CN110398320B - Gas leakage detection positioning method and system easy for continuous optimization - Google Patents
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Abstract
The application provides a gas leakage detection positioning method and system easy for continuous optimization, wherein the method comprises the following steps: establishing a leakage positioning model: the leakage positioning model is composed of a preset multilayer neural network; training of a leak localization model, comprising: 1) obtaining and preprocessing a training sample; 2) training a model; detecting and positioning, comprising: 1) obtaining and preprocessing a test sample; 2) and inputting the obtained data of the test sample into the trained leakage positioning model, and outputting a result, namely performing gas leakage positioning. The deep learning technology is adopted, a special network structure is constructed, massive actual detection data are learned, the performance superior to that of a traditional method can be obtained, the performance can be continuously optimized through continuous accumulation of detection data and improvement of a deep learning model, and accurate positioning of a gas leakage site can be achieved.
Description
Technical Field
The application relates to the technical field of computers, in particular to a gas leakage detection positioning method and system easy to continuously optimize.
Background
The information in this background section is disclosed only to enhance understanding of the general background of the application and is not necessarily to be construed as an admission or any form of suggestion that this information forms the prior art that is already known to a person of ordinary skill in the art.
As a clean and efficient energy source, the share of natural gas in the energy consumption proportion of cities and towns is gradually increasing, and the natural gas is increasingly and widely applied to the fields of cooking, heating, refrigeration, power generation, vehicles, air conditioners, clothes washing and the like. The pipeline transportation is used as the most main gas transportation mode and has the advantages of low manufacturing cost, low operation cost, good safety and large transportation volume. The vigorous development of town gas plays an important role in improving national economic level and developing social career, but with the large-area laying of a gas transmission and distribution pipe network, underground gas pipelines are more and more dense and complex, and the characteristics of the gas and uncertain factors of the pipelines in various aspects such as manufacturing, installation, use, management and the like make the pipelines have the possibility of gas leakage to a certain extent, so that great challenge is brought to the safe operation of the town gas pipe network. Therefore, research on a gas pipeline leakage detection method has attracted attention from countries around the world. In order to ensure the healthy and long-term operation of the town gas pipe network, security personnel can be provided with professional leakage detection equipment to regularly check the periphery of the pipe network so as to discover hidden operating troubles of the gas pipe network in time. The gas pipe network near the road can realize rapid detection through special vehicle-mounted gas leakage detection equipment, greatly improves the detection efficiency, and can realize large-area coverage detection in extremely short time. The equipment has the technical advantages of rapid large-area inspection, high speed and strong anti-interference capability.
The device is provided with a ppb level high-precision detection instrument, and is used for continuously detecting parameters such as methane concentration, ethane concentration, water concentration and the like at the frequency of 2Hz and judging the position and the range of a suspected gas leakage point according to detection data.
The traditional classical suspected leakage point positioning method is mostly realized according to an atmospheric diffusion model. The atmospheric diffusion model is a physical model based on data, and gas diffusion migration conditions under actual conditions are simulated by combining certain assumed conditions and parameters. At present, most atmospheric diffusion models are based on Gaussian diffusion models and evolve according to factors such as different scale ranges, terrains, climates and the like. The suspected leakage point location is the location of the back-thrust leakage point according to the atmospheric diffusion model.
However, in the traditional classical method, a physical model is constructed on the basis of theory, a series of parameters are determined through experimental data, so that an empirical formula is obtained, and the suspected leakage point is positioned, so that the following problems are inevitably caused:
1) the physical model is a theoretical model, and is difficult to be directly applied in practical situations, and generally certain assumptions and simplifications are set to realize engineering application, so theoretical and practical deviations exist.
2) A series of parameters of the empirical formula are determined through experiments, and experiments only aim at some general conditions and are difficult to adapt to variable and complex practical situations, so that great deviation of experimental conditions and practical situations exists.
3) The optimization of the traditional method generally starts from two aspects, one is theoretical upgrading, the evolution difficulty of the general theory is self-evident, and the period is very long; secondly, experiments are specially carried out aiming at specific areas or conditions, a series of parameters of empirical formulas are optimized, however, the experiment cost and the technical threshold are always high, and therefore continuous optimization aiming at a certain characteristic area or other conditions is difficult.
