CN114646022A - Special gas supply pipeline big data monitoring system and method - Google Patents
Special gas supply pipeline big data monitoring system and method Download PDFInfo
- Publication number
- CN114646022A CN114646022A CN202210309453.7A CN202210309453A CN114646022A CN 114646022 A CN114646022 A CN 114646022A CN 202210309453 A CN202210309453 A CN 202210309453A CN 114646022 A CN114646022 A CN 114646022A
- Authority
- CN
- China
- Prior art keywords
- pipeline
- monitoring
- unit
- point
- leakage
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
- F17D5/02—Preventing, monitoring, or locating loss
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
- F17D5/005—Protection or supervision of installations of gas pipelines, e.g. alarm
Abstract
The invention discloses a big data monitoring system and method for a special gas supply application pipeline, which comprises the following steps: a monitoring data acquisition module, a data management center, a pipeline monitoring module, a monitoring fault processing module, a pipeline leakage prediction module and an abnormity early warning module, the monitoring data acquisition module acquires the monitoring range data of the sensor of the monitoring pipeline and the image data of the original pipeline, data are stored through a data management center, the image, the pressure and the temperature of the special gas supply pipeline are monitored through a pipeline monitoring module, the monitoring mode of the pipeline is adjusted by the monitoring fault processing module when the sensor has a fault, and the maintenance of fault equipment is reminded, analyzing the position deviation condition of the easy corrosion point of the pipeline according to the pipeline deformation data through a pipeline leakage prediction module, the special gas leakage probability is predicted through the abnormity early warning module, early warning signals are sent out in time, the position of a leakage point is confirmed, the pipeline is helped to be maintained in time, and the leakage of the special gas in the pipeline is avoided.
Description
Technical Field
The invention relates to the technical field of gas supply pipeline monitoring, in particular to a special gas supply application pipeline big data monitoring system and method.
Background
The special gas is conveyed to the gas distributor through the gas cylinder cabinet, the gas distributor controls the supply pipeline to convey the special gas to the machine platform in a shunt way for use through the valve, and whether the gas leaks or corrodes the pipeline is monitored in the process of conveying the special gas through the pipeline, so that the safety in conveying the special gas is guaranteed;
in the prior art, the pressure and the temperature of pipeline are monitored by monitoring gas to obtain monitoring data, and whether the gas leaks is judged by judging whether the pressure is insufficient or not and whether the temperature is abnormal or not, so that the following defects exist: firstly, by using the existing monitoring mode, after the gas is found to leak during transportation through the pipeline, although the alarm can be given in time, the specific position where the gas leaks cannot be confirmed, so that the maintenance personnel cannot maintain the pipeline in time, the special gas leaks quickly, and the serious safety influence can be caused in a short time; secondly, influenced by internal factors and external factors, the gas supply pipeline, especially a bent pipeline, is likely to have deformation conditions, the judgment of the leakage position is affected, the positions of the pipeline after corrosion and gas leakage after specific change cannot be accurately judged without considering the deformation factors, and the monitoring precision is reduced.
Therefore, a system and method for monitoring big data of a special gas supply pipeline are needed to solve the above problems.
Disclosure of Invention
The present invention is directed to a system and method for monitoring big data of a pipeline for supplying a special gas, so as to solve the problems mentioned above in the background art.
In order to solve the technical problems, the invention provides the following technical scheme: the utility model provides a special gas supply application pipeline big data monitoring system which characterized in that: the system comprises: the system comprises a monitoring data acquisition module, a data management center, a pipeline monitoring module, a monitoring fault processing module, a pipeline leakage prediction module and an abnormity early warning module;
the monitoring data acquisition module is used for acquiring sensor monitoring range data of a monitoring pipeline and original pipeline image data; transmitting all the collected data to the data management center; the pipeline monitoring module is used for monitoring images, pressure and temperature of the special gas supply pipeline; the monitoring fault processing module is used for adjusting a pipeline monitoring mode when the sensor fails and reminding maintenance of fault equipment; the pipeline leakage prediction module is used for comparing the shot image with an original pipeline image and judging whether the bent pipeline deforms or not: if the pipeline deforms, comparing the bending degree and analyzing the position change of the bending point, predicting the corrosion position of the pipeline according to the pressure and temperature data, predicting whether the position change of the leakage point of the pipeline occurs according to the analysis result, and analyzing the position of the changed leakage point; the abnormity early warning module is used for predicting the probability of abnormity of the special gas supply pipeline according to the monitored data and sending out an early warning signal when the probability exceeds a threshold value.
Furthermore, the monitoring data acquisition module comprises a monitoring range acquisition unit and an original image acquisition unit, and the monitoring range acquisition unit acquires monitoring range data of the sensor; and the original pipeline image during installation is collected through the original image collecting unit, and all collected data is transmitted to the data management center for storage.
Furthermore, the pipeline monitoring module comprises a pipeline image shooting unit, a gas pressure monitoring unit and a pipeline temperature monitoring unit, and the current pipeline image is shot through the pipeline image shooting unit; the gas pressure monitoring unit monitors the gas pressure of the pipeline through a pressure sensor; the pipeline temperature monitoring unit monitors the temperature of the pipeline through a temperature sensor; the pipeline leakage prediction module comprises a pipeline deformation analysis unit, a bending degree comparison unit, a corrosion trend prediction unit and a leakage change analysis unit, and whether the bent part of the pipeline deforms or not is judged by comparing the current pipeline image with the original pipeline image through the pipeline deformation analysis unit: if the pipeline is deformed, the deformation degree of the pipeline is judged through the bending degree comparison unit, and the position change of a bending point is analyzed; predicting a corrosion point deviation trend of the pipeline through the corrosion trend prediction unit; and judging whether the position of the leakage point is changed or not by the leakage change analysis unit according to the bending degree of the pipeline and the change of the corrosion direction, and predicting the changed position of the leakage point.
Furthermore, the monitoring fault processing module comprises a detection segmentation unit, an equipment fault alarm unit, a monitoring mode adjusting unit and a maintenance alarm unit, and the detection segmentation unit is used for detecting pipeline points monitored by the sensor and measuring a monitoring interval distance; issuing an alarm by the equipment failure alarm unit when the sensor fails; monitoring the pipeline by the monitoring mode adjusting unit by using the remaining sensors which are not in fault, and adjusting the position of the monitored pipeline point; and sending the position of the fault sensor and reminding the maintenance of the sensor through the maintenance warning unit.
Further, the abnormity early warning module comprises an abnormity probability prediction unit and an abnormity early warning unit, the abnormity probability prediction unit predicts the gas leakage probability of the leakage point of the pipeline according to the monitored data, a probability threshold value is set, and when the probability exceeds the threshold value, an early warning signal is sent out through the abnormity early warning unit.
A big data monitoring method for a special gas supply pipeline is characterized by comprising the following steps: the method comprises the following steps:
s11: collecting sensor monitoring range data and original pipeline image data;
s12: monitoring the special gas supply pipeline, comparing the images, and judging whether the pipeline deforms or not and the deformation degree of the pipeline;
s13: predicting the corrosion position and the corrosion degree change of the pipeline, and analyzing the leakage position change of the pipeline by combining the deformation degree of the pipeline to obtain an analysis result;
s14: adjusting a monitoring mode when the sensor fails;
s15: and predicting the probability of gas leakage of the pipeline by combining the analysis result and the monitoring point position of the fault sensor, and performing gas leakage early warning.
Further, in steps S11-S12: the original pipeline image during installation is collected by the original image collecting unit, and the image is analyzed: the obtained random curved line equation of one curved pipeline is as follows: f (x), wherein x belongs to [ a, b ], the longest monitoring length of the sensor acquired by the monitoring range acquisition unit is as follows: l, calculating the curve length S of a random bent pipeline in the original pipeline image according to the following formula:
wherein f' (x) represents the derivative of f (x), and [ a, b ] represents the range from the starting point to the end point of the curve, the current pipeline image is shot by utilizing a pipeline image shooting unit, and the image is analyzed: in the same way, the curve equation corresponding to a random bent pipeline in the original pipeline image is calculated as follows: y ═ f (x), length S ', comparison of S and S': if S is equal to S', judging that the corresponding bent pipeline is not deformed; if S is not equal to S', judging that the corresponding bent pipeline deforms, and judging the deformation degree of the pipeline by using a bending degree comparison unit: the tangent line of the original curved pipeline curve is obtained as follows: y is Ax + B, calculating the abscissa Xi of the tangent point of the curve and the tangent line according to f' (Xi) and A, substituting Xi into a curve equation to obtain the ordinate f (Xi), obtaining the coordinates of the tangent point of the original curved pipeline curve and the current curved pipeline curve as (Xi, f (Xi)) and (Xi, F (Xi)), and calculating the offset distance di and the offset angle theta i of the bending point of one deformed curved pipeline according to the following formulas:
di＝|Xi-xi|；
the bending point offset distances of all the deformed bent pipelines are obtained to be d ═ d1, d 2.,. dm }, and the offset angle sets are theta ═ theta 1,. theta 2.,. theta m }, wherein m represents the number of the deformed bent pipelines, and the curve length of the bent pipelines is calculated in an integral mode so as to simplify the length calculation process of irregular curves and compare the curve length of the current pipelines with the original corresponding pipelines, thereby being beneficial to accurately judging whether the bent pipelines are deformed; the tangent point of the curve belongs to the point with the maximum bending degree on the curve, the position of the tangent point is obtained through a curve tangent equation, the deformation degree of the pipeline can be reflected to the maximum degree, the deviation data of the tangent point, namely the bending point, is calculated according to the obtained tangent point coordinate, the original deviation condition of the corrosion-prone point on the pipeline is indirectly reflected, and the position change of the corrosion-prone point on the pipeline can be judged.
Further, in step S13: predicting the corrosion point deviation trend of the pipeline by using a corrosion trend prediction unit: the method comprises the following steps of collecting the distance dj from a random point of historical corrosion leakage of a pipeline before a bent part of an original pipeline corresponding to a random deformed bent pipeline to a bent point, and calculating the distance D from the point of the current pipeline which is easy to generate corrosion leakage to the corresponding point according to the following formula:
D＝δ*(dj+di)；
and delta represents a pipeline deformation error coefficient, the offset distance of a point which is easy to generate corrosion leakage is calculated according to the offset distance of the bending point, and offset distance error factors caused by pipeline deformation are added, so that the accuracy of the offset judgment result of the corrosion leakage point is improved.
Further, in step S14: utilize monitoring range acquisition unit to gather sensor monitoring longest distance and be L, utilize and detect the segmentation unit and detect pipeline total length and be L ', carry out the segmentation to the pipeline and detect, segmentation length is L'/L, when the sensor trouble, utilizes equipment trouble alarm unit to send alarm signal, utilizes monitoring mode adjustment unit adjustment monitoring mode: and (3) offsetting all the sensors to the direction of the fault sensor by the offset distance: (L'/L)/2, the special gas supply line continues to be monitored after the sensor is deflected.
Further, in step S15: predicting the probability of gas leakage at the current point where the pipeline is easy to generate corrosion leakage by using an abnormal probability prediction unit: acquiring a corrosion degree coefficient Q when a corresponding point leaks in an original pipeline, monitoring the corrosion degree coefficient Q of the corresponding point before a sensor corresponding to a monitored range to which the corresponding point belongs fails, wherein the average time for the corrosion degree to be intensified is T, the time spent for the monitoring mode adjustment is T, and predicting the probability of gas leakage of the corresponding point
Setting the probability threshold to P ', comparing P and P', at P>P', namely when the prediction probability is larger than the threshold value, an abnormity early warning unit is used for sending a gas leakage early warning signal and leakage point position information, the time for pipeline corrosion needs to be accumulated, the longer the time is, the larger the corrosion degree is, if a sensor fails, a certain maintenance time is needed, and the period can not be monitoredThe pipeline is added with fault factors to predict the gas leakage probability of the pipeline, so that the accuracy of a prediction result is improved, and meanwhile, the pipeline is helped to be maintained in time, and the leakage of special gas in the pipeline is avoided.
Compared with the prior art, the invention has the following beneficial effects:
the method comprises the steps of collecting monitoring data of an original special gas supply pipeline, analyzing an original pipeline image and a current pipeline image, judging whether a bent part in the pipeline is deformed or not, analyzing the deformation degree, predicting the offset distance of a bent point according to the deformation data, indirectly reflecting the original offset condition of the corrosion-prone point on the pipeline, mapping the offset distance of the corrosion-prone leakage point through the offset distance of the bent point, adding an offset distance error factor caused by pipeline deformation, and improving the accuracy of the offset judgment result of the corrosion-prone leakage point; the method has the advantages that the original leakage condition of the corrosion points on the pipeline is judged by analyzing historical data, the gas leakage probability is predicted, the fault factor is added to predict the gas leakage probability of the pipeline, the accuracy of the prediction result is improved, the pipeline is timely maintained, and the leakage of special gas in the pipeline is avoided.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a block diagram of a special gas supply line big data monitoring system of the present invention;
FIG. 2 is a flow chart of a method for big data monitoring of a special gas supply pipeline according to the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Referring to fig. 1-2, the present invention provides the following technical solutions: the utility model provides a big data monitoring system of special gas supply application pipeline which characterized in that: the system comprises: the system comprises a monitoring data acquisition module S1, a data management center S2, a pipeline monitoring module S3, a monitoring fault processing module S4, a pipeline leakage prediction module S5 and an abnormity early warning module S6;
the monitoring data acquisition module S1 is used for acquiring sensor monitoring range data of a monitoring pipeline and original pipeline image data; transmitting all the collected data to a data management center S2; the pipeline monitoring module S3 is used for monitoring the image, pressure and temperature of the special gas supply pipeline; the monitoring fault processing module S4 is used for adjusting the pipeline monitoring mode when the sensor has a fault and reminding maintenance of fault equipment; the pipeline leakage prediction module S5 is configured to compare the captured image with the original pipeline image, and determine whether the curved pipeline is deformed: if the pipeline deforms, comparing the bending degree and analyzing the position change of the bending point, predicting the corrosion position of the pipeline according to the pressure and temperature data, predicting whether the position change of the pipeline leakage point occurs according to the analysis result, and analyzing the position of the changed leakage point; the abnormity early warning module S6 is used for predicting the probability of abnormity of the special gas supply pipeline according to the monitored data and sending out an early warning signal when the probability exceeds a threshold value.
The monitoring data acquisition module S1 comprises a monitoring range acquisition unit and an original image acquisition unit, and the monitoring range acquisition unit acquires the monitoring range data of the sensor; the original pipeline image at the time of installation is collected by the original image collecting unit, and all the collected data is transmitted to the data management center S2 to be stored.
The pipeline monitoring module S3 comprises a pipeline image shooting unit, a gas pressure monitoring unit and a pipeline temperature monitoring unit, and the current pipeline image is shot by the pipeline image shooting unit; the gas pressure monitoring unit monitors the gas pressure of the pipeline through a pressure sensor; the pipeline temperature monitoring unit monitors the temperature of the pipeline through a temperature sensor; the pipeline leakage prediction module S5 comprises a pipeline deformation analysis unit, a bending degree comparison unit, a corrosion trend prediction unit and a leakage change analysis unit, and judges whether the bent part of the pipeline deforms or not by comparing the current pipeline image with the original pipeline image through the pipeline deformation analysis unit: if the pipe is deformed, the deformation degree of the pipe is judged through a bending degree comparison unit, and the position change of a bending point is analyzed; predicting the corrosion point deviation trend of the pipeline through a corrosion trend prediction unit; and judging whether the position of the leakage point is changed or not by the leakage change analysis unit according to the bending degree of the pipeline and the change of the corrosion direction, and predicting the changed position of the leakage point.
The monitoring fault processing module S4 comprises a detection segmentation unit, an equipment fault alarm unit, a monitoring mode adjusting unit and a maintenance alarm unit, and is used for detecting the pipeline points monitored by the sensor and measuring the monitoring interval distance through the detection segmentation unit; sending an alarm when the sensor fails through an equipment failure alarm unit; monitoring the pipeline by using the remaining sensors which are not in fault through a monitoring mode adjusting unit, and adjusting the position of the monitored pipeline point; and sending the position of the fault sensor and reminding the maintenance of the sensor through the maintenance alarm unit.
The abnormity early warning module S6 comprises an abnormity probability prediction unit and an abnormity early warning unit, the abnormity probability prediction unit predicts the gas leakage probability of the pipeline leakage point according to the monitored data, a probability threshold value is set, and when the probability exceeds the threshold value, an early warning signal is sent out through the abnormity early warning unit.
A big data monitoring method for a special gas supply pipeline is characterized by comprising the following steps: the method comprises the following steps:
s11: collecting sensor monitoring range data and original pipeline image data;
s12: monitoring the special gas supply pipeline, comparing the images, and judging whether the pipeline deforms or not and the deformation degree of the pipeline;
s13: predicting the corrosion position and the corrosion degree change of the pipeline, and analyzing the leakage position change of the pipeline by combining the deformation degree of the pipeline to obtain an analysis result;
s14: adjusting a monitoring mode when the sensor fails;
s15: and predicting the probability of gas leakage of the pipeline by combining the analysis result and the monitoring point position of the fault sensor, and performing gas leakage early warning.
In steps S11-S12: the original pipeline image during installation is collected by an original image collecting unit, and the image is analyzed: the obtained random curved line equation of one curved pipeline is as follows: f (x), wherein x belongs to [ a, b ], the longest monitoring length of the sensor acquired by the monitoring range acquisition unit is as follows: l, calculating the curve length S of a random bent pipeline in the original pipeline image according to the following formula:
wherein f' (x) represents the derivative of f (x), and [ a, b ] represents the range from the starting point to the end point of the curve, and the current pipeline image is shot by the pipeline image shooting unit, and the image is analyzed: in the same way, the curve equation corresponding to a random bent pipeline in the original pipeline image is calculated as follows: y ═ f (x), length S ', comparison of S and S': if S is equal to S', judging that the corresponding bent pipeline is not deformed; if S is not equal to S', judging that the corresponding bent pipeline deforms, and judging the deformation degree of the pipeline by using a bending degree comparison unit: the tangent line of the original curved pipeline curve is obtained as follows: and y is Ax + B, calculating the abscissa Xi of a tangent point of the curve and the tangent point according to f' (Xi) -A, substituting Xi into a curve equation to obtain the ordinate f (Xi), obtaining the coordinates of the tangent point of the original curved pipeline curve and the current curved pipeline curve as (Xi, f (Xi)) and (Xi, F (Xi)), and calculating the bending point offset distance di and the offset angle theta i of one deformed curved pipeline according to the following formulas:
di＝|Xi-xi|；
the bending point offset distances of all the deformed bent pipelines are obtained to be d ═ d1, d 2.,. dm }, and the offset angle sets are theta ═ theta 1,. theta 2.,. theta m }, wherein m represents the number of the deformed bent pipelines, and the curve length of the bent pipelines is calculated in an integral mode so as to simplify the length calculation process of irregular curves and compare the curve length of the current pipelines with the original corresponding pipelines, thereby being beneficial to accurately judging whether the bent pipelines are deformed; the tangent point of the curve belongs to the point with the maximum bending degree on the curve, the position of the tangent point is obtained through a curve tangent equation, the deformation degree of the pipeline can be reflected to the maximum degree, the deviation data of the tangent point, namely the bending point, is calculated according to the obtained tangent point coordinate, the original deviation condition of the corrosion-prone point on the pipeline is indirectly reflected, and the position change of the corrosion-prone point on the pipeline can be conveniently judged.
In step S13: predicting the corrosion point deviation trend of the pipeline by using a corrosion trend prediction unit: the method comprises the following steps of collecting the distance dj from a random point of historical corrosion leakage of a pipeline before a bent part of an original pipeline corresponding to a random deformed bent pipeline to a bent point, and calculating the distance D from the point of the current pipeline which is easy to generate corrosion leakage to the corresponding point according to the following formula:
D＝δ*(dj+di)；
and delta represents a pipeline deformation error coefficient, the offset distance of a point which is easy to generate corrosion leakage is calculated according to the offset distance of the bending point, and offset distance error factors caused by pipeline deformation are added, so that the accuracy of the offset judgment result of the corrosion leakage point is effectively improved.
In step S14: utilize monitoring range acquisition unit to gather sensor monitoring longest distance and be L, utilize and detect the segmentation unit and detect pipeline total length and be L ', carry out the segmentation to the pipeline and detect, segmentation length is L'/L, when the sensor trouble, utilizes equipment trouble alarm unit to send alarm signal, utilizes monitoring mode adjustment unit adjustment monitoring mode: and (3) offsetting all the sensors to the direction of the fault sensor by the offset distance: (L'/L)/2, the special gas supply line continues to be monitored after the sensor is deflected.
In step S15: predicting the probability of gas leakage at the current point where the pipeline is easy to generate corrosion leakage by using an abnormal probability prediction unit: acquiring a corrosion degree coefficient Q when a corresponding point leaks in an original pipeline, monitoring the corrosion degree coefficient Q of the corresponding point before a sensor corresponding to a monitored range to which the corresponding point belongs fails, wherein the average time for the corrosion degree to be intensified is T, the time spent for the monitoring mode adjustment is T, and predicting the probability of gas leakage of the corresponding point
Setting the probability threshold to P ', comparing P and P', at P>P', namely when the prediction probability is larger than a threshold value, an abnormity early warning unit is used for sending a gas leakage early warning signal and leakage point position information, the time for pipeline corrosion needs to be accumulated, the longer the time is, the larger the corrosion degree is, if a sensor fails, a certain maintenance time is needed, and during the period, pipelines which cannot be monitored exist, fault factors are added to predict the pipeline gas leakage probability, so that the accuracy of a prediction result can be improved, and meanwhile, the pipeline can be timely maintained, and the leakage of special gas in the pipelines is avoided.
The first embodiment is as follows: the original pipeline image during installation is collected by the original image collecting unit, and the image is analyzed: the obtained random curved line equation of one curved pipeline is as follows: y ═ f (x) ═ x3，x∈[a，b]＝[1，2]According to the formula
Calculating the curve length of a random bent pipeline in the original pipeline image
Shooting the current pipeline image by utilizing a pipeline image shooting unit, and analyzing the image: in the same way, the curve equation corresponding to a random bent pipeline in the original pipeline image is calculated as follows: y ═ f (x) ═ 2x3Length of
Judging that the corresponding bent pipeline deforms, and judging the deformation degree of the pipeline by using a bending degree comparison unit: the tangent line of the original curved pipeline curve is obtained as follows: y is Ax + B is 6x +1, and the abscissa of the tangent point of the curve to the tangent line is calculated according to f' (xi) is A
Substituting xi into the curve equation to obtain the ordinate of the tangent point as
The coordinates of the tangent points of the original curved pipeline curve and the current curved pipeline curve are obtained
And (Xi, f (Xi)) (1, 2) according to the formula di ═ Xi-Xi |, and
calculating the offset distance of the bending point of a random deformed bent pipeline
The deviation angle theta i is approximately equal to 63 degrees, the distance from a random point of the pipeline which is subjected to corrosion leakage in the history before the bending part of the original pipeline corresponding to a random deformed bending pipeline is collected to a bending point is dj equal to 10, delta is set to 0.9, and the distance D between the point which is easy to generate corrosion leakage in the current pipeline and the corresponding point is computed to be approximately equal to 9.4 according to a formula D equal to delta (dj + di);
example two: predicting the probability of gas leakage at the current point where the pipeline is easy to generate corrosion leakage by using an abnormal probability prediction unit: the method comprises the steps of acquiring that the corrosion degree coefficient when a corresponding point leaks in an original pipeline is Q-8, monitoring that the corrosion degree coefficient of the corresponding point is Q-2, the average time when the corrosion degree is increased is T-10, the time spent on adjusting the monitoring mode is T-100 before a sensor corresponding to a monitored range to which the corresponding point belongs fails, and predicting the probability of gas leakage at the corresponding point
Setting the probability threshold to be P' 0.5<And P, when the prediction probability is larger than a threshold value, sending a gas leakage early warning signal and leakage point position information by using an abnormity early warning unit.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The utility model provides a big data monitoring system of special gas supply application pipeline which characterized in that: the system comprises: the system comprises a monitoring data acquisition module (S1), a data management center (S2), a pipeline monitoring module (S3), a monitoring fault processing module (S4), a pipeline leakage prediction module (S5) and an abnormity early warning module (S6);
the monitoring data acquisition module (S1) is used for acquiring sensor monitoring range data of a monitoring pipeline and original pipeline image data; transmitting all the collected data to the data management center (S2); the pipeline monitoring module (S3) is used for carrying out image, pressure and temperature monitoring on a special gas supply pipeline; the monitoring fault processing module (S4) is used for adjusting a pipeline monitoring mode when the sensor has a fault and reminding maintenance of fault equipment; the pipeline leakage prediction module (S5) is used for comparing the shot image with the original pipeline image and judging whether the bent pipeline deforms: if the pipeline deforms, comparing the bending degree and analyzing the position change of the bending point, predicting the corrosion position of the pipeline according to the pressure and temperature data, predicting whether the position change of the pipeline leakage point occurs according to the analysis result, and analyzing the position of the changed leakage point; the abnormity early warning module (S6) is used for predicting the probability of abnormity of the special gas supply pipeline according to the monitored data and sending out an early warning signal when the probability exceeds a threshold value.
2. The system for big data monitoring of the special gas supply pipeline according to claim 1, wherein: the monitoring data acquisition module (S1) comprises a monitoring range acquisition unit and an original image acquisition unit, and the monitoring range acquisition unit acquires monitoring range data of the sensor; the original pipeline image at the time of installation is collected by the original image collecting unit, and all collected data is transmitted to the data management center (S2) for storage.
3. The system for big data monitoring of the special gas supply pipeline according to claim 1, wherein: the pipeline monitoring module (S3) comprises a pipeline image shooting unit, a gas pressure monitoring unit and a pipeline temperature monitoring unit, and the current pipeline image is shot by the pipeline image shooting unit; the gas pressure monitoring unit monitors the gas pressure of the pipeline through a pressure sensor; the pipeline temperature monitoring unit monitors the temperature of the pipeline through a temperature sensor; the pipeline leakage prediction module (S5) comprises a pipeline deformation analysis unit, a bending degree comparison unit, a corrosion trend prediction unit and a leakage change analysis unit, and judges whether the bent part of the pipeline deforms or not by comparing the current pipeline image with the original pipeline image through the pipeline deformation analysis unit: if the pipe is deformed, the deformation degree of the pipe is judged through the bending degree comparison unit, and the position change of a bending point is analyzed; predicting a corrosion point deviation trend of the pipeline through the corrosion trend prediction unit; and judging whether the position of the leakage point is changed or not by the leakage change analysis unit according to the bending degree of the pipeline and the change of the corrosion direction, and predicting the changed position of the leakage point.
4. The system for big data monitoring of the special gas supply pipeline according to claim 1, wherein: the monitoring fault processing module (S4) comprises a detection segmentation unit, an equipment fault alarm unit, a monitoring mode adjusting unit and a maintenance alarm unit, and the detection segmentation unit is used for detecting pipeline points monitored by the sensor and measuring a monitoring interval distance; issuing an alarm by the equipment failure alarm unit when the sensor fails; monitoring the pipeline by using the remaining sensors which do not have faults through the monitoring mode adjusting unit, and adjusting the position of the monitored pipeline point; and sending the position of the fault sensor and reminding the maintenance of the sensor through the maintenance warning unit.
5. The special gas supply pipeline big data monitoring system according to claim 1, wherein: the abnormity early warning module (S6) comprises an abnormity probability prediction unit and an abnormity early warning unit, the abnormity probability prediction unit predicts the gas leakage probability of the pipeline leakage point according to the monitored data, a probability threshold value is set, and when the probability exceeds the threshold value, an early warning signal is sent out through the abnormity early warning unit.
6. A big data monitoring method for a special gas supply pipeline is characterized by comprising the following steps: the method comprises the following steps:
s11: collecting sensor monitoring range data and original pipeline image data;
s12: monitoring the special gas supply pipeline, comparing the images, and judging whether the pipeline deforms or not and the deformation degree of the pipeline;
s13: predicting the corrosion position and the corrosion degree change of the pipeline, and analyzing the leakage position change of the pipeline by combining the deformation degree of the pipeline to obtain an analysis result;
s14: adjusting a monitoring mode when the sensor fails;
s15: and predicting the probability of gas leakage of the pipeline by combining the analysis result and the monitoring point position of the fault sensor, and performing gas leakage early warning.
7. The method for monitoring the big data of the special gas supply pipeline according to claim 6, wherein the method comprises the following steps: in steps S11-S12: the original pipeline image during installation is collected by the original image collecting unit, and the image is analyzed: the curve equation of a random bent pipeline is obtained as follows: and y is f (x), x belongs to [ a, b ], the longest monitoring length of the sensor acquired by the monitoring range acquisition unit is as follows: l, calculating the curve length S of a random bent pipeline in the original pipeline image according to the following formula:
wherein f' (x) represents the derivative of f (x), and [ a, b ] represents the range from the starting point to the end point of the curve, the current pipeline image is shot by utilizing a pipeline image shooting unit, and the image is analyzed: in the same way, the curve equation corresponding to a random bent pipeline in the original pipeline image is calculated as follows: y ═ f (x), length S ', comparison of S and S': if S 'is equal to S', judging that the corresponding bent pipeline is not deformed; if S is not equal to S', judging that the corresponding bent pipeline deforms, and judging the deformation degree of the pipeline by using a bending degree comparison unit: the tangent line of the curve of the original bent pipeline is obtained as follows: y is Ax + B, calculating the abscissa Xi of the tangent point of the curve and the tangent line according to f' (Xi) and A, substituting Xi into a curve equation to obtain the ordinate f (Xi), obtaining the coordinates of the tangent point of the original curved pipeline curve and the current curved pipeline curve as (Xi, f (Xi)) and (Xi, F (Xi)), and calculating the offset distance di and the offset angle theta i of the bending point of one deformed curved pipeline according to the following formulas:
di＝|Xi-xi|；
the bending point offset distances of all deformed bent pipelines are set to be d ═ d1, d 2., dm, and the offset angles are set to be θ 1,. theta.2.,. theta.m, wherein m represents the number of deformed bent pipelines.
8. The method for monitoring the big data of the special gas supply pipeline according to claim 6, wherein the method comprises the following steps: in step S13: predicting the corrosion point deviation trend of the pipeline by using a corrosion trend prediction unit: the method comprises the following steps of collecting the distance dj from a random point of historical corrosion leakage of a pipeline before a bent part of an original pipeline corresponding to a random deformed bent pipeline to a bent point, and calculating the distance D from the point of the current pipeline which is easy to generate corrosion leakage to the corresponding point according to the following formula:
D＝δ*(dj+di)；
where δ represents the pipeline deformation error coefficient.
9. The method for monitoring big data of a special gas supply pipeline according to claim 6, wherein the method comprises the following steps: in step S14: utilize monitoring range acquisition unit to gather sensor monitoring longest distance and be L, utilize and detect the segmentation unit and detect pipeline total length and be L ', carry out the segmentation to the pipeline and detect, segmentation length is L'/L, when the sensor trouble, utilizes equipment trouble alarm unit to send alarm signal, utilizes monitoring mode adjustment unit adjustment monitoring mode: and (3) offsetting all the sensors to the direction of the fault sensor by the offset distance: (L'/L)/2, the special gas supply line continues to be monitored after the sensor is shifted.
10. The method for monitoring the big data of the special gas supply pipeline according to claim 6, wherein the method comprises the following steps: in step S15: predicting the probability of gas leakage at the current point where the pipeline is easy to generate corrosion leakage by using an abnormal probability prediction unit: obtaining a corrosion degree coefficient Q when a corresponding point leaks in an original pipeline, monitoring the corrosion degree coefficient Q of the corresponding point before a sensor corresponding to a monitored range to which the corresponding point belongs fails, wherein the average time when the corrosion degree is intensified is T, the time spent in the adjustment of a monitoring mode is T, and predicting the probability of gas leakage of the corresponding point
Setting the probability threshold to P ', comparing P and P', at P>And P', namely when the prediction probability is greater than the threshold value, sending a gas leakage early warning signal and leakage point position information by using the abnormity early warning unit.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210309453.7A CN114646022A (en) | 2022-03-27 | 2022-03-27 | Special gas supply pipeline big data monitoring system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210309453.7A CN114646022A (en) | 2022-03-27 | 2022-03-27 | Special gas supply pipeline big data monitoring system and method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114646022A true CN114646022A (en) | 2022-06-21 |
Family
ID=81995468
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210309453.7A Pending CN114646022A (en) | 2022-03-27 | 2022-03-27 | Special gas supply pipeline big data monitoring system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114646022A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115389707A (en) * | 2022-08-01 | 2022-11-25 | 江苏力之源智能电气有限公司 | SF6 gas monitoring and alarming system and method based on Internet of things |
| CN115662469A (en) * | 2022-12-06 | 2023-01-31 | 东莞先知大数据有限公司 | Water pipe leakage detecting method, electronic equipment and storage medium |
| CN115988034A (en) * | 2022-12-26 | 2023-04-18 | 广州市亿博信息技术有限公司 | Intelligent monitoring method and system for pipeline safety state and service platform |
| CN116412359A (en) * | 2023-04-13 | 2023-07-11 | 南雄市佛燃能源有限公司 | Natural gas leakage monitoring system and method |
-
2022
- 2022-03-27 CN CN202210309453.7A patent/CN114646022A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115389707A (en) * | 2022-08-01 | 2022-11-25 | 江苏力之源智能电气有限公司 | SF6 gas monitoring and alarming system and method based on Internet of things |
| CN115389707B (en) * | 2022-08-01 | 2024-03-29 | 江苏力之源智能电气有限公司 | SF6 gas monitoring and alarming system and method based on Internet of things |
| CN115662469A (en) * | 2022-12-06 | 2023-01-31 | 东莞先知大数据有限公司 | Water pipe leakage detecting method, electronic equipment and storage medium |
| CN115988034A (en) * | 2022-12-26 | 2023-04-18 | 广州市亿博信息技术有限公司 | Intelligent monitoring method and system for pipeline safety state and service platform |
| CN116412359A (en) * | 2023-04-13 | 2023-07-11 | 南雄市佛燃能源有限公司 | Natural gas leakage monitoring system and method |
| CN116412359B (en) * | 2023-04-13 | 2024-02-20 | 南雄市佛燃能源有限公司 | Natural gas leakage monitoring system and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114646022A (en) | Special gas supply pipeline big data monitoring system and method | |
| CN109524139B (en) | Real-time equipment performance monitoring method based on equipment working condition change | |
| US7082758B2 (en) | Hydraulic machine, system for monitoring health of hydraulic machine, and method thereof | |
| US8072340B2 (en) | Water leakage monitoring system | |
| US10962505B2 (en) | Methods and systems for measuring corrosion in-situ | |
| CN108360608B (en) | Pipe burst identification and positioning method for water delivery pipe of water supply system | |
| CN103792087A (en) | Parallel trial run fault monitoring and diagnosing method | |
| CN102288408B (en) | Apparatus and method for monitoring wear of a crosshead bearing in stroke diesel engine | |
| US11703457B2 (en) | Structure diagnosis system and structure diagnosis method | |
| WO1998011415A1 (en) | Event detection system and method | |
| CN102203433B (en) | Diagnostic and response systems and methods for fluid power systems | |
| JP2003271231A (en) | Estimation device of detector drift and monitor system of detector | |
| CN116541678B (en) | Pressure monitoring method and device for gas station safety pipeline | |
| CN116804412A (en) | Monitoring data processing method of hydraulic system | |
| CN110672283A (en) | Method and system for detecting and positioning water leakage of cold water pipeline in converter valve | |
| CN107655624B (en) | Pressure transmitter monitoring method | |
| EP3179165B1 (en) | Steam using facility management method, and steam using facility | |
| CN112797807A (en) | Temperature anomaly monitoring system and method | |
| CN116659630B (en) | Mass flowmeter standard meter on-line verification system based on Reynolds number compensation | |
| TWI742976B (en) | Structure diagnosis system and structure diagnosis method | |
| CN116630316B (en) | Belt fatigue detection alarm method and alarm system based on video analysis | |
| CN215931022U (en) | Device for measuring level value of drain tank | |
| CN114674278B (en) | Piston rod settlement monitoring system with threshold shielding function | |
| CN114970686A (en) | Multi-abnormal-mode-oriented partial information observable equipment fault detection method | |
| CN117537447A (en) | Air conditioner pipeline detection method and device, air conditioner and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |