Water supply network transformation method, device, electronic equipment and computer storage medium

Abstract

This invention provides a method, apparatus, electronic device, and computer storage medium for water supply network renovation. The method includes: determining target evaluation indicators based on the contribution rate of evaluation indicators during the water supply network renovation process; determining the subjective weights corresponding to the target evaluation indicators based on fuzzy hierarchical analysis and cluster weight analysis; determining the objective weights corresponding to the target evaluation indicators based on membership degree decision analysis; constructing an optimization objective function based on the subjective and objective weights, using a genetic algorithm to find the optimal solution, and determining the optimal solution as the combined weights; determining the membership degree set matrix based on the membership degree values ​​of the target evaluation indicators in the water supply network; determining the water supply network renovation level based on the combined weights and the membership degree set parameter range of the target evaluation; monitoring and maintaining the water supply network according to the renovation level; and enabling subjective and objective analysis of the target evaluation indicators during the water supply network renovation process to evaluate the network renovation level and implement renovation and maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of municipal water supply pipeline reconstruction, and in particular to a water supply network reconstruction method, device, electronic equipment and computer storage medium. Background Art

[0002] Urban water supply network is an important part of urban infrastructure, and plays a pivotal role in ensuring people's lives and urban development. The water supply network should be able to safely and reliably transport tap water to various water points in the city through water transmission and distribution pipelines at all levels, and meet the requirements of water quantity, water pressure and water quality. The investment cost of the water supply network in the entire water supply system generally accounts for more than 50% of the total project cost, while occupying a large amount of energy consumption, and also directly affects the performance and reliability of the entire water supply system.

[0003] There are many problems in the operation of water supply pipe networks, such as pipe aging: as time goes by, the water supply pipe networks in many cities have aged due to their long service life, causing the pipe materials to become brittle and prone to rupture, affecting water supply safety, as well as leakage problems, substandard pipe quality, and pipe network pressure regulation problems, etc. These problems greatly threaten the safe operation of the pipelines and affect the daily lives of residents. Therefore, it is necessary to appropriately transform the unreasonable pipe sections in the water supply pipe network system to ensure that the operation of the urban water supply pipe network is safer and more reliable.

[0004] However, currently, the damaged points of the pipelines are basically discovered through daily inspections and complaints from residents, and then repaired or replaced. There is a lack of a water supply network renovation method based on renovation assessment based on basic data of the water supply network, which can proactively evaluate the water supply network and determine the renovation level to facilitate subsequent repair guidance. Summary of the invention

[0005] The purpose of the present invention is to provide a water supply network reconstruction method, device, electronic equipment and computer storage medium, which can perform subjective and objective analysis on the target evaluation indicators in the water supply network reconstruction process, evaluate the network reconstruction level, and implement reconstruction maintenance.

[0006] In a first aspect, the present invention provides a water supply network reconstruction method, comprising:

[0007] Determine the target evaluation index based on the contribution rate of the evaluation index in the process of water supply network reconstruction;

[0008] Determine the subjective weights corresponding to the target evaluation indicators based on fuzzy hierarchical analysis and cluster weight analysis;

[0009] Determine the objective weights corresponding to the target evaluation indicators based on membership decision analysis;

[0010] Construct an optimized objective function based on subjective weights and objective weights, use genetic algorithm to find the optimal solution, and determine the optimal solution as the combined weight;

[0011] Determine the membership set matrix based on the membership values ​​of the target evaluation indicators in the water supply network;

[0012] Determine the level of water supply pipeline reconstruction based on the value range of membership set parameters of combined weight and target evaluation;

[0013] The water supply network shall be monitored and maintained according to its renovation level.

[0014] In some preferred embodiments of the present invention, the step of determining the target evaluation index based on the contribution rate of the evaluation index in the process of water supply network reconstruction includes:

[0015] Determine the evaluation indicators in the water supply network reconstruction process as raw data;

[0016] Construct the original sample matrix of evaluation indicators based on the original data;

[0017] The original sample matrix of the evaluation index is standardized to obtain a standardized matrix;

[0018] Determine the correlation coefficient matrix based on the covariance and standard deviation of the elements within the standardized matrix;

[0019] Solve the characteristic equation of the correlation coefficient matrix to determine the eigenvalues ​​of the correlation coefficient matrix;

[0020] Determine the contribution rate of the evaluation index based on the eigenvalue;

[0021] Sort the contribution rates from large to small, and select a preset number of evaluation indicators as target evaluation indicators.

[0022] In some preferred embodiments of the present invention, the step of determining the subjective weight corresponding to the target evaluation index based on fuzzy hierarchical analysis and cluster weight analysis includes:

[0023] Determine multiple basic weight sets of target evaluation indicators based on fuzzy hierarchical analysis; wherein the steps of fuzzy hierarchical analysis include: scaling the target evaluation indicators in pairs to determine the element importance matrix; determining the importance ranking matrix through importance calculation based on the elements in the element importance matrix; normalizing the importance ranking matrix and summing the column vectors to obtain weight coefficients; normalizing the weight coefficients to obtain the basic weight set of the target evaluation indicators;

[0024] The overlap of the basic weight set is calculated, and the basic weights whose overlap meets the preset threshold are determined as the intermediate weight set;

[0025] The intermediate weight set is calculated for cluster analysis, the average value of the intermediate weight set with the most cluster categories is calculated, and the average value is determined as the subjective weight.

[0026] In some preferred embodiments of the present invention, the step of determining the objective weight corresponding to the target evaluation index based on the membership decision analysis includes:

[0027] Obtain the membership value of the target evaluation index;

[0028] Determine an original decision matrix corresponding to the water supply network based on a preset membership value scale range and membership value;

[0029] The original decision matrix is ​​normalized and standardized to obtain a normalized decision matrix;

[0030] Calculate the standard deviation of the normalized decision matrix to obtain the standardized decision matrix;

[0031] determining a deviation decision matrix based on elements in the normalized decision matrix;

[0032] Based on the product of the standardized decision matrix and the deviation decision matrix, a weight decision matrix is ​​determined, and the objective weight is determined based on the weight decision matrix.

[0033] In some preferred embodiments of the present invention, the steps of constructing an optimized objective function based on subjective weights and objective weights, using a genetic algorithm to find an optimal solution, and determining the optimal solution as a combined weight include:

[0034] Construct an objective function based on subjective and objective weights;

[0035] Construct the constraint function corresponding to the objective function;

[0036] Genetic algorithm is used to solve the objective function and determine the initial population;

[0037] Determine the parent population based on the fitness of individuals in the initial population;

[0038] Performing evolutionary processing on the parent population to determine the offspring population; wherein the evolutionary processing includes at least one of the following: crossover or inheritance;

[0039] Based on the elite retention strategy, individuals in the offspring population are selected and the evolution process is repeated until the maximum number of iterations is reached to obtain the combined weight.

[0040] In some preferred embodiments of the present invention, the step of determining the water supply pipeline reconstruction level based on the combined weight and the membership set parameter value range of the target evaluation includes:

[0041] Multiply the combined weight of the target evaluation index by the parameter value range of the membership set of the target evaluation index;

[0042] The water supply network transformation score is determined by accumulating the product of each target evaluation index;

[0043] The renovation score of the water supply network can be used to determine the renovation level of the water supply network.

[0044] In some preferred embodiments of the present invention, the transformation level of the water supply network includes at least one of the following: healthy, sub-healthy, fragile and very fragile;

[0045] The transformation measures for the water supply network include at least one of the following: normal monitoring of the water supply network, increasing the number of inspections and maintenance of the water supply network, maintaining the water supply network based on evaluation indicators and pre-set threshold conditions, or maintaining the water supply network based on main indicators and pre-set maintenance conditions; wherein the main indicators are determined based on the evaluation indicators;

[0046] The transformation level of the water supply network corresponds to the transformation measures for the water supply network one by one.

[0047] In a second aspect, the present invention provides a water supply network transformation device, comprising:

[0048] A target indicator determination module is used to determine the target evaluation indicator based on the contribution rate of the evaluation indicator in the process of water supply network transformation;

[0049] The subjective weight processing module is used to determine the subjective weight corresponding to the target evaluation index based on fuzzy hierarchical analysis and cluster weight analysis;

[0050] An objective weight processing module is used to determine the objective weight corresponding to the target evaluation index based on membership decision analysis;

[0051] The combined weight processing module is used to construct an optimized objective function based on subjective weights and objective weights, use a genetic algorithm to find the optimal solution, and determine the optimal solution as the combined weight;

[0052] A membership set matrix determination module, used to determine the membership set matrix based on the membership values ​​of the target evaluation indicators in the water supply network;

[0053] A water supply pipeline renovation grade determination module is used to determine the water supply pipeline renovation grade based on the combined weight and the membership set parameter value range of the target evaluation;

[0054] The water supply network reconstruction module is used to monitor and maintain the water supply network according to the reconstruction level of the water supply network.

[0055] In a third aspect, the present invention provides an electronic device including a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the water supply network reconstruction method of the first aspect.

[0056] In a fourth aspect, the present invention provides a computer storage medium storing computer executable instructions. When the computer executable instructions are called and executed by a processor, the computer executable instructions prompt the processor to implement the water supply network reconstruction method of the first aspect.

[0057] The present invention brings the following beneficial effects:

[0058] The present invention provides a water supply network reconstruction method, device, electronic equipment and computer storage medium, the method comprising: determining target evaluation indicators based on the contribution rate of evaluation indicators in the process of water supply network reconstruction; determining the subjective weight corresponding to the target evaluation indicators based on fuzzy hierarchical analysis and cluster weight analysis; determining the objective weight corresponding to the target evaluation indicators based on membership decision analysis; constructing an optimized objective function based on subjective weights and objective weights, using a genetic algorithm to find the optimal solution, and determining the optimal solution as a combined weight; determining a membership set matrix based on the membership value of the target evaluation indicator in the water supply network; determining the water supply pipeline reconstruction level based on the combined weight and the membership set parameter value range of the target evaluation; monitoring and maintaining the water supply network according to the reconstruction level of the water supply network; subjective and objective analysis can be performed on the target evaluation indicators in the process of water supply network reconstruction, the network reconstruction level can be evaluated, and reconstruction maintenance can be implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0060] Figure 1 A flow chart of a water supply network transformation method provided by an embodiment of the present invention;

[0061] Figure 2 A flowchart for determining a target evaluation index provided by an embodiment of the present invention;

[0062] Figure 3 A schematic diagram of a standardized matrix provided by an embodiment of the present invention;

[0063] Figure 4 A schematic diagram of a correlation coefficient matrix provided by an embodiment of the present invention;

[0064] Figure 5 A schematic diagram of an indicator parameter contribution rate provided by an embodiment of the present invention;

[0065] Figure 6A water supply network topology diagram provided by an embodiment of the present invention;

[0066] Figure 7 A schematic structural diagram of a water supply network transformation device provided by an embodiment of the present invention;

[0067] Figure 8 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0068] Icons: 310-target indicator determination module; 320-subjective weight processing module; 330-objective weight processing module; 340-combined weight processing module; 350-membership set matrix determination module; 360-water supply pipeline renovation level determination module; 370-water supply network renovation module; 400-memory; 401-processor; 402-bus; 403-communication interface. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0070] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0072] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, or the positions or positional relationships in which the product of the invention is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0073] In addition, the terms "horizontal", "vertical", "overhanging" and the like do not mean that the components are required to be absolutely horizontal or overhanging, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0074] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0075] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0076] Embodiment 1

[0077] The present invention provides a water supply network transformation method, see Figure 1 The embodiment of the present invention shown in the figure provides a flow chart of a water supply network transformation method, the method comprising:

[0078] Step S102, determining the target evaluation index based on the contribution rate of the evaluation index in the process of water supply network reconstruction.

[0079] Specifically, in the process of water supply network reconstruction, it is necessary to first determine which evaluation indicators are crucial to the success of the project based on historical data and expert opinions. These evaluation indicators may include pipeline material, pipe age, leakage frequency, water quality, energy consumption, etc. By analyzing the contribution rate of these indicators, the target evaluation indicators with the greatest impact on the reconstruction effect can be identified.

[0080] Step S104, determining the subjective weight corresponding to the target evaluation index based on fuzzy hierarchy analysis and cluster weight analysis.

[0081] Specifically, fuzzy hierarchical analysis is a multi-criteria decision-making method that combines quantitative and qualitative analysis and is suitable for dealing with complex decision-making problems. Cluster weight analysis can help decision makers group different evaluation indicators according to similarities, thereby determining the relative importance of each indicator. In this step, a subjective weight is assigned to each target evaluation indicator by integrating expert opinions and evaluation criteria.

[0082] Step S106, determining the objective weight corresponding to the target evaluation index based on the membership decision analysis.

[0083] Specifically, membership decision analysis is a method based on fuzzy set theory, which can deal with the uncertainty and imprecision of evaluation indicators. By calculating the degree to which each target evaluation indicator belongs to different evaluation levels, the corresponding objective weight can be obtained. This step helps to objectively evaluate the importance of each indicator based on the indicator itself.

[0084] Step S108, constructing an optimized objective function based on the subjective weight and the objective weight, using a genetic algorithm to find the optimal solution, and determining the optimal solution as the combined weight.

[0085] Specifically, the subjective weights and objective weights are combined to construct an optimization objective function, which aims to balance the two weights to obtain a more accurate comprehensive weight. Genetic algorithms, a search algorithm that simulates natural selection and genetic mechanisms, are used to find the optimal solution. Genetic algorithms can efficiently find approximate optimal solutions in complex search spaces, thereby determining the final combined weights.

[0086] Step S110, determining a membership set matrix based on the membership values ​​of the target evaluation indicators in the water supply network.

[0087] Specifically, according to the membership values ​​of each target evaluation index in the water supply network, a membership set matrix is ​​constructed. This matrix will contain the membership information of each evaluation index corresponding to different evaluation levels, providing a basis for subsequent decision-making.

[0088] Step S112, determining the water supply pipeline renovation level based on the combined weight and the membership set parameter value range of the target evaluation.

[0089] Specifically, the combined weight obtained in step S108 and the membership set parameter value range in step S110 are used to comprehensively evaluate the transformation level of the water supply pipeline. This involves weighting all evaluation indicators according to their importance to obtain an overall evaluation result, based on which the priority and strategy of the transformation are determined.

[0090] Step S114, monitoring and maintaining the water supply network according to the modification level of the water supply network.

[0091] Specifically, according to the determined water supply network transformation level, the corresponding monitoring and maintenance plan will be implemented. For areas or parts with higher ratings, immediate action may be required to prevent potential failures and risks; while for areas with lower ratings, gradual improvements can be made based on resource conditions and planned arrangements.

[0092] The present invention provides a water supply network transformation method, which includes: determining a target evaluation index based on the contribution rate of the evaluation index in the process of water supply network transformation; determining the subjective weight corresponding to the target evaluation index based on fuzzy hierarchical analysis and cluster weight analysis; determining the objective weight corresponding to the target evaluation index based on membership decision analysis; constructing an optimized objective function based on subjective weight and objective weight, using genetic algorithm to find the optimal solution, and determining the optimal solution as a combined weight; determining a membership set matrix based on the membership value of the target evaluation index in the water supply network; determining the transformation level of the water supply pipeline based on the combined weight and the membership set parameter value range of the target evaluation; monitoring and repairing the water supply network according to the transformation level of the water supply network; subjective and objective analysis of the target evaluation index in the process of water supply network transformation, evaluating the transformation level of the network, and implementing transformation and maintenance. This not only helps to improve the operating efficiency and service quality of the water supply system, but also ensures the sustainable use of water resources and the healthy development of the urban environment.

[0093] Embodiment 2

[0094] The embodiment of the present invention provides another water supply network transformation method. Based on the above embodiment, in some preferred embodiments of the present invention, see Figure 2 The embodiment of the present invention shown in the figure provides a flowchart for determining a target evaluation index, and the steps of determining the target evaluation index based on the contribution rate of the evaluation index in the process of water supply network transformation include:

[0095] Step S202, determining the evaluation index in the process of water supply network reconstruction as original data.

[0096] Specifically, the collected original evaluation indicators include various performance parameters of the water supply network, such as flow rate, pressure, leakage rate, maintenance times, etc.

[0097] In some preferred embodiments of the present invention, firstly, the evaluation indicators related to the transformation of the water supply network are collected, and the evaluation indicators include four primary indicators and twenty secondary indicators; the four primary indicators in the evaluation indicators include indicators of environmental facilities around the water supply network, operating indicators of the water supply network, the number of pipe bursts in the water supply network, and basic attribute indicators of the water supply network. Among them, the indicators of environmental facilities around the water supply network include: road grade, soil corrosion degree, pH value, redox potential, pipeline load, and pipeline identification; the operating indicators of the water supply network include: network operating pressure, network terrain elevation, free water head, pipeline pressure load, flow rate, and pump station outlet pressure; the indicators of pipe bursts in the water supply network include: pipe diameter, pipe location, and number of pipe bursts; the basic attribute indicators of the water supply network include: pipe material, pipe age, pipe length, soil cover thickness, and pipe diameter.