In view of this, a method and a system for detecting and positioning gas leakage, which are easy to continuously optimize, are needed to accurately position gas leakage and efficiently continuously optimize for a specific area or other conditions.
Disclosure of Invention
Aiming at the defects of the prior art, the deep learning technology is adopted, a special network structure is constructed, massive actual detection data are learned, the performance which is more superior than that of the traditional classical method can be obtained, and the performance can be continuously optimized through continuous accumulation of the detection data and improvement of a deep learning model.
In order to solve the above technical problem, in a first aspect of the present application, a gas leakage detection positioning method easy to continuously optimize is provided, including the following steps:
establishing a leakage positioning model: the leakage positioning model is composed of a preset multilayer neural network;
training of a leak localization model, comprising:
1) acquisition and preprocessing of training samples: detecting the concentration of the gas required by each monitoring point, acquiring original data of the concentration of the required gas, preprocessing the original data of the concentration of the gas, acquiring net value data of the concentration of the gas, acquiring geographic coordinates of each monitoring point, acquiring the wind speed, wind direction and temperature of the environment where each monitoring point is located, acquiring the vehicle speed and the vehicle running direction, and acquiring a plurality of training samples;
2) model training: inputting the training samples into the preset multilayer neural network to obtain a trained leakage positioning model;
detecting and positioning, comprising:
1) obtaining and preprocessing a test sample: detecting the concentration of the gas required by a point to be monitored, acquiring original data of the concentration of the required gas, preprocessing the original data of the concentration of the gas, acquiring net value data of the concentration of the gas, acquiring geographic coordinates (including longitude and latitude) of the point to be monitored, acquiring the wind speed, the wind direction and the temperature of the environment where the point to be monitored is located, acquiring the vehicle speed and the vehicle running direction, and acquiring data of a test sample;
2) and inputting the obtained data of the test sample into the trained leakage positioning model, and outputting a result, namely performing gas leakage positioning.
In the establishment of the leakage localization model, further, the preset multilayer neural network is a neural network formed by one or a combination of all-connected, Attention, GRU and other time-sequence processing layers;
further, the preset multilayer neural network is a ResDense network based on an FC model (full connectivity model).
In training of the leak location model:
in order to more efficiently position and detect the gas leakage site, further, the required gas is selected from methane and/or ethane;
further, a concentration sensor is adopted to detect the concentration of the required gas in real time, so that the concentration data of the required gas is obtained;
further, preprocessing the raw gas concentration data to obtain net gas concentration data, including the following steps:
a, removing singular values: removing detection values with the concentration 2-3 times higher than the average value;
b calculating an environment background value: taking a moving average value of the window size of 120-160 as an environmental background value;
c calculating the net gas concentration: subtracting the environmental background value from the original gas concentration value to obtain the net gas concentration value;
furthermore, the coordinates of the required gas monitoring point can be obtained by adopting a positioning device, which can be conventionally achieved by technical personnel in the field, wherein the positioning device comprises but is not limited to a Beidou satellite navigation system (called Beidou positioning for short) or a global positioning system (called GPS for short);
furthermore, the wind speed and the wind direction of the environment where the monitoring point is located can be obtained by adopting an anemorumbometer or a wind speed and direction sensor, which can be operated by a person skilled in the art in a conventional way;
furthermore, the temperature of the environment where the monitoring point is located can be obtained by adopting a temperature sensor;
further, each training sample is input into a preset multilayer neural network as matrix data with 9 parameters in each row for global training until the output precision reaches the preset precision so as to obtain a trained model, wherein the 9 parameters include: methane (CH)4) Net value of concentration, ethane (C)2H6) Net concentration value, wind speed, wind direction (rectangular coordinate system), ambient temperature, vehicle speed, vehicle running direction (rectangular coordinate system), longitude and latitude of monitoring points; the number of columns of the matrix is set to be 2n +1, and n is 2-50; further, n is 5 to 20.
Furthermore, the trained model constructs a ResDense network based on an FC model (full connection model) for the ResNet network, wherein the depth of the ResDense network is 16-256 layers (comprising an input layer, a hidden layer, an output layer and the like), and the width of the ResDense network is 64-256 units; and the hidden layer activation function adopts tanh, and the output layer activation function LINEAR is used for regressing the position related information. 4 values are output to represent 4 components of the distance, distance error, direction and direction error of the suspected leakage point from the monitoring point respectively.
In the detection positioning, furthermore, the number of points to be monitored of the test sample is set to be (2n +1), the input data of the test sample is 9 x (2n +1), and n is 5-20;
furthermore, a GIS system is adopted to display the positioning result of the suspected leakage point in real time.
In a second aspect of the present application, there is provided an on-board gas leakage detection positioning system corresponding to any one of the above methods, which is easy to continuously optimize, comprising:
a detection module comprising:
the gas concentration detection device is used for detecting and acquiring data of required gas concentration;
the positioning detection device is used for acquiring the geographic coordinates of the monitoring points;
the wind speed and direction detection device is used for acquiring wind speed and direction data of the environment where the monitoring point is located;
the temperature measuring device is used for acquiring temperature data of the environment where the monitoring point is located;
the analysis module is used for analyzing and acquiring a required gas concentration net value according to the acquired data of the required gas concentration, and further analyzing and acquiring leakage risk value data of the monitoring point according to the required gas concentration net value, the geographic coordinate of the monitoring point, the wind speed, the wind direction, the temperature, the vehicle speed and the vehicle running direction through a preset multilayer neural network or a trained model;
the early warning display module: and the leakage point positioning data acquired by the analysis module is displayed.
In the detection module:
in order to detect the gas leakage more efficiently, the gas concentration detection device is a gas concentration sensor; the desired gas is methane and/or ethane;
further, the positioning detection device includes, but is not limited to, a Beidou satellite navigation system (abbreviated as Beidou positioning) or a global positioning system (abbreviated as GPS);
further, the wind speed and direction detection device is an anemoclinograph or a wind speed and direction sensor;
further, the temperature measuring device is a temperature sensor.
In the analysis module:
further, preprocessing the raw gas concentration data to obtain net gas concentration data, including the following steps:
1) removing singular values: removing detection values with the concentration 2-3 times higher than the average value;
2) calculating an environment background value: taking a moving average value of the window size of 120-160 as an environmental background value;
3) calculating the net gas concentration: and subtracting the environmental background value from the original gas concentration value to obtain the net gas concentration value.
Further, the original data of the wind direction and the vehicle running direction are preprocessed, and the angle needs to be converted into a rectangular coordinate, and the specific method comprises the following steps:
1) the angle is converted into polar coordinate representation with modulo 1;
2) the polar representation is converted to rectangular coordinates.
In the analysis module:
furthermore, the preset multilayer neural network is a model formed by combining one or more of full connection, Attention, GRU and other time sequence processing layers.
Further, the trained model constructs a ResDense network based on an FC model (full connection model) for the ResNet network, wherein the depth of the ResDense network is 16-256 layers (including an input layer, a hidden layer, an output layer and the like), and the width of the ResDense network is 64-256 units; and the hidden layer activation function adopts tanh, and the output layer activation function LINEAR is used for regressing the position related information. Outputting 4 values which respectively represent 4 components of the distance, the distance error, the direction and the direction error of the suspected leakage point from the monitoring point;
further, the number of the input points to be monitored is (2n +1), the input data of the test sample is 9 x (2n +1), n is 5-20, and the data measured at each monitoring point comprises: methane concentration, ethane concentration, geographical coordinates (longitude and latitude) of this monitoring point, wind speed, wind direction, ambient temperature, vehicle speed, and vehicle direction of travel;
furthermore, a GIS system is adopted to display the positioning result of the suspected leakage point in real time.
Compared with the prior art, the method has the following beneficial effects:
1. according to the method, the deep learning technology is adopted, a special network structure is constructed, massive actual detection data are learned, the performance superior to that of a traditional method can be obtained, and the performance can be continuously optimized through continuous accumulation of detection data and improvement of a deep learning model.
2. The method is based on a deep learning model, in the output of the analysis process, an activation function LINEAR is adopted for regression of position related information, 4 values are output to represent 4 components of the distance, the distance error, the direction and the direction error between a suspected leakage point position and a monitoring point respectively, and accurate positioning of the leakage point is achieved.
3. Compared with the prior art, the data selected by the method are real-time detection data which comprise required gas concentration, geographical coordinates of the monitoring points, wind speed, wind direction and temperature of the environment where the monitoring points are located, vehicle speed, vehicle running direction and the like, actual conditions can be completely covered, and the accuracy of positioning results is high.
4. The system and the method adopt the deep learning model, compared with the prior art, the subsequent targeted optimization or continuous improvement can be easier to carry out, and the detection cost is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a sample of raw data provided by an embodiment of the present application;
FIG. 2 is a schematic diagram of a gas leakage detection positioning model easy to continuously optimize provided by an embodiment of the application;
FIG. 3 is an exemplary structure of a leak location model provided by an embodiment of the present application;
FIG. 4 is a graph of raw, ambient background, and net values of ethane concentration provided by an embodiment of the present application;
FIG. 5 illustrates a suspected leak location area provided by an embodiment of the present application;
FIG. 6 is a flowchart of a leak location model training and testing process provided by an embodiment of the present application;
FIG. 7 is a flow chart of a gas leakage detection positioning system easy to continuously optimize provided by an embodiment of the application.
Fig. 8 is a schematic diagram of a neural network input form provided by an embodiment of the present application.
Fig. 9 is a schematic diagram of an output form of a neural network provided by an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Interpretation of terms:
in the present application, a GIS system is an integration of efficient acquisition, storage, update, operation, analysis, and display of any form of geographic information organized by computer hardware, software, geographic data, and system administrators, as is conventionally known by those skilled in the art.
In the present application, a monitoring point, also called a detection point, refers to a position where gas collected at a certain time point is located, and the parameters represented by the monitoring point are time and geographic position, for example, the position points where data are collected at different times at the same position are called two monitoring points.
The deep learning technology is to establish and simulate a neural network for analyzing and learning the human brain, which simulates the mechanism of the human brain to explain data (such as images, sounds and texts), can learn task data representation by pertinently designing a network structure, and automatically extract a series of characteristics to realize the autonomous learning of tasks. The deep learning can greatly optimize the traditional classical method, can perform quantitative analysis, and can also continuously optimize through two aspects of data accumulation and algorithm improvement.
In one embodiment of the present application, a gas leak detection positioning method easy to continuously optimize is provided, comprising the following steps:
establishing a leakage positioning model: the leakage positioning model is composed of a preset multilayer neural network;
training of a leak localization model, comprising:
1) acquisition and preprocessing of training samples: detecting the concentration of the gas required by each monitoring point, acquiring original data of the concentration of the required gas, preprocessing the original data of the concentration of the gas, acquiring net value data of the concentration of the gas, acquiring geographic coordinates of each monitoring point, acquiring the wind speed, wind direction and temperature of the environment where each monitoring point is located, acquiring the vehicle speed and the vehicle running direction, and acquiring a plurality of training samples;
2) model training: inputting the training samples into the preset multilayer neural network to obtain a trained leakage positioning model;
detecting and positioning, comprising:
1) obtaining and preprocessing a test sample: detecting the concentration of the gas required by a point to be monitored, acquiring original data of the concentration of the required gas, preprocessing the original data of the concentration of the gas, acquiring net value data of the concentration of the gas, acquiring geographic coordinates (longitude and latitude) of the point to be monitored, acquiring the wind speed, wind direction and temperature of the environment where the point to be monitored is located, and acquiring the vehicle speed and the vehicle running direction, thereby acquiring the data of a test sample;
2) and inputting the obtained data of the test sample into the trained leakage positioning model, and outputting a result, namely performing gas leakage positioning.
Based on the above embodiment, as another embodiment of the present application, the method further includes a step of gas leakage detection determination, through which it is determined whether gas is leaked; if so, the gas leakage detection positioning method easy to continuously optimize is adopted. Conventional gas leakage detection determination methods may be adopted by those skilled in the art, and are not described in detail herein.
Based on the above embodiments, as another embodiment of the present application, in the building of the leakage localization model, the preset multilayer neural network is a neural network formed by one or more combinations of full-connection, Attention, GRU, and other timing processing layers, and the neural network includes, but is not limited to, an input layer, a hidden layer, an output layer, and the like, and those skilled in the art can conventionally know the neural network according to actual situations.
Based on the above embodiment, in order to more efficiently detect the gas leak site, as another embodiment of the present application, in the training of the leak location model, methane and ethane are selected as the desired gases.
The leakage positioning is mainly to simulate the atmospheric diffusion model to analyze and position the leakage source, the atmospheric diffusion is regional and non-uniform, so the methane (CH) is required to be used4) And ethane (C)2H6) The position of the diffusion source is inferred according to the change of each measured value such as net concentration value, wind direction and wind speed, so that the diffusion source cannot be judged according to the value of the current monitoring point, the data of n monitoring points before and after the current time point can be adopted by comprehensively considering the conditions of the monitoring points in a certain range, and the data of each point comprises methane (CH)4) Net value of concentration, ethane (C)2H6) Concentration net value, wind speed, wind direction, environment temperature, vehicle speed, longitude and latitude and other sensor data.
The inventor analyzes and verifies the model and finds that methane (CH)4) Net value of concentration, ethane (C)2H6) The 9 parameters of net concentration value, wind speed, wind direction, environment temperature, vehicle speed, longitude and latitude are used as analysis data, so that the gas leakage point can be accurately positioned, and the traditional method also needs to measure various other parameters such as water concentration, flow and air pressure in the environment.
Based on the above embodiment, as another embodiment of the present application, the raw data sample is shown in fig. 1, and mainly relates to the concentration and latitude and longitude coordinates of methane and ethane, the carrier speed (vehicle) and direction, the wind speed and direction, and the like.
Based on the above embodiment, as another embodiment of the present application, in the building of the leakage localization model, the model mainly adopts a deep learning model based on a neural network, as shown in fig. 2, since the task mainly infers the position where the gas leakage occurs according to the variation of the measured values of the monitoring point series, the task is essentially a regression task. The input data is 9 x (2n +1) detection points, n is generally between 5 and 20, each monitoring point has m measurement values, and the measurement values adopted in the method mainly comprise methane (CH)4) Net value of concentration, ethane (C)2H6) The net concentration value, the wind speed, the wind direction (rectangular coordinate system), the vehicle speed, the driving direction (rectangular coordinate system), and the like, m takes 9, so that m × 2n +1 data are input each time, the input data scale is 99 to 369, the data scale is not large, but the inference logic is relatively complex, a more complex model and more data are needed, and therefore a deeper model is needed to learn advanced characteristics, and a resdensity network based on an FC model (fully connected model) is constructed with reference to a ResNet network, as shown in fig. 3.
Based on the above embodiment, in order to detect the gas leakage site more efficiently, as another embodiment of the present application, in the training of the leakage positioning model, the concentration sensor is adopted to detect the required gas concentration in real time, so as to obtain the required gas concentration data.
Since a certain amount of natural gas components exist in atmospheric environment, particularly urban road environment, in order to reduce the influence of environmental factors as much as possible and improve the accuracy of the model, the environmental background value needs to be calculated first, and then the net values of the concentrations of methane and ethane are used for subsequent analysis. Therefore, based on the above embodiment, in order to more efficiently detect and locate the gas leakage site, as another embodiment of the present application, in the training of the leakage location model, the method for preprocessing the raw gas concentration data to obtain the net gas concentration data includes the following steps:
a, removing singular values: removing detection values with the concentration 2-3 times higher than the average value;
b calculating an environment background value: taking a moving average value of the window size of 120-160 as an environmental background value;
c calculating the net gas concentration: and subtracting the environmental background value from the original gas concentration value to obtain the net gas concentration value.
The results are shown in fig. 4, taking the net ethane concentration calculation as an example.
It should be noted that the moving average is a series of averages obtained by performing an arithmetic average on several items of data in the time series in a way of progressing item by item on the time series data within a specified time period. If the number of data items averaged is N, it is referred to as N-term moving average, and N also becomes the window size.
The examples illustrate that: when the leak detection apparatus sequentially obtains a set of measurement values, a certain number of data are sequentially taken and an overall arithmetic average thereof is calculated, and the obtained data is called a moving average.
If the measured values (x) are obtained sequentially1，x2，x3，...，xn) Then, the whole arithmetic mean value of a certain number is taken in sequence. For example,
Based on the above embodiment, for detecting the gas leakage site more efficiently, as another embodiment of the present application, in the training of the leakage positioning model, the coordinates of the required gas monitoring point can be obtained by using the positioning device, which can be conventionally done by those skilled in the art, and the positioning system includes but is not limited to the beidou satellite navigation system or the GPS.
Based on the above embodiment, as another embodiment of the present application, in the training of the leakage localization model, the wind speed and the wind direction of the environment where the monitoring point is located can be obtained by using the wind speed and wind direction sensor, which is a routine operation that can be performed by those skilled in the art;
the angle values (the vehicle running direction and the wind direction) exist in the original data, because the angle values and the common numerical values have larger characteristic difference, on one hand, the angle represents the direction and does not represent the magnitude, for example, the east-north 30 degrees can not be considered to be larger than the east-north 15 degrees; on the other hand, the angle has a cyclic characteristic, for example, 0 degrees and 360 degrees are the same, and 0 degrees and 359 degrees are different by 1 degree or 359 degrees, but are different in numerical representation. Therefore, the direction cannot be directly used for modeling calculation, and preprocessing is required, and the method is as follows:
1) the angle is converted into polar coordinate representation with modulo 1;
2) the polar representation is converted to rectangular coordinates.
For example, 30 degrees is expressed as polar coordinates (1,30 °), and then converted into rectangular coordinates (0.866,0.5) by the following formula:
the representation of the vehicle running direction and the wind direction is numerical type, and the numerical value varies from-3.14 to 3.14 in radian.
Based on the above embodiment, as another embodiment of the present application, in the training of the leakage localization model, the temperature of the environment where the monitoring point is located may be obtained by using the temperature sensor device.
Based on the above embodiment, as another embodiment of the present application, in the training of the leakage localization model, each training sample is input into a preset multilayer neural network as matrix data with 9 parameters per row for global training until the output precision reaches the preset precision, so as to obtain a trained model, where the 9 parameters include: methane (CH)4) Net value of concentration, ethane (C)2H6) Net concentration, wind speed, wind direction (rectangular coordinate system), vehicle speed, direction of travel (rectangular coordinate system), longitude and latitude; the number of columns of the matrix is set to be 2n +1, and n is 2-50; the leakage positioning is mainly to simulate the atmospheric diffusion model to analyze and position the position of the leakage sourceSince atmospheric diffusion is regional and non-uniform, the estimation cannot be performed according to one monitoring point at the current time, so that n is selected to be not less than 2, and the number of the monitoring points is not too large; further, n is 5-20.
Based on the above embodiment, as another embodiment of the present application, in the training of the leakage localization model, the trained model constructs a resdensity network based on an FC model (full connectivity model) for the ResNet network. According to the actual training process, the model needs enough depth and width, the depth is generally 16-64 units and the width is 64-256 units. And the hidden layer activation function adopts tanh, and the output layer activation function LINEAR is used for regressing the position related information. The 4 values are output to represent 4 components of the distance, distance error, direction and direction error of the suspected leakage point from the monitoring point, so as to form a sector-shaped area where the suspected leakage point is located, as shown in fig. 5.
The model is verified:
the model training adopts actual detection data from 8 months in 2018 to 12 months in 2018, the verification data adopts actual detection data from 3 months in 2019 to 4 months in 2019, and meanwhile, the judgment result and the manual confirmation result of the existing traditional classical system and method are compared for evaluation so as to verify the performance of the model. Verification finds that model performance can be continuously optimized through data accumulation and model algorithm adjustment, accurate positioning of leakage points can be achieved, and the method is superior to traditional classical systems and methods in the aspects of accuracy and the like.
It should be noted that the input and output of the neural network are generally output in a matrix form, and in this embodiment, the input form of the parameters is shown in fig. 8, and the output form is shown in fig. 9.
Based on the above embodiments, as another embodiment of the present application, in the detection positioning, the acquisition or preprocessing method of the relevant data of the test sample is the same as that of the training sample.
Based on the above embodiment, as another embodiment of the present application, in the detection positioning, the number of the points to be monitored of the test sample is set to be (2n +1), the input data of the test sample is 9 × 2n +1, and n is 5 to 20.
Based on the above embodiment, in another embodiment of the present application, in the detection and positioning, a GIS system is used to display the positioning result of the suspected leakage point in real time.
Based on the above embodiment, in another embodiment of the present application, there is provided a gas leakage detection positioning method easy to continuously optimize, specifically including the following steps:
as shown in fig. 6, including data acquisition (methane (CH)4) Concentration value, ethane (C)2H6) Concentration value, wind speed, wind direction, vehicle speed, driving direction, monitoring point longitude and latitude), data standardization (data preprocessing), data cleaning (singular value elimination), data storage, verification, application and the like.
One embodiment of the present application provides an on-vehicle gas leakage detection positioning system who easily lasts optimization, includes:
a detection module comprising:
the gas concentration detection device is used for detecting and acquiring data of required gas concentration;
the positioning detection device is used for acquiring the geographic coordinates of the monitoring points;
the wind speed and direction detection device is used for acquiring wind speed and direction data of the environment where the monitoring point is located;
the temperature measuring device is used for acquiring temperature data of the environment where the monitoring point is located;
the analysis module is used for analyzing and acquiring a required gas concentration net value according to the acquired data of the required gas concentration, and further analyzing and acquiring leakage risk value data of the monitoring point according to the required gas concentration net value, the geographic coordinate of the monitoring point, the wind speed, the wind direction, the temperature, the vehicle speed and the vehicle running direction through a preset multilayer neural network or a trained model;
the early warning display module: and the leakage point positioning data acquired by the analysis module is displayed.
Based on the above-described embodiment, as another embodiment of the present application, as shown in fig. 7, the gas concentration detection apparatus includes a gas concentration sensor, and the desired gas includes methane and/or ethane. The gas concentration sensor is used for detecting the concentration of methane or ethane and transmitting the concentration to the analysis module in real time through a network.
Based on the above embodiment, as another embodiment of the present application, as shown in fig. 7, the monitoring point positioning device includes a beidou satellite navigation system for acquiring the geographic coordinates of the monitoring point, and transmitting the geographic coordinates to the analysis module in real time through a network.
Based on the above embodiments, as another embodiment of the present application, the anemorumbometer is an anemorumbometer, and the temperature measuring device is a temperature sensor.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system provided by the embodiment, the description is relatively simple because the system corresponds to the method provided by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present application are explained herein using specific examples, which are provided only to help understand the method and the core idea of the present application. It should be noted that, for those skilled in the art, it is possible to make several improvements and modifications to the present application without departing from the principle of the present application, and such improvements and modifications also fall within the scope of the claims of the present application.
It is further noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (9)
1. A gas leakage detection and positioning method easy for continuous optimization is characterized by comprising the following steps: establishing a leakage positioning model: the leakage positioning model is composed of a preset multilayer neural network;
training of a leak localization model, comprising:
1) acquisition and preprocessing of training samples: detecting the concentration of the gas required by each monitoring point, acquiring original data of the concentration of the required gas, preprocessing the original data of the concentration of the gas, acquiring net value data of the concentration of the gas, acquiring geographic coordinates of each monitoring point, acquiring the wind speed, wind direction and temperature of the environment where each monitoring point is located, acquiring the vehicle speed and the vehicle running direction, and acquiring a plurality of training samples;
2) model training: inputting the training samples into the preset multilayer neural network to obtain a trained leakage positioning model;
detecting and positioning, comprising:
1) obtaining and preprocessing a test sample: detecting the concentration of the gas required by a point to be monitored, acquiring original data of the concentration of the required gas, preprocessing the original data of the concentration of the gas, acquiring net value data of the concentration of the gas, acquiring the geographic coordinates of the point to be monitored, acquiring the wind speed, the wind direction and the temperature of the environment where the point to be monitored is located, and acquiring the vehicle speed and the vehicle running direction, thereby acquiring the data of a test sample;
2) inputting the obtained data of the test sample into the trained leakage positioning model, and outputting a result, namely performing gas leakage positioning;
constructing a ResDense network based on a full-connection model for a ResNet network by the trained model, wherein the depth is 16-256 layers, and the width is 64-256 units; the hidden layer activation function adopts tanh, and the output layer activation function adopts LINEAR to return the position related information; 4 values are output to represent 4 components of the distance, distance error, direction and direction error of the suspected leakage point from the monitoring point respectively.
