CN113446519A - Method, electronic device and storage medium for determining leakage degree of pipe network - Google Patents
Method, electronic device and storage medium for determining leakage degree of pipe network Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
- F17D5/02—Preventing, monitoring, or locating loss
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
- F17D5/005—Protection or supervision of installations of gas pipelines, e.g. alarm
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M3/00—Investigating fluid-tightness of structures
- G01M3/02—Investigating fluid-tightness of structures by using fluid or vacuum
- G01M3/26—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors
- G01M3/28—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors for pipes, cables or tubes; for pipe joints or seals; for valves ; for welds
- G01M3/2807—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors for pipes, cables or tubes; for pipe joints or seals; for valves ; for welds for pipes
- G01M3/2815—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors for pipes, cables or tubes; for pipe joints or seals; for valves ; for welds for pipes using pressure measurements
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Abstract
Embodiments of the present disclosure relate to a method, an electronic device, and a storage medium for determining a degree of leakage in a pipe network. According to the method, a plurality of first sample sets relating to the pressure of the pipe network are acquired at a predetermined sampling frequency in a plurality of consecutive sampling intervals; for each sampling interval, obtaining a second sample set based on a plurality of first sample sets collected in the sampling interval; selecting a plurality of second sample sets satisfying specific characteristics from all the second sample sets as training test samples; determining a pipe network pressure calculation model to be adopted based on the training test sample; and determining the leakage process of the pipe network based on the pipe network pressure calculation model to be adopted. Therefore, the leakage degree of the pressure of the pipe network can be determined quickly and accurately so as to prevent waste of energy.
Description
Technical Field
Embodiments of the present disclosure generally relate to the field of intelligent detection, and in particular, to a method, an electronic device, and a storage medium for determining a pipe network leakage degree.
Background
As the use of gases (e.g., compressed air, etc.) has become increasingly widespread, it is often desirable to transmit them from a gas supply device (such as an air compression station, etc.) to different gas users (e.g., gas use plants, etc.) through a network of pipes. However, in the process of transmitting gas through a pipe network, more gas often has to be transmitted due to leakage of pipes in the pipe network (referred to as pipe network leakage for short), and such increase of gas transmission amount causes further consumption of other energy sources (for example, electric energy), thereby causing huge energy waste and consumption cost. At present, the waste of energy caused by the leakage of a pipe network becomes one of the most common energy wastes in a factory. For example, in compressed air delivery systems, the leakage of average compressed air often accounts for 30% of the total compressed air production, which in turn can lead to tens of thousands of electricity costs per year. In addition, when the gas leakage phenomenon is increased, the pressure of the whole compressed air system is reduced, and if the pressure of the air system is maintained, an additional compressor must be started, which further increases the electricity consumption cost of the whole plant.
Therefore, there is a need for a method for determining the leakage level of a pipe network, which can rapidly and accurately determine the leakage level of the pipe network pressure, so that measures can be taken in time to prevent waste of energy.
Disclosure of Invention
In view of the above problems, the present disclosure provides a method and an electronic device for determining a leakage degree of a pipe network, which can quickly and accurately determine the leakage degree of the pipe network pressure, thereby preventing waste of energy.
According to a first aspect of the present disclosure, there is provided a method for determining a leakage degree of a pipe network, comprising: acquiring a plurality of first sample sets related to the pressure of the pipe network in a plurality of continuous sampling intervals at a predetermined sampling frequency, wherein each first sample set comprises the following first samples: the pressure of a gas production end and the pressure of a gas production end of the pipe network at corresponding sampling time points; for each sampling interval, obtaining a second set of samples based on a plurality of first sets of samples acquired during the sampling interval, the second set of samples comprising the following second samples: the average gas production end pressure, the average gas production end pressure change rate and the average gas production end pressure of the pipe network in the corresponding sampling interval; selecting a plurality of second sample sets satisfying specific characteristics from all the second sample sets as training test samples; determining a pipe network pressure calculation model to be adopted based on the training test sample; and determining the leakage degree of the pipe network based on the pipe network pressure calculation model to be adopted.
According to a second aspect of the present disclosure, there is provided an electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect of the disclosure.
In a third aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of the first aspect of the present disclosure.
In some embodiments, each first set of samples further comprises at least one of the following first samples: and the pipe network has instantaneous flow at a gas production end, gas production at the gas production end, gas temperature at the gas production end and gas temperature at the gas production end at corresponding sampling time points.
In some embodiments, the second set of samples further comprises at least one of the following second samples: the average gas production end instantaneous flow, the average gas production end gas production rate, the average gas production end gas temperature and the average gas production end gas temperature of the pipe network in the corresponding sampling interval.
In some embodiments, selecting a plurality of second sample sets satisfying the specific feature from all the second sample sets as training test samples comprises: and selecting a plurality of second sample sets in a plurality of time periods in which the average gas production end pressure change rate is kept constant from all the second sample sets as the training test samples, wherein each time period in the plurality of time periods is a subset of the plurality of continuous sampling intervals.
In some embodiments, the method further comprises exception handling for each second sample set before selecting a plurality of second sample sets satisfying the specific feature from all second sample sets as training test samples.
In some embodiments, exception handling for each second set of samples comprises: analyzing each second sample set to determine whether missing second samples or redundant second samples or abnormal second samples are included in the second sample set, in response to determining that missing second samples are included in the second sample set, interpolating or deleting the missing second samples; in response to determining that redundant second samples are included in the set of second samples, deleting the redundant second samples; in response to determining that a second sample of the second set of samples includes an anomaly, analyzing a cause of generation of the anomalous second sample; if the abnormal second sample is determined to be caused by the shutdown of the pipe network equipment, setting the second sample to zero; deleting the second sample set and deleting the second sample set in a previous sampling interval and a next sampling interval of the second sample set if the abnormal second sample is determined to be caused by the callback to zero of the corresponding sensor; deleting the second set of samples of the anomaly if it is determined that the second sample of the anomaly is due to a failure of the corresponding sensor.
In some embodiments, determining a pipe network pressure calculation model to employ based on the training test samples comprises: respectively training a plurality of pipe network pressure calculation models by utilizing different machine learning models based on the first subset of the training test samples; verifying the trained pipe network pressure calculation models respectively based on the second subset of the training test samples to select the pipe network pressure calculation model with the highest accuracy from the trained pipe network pressure calculation models; and optimizing the hyper-parameters of the selected pipe network pressure calculation model to obtain the pipe network pressure calculation model to be adopted.
In some embodiments, determining a degree of leakage in the pipe network based on the pipe network pressure calculation model comprises: obtaining a second set of samples for a current sampling interval based on a plurality of first sets of samples acquired at the predetermined sampling frequency within the current sampling interval; inputting a second sample set aiming at the current sampling interval into the pipe network pressure calculation model to be adopted so as to obtain the actual gas end pressure; determining the actual pressure drop of the pipe network based on the average gas production end pressure and the actual gas use end pressure in the second sample set aiming at the current sampling interval; calculating the normal pipe network pressure drop by using a standard pipe network pressure drop calculation formula; and determining the leakage degree of the pipe network based on the actual pipe network pressure drop and the normal pipe network pressure drop.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
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The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements.
FIG. 1 shows a graph comparing the pressure at the gas producing end and the gas using end of a piping network.
Fig. 2 shows a schematic diagram of a system 2 for a method of determining a degree of leakage in a pipe network according to an embodiment of the invention.
Fig. 3 shows a flow chart of a method 300 for determining a pipe network leak level according to an embodiment of the present disclosure.
Fig. 4 shows an illustrative schematic of a compressed air delivery system 400.
Fig. 5 shows a schematic diagram of a random forest model.
Fig. 6 illustrates a flow diagram of a method 600 for determining a ductwork pressure calculation model to employ, in accordance with an embodiment of the present disclosure.
Fig. 7 shows a flow chart of a method 700 for determining a degree of leakage in a pipe network based on a pipe network pressure calculation model, according to an embodiment of the present disclosure.
Fig. 8 shows a block diagram of an electronic device 800 according to an embodiment of the disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The term "include" and variations thereof as used herein is meant to be inclusive in an open-ended manner, i.e., "including but not limited to". Unless specifically stated otherwise, the term "or" means "and/or". The term "based on" means "based at least in part on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first," "second," and the like may refer to different or the same object. Other explicit and implicit definitions are also possible below.
As mentioned above, in the process of transporting gas through a pipe network, huge energy waste and cost are often incurred due to leakage of the pipes in the pipe network. Currently, ultrasonic detectors are commonly used to measure gas leaks in such networks by detecting the ultrasonic signals generated by the compressed gas leak to locate the leak and determine the severity of the leak. However, the ultrasonic detector is expensive, requires a special knowledge and skill of the operator, and is expensive to learn. In addition, because the ultrasonic probe of the ultrasonic detector belongs to disposable equipment, the cost problem is considered, and an operator cannot frequently use the ultrasonic probe to detect, so that the problem cannot be found in time when the leakage of a pipe network occurs, and further more energy waste is caused.
In addition, when the gas is transmitted from the gas supply device to each gas user through the pipe network, a certain pressure loss is usually generated, so that a certain difference (referred to as pressure drop) exists between the pressure at the gas production end of the pipe network and the pressure at the gas use end of the pipe network. Such pressure drops are typically caused by friction created by pipe network resistance (e.g., resistance caused by gas lines, valves, bends, changes in gas flow direction, throttling, etc.), and leakage from the pipe network. The actual pressure drop that occurs when gas flows in a network therefore typically includes the following two components: the first part is normal pressure drop caused by friction force generated by pipe network resistance, and the normal pressure drop is inevitable in a pipe network and can be usually calculated by adopting a standard pipe network pressure drop calculation formula; the second part is the pressure drop caused by gas leakage etc., which is often referred to as leakage pressure drop. It follows that pressure drop is an important parameter for determining the degree of leakage in a pipe network. Generally, a gas production end and a gas use end of a pipe network (for example, a gas use end of a use workshop) are respectively provided with a pressure sensor for measuring the pressure of the gas production end and the pressure of the gas use end of the pipe network. However, as shown in fig. 1, the gas demand of the gas using end often changes with the change of the working condition, and since components such as the gas storage tank and the pipeline in the pipe network have a certain volume, the pressure stabilizing function can be achieved, so the pressure change of the gas using end is not immediately shown on the pressure data of the gas generating end, but has a certain time delay. That is, the delay time between the pressure change of the gas using end and the pressure change of the gas producing end is related to the volume of the gas storage tank and the pipeline and the gas storage level of the current pipe network, for example. Therefore, the pressure at the gas production end and the pressure at the gas utilization end respectively detected by the pressure sensors cannot directly calculate the actual pressure drop of the pipe network, and therefore, the actual pressure drop cannot be used for accurately determining the leakage pressure drop of the pipe network.
To address at least in part one or more of the above issues and other potential issues, an example embodiment of the present disclosure provides a method for determining a pipe network leak level, comprising: acquiring a plurality of first sample sets related to the pressure of the pipe network in a plurality of continuous sampling intervals at a predetermined sampling frequency, wherein each first sample set comprises the following first samples: the pressure of a gas production end and the pressure of a gas production end of the pipe network at corresponding sampling time points; for each sampling interval, obtaining a second set of samples based on a plurality of first sets of samples acquired during the sampling interval, the second set of samples comprising the following second samples: the average gas production end pressure, the average gas production end pressure change rate and the average gas production end pressure of the pipe network in the corresponding sampling interval; selecting a plurality of second sample sets satisfying specific characteristics from all the second sample sets as training test samples; determining a pipe network pressure calculation model to be adopted based on the training test sample; and determining the leakage degree of the pipe network based on the pipe network pressure calculation model to be adopted. In this way, it is possible to determine the degree of leakage of the pressure in the network quickly and accurately, so that measures can be taken in time to prevent waste of energy.
Fig. 2 shows a schematic diagram of a system 2 for implementing a method for determining a pipe network leakage level according to an embodiment of the invention. As shown in fig. 2, system 2 includes a user terminal 10, a computing device 20, a server 30, and a network 40. User terminal 10, computing device 20, and server 30 may interact with data via network 40. Here, each user terminal 10 may be a mobile or fixed terminal of an end user, such as a mobile phone, a tablet computer, a desktop computer, or the like. The user terminal 10 may communicate with the server 30 providing the network monitoring service, for example, through a network monitoring application installed thereon, to transmit information to the server 30 and/or receive information from the server 30. The computing device 20 performs corresponding operations based on data from the user terminal 10 and/or the server 30. The computing device 20 may include at least one processor 210 and at least one memory 220 coupled to the at least one processor 210, the memory 220 having stored therein instructions 230 executable by the at least one processor 210, the instructions 230, when executed by the at least one processor 210, performing at least a portion of the method 100 as described below. Note that herein, computing device 20 may be part of server 30 or may be separate from server 30. The specific structure of computing device 20 or server 30 may be described, for example, in connection with FIG. 8, below.
Fig. 3 shows a flow chart of a method 300 for determining a pipe network leak level according to an embodiment of the present disclosure. The method 300 may be performed by the computing device 20 as shown in FIG. 2, or may be performed at the electronic device 800 shown in FIG. 8. It should be understood that method 300 may also include additional blocks not shown and/or may omit blocks shown, as the scope of the disclosure is not limited in this respect.
At step 302, a plurality of first sample sets relating to the pipe network pressure are acquired at a predetermined sampling frequency over a plurality of consecutive sampling intervals, each first sample set comprising at least the following first samples: and the pressure of the gas production end and the pressure of the gas use end of the pipe network at corresponding sampling time points. In some embodiments, to ensure the training effect of the pipe network pressure calculation model to be employed determined in step 308, each first sample set may further include at least one of the following first samples: the instantaneous flow of a gas production end, the accumulated flow of the gas production end, the gas temperature of the gas production end and the gas temperature of the gas production end of the pipe network at corresponding sampling time points. For example, in some embodiments, in addition to the gas production end pressure and the gas use end pressure, the gas production end instantaneous flow, the gas production end accumulated flow, the gas production end gas temperature, and the gas use end gas temperature are also collected at each sampling time point, so as to be used for determining the pipe network pressure calculation model to be sampled. Of course, more or fewer first samples may be collected, as long as the pipe network pressure calculation model to be used can be accurately determined.
In the present disclosure, the gas production end pressure at each sampling time point refers to the gas pressure at the gas supply end of the pipe network at the sampling time point, which may be collected by a pressure sensor provided at the gas supply end (e.g., a pressure sensor provided at the gas production end of the mother pipe in the example shown in fig. 4). The gas end pressure at each sampling time point refers to the gas pressure at the gas use end of the pipe network at that sampling time point, which may be collected by a pressure sensor provided at the gas use end (e.g., in the example shown in fig. 4, the pressure sensor provided at the gas use end of the corresponding gas use plant). The instantaneous gas production end flow rate at each sampling time point refers to the gas production end gas flow rate flowing through the pipe network at the sampling time point, which can be collected by a flow rate sensor provided at the gas supply end (for example, a flow rate sensor provided at the gas inlet end of the main pipe in the example shown in fig. 4). The gas production end accumulated flow at each sampling time point refers to the accumulated flow passing through the gas supply end from the last sampling time point to the sampling time point, and can be obtained by calculating the difference between the gas production end instantaneous flow collected at the sampling time point and the gas production end instantaneous flow collected at the previous sampling time point. The gas temperature at the gas production end at each sampling time point refers to the gas temperature at the gas supply end of the pipe network at that sampling time point, which can be collected by a temperature sensor (not shown in fig. 4) provided at that gas supply end. The gas end gas temperature at each sampling time point refers to the gas temperature at the gas end of the pipe network at that sampling time point, which may be collected by a temperature sensor (not shown in fig. 4) provided at the gas end.
In the present disclosure, the sampling frequency is selected depending on the sampling precision to be achieved, and the time length of each sampling interval may be selected depending on the sampling frequency. In some embodiments, the sampling frequency may be such that the first set of samples is taken, for example, once every 1 second or few seconds, in which case the sampling interval may be a time length of, for example, 1 minute or 3 minutes, and the total sampling time length may be a time of 1-2 months. In some embodiments, the sampling frequency may also be such that the first set of samples is taken, for example, every 1 minute or several minutes, in which case the sampling interval may be a length of time of, for example, 15 minutes or more, and the total sampling time length may be a time of 2 months or more.
At step 304, for each sampling interval, a second set of samples is obtained based on the plurality of first sets of samples acquired within the sampling interval, the second set of samples including at least the following second samples: the average gas production end pressure, the average gas production end pressure change rate and the average gas production end pressure of the pipe network in the corresponding sampling interval. In some embodiments, in the case that each first sample set further includes at least one first sample of gas generation end instantaneous flow, gas generation end accumulated flow, gas generation end gas temperature and gas use end gas temperature of the pipe network at the corresponding sampling time point, the second sample set further includes at least one of the following second samples, respectively: the average gas production end instantaneous flow, the average gas production end gas production rate, the average gas production end gas temperature and the average gas production end gas temperature of the pipe network in the corresponding sampling interval.
In the present disclosure, the average gas production end pressure in each sampling interval refers to an average value of a plurality of gas production end pressures at a plurality of sampling time points in the sampling interval. The average gas production end pressure change rate in each sampling interval refers to the average value of the gas production end pressure change rates at a plurality of sampling time points in the sampling interval, and the gas production end pressure change rate at each sampling time point can be obtained by subtracting the gas production end pressure at the last sampling time point from the gas production end pressure at the sampling time point and then dividing the gas production end pressure by the length of the sampling period. The average gas end pressure in each sampling interval refers to an average value of a plurality of gas end pressures at a plurality of sampling time points in the sampling interval. The average gas production end instantaneous flow in each sampling interval refers to the average value of a plurality of gas production end instantaneous flows at a plurality of sampling time points in the sampling interval. The average gas production rate at the gas production end in each sampling interval refers to an average value of the gas production rates at the gas production ends at a plurality of sampling time points in the sampling interval, and the gas production rate at the gas production end at each sampling time point can be obtained by subtracting the gas production end accumulated flow rate at the last sampling time point from the gas production end accumulated flow rate at the sampling time point, for example. The average gas production end gas temperature in each sampling interval refers to the average value of the gas production end gas temperatures at a plurality of sampling time points in the sampling interval. The average gas end gas temperature in each sampling interval refers to an average value of the gas end gas temperatures at a plurality of sampling time points in the sampling interval.
In the present disclosure, since different sensors (e.g., the above-mentioned pressure sensor, flow meter and temperature sensor) are installed at different positions of the pipe network, it is difficult to ensure that their sampling times are completely synchronized, and thus the times of the acquired first samples are not precisely aligned. For example, for a sample, the gas production end pressure may be collected by the gas production end pressure sensor at 12:01 and the gas end pressure may be collected by the gas end pressure sensor at 12:02, by way of example only. Therefore, by combining a plurality of first sample sets collected in each sampling interval into one second sample set in the above manner, the first samples in each time interval can be aligned to the same time period, so that the above problems can be well overcome.
In step 306, a plurality of second sample sets satisfying a specific feature are selected from all the second sample sets as training test samples. In some embodiments, a plurality of second sample sets in a plurality of time periods in which the average gas production end pressure change rate is kept constant are selected from all the second sample sets as training test samples, wherein each time period is a subset of a plurality of continuous sampling intervals. For example only, if the average gas production end pressure change rate is constant within the 40 th-70 th sampling interval, the 100 th-300 th sampling interval, and the 600 th-850 th sampling interval, a plurality of second sample sets within these time periods may be selected as training test samples.
In the disclosure, by selecting a plurality of second sample sets satisfying a specific characteristic as training test samples, for example, by selecting a plurality of second sample sets in a plurality of time periods in which an average gas production end pressure change rate is kept constant as training test samples, it is possible to help eliminate pressure changes caused by changes in operating conditions of a gas end pressure, and further enable a gas end pressure calculation model based on the training to output the gas end pressure from which the aforementioned changes in operating conditions and delay effects are eliminated.
In some embodiments, before selecting a plurality of second sample sets satisfying the specific feature from all the second sample sets as training test samples, exception handling is further performed on each second sample set. By such exception processing, noise that may be present in the sample data can be removed, thereby contributing to an improvement in the accuracy of training.
In particular, a method for exception handling for a second set of samples may comprise:
analyzing each second sample set to determine whether the second sample set comprises a missing second sample or a redundant second sample or an abnormal second sample; in response to determining that there are missing second samples in the second set of samples, interpolating the missing second samples or deleting the second set of samples; deleting the redundant second samples in response to determining that redundant second samples exist in the second set of samples; in response to determining that there is an anomalous second sample in the set of second samples, analyzing a cause of the anomalous second sample; if the second sample of the abnormity is determined to be caused by the shutdown of the pipe network equipment, setting the second sample to zero; if the abnormal second sample is determined to be caused by the callback zero of the corresponding sensor, deleting the second sample set, and deleting the second sample set in the previous sampling interval and the next sampling interval of the second sample set; if it is determined that the second sample of the anomaly is due to a failure of the corresponding sensor, the second set of samples is deleted.
In the present disclosure, for example, if the second sample set includes three second samples, that is, the average gas production end pressure change rate, and the average gas use end pressure of the pipe network in the corresponding sampling interval, but the obtained second sample set lacks one of the second samples, it is indicated that the second sample set includes the missing second sample. And if the obtained second sample set includes additional second samples, the second sample set is indicated to include redundant second samples.
The second sample may have various exceptions and therefore may need to be handled differently for different exception cases. For example, since there may still be small flow data in a ductwork plant (e.g., an air compressor) at shutdown, the flow data should be zeroed. For another example, a sensor such as a flow meter may be zeroed when it is full, so that the corresponding data is set to zero, and thus the correct value cannot be reflected, so that the corresponding second sample set and the two previous and subsequent second sample sets may be deleted, so as not to affect the training accuracy. As another example, by performing an overall analysis on all the second sample sets, it is often found that a certain second sample is clearly different from the adjacent second sample, and then further analysis is needed, if the problem is a non-normal jump caused by a sensor failure, it indicates that the corresponding second sample is wrong, and therefore the second sample set associated with the second sample needs to be directly deleted.
In step 308, based on the training test samples, a pipe network pressure calculation model to be employed is determined. In the present disclosure, based on the training test samples, a plurality of machine learning models (e.g., linear regression models, random forest models, LightGBM models, etc.) may be respectively used to fit the gas end pressure to train to obtain a plurality of pipe network pressure calculation models, and then one pipe network pressure calculation model with the highest accuracy is selected as the pipe network pressure calculation model to be used. The method 500 for determining the pipe network pressure calculation model to be employed is described in further detail below in conjunction with fig. 6.
Of course, a predetermined machine learning model can be selected empirically to fit the gas end pressure, so as to train to obtain a pipe network pressure calculation model to be used. In some embodiments, a random forest model is selected to train the pipe network pressure calculation model.
In the present disclosure, when training the pipe network pressure calculation model by using the random forest model, a plurality of irrelevant decision trees may be trained for the random forest model (e.g., the random forest model as shown in fig. 5), and then the average of the results obtained by these decision trees may be used as the final result, for example. In order to make the decision trees trained for the random forest model uncorrelated, feature sampling and sample sampling may be performed on training samples (i.e., in this disclosure, a first subset of training test samples mentioned later) respectively, so that the distributions of training sample data for different decision trees are inconsistent (because the decision trees trained using training sample data of different distributions are less correlated than the decision trees trained using the same training sample data), and then a certain number of features are randomly selected for each decision tree (the number of selected features is a hyperparameter and needs to be specified before model training), and meanwhile, with the use of a put-back sampling, the same number of samples as the training samples are selected as the training sample data of the decision tree. The method for performing the back sampling is a sampling method, for example, a training sample has 10000 pieces of data, and the method for performing the back sampling is to extract 1 piece of data from 10000 pieces of data each time, and then repeat 10000 times to obtain a data set of 10000 pieces of data.
It should be appreciated that in the present disclosure, a hyper-parameter is a configuration parameter of the model, which is not directly involved in the training process of the model. During training, the model parameters are adjusted and updated, but the hyper-parameters are set to default values. After model training is complete, these hyper-parameters can be adjusted again, so that the process can train more scientifically a more efficient machine learning model.
In step 310, the leakage level of the pipe network is determined based on the pipe network pressure calculation model to be used. After the calculation model of the pressure of the pipe network to be used is determined, the actual pressure drop of the pipe network and thus the leakage degree of the pipe network can be determined based on the model. A method for determining the extent of leakage in a pipe network based on a pipe network pressure calculation model is described in more detail below with reference to fig. 7.
By adopting the means, the leakage degree of the pipe network can be determined more accurately.
Fig. 6 illustrates a flow diagram of a method 600 for determining a ductwork pressure calculation model to employ, in accordance with an embodiment of the present disclosure. The method 600 may be performed by the computing device 20 as shown in FIG. 2, or may be performed at the electronic device 800 shown in FIG. 8. It should be understood that method 600 may also include additional blocks not shown and/or may omit blocks shown, as the scope of the disclosure is not limited in this respect.
In step 602, a plurality of pipe network pressure calculation models are respectively trained by using different machine learning models based on the first subset of training test samples. As mentioned previously, the plurality of pipe network pressure calculation models may be trained using, for example, a linear regression model, a random forest model, a Light Gradient Boosting Machine (LightGBM) model, and the like, respectively.
In step 604, the trained pipe network pressure calculation models are verified respectively based on the second subset of the training test samples, so as to select a pipe network pressure calculation model with the highest accuracy from the trained pipe network pressure calculation models. In some embodiments, the Mean Absolute Percentage Error (MAPE) may be used as a measure of the accuracy of the computational model of pipe network pressure. In these embodiments, the pipe network pressure calculation model with the smallest MAPE is selected as the model to be used.
For each trained pipe network pressure calculation model, MAPE can be calculated based on the following equation (1):
in the above equation (1), n represents the number of second sample sets in the second subset. y' represents the average gas end pressure in the ith second sample set in the second subset. Y represents the calculated gas end pressure of the corresponding trained pipe network pressure calculation model based on the ith second sample set in the second subset.
In some embodiments, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), symmetric Mean Absolute Percentage Error (sematic Mean Absolute Percentage Error, sempe), or R-squared (R-squared ) may also be used2) And the measurement method is similar to MAPE and is different from the formula, so that further description is omitted here.
In step 606, the hyper-parameters of the selected pipe network pressure calculation model are optimized to obtain the pipe network pressure calculation model to be used. In some embodiments, a grid search or bayesian optimization may be used to optimize the hyperparameters of the selected pipe network pressure calculation model.
By adopting the above means, the present disclosure is able to select the most appropriate model for pipe network pressure calculation.
Fig. 7 shows a flow chart of a method 700 for determining a degree of leakage in a pipe network based on a pipe network pressure calculation model, according to an embodiment of the present disclosure. The method 700 may be performed by the computing device 20 as shown in FIG. 2, or may be performed at the electronic device 800 shown in FIG. 8. It should be understood that method 700 may also include additional blocks not shown and/or may omit blocks shown, as the scope of the present disclosure is not limited in this respect.
At step 702, a second set of samples for a current sampling interval is obtained based on a plurality of first sets of samples acquired at a predetermined sampling frequency within the current sampling interval. The sampling frequency in step 702 may be the same as or different from the sampling frequency in step 302, depending on the actual situation.
In step 704, the second sample set for the current sampling interval is input into the pipe network pressure calculation model to obtain the actual gas end pressure. In the present disclosure, as mentioned above, the resulting gas end pressure is the gas end pressure excluding the operating condition changes and the pipe network delays.
In step 706, an actual pipe network pressure drop is determined based on the average gas production end pressure included in the second sample set for the current sampling interval and the actual gas end pressure obtained in step 704. In the present disclosure, the actual pipe network pressure drop may be obtained by subtracting the average gas production end pressure from the actual gas end pressure above.
At step 708, the normal pipe network pressure drop is calculated using a standard pipe network pressure drop calculation formula. In the present disclosure, step 708 may also be performed before step 702. In some embodiments of the present disclosure, the normal piping network pressure drop may be calculated using the following equation (2).
In the above-mentioned formula (2),
representing the normal pipe network pressure drop (in bar). Q represents the volume flow, namely the instantaneous flow (unit is L/s) of the gas production end of the pipe network. d represents the inner diameter of the pipe (in mm). L represents the pipe length (in m). And p represents the exhaust absolute pressure and represents the pressure of the gas end.
It should be noted that, because there are usually curves and pipes with different diameters in the pipe network, in the above formula, the pipe diameter d refers specifically to the direction of the gas transmission pipe, and for the pipe length L, in addition to the pipe length from the gas generation end to the gas generation end, the pipe diameter change and the curve condition are also converted into the pipe length L.
In step 710, the leakage level of the pipe network is determined based on the actual pipe network pressure drop and the normal pipe network pressure drop. In the present disclosure, the leakage level of the pipe network can be calculated by subtracting the normal pipe network pressure drop from the actual pipe network pressure drop.
By adopting the means, the leakage state of the pipe network can be accurately determined.
Fig. 8 illustrates a schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure. For example, the computing device 20 as shown in fig. 2 may be implemented by the electronic device 800. As shown, the electronic device 800 includes a Central Processing Unit (CPU) 801 that may perform various appropriate actions and processes according to computer program instructions stored in a Read Only Memory (ROM) 802 or loaded from a storage unit 808 into a Random Access Memory (RAM) 803. In the random access memory 803, various programs and data required for the operation of the electronic apparatus 800 can also be stored. The central processing unit 801, the read only memory 802 and the random access memory 803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
A plurality of components in the electronic apparatus 800 are connected to the input/output interface 805, including: an input unit 806, such as a keyboard, a mouse, a microphone, and the like; an output unit 807 such as various types of displays, speakers, and the like; a storage unit 808, such as a magnetic disk, optical disk, or the like; and a communication unit 809 such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The various processes and processes described above, such as methods 300, 400, 600, and 700, may be performed by the central processing unit 801. For example, in some embodiments, methods 300, 400, 600, and 700 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 808. In some embodiments, some or all of the computer program can be loaded and/or installed onto device 800 via read only memory 802 and/or communications unit 809. When loaded into the random access memory 803 and executed by the central processing unit 801, the computer program may perform one or more of the actions of the methods 300, 400, 600 and 700 described above.
The present disclosure relates to methods, apparatuses, systems, electronic devices, computer-readable storage media and/or computer program products. The computer program product may include computer-readable program instructions for performing various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein were chosen in order to best explain the principles of the embodiments, the practical application, or technical improvements to the techniques in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. A method for determining a leak level in a piping network, comprising:
acquiring a plurality of first sample sets related to the pressure of the pipe network in a plurality of continuous sampling intervals at a predetermined sampling frequency, wherein each first sample set comprises the following first samples: the pressure of a gas production end and the pressure of a gas production end of the pipe network at corresponding sampling time points;
for each sampling interval, obtaining a second set of samples based on a plurality of first sets of samples acquired during the sampling interval, the second set of samples comprising the following second samples: the average gas production end pressure, the average gas production end pressure change rate and the average gas production end pressure of the pipe network in the corresponding sampling interval;
selecting a plurality of second sample sets satisfying specific characteristics from all the second sample sets as training test samples;
determining a pipe network pressure calculation model to be adopted based on the training test sample; and
and determining the leakage degree of the pipe network based on the pipe network pressure calculation model to be adopted.
2. The method of claim 1, wherein each first set of samples further comprises at least one of the following first samples: the pipe network has gas production end instantaneous flow, gas production end accumulated flow, gas production end gas temperature and gas production end gas temperature at corresponding sampling time points.
3. The method of claim 2, wherein the second set of samples further comprises at least one of the following second samples: the average gas production end instantaneous flow, the average gas production end gas production rate, the average gas production end gas temperature and the average gas production end gas temperature of the pipe network in the corresponding sampling interval.
4. The method of claim 1, wherein selecting a plurality of second sample sets satisfying a particular feature from all second sample sets as training test samples comprises:
and selecting a plurality of second sample sets in a plurality of time periods in which the average gas production end pressure change rate is kept constant from all the second sample sets as the training test samples, wherein each time period in the plurality of time periods is a subset of the plurality of continuous sampling intervals.
5. The method of claim 1, wherein the method further comprises exception handling for each second sample set before selecting a plurality of second sample sets satisfying a particular feature from all second sample sets as training test samples.
6. The method of claim 5, wherein exception handling for each second set of samples comprises:
analyzing each second sample set to determine whether the second sample set comprises a missing second sample or a redundant second sample or an abnormal second sample;
in response to determining that the second set of samples includes a missing second sample, interpolating the missing second sample or deleting the second set of samples;
in response to determining that redundant second samples are included in the set of second samples, deleting the redundant second samples;
in response to determining that a second sample of the second set of samples includes an anomaly, analyzing a cause of generation of the anomalous second sample;
if the abnormal second sample is determined to be caused by the shutdown of the pipe network equipment, setting the second sample to zero;
deleting the second sample set and deleting the second sample set in a previous sampling interval and a next sampling interval of the second sample set if the abnormal second sample is determined to be caused by the callback to zero of the corresponding sensor;
deleting the second set of samples of the anomaly if it is determined that the second sample of the anomaly is due to a failure of the corresponding sensor.
7. The method of claim 1, wherein determining a pipe network pressure calculation model to employ based on the training test samples comprises:
respectively training a plurality of pipe network pressure calculation models by utilizing different machine learning models based on the first subset of the training test samples;
verifying the trained pipe network pressure calculation models respectively based on the second subset of the training test samples to select the pipe network pressure calculation model with the highest accuracy from the trained pipe network pressure calculation models;
and optimizing the hyper-parameters of the selected pipe network pressure calculation model to obtain the pipe network pressure calculation model to be adopted.
8. The method of claim 1, wherein calculating a leak level of the pipe network based on the pipe network pressure calculation model to be employed comprises:
obtaining a second set of samples for a current sampling interval based on a plurality of first sets of samples acquired at a predetermined sampling frequency within the current sampling interval;
inputting a second sample set aiming at the current sampling interval into the pipe network pressure calculation model to be adopted so as to obtain the actual gas end pressure;
determining the actual pressure drop of the pipe network based on the average gas production end pressure and the actual gas use end pressure in the second sample set aiming at the current sampling interval;
calculating the normal pipe network pressure drop by using a standard pipe network pressure drop calculation formula;
and determining the leakage degree of the pipe network based on the actual pipe network pressure drop and the normal pipe network pressure drop.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.
10. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-8.
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Address after: No. 118, Building C1, No.1 Qingsheng Avenue, Nansha District, Guangzhou City, Guangdong Province, 518109 Patentee after: Guangdong Mushroom IoT Technology Co.,Ltd. Country or region after: China Address before: 518109 room 2202, building 1, Huide building, Beizhan community, Minzhi street, Longhua District, Shenzhen, Guangdong Patentee before: MOGULINKER TECHNOLOGY (SHENZHEN) CO.,LTD. Country or region before: China |