CN117908585B - Intelligent regulation system and method for gas pipeline air pressure of industrial factory building - Google Patents
Intelligent regulation system and method for gas pipeline air pressure of industrial factory building Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 70
- 239000007789 gas Substances 0.000 claims description 133
- 230000007797 corrosion Effects 0.000 claims description 64
- 238000005260 corrosion Methods 0.000 claims description 64
- 238000012423 maintenance Methods 0.000 claims description 51
- 239000001257 hydrogen Substances 0.000 claims description 41
- 229910052739 hydrogen Inorganic materials 0.000 claims description 41
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 claims description 37
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- 238000004590 computer program Methods 0.000 claims description 15
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- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 56
- 239000003345 natural gas Substances 0.000 description 28
- 230000008569 process Effects 0.000 description 8
- 238000007726 management method Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 239000011241 protective layer Substances 0.000 description 4
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- 208000037656 Respiratory Sounds Diseases 0.000 description 2
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- 150000002431 hydrogen Chemical class 0.000 description 2
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Abstract
The invention relates to the technical field of gas conveying, and discloses an intelligent regulation system and method for gas pipeline pressure of an industrial factory building.
Description
Technical Field
The invention relates to the technical field of gas conveying, in particular to an intelligent regulation system and method for gas pipeline pressure of an industrial factory building.
Background
Natural gas is a clean and efficient fuel widely used for energy requirements such as heating, steam production, electricity generation and the like of factories, natural gas pipelines are used for conveying the natural gas to the factories, stable fuel supply is ensured, so that the production process of the factories is supported, some factories use natural gas generating sets to produce electricity so as to meet the power requirements of the factories or sell surplus electricity back to a power grid, in recent years, the related technology of hydrogen loading and conveying of the natural gas pipelines has been rapidly developed, and the operation state, equipment performance and safety maintenance requirements of the pipelines are obviously changed when hydrogen is mixed into the natural gas, so that the influence is not neglected;
Because of the large density difference between the hydrogen and the natural gas, after the pipeline stops conveying for a period of time, the phenomenon of gas layering can occur, which can lead to the accumulation of the hydrogen at the top of the pipeline, the accumulated hydrogen can lead to the hydrogen failure of the inner wall of the pipeline to a certain extent, the hydrogen failure refers to the material performance reduction, particularly the remarkable reduction of toughness and ductility, caused by the existence and diffusion of the hydrogen, so that the development of the technology not only represents innovative progress, but also brings new challenges and attention to the problems;
In the prior art, patent application publication number CN111045460a discloses a remote automatic control system, although the patent detects the concentration of natural gas in air through ultrasonic technology, and then judges whether to alarm according to the concentration of natural gas in air, but the natural gas in the patent is not introduced with hydrogen, so that it is not considered that when the pipeline stops conveying for a period of time, hydrogen can gather at the top of the pipeline, and the corrosion degree of the top of the inner wall of the pipeline can be accelerated, then when natural gas is conveyed again, the pipeline is easy to crack, and natural gas leakage is caused.
In view of the above, the present invention provides an intelligent adjustment system and method for gas pressure of industrial plant gas pipeline to solve the above problems.
Disclosure of Invention
In order to overcome the defects in the prior art, the embodiment of the invention provides an intelligent regulation system and method for the gas pressure of a gas pipeline of an industrial factory building.
In order to achieve the above purpose, the present invention provides the following technical solutions:
The intelligent regulation method for the gas pressure of the gas pipeline of the industrial factory building comprises the following steps:
s10: acquiring inner wall infrared information of M preset pipe sections, analyzing based on the inner wall infrared information, and judging whether at least one preset pipe section is abnormal or not, wherein the inner wall infrared information comprises H reflection time sets, and M and H are positive integer sets larger than zero;
S20: if at least one preset pipe section is abnormal, acquiring an outside top image of a corresponding pipe section, and acquiring maintenance grades based on the outside top image of the pipe section and a pre-constructed early warning model, wherein the maintenance grades comprise a first grade and a second grade;
S30: maintaining a preset pipe section based on the maintenance level, traversing the H reflection time sets, and generating a target inner wall corrosion coefficient based on the reflection time sets;
S40: and (3) acquiring gas inlet concentration and gas inlet interval, inputting the gas inlet concentration, the gas inlet interval and the corrosion coefficient of the inner wall of the target into a pre-constructed rate determination model, acquiring the gas inlet rate output by the rate determination model, wherein the gas inlet concentration refers to the concentration of hydrogen in the inlet preset pipe section, and the gas inlet interval refers to the interval between two adjacent hydrogen inlet times.
Further, the method for acquiring the inner wall infrared information of the M preset pipe sections comprises the following steps:
The intelligent floating ball device is arranged inside the preset pipe section, moves along the inside of the preset pipe section, emits infrared rays to detection points on the inner wall of the preset pipe section based on the infrared detection unit, receives the reflected infrared rays to form reflection time, and forms H reflection time sets.
Further, the method for judging whether at least one preset pipe section is abnormal based on the analysis of the inner wall infrared information comprises the following steps:
Traversing the H reflection time sets, judging whether an element in the reflection time sets is larger than a preset reflection time threshold, if not, calculating the time difference between the element and the adjacent element, and judging whether the time difference is smaller than the preset difference threshold, if so, judging whether the preset pipe section is abnormal, and if not, not.
Further, the method for acquiring the top image of the outer side of the corresponding pipe section comprises the following steps:
When the preset pipe section is judged to be abnormal, the intelligent floating ball device positioned in the preset pipe section sends position information, a background management system determines a label corresponding to the preset pipe section by combining the position information, an image acquisition mode is determined according to the label, the label comprises an overhead pipeline and a buried pipeline, and the image acquisition mode comprises a camera mode and a pipeline detection vehicle mode.
Further, the method for determining the image acquisition mode according to the label comprises the following steps:
When the tag is an overhead pipeline, the image acquisition mode is a camera mode;
when the tag is a buried pipeline, the image acquisition mode is a pipeline detection vehicle mode.
Further, the construction method of the early warning model comprises the following steps:
Acquiring i groups of data, wherein i is a positive integer greater than 1, the data comprises a top image and a history maintenance level on the outer side of a history pipe section, the top image and the history maintenance level on the outer side of the history pipe section are used as a sample set, the sample set is divided into a training set and a test set, a classifier is constructed, the top image on the outer side of the history pipe section in the training set is used as input data, the history maintenance level in the training set is used as output data, the classifier is trained, an initial classifier is obtained, the initial classifier is tested by using the test set, and the classifier meeting the preset accuracy is output to be used as an early warning model.
Further, traversing the H reflection time sets, and generating the target inner wall corrosion coefficient based on the reflection time sets comprises the following steps:
Based on the H reflection time sets, obtaining a reflection time average value and a reflection time maximum value of each reflection time set, calculating an inner wall corrosion coefficient according to the reflection time average value and the reflection time maximum value, and taking the maximum value of the H inner wall corrosion coefficients as a target inner wall corrosion coefficient.
Further, the method for calculating the inner wall corrosion coefficient according to the reflection time average value and the reflection time maximum value comprises the following steps:
Wct=Mrt＋/>；
Wherein Wct is the inner wall corrosion coefficient, mrt is the maximum value of reflection time, Is the reflection time average value,/>And/>Are weight coefficients.
Further, the method for constructing the rate determination model comprises the following steps:
The method comprises the steps of obtaining a sample data set, wherein the sample data set comprises historical gas inlet concentration, historical gas inlet interval, historical inner wall corrosion coefficient and historical gas inlet rate, dividing the sample data set into a sample training set and a sample testing set, constructing a regression network, taking the historical gas inlet concentration, the historical gas inlet interval and the historical inner wall corrosion coefficient in the sample training set as input data of the regression network, taking the historical gas inlet rate in the sample training set as output data of the regression network, training the regression network to obtain an initial regression network for predicting the real-time gas inlet rate, testing the initial regression network by utilizing the sample testing set, and outputting the regression network meeting the requirement of less than a preset error value as a rate determination model.
The intelligent regulation system of the gas pipeline air pressure of the industrial factory building is used for realizing the intelligent regulation method of the gas pipeline air pressure of the industrial factory building, and comprises the following steps:
An anomaly determination module: acquiring inner wall infrared information of M preset pipe sections, analyzing based on the inner wall infrared information, and judging whether at least one preset pipe section is abnormal or not, wherein the inner wall infrared information comprises H reflection time sets, and M and H are positive integer sets larger than zero;
The grade generation module: if at least one preset pipe section is abnormal, acquiring an outside top image of a corresponding pipe section, and acquiring maintenance grades based on the outside top image of the pipe section and a pre-constructed early warning model, wherein the maintenance grades comprise a first grade and a second grade;
And a coefficient generation module: maintaining a preset pipe section based on the maintenance level, traversing the H reflection time sets, and generating a target inner wall corrosion coefficient based on the reflection time sets;
and a rate regulation module: and acquiring the gas inlet concentration and the gas inlet interval, inputting the gas inlet concentration, the gas inlet interval and the target inner wall corrosion coefficient into a pre-constructed rate determination model, and acquiring the gas inlet rate output by the rate determination model.
An electronic device comprises a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor realizes the intelligent regulation method of the gas pipeline pressure of the industrial factory building when executing the computer program.
A computer readable storage medium, on which a computer program is stored, which when executed implements the above-mentioned intelligent regulation method for gas pressure in industrial plant gas pipelines.
Compared with the prior art, the invention has the beneficial effects that:
(1) According to the invention, firstly, whether the preset pipe section is abnormal or not is judged by acquiring the inner wall infrared information of the preset pipe section, then, a maintenance grade is obtained according to the top image of the outer side of the pipe section and a pre-built early warning model, the preset pipe section is maintained according to the maintenance grade, a target inner wall corrosion coefficient is generated according to a reflection time set, and then, the gas inlet rate is obtained by determining the model according to the gas inlet concentration, the gas inlet interval, the target inner wall corrosion coefficient and the pre-built rate, so that the intelligent regulation of the gas inlet rate is realized, and the problem that the natural gas leakage is caused by the occurrence of cracks in a pipeline when the natural gas is conveyed due to the existence of hydrogen in the natural gas is avoided;
(2) According to the invention, the intelligent floating ball device is used for acquiring the infrared information of the inner wall of the preset pipe section, determining the corrosion degree of the inner wall of the preset pipe section according to the infrared information of the inner wall, acquiring the surface image of the preset pipe section after the abnormality of the preset pipe section is determined, so that the whole pipe network can be rapidly maintained, and the intelligent floating ball device is not interfered by external factors in the detection process.
Drawings
FIG. 1 is a schematic diagram of an intelligent regulation method for the air pressure of an industrial plant air pipeline in the invention;
FIG. 2 is a schematic view showing hydrogen gas accumulated on the top of the inner wall of a pipeline according to the present invention;
FIG. 3 is a schematic diagram of a computer readable storage medium according to the present invention.
Reference numerals illustrate:
10. hydrogen gas; 20. a pipeline.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, the disclosure of this embodiment provides an intelligent adjustment method for gas pressure in a gas pipeline of an industrial factory building, which includes:
s10: acquiring inner wall infrared information of M preset pipe sections, analyzing based on the inner wall infrared information, and judging whether at least one preset pipe section is abnormal or not, wherein the inner wall infrared information comprises H reflection time sets, and M and H are positive integer sets larger than zero;
It should be noted that, the natural gas transportation in the factory is usually completed by a pipe network, the pipe network is usually composed of a plurality of pipe sections, the pipe sections are connected by valve bodies or flange discs and other parts, the pipe network is composed of M preset pipe sections, and each preset pipe section is provided with a corresponding label and is prestored in the system;
The method for acquiring the inner wall infrared information of the M preset pipe sections comprises the following steps:
An intelligent floating ball device is arranged in the preset pipe section, moves along the inside of the preset pipe section, emits infrared rays to detection points on the inner wall of the preset pipe section based on the infrared detection unit, and receives the reflected infrared rays to form reflection time, so that H reflection time sets are formed;
It can be understood that the inner wall of each preset pipe section can be divided into several detection areas, a plurality of detection points exist in each detection area, each detection point corresponds to one reflection time, so each preset pipe section corresponds to a plurality of reflection time sets, and thus the inner wall infrared information comprises H reflection time sets, the intelligent floating ball device comprises but not limited to an infrared detection unit, a GPS positioning unit, a communication unit, a control processing unit and a magnetic attraction/repulsion unit, the magnetic attraction/repulsion unit is used for enabling the intelligent floating ball device to be fixed on the inner wall of the preset pipe section according to instructions, and a plurality of sensors are prevented from being arranged inside the preset pipe section through the intelligent floating ball device, so that the monitoring cost is reduced;
the method for judging whether at least one preset pipe section is abnormal or not based on the analysis of the inner wall infrared information comprises the following steps:
traversing the H reflection time sets, judging whether an element in the reflection time sets is larger than a preset reflection time threshold, if not, further calculating a time difference between the element and an adjacent element, if so, judging whether the time difference is smaller than the preset difference threshold, if so, judging whether the preset pipe section is abnormal, and if not, not;
It is noted that, the above-mentioned reflection time set has a plurality of elements, each element is the reflection time that the check point corresponds to, then judge whether the element in the reflection time set is greater than the logic of the threshold value of preset reflection time, if there is an element greater than the threshold value of preset reflection time in the reflection time set, indicate that the check point corresponding to this element corrodes the phenomenon to be serious, or there is a large error in the course that the intelligent floating ball device detects this check point too, therefore need to confirm further, because corrode the pipe wall and usually appear the crackle, therefore will have the phenomenon that corrodes the serious between the adjacent check points, judge whether is smaller than the threshold value of preset difference through the time difference between this element and adjacent element, if is smaller than, indicate this check point as the base point, the adjacent check point also appears the serious corrosion phenomenon, therefore has excluded the possibility that detects the great error;
S20: if at least one preset pipe section is abnormal, acquiring an outside top image of a corresponding pipe section, and acquiring maintenance grades based on the outside top image of the pipe section and a pre-constructed early warning model, wherein the maintenance grades comprise a first grade and a second grade;
In this embodiment, the method for acquiring the top image of the outer side of the corresponding pipe section includes:
When the preset pipe section is judged to be abnormal, the intelligent floating ball device positioned in the preset pipe section sends position information, a background management system determines a label corresponding to the preset pipe section by combining the position information, an image acquisition mode is determined according to the label, the label comprises an overhead pipeline and a buried pipeline, and the image acquisition mode comprises a camera mode and a pipeline detection vehicle mode;
the method for determining the image acquisition mode according to the label comprises the following steps:
When the tag is an overhead pipeline, the image acquisition mode is a camera mode;
when the tag is a buried pipeline, the image acquisition mode is a pipeline detection vehicle mode;
It should be noted that two modes of pipeline laying in a factory exist, one mode is to set along a wall, the other mode is to set in a semi-buried mode, if a camera is needed to be arranged in a nearby area along the pipeline of the wall correspondingly, then the surface image of the pipeline can be obtained, if the pipeline is in the semi-buried mode, then a pipeline detection vehicle is generally used to move along the direction of pipeline wiring, an image is obtained through the camera, each preset pipeline section is provided with a corresponding label, the label indicates the laying mode of the preset pipeline section, each preset pipeline section also has corresponding position information, the label and the position information are all pre-stored in a background management system, it can be understood that, as shown in fig. 2, 20 is a pipeline, 10 is hydrogen in the pipeline, after the pipeline stops conveying for a period of time, the hydrogen can be gathered at the top of the pipeline, and the corrosion degree of the top of the inner wall of the pipeline can be accelerated, therefore, the top of the preset pipeline section is more serious than other parts, the corrosion degree of the top of the preset pipeline section needs to be obtained, and whether maintenance or replacement is needed is judged;
The construction method of the early warning model comprises the following steps:
acquiring i groups of data, wherein i is a positive integer greater than 1, the data comprises a top image and a history maintenance level on the outer side of a history pipe section, the top image and the history maintenance level on the outer side of the history pipe section are used as a sample set, the sample set is divided into a training set and a test set, a classifier is constructed, the top image on the outer side of the history pipe section in the training set is used as input data, the history maintenance level in the training set is used as output data, the classifier is trained to obtain an initial classifier, the test set is used for testing the initial classifier, a classifier meeting the preset accuracy is output as an early warning model, and the classifier is preferably one of a naive Bayesian model or a support vector machine model;
It is noted that the top image on the outer side of the history pipe section comprises an image with cracks on the surface of the history pipe section and an image without cracks on the surface of the history pipe section, the corresponding history maintenance level comprises a first level and a second level, the input is the image with cracks on the surface of the history pipe section, the corresponding output is the first level, the input is the image without cracks on the surface of the history pipe section, the corresponding output is the second level, and the model is trained in this way;
The intelligent floating ball device is used for acquiring the inner wall infrared information of the preset pipe section, determining the corrosion degree of the inner wall of the preset pipe section according to the inner wall infrared information, acquiring the surface image of the preset pipe section after the abnormality of the preset pipe section is determined, and therefore the whole pipe network can be rapidly maintained, and the intelligent floating ball device is not interfered by external factors in the detection process;
S30: maintaining a preset pipe section based on the maintenance level, traversing the H reflection time sets, and generating a target inner wall corrosion coefficient based on the reflection time sets;
In this embodiment, the method for maintaining the preset pipe section based on the maintenance level includes:
When the maintenance level is the first level, replacing the preset pipe section;
When the maintenance level is the second level, reinforcing the preset pipe section;
When the maintenance level is the first level, the outside of the preset pipe section is indicated to be cracked, workers are required to be informed of replacement in time, when the maintenance level is the second level, the outside of the preset pipe section is indicated to be not cracked, but the inner wall of the preset pipe section is severely corroded, the preset pipe section is required to be reinforced, for example, a protective layer is welded on the outside of the preset pipe section again, and the protective layer can be made of the same material as the preset pipe section;
the method for generating the corrosion coefficient of the inner wall of the target based on the reflection time sets comprises the following steps of:
Based on the H reflection time sets, obtaining a reflection time average value and a reflection time maximum value of each reflection time set, calculating an inner wall corrosion coefficient according to the reflection time average value and the reflection time maximum value, and taking the maximum value of the H inner wall corrosion coefficients as a target inner wall corrosion coefficient;
the method for calculating the inner wall corrosion coefficient according to the reflection time average value and the reflection time maximum value comprises the following steps:
Wct=Mrt＋/>；
Wherein Wct is the inner wall corrosion coefficient, mrt is the maximum value of reflection time, Is the reflection time average value,/>AndAre all weight coefficients;
it will be appreciated that the number of components, And/>The purpose of generating the corrosion coefficient of the inner wall of the target is to avoid cracks on the surface of the preset pipe section due to pressure caused by the too high gas inlet rate in the subsequent gas inlet process according to the determination of the person skilled in the art according to the actual situation;
S40: acquiring gas inlet concentration and gas inlet interval, inputting the gas inlet concentration, the gas inlet interval and the corrosion coefficient of the inner wall of the target into a pre-constructed rate determination model, acquiring the gas inlet rate output by the rate determination model, wherein the gas inlet concentration refers to the concentration of hydrogen in a preset pipe section, and the gas inlet interval refers to the interval between two adjacent hydrogen inlet times;
Specifically, the gas inlet concentration refers to the concentration of hydrogen in a preset pipe section, the gas inlet concentration can be directly obtained through a gas sensor, because the hydrogen injected into a natural gas pipeline can be uniformly distributed in a certain time, the hydrogen occupies a relatively high proportion in the natural gas, the gas inlet rate is relatively high, the risk of hydrogen failure caused by the fact that the local hydrogen concentration in the preset pipe section is too high is easily caused, the gas inlet interval refers to the interval between two adjacent hydrogen inlet times, in general, the gas inlet interval is fixed, after the pipeline stops conveying for a period of time, the hydrogen can be gathered at the top of the pipeline, the longer the gas inlet interval is, the higher the local gathering degree of the hydrogen is, the natural gas leakage is easily caused by the fact that the preset pipe section is cracked in the process of the hydrogen re-inlet, and the same target inner wall corrosion coefficient is larger, so that the crack is more easily caused in the preset pipe section is shown;
The method for constructing the rate determination model comprises the following steps:
Obtaining a sample data set, wherein the sample data set comprises a historical gas inlet concentration, a historical gas inlet interval, a historical inner wall corrosion coefficient and a historical gas inlet rate, dividing the sample data set into a sample training set and a sample testing set, constructing a regression network, taking the historical gas inlet concentration, the historical gas inlet interval and the historical inner wall corrosion coefficient in the sample training set as input data of the regression network, taking the historical gas inlet rate in the sample training set as output data of the regression network, training the regression network to obtain an initial regression network for predicting the real-time gas inlet rate, testing the initial regression network by using the sample testing set, and outputting the regression network meeting a value smaller than a preset error as a rate determination model, wherein the regression network is preferably a neural network model;
It can be understood that the gas introducing rate refers to the introducing amount of hydrogen in unit time, in this embodiment, by acquiring the inner wall infrared information of the preset pipe section, judging whether the preset pipe section is abnormal, then obtaining a maintenance level according to the top image of the outer side of the pipe section and a pre-built early warning model, maintaining the preset pipe section according to the maintenance level, generating a target inner wall corrosion coefficient according to the set of reflection times, and determining the model according to the gas introducing concentration, the gas introducing interval, the target inner wall corrosion coefficient and the pre-built rate, so as to obtain the gas introducing rate, thereby realizing intelligent adjustment of the gas introducing rate and avoiding the occurrence of cracks in the pipeline when natural gas is conveyed due to the existence of hydrogen in the natural gas.
Example 2
Based on embodiment 1, this implementation provides industrial factory building gas pipeline atmospheric pressure intelligent regulation system, includes:
An anomaly determination module: acquiring inner wall infrared information of M preset pipe sections, analyzing based on the inner wall infrared information, and judging whether at least one preset pipe section is abnormal or not, wherein the inner wall infrared information comprises H reflection time sets, and M and H are positive integer sets larger than zero;
It should be noted that, the natural gas transportation in the factory is usually completed by a pipe network, the pipe network is usually composed of a plurality of pipe sections, the pipe sections are connected by valve bodies or flange discs and other parts, the pipe network is composed of M preset pipe sections, and each preset pipe section is provided with a corresponding label and is prestored in the system;
The method for acquiring the inner wall infrared information of the M preset pipe sections comprises the following steps:
An intelligent floating ball device is arranged in the preset pipe section, moves along the inside of the preset pipe section, emits infrared rays to detection points on the inner wall of the preset pipe section based on the infrared detection unit, and receives the reflected infrared rays to form reflection time, so that H reflection time sets are formed;
It can be understood that the inner wall of each preset pipe section can be divided into several detection areas, a plurality of detection points exist in each detection area, each detection point corresponds to one reflection time, so each preset pipe section corresponds to a plurality of reflection time sets, and thus the inner wall infrared information comprises H reflection time sets, the intelligent floating ball device comprises but not limited to an infrared detection unit, a GPS positioning unit, a communication unit, a control processing unit and a magnetic attraction/repulsion unit, the magnetic attraction/repulsion unit is used for enabling the intelligent floating ball device to be fixed on the inner wall of the preset pipe section according to instructions, and a plurality of sensors are prevented from being arranged inside the preset pipe section through the intelligent floating ball device, so that the monitoring cost is reduced;
the method for judging whether at least one preset pipe section is abnormal or not based on the analysis of the inner wall infrared information comprises the following steps:
traversing the H reflection time sets, judging whether an element in the reflection time sets is larger than a preset reflection time threshold, if not, further calculating a time difference between the element and an adjacent element, if so, judging whether the time difference is smaller than the preset difference threshold, if so, judging whether the preset pipe section is abnormal, and if not, not;
It is noted that, in the above-mentioned middle reflection time set, there are multiple elements, each element is the reflection time that the check point corresponds to, then judge whether the element in the reflection time set is greater than the logic of the threshold value of preset reflection time, if there is an element greater than the threshold value of preset reflection time in the reflection time set, indicate that the check point corrosion phenomenon that the element corresponds to is serious, or there is a large error in the course that the intelligent floating ball device detects the check point too, therefore need to confirm further, because the pipe wall of corroding will usually appear the crackle, therefore will have the phenomenon that corrodes relatively serious between the adjacent check points, judge whether is smaller than the threshold value of preset difference through the time difference between the element and adjacent element, if is smaller than, indicate that the check point is the base point, the adjacent check point also appears relatively serious corrosion phenomenon, therefore has excluded the possibility that the detection has relatively large error;
The grade generation module: if at least one preset pipe section is abnormal, acquiring an outside top image of a corresponding pipe section, and acquiring maintenance grades based on the outside top image of the pipe section and a pre-constructed early warning model, wherein the maintenance grades comprise a first grade and a second grade;
In this embodiment, the method for acquiring the top image of the outer side of the corresponding pipe section includes:
When the preset pipe section is judged to be abnormal, the intelligent floating ball device positioned in the preset pipe section sends position information, a background management system determines a label corresponding to the preset pipe section by combining the position information, an image acquisition mode is determined according to the label, the label comprises an overhead pipeline and a buried pipeline, and the image acquisition mode comprises a camera mode and a pipeline detection vehicle mode;
The method for determining the image acquisition mode according to the label comprises the following steps:
When the tag is an overhead pipeline, the image acquisition mode is a camera mode;
when the tag is a buried pipeline, the image acquisition mode is a pipeline detection vehicle mode;
It should be noted that, two modes of pipeline laying in factories exist, one mode is to set along a wall, the other mode is to set in a semi-buried mode, then a camera is required to be arranged in a nearby area of the corresponding pipeline along the wall to acquire a surface image of the pipeline, then a pipeline detection vehicle is generally used to move along the direction of pipeline wiring to acquire an image through the camera, each preset pipeline section is provided with a corresponding tag, the tag indicates the laying mode of the preset pipeline section, each preset pipeline section is also provided with corresponding position information, the tag and the position information are all pre-stored in a background management system, it is to be understood that when the pipeline is stopped to be conveyed for a period of time, hydrogen is gathered at the top of the pipeline, and the corrosion degree of the top of the inner wall of the pipeline is accelerated, so that compared with other parts, the top of the preset pipeline section is more severely corroded, the image of the outside of the preset pipeline section is required to be acquired, and whether maintenance or replacement is required or not;
The construction method of the early warning model comprises the following steps:
acquiring i groups of data, wherein i is a positive integer greater than 1, the data comprises a top image and a history maintenance level on the outer side of a history pipe section, the top image and the history maintenance level on the outer side of the history pipe section are used as a sample set, the sample set is divided into a training set and a test set, a classifier is constructed, the top image on the outer side of the history pipe section in the training set is used as input data, the history maintenance level in the training set is used as output data, the classifier is trained to obtain an initial classifier, the test set is used for testing the initial classifier, a classifier meeting the preset accuracy is output as an early warning model, and the classifier is preferably one of a naive Bayesian model or a support vector machine model;
It is noted that the top image on the outer side of the history pipe section comprises an image with cracks on the surface of the history pipe section and an image without cracks on the surface of the history pipe section, the corresponding history maintenance level comprises a first level and a second level, the input is the image with cracks on the surface of the history pipe section, the corresponding output is the first level, the input is the image without cracks on the surface of the history pipe section, the corresponding output is the second level, and the model is trained in this way;
The intelligent floating ball device is used for acquiring the inner wall infrared information of the preset pipe section, determining the corrosion degree of the inner wall of the preset pipe section according to the inner wall infrared information, acquiring the surface image of the preset pipe section after the abnormality of the preset pipe section is determined, and therefore the whole pipe network can be rapidly maintained, and the intelligent floating ball device is not interfered by external factors in the detection process;
And a coefficient generation module: maintaining a preset pipe section based on the maintenance level, traversing the H reflection time sets, and generating a target inner wall corrosion coefficient based on the reflection time sets;
In this embodiment, the method for maintaining the preset pipe section based on the maintenance level includes:
When the maintenance level is the first level, replacing the preset pipe section;
When the maintenance level is the second level, reinforcing the preset pipe section;
When the maintenance level is the first level, the outside of the preset pipe section is indicated to be cracked, workers are required to be informed of replacement in time, when the maintenance level is the second level, the outside of the preset pipe section is indicated to be not cracked, but the inner wall of the preset pipe section is severely corroded, the preset pipe section is required to be reinforced, for example, a protective layer is welded on the outside of the preset pipe section again, and the protective layer can be made of the same material as the preset pipe section;
the method for generating the corrosion coefficient of the inner wall of the target based on the reflection time sets comprises the following steps of:
Based on the H reflection time sets, obtaining a reflection time average value and a reflection time maximum value of each reflection time set, calculating an inner wall corrosion coefficient according to the reflection time average value and the reflection time maximum value, and taking the maximum value of the H inner wall corrosion coefficients as a target inner wall corrosion coefficient;
the method for calculating the inner wall corrosion coefficient according to the reflection time average value and the reflection time maximum value comprises the following steps:
Wct=Mrt＋/>；
Wherein Wct is the inner wall corrosion coefficient, mrt is the maximum value of reflection time, Is the reflection time average value,/>AndAre all weight coefficients;
it will be appreciated that the number of components, And/>The purpose of generating the corrosion coefficient of the inner wall of the target is to avoid cracks on the surface of the preset pipe section due to pressure caused by the too high gas inlet rate in the subsequent gas inlet process according to the determination of the person skilled in the art according to the actual situation;
and a rate regulation module: acquiring gas inlet concentration and gas inlet interval, inputting the gas inlet concentration, the gas inlet interval and the corrosion coefficient of the inner wall of the target into a pre-constructed rate determination model, and acquiring the gas inlet rate output by the rate determination model;
Specifically, the gas inlet concentration refers to the ratio of hydrogen in natural gas, the gas inlet concentration can be directly obtained through a gas sensor, because the hydrogen injected into a natural gas pipeline needs a certain time to be uniformly distributed, the ratio of the hydrogen in the natural gas is relatively high, the gas inlet rate is relatively high, the risk of hydrogen failure caused by the fact that the local hydrogen concentration in a preset pipeline section is easily caused to be too high is easily caused, the gas inlet interval refers to the interval of two hydrogen inlet times, in general, the gas inlet interval is fixed, after the pipeline stops conveying for a period of time, the hydrogen can be gathered at the top of the pipeline, the longer the gas inlet interval is, the higher the local gathering degree of the hydrogen is, the natural gas leakage is easily caused in the process of leading to the occurrence of cracks in the preset pipeline section, and the same target inner wall corrosion coefficient is relatively high, so that the cracks are easily caused in the preset pipeline section is shown;
The method for constructing the rate determination model comprises the following steps:
Obtaining a sample data set, wherein the sample data set comprises a historical gas inlet concentration, a historical gas inlet interval, a historical inner wall corrosion coefficient and a historical gas inlet rate, dividing the sample data set into a sample training set and a sample testing set, constructing a regression network, taking the historical gas inlet concentration, the historical gas inlet interval and the historical inner wall corrosion coefficient in the sample training set as input data of the regression network, taking the historical gas inlet rate in the sample training set as output data of the regression network, training the regression network to obtain an initial regression network for predicting the real-time gas inlet rate, testing the initial regression network by using the sample testing set, and outputting the regression network meeting a value smaller than a preset error as a rate determination model, wherein the regression network is preferably a neural network model;
It can be understood that the gas introducing rate refers to the introducing amount of hydrogen in unit time, in this embodiment, by acquiring the inner wall infrared information of the preset pipe section, judging whether the preset pipe section is abnormal, then obtaining a maintenance level according to the top image of the outer side of the pipe section and a pre-built early warning model, maintaining the preset pipe section according to the maintenance level, generating a target inner wall corrosion coefficient according to the set of reflection times, and determining the model according to the gas introducing concentration, the gas introducing interval, the target inner wall corrosion coefficient and the pre-built rate, so as to obtain the gas introducing rate, thereby realizing intelligent adjustment of the gas introducing rate and avoiding the occurrence of cracks in the pipeline when natural gas is conveyed due to the existence of hydrogen in the natural gas.
Example 3
The embodiment discloses an electronic device, which comprises a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor realizes the intelligent regulation method for the gas pipeline pressure of the industrial factory building provided by the methods when executing the computer program.
Since the electronic device described in this embodiment is an electronic device used to implement the method for intelligently adjusting the air pressure of the gas pipeline in the industrial factory building according to the embodiment of the present application, based on the method for intelligently adjusting the air pressure of the gas pipeline in the industrial factory building described in the embodiment of the present application, those skilled in the art can understand the specific implementation manner of the electronic device in this embodiment and various modifications thereof, so how to implement the method in this embodiment of the present application for this electronic device will not be described in detail herein. As long as the person skilled in the art implements the electronic device adopted by the intelligent adjustment method for the air pressure of the gas pipeline of the industrial factory building in the embodiment of the application, the electronic device belongs to the scope of protection required by the application.
Example 4
As shown in fig. 3, the disclosure of the present embodiment provides a computer readable storage medium, on which a computer program is stored, where the computer program is executed to implement the method for intelligently adjusting the air pressure of a gas pipeline of an industrial plant.
The above formulas are all formulas with dimensionality removed and numerical value calculated, the formulas are formulas with the latest real situation obtained by software simulation by collecting a large amount of data, and preset parameters, weights and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center over a wired network or a wireless network. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely one, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (8)
1. The intelligent regulation method for the gas pressure of the gas pipeline of the industrial factory building is characterized by comprising the following steps:
s10: acquiring inner wall infrared information of M preset pipe sections, analyzing based on the inner wall infrared information, and judging whether at least one preset pipe section is abnormal or not, wherein the inner wall infrared information comprises H reflection time sets, and M and H are positive integer sets larger than zero;
S20: if at least one preset pipe section is abnormal, acquiring an outside top image of a corresponding pipe section, and acquiring maintenance grades based on the outside top image of the pipe section and a pre-constructed early warning model, wherein the maintenance grades comprise a first grade and a second grade;
S30: maintaining a preset pipe section based on the maintenance level, traversing the H reflection time sets, and generating a target inner wall corrosion coefficient based on the reflection time sets;
S40: acquiring gas inlet concentration and gas inlet interval, inputting the gas inlet concentration, the gas inlet interval and the corrosion coefficient of the inner wall of the target into a pre-constructed rate determination model, and acquiring the gas inlet rate output by the rate determination model, wherein the gas inlet concentration refers to the concentration of hydrogen in a preset pipe section, and the gas inlet interval refers to the interval between two adjacent hydrogen inlet times;
The construction method of the early warning model comprises the following steps:
Acquiring i groups of data, wherein i is a positive integer greater than 1, the data comprises a top image and a history maintenance level on the outer side of a history pipe section, the top image and the history maintenance level on the outer side of the history pipe section are used as a sample set, the sample set is divided into a training set and a test set, a classifier is constructed, the top image on the outer side of the history pipe section in the training set is used as input data, the history maintenance level in the training set is used as output data, the classifier is trained to obtain an initial classifier, the initial classifier is tested by using the test set, and the classifier meeting the preset accuracy is output as an early warning model;
The method for traversing the H reflection time sets and generating the corrosion coefficient of the inner wall of the target based on the reflection time sets comprises the following steps:
Based on the H reflection time sets, obtaining a reflection time average value and a reflection time maximum value of each reflection time set, calculating an inner wall corrosion coefficient according to the reflection time average value and the reflection time maximum value, and taking the maximum value of the H inner wall corrosion coefficients as a target inner wall corrosion coefficient;
the method for calculating the inner wall corrosion coefficient according to the reflection time average value and the reflection time maximum value comprises the following steps:
；
Wherein Wct is the inner wall corrosion coefficient, mrt is the maximum value of reflection time, Is the reflection time average value,/>And/>Are all weight coefficients;
the method for constructing the rate determination model comprises the following steps:
The method comprises the steps of obtaining a sample data set, wherein the sample data set comprises historical gas inlet concentration, historical gas inlet interval, historical inner wall corrosion coefficient and historical gas inlet rate, dividing the sample data set into a sample training set and a sample testing set, constructing a regression network, taking the historical gas inlet concentration, the historical gas inlet interval and the historical inner wall corrosion coefficient in the sample training set as input data of the regression network, taking the historical gas inlet rate in the sample training set as output data of the regression network, training the regression network to obtain an initial regression network for predicting the real-time gas inlet rate, testing the initial regression network by utilizing the sample testing set, and outputting the regression network meeting the requirement of less than a preset error value as a rate determination model.
2. The intelligent regulation method for the gas pressure of the gas pipeline of the industrial plant according to claim 1, wherein the method for acquiring the infrared information of the inner walls of the M preset pipe sections comprises the following steps:
The intelligent floating ball device is arranged inside the preset pipe section, moves along the inside of the preset pipe section, emits infrared rays to detection points on the inner wall of the preset pipe section based on the infrared detection unit, receives the reflected infrared rays to form reflection time, and forms H reflection time sets.
3. The intelligent regulation method of the gas pressure of the gas pipeline of the industrial plant according to claim 2, wherein the method for judging whether at least one preset pipe section is abnormal based on the analysis of the inner wall infrared information comprises the following steps:
Traversing the H reflection time sets, judging whether an element in the reflection time sets is larger than a preset reflection time threshold, if not, calculating the time difference between the element and the adjacent element, and judging whether the time difference is smaller than the preset difference threshold, if so, judging whether the preset pipe section is abnormal, and if not, not.
4. The intelligent regulation method for the gas pressure of the gas pipeline of the industrial plant according to claim 3, wherein the method for acquiring the top image of the outer side of the corresponding pipe section comprises the following steps:
When the preset pipe section is judged to be abnormal, the intelligent floating ball device positioned in the preset pipe section sends position information, a background management system determines a label corresponding to the preset pipe section by combining the position information, an image acquisition mode is determined according to the label, the label comprises an overhead pipeline and a buried pipeline, and the image acquisition mode comprises a camera mode and a pipeline detection vehicle mode.
5. The intelligent regulation method for the air pressure of the gas pipeline of the industrial plant according to claim 4, wherein the method for determining the image acquisition mode according to the tag comprises the following steps:
When the tag is an overhead pipeline, the image acquisition mode is a camera mode;
when the tag is a buried pipeline, the image acquisition mode is a pipeline detection vehicle mode.
6. An industrial plant gas pipeline air pressure intelligent regulation system for implementing the industrial plant gas pipeline air pressure intelligent regulation method as claimed in any one of claims 1-5, characterized by comprising:
An anomaly determination module: acquiring inner wall infrared information of M preset pipe sections, analyzing based on the inner wall infrared information, and judging whether at least one preset pipe section is abnormal or not, wherein the inner wall infrared information comprises H reflection time sets, and M and H are positive integer sets larger than zero;
The grade generation module: if at least one preset pipe section is abnormal, acquiring an outside top image of a corresponding pipe section, and acquiring maintenance grades based on the outside top image of the pipe section and a pre-constructed early warning model, wherein the maintenance grades comprise a first grade and a second grade;
And a coefficient generation module: maintaining a preset pipe section based on the maintenance level, traversing the H reflection time sets, and generating a target inner wall corrosion coefficient based on the reflection time sets;
and a rate regulation module: and acquiring the gas inlet concentration and the gas inlet interval, inputting the gas inlet concentration, the gas inlet interval and the target inner wall corrosion coefficient into a pre-constructed rate determination model, and acquiring the gas inlet rate output by the rate determination model.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor implements the intelligent regulation method of the gas duct pressure of an industrial plant according to any one of claims 1 to 5 when executing the computer program.
8. A computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program is executed to implement the intelligent regulation method for the gas pressure of the industrial plant gas pipeline according to any one of claims 1 to 5.
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| AU2020101405A4 (en) * | 2020-07-18 | 2020-08-20 | Guizhou Minzu University | An Efficient Distributed Method for Real-Time Traffic Flow Prediction |
| US11487273B1 (en) * | 2021-04-30 | 2022-11-01 | Dalian University Of Technology | Distributed industrial energy operation optimization platform automatically constructing intelligent models and algorithms |
| CN116090322A (en) * | 2021-10-30 | 2023-05-09 | 中国石油化工股份有限公司 | Intelligent decision visualization system for pipeline integrity |
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