2. The method of claim 1, wherein in the building of the leak localization model, the predetermined multi-layer neural network is a neural network formed by one or more combinations of fully connected, Attention, and GRU timing processing layers;
further, the preset multilayer neural network is a ResDense network based on a full-connection model.
3. The method of claim 1, wherein in the training of the leak location model, the desired gas is methane and/or ethane;
further, a concentration sensor is adopted to detect the concentration of the required gas in real time;
further, preprocessing the raw gas concentration data to obtain net gas concentration data, including the following steps:
a, removing singular values: removing detection values with the concentration 2-3 times higher than the average value;
b calculating an environment background value: taking a moving average value of the window size of 120-160 as an environmental background value;
c calculating the net gas concentration: and subtracting the environmental background value from the original gas concentration value to obtain the net gas concentration value.
4. The method of claim 1, wherein in the training of the leakage localization model, the coordinates of the required gas monitoring point can be obtained by using a localization device, wherein the localization device is a Beidou satellite navigation system or a global positioning system;
furthermore, an anemorumbometer or an anemorumbometer sensor can be used for acquiring the wind speed and the wind direction of the environment where the monitoring point is located;
the temperature of the environment where the monitoring point is located can be obtained by adopting the temperature sensor.
5. The method of claim 1, wherein in the training of the leakage localization model, each training sample is input into a preset multilayer neural network as matrix data with 9 parameters per row for global training until the output precision reaches a preset precision, thereby obtaining a trained model, wherein the 9 parameters comprise: net methane concentration, net ethane concentration, wind speed, wind direction, temperature, vehicle speed, direction of travel, longitude and latitude of the monitoring point.
6. The method according to claim 1, wherein in the test positioning, the number of points to be monitored of the test sample is set to 2n +1, the input data of the test sample is 9 x (2n +1), and n is 5 to 20.
7. The method of claim 1, wherein in the detecting and locating, a GIS system is used to display the locating result of the suspected leakage points in real time.
8. A gas leakage detection and positioning system easy to continuously optimize corresponding to the method of any one of claims 1-7, and characterized by comprising:
a detection module comprising:
the gas concentration detection device is used for detecting and acquiring data of required gas concentration;
the positioning detection device is used for acquiring the geographic coordinates of the monitoring points;
the wind speed and direction detection device is used for acquiring wind speed and direction data of the environment where the monitoring point is located;
the temperature measuring device is used for acquiring temperature data of the environment where the monitoring point is located;
the analysis module is used for analyzing and acquiring a required gas concentration net value according to the acquired data of the required gas concentration, and further analyzing and acquiring leakage risk value data of the monitoring point according to the required gas concentration net value, the geographic coordinate of the monitoring point, the wind speed, the wind direction, the temperature, the vehicle speed and the vehicle running direction through a preset multilayer neural network or a trained model;
the early warning display module: and the leakage point positioning data acquired by the analysis module is displayed.
9. The system of claim 8, wherein the trained model constructs a fully connected model-based resdensity network for the ResNet network, wherein the depth is 16-256 layers and the width is 64-256 cells; the hidden layer activation function adopts tanh, and the output layer activation function adopts LINEAR to return the position related information; 4 values are output to represent 4 components of the distance, distance error, direction and direction error of the suspected leakage point from the monitoring point respectively.
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