CN115854258A - Steam pipe network leakage point online inspection method based on time sequence - Google Patents
Steam pipe network leakage point online inspection method based on time sequence Download PDFInfo
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Abstract
The invention discloses a steam pipe network leakage point online inspection method based on a time sequence, which comprises the following steps: acquiring multi-source sensing data acquired by all steam pressure sensors on a steam pipe network to obtain an original sensor data set; respectively carrying out data correlation detection and time sequence continuity detection on multi-source sensing data of the same steam pressure sensor in an original data set to obtain a data correlation detection abnormal set omega m and a time sequence continuity detection abnormal set omega c; fusing a data correlation detection abnormal set omega m and a time sequence continuity detection abnormal set omega c; and determining leakage point information in the steam pipe network according to the fusion result. The steam pipe network leakage point online inspection method based on the time sequence provided by the invention utilizes a multi-source sensing data abnormity detection strategy based on the time sequence to carry out intelligent inspection on steam pipe network equipment, detects and analyzes the operation state of the steam pipe network equipment at any time, and judges the future development and operation state of a steam pipe network equipment system.
Description
Technical Field
The invention relates to the technical field of steam pipe network equipment improvement, in particular to a steam pipe network leakage point online inspection method based on a time sequence.
Background
At present, cigarette enterprises do not need steam energy sources all the time, and once a steam pipe network system goes wrong, the production of the enterprises is greatly influenced. Along with the development of society, the demand of cigarette enterprises on steam energy is continuously increased, the scale of production and construction is also larger and larger, and some equipment safety accidents can happen in the operation process of steam pipe network equipment, so that a large amount of energy is wasted. Therefore, it is very important to enhance the inspection work and inspection quality of the steam pipe network.
Steam pipe network patrols and examines an important link in whole production system, can ensure the even running of kinetic energy equipment, along with the continuous expansion of cigarette production scale, the work load that the pipe network was patrolled and examined also corresponding increase. In current steam pipe network equipment patrols and examines, mainly rely on operation and maintenance personnel's on-the-spot inspection and the work experience to carry out the judgement of trouble, traditional information system's monitoring system is difficult to adapt to current information-based development speed simultaneously, consequently patrols and examines the in-process, often is difficult to discover trouble problem and hidden danger in time.
Therefore, an online inspection method for leakage points of a steam pipe network based on time series is needed.
Disclosure of Invention
The invention aims to provide a steam pipe network leakage point online inspection method based on a time sequence, which aims to solve the problems in the prior art, realize intelligent inspection of equipment by using a multi-source sensing data anomaly detection strategy based on the time sequence, detect and analyze the operation conditions of the steam pipe network equipment at any time, and judge the future development and operation conditions of a steam pipe network equipment system.
The invention provides a steam pipe network leakage point online inspection method based on a time sequence, which comprises the following steps:
acquiring multi-source sensing data acquired by all steam pressure sensors on a steam pipe network to obtain an original sensor data set, wherein the multi-source sensing data comprises various types of steam data;
respectively carrying out data correlation detection and time sequence continuity detection on multi-source sensing data aiming at the same steam pressure sensor in the original sensor data set to respectively obtain a data correlation detection abnormal set omega m and a time sequence continuity detection abnormal set omega c;
fusing the data correlation detection abnormal set omega m and the time sequence continuity detection abnormal set omega c;
and determining leakage point information in the steam pipe network according to the fusion result of the time sequence continuity detection abnormal set omega c and the data correlation detection abnormal set omega m.
The steam pipe network leakage point online inspection method based on the time series is characterized in that the multi-source sensing data acquired by each steam pressure sensor preferably comprises a pressure value, a flow rate value and a steam quantity.
The steam pipe network leakage point online inspection method based on the time sequence, wherein preferably, the data correlation detection and the time sequence continuity detection are respectively performed on the multi-source sensing data of the same steam pressure sensor in the original sensor data set to respectively obtain a data correlation detection abnormal set Ω m and a time sequence continuity detection abnormal set Ω c, and specifically includes:
extracting multi-source sensing data corresponding to each steam pressure sensor according to the original sensor data set to obtain a single sensor steam data set of each steam pressure sensor;
performing data correlation detection on multi-source sensor data in the single sensor data set of each steam pressure sensor to obtain a data correlation detection abnormal set omega m, wherein the multi-source sensor data represent different types of steam data;
and carrying out time sequence continuity detection on unary sensing data in the single sensor data set of each steam pressure sensor to obtain a time sequence continuity detection abnormal set omega c, wherein the unary sensing data represents the same type of steam data.
The steam pipe network leakage point online inspection method based on the time sequence, wherein preferably, the extracting multi-source sensing data corresponding to each steam pressure sensor according to the original sensor data set to obtain a single sensor steam data set of each steam pressure sensor specifically includes:
extracting multi-source sensing data corresponding to a single steam pressure sensor in the original sensor data set to serve as a single sensor abnormal detection ID set, wherein the number of the single abnormal detection ID set is consistent with that of the steam pressure sensors;
using the single sensor anomaly detection ID set as the single sensor vapor data set.
The steam pipe network leakage point online inspection method based on the time sequence, wherein preferably, the fusing the data correlation detection anomaly set Ω m and the time sequence continuity detection anomaly set Ω c specifically includes:
positioning a steam pipe network corresponding to correlation sensing data in the data correlation detection abnormal set omega m by using the time sequence continuity detection abnormal set omega c, detecting multi-source sensor abnormal data in the abnormal set omega c according to the time sequence continuity, eliminating steam data without abnormality in the data correlation detection abnormal set omega m, and storing the steam data without abnormality in a normal sensor data set omega del;
re-screening a correct data set omega r which accords with the correlation relation in the data correlation detection abnormal set omega m by using the time sequence continuity detection abnormal set omega c, and storing the screened sensing data which accords with the correct correlation relation and has data abnormality in a newly added abnormal data set omega add;
and fusing the results of the time sequence continuity detection abnormal set omega c and the data correlation detection abnormal set omega m by using the normal sensor data set omega del and the newly added abnormal data set omega add.
The steam pipe network leakage point online inspection method based on the time sequence, wherein preferably, the locating, by using the time sequence continuity detection anomaly set Ω c, the steam pipe network corresponding to the correlation sensing data in the data correlation detection anomaly set Ω m specifically includes:
drawing a time-varying curve of the multi-source sensing data in the time sequence continuity detection abnormal set omega c according to the time sequence continuity detection abnormal set omega c to obtain a multi-source sensing data varying curve;
and positioning the steam pipe network corresponding to the correlation sensing data in the data correlation detection abnormal set omega m according to the change condition of the curve slope in the multi-source sensing data change curve.
The steam pipe network leakage point online inspection method based on the time sequence, as described above, preferably, the fusing the results of the time sequence continuity detection abnormal set Ω c and the data correlation detection abnormal set Ω m by using the normal sensor data set Ω del and the newly added abnormal data set Ω add, specifically including:
and removing the normal sensor data set omega del and adding the newly added abnormal data set omega add to obtain a fusion result in the union set of the time sequence continuity detection abnormal set omega c and the data correlation detection abnormal set omega m.
The steam pipe network leakage point online inspection method based on the time sequence, wherein preferably, the determining leakage point information in the steam pipe network according to the fusion result of the time sequence continuity detection anomaly set Ω c and the data correlation detection anomaly set Ω m specifically includes:
and determining the positions of the leakage points in the steam pipe network and the abnormal sensor data of the positions of the leakage points according to the fusion result of the time sequence continuity detection abnormal set omega c and the data correlation detection abnormal set omega m.
The steam pipe network leakage point online inspection method based on the time sequence preferably further includes:
and screening the pressure values in the steam pipe network in the three-dimensional intelligent system according to the fusion result, and marking and early warning the position of the pipeline with the leakage point in the steam pipe network in the three-dimensional intelligent system.
The invention provides a steam pipe network leakage point online inspection method based on a time sequence, which is characterized in that multi-source sensing data of each steam pressure sensor are obtained to obtain distributed sensing data, corresponding data are correspondingly processed on computing resources close to a data source as much as possible by utilizing the idea of edge computing big data processing, the overall efficiency of data processing is improved while the pressure of network transmission bandwidth is reduced, and corresponding data fusion operation is carried out on data detection results of DCD and TCD, so that the defects of DCD and TCD detection are effectively avoided, the online detection of the leakage point of the steam pipe network is realized, and the working efficiency of maintenance personnel is effectively improved; the system can more rapidly judge the impurities and other approximate position information influencing the pressure curve, and can mark and early warn in a three-dimensional system, so that workers can check and analyze in real time and effectively communicate with field workers, and timely and accurate fault treatment or accident avoidance can be realized.
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In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described with reference to the accompanying drawings, in which:
fig. 1 is a flowchart of an embodiment of a steam pipe network leakage point online inspection method based on a time sequence provided by the invention.
Detailed Description
Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. The description of the exemplary embodiments is merely illustrative and is in no way intended to limit the disclosure, its application, or uses. The present disclosure may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. It should be noted that: the relative arrangement of parts and steps, the composition of materials, numerical expressions and numerical values set forth in these embodiments are to be construed as merely illustrative, and not as limitative, unless specifically stated otherwise.
As used in this disclosure, "first", "second": and the like, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element preceding the word covers the element listed after the word, and does not exclude the possibility that other elements are also covered. "upper", "lower", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
In the present disclosure, when a specific component is described as being located between a first component and a second component, there may or may not be intervening components between the specific component and the first component or the second component. When it is described that a specific component is connected to other components, the specific component may be directly connected to the other components without having an intervening component, or may be directly connected to the other components without having an intervening component.
All terms (including technical or scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs unless specifically defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
As shown in fig. 1, the steam pipe network leakage point online inspection method based on the time sequence provided in this embodiment specifically includes the following steps in an actual execution process:
s1, obtaining multi-source sensing data collected by all steam pressure sensors on the steam pipe network to obtain an original sensor data set, wherein the multi-source sensing data comprises various types of steam data.
The initialization operation of the corresponding data variables is completed through the step S1, namely, the acquired parameters are put into a data set, so that the subsequent processing is facilitated.
The multi-source sensing data collected by each steam pressure sensor comprises a pressure value, a flow rate value and a steam amount. It should be noted that the type of the steam data is not specifically limited in the present invention, and the parameter values capable of reflecting the leak point of the detection pipe network all belong to the multi-source sensing data.
Distributed sensing data are obtained by obtaining multi-source sensing data of each steam pressure sensor, corresponding data are correspondingly processed on computing resources close to a data source as much as possible by utilizing the idea of big data processing of edge computing, and the overall efficiency of data processing can be improved while the pressure of network transmission bandwidth is reduced.
And S2, respectively carrying out Data Correlation Detection (DCD) and time sequence continuity detection (TCD) on multi-source sensing data aiming at the same steam pressure sensor in the original sensor data set so as to respectively obtain a data correlation detection abnormal set omega m and a time sequence continuity detection abnormal set omega c.
Wherein, the Data Correlation Detection (DCD) is used for detecting the correlation between data and data, and the time sequence continuity detection (TCD) is used for detecting the continuity of the data in time sequence. In an embodiment of the steam pipe network leakage point online inspection method based on the time sequence, the step S2 may specifically include:
and S21, extracting multi-source sensing data corresponding to each steam pressure sensor according to the original sensor data set to obtain a single sensor steam data set of each steam pressure sensor.
In an embodiment of the steam pipe network leakage point online inspection method based on the time sequence, the step S21 may specifically include:
step S211, extracting multi-source sensing data corresponding to a single steam pressure sensor from the original sensor data set to serve as a single sensor abnormality detection ID set, wherein the number of the single abnormality detection ID set is consistent with that of the steam pressure sensors.
And S212, taking the single sensor abnormality detection ID set as the single sensor steam data set.
And S22, carrying out data correlation detection on multi-source sensor data in the single sensor data set of each steam pressure sensor to obtain a data correlation detection abnormal set omega m, wherein the multi-source sensor data represent different types of steam data.
And S23, carrying out time sequence continuity detection on the unary sensing data in the single sensor data set of each steam pressure sensor to obtain a time sequence continuity detection abnormal set omega c, wherein the unary sensing data represent the same type of steam data.
For specific detection methods of Data Correlation Detection (DCD) and Timing Continuity Detection (TCD), reference may be made to the prior art, and details thereof are not repeated herein.
And S3, fusing the data correlation detection abnormal set omega m and the time sequence continuity detection abnormal set omega c.
In an embodiment of the steam pipe network leakage point online inspection method based on the time sequence, the step S3 may specifically include:
step S31, positioning a steam pipe network corresponding to the correlation sensing data in the data correlation detection abnormal set omega m by using the time sequence continuity detection abnormal set omega c, detecting multisource sensor abnormal data in the abnormal set omega c according to the time sequence continuity, eliminating the steam data without abnormality in the data correlation detection abnormal set omega m, and storing the steam data without abnormality in a normal sensor data set omega del.
In an embodiment of the present invention, the extracting, according to the original sensor data set, multi-source sensing data corresponding to each steam pressure sensor to obtain a single sensor steam data set of each steam pressure sensor specifically includes:
step S311, drawing a time-varying curve of the multi-source sensing data in the time sequence continuity detection abnormal set omega c according to the time sequence continuity detection abnormal set omega c to obtain a multi-source sensing data varying curve.
Step S312, according to the change condition of the curve slope in the multi-source sensing data change curve, positioning the steam pipe network corresponding to the correlation sensing data in the data correlation detection abnormal set omega m.
Illustratively, the leakage point of the steam pipe network can be accurately positioned according to the sudden change points in the pressure value curve and the flow velocity value curve, and if the sudden change occurs, the leakage point of the pipe network near the point is indicated.
And S32, screening a correct data set omega r which accords with the correlation in the data correlation detection abnormal set omega m again by using the time sequence continuity detection abnormal set omega c, and storing the screened sensing data which accords with the correct correlation and has data abnormality in a newly-added abnormal data set omega add.
The criterion for re-screening the correct data set Ω r meeting the correlation in the data correlation detection abnormal set Ω m may be a change of the correlation between the pressure data measured by the pressure sensor and changes of the steam amount and the flow rate.
And S33, fusing the results of the time sequence continuity detection abnormal set omega c and the data correlation detection abnormal set omega m by using the normal sensor data set omega del and the newly added abnormal data set omega add.
Illustratively, in the fusion process, in the union of the time-series continuity detection abnormal set Ω c and the data correlation detection abnormal set Ω m, the normal sensor data set Ω del is removed, and the newly added abnormal data set Ω add is added to obtain the final fusion result, so that the effective fusion of the abnormal data results can be realized through the fusion operation. By integrating the data set, abnormal data which cannot be found by TCD detection and DCD detection can be supplemented, corresponding data without abnormality is eliminated, the two detection methods are made up for each other, and the accuracy of the final result is ensured.
The invention provides a Sensing data Anomaly Detection (ADMSD _ TS) strategy based on Time Series, which can perform corresponding data fusion operation on data Detection results of DCD and TCD, thereby effectively avoiding the defects of the two algorithms, and the Detection result of the ADMSD _ TS algorithm is obviously superior to that of a single TCD and a single abnormal data Detection method of DCD.
And S4, determining leakage point information in the steam pipe network according to a fusion result of the time sequence continuity detection abnormal set omega c and the data correlation detection abnormal set omega m.
Specifically, according to the fusion result of the time sequence continuity detection abnormal set Ω c and the data correlation detection abnormal set Ω m, the leakage point position in the steam pipe network and the abnormal sensor data of the leakage point position are determined. In one embodiment of the invention, the fused exception dataset is saved in a result array.
Further, in an embodiment of the present invention, the online routing inspection of the leakage point of the steam pipe network based on the time series further includes:
and S5, screening the pressure value in the steam pipe network in a three-dimensional intelligent (AI) system according to the fusion result, and marking and early warning the position of the pipeline with the leakage point in the steam pipe network in the three-dimensional intelligent system.
According to the steam pipe network leakage point online inspection method based on the time sequence, the multi-source sensing data of each steam pressure sensor are obtained, the distributed sensing data are obtained, corresponding data are correspondingly processed on computing resources close to a data source as far as possible by utilizing the idea of edge computing big data processing, the overall efficiency of data processing is improved while the network transmission bandwidth pressure is reduced, and meanwhile, corresponding data fusion operation is carried out on data detection results of DCD and TCD, so that the defects existing in DCD and TCD detection are effectively avoided, the online detection of the leakage point of the steam pipe network is realized, and the working efficiency of maintenance personnel is effectively improved; the system can more rapidly judge the impurities and other approximate position information influencing the pressure curve, and can mark and early warn in a three-dimensional system, so that workers can check and analyze in real time and effectively communicate with field workers, and timely and accurate fault treatment or accident avoidance can be realized.
Thus, various embodiments of the present disclosure have been described in detail. Some details well known in the art have not been described in order to avoid obscuring the concepts of the present disclosure. It will be fully apparent to those skilled in the art from the foregoing description how to practice the presently disclosed embodiments.
Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the foregoing examples are for purposes of illustration only and are not intended to limit the scope of the present disclosure. It will be understood by those skilled in the art that various changes may be made in the above embodiments or equivalents may be substituted for elements thereof without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.
Claims (9)
1. A steam pipe network leakage point online inspection method based on time series is characterized by comprising the following steps:
acquiring multi-source sensing data acquired by all steam pressure sensors on a steam pipe network to obtain an original sensor data set, wherein the multi-source sensing data comprises various types of steam data;
respectively carrying out data correlation detection and time sequence continuity detection on multi-source sensing data aiming at the same steam pressure sensor in the original sensor data set to respectively obtain a data correlation detection abnormal set omega m and a time sequence continuity detection abnormal set omega c;
fusing the data correlation detection abnormal set omega m and the time sequence continuity detection abnormal set omega c;
and determining leakage point information in the steam pipe network according to the fusion result of the time sequence continuity detection abnormal set omega c and the data correlation detection abnormal set omega m.
2. The steam pipe network leakage point online inspection method based on the time sequence according to claim 1, wherein the multi-source sensing data collected by each steam pressure sensor comprises a pressure value, a flow rate value and a steam amount.
3. The steam pipe network leakage point online inspection method based on the time sequence according to claim 1, wherein the data correlation detection and the time sequence continuity detection are respectively performed on multi-source sensing data for the same steam pressure sensor in the original sensor data set to respectively obtain a data correlation detection abnormal set Ω m and a time sequence continuity detection abnormal set Ω c, and specifically comprises:
extracting multi-source sensing data corresponding to each steam pressure sensor according to the original sensor data set to obtain a single sensor steam data set of each steam pressure sensor;
performing data correlation detection on multi-source sensor data in the single sensor data set of each steam pressure sensor to obtain a data correlation detection abnormal set omega m, wherein the multi-source sensor data represent different types of steam data;
and carrying out time sequence continuity detection on unary sensing data in the single sensor data set of each steam pressure sensor to obtain a time sequence continuity detection abnormal set omega c, wherein the unary sensing data represents the same type of steam data.
4. The steam pipe network leakage point online inspection method based on the time sequence according to claim 3, wherein the extracting of the multi-source sensing data corresponding to each steam pressure sensor according to the original sensor data set to obtain a single sensor steam data set of each steam pressure sensor specifically comprises:
extracting multi-source sensing data corresponding to a single steam pressure sensor from the original sensor data set to serve as a single sensor abnormality detection ID set, wherein the number of the single abnormality detection ID set is consistent with that of the steam pressure sensors;
using the single sensor anomaly detection ID set as the single sensor vapor data set.
5. The steam pipe network leakage point online inspection method based on the time sequence according to claim 1, wherein the fusion of the data correlation detection anomaly set Ω m and the time sequence continuity detection anomaly set Ω c specifically comprises:
positioning a steam pipe network corresponding to correlation sensing data in the data correlation detection abnormal set omega m by using the time sequence continuity detection abnormal set omega c, detecting multi-source sensor abnormal data in the abnormal set omega c according to the time sequence continuity, eliminating steam data without abnormality in the data correlation detection abnormal set omega m, and storing the steam data without abnormality in a normal sensor data set omega del;
screening a correct data set omega r which accords with the correlation in the data correlation detection abnormal set omega m again by using the time sequence continuity detection abnormal set omega c, and storing the screened sensing data which accords with the correct correlation and has data abnormality in a newly added abnormal data set omega add;
and fusing the results of the time sequence continuity detection abnormal set omega c and the data correlation detection abnormal set omega m by using the normal sensor data set omega del and the newly added abnormal data set omega add.
6. The steam pipe network leakage point online inspection method based on the time sequence according to claim 5, wherein the positioning of the steam pipe network corresponding to the correlation sensing data in the data correlation detection anomaly set Ω m by using the time sequence continuity detection anomaly set Ω c specifically comprises:
drawing a time-varying curve of the multi-source sensing data in the time sequence continuity detection abnormal set omega c according to the time sequence continuity detection abnormal set omega c to obtain a multi-source sensing data varying curve;
and positioning the steam pipe network corresponding to the correlation sensing data in the data correlation detection abnormal set omega m according to the change condition of the curve slope in the multi-source sensing data change curve.
7. The steam pipe network leakage point online inspection method based on the time sequence according to claim 5, wherein the fusing of the results of the time sequence continuity detection anomaly set Ω c and the data correlation detection anomaly set Ω m by using the normal sensor data set Ω del and the newly added anomaly data set Ω add specifically comprises:
and removing the normal sensor data set omega del and adding the newly added abnormal data set omega add to obtain a fusion result in the union set of the time sequence continuity detection abnormal set omega c and the data correlation detection abnormal set omega m.
8. The steam pipe network leakage point online inspection method based on the time sequence according to claim 1, wherein the determining of the leakage point information in the steam pipe network according to the fusion result of the time sequence continuity detection anomaly set Ω c and the data correlation detection anomaly set Ω m specifically comprises:
and determining the positions of the leakage points in the steam pipe network and the abnormal sensor data of the positions of the leakage points according to the fusion result of the time sequence continuity detection abnormal set omega c and the data correlation detection abnormal set omega m.
9. The steam pipe network leakage point online inspection method based on the time sequence according to claim 1, wherein the steam pipe network leakage point online inspection based on the time sequence further comprises:
and screening the pressure value in the steam pipe network in the three-dimensional intelligent system according to the fusion result, and marking and early warning the position of the pipeline with the leakage point in the steam pipe network in the three-dimensional intelligent system.
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| CN117370906A (en) * | 2023-08-21 | 2024-01-09 | 长江生态环保集团有限公司 | Tube explosion detection and performance evaluation method based on single-point and time sequence anomaly detection |
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| CN117370906A (en) * | 2023-08-21 | 2024-01-09 | 长江生态环保集团有限公司 | Tube explosion detection and performance evaluation method based on single-point and time sequence anomaly detection |
| CN117370906B (en) * | 2023-08-21 | 2024-05-10 | 长江生态环保集团有限公司 | Tube explosion detection and performance evaluation method based on single-point and time sequence anomaly detection |
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