CN115062192A - Gas pipeline detection data automatic alignment method based on spatial analysis - Google Patents
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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Abstract
The invention relates to a gas pipeline detection data automatic alignment method based on spatial analysis, which comprises the following steps: collecting pipeline regular inspection data, analyzing spatial characteristics of the regular inspection data, and dividing the regular inspection data into point location data and line type data, wherein the fine granularity of the line type data is smaller than that of the point location data; selecting the smallest fine granularity in the linear data as spatial reference data aligned with the GIS pipeline data; establishing an ID association table of the linear data and the GIS pipeline data; and analyzing the incidence relation and the hierarchy in the ID incidence table, and aligning the scheduled inspection data of the pipeline with the GIS pipeline data based on the incidence relation and the hierarchy. The invention realizes the automatic alignment of the scheduled inspection data and the GIS pipeline data by designing a detection data structure and a matched space analysis method on the premise of not increasing the scheduled inspection data arrangement work.
Description
Technical Field
The invention relates to the technical field of gas pipeline data detection, in particular to a gas pipeline detection data automatic alignment method based on spatial analysis.
Background
The town natural gas pipeline is used as a main medium for town gas transportation and is a main infrastructure concerned by pipe network production and operation. In order to ensure the safety of the pipeline, the pipeline is periodically detected, and data of the pipeline and the environment where the pipeline is located are collected so as to judge the safety condition of the pipeline. However, the pipeline detection data collection usually uses the detection section of the detection task as a unit for collection, and the data granularity of the pipeline detection data collection cannot correspond to the pipelines in the gas enterprise GIS system one by one, so that the digitization of the detection data faces great difficulty. Therefore, in order to implement digitization of the detection data without increasing complexity of the detection data arrangement work, further improvement is needed for the detection method.
Disclosure of Invention
The invention aims to realize automatic alignment of the scheduled inspection data and GIS pipeline data by designing a detection data structure and a matched spatial analysis method on the premise of not increasing the scheduled inspection data (namely detection data) arrangement work, and provides a gas pipeline detection data automatic alignment method based on spatial analysis.
In order to achieve the above object, the embodiments of the present invention provide the following technical solutions:
the automatic alignment method of the gas pipeline detection data based on the spatial analysis comprises the following steps:
step S1, collecting pipeline regular inspection data, analyzing the spatial characteristics of the regular inspection data, and dividing the regular inspection data into point location data and line type data, wherein the fine granularity of the line type data is smaller than that of the point location data;
step S2, selecting the smallest fine granularity in the linear data as the spatial reference data aligned with the GIS pipeline data; establishing an ID association table of the linear data and the GIS pipeline data;
and step S3, analyzing the incidence relation and the hierarchy in the ID incidence table, and aligning the scheduled inspection data of the pipeline with the GIS pipeline data based on the incidence relation and the hierarchy.
The service data collected by the regular inspection data on the service comprises soil corrosivity detection, stray current detection, anticorrosive layer quality detection, anticorrosive layer non-excavation and buried depth detection, cathode protection effectiveness detection and pipeline pressure occupation; the collection mode of the regular inspection data on the service comprises point-to-point, segmentation and segmentation-to-multipoint;
wherein, the collection modes of soil corrosivity detection, stray current detection, cathode protection effectiveness detection and pipeline pressure occupation are point-by-point; the collection mode of the quality detection of the anticorrosive coating is segmented; the collection mode of non-excavation and burial depth detection of the anticorrosive coating is multi-point according to sections;
the business data collected according to the collection mode of the point location is point location data, and the business data collected according to the collection mode of the subsection and the subsection multipoint is linear data; the fine granularity of the linear data is smaller than that of the point position data, and the fine granularity of the service data of the multi-point according to the segments is smaller than that of the service data according to the segments.
The step of establishing the ID association table of the linear data and the GIS pipeline data comprises the following steps:
the method comprises the steps that line type data of multiple points in a segmentation mode are used as spatial reference data aligned with GIS pipeline data, the line type data comprise a plurality of detection sections, and each detection section comprises a plurality of detection point positions;
performing spatial buffer analysis on each detection section, vertically buffering line segments formed by a single detection section within a preset distance range d on two sides of a line segment to form a surface graph expressed by a strip, and taking the line segments of the GIS pipeline contained in the surface graph as the pipe segments of the detection section;
and the formed ID association table is a mapping relation of a detection section corresponding to a plurality of GIS pipe sections.
And analyzing the incidence relation and the hierarchy in the ID incidence table to form a main data form, wherein the main data form comprises a detection task ID, a detection section ID, a GIS pipe section main ID, a GIS pipe section auxiliary ID and detection data.
Compared with the prior art, the invention has the following beneficial effects:
the invention realizes classification of the pipeline regular inspection data by analyzing the spatial characteristics of the pipeline regular inspection data, completes calculation of corresponding incidence relation with GIS pipeline data by mainly describing linear data with minimum fine granularity, completes alignment of original detection data to the GIS pipeline data by combining the incidence design of the main data of the acquired data, realizes automatic alignment of the regular inspection data and the GIS pipeline data, and provides a realization scheme for digitalization of the regular inspection data.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of spatial correlation analysis between a detection segment and a GIS pipe segment according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating a hierarchy associated with primary data according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Also, in the description of the present invention, the terms "first", "second", and the like are used solely for distinguishing between descriptions and not necessarily for describing or implying any actual such relationship or order between such entities or operations.
Example 1:
the invention is realized by the following technical scheme, as shown in figure 1, the automatic alignment method of the gas pipeline detection data based on the space analysis comprises the following steps:
and step S1, collecting pipeline scheduled inspection data, analyzing the spatial characteristics of the scheduled inspection data, and dividing the scheduled inspection data into point location data and line type data, wherein the fine granularity of the line type data is smaller than that of the point location data.
The main collected service data and collection mode of the pipeline scheduled inspection data on the service are shown in table 1:
table 1 service data and collection mode mainly collected in service by pipeline scheduled inspection data
According to the collection mode of the service data, the service data of 'non-excavation and buried depth detection of the anticorrosive coating' are collected in a sectional multipoint mode, and can be judged to be linear data represented by multiple points; the 'anti-corrosion layer quality detection' service data adopts a collection mode according to sections, is linear data represented by a plurality of sections, and other service data adopts a collection mode according to point positions.
Therefore, according to the collection mode of the service data, the service data can be divided into point location data and line type data, where the sequence numbers 1, 2, 5, and 6 in table 1 are the point location data, and the sequence numbers 3 and 4 are the line type data, where the fine granularity of the line type data is smaller than that of the point location data.
Step S2, selecting the smallest fine granularity in the linear data as the spatial reference data aligned with the GIS pipeline data; and establishing an ID association table of the linear data and the GIS pipeline data.
The line data in step S1 includes service data collected by segments and points, and then the line data describing the finest granularity, that is, the service data collected by segments and points, is selected from the line data as spatial reference data aligned with the GIS line data.
The service data records of the non-excavation and burial depth detection of the anticorrosive coating are specifically shown in the table 2-1 and the table 2-2. Table 2-1 shows a detection segment 01, which includes a plurality of spots; table 2-2 shows a detection segment 02, which also contains a plurality of spots.
TABLE 2-1 record of service data for non-excavation and buried depth detection of anticorrosive coating of detection section 01
TABLE 2-2 record of service data for non-excavation and buried depth detection of anticorrosive coating of detection section 02
According to the data characteristics of the tables 2-1 and 2-2, each detection segment is expressed through multiple points, meets the selection requirement of reference data, and is selected as the reference data for aligning the GIS pipeline data. Each detection section is provided with a series of detection points, and the detection points are sequentially connected to form the line segment description of the detection section. Due to the error of coordinate acquisition, the geometric position of the detection section is basically coincident with the geometric position of GIS pipeline data, but not completely coincident. Therefore, it is necessary to establish a relationship between the detection segment and the GIS pipeline, as shown in fig. 2, a spatial buffer analysis is performed on the line segment formed by the detection segment, that is, the line segment formed by the detection segment 01 is vertically buffered in a certain distance range d at two sides of the line segment, so as to form a surface graph expressed by a strip, and the line segment of the GIS pipeline included in the surface graph is used as the pipe segment corresponding to the detection segment 01.
After spatial analysis, for the detection segment 01, the ID association table of the GIS pipe segment and the detection segment 01 shown in table 3 can be obtained:
TABLE 3 ID Association Table of GIS pipe segment and detection segment 01
The aim of data alignment is to align the detected service data to each GIS pipe segment as an attribute representing the state of the pipeline. However, data collection with heavy business is not directly targeted to the GIS pipe sections, and collected business data are represented by different geometric figures in space, so that main data association and hierarchy need to be combed, and meanwhile, definition and alignment are performed to the GIS pipe sections.
In the embodiment, point data and line data are involved, based on the ID association table shown in table 3, step S3 shows the alignment process and logic of the line data by taking point data anti-corrosion layer quality detection as an example; taking the stray current of the point location data as an example, the alignment process and logic of the point location data are shown.
And step S3, analyzing the incidence relation and the hierarchy in the ID incidence table, and aligning the scheduled inspection data of the pipeline with the GIS pipeline data based on the incidence relation and the hierarchy.
The main data is an objectified main body to which a service attribute can be added, and is generally represented by a corresponding ID identifier, such as a primary key ID of a pipeline in a database and a number of a detection segment, and the main data system of this embodiment is shown in fig. 3. In fig. 3, the detection task ID is an identifier of one detection task, each detection task performs detection on a plurality of detection segments, the plurality of detection segments belong to one detection task, and one detection segment establishes one-to-many association with the GIS pipe segment through spatial analysis, as shown in table 3, so that the entire main data association relationship and hierarchy are established. The segment auxiliary ID in fig. 3 is generally an ID added to a segment in the GIS pipeline data according to a specific requirement on the service data, and represents an auxiliary management unit higher than the GIS segment ID by one level. If a road section comprises a plurality of GIS pipe sections, the main data form shown in the table 4 can be formed finally:
TABLE 4 Master data modality
For the point location data, the data recording format such as stray current is shown in table 5:
TABLE 5 data recording of stray currents
Therefore, by analyzing the buffer area of the GIS pipe section, the stray current measuring point position in the buffer area range of the GIS pipe section is aligned to the GIS pipe section, generally only one GIS pipe section is aligned, and the stray current of other GIS pipe sections is empty. And stray current detection is to represent the stray current level of a GIS pipe section included in the whole detection task by the point position, so that a detection section needs to be checked reversely by an aligned GIS pipe section, and then the stray current of the GIS pipe section belonging to the detection section is filled into the detected stray current value. The results of the alignment are shown in table 6:
TABLE 6 stray current alignment results of detection section 01 and GIS pipe section
For line type data submitted in test sections, such as corrosion protection quality test data, the form is shown in table 7:
TABLE 7 detection of Corrosion protection quality data for test subsegment 0101
The detection sub-segment 0101 represents one of the segment bit detection segments 01. According to the starting point mileage described by each record and the detection section to which the starting point mileage belongs, a corresponding measurement point set contained in the detection section 0101 is queried and obtained from the table 2-1, the 1 st record in the table 7 contains measurement points with mileage from 0 to 499m, and 1 to 6 records corresponding to the table 1-1, and the GIS pipe sections contained in the data of the detection subsection 0101 can be obtained by performing spatial buffer analysis on the broken line sections formed by the 6 points, so that the corresponding relationship and the alignment result of the data are obtained, as shown in the table 8:
table 8 alignment result of anticorrosive layer quality detection data of detection field 0101 and GIS pipe section
The detection service attributes on the detection subsections can be distributed to GIS pipe sections g1 and g2 through the corresponding relation, and alignment of linear data and GIS pipeline data is achieved.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered 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.
Claims (4)
1. The automatic alignment method of the gas pipeline detection data based on the spatial analysis is characterized by comprising the following steps: the method comprises the following steps:
step S1, collecting pipeline regular inspection data, analyzing the spatial characteristics of the regular inspection data, and dividing the regular inspection data into point location data and linear data, wherein the fine granularity of the linear data is smaller than that of the point location data;
step S2, selecting the smallest fine granularity in the linear data as the spatial reference data aligned with the GIS pipeline data; establishing an ID association table of the linear data and the GIS pipeline data;
and step S3, analyzing the incidence relation and the hierarchy in the ID incidence table, and aligning the scheduled inspection data of the pipeline with the GIS pipeline data based on the incidence relation and the hierarchy.
2. The gas pipeline detection data automatic alignment method based on the spatial analysis according to claim 1, characterized in that: the service data collected by the regular inspection data on the service comprises soil corrosivity detection, stray current detection, anticorrosive layer quality detection, anticorrosive layer non-excavation and buried depth detection, cathode protection effectiveness detection and pipeline pressure occupation; the collection mode of the regular inspection data on the service comprises point-to-point, segmentation and segmentation-to-multipoint;
wherein, the collection modes of soil corrosivity detection, stray current detection, cathode protection effectiveness detection and pipeline pressure occupation are point-by-point; the collection mode of the quality detection of the anticorrosive coating is segmented; the collection mode of non-excavation and burial depth detection of the anticorrosive coating is multi-point according to sections;
the business data collected according to the collection mode of the point location is point location data, and the business data collected according to the collection mode of the subsection and the subsection multipoint is linear data; the fine granularity of the linear data is smaller than that of the point position data, and the fine granularity of the service data of the multi-point according to the segments is smaller than that of the service data according to the segments.
3. The gas pipeline detection data automatic alignment method based on the spatial analysis as claimed in claim 2, characterized in that: the step of establishing the ID association table of the linear data and the GIS pipeline data comprises the following steps:
the method comprises the steps that line type data of multiple points in a segmentation mode are used as spatial reference data aligned with GIS pipeline data, the line type data comprise a plurality of detection sections, and each detection section comprises a plurality of detection point positions;
performing spatial buffer analysis on each detection section, vertically buffering a line segment formed by a single detection section within a preset distance range d on two sides of the line segment to form a surface graph expressed by a strip, and taking the line segment of a GIS pipeline contained in the surface graph as the pipe segment of the detection section;
and the formed ID association table is a mapping relation of one detection section corresponding to a plurality of GIS pipe sections.
4. The gas pipeline detection data automatic alignment method based on the spatial analysis according to claim 1, characterized in that: and analyzing the incidence relation and the hierarchy in the ID incidence table to form a main data form, wherein the main data form comprises a detection task ID, a detection section ID, a GIS pipe section main ID, a GIS pipe section auxiliary ID and detection data.
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Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050149561A1 (en) * | 2003-12-29 | 2005-07-07 | Jungle Lasers, Llc | Method and apparatus for creating and maintaining a GIS |
| US20060235611A1 (en) * | 2005-04-18 | 2006-10-19 | Dataforensics, Llc | Systems and methods for recording and reporting data collected from a remote location |
| US8548757B1 (en) * | 2013-01-14 | 2013-10-01 | RCP Inc. | Method for calculating maximum allowable operating pressure and maximum operating pressure of a pipeline |
| US8548756B1 (en) * | 2013-01-14 | 2013-10-01 | RCP Inc. | System for calculating maximum allowable operating pressure and maximum operating pressure of a pipeline |
| CN105303045A (en) * | 2015-10-27 | 2016-02-03 | 中国石油天然气股份有限公司 | Long-distance pipeline linear data association rule mining method |
| CN109886506A (en) * | 2019-03-14 | 2019-06-14 | 重庆大学 | A kind of water supply network booster risk analysis method |
| CN111105107A (en) * | 2018-10-25 | 2020-05-05 | 中国石油化工股份有限公司 | Oil and gas pipeline risk management system based on quantitative risk evaluation |
| CN111274683A (en) * | 2020-01-15 | 2020-06-12 | 深圳四维集思技术服务有限公司 | Oil-gas pipeline data alignment management method |
| CN111551967A (en) * | 2020-06-11 | 2020-08-18 | 沈阳龙昌管道检测中心 | Indirect detection data alignment method using GPS coordinates and application thereof |
| CN111612301A (en) * | 2020-04-17 | 2020-09-01 | 北京市燃气集团有限责任公司 | Weight self-adjustment-based gas buried pipeline leakage risk assessment method and device |
| CN111667132A (en) * | 2019-03-07 | 2020-09-15 | 中国石油化工股份有限公司 | Intelligent analysis decision-making system for pipeline integrity |
| CN113032380A (en) * | 2021-03-25 | 2021-06-25 | 中国石油管道局工程有限公司 | Method, system and equipment for aligning internal and external detection data of pipeline |
| CN113485984A (en) * | 2021-06-07 | 2021-10-08 | 深圳市燃气集团股份有限公司 | Gas pipeline state data processing method and system |
| CN113486494A (en) * | 2021-06-07 | 2021-10-08 | 深圳市燃气集团股份有限公司 | Gas pipeline state monitoring decision-making method and system |
| CN113971463A (en) * | 2021-10-13 | 2022-01-25 | 杭州英集动力科技有限公司 | Heat supply pipeline risk distribution analysis method and routing inspection path planning system |
| CN113986894A (en) * | 2021-09-28 | 2022-01-28 | 深圳市燃气集团股份有限公司 | Method and system for aligning pipeline data after gas pipeline relocation and storage medium |
| CN114458967A (en) * | 2020-10-22 | 2022-05-10 | 中国石油化工股份有限公司 | Memory, oil and gas pipeline monitoring and early warning method, device and equipment |
-
2022
- 2022-08-22 CN CN202211007134.7A patent/CN115062192B/en active Active
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050149561A1 (en) * | 2003-12-29 | 2005-07-07 | Jungle Lasers, Llc | Method and apparatus for creating and maintaining a GIS |
| US20060235611A1 (en) * | 2005-04-18 | 2006-10-19 | Dataforensics, Llc | Systems and methods for recording and reporting data collected from a remote location |
| US8548757B1 (en) * | 2013-01-14 | 2013-10-01 | RCP Inc. | Method for calculating maximum allowable operating pressure and maximum operating pressure of a pipeline |
| US8548756B1 (en) * | 2013-01-14 | 2013-10-01 | RCP Inc. | System for calculating maximum allowable operating pressure and maximum operating pressure of a pipeline |
| CN105303045A (en) * | 2015-10-27 | 2016-02-03 | 中国石油天然气股份有限公司 | Long-distance pipeline linear data association rule mining method |
| CN111105107A (en) * | 2018-10-25 | 2020-05-05 | 中国石油化工股份有限公司 | Oil and gas pipeline risk management system based on quantitative risk evaluation |
| CN111667132A (en) * | 2019-03-07 | 2020-09-15 | 中国石油化工股份有限公司 | Intelligent analysis decision-making system for pipeline integrity |
| CN109886506A (en) * | 2019-03-14 | 2019-06-14 | 重庆大学 | A kind of water supply network booster risk analysis method |
| CN111274683A (en) * | 2020-01-15 | 2020-06-12 | 深圳四维集思技术服务有限公司 | Oil-gas pipeline data alignment management method |
| CN111612301A (en) * | 2020-04-17 | 2020-09-01 | 北京市燃气集团有限责任公司 | Weight self-adjustment-based gas buried pipeline leakage risk assessment method and device |
| CN111551967A (en) * | 2020-06-11 | 2020-08-18 | 沈阳龙昌管道检测中心 | Indirect detection data alignment method using GPS coordinates and application thereof |
| CN114458967A (en) * | 2020-10-22 | 2022-05-10 | 中国石油化工股份有限公司 | Memory, oil and gas pipeline monitoring and early warning method, device and equipment |
| CN113032380A (en) * | 2021-03-25 | 2021-06-25 | 中国石油管道局工程有限公司 | Method, system and equipment for aligning internal and external detection data of pipeline |
| CN113485984A (en) * | 2021-06-07 | 2021-10-08 | 深圳市燃气集团股份有限公司 | Gas pipeline state data processing method and system |
| CN113486494A (en) * | 2021-06-07 | 2021-10-08 | 深圳市燃气集团股份有限公司 | Gas pipeline state monitoring decision-making method and system |
| CN113986894A (en) * | 2021-09-28 | 2022-01-28 | 深圳市燃气集团股份有限公司 | Method and system for aligning pipeline data after gas pipeline relocation and storage medium |
| CN113971463A (en) * | 2021-10-13 | 2022-01-25 | 杭州英集动力科技有限公司 | Heat supply pipeline risk distribution analysis method and routing inspection path planning system |
Non-Patent Citations (4)
| Title |
|---|
| 杨贺等: "油气管道多轮内检测数据对齐算法研究及应用", 《管道技术与设备》 * |
| 肖靖峰等: "基于ArcGIS平台的厂区地下管网空间分析", 《计算机应用》 * |
| 董绍华等: "基于大数据的管道系统数据分析模型及应用", 《油气储运》 * |
| 金剑等: "油气长输管道大数据整合技术方法研究", 《油气田地面工程》 * |
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