CN110543719A - A method for predicting leakage of water supply pipeline based on spatial metering model - Google Patents
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Abstract
Description
技术领域technical field
本发明涉及一种基于空间计量模型的供水管道漏失预测方法,属于供水管道漏失技术领域。The invention relates to a method for predicting leakage of water supply pipelines based on a spatial metering model, and belongs to the technical field of leakage of water supply pipelines.
背景技术Background technique
随着社会的发展,城市化进程在不断的向前推进,供水管道的漏损问题正在成为供水系统中面临的重要难题之一。长期以来,我国城市供水系统的管道漏损率都居高不下,不仅阻碍了供水企业的运营与发展,而且严重影响了城市供水的效率。因此如何探究出导致供水管道漏损的因素,如何提出有效的降低管道漏损的方法与对策,如何准确的预测出管道漏损,如何精确的对供水管道漏损点进行定位,减少管道漏损事故的发生,是目前亟需解决的问题。With the development of society and the continuous advancement of urbanization, the leakage of water supply pipes is becoming one of the important problems faced by the water supply system. For a long time, the leakage rate of pipelines in my country's urban water supply system has remained high, which not only hinders the operation and development of water supply enterprises, but also seriously affects the efficiency of urban water supply. Therefore, how to explore the factors that cause the leakage of water supply pipelines, how to propose effective methods and countermeasures to reduce pipeline leakage, how to accurately predict pipeline leakage, and how to accurately locate the leakage points of water supply pipelines to reduce pipeline leakage The occurrence of accidents is an urgent problem that needs to be solved at present.
发明内容SUMMARY OF THE INVENTION
本发明为了减少管道漏损事故的发生,提出了一种基于空间计量模型的供水管道漏失预测方法,所采取的技术方案如下:In order to reduce the occurrence of pipeline leakage accidents, the present invention proposes a method for predicting leakage of water supply pipelines based on a spatial metering model, and the adopted technical solutions are as follows:
一种基于空间计量模型的供水管道漏失预测方法,所述供水管道漏失预测方法包括:A method for predicting leakage of water supply pipelines based on a spatial metering model, the method for predicting leakage of water supply pipelines includes:
第一步、通过EPANET软件结合动态水力模型获取预测对象、空间、数据以及因素用于分析其对管道漏损的影响程度;The first step is to obtain prediction objects, spaces, data and factors through EPANET software combined with dynamic hydraulic model to analyze its influence on pipeline leakage;
第二步、通过空间相关性分析确定管道之间存在的空间相关性;The second step is to determine the spatial correlation between the pipelines through spatial correlation analysis;
第三步、利用MATLAB软件中的jplv工具箱对供水区域A的管道漏损数据进行LM检验确定空间计量模型;The third step is to use the jplv toolbox in the MATLAB software to perform LM inspection on the pipeline leakage data of the water supply area A to determine the spatial measurement model;
第四步、利用第三步确定的模型计算出各参数估计系数,然后将各参数估计系数通过直接效应与间接效应来对各因素进行解释,获得直接效应与间接效应的估计结果;利用所述直接效应与间接效应的估计结果分析各因素对管道漏损的影响。The fourth step is to use the model determined in the third step to calculate the estimated coefficients of each parameter, and then use the estimated coefficients of each parameter to explain each factor through the direct effect and indirect effect, and obtain the estimated results of the direct effect and indirect effect; The estimation results of direct effect and indirect effect analyze the influence of various factors on pipeline leakage.
进一步地,所述第一步所述获取预测对象、空间、数据以及因素的过程包括:Further, the process of acquiring prediction objects, spaces, data and factors in the first step includes:
步骤一、通过EPANET软件,利用供水管道动态水力模型,以一小时为一个时间节点,模拟某天24个时间段的数据,获取该市漏损情况最为严重的供水区域A的管道数据作为预测对象;Step 1. Using the EPANET software, use the dynamic hydraulic model of the water supply pipeline to simulate the data of 24 time periods in a day with one hour as a time node, and obtain the pipeline data of the water supply area A with the most serious leakage in the city as the prediction object ;
步骤二、按管道的不同分为多个空间,每一个管道作为一个空间,以便完成模型的建立、分析及预测;Step 2. Divide into multiple spaces according to the difference of the pipeline, and each pipeline is used as a space to complete the establishment, analysis and prediction of the model;
步骤三、将步骤一所述24个时间段中的前20个时间段的数据进行建模,探究供水管道漏损率的影响因素,后4个时间段的数据用来预测,验证模型的可靠性;Step 3. Model the data of the first 20 time periods in the 24 time periods described in step 1 to explore the influencing factors of the leakage rate of the water supply pipeline. The data of the last 4 time periods are used for prediction to verify the reliability of the model. sex;
步骤四、选取管径、管龄、管材、管长、覆土厚度、管道运行压力和管道水流速度这7个因素作为解释变量,用于分析所述7个因素对管道漏损的影响程度。Step 4: Select 7 factors of pipe diameter, pipe age, pipe material, pipe length, thickness of covering soil, pipeline operating pressure and pipeline water flow velocity as explanatory variables to analyze the influence degree of the 7 factors on pipeline leakage.
进一步地,步骤二所述空间的具体数量为39个。Further, the specific number of the spaces described in step 2 is 39.
进一步地,第二步所述通过空间相关性分析确定管道之间存在的空间相关性的过程包括:Further, the process of determining the spatial correlation between the pipelines through the spatial correlation analysis described in the second step includes:
步骤1、利用基于地理位置与漏损率相结合的方法设立空间权重矩阵,所述空间权重矩阵为:Step 1. Use a method based on a combination of geographic location and leakage rate to establish a spatial weight matrix, where the spatial weight matrix is:
其中,与分别为管道i与管道j的前20个时间段的漏损率平均值;in, and are the average leakage rates of pipeline i and pipeline j in the first 20 time periods;
步骤2、利用GeoDa软件进行空间自相关分析,利用前20个时间段里供水区域A各指标数据的平均值数据来得到该供水区域各管道漏失的Moran’s I指数值和莫兰散点图;Step 2. Use GeoDa software to perform spatial autocorrelation analysis, and use the average data of each index data of water supply area A in the first 20 time periods to obtain the Moran's I index value and Moran scatter plot of the leakage of each pipeline in this water supply area;
步骤3、根据Moran’s I指数值和莫兰散点图获得管道之间存在的空间相关性。Step 3. Obtain the spatial correlation between the pipelines according to Moran's I index value and Moran's scatter plot.
进一步地,第三步所述LM检验确定空间计量模型的过程包括:Further, the process of determining the spatial econometric model by the LM test described in the third step includes:
步骤a:利用OLS回归方法获得OLS回归的随机效应模型;Step a: Use the OLS regression method to obtain the random effect model of the OLS regression;
步骤b:两个LM(拉格朗日乘数检验)判断LM-error和LM-lag;Step b: Two LM (Lagrange multiplier test) to judge LM-error and LM-lag;
步骤c:如果判断结果为:LM-error和LM-lag两个检验都不显著,则保持OLS结果;如果判断结果为两个检验结果都显著,则执行步骤d;如果判断结果为只有一个显著,那么,判断为LM-error显著时,则选用SEM模型(空间误差模型,Spatial Error Model,SEM);判断为LM-lag显著时,则选用SAR模型(空间自回归模型,Spatial Auto Regressive model,SAR);Step c: If the judgment result is that both LM-error and LM-lag tests are not significant, keep the OLS result; if the judgment result is that both test results are significant, perform step d; if the judgment result is that there is only one significant , then, when it is judged that the LM-error is significant, the SEM model (Spatial Error Model, SEM) is used; when the LM-lag is judged to be significant, the SAR model (spatial autoregressive model, Spatial Auto Regressive model, SEM) is used. SAR);
步骤d:进一步检验RLM-lag和RLM-error,并对RLM-lag和RLM-error的检验结果进行判断;如果判断结果为两个检验结果都不显著,选用OLS回归模型;如果判断结果为RLM-error显著,则选用SEM模型(空间误差模型,Spatial Error Model,SEM);如果判断结果为RLM-lag显著,则选用SAR模型(空间自回归模型,Spatial Auto Regressive model,SAR)。Step d: further test RLM-lag and RLM-error, and judge the test results of RLM-lag and RLM-error; if the judgment result is that both test results are not significant, select the OLS regression model; if the judgment result is RLM If the -error is significant, the SEM model (Spatial Error Model, SEM) is selected; if the judgment result is that the RLM-lag is significant, the SAR model (Spatial Auto Regressive model, SAR) is selected.
其中,LM-error、LM-lag、RLM-lag和RLM-error分别代表4种不同的统计量,即变量空间相关性检验(LM-lag),误差项空间相关性检验(LM-error),稳健的变量空间相关性检验(RLM-lag),稳健的误差项空间相关性检验(RLM-error)。Among them, LM-error, LM-lag, RLM-lag and RLM-error respectively represent four different statistics, namely variable spatial correlation test (LM-lag), error term spatial correlation test (LM-error), Robust variable spatial correlation test (RLM-lag), robust error term spatial correlation test (RLM-error).
本发明有益效果:Beneficial effects of the present invention:
本发明基于空间计量模型提出了城市供水管网管段漏失预测方法,探索了供水区域A的管道漏损的空间分布,建立了基于管段物理拓扑连接的空间自回归模型,分析了可以实现对城市供水管网管段级漏损的重要性排序和漏失预测,为城市供水管网管段改造提供了一种全新的策略和技术手段。Based on the spatial measurement model, the present invention proposes a method for predicting the leakage of urban water supply pipe network sections, explores the spatial distribution of pipeline leakage in the water supply area A, establishes a spatial autoregressive model based on the physical topological connection of pipe sections, and analyzes the ability to realize urban water supply. The importance ordering and prediction of leakage at the section level of the pipe network provide a new strategy and technical means for the reconstruction of the urban water supply pipe network.
附图说明Description of drawings
图1为本发明所述LM检验流程图;Fig. 1 is the LM inspection flow chart of the present invention;
图2为本发明供水区域A的Moran散点图;Fig. 2 is the Moran scatter diagram of the water supply area A of the present invention;
图3为本发明所述方法在20时至21时预测值与真实值对比图。FIG. 3 is a comparison diagram of the predicted value and the actual value of the method according to the present invention from 20:00 to 21:00.
具体实施方式Detailed ways
下面结合具体实施例对本发明做进一步说明,但本发明不受实施例的限制。The present invention will be further described below in conjunction with specific embodiments, but the present invention is not limited by the embodiments.
实施例1:Example 1:
一种基于空间计量模型的供水管道漏失预测方法,所述供水管道漏失预测方法包括:A method for predicting leakage of water supply pipelines based on a spatial metering model, the method for predicting leakage of water supply pipelines includes:
第一步、通过EPANET软件结合动态水力模型获取预测对象、空间、数据以及因素用于分析其对管道漏损的影响程度;The first step is to obtain prediction objects, spaces, data and factors through EPANET software combined with dynamic hydraulic model to analyze its influence on pipeline leakage;
第二步、通过空间相关性分析确定管道之间存在的空间相关性;The second step is to determine the spatial correlation between the pipelines through spatial correlation analysis;
第三步、利用MATLAB软件中的jplv工具箱对供水区域A的管道漏损数据进行LM检验确定空间计量模型;The third step is to use the jplv toolbox in the MATLAB software to perform LM inspection on the pipeline leakage data of the water supply area A to determine the spatial measurement model;
第四步、利用第三步确定的模型计算出各参数估计系数,然后将各参数估计系数通过直接效应与间接效应来对各因素进行解释,获得直接效应与间接效应的估计结果;利用所述直接效应与间接效应的估计结果分析各因素对管道漏损的影响。The fourth step is to use the model determined in the third step to calculate the estimated coefficients of each parameter, and then use the estimated coefficients of each parameter to explain each factor through the direct effect and indirect effect, and obtain the estimated results of the direct effect and indirect effect; The estimation results of direct effect and indirect effect analyze the influence of various factors on pipeline leakage.
其中,所述第一步所述获取预测对象、空间、数据以及因素的过程包括:Wherein, the process of obtaining prediction objects, spaces, data and factors in the first step includes:
步骤一、通过EPANET软件,利用供水管道动态水力模型,以一小时为一个时间节点,模拟某天24个时间段的数据,获取该市漏损情况最为严重的供水区域A的管道数据作为预测对象;Step 1. Using the EPANET software, use the dynamic hydraulic model of the water supply pipeline to simulate the data of 24 time periods in a day with one hour as a time node, and obtain the pipeline data of the water supply area A with the most serious leakage in the city as the prediction object ;
步骤二、按管道的不同分为多个空间,每一个管道作为一个空间,以便完成模型的建立、分析及预测;其中,所述空间的具体数量为39个;Step 2: Divide into multiple spaces according to different pipelines, and each pipeline is used as a space to complete the establishment, analysis and prediction of the model; wherein, the specific number of the spaces is 39;
步骤三、将步骤一所述24个时间段中的前20个时间段的数据进行建模,探究供水管道漏损率的影响因素,后4个时间段的数据用来预测,验证模型的可靠性;Step 3. Model the data of the first 20 time periods in the 24 time periods described in step 1 to explore the influencing factors of the leakage rate of the water supply pipeline. The data of the last 4 time periods are used for prediction to verify the reliability of the model. sex;
步骤四、选取管径、管龄、管材、管长、覆土厚度、管道运行压力和管道水流速度这7个因素作为解释变量,用于分析所述7个因素对管道漏损的影响程度。Step 4: Select 7 factors of pipe diameter, pipe age, pipe material, pipe length, thickness of covering soil, pipeline operating pressure and pipeline water flow velocity as explanatory variables to analyze the influence degree of the 7 factors on pipeline leakage.
本实施例所分析的数据来源于我国北方M市的供水管道。该市已经为城市供水管道建立了动态水力学模型,可以实现管道状况的实时仿真。因此,通过EPANET软件,利用供水管道动态水力模型,以一小时为一个时间节点,模拟某天24个时间段的数据,获取该市漏损情况最为严重的供水区域A的管道数据作为研究对象,按管道的不同分为39个空间,每一个管道作为一个空间,以便完成模型的建立、分析及预测。本实施例采用前20个时间段的数据进行建模,探究供水管道漏损率的影响因素,后4个时间段的数据用来预测,验证模型的可靠性。The data analyzed in this example comes from the water supply pipeline in M city in northern my country. The city has built a dynamic hydraulic model for the city's water supply pipeline, which enables real-time simulation of pipeline conditions. Therefore, through the EPANET software, using the dynamic hydraulic model of the water supply pipeline, taking one hour as a time node, simulating the data of 24 time periods in a day, and obtaining the pipeline data of the water supply area A with the most serious leakage in the city as the research object. It is divided into 39 spaces according to the different pipelines, and each pipeline is used as a space to complete the establishment, analysis and prediction of the model. In this example, the data of the first 20 time periods are used for modeling, to explore the influencing factors of the leakage rate of the water supply pipeline, and the data of the last 4 time periods are used to predict and verify the reliability of the model.
本实施例选取管径、管龄、管材、管长、覆土厚度、管道运行压力和管道水流速度这7个因素作为解释变量,分析这些因素对管道漏损的影响程度。表1为选取的相关指标的说明。In this example, seven factors, such as pipe diameter, pipe age, pipe material, pipe length, thickness of covering soil, pipeline operating pressure and pipeline water flow velocity, are selected as explanatory variables, and the degree of influence of these factors on pipeline leakage is analyzed. Table 1 is a description of the selected relevant indicators.
表1管道漏损相关指标Table 1 Pipeline leakage related indicators
由于变量中包含连续型变量和离散型变量,为了研究方便,首先需要对变量进行预处理。对于连续型变量,为消除不同量纲对模型估计结果产生的偏误,需对其做对数化处理;对于离散型变量,需将其转化为0-1变量。即把漏损率、管径、管龄、管长、覆土厚度、管道压力和管道水流速度这些变量取对数,把变量管材转化为0-1变量,将其拆分成4个变量,分别为是否普通铸铁管(isA),是否钢管(isB),是否PVC管(isC)和是否球墨铸铁管(isD),处理之后的部分数据如表2所示,表中各字母的含义如表1所示。Since the variables include continuous variables and discrete variables, for the convenience of research, the variables need to be preprocessed first. For continuous variables, logarithmization is required in order to eliminate the bias of the model estimation results caused by different dimensions; for discrete variables, it needs to be converted into 0-1 variables. That is to take the logarithm of the variables such as leakage rate, pipe diameter, pipe age, pipe length, covering soil thickness, pipe pressure and pipe water flow velocity, convert the variable pipe material into a 0-1 variable, and split it into 4 variables, respectively. Whether it is an ordinary cast iron pipe (isA), whether it is a steel pipe (isB), whether it is a PVC pipe (isC) and whether it is a ductile iron pipe (isD), some data after processing are shown in Table 2, and the meaning of each letter in the table is shown in Table 1 shown.
表2预处理后的部分数据Table 2 Part of the data after preprocessing
第二步所述通过空间相关性分析确定管道之间存在的空间相关性的过程包括:The process of determining the spatial correlation between the pipelines through the spatial correlation analysis in the second step includes:
步骤1、利用基于地理位置与漏损率相结合的方法设立空间权重矩阵,所述空间权重矩阵为:Step 1. Use a method based on a combination of geographic location and leakage rate to establish a spatial weight matrix, where the spatial weight matrix is:
其中,与分别为管道i与管道j的前20个时间段的漏损率平均值;in, and are the average leakage rates of pipeline i and pipeline j in the first 20 time periods;
步骤2、利用GeoDa软件进行空间自相关分析,利用前20个时间段里供水区域A各指标数据的平均值数据来得到该供水区域各管道漏失的Moran’s I指数值和莫兰散点图,如图2所示;Step 2. Use GeoDa software to perform spatial autocorrelation analysis, and use the average data of each index data of water supply area A in the first 20 time periods to obtain the Moran's I index value and Moran scatter diagram of the leakage of each pipeline in the water supply area, as shown in the figure. As shown in Figure 2;
步骤3、根据Moran’s I指数值和莫兰散点图获得管道之间存在的空间相关性。Step 3. Obtain the spatial correlation between the pipelines according to Moran's I index value and Moran's scatter plot.
如图2所示,Moran’s I指数值为0.468,相应的p值为0.001,在显著性为0.01的情况下可以认为管道漏损之间存在着显著的正的空间自相关性;Moran散点图的横坐标为被解释变量,纵坐标为被解释变量与空间权重矩阵的乘积,常用来研究空间特征。从图2所示的莫兰散点图可以看出各管道的分布情况,各管道基本分布在第一和第三象限,说明管道之间存在着显著的空间相关性,且是正向影响的,即相邻管道之间存在相同变化的漏失率。进一步说明,与有高漏失率管道相邻的管道也有较高的漏失率,同样,低漏失率管道邻接的管道漏失率也不会高。As shown in Figure 2, the Moran's I index value is 0.468, and the corresponding p value is 0.001. When the significance is 0.01, it can be considered that there is a significant positive spatial autocorrelation between pipeline leakage; Moran scatter plot The abscissa is the explained variable, and the ordinate is the product of the explained variable and the spatial weight matrix, which is often used to study spatial characteristics. From the Moran scatter plot shown in Figure 2, we can see the distribution of each pipeline. The pipelines are basically distributed in the first and third quadrants, indicating that there is a significant spatial correlation between the pipelines, and it has a positive impact. That is, there is the same variation in the leakage rate between adjacent pipes. It is further explained that the pipeline adjacent to the pipeline with a high leakage rate also has a higher leakage rate, and similarly, the leakage rate of the pipeline adjacent to the pipeline with a low leakage rate will not be high.
如图1所示,第三步所述LM检验确定空间计量模型的过程包括:As shown in Figure 1, the process of determining the spatial econometric model by the LM test described in the third step includes:
步骤a:利用OLS回归方法获得OLS回归的随机效应模型;其中,随机效应模型的具体形式如下:Step a: Obtain the random effect model of OLS regression by using the OLS regression method; wherein, the specific form of the random effect model is as follows:
其中,Rit为被解释变量,即漏损率,ρ为空间自回归系数,W为空间权重矩阵,Dit,ageit,…,Vit为解释变量,β1,β2,…β11为解释变量估计系数,反映解释变量对被解释变量的影响程度,α为随机效应模型项,εit为随机误差向量。Among them, R it is the explained variable, namely the leakage rate, ρ is the spatial autoregressive coefficient, W is the spatial weight matrix, D it ,age it ,…,V it is the explanatory variable, β 1 ,β 2 ,…β 11 The coefficient is estimated for the explanatory variable, reflecting the degree of influence of the explanatory variable on the explained variable, α is the random effect model term, and ε it is the random error vector.
步骤b:两个LM(拉格朗日乘数检验)判断LM-error和LM-lag;Step b: Two LM (Lagrange multiplier test) to judge LM-error and LM-lag;
步骤c:如果判断结果为:LM-error和LM-lag两个检验都不显著,则保持OLS结果;如果判断结果为两个检验结果都显著,则执行步骤d;如果判断结果为只有一个显著,那么,判断为LM-error显著时,则选用SEM模型(空间误差模型,Spatial Error Model,SEM);判断为LM-lag显著时,则选用SAR模型(空间自回归模型,Spatial Auto Regressive model,SAR);Step c: If the judgment result is that both LM-error and LM-lag tests are not significant, keep the OLS result; if the judgment result is that both test results are significant, perform step d; if the judgment result is that there is only one significant , then, when it is judged that the LM-error is significant, the SEM model (Spatial Error Model, SEM) is used; when the LM-lag is judged to be significant, the SAR model (spatial autoregressive model, Spatial Auto Regressive model, SEM) is used. SAR);
步骤d:进一步检验RLM-lag和RLM-error,并对RLM-lag和RLM-error的检验结果进行判断;如果判断结果为两个检验结果都不显著,选用OLS回归模型;如果判断结果为RLM-error显著,则选用SEM模型(空间误差模型,Spatial Error Model,SEM);如果判断结果为RLM-lag显著,则选用SAR模型(空间自回归模型,Spatial Auto Regressive model,SAR)。Step d: further test RLM-lag and RLM-error, and judge the test results of RLM-lag and RLM-error; if the judgment result is that both test results are not significant, select the OLS regression model; if the judgment result is RLM If the -error is significant, the SEM model (Spatial Error Model, SEM) is selected; if the judgment result is that the RLM-lag is significant, the SAR model (Spatial Auto Regressive model, SAR) is selected.
其中,LM-error、LM-lag、RLM-lag和RLM-error分别代表4种不同的统计量,即变量空间相关性检验(LM-lag),误差项空间相关性检验(LM-error),稳健的变量空间相关性检验(RLM-lag),稳健的误差项空间相关性检验(RLM-error)。Among them, LM-error, LM-lag, RLM-lag and RLM-error respectively represent four different statistics, namely variable spatial correlation test (LM-lag), error term spatial correlation test (LM-error), Robust variable spatial correlation test (RLM-lag), robust error term spatial correlation test (RLM-error).
第四步所述解释分析的具体过程如下:The specific process of the interpretation analysis described in the fourth step is as follows:
根据式(1)所示的模型初步设定,利用MATLAB软件,计算出各参数估计系数,各系数估计值如表3的第二列。对于空间面板自回归模型来说,由于空间效应的存在,模型各参数的估计系数不能直接表示各因素的影响程度,需要通过直接效应与间接效应来对各因素进行解释。直接效应与间接效应的估计结果如表3所示,通过对直接效应与间接效应的估计,可以分析各因素对管道漏损的影响。According to the initial setting of the model shown in formula (1), using MATLAB software, the estimated coefficients of each parameter are calculated, and the estimated values of each coefficient are shown in the second column of Table 3. For the spatial panel autoregressive model, due to the existence of spatial effects, the estimated coefficients of each parameter of the model cannot directly represent the degree of influence of each factor, and each factor needs to be explained through direct and indirect effects. The estimation results of direct effect and indirect effect are shown in Table 3. By estimating direct effect and indirect effect, the influence of various factors on pipeline leakage can be analyzed.
(1)空间因素:空间自回归模型估计结果中,空间滞后系数ρ=0.408,且在0.01的水平下显著,说明管道的漏损在很大程度上会受到其邻接管道漏损情况的影响,随着邻接管道漏损情况的加重,自身管道的漏损情况也会变得更加严重。(1) Spatial factor: In the estimation results of the spatial autoregressive model, the spatial lag coefficient ρ=0.408, and it is significant at the level of 0.01, indicating that the leakage of the pipeline is largely affected by the leakage of its adjacent pipelines. As the leakage of the adjacent pipeline increases, the leakage of the own pipeline will also become more serious.
(2)管道性状:首先,在0.1的显著水平下,管径和管材的其中三个0-1变量(是否普通铸铁管、是否PVC管和是否球墨铸铁管)的直接效应通过了检验,且管材的三个0-1变量系数都为正。管材的四个0-1变量中,系数最大的是“是否是球墨铸铁管”,值为0.2636,系数最小的是“是否是钢管”,值为0.0369,两者相差0.2267,表明如果管材从球墨铸铁管更换为钢管,则漏损率可以降低0.2267个单位;管径通过了0.1水平下的显著性检验,其回归系数为负,即随着管径的增大,漏损会降低;而管长没有通过显著性检验,即管长对管道漏损率的影响不显著;管龄也没有通过显著性检验,可能是因为本章用于研究的数据中,管龄数据分布不均匀,基本都是49年,只有个别的7年和29年,使得管龄对管道漏损不显著。在间接效应的估计结果中,只有管径通过了0.1的显著性检验,说明管径的大小会影响到邻接管道的漏损,管道性状中的其他因素不会影响到邻接管道。(2) Pipe properties: First, at a significant level of 0.1, the direct effects of three 0-1 variables of pipe diameter and pipe material (whether ordinary cast iron pipe, whether PVC pipe and whether ductile iron pipe are not) passed the test, and All three coefficients of 0-1 variables for the pipe are positive. Among the four 0-1 variables of the pipe, the largest coefficient is "whether it is a ductile iron pipe", the value is 0.2636, and the smallest coefficient is "whether it is a steel pipe", the value is 0.0369, the difference between the two is 0.2267, indicating that if the pipe is made from ductile iron, the value is 0.2267. If the cast iron pipe is replaced by a steel pipe, the leakage rate can be reduced by 0.2267 units; the pipe diameter has passed the significance test at the 0.1 level, and its regression coefficient is negative, that is, as the pipe diameter increases, the leakage loss will decrease; The length did not pass the significance test, that is, the effect of the pipe length on the leakage rate of the pipeline was not significant; the pipe age did not pass the significance test, probably because the data used in this chapter for the study, the pipe age data distribution is uneven, basically all 49 years, there are only a few 7 years and 29 years, so that the pipe age is not significant for pipeline leakage. In the estimation results of indirect effects, only the pipe diameter passed the significance test of 0.1, indicating that the size of the pipe diameter will affect the leakage of the adjacent pipes, and other factors in the pipe properties will not affect the adjacent pipes.
(3)环境因素:覆土厚度的系数为负,在0.05的水平下显著,覆土厚度越大,管道受到地面承载影响越小,使得管道的漏损越小;并且其直接效应与间接效应都显著,说明覆土厚度不仅会影响到自身管道的漏损,还会影响到邻接管道的漏损。管道的漏损会同时受到自身管道及相邻管道的地面承载的影响。(3) Environmental factors: the coefficient of the thickness of the covering soil is negative, which is significant at the level of 0.05. The greater the thickness of the covering soil, the less the pipeline is affected by the ground load, and the smaller the leakage of the pipeline; and its direct and indirect effects are significant. , indicating that the thickness of the covering soil will not only affect the leakage of its own pipeline, but also affect the leakage of the adjacent pipeline. The leakage of the pipeline will be affected by the ground bearing of the own pipeline and the adjacent pipeline at the same time.
(4)运行因素:管道运行压力和管道水流速度都通过了0.05水平下的显著性检验,其回归系数分别为0.346和0.182,而且直接效应与间接效应都显著,表明随着管道压力和管道水流速度的增加,管道漏损会增加;过大的压力与流速不仅会给自身管道带来漏损,同时也会对相邻接的管道构成威胁。(4) Operational factors: both the pipeline operating pressure and the pipeline water flow velocity have passed the significance test at the 0.05 level, and the regression coefficients are 0.346 and 0.182, respectively, and the direct and indirect effects are both significant, indicating that with the pipeline pressure and pipeline water flow As the speed increases, the leakage of the pipeline will increase; excessive pressure and flow velocity will not only cause leakage of the own pipeline, but also pose a threat to the adjacent pipeline.
根据确定的方程式(1),利用后面4个时间段的管径、管材、管道运行压力、管道水流速度和覆土厚度值,计算得出供水区域A的各管道漏损率的预测值,并检验预测效果。下表只列出了20时至21时这个时间段的预测结果,预测相对误差在[0.0051,0.0364]范围内,预测效果较好。According to the determined equation (1), the predicted value of the leakage rate of each pipeline in the water supply area A is calculated and checked by using the pipe diameter, pipe material, pipe operating pressure, pipe water flow velocity and covering soil thickness values in the following four time periods. predict the effect. The following table only lists the prediction results for the time period from 20:00 to 21:00. The relative error of the prediction is in the range of [0.0051, 0.0364], and the prediction effect is better.
根据表4的管道漏损预测结果,可以发现供水区域A的各管道漏损情况,漏损最严重的是G01525管道,通过查找,发现该管道位于几个供水区域的交汇处,处于城市供水主干线。漏损最少的是G01428管道,处于支线,供水压力较小。图3是该时间段的预测值与真实值的折线图,可以看出两条折线几乎重合,拟合效果较好。According to the prediction results of pipeline leakage in Table 4, it can be found that the leakage of each pipeline in water supply area A, the most serious leakage is the G01525 pipeline. trunk. The least leakage is the G01428 pipeline, which is in the branch line and has a lower water supply pressure. Figure 3 is a line graph of the predicted value and the actual value in this time period. It can be seen that the two broken lines almost overlap, and the fitting effect is good.
表3回归系数、直接效应和间接效应估计结果Table 3 Regression coefficient, direct effect and indirect effect estimation results
注:表中*,**和***分别表示在10%,5%和1%的水平下显著Note: *, ** and *** in the table represent significant levels at 10%, 5% and 1%, respectively
表4管道漏损预测情况Table 4 Prediction of pipeline leakage
虽然本发明已以较佳的实施例公开如上,但其并非用以限定本发明,任何熟悉此技术的人,在不脱离本发明的精神和范围内,都可以做各种改动和修饰,因此本发明的保护范围应该以权利要求书所界定的为准。Although the present invention has been disclosed above with preferred embodiments, it is not intended to limit the present invention. Anyone who is familiar with this technology can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, The protection scope of the present invention should be defined by the claims.
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