invention introduces the concept of water age entropy into the water quality evaluation of the pipe network, and predicts the residual chlorine value of the pipe network by using the water age entropy, which has guiding significance for the water quality management and safety assurance of the pipe network.

[0099] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for predicting water quality of a water supply network based on water age entropy, characterized in that: The method comprises the following steps: S1. Establishing a water supply network model based on the actual water supply network, wherein the water supply network model at least includes a topological structure, a pipe length, and pipe nodes corresponding to the actual water supply network; S2. Using the water supply network model to simulate the water supply of the actual water supply network, and counting the flow rate of each pipeline and the water age of each pipeline node at a certain sampling time interval; S3, calculating the water age entropy of each pipeline node at different sampling points according to the flow rate of each pipeline and the water age of each pipeline node counted by the water supply network model; S4. Detect the water quality influencing factors of each pipeline node of the actual water supply network at a certain sampling time interval, and obtain the water quality influencing factors of each pipeline node at different sampling points; S5, using the water age entropy of each pipeline node at different sampling points obtained in step S3 and the water quality influencing factor of each pipeline node at different sampling points obtained in step S4, calculate the correlation coefficient between the water age entropy and the water quality influencing factor of each pipeline node at different sampling points; S6, using the water age entropy of each pipeline node at different sampling points obtained in step S3 and the water quality influencing factors of each pipeline node at different sampling points obtained in step S4, fitting a water age entropy-water quality influencing factor relationship curve of each pipeline node; S7, using the water supply network model to simulate the water supply of the actual water supply network, and counting the flow of each pipeline at a certain time, the water age of each pipeline node, and calculating the water age entropy of each pipeline node at a certain time; The water quality influencing factors of each pipeline node at a certain time are predicted by using the fitted water age entropy-water quality influencing factor relationship curve of each pipeline node and the calculated water age entropy of each pipeline node at a certain time.

2. The method according to claim 1, characterized in that In step S3, the water age entropy of each pipeline node at different sampling points is calculated by the following method: Among them, S i represents the water age entropy of a sampling point pipeline node i; D i represents the upstream adjacent pipeline node set of pipeline node i; Q xi represents the flow rate from the upstream pipeline node x of a sampling point pipeline node i to pipeline node i, m 3 / h; T xi represents the water age of pipeline xi between a sampling point pipeline node i and pipeline node x; Q i represents the total flow rate of a sampling point into the pipeline node i, m 3 / h; T i represents the water age of a sampling point pipeline node i; S x Represents the water age entropy of a sampling point pipeline node x.

3. The method according to claim 1, characterized in that: In step S3, the method further includes introducing a maximum water age entropy index to perform reliability evaluation on the calculated water age entropy of each pipeline node at different sampling points.

4. The method according to claim 3, characterized in that The water age entropy of each pipeline node is used to evaluate the reliability by the following method: Among them, De i represents the reliability of the water age entropy of pipeline node i; S i represents the calculated water age entropy of a sampling point pipeline node i; S i，max Represents the maximum water age entropy index of pipeline node i.

5. The method according to claim 1, characterized in that: In step S3, the method further includes evaluating the reliability of the established water supply network model by using the reliability of the water age entropy of each pipeline node.

6. The method according to claim 5, characterized in that The established water supply network model is evaluated for reliability through the following methods: in, represents the reliability of the established water supply network model; I represents the number of pipeline nodes in the established water supply network model; De i represents the reliability of the water age entropy of pipeline node i; w i Represents the weight coefficient of pipeline node i.

7. The method according to claim 1, characterized in that In step S5, the correlation coefficient between the water age entropy and the water quality influencing factors of each pipeline node at different sampling points is calculated by the following method: Among them, Correl (S i , Y i ) represents the correlation coefficient between the water age entropy and water quality influencing factors of a sampling point pipeline node i; S i represents the water age entropy of a sampling point pipeline node i; Y i represents the water quality influencing factors of a sampling point pipeline node i; represents the mean water age entropy of pipeline node i at all sampling points; Represents the mean of water quality influencing factors at all sampling points and pipeline node i.

8. The method according to claim 1, characterized in that In step S7, a risk warning model is established, the fitted water age entropy-water quality influencing factor relationship curve of each pipeline node is embedded into the established risk warning model, and a risk warning value is set in the risk warning model; When it is predicted that the water quality influencing factors of a certain pipeline node at a certain time exceed the risk warning value, the risk warning model will issue a water quality risk warning.

9. A system for predicting water quality of a water supply network based on water age entropy, characterized in that: The system is used to perform the method according to any one of claims 1 to 8.

10. A computing storage medium, characterized in that: The computer storage medium is used to store computer-executable instructions, and the computer-executable instructions are used to execute the method of any one of claims 1 to 8.

Images