Water supply network pipe burst monitoring method based on cooperation gain of physical sensor

Abstract

The invention discloses a water supply network pipe burst monitoring method based on cooperative gain of a physical sensor, and the method comprises the steps: taking a ratio of a pressure drop value detected by a corresponding physical sensor during pipe burst to a pressure standard deviation detected by a corresponding physical sensor under a normal working condition as the sensitivity of a pressure monitoring point for the first time; the sensitivity of the pressure monitoring point can accurately reflect the sensitivity of the physical sensor for detecting the pressure during pipe explosion, the pipe sections with similar sensitivity can be clustered based on the sensitivity of the pressure monitoring point, and the optimization efficiency of a subsequent genetic algorithm is improved. The physical sensor combination corresponding to the virtual sensor obtained by using the constraint condition of the genetic algorithm and target function optimization provided by the invention can obviously reduce the coverage blind area of the pipe network and has a heightened error-tolerant rate, that is, the physical sensor combination can better exchange information so as to obtain more accurate pipe burst monitoring.

Description

Technical Field

[0001] The invention belongs to the field of urban water supply network monitoring, and in particular relates to a water supply network burst monitoring method based on physical sensor cooperative gain. Background Art

[0002] Pipe burst and leakage are a major problem that threatens the safety and reliability of water supply. Not only will it lead to a large amount of precious clean water resources being wasted, but it will also lead to chain problems such as water pollution, ground collapse, and insufficient water supply pressure, which will cause economic losses to water companies and seriously affect the normal production and life of residents. Developing more accurate pipe burst and leakage monitoring technology is an important measure to respond to the national leakage control policy and improve the operation and service capabilities of the pipe network.

[0003] With the popularization and application of sensors, the real-time monitoring platform of the pipe network based on data acquisition and supervisory control system (SCADA) has gradually become an essential foundation for supporting the digital management and intelligent operation of the water supply network. However, due to the huge scale of the urban water supply network and the limitation of hardware equipment layout cost, monitoring equipment can only be deployed at limited nodes. This makes the spatial distribution of the monitoring system performance highly unbalanced, and there are many monitoring coverage blind spots in areas where sensors are sparsely deployed. Therefore, how to improve the monitoring system's recognition accuracy and monitoring breadth of pipe bursts as much as possible with limited monitoring equipment is a key research topic.

[0004] The current methods for burst pipe identification based on real-time monitoring can be roughly divided into two categories: data-driven methods and methods based on hydraulic models. Data-driven methods usually do not rely on hydraulic models, and directly mine information from historical monitoring data to predict and judge the system status. Although this method avoids the precise calibration of the hydraulic model, it is easily disturbed by missing and erroneous data and it is difficult to obtain data labels. Therefore, this method is less applicable to actual pipe networks with limited label data. Therefore, the method based on hydraulic models is often the preferred solution for burst pipe monitoring and identification. It generally judges the status of the pipe network by comparing the measured values ​​with the alarm threshold of the model.

[0005] Current hydraulic model-based methods usually monitor pipe bursts by independently setting alarm thresholds for each pressure sensor. Based on this type of independent decision-making monitoring method, the pipe network can be further divided into Boolean physical coverage zones centered on each sensor. Under this partitioning concept, all nodes of the pipe network should be within the sensing range of at least one sensor to achieve full coverage of the area.

[0006] However, as mentioned above, due to the limitation of sensor layout density in practical applications, the pipe network is prone to blind spots. This is one of the disadvantages of sensors making independent decisions on pipe bursts. On the other hand, independent decisions by sensors also give pipe burst judgments too low a tolerance rate, making it impossible to make more accurate judgments by exchanging information with other monitoring points. The above defects faced by independent decisions by sensors have not received enough attention in previous studies on pipe burst monitoring. Summary of the invention

[0007] The present invention provides a pipe burst monitoring method for a water supply pipe network based on the cooperative gain of physical sensors, which can improve the pipe burst identification accuracy while expanding the pipe burst monitoring coverage.

[0008] A specific embodiment of the present invention provides a water supply network pipe burst monitoring method based on physical sensor cooperative gain, comprising:

[0009] The burst simulation is performed on each pipe section of the water supply network to obtain the pressure monitoring point sensitivity sequence of each pipe section. The pressure monitoring point sensitivity is the ratio of the pressure drop value detected by the corresponding physical sensor when the pipe bursts to the pressure standard deviation of the corresponding physical sensor monitoring value under normal working conditions. Based on the normalized pressure monitoring point sensitivity sequence of each pipe section, the K-means clustering algorithm is used to cluster each pipe section to obtain the cluster center pipe section set.

[0010] The optimal virtual sensor of each cluster center pipe section is selected by genetic algorithm, and multiple optimal virtual sensors corresponding to the cluster center pipe section set are deduplicated to obtain a physical sensor cooperative gain system that can improve pipe burst monitoring.

[0011] The objective function of the genetic algorithm is to maximize the virtual sensor monitoring sensitivity, which is the ratio of the sum of the pressure drop values ​​detected by multiple physical sensors fused by the virtual sensor when the pipe bursts to the pressure standard deviation of the virtual sensor monitoring value under normal working conditions, and the virtual sensor is composed of multiple physical sensors;

[0012] The constraint condition of the genetic algorithm is that the maximum value of the monitoring sensitivity of the virtual sensor of each cluster center pipe segment is greater than the maximum value of the sensitivity of each physical sensor monitoring point.

[0013] Preferably, the sensitivity sequence S of the pressure monitoring points when the jth pipe section bursts j for:

[0014] S j =[s j1 ,s j2 ,…s ji …,s jN ]

[0015]

[0016] Where N is the number of pressure monitoring points, s ji is the sensitivity of the i-th pressure monitoring point when the j-th pipe section bursts, The occurrence intensity is The pressure drop value detected at the i-th pressure monitoring point when the pipe bursts, σ i is the pressure standard deviation of the physical sensor monitoring value at the i-th pressure monitoring point under normal working conditions.

[0017] Preferably, the objective function of the genetic algorithm is:

[0018]

[0019]

[0020]

[0021] in, The strength of the pipe segment j is When the pipe bursts, the gth virtual sensor v g The pressure drop value, is the gth virtual sensor v g The burst pressure drop value of the fused mth physical sensor, K is the gth virtual sensor v g The number of physical sensors fused, is the virtual sensor v g The standard deviation of pressure, H gm is the virtual sensor v g The pressure monitoring value of the mth physical sensor is fused, Refers to the virtual sensor v g The covariance of the pressure monitoring values ​​between the fused m1th physical sensor and the m2th physical sensor, when m1=m2, the covariance is the variance of the physical sensor.

[0022] Preferably, the constraints of the genetic algorithm are:

[0023] Subject to:△E j >0

[0024] △E j =E vj -E wj

[0025]

[0026]

[0027] Among them, △Ej is the sensitivity increase of the virtual sensor compared to the physical sensor when pipe burst occurs in pipe segment j, E wj is the maximum sensitivity of the pressure monitoring point of the physical sensor corresponding to the pipe section j when the pipe bursts, E vj is the maximum sensitivity of the virtual sensor pressure monitoring when pipe section j bursts, N is the number of physical sensors, 2 N -N-1 is the number of virtual sensors generated by the collaboration, The strength of the burst pipe.

[0028] Preferably, a genetic algorithm is used to select the optimal virtual sensor for each cluster center pipe segment, including:

[0029] S1. Setting initial values ​​of parameters of the genetic algorithm, wherein the parameters of the genetic algorithm include the number of physical sensors, population size, number of iterations, crossover probability and mutation probability, constructing a population based on virtual sensors with different gene combinations, and treating each virtual sensor as an individual;

[0030] S2, combining the physical sensors in the cluster center pipe segment to construct a plurality of virtual sensors, and constructing an initial parent population based on the plurality of virtual sensors;

[0031] S3. In the initial parent population, replace the individuals that do not meet the constraints so that all individuals in the initial parent population meet the constraints;

[0032] S4. Use the objective function to evaluate the fitness of individuals in the initial parent population that meet the constraints, and retain the individuals with the best fitness;

[0033] S5. Perform crossover and mutation operations on the initial parent population that meets the constraints to obtain the offspring population, and degenerate the individuals in the offspring population that do not meet the constraints to the original individuals;

[0034] S6, after merging the parent population and the child population that meet the constraints, evaluate the fitness of the individuals through the objective function, retain the individuals with the best fitness, and select the top X high fitness individuals of no more than the population size X according to the fitness sorting to form a new parent population;

[0035] S7. Repeat steps S3-S6 until the number of iterations is reached, and the retained individual with the best fitness is evaluated again through the objective function, and the individual with the highest fitness is used as the optimal virtual sensor of the cluster center segment.

[0036] Preferably, the iteration number of the genetic algorithm is 50-200 times, the crossover probability is 0.2-0.5, and the mutation probability is 0.2-0.3.

[0037] Preferably, multiple optimal virtual sensors corresponding to the cluster center pipe segment set are deduplicated to obtain a physical sensor cooperative gain system capable of improving pipe burst monitoring, including:

[0038] A genetic algorithm is used to screen out the optimal virtual sensor of each cluster center pipe section in the cluster center pipe section set to obtain multiple optimal virtual sensors, and duplicate virtual sensors in the multiple optimal virtual sensors are removed to obtain a physical sensor cooperative gain system.

[0039] Preferably, the parameters of the K-means clustering algorithm include the pipe network hydraulic model, the location of the pressure monitoring point, the number of clusters, and the simulated pipe burst intensity.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The present invention uses for the first time the ratio of the pressure drop value detected by the corresponding physical sensor when the pipe bursts to the pressure standard deviation of the corresponding physical sensor monitoring value under normal working conditions as the pressure monitoring point pipe burst monitoring sensitivity. The monitoring sensitivity can accurately reflect the pressure response sensitivity of the physical sensor in pipe burst monitoring. Based on the pressure monitoring sensitivity, pipe sections with similar sensor pipe burst response characteristics can be clustered, thereby improving the optimization efficiency of the subsequent genetic algorithm.

[0042] The physical sensor combination corresponding to the virtual sensor obtained by optimizing the constraint conditions and objective function of the genetic algorithm provided by the present invention can significantly reduce the coverage blind area of ​​the pipeline network and have a higher fault tolerance rate, that is, the physical sensor combination can better obtain more accurate pipe burst monitoring judgment through information fusion. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A flow chart of a water supply network pipe burst cooperative gain monitoring system design based on pressure monitoring points provided by a specific embodiment of the present invention;

[0044] Figure 2 A schematic diagram of a pipe network provided in Example 1;

[0045] Figure 3 Provide a comparison chart of confidence information coverage of the physical system and the gain system at different simulation times for Example 1;

[0046] Figure 4 A comparison chart of the number of unmonitorable pipe sections and the minimum coverage flow rate of the physical system and the gain system at different simulation times provided in Example 1;

[0047] Figure 5 This is a comparison chart of the false alarm rate and missed alarm rate of the physical system and the gain system at different simulation times provided in Example 1. DETAILED DESCRIPTION

[0048] The purpose of the present invention is to develop a water supply pipe burst monitoring method based on sensor cooperative gain to improve the monitoring range and accuracy of water supply pipe burst. The specific implementation method of the present invention is further described in detail below in conjunction with the accompanying drawings. The present invention is specifically applied to pipe burst monitoring based on pressure monitoring points in urban water distribution systems.

[0049] In the face of the problem that the existing pipe network burst monitoring model is prone to blind spots and has a low fault tolerance rate, the specific embodiment of the present invention uses the maximum value of the ratio of the pressure drop value detected by multiple physical sensors fused by the virtual sensor when the pipe bursts and the pressure standard deviation of the virtual sensor monitoring value under normal working conditions as the objective function of the genetic algorithm optimization, thereby providing a suitable physical sensor cooperation combination for each pipe section, improving the fault tolerance rate and reducing the monitoring blind spots, and achieving the purpose of accurately monitoring the pipe burst.

[0050] A specific embodiment of the present invention provides a water supply network pipe burst monitoring method based on physical sensor cooperative gain, comprising:

[0051] S1. Construct the initial parameter values ​​of the K-means clustering algorithm based on burst sensitivity based on different water supply systems: The initial parameter values ​​of the K-means clustering algorithm include the hydraulic model of the pipe network, the location of the existing pressure monitoring points, the number of cluster centers, and the simulated burst intensity. The M-root pipe segment sequence [p1, p2, …, p j ,…,p M ].

[0052] S2. Traverse all pipe sections and perform pipe burst simulation under simulated pipe burst intensity to obtain a pressure monitoring point sensitivity sequence for pipe burst in each pipe section, and perform normalization on the sequence to obtain a normalized sensitivity sequence for pressure monitoring points for pipe burst in each pipe section. The normalized sensitivity matrix is ​​constructed from the normalized sensitivity sequence of the pressure monitoring points of each pipe section where the pipe bursts.

[0053] The sensitivity sequence S of the pressure monitoring point (physical sensor) of the j-th pipe section burst provided by the specific embodiment of the present invention is j for:

[0054] S j =[s j1 ,s j2 ,…s ji …,s jN ]

[0055]

[0056] Where N is the number of pressure monitoring points, s ji is the sensitivity of the i-th pressure monitoring point when the j-th pipe section bursts, The occurrence intensity is The pressure drop value detected by the physical sensor at the i-th pressure monitoring point when the pipe bursts, σ i is the pressure standard deviation of the monitoring value at the i-th pressure monitoring point under normal working conditions. The sensitivity of the pressure monitoring point provided by the present invention can better reflect the sensitivity of the physical sensor in monitoring pipe burst. The reason is that a relatively small standard deviation σ represents a relatively small pressure fluctuation of the sensor under normal working conditions. The pressure under normal working conditions is relatively stable. The pressure drop after the pipe burst will have a more obvious deviation compared with the normal working conditions, and the sensor has a higher sensitivity to the pipe burst event. If the standard deviation σ of the sensor under normal working conditions is relatively large, it means that the pressure value fluctuates greatly under normal working conditions, and the pressure drop signal after the pipe burst is easily submerged in the normal fluctuation, and the sensor has a lower sensitivity to the pipe burst event.

[0057] The normalized sensitivity matrix provided by the specific embodiment of the present invention for

[0058]

[0059] Where N is the number of pressure monitoring points and M is the number of pipe sections.

[0060] S3, based on the normalized sensitivity matrix, use the K-means clustering algorithm to cluster each pipe segment to obtain the cluster center pipe segment set, that is, the T root cluster center pipe segment sequence [p1, p2, ..., p l ,…,p T ], the present invention clusters each pipe section based on the normalized sensitivity matrix, and realizes that the pipe sections with similar sensor burst responses are divided into one cluster, which is beneficial to the optimization of the ordered genetic algorithm and improves the optimization efficiency.

[0061] S4. The specific embodiment of the present invention uses a genetic algorithm to select the optimal virtual sensor for each cluster center pipe segment, including:

[0062] S41. Setting initial values ​​of parameters of the genetic algorithm, wherein the parameters of the genetic algorithm include the number of physical sensors, population size, number of iterations, objective function, constraints, crossover probability and mutation probability. Constructing a population of virtual sensors based on different gene combinations, i.e., cooperative combinations of physical sensors, and treating each virtual sensor as an individual. The genes referred to in the specific embodiment of the present invention are physical sensors.

[0063] The objective function provided by the specific embodiment of the present invention is the maximum value of the virtual sensor burst monitoring sensitivity. The virtual sensor pressure monitoring sensitivity is the ratio of the pressure drop value detected by multiple physical sensors fused by the virtual sensor when the pipe bursts to the pressure standard deviation of the virtual sensor monitoring value under normal working conditions. The virtual sensor provided by the specific embodiment of the present invention is obtained by the cooperation of multiple physical sensors. The purpose of constructing the objective function in the present invention is to find the optimal combination of physical sensors corresponding to the cluster center pipe section, so that when a pipe burst occurs in the cluster center pipe section, the virtual sensor fused by the optimal combination of physical sensors has a higher pipe burst sensitivity, that is, it has fewer blind spots and a higher fault tolerance rate.

[0064] In a specific embodiment, the objective function of the genetic algorithm is:

[0065]

[0066]

[0067]

[0068] in, The strength of the pipe segment j is When the pipe bursts, the gth virtual sensor v g The pressure drop value, is the gth virtual sensor v g The burst pressure drop value of the fused mth physical sensor, K is the gth virtual sensor v g The number of physical sensors fused, is the virtual sensor v g The standard deviation of pressure, H gm is the virtual sensor v g The pressure monitoring value of the mth physical sensor is fused, Refers to the virtual sensor v g The covariance of the pressure monitoring values ​​between the fused m1th physical sensor and the m2th physical sensor, when m1=m2, the covariance is the variance of the physical sensor.

[0069] The constraint condition provided by the specific embodiment of the present invention is that the maximum value of the sensitivity of the virtual sensor for pipe burst monitoring of each cluster center pipe segment is greater than the maximum value of the sensitivity of each physical monitoring point.

[0070] The constraints of the genetic algorithm provided by the specific embodiment of the present invention are:

[0071] Subject to:△E j >0

[0072] △E j =Evj -E wj

[0073]

[0074]

[0075] Among them, △E j is the sensitivity increase of the virtual sensor relative to the physical sensor when pipe burst occurs in pipe segment j, E wj is the maximum sensitivity of the pressure monitoring point of the physical sensor corresponding to the pipe section j when the pipe bursts, E vj is the maximum value of the virtual sensor pressure monitoring sensitivity when pipe section j bursts, N is the number of physical sensors, 2 N -N-1 is the number of virtual sensors generated by the collaboration, The strength of the burst pipe.

[0076] S42, combining the physical sensors in the cluster center pipe section, that is, randomly generating a water supply network pressure monitoring point cooperation plan to construct a plurality of virtual sensors, and constructing an initial parent population based on the plurality of virtual sensors.

[0077] The initial parent population V with a population size of Z provided in a specific embodiment of the present invention is:

[0078]

[0079] S43. According to the constraint conditions, replace the individuals in the initial parent population of step S42 that do not meet the constraint conditions, so that the individuals in the initial parent population all meet the constraint conditions.

[0080] S44. Use the objective function to evaluate the fitness of individuals in the initial parent population that meet the constraints, and retain the individuals with the best fitness.

[0081] S45. Perform crossover and mutation operations on the initial parent population that meets the constraints to obtain the offspring population, and degenerate the individuals in the offspring population that do not meet the constraints to the original individuals.

[0082] S46. After merging the parent population and the child population that meet the constraints, the fitness of the individuals is evaluated through the objective function, and the individuals with the best fitness are retained. At the same time, the top Z high-fitness individuals of no more than the population size Z are selected according to the fitness sorting to form a new parent population.

[0083] S47. Repeat steps S43-S46 until the number of iterations is reached, evaluate the retained individual with the best fitness again through the objective function, use the individual with the highest fitness as the optimal virtual sensor of the cluster center segment, and add the optimal virtual sensor to the gain system.

[0084] S48. De-duplication of multiple optimal virtual sensors corresponding to the cluster center pipe section set is performed to obtain a physical sensor cooperative gain system that can achieve improved pipe burst monitoring: a genetic algorithm is used to screen out the optimal virtual sensor of each cluster center pipe section in the cluster center pipe section set to obtain an optimal virtual sensor set, and duplicate virtual sensors in the optimal virtual sensor set are removed to obtain a physical sensor cooperative gain system.

[0085] The specific embodiment of the present invention provides a specific process of constructing a cooperative gain objective function and constraint conditions, including:

[0086] For a pipe burst monitoring system consisting of N pressure monitoring points, s i ∈S(i=1,...,N). For a monitoring point s i , whose measured value is H i , approximately obey If a pipe segment j has a strength burst pipe, pressure monitoring points i A pressure drop signal will be received Therefore, based on the assumptions of unexploded pipes and exploded pipes, the readings of the pressure monitoring points can be obtained as follows:

[0087]

[0088]

[0089]

[0090] in, and They represent the two hypotheses of unexploded pipe and exploded pipe respectively, H i is the monitoring point s i The pressure measurement value, is the pressure value when the pipe is not burst. is the pressure drop caused by pipe burst, is the monitoring point s i The pressure alarm threshold.

[0091] according to Assumption (no burst pipe), monitoring point s i The false alarm rate can be expressed as:

[0092]

[0093] Where F(x) is the cumulative probability distribution function of the standard normal distribution.

[0094] According to the Neyman-Pearson criterion, the alarm threshold can be determined by ensuring that the false alarm rate does not exceed the specified value, setting the monitoring point s i The upper limit of false positive rate is but:

[0095]

[0096] According to formula (4) (6), we can get monitoring point s i Alarm threshold

[0097]

[0098] When there is a pipe burst in the system, (burst pipe) hypothesis, monitoring point s i The underreporting rate of pipe burst events is:

[0099]

[0100] In the specific embodiment of the present invention, the measured values ​​of the physical sensors are fused by the sum rule in the value fusion principle to generate the virtual sensor monitoring value. g1 、w g2 ... gk Reading H g1 , H g2 ...H gk Add and get the virtual sensor v g Monitoring value:

[0101]

[0102] Where k is the cooperation degree of the virtual sensor, k≤N, w gm It means that the virtual sensor v g The mth physical sensor associated.

[0103] The virtual sensor v provided by the specific embodiment of the present invention g The alarm model is as follows:

[0104]

[0105]

[0106]

[0107]

[0108] According to formula (8), it is easy to get:

[0109]

[0110]

[0111]

[0112] in, is the virtual sensor v g The mean pressure, is the virtual sensor v g The standard deviation of pressure.

[0113] The specific embodiment of the present invention is based on (unexploded pipe) assumption, virtual sensor v g The false alarm rate can be expressed as:

[0114]

[0115] To avoid additional false alarms, set the same false alarm rate threshold as a single physical sensor. The Neyman-Pearson criterion is used to determine the virtual sensor alarm threshold:

[0116]

[0117] When there is a pipe burst in the system, (burst pipe) assumption, the false alarm rate of the virtual sensor for the burst pipe event is shown in formula (18):

[0118]

[0119] It is expected that the constructed virtual sensor can reduce the missed alarm rate of pipe burst without causing additional false alarms. However, due to the particularity of the water supply system, not all cooperation of physical sensors can achieve the gain effect. Instead, their simple combination may lead to the deterioration of monitoring performance. In order to achieve the above goals, it is necessary to reasonably design the combination scheme and degree of cooperation of the physical sensors involved in the cooperation. The gain system design is expressed as an optimization problem.

[0120] Assume that the physical sensors constitute a set W = {w1,w2,…,w r ,…w N}, virtual sensors constitute a set where w r ,v g Represent the rth physical sensor and the gth virtual sensor respectively. According to the false alarm rate formulas (8)(18) of physical sensors and virtual sensors, when pipeline j When the burst flow rate is large or small, any physical sensor w r With virtual sensor v g The decrease in the false negative rate between for:

[0121]

[0122] in, It is the ratio of the deviation degree of the abnormal value after the pipe burst to the normal standard deviation, which reflects the monitoring sensitivity of the sensor to the pipe burst event.

[0123] Maximize the drop in false negative rate As a design goal to achieve gain system optimization. Considering that for a certain event The maximum sensitivity of a physical sensor is a constant, so maximizing It is equivalent to finding the virtual sensor with the greatest sensitivity.

[0124]

[0125]

[0126]

[0127] in, is the virtual sensor v g The strength of pipe segment j is The pressure drop value of the burst pipe, is the virtual sensor v g The burst pressure drop value of the fused mth physical sensor, K is the virtual sensor v g The number of physical sensors fused, is the virtual sensor v g The standard deviation of pressure, H gm is the virtual sensor v g The pressure monitoring value of the mth physical sensor is fused. On the basis of finding the most sensitive virtual sensor, the selected virtual sensor also needs to meet the constraint that the sensitivity improvement is greater than 0. For the physical sensor, when pipeline j When the burst flow rate is large or small, the maximum sensitivity of the physical sensor is:

[0128]

[0129] The sensitivity gain of the virtual sensor can be expressed as the sensitivity of the virtual sensor minus the maximum sensitivity of the physical sensor:

[0130] △E j =E vj -E wj (24) To achieve the virtual sensor gain, the constraints that need to be met are:

[0131] Subject to: △E j>0 (25)

[0132] △E j =E vj -E wj (26)

[0133]

[0134] Among them, △E j is the improvement of the burst sensitivity of the virtual sensor with respect to pipe segment j, E wj is the maximum burst sensitivity of the physical sensor with respect to pipe segment j, E vj is the maximum burst sensitivity of the virtual sensor with respect to pipe segment j, N is the number of physical sensors, 2 N -N-1 is the number of virtual sensors generated by the collaboration.

[0135] A specific embodiment of the present invention provides a pipe burst alarm model for physical sensors and virtual sensors. The present invention evaluates the reduction in the false alarm rate of virtual sensors compared to the original optimal physical sensors. The proposed K-means clustering algorithm based on pipe burst sensitivity of pipe segments focuses on reducing the number of similar pipe burst response segments to improve the optimization efficiency when applying genetic algorithms in subsequent traversal of pipe segments. The proposed genetic algorithm avoids traversing exponential virtual sensor cooperation schemes when there are many physical sensors by setting the goal and constraints of maximum positive sensitivity improvement, and can obtain a relatively optimal solution under limited computing power. In general, the present invention not only improves the pipe burst identification accuracy of the water supply network pipe burst monitoring system through the cooperation of sensors, but also provides an efficient and convenient coupling algorithm for the design of the optimal cooperation scheme of sensors.

[0136] The confidence information coverage provided by the present invention means: given a monitoring point s i The upper limit of false positive rate and false negative rate When the strength of pipe segment j is When the pipe bursts, the monitoring point s i The false positive rate is less than And the false negative rate is less than Then the pipe section j is called the monitoring point s i Coverage. The pipe burst monitoring within the coverage of the monitoring point has sufficient confidence. The high-sensitivity virtual sensor generated by the cooperation of physical sensors in the present invention has a lower false alarm rate than the original physical sensor while meeting the upper limit of the false alarm rate. Therefore, it is expected to achieve a larger area coverage and reduce the coverage blind area.

[0137] In one embodiment, the water supply network provided in this embodiment is composed of 2 reservoirs, 782 nodes and 905 pipelines. Figure 2 shown.

[0138] The main parameters of the genetic algorithm provided in this embodiment are set as follows: the number of pressure sensors is 8, the population size is 100, the number of iterations is 200, the crossover probability is 0.2, and the mutation probability is 0.2. Figure 1 The design process shown in the figure, the final gain system includes 13 virtual sensor combinations with the highest pipe burst sensitivity, as shown in Table 1, including 3 physical sensors and 10 virtual sensors. The embodiment of the present invention uses the generated gain system for pipe burst monitoring in this case, and uses four indicators, namely, confidence information coverage (CIC), minimum coverage flow (MCF), number of unmonitored pipe sections, and alarm probability, to compare and evaluate the original monitoring system and the gain monitoring system proposed by the present invention.

[0139] Table 1 shows the gain system sensor composition provided in Example 1

[0140]

[0141] exist Under the condition of burst outflow, the burst delay simulation of the pipe network is carried out for 24 hours a day, and the results are Figure 3 The figure shows the changes in the confidence information coverage of the physical monitoring system and the gain system and the average flow rate of the pipe section at different times. Figure 3 It can be seen that the confidence information coverage of the pipe network by the gain system proposed in the present invention is higher than that of the original physical monitoring system at any time, and the average coverage increase is as high as 25.34%. The confidence information coverage fluctuates over time, which is mainly due to the positive correlation between the intensity of the burst pressure drop signal and the flow rate of the pipe network.

[0142] Further simulations of the changes in the average minimum coverage flow (MCF) and the number of unmonitored pipe sections at different times show the following results: Figure 4 As shown. Figure 4 It can be seen that the minimum coverage flow of the gain system (blue five-pointed star) at each moment is significantly lower than that of the physical system (blue triangle), with an average reduction of up to 29.69m 3 / h. This reflects the outstanding benefits of the sensor cooperative gain method proposed in the present invention in improving the accuracy of pipe burst identification. At the same time, the gain system (red five-pointed star) also significantly reduces the number of unmonitored pipe sections compared to the physical monitoring system (red triangle). Especially at 4, 21 pipe sections that were originally unmonitored due to pipe burst were identified by the monitoring system under the effect of gain. This shows that the cooperative gain method proposed in the present invention can improve the monitoring system's ability to monitor pipe bursts in blind areas.

[0143] In order to verify the effect of the cooperative gain method proposed in this invention in improving the alarm accuracy, the false alarm rate and average missed alarm rate of the system before and after cooperation at different times are calculated using the proposed calculation formula. The results are as follows: Figure 5As shown. As can be seen from the figure, the false alarm rate of the gain system (red five-pointed star) is basically unchanged compared with the physical system (red triangle), and the maximum increase is only 0.16%. For 24 simulation moments, the average missed alarm rate of the gain system (blue five-pointed star) for each pipe section is less than that of the physical system (blue triangle), and the maximum average missed alarm rate reduction is 11.81% when the degree is 4. This shows that the cooperative gain method proposed in the present invention can reduce the missed alarm rate of pipe bursts without worsening the false alarm rate, thereby improving the alarm accuracy of the monitoring system.

[0144] In summary, the sensor cooperative gain-based method proposed in the present invention can be more effectively used for pipe burst monitoring in water supply systems than traditional physical monitoring systems.

Claims

1. A method for monitoring pipe burst in a water supply network based on cooperative gain of physical sensors, characterized in that: include: The burst simulation is performed on each pipe section of the water supply network to obtain the pressure monitoring point sensitivity sequence of each pipe section. The pressure monitoring point sensitivity is the ratio of the pressure drop value detected by the corresponding physical sensor when the pipe bursts to the pressure standard deviation of the corresponding physical sensor monitoring value under normal working conditions. Based on the normalized pressure monitoring point sensitivity sequence of each pipe section, the K-means clustering algorithm is used to cluster each pipe section to obtain the cluster center pipe section set. The optimal virtual sensor of each cluster center pipe section is selected by genetic algorithm, and multiple optimal virtual sensors corresponding to the cluster center pipe section set are deduplicated to obtain a physical sensor cooperative gain system that can improve pipe burst monitoring. The objective function of the genetic algorithm is to maximize the virtual sensor monitoring sensitivity, which is the ratio of the sum of the pressure drop values ​​detected by multiple physical sensors fused by the virtual sensor when the pipe bursts to the pressure standard deviation of the virtual sensor monitoring value under normal working conditions, and the virtual sensor is composed of multiple physical sensors; The constraint condition of the genetic algorithm is that the maximum value of the monitoring sensitivity of the virtual sensor of each cluster center pipe segment is greater than the maximum value of the sensitivity of each physical sensor monitoring point.

2. The water supply pipe burst monitoring method based on physical sensor cooperative gain according to claim 1 is characterized in that: The sensitivity sequence S of the pressure monitoring points when the jth pipe section bursts j for: S j =[s j1 ,s j2 ,…s ji …,s jN ] Where N is the number of pressure monitoring points, s ji is the sensitivity of the i-th pressure monitoring point when the j-th pipe section bursts, The occurrence intensity is The pressure drop value detected at the i-th pressure monitoring point when the pipe bursts, σ i is the pressure standard deviation of the physical sensor monitoring value at the i-th pressure monitoring point under normal working conditions.

3. The water supply pipe burst monitoring method based on physical sensor cooperative gain according to claim 1 is characterized in that: The objective function of the genetic algorithm is: in, The strength of the pipe segment j is When the pipe bursts, the gth virtual sensor v g The pressure drop value, is the gth virtual sensor v g The burst pressure drop value of the fused mth physical sensor, K is the gth virtual sensor v g The number of physical sensors fused, is the virtual sensor v g The standard deviation of pressure, H gm is the virtual sensor v g The pressure monitoring value of the mth physical sensor is fused, Refers to the virtual sensor v g The covariance of the pressure monitoring values ​​between the fused m1th physical sensor and the m2th physical sensor, when m1=m2, the covariance is the variance of the physical sensor.

4. The water supply pipe burst monitoring method based on physical sensor cooperative gain according to claim 1 is characterized in that: The constraints of the genetic algorithm are: Subject to:△E j >0 △E j =And vj -AND wj Among them, △E j is the sensitivity increase of the virtual sensor compared to the physical sensor when pipe burst occurs in pipe segment j, E wj is the maximum sensitivity of the pressure monitoring point of the physical sensor corresponding to the pipe section j when the pipe bursts, E vj is the maximum value of the virtual sensor pressure monitoring sensitivity when pipe section j bursts, N is the number of physical sensors, 2 N -N-1 is the number of virtual sensors generated by the collaboration, The strength of the burst pipe.

5. The water supply pipe burst monitoring method based on physical sensor cooperative gain according to claim 1 is characterized in that: Genetic algorithm is used to select the optimal virtual sensor for each cluster center pipe segment, including: S1. Setting initial values ​​of parameters of the genetic algorithm, wherein the parameters of the genetic algorithm include the number of physical sensors, population size, number of iterations, crossover probability and mutation probability, constructing a population based on virtual sensors with different gene combinations, and treating each virtual sensor as an individual; S2, combining the physical sensors in the cluster center pipe segment to construct a plurality of virtual sensors, and constructing an initial parent population based on the plurality of virtual sensors; S3. In the initial parent population, replace the individuals that do not meet the constraints so that all individuals in the initial parent population meet the constraints; S4. Use the objective function to evaluate the fitness of individuals in the initial parent population that meet the constraints, and retain the individuals with the best fitness; S5. Perform crossover and mutation operations on the initial parent population that meets the constraints to obtain the offspring population, and degenerate the individuals in the offspring population that do not meet the constraints to the original individuals; S6, after merging the parent population and the child population that meet the constraints, evaluate the fitness of the individuals through the objective function, retain the individuals with the best fitness, and select the top X high fitness individuals of no more than the population size X according to the fitness sorting to form a new parent population; S7. Repeat steps S3-S6 until the number of iterations is reached, and the retained individual with the best fitness is evaluated again through the objective function, and the individual with the highest fitness is used as the optimal virtual sensor of the cluster center segment.

6. The water supply pipe burst monitoring method based on physical sensor cooperative gain according to claim 5 is characterized in that: The iteration number of the genetic algorithm is 50-200 times, the crossover probability is 0.2-0.5, and the mutation probability is 0.2-0.

3.

7. The water supply network burst monitoring method based on physical sensor cooperative gain according to claim 1 is characterized in that: The multiple optimal virtual sensors corresponding to the cluster center pipe section set are deduplicated to obtain a physical sensor cooperative gain system that can improve pipe burst monitoring, including: A genetic algorithm is used to screen out the optimal virtual sensor of each cluster center pipe section in the cluster center pipe section set to obtain multiple optimal virtual sensors, and duplicate virtual sensors in the multiple optimal virtual sensors are removed to obtain a physical sensor cooperative gain system.

8. The water supply pipe burst monitoring method based on physical sensor cooperative gain according to claim 1 is characterized in that: The parameters of the K-means clustering algorithm include the pipe network hydraulic model, the location of the pressure monitoring point, the number of clusters, and the simulated pipe burst intensity.

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