CN114352947A - Gas pipeline leakage detection method, system and device and storage medium - Google Patents
Gas pipeline leakage detection method, system and device and storage medium Download PDFInfo
- Publication number
- CN114352947A CN114352947A CN202111494477.6A CN202111494477A CN114352947A CN 114352947 A CN114352947 A CN 114352947A CN 202111494477 A CN202111494477 A CN 202111494477A CN 114352947 A CN114352947 A CN 114352947A
- Authority
- CN
- China
- Prior art keywords
- gas
- sequence
- gas pipeline
- leakage
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 54
- 238000012549 training Methods 0.000 claims abstract description 110
- 238000000034 method Methods 0.000 claims abstract description 35
- 230000000306 recurrent effect Effects 0.000 claims abstract description 34
- 238000013528 artificial neural network Methods 0.000 claims abstract description 33
- 238000012545 processing Methods 0.000 claims abstract description 19
- 238000010606 normalization Methods 0.000 claims abstract description 16
- 238000012163 sequencing technique Methods 0.000 claims abstract description 13
- 230000006870 function Effects 0.000 claims description 33
- 230000015654 memory Effects 0.000 claims description 9
- 238000012360 testing method Methods 0.000 claims description 9
- 239000000126 substance Substances 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 3
- 238000013473 artificial intelligence Methods 0.000 abstract description 2
- 238000007689 inspection Methods 0.000 description 9
- 230000009194 climbing Effects 0.000 description 6
- 230000003287 optical effect Effects 0.000 description 6
- 239000013307 optical fiber Substances 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 238000004880 explosion Methods 0.000 description 2
- 238000003331 infrared imaging Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 241000135164 Timea Species 0.000 description 1
- 230000001133 acceleration Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000008034 disappearance Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000000191 radiation effect Effects 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 230000006403 short-term memory Effects 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 238000010183 spectrum analysis Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
Images
Landscapes
- Examining Or Testing Airtightness (AREA)
Abstract
The invention discloses a method, a system, a device and a storage medium for detecting leakage of a gas pipeline, wherein the method comprises the following steps: acquiring first data of a preset gas pipeline, wherein the first data comprises pipeline pressure, gas flow and gas concentration; carrying out time sequencing and normalization processing on the first data to obtain a first sequence, wherein the first sequence comprises a pipeline pressure sequence, a gas flow sequence and a gas concentration sequence; determining a training sample and a corresponding label according to the first sequence and a preset expert scoring model, and further obtaining a training data set according to the training sample and the label; and inputting the training data set into a pre-constructed recurrent neural network for training to obtain a trained gas pipeline leakage recognition model, and then recognizing and detecting the gas pipeline to be detected according to the gas pipeline leakage recognition model. The invention improves the accuracy and the detection efficiency of the gas pipeline leakage detection, can accurately position the position of the gas pipeline leakage, and can be widely applied to the technical field of artificial intelligence.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a method, a system and a device for detecting leakage of a gas pipeline and a storage medium.
Background
With the acceleration of the urbanization process, the laying and transmission of gas pipelines become important contents for urban construction. However, safety problems in the operation process of the gas pipeline, such as gas leakage, gas explosion and the like, pose great threats to the social and people property safety. The existing gas pipeline detection methods include a manual inspection method, an in-pipe intelligent climbing detection method, an infrared imaging detection method and a distributed optical fiber detection method.
1) The manual inspection method needs inspection workers to hold a gas leak detector or a leak detection vehicle to regularly inspect along a pipeline laying path, judges whether gas leakage exists or not through various modes such as seeing, smelling and listening, and is heavy in work and prone to the occurrence of the condition of missed detection and false detection.
2) The intelligent climbing machine detection method in the pipe is provided with various sensors to form an intelligent climbing machine detection system, the pressure, the flow, the temperature and the integrity of the pipe wall in the pipe can be detected by utilizing the climbing machine, but the climbing machine is only suitable for the pipelines without too many elbows and joints, and the operation of the climbing machine needs abundant experience.
3) Infrared imaging method. When the pipeline leaks, the temperature field of soil around the leakage point changes, the infrared remote sensing camera device can record the geothermal radiation effect around the gas transmission pipeline, and the leakage position can be detected by spectral analysis. The method can accurately position the leakage point, has high sensitivity, but is not suitable for detecting leakage of pipelines buried deeply.
4) Distributed optical fiber leak detection method. One optical cable is laid side by side along the pipeline near the pipeline, or a communication optical cable laid in the same ditch with the pipeline can be utilized, according to the interference principle of the optical fiber, when the pipeline leaks, the test optical fiber near the pipeline leakage point is caused to generate stress strain, so that the optical wave phase modulation is caused at the position, and the optical wave generating the phase modulation is respectively transmitted to two ends of the sensor along the optical fiber. The two photoelectric detection sensors are used for detecting the time difference of the change of the interference signals at the two ends, so that the position of the leakage can be accurately calculated, but the detection method has higher cost.
From the above, the gas pipeline detection method in the prior art has the disadvantages of high cost, low accuracy, limited use scene and the like.
Disclosure of Invention
The present invention aims to solve at least to some extent one of the technical problems existing in the prior art.
Therefore, an object of the embodiments of the present invention is to provide a method for detecting a leakage of a gas pipeline, which improves the accuracy and detection efficiency of the detection of the leakage of the gas pipeline, avoids false detection and missing detection of manual inspection, can accurately locate the position of the leakage of the gas pipeline, and facilitates subsequent fault processing.
Another object of an embodiment of the present invention is to provide a gas pipeline leakage detection system.
In order to achieve the technical purpose, the technical scheme adopted by the embodiment of the invention comprises the following steps:
in a first aspect, an embodiment of the present invention provides a gas pipeline leakage detection method, including the following steps:
acquiring first data of a preset gas pipeline, wherein the first data comprises pipeline pressure, gas flow and gas concentration;
carrying out time sequencing and normalization processing on the first data to obtain a first sequence, wherein the first sequence comprises a pipeline pressure sequence, a gas flow sequence and a gas concentration sequence;
determining a training sample and a corresponding label according to the first sequence and a preset expert scoring model, and further obtaining a training data set according to the training sample and the label;
inputting the training data set into a pre-constructed recurrent neural network for training to obtain a trained gas pipeline leakage recognition model, and then recognizing and detecting the gas pipeline to be detected according to the gas pipeline leakage recognition model.
Further, in an embodiment of the present invention, the step of acquiring the first data of the preset gas pipeline specifically includes:
the method comprises the steps that first data of a preset gas pipeline and position data of gas pipeline sensors are collected through the plurality of gas pipeline sensors, the plurality of gas pipeline sensors comprise NB-IoT pressure sensors, NB-IoT flow sensors, NB-IoT concentration sensors and GPS locators, and the plurality of gas pipeline sensors are installed in the preset gas pipeline at intervals according to preset distances.
Further, in an embodiment of the present invention, the step of performing time sequencing and normalization on the first data to obtain a first sequence specifically includes:
carrying out time sequencing and normalization processing on the first data to obtain a pipeline pressure sequence, a gas flow sequence and a gas concentration sequence corresponding to each gas pipeline sensor;
and marking the pipeline pressure sequence, the gas flow sequence and the gas concentration sequence according to the position data to obtain the first sequence.
Further, in an embodiment of the present invention, the step of determining a training sample and a corresponding label according to the first sequence and a preset expert scoring model, and further obtaining a training data set according to the training sample and the label specifically includes:
acquiring two groups of first sequences at adjacent positions, and determining time sequence data corresponding to two gas pipeline sensors at the adjacent positions at each moment;
grading the time sequence data according to a preset expert grading model, and determining a gas leakage grading result;
determining a training sample according to the time sequence data, and determining a label of the training sample according to the gas leakage scoring result;
and constructing a training data set according to the training samples and the labels.
Further, in an embodiment of the present invention, the expert scoring model is:
where l denotes the gas leakage class, l 1,2,3,4,5, l 1 corresponds to uncontrolled leakage, l 2 corresponds to severe leakage, l 3 corresponds to general leakage, l 4 corresponds to slight leakage, l 5 corresponds to no leakage,
indicates the position Sm+1At tnThe pressure of the pipe at the moment of time,
indicates the position SmAt tnLine pressure at time, delta2|lAnd delta1|lRespectively represent the upper and lower limits of pipeline pressure when the gas leakage grade is l,
indicates the position Sm+1At tnThe gas flow at the moment is controlled by the controller,
indicates the position SmAt tnGas flow at a time, epsilon2|lAnd ε1|lRespectively represents the upper limit and the lower limit of the gas flow when the gas leakage grade is l,
indicates the position Sm+1At tnThe concentration of the gas at the moment of time,
indicates the position SmAt tnGas concentration at time, delta2|lAnd delta1|lRespectively representing the upper and lower limits of the gas concentration when the gas leakage grade is l.
Further, in an embodiment of the present invention, the step of inputting the training data set into a pre-constructed recurrent neural network for training to obtain a trained gas pipeline leakage recognition model specifically includes:
inputting the training data set into the recurrent neural network to obtain a prediction classification result;
determining a loss value of the recurrent neural network according to the prediction classification result and the label;
updating parameters of the recurrent neural network through a back propagation algorithm according to the loss value;
and when the loss value reaches a preset first threshold value or the iteration times reaches a preset second threshold value or the test precision reaches a preset third threshold value, stopping training to obtain a trained gas pipeline leakage recognition model.
Further, in one embodiment of the present invention, the recurrent neural network is formed by stacking two layers of LSTM networks, and the loss function of the recurrent neural network is:
wherein the content of the first and second substances,
represents tnThe value of the tag that is true at the time,
prediction values representing a recurrent neural networkAnd N represents the total number of time steps.
In a second aspect, an embodiment of the present invention provides a gas pipeline leakage detection system, including:
the system comprises a first data acquisition module, a second data acquisition module and a data processing module, wherein the first data acquisition module is used for acquiring first data of a preset gas pipeline, and the first data comprises pipeline pressure, gas flow and gas concentration;
the first sequence determination module is used for carrying out time sequencing and normalization processing on the first data to obtain a first sequence, and the first sequence comprises a pipeline pressure sequence, a gas flow sequence and a gas concentration sequence;
the training data set construction module is used for determining a training sample and a corresponding label according to the first sequence and a preset expert scoring model, and further obtaining a training data set according to the training sample and the label;
and the model training and identifying module is used for inputting the training data set into a pre-constructed recurrent neural network for training to obtain a trained gas pipeline leakage identifying model, and then identifying and detecting the gas pipeline to be detected according to the gas pipeline leakage identifying model.
In a third aspect, an embodiment of the present invention provides a gas pipeline leakage detection apparatus, including:
at least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to implement a gas pipeline leak detection method as described above.
In a fourth aspect, the present invention also provides a computer-readable storage medium, in which a program executable by a processor is stored, and when the program executable by the processor is executed by the processor, the program is used to execute a gas pipeline leakage detection method as described above.
Advantages and benefits of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention:
the embodiment of the invention considers a plurality of data sources in the gas pipeline, trains the gas pipeline leakage recognition model by adopting multi-dimensional time sequence data, and simultaneously adopts a training mode of continuously optimizing the model, thereby improving the accuracy of gas pipeline leakage detection; the labor cost and the time cost of the gas pipeline inspection can be reduced, and the false detection and the missing detection of the manual inspection are avoided; the data of the gas pipeline can be remotely monitored, and the efficiency of detecting the leakage of the gas pipeline is improved; in addition, the position of gas pipeline leakage can be accurately positioned according to the pre-laid data acquisition points, and subsequent fault treatment is facilitated.
Drawings
In order to more clearly illustrate the technical solution in the embodiment of the present invention, the following description is made on the drawings required to be used in the embodiment of the present invention, and it should be understood that the drawings in the following description are only for convenience and clarity of describing some embodiments in the technical solution of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart illustrating steps of a method for detecting a leakage of a gas pipeline according to an embodiment of the present invention;
fig. 2 is a data flow diagram of a gas pipeline leakage detection method according to an embodiment of the present invention;
FIG. 3 is a block diagram of a gas pipeline leakage detection system according to an embodiment of the present invention;
fig. 4 is a block diagram of a gas pipeline leakage detection device according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
In the description of the present invention, the meaning of a plurality is two or more, if there is a description to the first and the second for the purpose of distinguishing technical features, it is not understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features or implicitly indicating the precedence of the indicated technical features. Furthermore, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.
Referring to fig. 1, an embodiment of the present invention provides a gas pipeline leakage detection method, which specifically includes the following steps:
s101, acquiring first data of a preset gas pipeline, wherein the first data comprise pipeline pressure, gas flow and gas concentration.
