技术领域technical field
本发明涉及管道检测技术领域，尤其涉及一种管道流量敏感性矩阵漏损检测方法。The invention relates to the technical field of pipeline detection, in particular to a pipeline flow sensitivity matrix leakage detection method.
背景技术Background technique
城市管网是工业社会的重要基础设施之一，在经济发展和人们的正常生活中占据着重要的地位。在我国，由于城市化的进程不断加快，为了适应城市发展的需要，城市供水管网也随之快速改造扩建，但是管网大多数凭经验进行布局，使得管网系统改扩建缺乏科学依据，导致供水负荷不均匀，容易出现漏损，严重的时候会出现爆管。老城区的供水管网日益老化，出现了腐蚀，强度减低，容易出现漏损。根据2014年统计，全国的漏损总量达到65亿m3，漏损率达到15％。这些漏损造成了国家重大经济损失。然而，现有的方法不能定位到漏损管道，只能定位到节点，一个节点最少连接两根管道，排查范围很大，需要耗费大量的人力物力。Urban pipe network is one of the important infrastructures of industrial society, and plays an important role in economic development and people's normal life. In my country, due to the continuous acceleration of urbanization, in order to meet the needs of urban development, the urban water supply network has also been rapidly reconstructed and expanded. The water supply load is uneven, and leakage is prone to occur, and in severe cases, pipe bursts will occur. The water supply network in the old urban area is aging day by day, corroded, weakened in strength and prone to leakage. According to statistics in 2014, the total amount of leakage in the country reached 6.5 billion m3 , and the leakage rate reached 15%. These leaks have caused significant economic losses to the country. However, the existing methods cannot locate leaking pipelines, but can only locate nodes. A node is connected to at least two pipelines. The scope of investigation is very large, and a lot of manpower and material resources are required.
发明内容Contents of the invention
为解决上述问题，本发明提供一种管道流量敏感性矩阵漏损检测方法，至少部分解决上述技术问题。In order to solve the above-mentioned problems, the present invention provides a pipeline flow sensitivity matrix leakage detection method, which at least partly solves the above-mentioned technical problems.
为此，本发明提供一种管道流量敏感性矩阵漏损检测方法，包括：For this reason, the present invention provides a pipeline flow sensitivity matrix leakage detection method, comprising:
获取管网信息，所述管网信息包括各个管道的管径、管长、管道摩阻、节点需水量；Acquiring pipe network information, the pipe network information includes pipe diameter, pipe length, pipe friction, and node water demand of each pipe;
根据所述管网信息形成正常运行状态之下管网的水力模型；Forming a hydraulic model of the pipe network under normal operating conditions according to the pipe network information;
根据所述水力模型、节点压力、管道流量获得敏感性矩阵；Obtain a sensitivity matrix according to the hydraulic model, node pressure, and pipeline flow;
根据所述敏感性矩阵的对应元素形成梯度向量；forming a gradient vector from corresponding elements of said sensitivity matrix;
获取发生漏损之后管网监测点的压力变化量和流量变化量；Obtain the pressure change and flow change of the monitoring points of the pipe network after leakage occurs;
根据最小二乘法、所述梯度向量、所述压力变化量以及所述流量变化量获得各个管道的残差；Obtain the residual error of each pipeline according to the least square method, the gradient vector, the pressure variation and the flow variation;
确认残差最小的管道为漏损管道。Confirm that the pipeline with the smallest residual error is the leaky pipeline.
可选的，所述敏感性矩阵为Optionally, the sensitivity matrix is
其中，a、b是管道上下游节点，管道流量的敏感性矩阵表示为对应管道上下游节点流量敏感性矩阵元素和的二分之一；Among them, a and b are the upstream and downstream nodes of the pipeline, and the sensitivity matrix of the pipeline flow is expressed as one-half of the element sum of the flow sensitivity matrix of the corresponding upstream and downstream nodes of the pipeline;
所述节点流量敏感性矩阵为The node traffic sensitivity matrix is
其中，B为对角矩阵where B is a diagonal matrix
A为n×m关联矩阵，用于描述管网的拓扑关系，n与m分别为节点数与管道数。A is an n×m correlation matrix, which is used to describe the topological relationship of the pipe network, where n and m are the number of nodes and the number of pipes, respectively.
可选的，所述根据所述管网信息形成正常运行状态之下管网的水力模型的步骤包括：Optionally, the step of forming a hydraulic model of the pipeline network under normal operating conditions according to the pipeline network information includes:
利用EPANET软件形成所述水力模型。The hydraulic model was developed using EPANET software.
可选的，所述根据所述水力模型、节点压力、管道流量获得敏感性矩阵的步骤包括：Optionally, the step of obtaining a sensitivity matrix according to the hydraulic model, node pressure, and pipeline flow includes:
利用解析法获得所述敏感性矩阵。The sensitivity matrix is obtained analytically.
本发明具有下述有益效果：The present invention has following beneficial effect:
本发明提供的管道流量敏感性矩阵漏损检测方法，包括：获取管网信息，所述管网信息包括各个管道的管径、管长、管道摩阻、节点需水量；根据所述管网信息形成正常运行状态之下管网的水力模型；根据所述水力模型、节点压力、管道流量获得敏感性矩阵；根据所述敏感性矩阵的对应元素形成梯度向量；获取发生漏损之后管网监测点的压力变化量和流量变化量；根据最小二乘法、所述梯度向量、所述压力变化量以及所述流量变化量获得各个管道的残差；确认残差最小的管道为漏损管道。本发明提供的漏损检测方法减少了维修人员的排查范围，缩短了排查时间。因此，本发明提供的技术方案可以快速找到漏损点进行维修，最终减少了漏损时间和经济损失。The pipeline flow sensitivity matrix leakage detection method provided by the present invention includes: obtaining pipeline network information, and the pipeline network information includes the pipe diameter, pipe length, pipeline friction resistance, and node water demand of each pipeline; according to the pipeline network information Form a hydraulic model of the pipeline network under normal operating conditions; obtain a sensitivity matrix according to the hydraulic model, node pressure, and pipeline flow; form a gradient vector according to the corresponding elements of the sensitivity matrix; obtain monitoring points of the pipeline network after leakage occurs The pressure variation and the flow variation; obtain the residual error of each pipeline according to the least square method, the gradient vector, the pressure variation and the flow variation; confirm that the pipeline with the smallest residual error is the leakage pipeline. The leakage detection method provided by the invention reduces the inspection range of maintenance personnel and shortens the inspection time. Therefore, the technical solution provided by the present invention can quickly find the leakage point for maintenance, and finally reduce the leakage time and economic loss.
附图说明Description of drawings
图1为本发明实施例一提供的基于管道流量敏感性矩阵漏损检测方法的流程图；Fig. 1 is a flow chart of the leakage detection method based on the pipeline flow sensitivity matrix provided by Embodiment 1 of the present invention;
图2为本发明实施例一提供的水力模型的结构示意图；FIG. 2 is a schematic structural view of a hydraulic model provided by Embodiment 1 of the present invention;
图3为本发明实施例一提供的不同管道发生漏损之后的节点变化量；Fig. 3 is the change amount of nodes after leakage occurs in different pipelines provided by Embodiment 1 of the present invention;
图4为本发明实施例一提供的管道1发生漏损之后进行漏损定位的结果示意图；Fig. 4 is a schematic diagram of the result of leak location after the leakage of the pipeline 1 provided by Embodiment 1 of the present invention;
图5为本发明实施例一提供的管道1发生漏损之后进行50次漏损定位实验的结果统计示意图；Fig. 5 is a statistical schematic diagram of the results of 50 leak location experiments after the leakage of the pipeline 1 provided by Embodiment 1 of the present invention;
图6为本发明实施例一提供的管道1发生漏损之后进行漏损定位的一种排查范围示意图；Fig. 6 is a schematic diagram of an investigation scope for leak location after the leakage occurs in the pipeline 1 provided by Embodiment 1 of the present invention;
图7为本发明实施例一提供的管道1发生漏损之后进行漏损定位的另一种排查范围示意图。FIG. 7 is a schematic diagram of another investigation scope for leak location after the leakage occurs in the pipeline 1 according to Embodiment 1 of the present invention.
具体实施方式Detailed ways
为使本领域的技术人员更好地理解本发明的技术方案，下面结合附图对本发明提供的管道流量敏感性矩阵漏损检测方法进行详细描述。In order to enable those skilled in the art to better understand the technical solution of the present invention, the pipeline flow sensitivity matrix leakage detection method provided by the present invention will be described in detail below with reference to the accompanying drawings.
实施例一Embodiment one
管网漏损定位可以大致分为两类：1)通过收集的管网漏损监测数据建立数据模型进行漏损检测；2)构建水力模型进行漏损检测，通过水力模型拟合漏损状态定位漏损。第一类是建立数据模型，利用数据模型对漏损进行检测，常用的方法是通过神经网络将收集到的漏损检测数据对神经网络进行训练，将训练好的模型用于漏损检测。还可以利用支持向量机、流量和压力等数据判断该区域是否存在漏损。然而，利用数据模型进行漏损检测需要大量的覆盖全部管道的漏损检测数据才能达到好的检测效果，在实际中很难收集到足够的数据。第二类是建立机理模型，利用水力模型拟合管网的漏损状态。这种方法能在没有大量监测数据的情况下进行漏损检测。然而，现有的方法不能定位到漏损管道，只能定位到供水节点，一个节点最少连接两根管道，排查范围很大，需要耗费大量的人力物力。Pipeline network leakage location can be roughly divided into two categories: 1) Build a data model for leakage detection through the collected pipeline network leakage monitoring data; 2) Build a hydraulic model for leakage detection, and use the hydraulic model to fit the leakage state location Leakage. The first type is to establish a data model and use the data model to detect leakage. The common method is to train the neural network with the collected leakage detection data through the neural network, and use the trained model for leakage detection. Data such as support vector machines, flow and pressure can also be used to determine whether there is leakage in the area. However, using a data model for leak detection requires a large amount of leak detection data covering all pipelines to achieve a good detection effect, and it is difficult to collect enough data in practice. The second category is to establish a mechanism model and use a hydraulic model to fit the leakage state of the pipe network. This approach enables leak detection without extensive monitoring data. However, the existing methods cannot locate leaking pipes, but only water supply nodes. A node is connected to at least two pipes. The scope of investigation is very large, and a lot of manpower and material resources are required.
本实施例首先获取管网的管径、管长、管道摩阻、节点需水量等信息，再利用EPANET构建水力模型，EPANET是美国国家环境保护局开发的开源软件，接着利用解析法计算节点压力、管道流量对管道流量的敏感性矩阵。本实施例根据供水管网的连续性方程和能量方程的微分式(1)推导上述敏感性矩阵。In this embodiment, first obtain information such as the pipe diameter, pipe length, pipe friction, and node water demand of the pipe network, and then use EPANET to construct a hydraulic model. EPANET is an open source software developed by the US Environmental Protection Agency, and then use the analytical method to calculate the node pressure. , The sensitivity matrix of pipeline flow to pipeline flow. In this embodiment, the above-mentioned sensitivity matrix is derived according to the continuity equation and the differential equation (1) of the energy equation of the water supply pipe network.
其中，Δq、ΔQ和Δh·分别为管道流量变化向量、节点流量变化向量和管道漏损变化向量，A为n×m关联矩阵，用于描述管网的拓扑关系，n与m分别为节点数与管道数。A矩阵之元素的确定方法如下：Among them, Δq, ΔQ, and Δh are pipeline flow change vectors, node flow change vectors, and pipeline leakage change vectors, respectively, A is an n×m correlation matrix, which is used to describe the topological relationship of the pipe network, and n and m are the number of nodes with the number of pipes. The determination method of the elements of A matrix is as follows:
然后，本实施例对海曾-威廉方程(3)求偏微分带入公式(1)获得Then, in this embodiment, the partial differential of the Hazen-Williams equation (3) is brought into the equation (1) to obtain
其中，k为单位换算系数，d、l、q及c为管道的管径、管长、流量以及海曾-威廉系数，从而可以求得节点水压、管道流量对节点流量的敏感性矩阵(4)。Among them, k is the unit conversion coefficient, and d, l, q, and c are the pipe diameter, pipe length, flow rate, and Hazen-Williams coefficient of the pipeline, so that the sensitivity matrix of node water pressure and pipeline flow to node flow can be obtained ( 4).
其中，B为对角矩阵where B is a diagonal matrix
本实施例把漏损等效至两个节点之上，根据这两个节点进一步推导管道流量的敏感性矩阵，利用最小二乘拟合和确定漏损发生的管道。In this embodiment, the leakage is equivalent to two nodes, and the sensitivity matrix of the pipeline flow is further derived based on these two nodes, and the pipeline where the leakage occurs is fitted and determined by least squares.
本实施例构建的目标函数为The objective function constructed in this embodiment is
其中，为与漏损管道l连接的a、b节点的等效漏损量，wH，wq分别为水压与流量的权重系数，此处本实施例为监测误差方差的倒数，Hi与qj分别表示漏损状态之下水压与流量监测值，nH、mq分别表示压力检测点数量和流量监测点数量。本实施例提供的目标函数的意义为：调整管道l连接的节点a、b的等效漏损量，使得模型的计算值与漏损状态之下的监测值尽量匹配。本实施例提供的漏损检测方法减少了维修人员的排查范围，缩短了排查时间。因此，本实施例提供的技术方案可以快速找到漏损点进行维修，最终减少了漏损时间和经济损失。in, is the equivalent leakage amount of nodes a and b connected to the leakage pipeline l, wH , wq are the weight coefficients of water pressure and flow rate respectively, and this embodiment is the reciprocal of the monitoring error variance here, Hi and qj respectively represent the water pressure and flow monitoring values under the state of leakage, nH, mq respectively represent the number of pressure detection points and the number of flow monitoring points. The meaning of the objective function provided in this embodiment is to adjust the equivalent leakage of the nodes a and b connected by the pipeline l, so that the calculated value of the model matches the monitored value under the leakage state as much as possible. The leakage detection method provided in this embodiment reduces the inspection scope of the maintenance personnel and shortens the inspection time. Therefore, the technical solution provided by this embodiment can quickly find the leakage point for maintenance, and finally reduce the leakage time and economic loss.
本实施例对公式(6)进行二元函数一阶泰勒展开，其中公式(7)为The present embodiment carries out binary function first-order Taylor expansion to formula (6), wherein formula (7) is
本实施例假设发生漏损时的漏损量为ΔQl，将漏损量假设为管道两端节点的需水量增加，而且增加量相同，即因此，公式(7)可以表示为In this embodiment, it is assumed that the amount of leakage when leakage occurs is ΔQl , and the amount of leakage is assumed to be the increase in water demand at the nodes at both ends of the pipeline, and the amount of increase is the same, that is, Therefore, formula (7) can be expressed as
本实施例可以将管道流量的敏感性矩阵解析式近似为：In this embodiment, the analytical expression of the sensitivity matrix of the pipeline flow can be approximated as:
其中，ΔH0＝H-H(Qa，Qb)，Δq0＝q-qQaQb)，H和q分别为漏损状态之下的水压与流量的监测向量。因此，公式(8)进行加权最小二乘回归，解得管道的漏损量为：Among them, ΔH0 =HH(Qa , Qb ), Δq0 =q-qQa Qb ), H and q are monitoring vectors of water pressure and flow rate under the leakage state, respectively. Therefore, formula (8) performs weighted least squares regression, and the leakage of the pipeline is solved as follows:
本实施例提供的管网水力模型之中水压与流量的变化值为：In the hydraulic model of the pipe network provided in this embodiment, the change values of water pressure and flow are:
将公式(10)代入公式(11)可得：Substituting formula (10) into formula (11) can get:
本实施例提供的技术方案可以快速找到漏损点进行维修，最终减少了漏损时间和经济损失。本实施例通过公式(13)获得目标函数的残差，公式(13)为The technical solution provided by this embodiment can quickly find the leakage point for maintenance, and finally reduce the leakage time and economic loss. In this embodiment, the residual error of the objective function is obtained by formula (13), and formula (13) is
本实施例将残差最小的管道确认为漏损管道，从而减少了维修人员的排查范围，缩短了排查时间。因此，本实施例提供的技术方案可以快速找到漏损点进行维修，最终减少了漏损时间和经济损失。In this embodiment, the pipeline with the smallest residual error is confirmed as the leaking pipeline, thereby reducing the investigation scope of the maintenance personnel and shortening the investigation time. Therefore, the technical solution provided by this embodiment can quickly find the leakage point for maintenance, and finally reduce the leakage time and economic loss.
下面以一个复杂的模拟管网为例对本实施例的技术方案进行具体说明。The technical solution of this embodiment will be specifically described below by taking a complex simulated pipe network as an example.
图1为本发明实施例一提供的基于管道流量敏感性矩阵漏损检测方法的流程图。如图1所示，本实施例利用EPANET构建水力模型及其管网结构，管网中有两个水泵，而且有一个高位水池为管网供水。管网中设置有14个压力监测点和2个流量监测点，位置如图1所示。本实施例利用EPANET对不同管道的漏损进行FIG. 1 is a flow chart of a leakage detection method based on a pipeline flow sensitivity matrix provided by Embodiment 1 of the present invention. As shown in Figure 1, this embodiment uses EPANET to build a hydraulic model and its pipe network structure. There are two water pumps in the pipe network, and a high-level pool supplies water for the pipe network. There are 14 pressure monitoring points and 2 flow monitoring points in the pipe network, the locations are shown in Figure 1. This embodiment utilizes EPANET to carry out the leakage of different pipelines
模拟，模拟网管发生漏损之后监测点的压力、流量的变化量。图2为本发明实施例一提供的水力模型的结构示意图，图3为本发明实施例一提供的不同管道发生漏损之后的节点变化量。本实施例模拟的管道漏损量为20L/S，部分测压节点的压力变化如图2所示，对于不同管道的漏损，测压节点的变化量和变化规律是不同的，这也是本方法进行漏损定位的基础。为了评估监测误差的不确定性对漏损定位结果的影响，使得本实施例提供的实验更加符合实际管网的工作情况，本实施例在监测值的随机误差采用蒙特卡洛模拟产生，节点水压监测误差的方差σH＝0.1m。由于误差的不确定性导致定位的不确定性，本实施例进行50次漏损定位实验。Simulation, simulating the changes in the pressure and flow of the monitoring point after the leakage of the network management system occurs. Fig. 2 is a schematic structural diagram of a hydraulic model provided in Embodiment 1 of the present invention, and Fig. 3 is a node change amount after leakage occurs in different pipelines provided in Embodiment 1 of the present invention. The leakage of the pipeline simulated in this embodiment is 20L/S, and the pressure changes of some pressure measurement nodes are shown in Figure 2. For the leakage of different pipelines, the variation and change law of the pressure measurement nodes are different, which is also the reason for this method for leak location. In order to evaluate the influence of the uncertainty of the monitoring error on the leakage location results, so that the experiments provided in this example are more in line with the actual working conditions of the pipe network, the random errors of the monitoring values in this example are generated by Monte Carlo simulation, and the node water The variance of pressure monitoring error σH =0.1m. Since the uncertainty of the error leads to the uncertainty of the location, 50 leak location experiments are carried out in this embodiment.
当管道1发生漏损之后，本实施例以其中一次定位实验来说明漏损检测方法。When the pipeline 1 leaks, this embodiment uses one of the positioning experiments to illustrate the leak detection method.
本实施例提供的本实验之中监测点的变化向量为The change vector of the monitoring point in this experiment provided by the present embodiment is
管道1发生漏损之后，本实施例利用最小二乘法进行拟合，获得节点压力、管道流量对管道流量敏感性矩阵的对应元素，从而形成梯度向量，所述梯度向量为After the pipeline 1 leaks, this embodiment uses the least squares method to fit, and obtains the node pressure, the corresponding elements of the pipeline flow sensitivity matrix to the pipeline flow, thereby forming a gradient vector, and the gradient vector is
本实施例利用加权最小二乘法求解管道的漏损量为In this embodiment, the weighted least squares method is used to solve the leakage loss of the pipeline as
本实施例提供的漏损检测方法减少了维修人员的排查范围，缩短了排查时间。因此，本实施例提供的技术方案可以快速找到漏损点进行维修，最终减少了漏损时间和经济损失。当管道1漏损量为ΔQ1时，本实施例根据管网水力模型计算水压与流量的变化值为The leakage detection method provided in this embodiment reduces the inspection scope of the maintenance personnel and shortens the inspection time. Therefore, the technical solution provided by this embodiment can quickly find the leakage point for maintenance, and finally reduce the leakage time and economic loss. When the leakage of pipeline 1 is ΔQ1 , this embodiment calculates the change value of water pressure and flow according to the hydraulic model of the pipe network as
因此，本实施例获得残差为Therefore, the residual obtained in this embodiment is
本实施例对残差绝对值进行求和，获得总的残差，同理遍历每个管道，求得各个管道的残差，最终结果如图4所示。图4为本发明实施例一提供的管道1发生漏损之后进行漏损定位的结果示意图。管道1的残差最小，因此管道1为漏损管道。In this embodiment, the absolute value of the residual is summed to obtain the total residual, and similarly, each pipeline is traversed to obtain the residual of each pipeline. The final result is shown in FIG. 4 . Fig. 4 is a schematic diagram of the results of leak location after the pipeline 1 leaks according to Embodiment 1 of the present invention. Pipeline 1 has the smallest residual, so Pipeline 1 is a leaky pipe.
本实施例提供的漏损检测方法减少了维修人员的排查范围，缩短了排查时间。因此，本实施例提供的技术方案可以快速找到漏损点进行维修，最终减少了漏损时间和经济损失。The leakage detection method provided in this embodiment reduces the inspection scope of the maintenance personnel and shortens the inspection time. Therefore, the technical solution provided by this embodiment can quickly find the leakage point for maintenance, and finally reduce the leakage time and economic loss.
图5为本发明实施例一提供的管道1发生漏损之后进行50次漏损定位实验的结果统计示意图。本实施例进行50次定位实验，对漏损定位情况进行统计，给出了漏损定位到的管道ID以及定位到的次数。在50次定位之中，有42次定位到管道1，4次定位到管道7，2次定位到管道2和管道15。由此可见，多数时候可以定位到正确的漏损管道，本实施例计算获得的平均漏损量为20.059L/s，与真实漏损(20L/s)十分接近。FIG. 5 is a statistical diagram of the results of 50 leak location experiments after the leakage of the pipeline 1 provided by Embodiment 1 of the present invention. In this embodiment, 50 location experiments are carried out, statistics are made on leakage location, and the ID of the pipeline where the leakage is located and the number of times of location are given. Among the 50 positionings, there are 42 positionings to pipeline 1, 4 positionings to pipeline 7, and 2 positionings to pipelines 2 and 15. It can be seen that the correct leakage pipeline can be located most of the time, and the average leakage calculated in this embodiment is 20.059 L/s, which is very close to the real leakage (20 L/s).
图6为本发明实施例一提供的管道1发生漏损之后进行漏损定位的一种排查范围示意图。如图6所示，被定位的四根管道在图6之中被圈出，漏损定位在图6所示的范围之中，因此，定位不准确的管道也是非常接近真实的漏损管道。因此，本实施例提供的实验充分证明了漏损检测方法的可行性和正确性。本实施例提供的漏损检测方法减少了维修人员的排查范围，缩短了排查时间。本实施例提供的漏损检测方法减少了维修人员的排查范围，缩短了排查时间。因此，本实施例提供的技术方案可以快速找到漏损点进行维修，最终减少了漏损时间和经济损失。FIG. 6 is a schematic diagram of an investigation scope for leak location after a leak occurs in the pipeline 1 according to Embodiment 1 of the present invention. As shown in Figure 6, the four positioned pipelines are circled in Figure 6, and the leakage is located within the range shown in Figure 6. Therefore, the pipelines with inaccurate positioning are also very close to the real leaky pipelines. Therefore, the experiment provided in this embodiment fully proves the feasibility and correctness of the leakage detection method. The leakage detection method provided in this embodiment reduces the inspection scope of the maintenance personnel and shortens the inspection time. The leakage detection method provided in this embodiment reduces the inspection scope of the maintenance personnel and shortens the inspection time. Therefore, the technical solution provided by this embodiment can quickly find the leakage point for maintenance, and finally reduce the leakage time and economic loss.
本实施例也可以利用同样的方法对不同管道进行漏损模拟，然后进行漏损定位。由于涉及数据太多，本实施例不全部展示，只展示部分管道发生漏损之后的定位统计情况如表1所示。1表反映出相似的性质，多数情况定位到正确的漏损管道，定位不准确的管道也在漏损管道的附近。In this embodiment, the same method can also be used to perform leakage simulation on different pipelines, and then perform leakage location. Due to too much data involved, this embodiment does not show all of them, but only shows the location statistics of some pipelines after leakage, as shown in Table 1. Table 1 reflects similar properties. In most cases, the correct leaky pipeline is located, and the inaccurately located pipeline is also near the leaky pipeline.
表1部分管道发生漏损之后的一种定位统计数据Table 1 A kind of positioning statistical data after the leakage of some pipelines
下面将本实施例提供的漏损检测方法与利用基于节点流量敏感性矩阵的定位方法进行对比。这种方法可以定位到节点，节点附近发生漏损。但是一个节点至少连接两个管道，在定位正确的情况下排查范围已经很大了。在定位不准确的情况下，排查定位节点附近的节点连接的管道，那么排查量会成指数增长。本实施例提供的漏损检测方法减少了维修人员的排查范围，缩短了排查时间。The following compares the leakage detection method provided by this embodiment with the location method based on the node traffic sensitivity matrix. This method can locate the node, and the leakage occurs near the node. But a node is connected to at least two pipelines, and the scope of investigation is already very large when the positioning is correct. In the case of inaccurate positioning, check the pipelines connected to nodes near the positioning node, and the amount of checking will increase exponentially. The leakage detection method provided in this embodiment reduces the inspection scope of the maintenance personnel and shortens the inspection time.
图7为本发明实施例一提供的管道1发生漏损之后进行漏损定位的另一种排查范围示意图。如图7所示，利用同样的方法，当管道1发生漏损之后，进行50次漏损定位实验，图7示出了漏损定位的排查范围，与图6对比可以看出，本实施例提供的漏损检测方法明显缩小了漏损排查范围。FIG. 7 is a schematic diagram of another investigation scope for leak location after the leakage occurs in the pipeline 1 according to Embodiment 1 of the present invention. As shown in Figure 7, using the same method, after a leak occurred in the pipeline 1, 50 leak location experiments were performed. Figure 7 shows the scope of investigation for leak location. Compared with Figure 6, it can be seen that The leakage detection method provided significantly reduces the scope of leakage investigation.
本实施例对不同管道进行漏损模拟，然后进行漏损定位，对漏损定位情况做统计。部分定位结果展示如表2所示。从表2可以看出，漏损定位情况表现出相似的性质，这个方法也可以大致定位出漏损节点的范围，但是漏损排查范围比本实施例的排查范围要大很多。由此可以说明本实施例提供的技术方案的优越性，大大减少了漏损的排查范围。In this embodiment, leakage simulation is performed on different pipelines, and then leakage location is performed, and statistics are made on the leakage location situation. Some positioning results are shown in Table 2. It can be seen from Table 2 that the location of leaks shows similar properties. This method can also roughly locate the range of leaky nodes, but the scope of leak detection is much larger than that of this embodiment. Therefore, it can be explained that the technical solution provided by this embodiment is superior, and the scope of troubleshooting for leakage is greatly reduced.
表2部分管道发生漏损之后的另一种定位统计数据Table 2 Another kind of positioning statistics data after the leakage of some pipelines
本实施例提供的管道流量敏感性矩阵漏损检测方法，包括：获取管网信息，所述管网信息包括各个管道的管径、管长、管道摩阻、节点需水量；根据所述管网信息形成正常运行状态之下管网的水力模型；根据所述水力模型、节点压力、管道流量获得敏感性矩阵；根据所述敏感性矩阵的对应元素形成梯度向量；获取发生漏损之后管网监测点的压力变化量和流量变化量；根据最小二乘法、所述梯度向量、所述压力变化量以及所述流量变化量获得各个管道的残差；确认残差最小的管道为漏损管道。本实施例提供的漏损检测方法减少了维修人员的排查范围，缩短了排查时间。因此，本实施例提供的技术方案可以快速找到漏损点进行维修，最终减少了漏损时间和经济损失。The pipeline flow sensitivity matrix leakage detection method provided in this embodiment includes: obtaining pipe network information, the pipe network information including the pipe diameter, pipe length, pipe friction, and node water demand of each pipe; according to the pipe network Information forms a hydraulic model of the pipeline network under normal operating conditions; obtains a sensitivity matrix according to the hydraulic model, node pressure, and pipeline flow; forms a gradient vector according to the corresponding elements of the sensitivity matrix; obtains the pipeline network monitoring value after leakage occurs The pressure variation and the flow variation of the point; according to the least square method, the gradient vector, the pressure variation and the flow variation, the residual error of each pipeline is obtained; the pipeline with the smallest residual error is confirmed as the leakage pipeline. The leakage detection method provided in this embodiment reduces the inspection scope of the maintenance personnel and shortens the inspection time. Therefore, the technical solution provided by this embodiment can quickly find the leakage point for maintenance, and finally reduce the leakage time and economic loss.
可以理解的是，以上实施方式仅仅是为了说明本发明的原理而采用的示例性实施方式，然而本发明并不局限于此。对于本领域内的普通技术人员而言，在不脱离本发明的精神和实质的情况下，可以做出各种变型和改进，这些变型和改进也视为本发明的保护范围。It can be understood that, the above embodiments are only exemplary embodiments adopted for illustrating the principle of the present invention, but the present invention is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also regarded as the protection scope of the present invention.
| Application Number | Priority Date | Filing Date | Title |
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| CN201711341116.1ACN107869653B (en) | 2017-12-14 | 2017-12-14 | A Leakage Detection Method of Pipeline Flow Sensitivity Matrix |
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| CN201711341116.1ACN107869653B (en) | 2017-12-14 | 2017-12-14 | A Leakage Detection Method of Pipeline Flow Sensitivity Matrix |
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| CN201711341116.1AActiveCN107869653B (en) | 2017-12-14 | 2017-12-14 | A Leakage Detection Method of Pipeline Flow Sensitivity Matrix |
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