[0098] Step S204: construct an original sample matrix of evaluation indicators based on the original data.

[0099] Specifically, these indicators are combined into a multidimensional data set, where each row represents an observation sample (such as a pipe network section or a measurement value at a time point), and each column represents an evaluation indicator. This data set is organized into an original sample matrix to provide a basis for subsequent data processing.

[0100] The statistical indicators involved in the pipeline renovation process of the existing water supply network system over the years are collected and sorted, and all indicators are statistically summarized and sorted. The original sample evaluation indicators are used to establish the evaluation indicator original sample matrix K, as shown in formula (1):

[0101] (1)

[0102] in, is the value of the pth indicator of the nth sample.

[0103] Step S206, normalizing the original sample matrix of the evaluation index to obtain a standardized matrix.

[0104] Specifically, since different evaluation indicators may have different dimensions and numerical ranges, in order to eliminate the impact of these differences on the analysis results, the original sample matrix needs to be standardized. The purpose of standardization is to convert the data of each indicator into a dimensionless form. The processed indicators constitute a standardized matrix, which is convenient for subsequent statistical analysis.

[0105] In some preferred embodiments of the present invention, the original sample matrix K of the evaluation index is centered and standardized based on formulas (2) and (3) to obtain the standardized matrix J, as shown in formula (4).

[0106] (2)

[0107] (3)

[0108] J (4)

[0109] in, is the average value of a certain evaluation index, is the variance of a certain evaluation index, j 11 、j 12 …j np Represents the elements of the normalized matrix J.

[0110] For example, see Figure 3A schematic diagram of a standardized matrix provided by an embodiment of the present invention is shown, in which 17 pipe sections are selected from multiple pipe sections, 20 indicators are obtained to construct an original sample matrix, and the standardized matrix is ​​obtained by processing, and the 20 indicators are: pipe age, number of pipe bursts, pipeline load, free water head, cover thickness, road grade, soil corrosion degree, pH value, redox potential, pipe material, pipeline identification, pipe network operating pressure, pipe network terrain elevation, pipe network pressure load, flow rate, pump station outlet pressure, pipe diameter of pipe burst, pipe burst position, pipe length and pipe diameter.

[0111] Step S208: determining a correlation coefficient matrix based on the covariance and standard deviation of the elements in the standardized matrix.

[0112] Specifically, the correlation coefficient matrix reflects the linear correlation between different evaluation indicators. The correlation coefficient matrix can be obtained by calculating the covariance and standard deviation between the elements in the standardized matrix. The value range of the correlation coefficient is between -1 and 1. A value close to 1 indicates a positive correlation between the two indicators, a value close to -1 indicates a negative correlation, and a value close to 0 indicates no obvious linear relationship.

[0113] In some preferred embodiments of the present invention, the correlation coefficient matrix of the standardized matrix J is calculated based on formula (5): , and get the correlation coefficient matrix R, Represents each element in the matrix R, based on Figure 3 The correlation coefficient matrix determined by the standardized matrix shown is as follows Figure 4 The figure shows a schematic diagram of a correlation coefficient matrix provided by an embodiment of the present invention.

[0114] (5)

[0115] in, For indicators and The covariance of For indicators The standard deviation of For indicators The standard deviation of .

[0116] Step S210, solving the characteristic equation of the correlation coefficient matrix to determine the eigenvalues ​​of the correlation coefficient matrix.

[0117] Specifically, eigenvalue analysis is a commonly used multivariate statistical method to identify key patterns and structures in data. By solving the characteristic equation of the correlation coefficient matrix, a series of eigenvalues ​​can be obtained. These eigenvalues ​​represent the contribution of each pattern to the data variation.

[0118] In some preferred embodiments of the present invention, based on the correlation coefficient matrix The characteristic equation of , get the eigenvalue of p of matrix R , … , , respectively 3.639, 2.802, 2.332, 1.856, 1.686, 1.438, 1.089, 1.007, 0.931, 0.764, 0.715, 0.475, 0.354, 0.302, 0.197, 0.12, 0.111, 0.086, 0.051, and 0.045.

[0119] Step S212: determining the contribution rate of the evaluation index based on the characteristic value.

[0120] Specifically, the size of the eigenvalue can be used to determine the importance of the corresponding pattern. The contribution rate is an indicator that measures the proportion of the eigenvalue in the total variation. Usually, we calculate the contribution rate of each eigenvalue and use it as a measure of the importance of the pattern corresponding to the eigenvalue in the entire data set.

[0121] In some preferred embodiments of the present invention, the variance contribution rate and cumulative variance contribution rate of the principal component are calculated based on the above-mentioned eigenvalues ​​based on formulas (6) and (7), and the calculation results of the 20 index parameters are as follows: Figure 5 A schematic diagram of an indicator parameter contribution rate provided by an embodiment of the present invention is shown.

[0122] (6)

[0123] (7)

[0124] Wherein, α is the variance contribution rate, γ is the cumulative variance contribution rate, and the cumulative variance contribution rate is generally required to be ≥ 0.9 to 0.95. Wherein, p is the pth index, and in this embodiment, there are 20 cumulative indexes, which means that cumulative i=1, 2, 3…, 20; is the characteristic value of the i-th indicator; m represents the cumulative m-th indicator, 1≤m≤p.

[0125] Step S214, sorting the contribution rates from large to small, and selecting a preset number of evaluation indicators as target evaluation indicators.

[0126] Specifically, the evaluation indicators are ranked according to their contribution rates, and the indicators with the highest contribution rates are selected as target evaluation indicators. These target evaluation indicators will serve as the basis for subsequent evaluation of the health status of the water supply network and formulation of transformation plans.

[0127] In some preferred embodiments of the present invention, the contribution rate can be obtained according to the calculation results. For the top five contribution rates, the pipe age is 18.196%, the number of pipe bursts is 14.011%, the pipe load is 11.658%, the free water head is 9.278%, and the soil cover thickness is 8.432%. Therefore, it can be obtained that the pipe age, the number of pipe bursts, the pipe load, the free water head, and the soil cover thickness are more important indicator parameters in the original data.

[0128] Through the above steps, the key indicators that have the greatest impact on the health status of the water supply network can be screened out, providing a scientific basis for subsequent weight allocation and comprehensive evaluation. This method can not only reduce the complexity of data processing, but also ensure that key information is not ignored, thereby improving the accuracy and efficiency of water supply network transformation evaluation.

[0129] Further, in some preferred embodiments of the present invention, in the water supply network assessment and transformation method, the improved cluster weight analysis method (Fuzzy Analytic Hierarchy Process, FAHP) is a method for determining the subjective weight corresponding to the target evaluation index. FAHP is an extension of the traditional analytic hierarchy process (AHP), which takes into account the fuzziness and subjectivity of human judgment. The steps of determining the subjective weight corresponding to the target evaluation index based on fuzzy hierarchy analysis and cluster weight analysis include the following steps A1 to A4:

[0130] Step A1, determining multiple basic weight sets of target evaluation indicators based on fuzzy hierarchical analysis; wherein the steps of fuzzy hierarchical analysis include: scaling the target evaluation indicators in pairs to determine the element importance matrix; determining the importance ranking matrix through importance calculation based on the elements in the element importance matrix; normalizing the importance ranking matrix and summing the column vectors to obtain weight coefficients; normalizing the weight coefficients to obtain the basic weight set of the target evaluation indicators.

[0131] Specifically, experts or decision makers need to compare each target evaluation indicator in pairs to determine their relative importance. This comparison is usually based on a scale, such as the 1-9 scale, where 1 means that two elements are equally important and 9 means that one element is extremely important. These comparison results form an element importance matrix, and the element a of the matrix is ij Indicates the importance of indicator i relative to indicator j.

[0132] In some preferred embodiments of the present invention, the indicator element importance matrix YZ is first established, such as formula (8).

[0133] (8)

[0134] (9)

[0135] in, is the relative importance of the i-th indicator element compared to the j-th indicator element. If i is more important than j, then =2, and the same importance is = 1, i is less important than j, then =0.

[0136] For example, based on pipe age, number of pipe bursts, pipe load, free water head, and cover thickness, an element importance matrix as shown in the following formula (10) can be determined.

[0137] (10)

[0138] Since direct comparison may have consistency issues, it is necessary to convert the indicator element importance matrix into an importance ranking matrix. The elements of the importance ranking matrix are obtained by the geometric mean of the corresponding elements in the element importance matrix or other methods, which can ensure the consistency of the importance ranking matrix. The elements of the importance ranking matrix represent the adjusted relative importance.

[0139] In some preferred embodiments of the present invention, the importance ranking index Ri is calculated based on formula (11), and further, the importance ranking matrix PX is determined based on formula (12).

[0140] (11)

[0141] (12)

[0142] in, , , , is the sum of the elements in the i-th row of the indicator element importance matrix YS, It is the sum of the elements in the j-th row of the indicator element importance matrix YZ.

[0143] For example, based on the element importance matrix shown in formula (10), the corresponding importance index is Ri ={8, 5, 8, 2, 2}, the importance ranking matrix is ​​as follows (13).

[0144] (13)

[0145] To obtain the weight coefficients, the importance ranking matrix needs to be normalized. This usually involves dividing each element by the sum of its column, resulting in a normalized matrix. The weight coefficient for each indicator is then obtained by summing each row of the normalized matrix.

[0146] In some preferred embodiments of the present invention, the importance ranking matrix is ​​column-vector normalized and then the sum of each column is calculated based on formula (14).

[0147] (14)

[0148] in, is the weight coefficient, are the elements in the normalized matrix.

[0149] Exemplarily, column vector normalization is performed on equation (13) to obtain a normalized matrix, as shown in equation (15).

[0150] (15)

[0151] To ensure that all weight coefficients add up to 1, the weight coefficients need to be normalized. This involves dividing each weight coefficient by the sum of all weight coefficients to obtain the final subjective weight. These weights reflect the relative importance of each target evaluation indicator in the mind of the decision maker.

[0152] In some preferred embodiments of the present invention, the weight coefficient is normalized based on formula (16) to obtain the basic weight.

[0153] (16)

[0154] For example, the pipeline load (C1), free water head (C2), number of pipe bursts (C3), soil cover thickness (C4) and pipe age (C5) are substituted into the calculation to finally obtain a set of basic weight sets of the importance ranking matrix: .

[0155] Step A2, calculating the overlap of the basic weight sets, and determining the basic weights whose overlap meets a preset threshold as the intermediate weight set.

[0156] Step A3, calculating the intermediate weight set to perform cluster analysis, calculating the average value of the intermediate weight set with the most cluster categories, and determining the average value as the subjective weight.

[0157] Specifically, a total of n groups of subjective weight vector data are collected through the above method, which are called evaluation index samples. In this implementation case, no less than 100 groups of data can be selected;

[0158] The first group is denoted as A1={a 11 、a 12 、a 13 、a 14 、a 15};

[0159] The second group is denoted as A2={a21 、a 22 、a 23 、a 24 、a 25};

[0160] The third group is denoted as A3={a 31 、a 32 、a 33 、a 34 、a 35};

[0161] …

[0162] The nth group is denoted as An={a n1 、a n2 、a n3 、a n4 、a n5};

[0163] First, the first item database of each group of data in the evaluation index sample, that is, { a 11 、a 21 、a 31 … a n1}Calculate the overlap degree P, the calculation formula is as follows:

[0164] Assume that the elements in {a11, a21, a31… an1} constitute the interval domain of the largest interval and that it is K. Then the domain of the largest intersection interval in the interval is set to J (That is, the number of weight values ​​contained in this interval is the largest), then P=K / J .

[0165] For example, if the first database is {0.12, 0.28, 0.25, 0.15, 0.53, 0.32, 0.45, 0.35, 0.27, 0.50, 0.42, 0.33, 0.44, 0.19, 0.22, 0.49, 0.24, 0.51}, we can get the maximum interval definition domain K=0.53-0.12=0.41; the coverage domain of the largest intersection interval in the interval is J =0.51-0.15=0.36, then p=0.36 / 0.41=87.8%, and the degree of overlap is less than 95%.

[0166] When the calculated coincidence degree is greater than 95%, the weight center position is calculated. The calculation steps of the weight center position are as follows:

[0167] First, remove the maximum and minimum values ​​in the first database, and average the remaining databases. After obtaining the average value, subtract the data in the first database from the average value, take the positive value of the difference and sort, and remove the data with the maximum value. For example, the first database is { 0.12, 0.28, 0.25, 0.15, 0.53, 0.32, 0.45, 0.35, 0.27, 0.50, 0.42, 0.33, 0.44, 0.19, 0.22, 0.49, 0.24, 0.51}, the average value is 0.338, the positive values ​​of the differences between other values ​​and the average value are calculated and sorted, such as the difference between 0.12 and the average value is =|0.12-0.338|=0.218, the difference between 0.28 and the average value of 0.338 is =|0.28-0.338|=0.058, and so on. It can be obtained that the difference between 0.12 and the average value of 0.338 is the largest, so it is deleted.

[0168] Recalculate the overlap and repeat the above operation until the overlap is ≥ 95%; redefine the new first database as J1=[j 11 、j 12 、j 13 …j 1m ], (m<n), which is the intermediate weight set;

[0169] Similarly, the overlap degree of the second database in the n groups of data is calculated, and the new second database with overlap degree ≥ 95% is redefined as J2=[j 21 、j 22 、j 23 …j 2m ]; the new third database is redefined as J3=[j 31 、j 32 、j 33 …j 3m ]; the new fourth database is redefined as J4=[j 41 、j 42 、j 43 …j 4m ]; the new fifth database is redefined as J5=[j 51 、j 52 、j 53 …j 5m ];

[0170] The first, second, third, fourth and fifth intermediate weight sets are clustered and analyzed respectively, and the group with the most cluster categories is selected. The average value of the data belonging to this group is calculated, and the average value obtained is the weight value of the corresponding target evaluation indicator.

[0171] Finally, the subjective weight value ZG of the target evaluation index can be obtained. .

[0172] Through the above steps, we can get the subjective weight of each target evaluation indicator. These weights will be combined with the objective weights determined later to form a combined weight for comprehensive evaluation of the health status of the water supply network. Although this process involves some subjective judgments, the application of the FAHP method can ensure that these subjective judgments are logically consistent and can reflect the decision makers' true views on the importance of different indicators.

[0173] Furthermore, the membership decision matrix algorithm is a method for determining the objective weights corresponding to the target evaluation indicators. The membership decision matrix algorithm is a mathematical tool that can be used to process large and complex data. It can discover patterns and internal logical relationships in the data without any additional information, using only the data itself. It is not subject to human will and is scientific and rigorous. In some preferred embodiments of the present invention, the step of determining the objective weights corresponding to the target evaluation indicators based on the membership decision analysis includes the following steps B1 to B6:

[0174] Step B1, obtaining the membership value of the target evaluation index.

[0175] Specifically, it is necessary to collect data related to the target evaluation indicators, which reflect the performance of the water supply network on different indicators. For example, these data may include the leakage rate, maintenance frequency, water quality data, etc. These data constitute the membership value of the evaluation indicators, which are the basis for subsequent analysis.

[0176] In some preferred embodiments of the present invention, the parameters with the greatest impact on the water supply pipeline reconstruction process are firstly clarified to include pipeline load (C1), free water head (C2), number of pipe bursts (C3), soil cover thickness (C4) and pipe age (C5), which are used as conditional attributes C, C=(C1, C2, C3, C4, C5). The membership values ​​of pipeline load (C1), free water head (C2), number of pipe bursts (C3), soil cover thickness (C4) and pipe age (C5) of each section of the pipeline are obtained.

[0177] Step B2, determining the original decision matrix corresponding to the water supply network based on the preset membership value scale range and membership value, as shown in Table 1 for details.

[0178] Table 1 Original decision matrix

[0179]

[0180] Specifically, in the membership decision matrix algorithm, the decision table is a key concept. It is a two-dimensional table that contains conditional attributes (i.e., evaluation indicators) and value range attributes (i.e., evaluation results). According to the preset membership value scale range, the actual membership value data is converted into discrete values ​​to construct a decision table for the water supply network. This decision table will be used for subsequent attribute dependency analysis.

[0181] In some preferred embodiments of the present invention, the decision attribute D=(d) refers to the number of pipeline renovation and maintenance times, and the decision attribute value range V=(1, 2, 3, 4, 5, 6, 7, 8, 9), wherein each attribute value ranges from 1 to 9 and represents the degree of water supply pipeline from fragile to healthy, such as 1 is the most fragile, 9 is the healthiest, and so on.

[0182] For example, Tables 2 to 6 show the membership of pipeline load (C1), free water head (C2), number of pipe bursts (C3), soil cover thickness (C4) and pipe age (C5).

[0183] Table 2 Membership parameter table of pipeline load

[0184]

[0185] Table 3 Membership parameter table of free head

[0186]

[0187] Table 4 Membership parameter table of pipe burst times

[0188]

[0189] Table 5 Membership parameter table of cover soil thickness

[0190]

[0191] Table 6 Membership parameter table of pipe age

[0192]

[0193] In this embodiment, the original decision matrix shown below can be obtained:

[0194] ;

[0195] Step B3, normalize the original decision matrix to obtain a normalized decision matrix.

[0196] Specifically, the constructed matrix table of target evaluation index load, free water head, number of pipe bursts, cover thickness and pipe age is normalized to obtain a normalized decision matrix A. The normalized standard calculation formulas (17) and (18) are shown. If the target evaluation index is negatively correlated, formula (17) is used for normalization and standardization. If the target evaluation index is positively correlated, formula (18) is used for normalization and standardization.

[0197] (17)

[0198] (18)

[0199] It represents the value range of each target evaluation index in the decision matrix table. Represents the maximum value domain of the column vector within the same target evaluation index, Represents the minimum value range of the column vector within the same target evaluation index, Represents the elements in the normalized decision matrix A.

[0200] This implementation case can obtain the following normalized decision matrix A:

[0201] ;

[0202] Step B4, calculate the standard deviation of the normalized decision matrix to obtain the standardized decision matrix BZ.

[0203] The calculation formulas for each element in the standardized decision matrix are shown in (19) and (20):

[0204] (19)

[0205] (20)

[0206] in, represents the standard deviation of each target evaluation indicator, The average value of each target evaluation index, Represents each element in the standardized decision matrix.

[0207] The standardized decision matrix that can be obtained from this implementation case is as follows:

[0208] ;

[0209] Step B5, determining the deviation decision matrix based on the elements in the normalized decision matrix.

[0210] Specifically, calculate the importance evaluation of the overall target evaluation index, represented by ZY, and the calculation formula is shown in (21). Eliminate any one of the existing target evaluation indicators and evaluate the importance of the target evaluation indicators of the remaining items, represented by SY, and the calculation formula is shown in (22). The importance evaluation value of the overall target evaluation index is subtracted from the importance evaluation of the target evaluation indicators of the remaining items, and the matrix composed of their absolute values ​​is defined as the deviation decision matrix PC. The calculation formula is shown in (23), which is as follows:

[0211] (twenty one)

[0212] The ZY matrix can be obtained, .

[0213] (twenty two)

[0214] The SY matrix can be obtained as follows:

[0215] ;

[0216] (twenty three)

[0217] Taking the element in the first row and first column of the PC matrix as an example, we can calculate: |0.920-0.863|=0.057. By analogy, we can get the PC matrix:

[0218] ;

[0219] Step B6, determining a weight decision matrix based on the product of the standardized decision matrix and the deviation decision matrix, and determining the objective weight based on the weight decision matrix.

[0220] Specifically, by multiplying the standardized decision matrix with the deviation decision matrix, we can get the weight decision matrix QZ. The objective weight KG can be obtained by dividing each element of the weight decision matrix by the sum of each element. The calculation formula is shown in (24) and (25):

[0221] (twenty four)

[0222] Taking the element in the first row and first column of QZ as an example, the calculation result is: 1.105×0.057+0.564×0.092+1.105×0.057+0.338×0.137+1.240×0.193+2.142×0.650=0.778.

[0223] By analogy, we can get KG=[0.778 0.617 0.439 0.772 0.477].

[0224] The sum of the above matrix elements is equal to 0.778+0.617+0.439+0.772+0.477=3.083.

[0225] (25)

[0226] It can be obtained that the weight of load (C1) = 0.778 / 3.083 = 0.252; the weight of free head (C2) = 0.617 / 3.083 = 0.200; the weight of number of pipe bursts (C3) = 0.439 / 3.083 = 0.142; the weight of cover thickness (C4) = 0.772 / 3.083 = 0.250; the weight of pipe age (C5) = 0.477 / 3.083 = 0.155.

[0227] That is, the objective weight value of the target evaluation index:

[0228] ;

[0229] Through the above steps, we can use the membership decision matrix algorithm to determine the objective weight of the target evaluation index. The advantage of this method is that it does not require any prior knowledge or additional information, and there is no human subjective judgment factor that causes errors in the results. Instead, this method relies entirely on the inherent logical relationship between the indicators and the objective results obtained by the underlying laws. This makes the determination process of the objective weight have a strong logical deduction process, with high transparency and credibility. At the same time, because the membership decision matrix algorithm has advantages in dealing with large and complex systems, by constructing the original matrix, normalized matrix, deviation decision matrix and weight matrix, the data in extremely complex systems such as water supply networks are processed and the logical relationship and internal correlation behind the data are mined to obtain objective and fair weight assignments. Compared with the currently commonly used objective weight methods, such as factor analysis, principal component analysis, entropy weight method and Delphi method, the function framework constructed by this method is simple and more logical. The calculation process is completely based on the data itself, without the interference of subjective human factors. The calculation process is objective and fair, and the results are basically consistent with the actual results.

[0230] Furthermore, in some preferred embodiments of the present invention, the steps of constructing an optimized objective function based on subjective weights and objective weights, using a genetic algorithm to find an optimal solution, and determining the optimal solution as a combined weight include:

[0231] Construct an objective function based on subjective weights and objective weights; construct a constraint function corresponding to the objective function; use a genetic algorithm to solve the objective function and determine the initial population; determine the parent population based on the fitness of individuals in the initial population; perform evolutionary processing on the parent population to determine the offspring population; wherein the evolutionary processing includes at least one of the following: crossover or inheritance; screen individuals in the offspring population based on an elite retention strategy, and repeat the evolutionary processing until the maximum number of iterations is reached to obtain the combined weight.

[0232] Specifically, construct the objective function F, as shown in formula (26):

[0233] (26)

[0234] (27)

[0235] in, is the weight and The measure of similarity between them, n represents the number of evaluation indicators, The empirical factor of the i-th evaluation index is a value between 0 and 1. When it is close to 1, it means that it relies more on subjective weights; when it is close to 0, it means that it relies more on objective weights. represents the combined weight of the i-th evaluation index, represents the subjective weight of the i-th evaluation index, represents the objective weight of the i-th evaluation index, and For all The maximum and minimum values ​​in are used to standardize the similarity measure to ensure that the similarity value is between 0 and 1. λ represents the regularization coefficient, which is used to control the smoothness of the solution or prevent overfitting.

[0236] The constraint function for constructing the objective function is: (1) Normalization of weights: ; (2) Non-negativity of weights: ≥0; (3) Limit: 0≤ ≤1;

[0237] The traditional genetic algorithm is used to solve the objective function. First, each individual (i.e., a set of possible solutions) is encoded as a chromosome. In this embodiment, each chromosome will be an array of length 5, representing the combined weight of 5 evaluation indicators; the initial population is randomly generated, and each individual is a set of The solution satisfies the normalization constraint.

[0238] Construct a fitness function, which calculates the fitness of each individual, and the fitness is inversely proportional to the objective function value; use the roulette wheel selection method to select individuals according to their fitness.

[0239] Crossover and mutation: use the single-point crossover method to generate offspring between parent individuals, and make small random perturbations to the weights of individuals with a certain probability; elite retention strategy: retain the best individuals in each generation. If the maximum number of iterations is reached or the fitness is no longer significantly improved, it will terminate.

[0240] For example, in this embodiment, given known conditions, the number of evaluation indicators ;

[0241] Subjective weight ;

[0242] Objective weight ;

[0243] Initial experience factor ;

[0244] The combined weight can be obtained .

[0245] Through the above steps, the present invention constructs an optimization decision-making target model and adopts a genetic algorithm to achieve the optimal solution of the combined weights. Different from the currently commonly used method of achieving weight combination by setting a proportional factor for subjective weights and objective weights, the present method is based on constructing an objective function to achieve a clever fusion of subjective and objective weights to obtain the final combined weight value.

[0246] This method relies entirely on the objective results obtained by the inherent logical relationship and potential laws between the data. This makes the process of determining the objective weight have a strong logical deduction process, with high transparency and credibility. At the same time, due to its advantages in dealing with large and complex systems, the function framework constructed by this method is simple and more logical. The calculation process is completely based on the data itself, and the processing results are not subject to human will. The calculation process is scientific and rigorous, and the calculation results are objective and fair, providing scientific guidance for guiding pipeline risk assessment.

[0247] Further, in some preferred embodiments of the present invention, the step of determining the water supply pipeline reconstruction level based on the combined weight and the membership set parameter value range of the target evaluation includes the following steps D1 to D3:

[0248] Step D1, multiplying the combined weight of the target evaluation index by the membership set parameter value range of the target evaluation index.

[0249] Step D2, the cumulative sum of the products of the evaluation indicators of each target is used to determine the water supply network reconstruction score.

[0250] Step D3, the transformation score of the water supply network can be used to determine the transformation level of the water supply network.

[0251] Specifically, we have obtained the subjective and objective weights of the target evaluation indicators through the improved cluster weight analysis method (FAHP) and the membership decision matrix algorithm. These weights are combined into a table, namely the combined weight table. At the same time, based on the actual measurement or statistical data of the water supply network, we have constructed a membership set matrix, which reflects the membership of each evaluation indicator under different degrees of transformation. The product of the combined weight table and the membership set matrix, that is, multiplying the weight of each evaluation indicator by its corresponding membership, is used as the evaluation attribute, which will be used for the subsequent transformation index calculation.

[0252] Furthermore, the membership distribution function is established for the evaluation indicators of load, free water head, number of pipe bursts, soil cover thickness and pipe age. The distribution of evaluation indicators is shown in Table 7, where X1~X4 represent the limit values ​​of four levels. The calculation formulas are as follows (28) to (35):

[0253] Table 7 Evaluation index level distribution

[0254]

[0255] Membership function of load index It can be expressed as formula (28):

[0256] (28)

[0257] Among them, C i Represents the measured value of the evaluation index; i represents the i-th pipeline.

[0258] Membership function of free head index It can be expressed as formula (29):

[0259] (29)

[0260] Among them, C i Represents the measured value of the evaluation index; i represents the i-th pipeline.

[0261] Membership function of pipe burst frequency index It can be expressed as formula (30):

[0262] (30)

[0263] Among them, C i Represents the measured value of the evaluation index; i represents the i-th pipeline.

[0264] Membership function of soil cover thickness index It can be expressed as formula (31):

[0265] (31)

[0266] Among them, C i Represents the measured value of the evaluation index; i represents the i-th pipeline.

[0267] Membership function of pipe age index It can be expressed as formula (32):

[0268] (32)

[0269] Where Ci represents the measured value of the evaluation index; i represents the i-th pipeline.

[0270] According to the comprehensive statistical analysis of the data of each pipeline section in the pipeline network system to be evaluated, the classification is carried out according to the above-mentioned membership function analysis, and the membership distribution matrix of each pipeline section is formed as follows: .

[0271] For example, the load on a section of pipeline is 18KN / m 2 , the free head is 16 meters, the number of pipe bursts is 1, the cover thickness is 1m, and the pipe age is 12 years, then the above calculation formula can be used to obtain:

[0272] Load, L 11 =0.72; free head, L 12 =0.224; Number of pipe bursts, L 13 =1; soil cover thickness, L 14 =0.45; pipe age, L 15 =0.37; then the membership distribution matrix of the pipeline in this section is .

[0273] For further information, see Figure 6 The illustrated embodiment of the present invention provides a water supply network topology diagram, in which each two points represent a section of pipe, and N sections of pipes in the diagram constitute the entire pipe network system.

[0274] For the N pipes in the entire pipe network system, the membership distribution matrix cluster is represented by Lr, as shown in the following formula (33):

[0275] (33)

[0276] By performing matrix synthesis calculation on the evaluation index weight set Q = [0.308, 0.164, 0.291, 0.124, 0.113] and the membership distribution matrix set group Lr, the pipeline transformation influencing factor analysis and evaluation model Z is constructed as shown in formula (34).

[0277] (34)

[0278] Exemplary:

[0279] = .

[0280] Once we have the assessment attributes, we need to weight them and average them to calculate a comprehensive renovation index. This process requires us to consider different renovation degree assignments, which are pre-set based on the urgency and necessity of the renovation. For example, a severely aged network would have a higher renovation degree assignment, while a network in fair condition might have a lower renovation degree assignment. We obtain the renovation index by multiplying the assessment attributes with the corresponding renovation degree assignment, then summing all the results and dividing by the total renovation degree assignment. This index reflects the overall health of the water supply network and the urgency of renovation.

[0281] In some preferred embodiments of the present invention, according to the transformation index analysis of the transformation scheme evaluation index, the transformation degree is assigned 1, 2, 3, 4, and 5 respectively, and the evaluation model Z is weighted averaged to obtain the transformation index F. The calculation formula (35) is as follows:

[0282] (35)

[0283] Determine the pipeline health level based on the transformation degree assignment table in Table 8.

[0284] Table 8 Transformation degree assignment table

[0285]

[0286] For example, . Through judgment, the pipe network system is in a fragile state, the water supply is at risk, and timely management and maintenance are needed.

[0287] Further statistical analysis of each section of the water supply network system was carried out to divide the different levels into categories; the final results continue to refer to Figure 6 As shown, the pipes in area two indicate health; the pipes in area three indicate sub-health; the pipes in area one indicate fragility; and the pipes in area four indicate fragility.

[0288] Furthermore, in some preferred embodiments of the present invention, the transformation level of the water supply network includes at least one of the following: healthy, sub-healthy, fragile and very fragile; the transformation measures for the water supply network include at least one of the following: normal monitoring of the water supply network, increasing the number of inspections and maintenance of the water supply network, maintaining the water supply network based on evaluation indicators and pre-set threshold conditions, or maintaining the water supply network based on main indicators and pre-set maintenance conditions; wherein the main indicators are determined based on the evaluation indicators; the transformation level of the water supply network and the transformation measures for the water supply network correspond one to one.

[0289] For example, corresponding measures should be taken for four levels of pipelines:

[0290] The first level is a healthy network, where no engineering measures are required and the water supply network is monitored normally.

[0291] The second level is the sub-healthy pipe network, which needs to be inspected and maintained more frequently. The inspection and maintenance contents are as follows:

[0292] 1) Monitor the flow of the older pipes in the area, record the flow with the flow meters installed at the inlet and outlet ends of the pipe section, and calculate whether there is a flow difference. If there is a flow difference and the flow difference is ≥10L / S, it means that the pipe section has leakage and needs to be repaired.

[0293] 2) For pipes with a small thickness of soil covering in this area, record whether there is often the influence of dynamic loads such as large vehicles on them. If so, the pipes need to be reinforced. Monitor the free water head pressure of the pipes in a long-term sequence to see whether the free water head pressure inside the pipes meets the requirements under the highest daily maximum, fire conditions and the most unfavorable accident conditions. If not, the head of the water supply plant's outlet pump should be set and a variable frequency pump should be used.

[0294] The third level is the fragile network, which requires the renewal and maintenance of pipelines in fragile areas, and the maintenance of the water supply network based on the evaluation indicators and pre-set threshold conditions; the renewal and maintenance steps are as follows:

[0295] 1) First, identify the most vulnerable pipe network areas and directly replace pipes that are more than 10 years old and have burst more than 3 times.

[0296] 2) If the pipe age is less than 10 years, the number of pipe bursts is in a sub-healthy state, and the load is in a healthy state, determine the size of the free water head. If the free water head is less than 12 meters, perform pipe network leveling to increase the head of the water supply plant's outlet pump station; if the free water head is between 12 and 28 meters, it is necessary to add a local secondary pump station to the area to achieve pressurization; if the free water head is greater than 28 meters, it is necessary to reduce the pipe network pressure in the area, and reduce the head of the water supply plant's outlet pump station through pipe network leveling.

[0297] 3) If the pipe age is less than 10 years, the number of pipe bursts is sub-healthy or pathological, and the pipe load is less than 5KN / m2, it means that the pipe's bearing capacity is insufficient. Determine whether the pipe is a metal pipe or a concrete pipe. If so, and the free water head pressure is greater than 28 meters, the pipe pressure may be too high. Reduce the free water head of the pipe and reduce the head of the water supply plant's outlet pump station through pipe network leveling; if the free water head pressure is between 12 and 28 meters, the pipe may be partially damaged, leaking, or otherwise damaged, and the pipe needs to be replaced or repaired; if it is a plastic pipe and the free water head pressure is within the range of 12 to 28 meters, replace it with a metal pipe or concrete pipe to increase the load strength of the pipe.

[0298] 4) If the pipe age is less than 10 years, the pipe load is in a healthy state, and the number of pipe bursts is sub-healthy or pathological, determine the pipe cover thickness. If the pipe cover thickness is less than 0.5 meters and is on a motor vehicle lane, it may be that the external dynamic load has caused damage to the pipe and caused the pipe to burst. The pipe needs to be reinforced with reinforced concrete to increase its strength.

[0299] The fourth level is a very fragile pipe network, which requires principal component analysis and the formulation of corresponding maintenance strategies. The water supply pipe network is maintained based on the main indicators and pre-set maintenance conditions; the maintenance strategies are as follows:

[0300] 1) First, identify the vulnerable pipeline area. Generally, there are many index factors caused by vulnerable pipelines, and it is necessary to conduct principal component analysis and rank the contribution of the index factors.

[0301] 2) Analyze the top three factors, there are 6 ways to arrange and combine the five indicators, and the indicators of the top three pipelines are in a sub-healthy or pathological state.

[0302] 3) In the first case, the top three influencing factors are: load, free water head and number of pipe bursts. This situation indicates that the pipeline load is low, the free water head is high, and the internal pressure of the pipeline is high, resulting in frequent pipe bursts. In this case, the transformation method should be to update the pipeline in time and replace it with a pipeline with higher load strength.

[0303] 4) In the second case, the first three main influencing factors are: load, number of pipe bursts and soil thickness. This situation indicates that the pipeline load is low and the pipeline is located in an area with large external loads such as motor vehicle lanes. Therefore, the first option is to reinforce the pipeline with reinforced concrete, and the second option is to replace it with metal pipes or reinforced concrete pipes.

[0304] 5) In the third case, the first three main influencing factors are: load, soil cover thickness and pipe age. This situation shows that the pipeline's own load strength has gradually decreased due to long-term operation, and the small soil cover thickness has caused external dynamic loads to oppress it. Therefore, the measures taken are to update the pipeline or reinforce and strengthen the damaged section of the pipeline.

[0305] 6) In the fourth case, the first three main influencing factors are: free water head, number of pipe bursts and soil thickness. This situation shows that the internal free water head pressure of the pipeline is large, and the soil thickness of the external pipeline is small, resulting in damage to the pipeline by external dynamic load. Therefore, it is necessary to calculate the pipe network adjustment, reduce the head of the pipeline outlet pump station, and reinforce the pipe section with reinforced concrete to reduce the impact of external dynamic loads.

[0306] 7) In the fifth case, the first three influencing factors are: free water head, soil cover thickness and pipe age. This situation indicates that the free water head pressure inside the pipeline is large, the pipe age is large, and the soil cover thickness is small. It is necessary to calculate the pipe network adjustment, reduce the head of the pipeline outlet pump station, and reinforce the pipe section with reinforced concrete to reduce the impact of external dynamic loads.

[0307] 8) In the sixth case, the first three factors are: number of pipe bursts, soil cover thickness and pipe age. This indicates that the pipe is old, its own load strength is reduced, and the soil cover thickness is small. The external load interferes with the pipe, causing more pipe bursts. Therefore, it is necessary to replace the pipe or strengthen the local load-weak area with reinforced concrete.

[0308] The renovation index is a quantitative data that provides decision makers with an intuitive reference for the priority and necessity of water supply network renovation. A high renovation index means that there are major problems in the network and renovation needs to be prioritized; while a low renovation index means that the network is in good condition and the urgency of renovation is not high. This method can ensure the rational allocation of renovation resources and prioritize the problems that need the most attention, thereby improving the efficiency and reliability of the entire water supply system.

[0309] In practical applications, this method can flexibly adapt to different water supply network systems and different transformation needs. By adjusting the combination weights, membership set matrix and transformation degree assignment, the characteristics and transformation needs of water supply networks in different regions and types can be reflected. In addition, this method can also be used as a dynamic evaluation tool. With the update of data and the adjustment of weights, the health status of the water supply network can be continuously monitored and transformation suggestions can be made in a timely manner.

[0310] Embodiment 3

[0311] Based on the above embodiments, the present invention provides a water supply network transformation device. Figure 7 The structural diagram of a water supply network transformation device provided by an embodiment of the present invention is shown, and the device includes:

[0312] The target indicator determination module 310 is used to determine the target evaluation indicator based on the contribution rate of the evaluation indicator in the process of water supply network reconstruction.

[0313] The subjective weight processing module 320 is used to determine the subjective weight corresponding to the target evaluation index based on fuzzy hierarchical analysis and cluster weight analysis.

[0314] The objective weight processing module 330 is used to determine the objective weight corresponding to the target evaluation index based on the membership decision analysis.

[0315] The combined weight processing module 340 is used to construct an optimized objective function based on subjective weights and objective weights, use a genetic algorithm to find the optimal solution, and determine the optimal solution as the combined weight.

[0316] The membership set matrix determination module 350 is used to determine the membership set matrix based on the membership values ​​of the target evaluation indicators in the water supply network.

[0317] The water supply pipeline renovation grade determination module 360 ​​is used to determine the water supply pipeline renovation grade based on the combined weight and the membership set parameter value range of the target evaluation.

[0318] The water supply network reconstruction module 370 is used to monitor and maintain the water supply network according to the reconstruction level of the water supply network.

[0319] In some preferred embodiments of the present invention, the target indicator determination module 310 is used to determine the evaluation indicators in the process of water supply network transformation as original data; construct an original sample matrix of evaluation indicators based on the original data; standardize the original sample matrix of evaluation indicators to obtain a standardized matrix; determine the correlation coefficient matrix based on the covariance and standard deviation of the elements in the standardized matrix; solve the characteristic equation of the correlation coefficient matrix to determine the eigenvalue of the correlation coefficient matrix; determine the contribution rate of the evaluation indicator based on the eigenvalue; sort the contribution rate from large to small, and select a preset number of evaluation indicators as target evaluation indicators.

[0320] In some preferred embodiments of the present invention, the subjective weight processing module 320 is used to determine multiple basic weight sets of target evaluation indicators based on fuzzy hierarchical analysis; wherein, the steps of fuzzy hierarchical analysis include: scaling the target evaluation indicators in pairs to determine the element importance matrix; determining the importance ranking matrix through importance calculation based on the elements in the element importance matrix; normalizing the importance ranking matrix and summing the column vectors to obtain the weight coefficients; normalizing the weight coefficients to obtain the basic weight set of the target evaluation indicator; calculating the overlap of the basic weight set, and determining the basic weight whose overlap meets the preset threshold as the intermediate weight set; calculating the intermediate weight set for cluster analysis, calculating the average value of the intermediate weight set with the most cluster categories, and determining the average value as the subjective weight.

[0321] In some preferred embodiments of the present invention, the objective weight processing module 330 is used to obtain the membership value of the target evaluation index; determine the original decision matrix corresponding to the water supply network based on the preset membership value scale range and membership value; normalize and standardize the original decision matrix to obtain a normalized decision matrix; calculate the standard deviation of the normalized decision matrix to obtain a standardized decision matrix; determine the deviation decision matrix based on the elements in the normalized decision matrix; determine the weight decision matrix based on the product of the standardized decision matrix and the deviation decision matrix, and determine the objective weight based on the weight decision matrix.

[0322] In some preferred embodiments of the present invention, the combined weight processing module 340 is used to construct an objective function based on subjective weights and objective weights; construct a constraint function corresponding to the objective function; use a genetic algorithm to solve the objective function and determine the initial population; determine the parent population based on the fitness of individuals in the initial population; perform evolutionary processing on the parent population to determine the offspring population; wherein the evolutionary processing includes at least one of the following: crossover or inheritance; screen individuals in the offspring population based on an elite retention strategy, and repeat the evolutionary processing to support reaching a maximum number of iterations to obtain a combined weight.

[0323] In some preferred embodiments of the present invention, the water supply pipeline renovation level determination module 360 ​​is used to multiply the combined weight of the target evaluation indicators by the membership set parameter value range of the target evaluation indicators; the cumulative sum of the products of each target evaluation indicator is used to determine the water supply network renovation score; the renovation score of the water supply network can be used to judge the renovation level of the water supply network.

[0324] In some preferred embodiments of the present invention, the transformation levels of the water supply network include: healthy, sub-healthy, fragile and very fragile; the water supply network transformation module 370 is used to perform normal monitoring on the water supply network with a healthy transformation level; increase the number of inspections and maintenance for the water supply network with a sub-healthy transformation level; perform maintenance on the water supply network with a fragile transformation level based on evaluation indicators and pre-set threshold conditions; and determine main indicators based on the evaluation indicators for the water supply network with a very fragile transformation level, and perform maintenance based on the main indicators and pre-set maintenance conditions.

[0325] Technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, the specific working process of the water supply pipe network modification device described above can refer to the corresponding process in the aforementioned embodiment of the water supply pipe network modification method, and will not be repeated here.

[0326] Embodiment 4

[0327] The embodiment of the present invention also provides an electronic device for running a water supply network transformation method; see Figure 8 The structural schematic diagram of an electronic device provided by an embodiment of the present invention is shown, and the electronic device includes a memory 400 and a processor 401, wherein the memory 400 is used to store one or more computer instructions, and the one or more computer instructions are executed by the processor 401 to implement the above-mentioned water supply network transformation method.

[0328] Further, Figure 8 The electronic device shown further includes a bus 402 and a communication interface 403 , and the processor 401 , the communication interface 403 and the memory 400 are connected via the bus 402 .

[0329] The memory 400 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 403 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used. The bus 402 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0330] The processor 401 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 401. The above processor 401 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present invention can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module may be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 400, and the processor 401 reads the information in the memory 400 and completes the steps of the method of the above embodiment in combination with its hardware.

[0331] An embodiment of the present invention further provides a computer storage medium storing computer executable instructions. When the computer executable instructions are called and executed by a processor, the computer executable instructions prompt the processor to implement the above-mentioned business recommendation method. The specific implementation can be found in the method embodiment and will not be repeated here.

[0332] The computer program products of the water supply network reconstruction method, device and electronic device provided in the embodiments of the present invention include a computer storage medium storing program code. The instructions included in the program code can be used to execute the methods in the previous method embodiments. The specific implementation can be found in the method embodiments and will not be repeated here.

[0333] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and / or device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0334] In addition, in the description of the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0335] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0336] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A water supply network reconstruction method, characterized in that: include: Determine the target evaluation index based on the contribution rate of the evaluation index in the process of water supply network reconstruction; Determine the subjective weight corresponding to the target evaluation index based on fuzzy hierarchical analysis and cluster weight analysis; Determine the objective weight corresponding to the target evaluation index based on membership decision analysis; Constructing an optimized objective function based on the subjective weight and the objective weight, using a genetic algorithm to find an optimal solution, and determining the optimal solution as a combined weight; Determining a membership set matrix based on the membership values ​​of the target evaluation indicators in the water supply network; Determine the water supply pipeline reconstruction level based on the combined weight and the membership set parameter value range of the target evaluation; Monitoring and maintaining the water supply network according to the modification level of the water supply network; The step of determining the subjective weight corresponding to the target evaluation index based on fuzzy hierarchical analysis and cluster weight analysis includes: Determine multiple basic weight sets of target evaluation indicators based on the fuzzy hierarchy analysis; wherein the steps of the fuzzy hierarchy analysis include: scaling the target evaluation indicators in pairs to determine the element importance matrix; determining the importance ranking matrix by importance calculation based on the elements in the element importance matrix; normalizing the importance ranking matrix and summing the column vectors to obtain weight coefficients; normalizing the weight coefficients to obtain the basic weight set of the target evaluation indicators; Calculating the degree of overlap of the basic weight set, and determining the basic weights whose degree of overlap meets a preset threshold as the intermediate weight set; The intermediate weight sets are calculated to perform cluster analysis, an average value is calculated for the intermediate weight sets with the most cluster categories, and the average value is determined as the subjective weight.

2. The water supply network reconstruction method according to claim 1, characterized in that: The steps of determining the target evaluation index based on the contribution rate of the evaluation index in the process of water supply network reconstruction include: Determine the evaluation indicators in the water supply network reconstruction process as raw data; Constructing an original sample matrix of evaluation indicators based on the original data; Standardizing the original sample matrix of the evaluation index to obtain a standardized matrix; Determining a correlation coefficient matrix based on the covariance and standard deviation of the elements in the standardized matrix; Solving the characteristic equation of the correlation coefficient matrix to determine the eigenvalues ​​of the correlation coefficient matrix; Determining a contribution rate of the evaluation index based on the characteristic value; The contribution rates are sorted from large to small, and a preset number of evaluation indicators are selected as the target evaluation indicators.

3. The water supply network reconstruction method according to claim 1, characterized in that: The step of determining the objective weight corresponding to the target evaluation index based on membership decision analysis includes: Obtain the membership value of the target evaluation index; Determine an original decision matrix corresponding to the water supply network based on a preset membership value scale range and membership value; Performing normalization processing on the original decision matrix to obtain a normalized decision matrix; Calculating the standard deviation of the normalized decision matrix to obtain a standardized decision matrix; Determining a deviation decision matrix based on elements in the normalized decision matrix; A weight decision matrix is ​​determined based on the product of the standardized decision matrix and the deviation decision matrix, and the objective weight is determined based on the weight decision matrix.

4. The water supply network reconstruction method according to claim 1, characterized in that: The steps of constructing an optimized objective function based on the subjective weight and the objective weight, finding an optimal solution using a genetic algorithm, and determining the optimal solution as a combined weight include: constructing an objective function based on the subjective weight and the objective weight; Constructing a constraint function corresponding to the objective function; Using a genetic algorithm to solve the objective function and determine an initial population; Determining a parent population based on the fitness of individuals in the initial population; Performing evolution processing on the parent population to determine the offspring population; wherein the evolution processing includes at least one of the following: crossover or inheritance; The individuals in the offspring population are screened based on an elite retention strategy, and the evolution process is repeated until a maximum number of iterations is reached to obtain a combined weight.

5. The water supply network reconstruction method according to claim 1, characterized in that: The step of determining the water supply pipeline reconstruction level based on the combined weight and the membership set parameter value range of the target evaluation includes: Multiplying the combined weight of the target evaluation index by the membership set parameter value range of the target evaluation index; The water supply network transformation score is determined by accumulating the product of each target evaluation index; The renovation score of the water supply network can be used to determine the renovation level of the water supply network.

6. The water supply network reconstruction method according to claim 1, characterized in that: The transformation level of the water supply network includes at least one of the following: healthy, sub-healthy, fragile and very fragile; The transformation measures for the water supply network include at least one of the following: normal monitoring of the water supply network, increasing the number of inspections and maintenance of the water supply network, maintaining the water supply network based on the evaluation index and a preset threshold condition, or maintaining the water supply network based on a main index and a preset maintenance condition; wherein the main index is determined based on the evaluation index; The transformation level of the water supply network and the transformation measures for the water supply network correspond one to one.

7. A water supply network transformation device, characterized in that: include: A target indicator determination module is used to determine the target evaluation indicator based on the contribution rate of the evaluation indicator in the process of water supply network transformation; A subjective weight processing module, used to determine the subjective weight corresponding to the target evaluation index based on fuzzy hierarchical analysis and cluster weight analysis; An objective weight processing module, used to determine the objective weight corresponding to the target evaluation index based on membership decision analysis; A combined weight processing module, used to construct an optimized objective function based on the subjective weight and the objective weight, use a genetic algorithm to find an optimal solution, and determine the optimal solution as the combined weight; A membership set matrix determination module, used to determine the membership set matrix based on the membership values ​​of the target evaluation indicators in the water supply network; A water supply pipeline renovation grade determination module, used to determine the water supply pipeline renovation grade based on the combined weight and the membership set parameter value range of the target evaluation; A water supply network transformation module, used to monitor and maintain the water supply network according to the transformation level of the water supply network; The subjective weight processing module is used to determine multiple basic weight sets of target evaluation indicators based on the fuzzy hierarchical analysis; wherein the steps of the fuzzy hierarchical analysis include: scaling the target evaluation indicators in pairs to determine the element importance matrix; determining the importance ranking matrix through importance calculation based on the elements in the element importance matrix; normalizing the importance ranking matrix and summing the column vectors to obtain the weight coefficients; normalizing the weight coefficients to obtain the basic weight set of the target evaluation indicators; calculating the overlap of the basic weight set, and determining the basic weight whose overlap meets the preset threshold as the intermediate weight set; calculating the intermediate weight set for cluster analysis, calculating the average value of the intermediate weight set with the most cluster categories, and determining the average value as the subjective weight.

8. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the water supply network reconstruction method described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that The computer storage medium stores computer executable instructions. When the computer executable instructions are called and executed by the processor, the computer executable instructions prompt the processor to implement the water supply network reconstruction method described in any one of claims 1 to 6.

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