Specifically, when the gas pipeline leaks, pipeline pressure, gas flow and gas concentration are changed, and the pipeline pressure, the gas flow and the gas concentration of each position of the gas pipeline are collected through various sensors, so that a data source can be continuously provided for subsequent model training.
Further as an optional implementation manner, the step of acquiring first data of a preset gas pipeline specifically includes:
the method comprises the steps that first data of a preset gas pipeline and position data of gas pipeline sensors are collected through the plurality of gas pipeline sensors, the plurality of gas pipeline sensors comprise NB-IoT pressure sensors, NB-IoT flow sensors, NB-IoT concentration sensors and GPS locators, and the plurality of gas pipeline sensors are installed in the preset gas pipeline at intervals according to preset distances.
Specifically, the gas pipeline sensor integrates an NB-IoT pressure sensor, an NB-IoT flow sensor, an NB-IoT concentration sensor and a GPS positioner, and the gas pipeline sensor is installed in the gas pipeline at intervals according to a set distance in advance and used for acquiring data of pressure, gas flow and gas concentration of the pipeline in a fixed point position in real time.
As shown in fig. 2, the pipeline pressure, the gas flow and the gas concentration data collected by each gas pipeline sensor can be uploaded to the internet of things management platform through the NB-IoT communication module, and meanwhile, the position data of each gas pipeline sensor is also uploaded to the internet of things management platform through the GPS, so that subsequent processing of the data is facilitated.
S102, carrying out time sequencing and normalization processing on the first data to obtain a first sequence, wherein the first sequence comprises a pipeline pressure sequence, a gas flow sequence and a gas concentration sequence.
Specifically, time series data of each index can be formed through time series processing, and the complexity of the data can be reduced through normalization processing, so that the calculated amount is reduced, and the training efficiency of the neural network model is improved. Step S102 specifically includes the following steps:
s1021, carrying out time sequencing and normalization processing on the first data to obtain a pipeline pressure sequence, a gas flow sequence and a gas concentration sequence corresponding to each gas pipeline sensor;
and S1022, marking the pipeline pressure sequence, the gas flow sequence and the gas concentration sequence according to the position data to obtain a first sequence.
Specifically, the data center in the management platform of the internet of things performs time sequence serialization and data normalization processing on the acquired pipeline pressure, gas flow and gas concentration data, and a pipeline pressure sequence { P (t) } can be obtained after processing1),P(t2),P(t3),...,P(tn) The sequence of gas flow rate (L (t))1),L(t2),L(t3),...,L(tn) And a gas concentration series { C (t) }1),C(t2),C(t3),...,C(tn)}。
Each gas pipeline sensor corresponds to a pipeline pressure sequence, a gas flow sequence and a gas concentration sequence, in order to distinguish data collected by different gas pipeline sensors, position data collected by each gas pipeline sensor can be marked, and the marked pipeline pressure sequence, gas flow sequence and gas concentration sequence are obtained.
S103, determining a training sample and a corresponding label according to the first sequence and a preset expert scoring model, and further obtaining a training data set according to the training sample and the label.
Specifically, two groups of time series data of adjacent positions are selected from the first sequence determined in the previous step, and are scored through a preset expert scoring model to obtain a scoring result corresponding to the time series data, namely a sample label, so that a training data set can be formed. Step S103 specifically includes the following steps:
s1031, acquiring two groups of first sequences at adjacent positions, and determining time sequence data corresponding to two gas pipeline sensors at the adjacent positions at each moment;
s1032, scoring the time sequence data according to a preset expert scoring model, and determining a gas leakage scoring result;
s1033, determining a training sample according to the time sequence data, and determining a label of the training sample according to the gas leakage scoring result;
s1034, constructing a training data set according to the training samples and the labels.
Specifically, in order to train a detection model of gas leakage, an expert scoring model is first used to judge a gas leakage event, and a training sample is collected. Setting two adjacent acquisition positions SmAnd Sm+1At tnThe pipeline pressure, the gas flow and the gas concentration data which are collected at all times are respectively
And
and selecting data of the same index at the two positions at the moment to form a data group, inputting the data group into a preset expert scoring model for scoring to obtain a scoring result, and then forming a training sample and a label according to the data group and the scoring result.
As a further optional implementation, the expert scoring model is:
where l denotes the gas leakage class, l 1,2,3,4,5, l 1 corresponds to uncontrolled leakage, l 2 corresponds to severe leakage, l 3 corresponds to general leakage, l 4 corresponds to slight leakage, l 5 corresponds to no leakage,
indicates the position Sm+1At tnThe pressure of the pipe at the moment of time,
indicates the position SmAt tnLine pressure at time, delta2|lAnd delta1|lRespectively represent the upper and lower limits of pipeline pressure when the gas leakage grade is l,
indicates the position Sm+1At tnThe gas flow at the moment is controlled by the controller,
indicates the position SmAt tnGas flow at a time, epsilon2|lAnd ε1|lRespectively represents the upper limit and the lower limit of the gas flow when the gas leakage grade is l,
indicates the position Sm+1At tnThe concentration of the gas at the moment of time,
indicates the position SmAt tnGas concentration at time, delta2|lAnd delta1|lRespectively representing the upper and lower limits of the gas concentration when the gas leakage grade is l.
Specifically, the expert scoring model can score each index in the first sequence of two adjacent positions, and when the difference value of certain index data falls into a corresponding interval, the gas leakage grade corresponding to the index can be determined; and respectively determining the gas leakage grades corresponding to the three indexes of the pipeline pressure, the gas flow and the gas concentration, and then performing weighted summation on the grades of the pipeline pressure, the gas flow and the gas concentration to obtain the final gas leakage grade. When the gas leakage grade obtained by the data collected at the same time at two adjacent positions is 1-4, judging that the gas leakage exists in the pipeline between the two positions, and otherwise, judging that the gas pipe does not leak. The dangerous grade of gas leakage is divided into first-level leakage (uncontrollable leakage), second-level leakage (serious leakage), third-level leakage (general leakage) and fourth-level leakage (slight leakage).
Alternatively, the position S may be fixedmAnd Sm+1At tnTime sequence data respectively collected at different times
And
combined into a vector
Taking the vector as a training sample, the obtained training data set can be recorded as
Using the training data set as input to a recurrent neural network, wherein the input vector
Is set to tstepI.e. at intervals tstepAcquisition position SmAnd Sm+1Time series data of (a). Grading result of expert grading model at the same time
As a label for the training sample. When the scoring result is first-level leakage, the label value
When the scoring result is secondary leakage, the label value
When the scoring result is three-level leakage, the label value
When the scoring result is four-level leakage, the label value
When the scoring result is no leakage, the label value
S104, inputting the training data set into a pre-constructed recurrent neural network for training to obtain a trained gas pipeline leakage recognition model, and then recognizing and detecting the gas pipeline to be detected according to the gas pipeline leakage recognition model.
Specifically, as shown in fig. 2, the AI central station of the internet of things management platform trains the gas leakage detection model using a recurrent neural network. 70% of the data collected in the above steps can be used for supervised training of the model, and 30% of the data can be used for testing of the model.
Further as an optional implementation manner, the step of inputting the training data set into a pre-constructed recurrent neural network for training to obtain a trained gas pipeline leakage recognition model specifically includes:
a1, inputting the training data set into a recurrent neural network to obtain a prediction classification result;
a2, determining the loss value of the recurrent neural network according to the prediction classification result and the label;
a3, updating parameters of the recurrent neural network through a back propagation algorithm according to the loss value;
and A4, stopping training when the loss value reaches a preset first threshold value or the iteration number reaches a preset second threshold value or the test precision reaches a preset third threshold value, and obtaining a trained gas pipeline leakage recognition model.
Specifically, after the data in the training data set is input into the initialized recurrent neural network, the prediction classification result output by the model can be obtained, and the accuracy of the gas pipeline leakage identification model can be evaluated by the prediction classification result and the label, so that the parameters of the model are updated. For gas pipeline leakage identification, the accuracy of the model prediction result can be measured by a Loss Function (Loss Function), the Loss Function is defined on a single training data and is used for measuring the prediction error of the training data, and specifically, the Loss value of the training data is determined according to the label of the single training data and the prediction result of the model on the training data. In actual training, a training data set has many training data, so a Cost Function (Cost Function) is generally adopted to measure the overall error of the training data set, and the Cost Function is defined on the whole training data set and is used for calculating the average value of prediction errors of all the training data, so that the prediction effect of the model can be measured better. For a general machine learning model, based on the cost function, and a regularization term for measuring the complexity of the model, the regularization term can be used as a training objective function, and based on the objective function, the loss value of the whole training data set can be obtained. There are many kinds of commonly used loss functions, such as 0-1 loss function, square loss function, absolute loss function, logarithmic loss function, cross entropy loss function, etc. all can be used as the loss function of the machine learning model, and are not described one by one here. In the embodiment of the application, a loss function can be selected from the loss functions to determine the loss value of the training. And updating the parameters of the model by adopting a back propagation algorithm based on the trained loss value, and iterating for several rounds to obtain the trained point cloud tower identification model. Specifically, the number of iteration rounds may be preset, or training may be considered complete when the test set meets the accuracy requirement.
As a further optional implementation, the recurrent neural network is formed by stacking two LSTM networks, and the loss function of the recurrent neural network is:
wherein the content of the first and second substances,
represents tnThe value of the tag that is true at the time,
represents the predicted value of the recurrent neural network, and N represents the total number of time steps.
Specifically, the recurrent neural network of the embodiment of the invention is formed by stacking two layers of LSTM (long short term memory network), and the input layer inputs a training data set
Meanwhile, Layer Normalization is adopted in the model to avoid the problems of gradient disappearance and gradient explosion in the training process, the training speed and the training precision of the model can be improved, and the model is more stable.
Setting the hyper-parameters of the model, training the model by using a BPTT (Back-Propagation Through Time) algorithm, and simultaneously optimizing the target by using the following cross entropy function:
wherein the content of the first and second substances,
represents tnThe value of the tag that is true at the time,
represents the predicted value of the recurrent neural network, and N represents the total number of time steps.
Optionally, the model is trained by using a training data set, the training precision can be visualized, when the training precision reaches 100%, the trained model is tested by using a test set, then the setting of the model hyper-parameters is continuously adjusted, and the training step is repeated until the testing precision of the model reaches 100%, so that the trained gas pipeline leakage recognition model can be obtained.
After the gas pipeline leakage recognition model is obtained through training, the model can be directly used for detecting the leakage condition of the gas pipeline, and the model can be used for outputting the time sequence data of the pipeline pressure, the gas concentration and the gas flow of each position of the gas pipeline to be detected, so that the monitoring result can be directly obtained. Compared with an expert scoring model, a manual detection method and other methods, the trained gas pipeline leakage identification model has a better detection effect, monitoring data (gas pipeline pressure, gas concentration, gas flow and GPS (global positioning system) position) of a gas pipeline and a positioning result of a gas leakage pipeline section are displayed on an IOC (internet of things) large screen of the management platform of the Internet of things, then information is synchronized to a gas management system, the gas leakage problem is conveniently judged and analyzed according to the leakage grade, and emergency measures and solutions are made.
In a business scene of a gas company, the method can be used for remotely monitoring the gas pipeline, and meanwhile, the gas leakage condition can be predicted and early warned without on-site investigation and inspection, so that the manpower input cost is reduced, and the remote monitoring and maintenance of the gas pipeline are facilitated.
The method steps of the embodiments of the present invention are described above. As shown in fig. 2, in the embodiment of the present invention, a gas pipeline sensor is additionally installed on a gas pipeline, the sensor integrates a NB-IoT-based pressure sensor, a flow sensor, a concentration sensor and a GPS locator, and is configured to collect pipeline pressure, gas flow, gas concentration and position data of the sensor of the gas pipeline in real time, transmit the data to an internet of things management platform through an NB-IoT communication module, perform time sequencing and data normalization on the collected data by a data console in the internet of things management platform, use preprocessed data as input, perform supervised training and testing by an AI console using a recurrent neural network, and finally feed back the monitored data and predicted gas leakage information to a gas pipeline management system.
It should be appreciated that in the embodiment of the invention, a plurality of data sources in the gas pipeline are considered, the multidimensional time sequence data is adopted to train the gas pipeline leakage identification model, and meanwhile, the training mode of the continuous optimization model is adopted, so that the accuracy of gas pipeline leakage detection is improved; the labor cost and the time cost of the gas pipeline inspection can be reduced, and the false detection and the missing detection of the manual inspection are avoided; the data of the gas pipeline can be remotely monitored, and the efficiency of detecting the leakage of the gas pipeline is improved; in addition, the position of gas pipeline leakage can be accurately positioned according to the pre-laid data acquisition points, and subsequent fault treatment is facilitated.
Referring to fig. 3, an embodiment of the present invention provides a gas pipeline leakage detection system, including:
the first data acquisition module is used for acquiring first data of a preset gas pipeline, wherein the first data comprises pipeline pressure, gas flow and gas concentration;
the first sequence determination module is used for carrying out time sequencing and normalization processing on the first data to obtain a first sequence, and the first sequence comprises a pipeline pressure sequence, a gas flow sequence and a gas concentration sequence;
the training data set building module is used for determining training samples and corresponding labels according to the first sequence and a preset expert scoring model, and further obtaining a training data set according to the training samples and the labels;
and the model training and recognition module is used for inputting the training data set into a pre-constructed recurrent neural network for training to obtain a trained gas pipeline leakage recognition model, and then recognizing and detecting the gas pipeline to be detected according to the gas pipeline leakage recognition model.
The contents in the above method embodiments are all applicable to the present system embodiment, the functions specifically implemented by the present system embodiment are the same as those in the above method embodiment, and the beneficial effects achieved by the present system embodiment are also the same as those achieved by the above method embodiment.
Referring to fig. 4, an embodiment of the present invention provides a gas pipeline leakage detection apparatus, including:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor is caused to implement the gas pipeline leakage detection method.
The contents in the above method embodiments are all applicable to the present apparatus embodiment, the functions specifically implemented by the present apparatus embodiment are the same as those in the above method embodiments, and the advantageous effects achieved by the present apparatus embodiment are also the same as those achieved by the above method embodiments.
Embodiments of the present invention also provide a computer-readable storage medium, in which a program executable by a processor is stored, and the program executable by the processor is used for executing the gas pipeline leakage detection method.
The computer-readable storage medium of the embodiment of the invention can execute the gas pipeline leakage detection method provided by the embodiment of the method of the invention, can execute any combination of the implementation steps of the embodiment of the method, and has corresponding functions and beneficial effects of the method.
The embodiment of the invention also discloses a computer program product or a computer program, which comprises computer instructions, and the computer instructions are stored in a computer readable storage medium. The computer instructions may be read by a processor of a computer device from a computer-readable storage medium, and executed by the processor to cause the computer device to perform the method illustrated in fig. 1.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. 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/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the above-described functions and/or features may be integrated in a single physical device and/or software module, or one or more of the functions and/or features may be implemented in a separate physical device or software module. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer, given the nature, function, and internal relationship of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
The above functions, if implemented in the form of software functional units and sold or used as a separate product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the above method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Further, the computer readable medium could even be paper or another suitable medium upon which the above described program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the foregoing description of the specification, reference to the description of "one embodiment/example," "another embodiment/example," or "certain embodiments/examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A gas pipeline leakage detection method is characterized by comprising the following steps:
acquiring first data of a preset gas pipeline, wherein the first data comprises pipeline pressure, gas flow and gas concentration;
carrying out time sequencing and normalization processing on the first data to obtain a first sequence, wherein the first sequence comprises a pipeline pressure sequence, a gas flow sequence and a gas concentration sequence;
determining a training sample and a corresponding label according to the first sequence and a preset expert scoring model, and further obtaining a training data set according to the training sample and the label;
inputting the training data set into a pre-constructed recurrent neural network for training to obtain a trained gas pipeline leakage recognition model, and then recognizing and detecting the gas pipeline to be detected according to the gas pipeline leakage recognition model.
2. The gas pipeline leakage detection method according to claim 1, wherein the step of obtaining the first data of the preset gas pipeline specifically comprises:
the method comprises the steps that first data of a preset gas pipeline and position data of gas pipeline sensors are collected through the plurality of gas pipeline sensors, the plurality of gas pipeline sensors comprise NB-IoT pressure sensors, NB-IoT flow sensors, NB-IoT concentration sensors and GPS locators, and the plurality of gas pipeline sensors are installed in the preset gas pipeline at intervals according to preset distances.
3. The method according to claim 2, wherein the step of performing the time-sequencing and normalization processing on the first data to obtain a first sequence specifically comprises:
carrying out time sequencing and normalization processing on the first data to obtain a pipeline pressure sequence, a gas flow sequence and a gas concentration sequence corresponding to each gas pipeline sensor;
and marking the pipeline pressure sequence, the gas flow sequence and the gas concentration sequence according to the position data to obtain the first sequence.
4. The method according to claim 3, wherein the step of determining a training sample and a corresponding label according to the first sequence and a preset expert scoring model, and further obtaining a training data set according to the training sample and the label specifically comprises:
acquiring two groups of first sequences at adjacent positions, and determining time sequence data corresponding to two gas pipeline sensors at the adjacent positions at each moment;
grading the time sequence data according to a preset expert grading model, and determining a gas leakage grading result;
determining a training sample according to the time sequence data, and determining a label of the training sample according to the gas leakage scoring result;
and constructing a training data set according to the training samples and the labels.
5. The gas pipeline leakage detection method according to claim 4, wherein the expert scoring model is:
where l denotes the gas leakage class, l 1,2,3,4,5, l 1 corresponds to uncontrolled leakage, l 2 corresponds to severe leakage, l 3 corresponds to general leakage, l 4 corresponds to slight leakage, l 5 corresponds to no leakage,
indicates the position Sm+1At tnThe pressure of the pipe at the moment of time,
indicates the position SmAt tnLine pressure at time, delta2|lAnd delta1|lRespectively represent the upper and lower limits of pipeline pressure when the gas leakage grade is l,
indicates the position Sm+1At tnThe gas flow at the moment is controlled by the controller,
indicates the position SmAt tnGas flow at a time, epsilon2|lAnd ε1|lRespectively represents the upper limit and the lower limit of the gas flow when the gas leakage grade is l,
indicates the position Sm+1At tnThe concentration of the gas at the moment of time,
indicates the position SmAt tnGas concentration at time, delta2|lAnd delta1|lRespectively representing the upper and lower limits of the gas concentration when the gas leakage grade is l.
6. The method for detecting the leakage of the gas pipeline according to claim 4, wherein the step of inputting the training data set into a pre-constructed recurrent neural network for training to obtain a trained gas pipeline leakage recognition model specifically comprises:
inputting the training data set into the recurrent neural network to obtain a prediction classification result;
determining a loss value of the recurrent neural network according to the prediction classification result and the label;
updating parameters of the recurrent neural network through a back propagation algorithm according to the loss value;
and when the loss value reaches a preset first threshold value or the iteration times reaches a preset second threshold value or the test precision reaches a preset third threshold value, stopping training to obtain a trained gas pipeline leakage recognition model.
7. The gas pipeline leakage detection method according to any one of claims 1 to 6, wherein the recurrent neural network is formed by stacking two layers of LSTM networks, and the loss function of the recurrent neural network is:
wherein the content of the first and second substances,
represents tnThe value of the tag that is true at the time,
represents the predicted value of the recurrent neural network, and N represents the total number of time steps.
8. A gas pipeline leak detection system, comprising:
the system comprises a first data acquisition module, a second data acquisition module and a data processing module, wherein the first data acquisition module is used for acquiring first data of a preset gas pipeline, and the first data comprises pipeline pressure, gas flow and gas concentration;
the first sequence determination module is used for carrying out time sequencing and normalization processing on the first data to obtain a first sequence, and the first sequence comprises a pipeline pressure sequence, a gas flow sequence and a gas concentration sequence;
the training data set construction module is used for determining a training sample and a corresponding label according to the first sequence and a preset expert scoring model, and further obtaining a training data set according to the training sample and the label;
and the model training and identifying module is used for inputting the training data set into a pre-constructed recurrent neural network for training to obtain a trained gas pipeline leakage identifying model, and then identifying and detecting the gas pipeline to be detected according to the gas pipeline leakage identifying model.
9. A gas pipeline leakage detection device, characterized by, includes:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement a gas pipeline leak detection method as claimed in any one of claims 1 to 7.
10. A computer readable storage medium in which a processor executable program is stored, wherein the processor executable program when executed by a processor is for performing a gas pipeline leak detection method as claimed in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111494477.6A CN114352947B (en) | 2021-12-08 | 2021-12-08 | Gas pipeline leakage detection method, system, device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111494477.6A CN114352947B (en) | 2021-12-08 | 2021-12-08 | Gas pipeline leakage detection method, system, device and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114352947A true CN114352947A (en) | 2022-04-15 |
| CN114352947B CN114352947B (en) | 2024-03-12 |
Family
ID=81097115
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111494477.6A Active CN114352947B (en) | 2021-12-08 | 2021-12-08 | Gas pipeline leakage detection method, system, device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114352947B (en) |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114777030A (en) * | 2022-05-23 | 2022-07-22 | 中用科技有限公司 | Hazardous gas monitoring method based on NB-IOT technology |
| CN115063950A (en) * | 2022-05-20 | 2022-09-16 | 南通中油燃气有限责任公司 | Intelligent monitoring method and system for gas leakage in open space of station |
| CN115127036A (en) * | 2022-09-01 | 2022-09-30 | 北京云庐科技有限公司 | Municipal gas pipe network leakage positioning method and system |
| CN115330094A (en) * | 2022-10-14 | 2022-11-11 | 成都秦川物联网科技股份有限公司 | Intelligent gas pipeline service life prediction method, internet of things system, device and medium |
| CN115330361A (en) * | 2022-10-14 | 2022-11-11 | 成都秦川物联网科技股份有限公司 | Indoor safety-based gas leakage prediction method and intelligent gas Internet of things system |
| CN115681821A (en) * | 2022-12-13 | 2023-02-03 | 成都秦川物联网科技股份有限公司 | Automatic odorizing control method for intelligent gas equipment management and Internet of things system |
| CN116055514A (en) * | 2022-11-08 | 2023-05-02 | 成都秦川物联网科技股份有限公司 | Natural gas synergistic control method, intelligent gas Internet of things system, device and medium |
| CN116164243A (en) * | 2023-04-26 | 2023-05-26 | 北京理工大学 | Hydrogen leakage detection positioning system and method |
| CN116336400A (en) * | 2023-05-30 | 2023-06-27 | 克拉玛依市百事达技术开发有限公司 | Baseline detection method for oil and gas gathering and transportation pipeline |
| CN116625438A (en) * | 2023-07-25 | 2023-08-22 | 克拉玛依市燃气有限责任公司 | Gas pipe network safety on-line monitoring system and method thereof |
| CN116772944A (en) * | 2023-08-25 | 2023-09-19 | 克拉玛依市燃气有限责任公司 | Intelligent monitoring system and method for gas distribution station |
| CN116781430A (en) * | 2023-08-24 | 2023-09-19 | 克拉玛依市燃气有限责任公司 | Network information security system and method for gas pipe network |
| CN117114369A (en) * | 2023-10-23 | 2023-11-24 | 成都秦川物联网科技股份有限公司 | Intelligent gas emergency gas supply disposal method, internet of things system, device and medium |
| US12033487B2 (en) | 2022-10-14 | 2024-07-09 | Chengdu Qinchuan Iot Technology Co., Ltd. | Methods for gas leakage emergency treatment based on indoor safety and smart gas internet of things systems |
Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DD226371A1 (en) * | 1984-05-14 | 1985-08-21 | Zentrales Entwurfs U Konstrukt | METHOD FOR THE EXPERIMENTAL MATHEMATICAL GUIDANCE FOR HOUSEHOLD EXTRACTION TESTS |
| RU2279140C1 (en) * | 2005-04-06 | 2006-06-27 | Сергей Яковлевич Барбулев | Device for training specialists in removal of gas leaks from gas pipelines |
| CN108167653A (en) * | 2017-12-23 | 2018-06-15 | 浙江大学 | Public supply mains booster detection method based on dynamic neural network prediction |
| US20180320828A1 (en) * | 2017-05-08 | 2018-11-08 | 64Seconds, Inc. | Integrity assessment of a pipeline network |
| CN109308494A (en) * | 2018-09-27 | 2019-02-05 | 厦门服云信息科技有限公司 | LSTM Recognition with Recurrent Neural Network model and network attack identification method based on this model |
| CN109359698A (en) * | 2018-10-30 | 2019-02-19 | 清华大学 | Leakage loss recognition methods based on long Memory Neural Networks model in short-term |
| KR20190018293A (en) * | 2017-08-14 | 2019-02-22 | 오토시맨틱스 주식회사 | Diagnosis method for Detecting Leak of Water Supply Pipe using Deep Learning by Acoustic Signature |
| CN110242865A (en) * | 2019-07-09 | 2019-09-17 | 北京讯腾智慧科技股份有限公司 | A kind of gas leakage detection determination method and system being easy to Continuous optimization |
| CN111783945A (en) * | 2020-06-22 | 2020-10-16 | 东北石油大学 | Long oil transmission pipeline leakage real-time detection system based on transfer learning LSTM |
| CN111950693A (en) * | 2019-05-14 | 2020-11-17 | 辉达公司 | Neural network inference using attenuation parameters |
| CN112149750A (en) * | 2020-09-29 | 2020-12-29 | 浙江大学 | Water supply network pipe burst identification data driving method |
| CN112332444A (en) * | 2020-09-14 | 2021-02-05 | 华北电力大学（保定） | Microgrid energy management system based on digital twins |
| US20210087925A1 (en) * | 2019-09-25 | 2021-03-25 | Halliburton Energy Services, Inc. | Systems and methods for real-time hydraulic fracture control |
| KR20210048100A (en) * | 2019-10-23 | 2021-05-03 | 서울대학교산학협력단 | Condition monitoring data generating apparatus and method using generative adversarial network |
| CN112749761A (en) * | 2021-01-22 | 2021-05-04 | 上海机电工程研究所 | Enemy combat intention identification method and system based on attention mechanism and recurrent neural network |
| CN112836758A (en) * | 2021-02-08 | 2021-05-25 | 浙江工业大学 | Independent metering area pipe network leakage detection method based on double-layer circulating neural network |
| CN112903211A (en) * | 2021-03-29 | 2021-06-04 | 广州燃气集团有限公司 | Equipment and method for detecting tightness of gas pipeline |
| US20210216852A1 (en) * | 2018-08-09 | 2021-07-15 | Flowstate Technologies Llc | Leak detection with artificial intelligence |
| CN113364751A (en) * | 2021-05-26 | 2021-09-07 | 北京电子科技职业学院 | Network attack prediction method, computer-readable storage medium, and electronic device |
| CN113642023A (en) * | 2021-08-25 | 2021-11-12 | 北京恒安嘉新安全技术有限公司 | Data security detection model training method, data security detection device and equipment |
-
2021
- 2021-12-08 CN CN202111494477.6A patent/CN114352947B/en active Active
Patent Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DD226371A1 (en) * | 1984-05-14 | 1985-08-21 | Zentrales Entwurfs U Konstrukt | METHOD FOR THE EXPERIMENTAL MATHEMATICAL GUIDANCE FOR HOUSEHOLD EXTRACTION TESTS |
| RU2279140C1 (en) * | 2005-04-06 | 2006-06-27 | Сергей Яковлевич Барбулев | Device for training specialists in removal of gas leaks from gas pipelines |
| US20180320828A1 (en) * | 2017-05-08 | 2018-11-08 | 64Seconds, Inc. | Integrity assessment of a pipeline network |
| KR20190018293A (en) * | 2017-08-14 | 2019-02-22 | 오토시맨틱스 주식회사 | Diagnosis method for Detecting Leak of Water Supply Pipe using Deep Learning by Acoustic Signature |
| CN108167653A (en) * | 2017-12-23 | 2018-06-15 | 浙江大学 | Public supply mains booster detection method based on dynamic neural network prediction |
| US20210216852A1 (en) * | 2018-08-09 | 2021-07-15 | Flowstate Technologies Llc | Leak detection with artificial intelligence |
| CN109308494A (en) * | 2018-09-27 | 2019-02-05 | 厦门服云信息科技有限公司 | LSTM Recognition with Recurrent Neural Network model and network attack identification method based on this model |
| CN109359698A (en) * | 2018-10-30 | 2019-02-19 | 清华大学 | Leakage loss recognition methods based on long Memory Neural Networks model in short-term |
| CN111950693A (en) * | 2019-05-14 | 2020-11-17 | 辉达公司 | Neural network inference using attenuation parameters |
| CN110242865A (en) * | 2019-07-09 | 2019-09-17 | 北京讯腾智慧科技股份有限公司 | A kind of gas leakage detection determination method and system being easy to Continuous optimization |
| US20210087925A1 (en) * | 2019-09-25 | 2021-03-25 | Halliburton Energy Services, Inc. | Systems and methods for real-time hydraulic fracture control |
| KR20210048100A (en) * | 2019-10-23 | 2021-05-03 | 서울대학교산학협력단 | Condition monitoring data generating apparatus and method using generative adversarial network |
| CN111783945A (en) * | 2020-06-22 | 2020-10-16 | 东北石油大学 | Long oil transmission pipeline leakage real-time detection system based on transfer learning LSTM |
| CN112332444A (en) * | 2020-09-14 | 2021-02-05 | 华北电力大学（保定） | Microgrid energy management system based on digital twins |
| CN112149750A (en) * | 2020-09-29 | 2020-12-29 | 浙江大学 | Water supply network pipe burst identification data driving method |
| CN112749761A (en) * | 2021-01-22 | 2021-05-04 | 上海机电工程研究所 | Enemy combat intention identification method and system based on attention mechanism and recurrent neural network |
| CN112836758A (en) * | 2021-02-08 | 2021-05-25 | 浙江工业大学 | Independent metering area pipe network leakage detection method based on double-layer circulating neural network |
| CN112903211A (en) * | 2021-03-29 | 2021-06-04 | 广州燃气集团有限公司 | Equipment and method for detecting tightness of gas pipeline |
| CN113364751A (en) * | 2021-05-26 | 2021-09-07 | 北京电子科技职业学院 | Network attack prediction method, computer-readable storage medium, and electronic device |
| CN113642023A (en) * | 2021-08-25 | 2021-11-12 | 北京恒安嘉新安全技术有限公司 | Data security detection model training method, data security detection device and equipment |
Non-Patent Citations (1)
| Title |
|---|
| 于涛;刘丽君;陈泓君;于瑶;: "长输油气管道大数据挖掘与应用", 物联网学报, no. 03 * |
Cited By (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115063950A (en) * | 2022-05-20 | 2022-09-16 | 南通中油燃气有限责任公司 | Intelligent monitoring method and system for gas leakage in open space of station |
| CN115063950B (en) * | 2022-05-20 | 2024-04-05 | 南通中油燃气有限责任公司 | Intelligent monitoring method and system for open space gas leakage of station |
| CN114777030B (en) * | 2022-05-23 | 2023-09-05 | 中用科技有限公司 | Dangerous chemical gas monitoring method based on NB-IOT technology |
| CN114777030A (en) * | 2022-05-23 | 2022-07-22 | 中用科技有限公司 | Hazardous gas monitoring method based on NB-IOT technology |
| CN115127036A (en) * | 2022-09-01 | 2022-09-30 | 北京云庐科技有限公司 | Municipal gas pipe network leakage positioning method and system |
| CN115330094A (en) * | 2022-10-14 | 2022-11-11 | 成都秦川物联网科技股份有限公司 | Intelligent gas pipeline service life prediction method, internet of things system, device and medium |
| CN115330361A (en) * | 2022-10-14 | 2022-11-11 | 成都秦川物联网科技股份有限公司 | Indoor safety-based gas leakage prediction method and intelligent gas Internet of things system |
| US12033487B2 (en) | 2022-10-14 | 2024-07-09 | Chengdu Qinchuan Iot Technology Co., Ltd. | Methods for gas leakage emergency treatment based on indoor safety and smart gas internet of things systems |
| US11898704B2 (en) | 2022-10-14 | 2024-02-13 | Chengdu Qinchuan Iot Technology Co., Ltd. | Methods and Internet of Things systems for smart gas pipeline life prediction based on safety |
| US11776378B2 (en) | 2022-10-14 | 2023-10-03 | Chengdu Qinchuan Iot Technology Co., Ltd. | Methods for predicting gas leakage based on indoor safety and smart gas Internet of Things systems |
| CN116055514A (en) * | 2022-11-08 | 2023-05-02 | 成都秦川物联网科技股份有限公司 | Natural gas synergistic control method, intelligent gas Internet of things system, device and medium |
| CN115681821B (en) * | 2022-12-13 | 2023-04-07 | 成都秦川物联网科技股份有限公司 | Automatic odorizing control method for intelligent gas equipment management and Internet of things system |
| US11828422B2 (en) | 2022-12-13 | 2023-11-28 | Chengdu Qinchuan Iot Technology Co., Ltd. | Methods and internet of things systems for controlling automatic odorization of smart gas device management |
| CN115681821A (en) * | 2022-12-13 | 2023-02-03 | 成都秦川物联网科技股份有限公司 | Automatic odorizing control method for intelligent gas equipment management and Internet of things system |
| CN116164243A (en) * | 2023-04-26 | 2023-05-26 | 北京理工大学 | Hydrogen leakage detection positioning system and method |
| CN116336400B (en) * | 2023-05-30 | 2023-08-04 | 克拉玛依市百事达技术开发有限公司 | Baseline detection method for oil and gas gathering and transportation pipeline |
| CN116336400A (en) * | 2023-05-30 | 2023-06-27 | 克拉玛依市百事达技术开发有限公司 | Baseline detection method for oil and gas gathering and transportation pipeline |
| CN116625438B (en) * | 2023-07-25 | 2023-12-01 | 克拉玛依市燃气有限责任公司 | Gas pipe network safety on-line monitoring system and method thereof |
| CN116625438A (en) * | 2023-07-25 | 2023-08-22 | 克拉玛依市燃气有限责任公司 | Gas pipe network safety on-line monitoring system and method thereof |
| CN116781430B (en) * | 2023-08-24 | 2023-12-01 | 克拉玛依市燃气有限责任公司 | Network information security system and method for gas pipe network |
| CN116781430A (en) * | 2023-08-24 | 2023-09-19 | 克拉玛依市燃气有限责任公司 | Network information security system and method for gas pipe network |
| CN116772944B (en) * | 2023-08-25 | 2023-12-01 | 克拉玛依市燃气有限责任公司 | Intelligent monitoring system and method for gas distribution station |
| CN116772944A (en) * | 2023-08-25 | 2023-09-19 | 克拉玛依市燃气有限责任公司 | Intelligent monitoring system and method for gas distribution station |
| CN117114369B (en) * | 2023-10-23 | 2024-01-02 | 成都秦川物联网科技股份有限公司 | Intelligent gas emergency gas supply disposal method, internet of things system, device and medium |
| CN117114369A (en) * | 2023-10-23 | 2023-11-24 | 成都秦川物联网科技股份有限公司 | Intelligent gas emergency gas supply disposal method, internet of things system, device and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114352947B (en) | 2024-03-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114352947B (en) | Gas pipeline leakage detection method, system, device and storage medium | |
| CN107480705A (en) | A kind of running water pipe leakage detection method | |
| CN107949812A (en) | For detecting the abnormal combined method in water distribution system | |
| Ma et al. | Advances in corrosion growth modeling for oil and gas pipelines: A review | |
| CN111222743B (en) | Method for judging vertical offset distance and threat level of optical fiber sensing event | |
| US20230221207A1 (en) | Methods and internet of things systems for determining gas leakages based on smart gas | |
| CN113878214B (en) | Welding quality real-time detection method and system based on LSTM and residual distribution | |
| CN109886433A (en) | The method of intelligent recognition city gas pipeline defect | |
| CN115127037B (en) | Water supply pipe network leakage positioning method and system | |
| CN116382100B (en) | Oil and gas pipeline detection control system and control method | |
| CN116482227B (en) | Pipeline corrosion monitoring method, device and system | |
| CN111191855B (en) | Water quality abnormal event identification and early warning method based on pipe network multi-element water quality time sequence data | |
| CN111486345B (en) | Grain depot underground pipe network liquid leakage on-line monitoring and early warning method and device | |
| CN106706119B (en) | Vibration source identification method and system based on signal frequency domain characteristics | |
| Wang et al. | The replacement of dysfunctional sensors based on the digital twin method during the cutter suction dredger construction process | |
| CN117540329B (en) | Online early warning method and system for defects of drainage pipe network based on machine learning | |
| CN114739891A (en) | Floating type offshore wind power mooring system corrosion state detection and evaluation system | |
| CN115063337A (en) | Intelligent maintenance decision-making method and device for buried pipeline | |
| Ikeda et al. | Hammering acoustic analysis using machine learning techniques for piping inspection | |
| CN116754022A (en) | On-line detection emergency early warning method and system for cable tunnel | |
| CN116306262A (en) | Galvanized steel corrosion prediction method based on ensemble learning algorithm | |
| CN116026449A (en) | Vibration positioning monitoring system based on single-core optical fiber sensing | |
| CN110348123B (en) | AIC-RBF-based oil and gas pipeline extrusion deformation estimation method | |
| CN114413832A (en) | Road monitoring system and method based on optical fiber sensing | |
| Miao et al. | Leakage diagnosis of natural gas pipeline based on multi-source heterogeneous information fusion |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |