CN115033612A - Underground pipe network longitudinal section analysis method realized based on POSTGRESQL - Google Patents
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Abstract
A underground pipe network vertical section analysis method based on POSTGRESQL is realized, and comprises the following steps of inputting a pipe table name and serial numbers of two sections of pipes to be inquired; step two, the number of serial numbers of two sections of pipelines in a pipeline table is inquired by sql, if the serial numbers are equal to 2, the step three is entered, and if the serial numbers are not equal to 2, the return to a null record is exited; step three, judging whether the two pipe sections are adjacent according to the starting and ending point numbers of the two pipe sections, entering the step four when the two pipe sections are adjacent, and entering the step five when the two pipe sections are not adjacent; fourthly, the sql inquires numbers of two pipe sections in a pipeline table, lengths of the pipe sections, horizontal projection distance and other attribute information in the table to return; and step five, the two pipe sections are not adjacent, all the path paths from the starting pipe section to the ending pipe section are inquired, and the serial number, the length of the pipe section, the horizontal projection distance and other attribute information in the table are recorded in the traversing process. If the traversal reaches the end pipe section, the input serial numbers of the two pipe sections, the pipe section length, the horizontal projection distance and other attribute information in the table are combined to be used as a vertical section result to be returned, and meanwhile, the traversal is ended. The method realizes the query analysis algorithm of the vertical section attributes and the spatial information of two or more sections of the communication pipelines, and has the characteristics of no need of processing data, quick query of the reached path and recording of the vertical section attributes and the spatial attributes of the pipe network in the traversal process.
Description
Technical Field
The patent belongs to the technical field of geographic information, relates to an underground pipeline network analysis module, and relates to an underground pipe network vertical section analysis method based on POSTGRESQL.
Background
The Postgresql database is a powerful open source relational database. POSTGIS is a plug-in to the Postgresql database, where open source GIS space data processing and algorithm modules are provided. A space data processing process function is defined by combining a POSTGIS plug-in with a Postgresql database process function, and a desired space data processing result can be obtained by only querying the Postgresql database through a simple sql function at the back end. The underground pipe network belongs to a network vector data set, is very suitable for POSTGIS storage, and can realize the network analysis of the underground pipe network by combining a POSTGIS space analysis algorithm and a Postgresql database self-defined process function. This patent is based on this development.
The underground pipe network vertical section analysis belongs to undirected graph network analysis. A star and Dijkstra algorithms are provided in a POSTGRESQL database for undirected graph analysis, underground pipe network vertical section analysis can be performed based on the two algorithms, and the defects are shown in the following steps:
1. the pipeline data needs to be processed into a data structure conforming to a POSTGRESQL network data set;
2. after the communication path is inquired, the pipeline length and the horizontal projection length are calculated according to the analysis specification of the longitudinal section of the pipe network and the pipeline attribute which is not the final result but needs to be correlated;
because all the through paths can be traversed, and the analysis of the longitudinal section of the pipe network only needs to find the through paths, the query efficiency is not high under the requirement.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a method for analyzing the vertical section of the underground pipe network based on POSTGRESQL, which realizes the query and analysis algorithm of the vertical section attributes and the spatial information of two or more sections of the connected pipelines and has the characteristics of no need of processing data, quick query of the arrived path and recording of the vertical section attributes and the spatial attributes of the pipe network in the traversal process.
In order to achieve the purpose, the invention adopts the technical scheme that:
a underground pipe network longitudinal section analysis method based on POSTGRESQL comprises the following steps:
step 2, the number of the serial numbers of the two sections of pipelines in a pipeline table is inquired by sql, if the serial numbers of the two sections of pipelines are equal to 2, the step 3 is entered, and if the serial numbers of the two sections of pipelines are not equal to 2, the step exits and returns to a null record;
step 3, judging whether the two pipe sections are adjacent according to the starting and ending point numbers of the two pipe sections, entering the step 4 when the two pipe sections are adjacent, and entering the step 5 when the two pipe sections are not adjacent;
step 4, the sql inquires the serial numbers of two pipe sections in the pipeline table, the lengths of the pipe sections, the horizontal projection distance and other attribute information in the table to return;
and 5, the two pipe sections are not adjacent, all the path paths from the starting pipe section to the ending pipe section are inquired, the serial number, the length of the pipe section, the horizontal projection distance and other attribute information in the table are recorded in the traversing process, if the traversing reaches the ending pipe section, the input serial number, the length of the pipe section, the horizontal projection distance and the other attribute information in the table are used as a longitudinal section result to be returned, and meanwhile, the traversing is ended.
The step of step 5 is as follows:
step 5.1: initializing each variable:
adding line1_ s _ point and line1_ e _ point, and further recording all queried node arrays all _ check _ point _ array; adding an Objson object character string of pipeline1, carrying out a path array point _ path _ array, wherein a firstPoint in the Objson is line1_ s _ point, and a lastPoint is line1_ e _ point; adding an Objson object character string of pipeline1, carrying out a path array point _ path _ array, wherein a firstPoint in the Objson is line1_ e _ point, and a lastPoint is line1_ s _ point; adding line1_ s _ point and line1_ e _ point, and traversing node array current _ check _ point _ array;
and step 5.2: circularly judging whether the current _ check _ point _ array is greater than 0 or not, if so, entering the step 5.3, and otherwise, returning to the empty record;
step 5.3: identify init _ source, initialize cursor identify init _ source =0, function: identifying an opportunity to empty current _ check _ point _ array;
step 5.4: circularly traversing records with starting points or end points equal to current _ check _ point _ array in the pipeline table, recording duplication, and traversing each record temprow;
step 5.5: judging whether temprow reaches the tail of the cycle, if yes, entering step 5.2, and if not, entering step 5.6;
the step of step 5.6 is as follows:
step 5.6.1: acquiring a current traversal pieTable record temprow; meanwhile, whether init _ source is equal to 0 is judged, and if so, current _ check _ point _ array is assigned to null; then the init _ source is assigned to be 1 again; according to the starting and ending point temprow.s _ point of temprow, temprow.e _ point, if all _ check _ point _ array contains temprow.e _ point and does not contain temprow.s _ point, go to step 5.6.2, and if all _ check _ point _ array contains temprow.s _ point and does not contain temprow.e _ point, go to step 5.6.3.
The steps of step 5.6.2 are as follows:
step 5.6.2.1: judging that the all _ check _ point _ array contains temprow.e _ point and does not contain temprow.s _ point, and entering step 5.6.2.2 if the condition is met;
step 5.6.2.2: if the temperature s _ point is equal to line2_ e _ point or line2_ s _ point, the process proceeds to step 5.6.2.3 if the condition is satisfied, and proceeds to step 5.6.2.4 if the condition is not satisfied
Step 5.6.2.3: traversing each piece of data in point _ path _ array, querying json data of which the last lastpoint of the current json data is equal to temprow.e _ point, splicing the Objson object of temprow and the Objson object of line2 after the data, returning records, and exiting two-layer loop
Step 5.6.2.4: constructing an Objson object according to temprow, traversing each piece of data in point _ path _ array, screening out all the last piece of data with lastpoint equal to temprow.e _ point, respectively splicing each piece of data with the Objson object to generate new data, adding each piece of new data to a point _ path _ array, and then entering step 5.6.2.5;
step 5.6.2.5: adding temprow.s _ point to the arrays of all _ check _ point _ array and current _ check _ point _ array, and then entering step 5.5 again;
the step of step 5.6.3 is as follows:
step 5.6.3.1: judging that the all _ check _ point _ array contains temprow.s _ point and does not contain temprow.e _ point, and entering step 5.6.3.2 if the condition is met;
step 5.6.3.2: if the temperature _ point is equal to line2_ e _ point or line2_ s _ point, the process goes to step 5.6.3.3 if the condition is satisfied, and the process goes to step 5.6.3.4 if the condition is not satisfied;
step 5.6.3.3: traversing each piece of data in the point _ path _ array, inquiring json data of which the last lastpoint of the current json data is equal to temprow.s _ point, splicing an Objson object of temprow and an Objson object of line2 after the data, returning to records, and exiting from two-layer circulation;
step 5.6.3.4: constructing an Objson object according to temprow, traversing each piece of data in point _ path _ array, screening out all the last piece of data with lastpoint equal to temprow.s _ point, respectively splicing each piece of data with the Objson object to generate new data, adding each piece of new data to a point _ path _ array, and then entering step 5.6.3.5;
step 5.6.3.5: e _ point is added to the all _ check _ point _ array and current _ check _ point _ array arrays, and then step 5.5 is entered again.
And the step 2, the step 4, the step 5.2, the step 5.6.2.3 and the step 5.6.3.3 are all output.
The step 5.2 is a first layer cycle, and the step 5.5 is a second layer cycle.
The invention has the beneficial effects that:
1. the POSTGRESQL database self-defined function is adopted to realize the analysis of the vertical section of the pipe network, the method is novel, and the method is effectively integrated with the database.
2. The method realizes the analysis of two or more than two sections of pipe network longitudinal sections, the analysis result carries all information required by the display of the pipe network longitudinal sections, the method is flexible, and the method can meet the analysis requirements of all the pipe network longitudinal sections.
3. And (4) storing the original pipe point and pipeline data into a POSTGRESQL database, and calling the method to complete the analysis of the longitudinal section of the pipe network. Compared with the tap hypergraph software in the domestic GIS industry, the pipe network data is analyzed after being topologically constructed. Therefore, the method has the characteristic that analysis can be carried out without processing data.
4. The pipe network analysis method is developed based on a POSTGRESQL database process function, and the query efficiency is higher than that of a language (C + + \\ JAVA and the like) and database development mode. Compared with the domestic GIS industry faucet hypergraph software, the vertical section pipe network analysis efficiency is improved remarkably.
Drawings
FIG. 1 is a diagram of the overall flow of a profile analysis algorithm;
FIG. 2 is a common data structure definition in a profile analysis algorithm flow;
FIG. 3 is an overall flowchart of step five;
FIG. 4 is a flow chart of each substep of step five;
FIG. 5 is an overall flowchart of step 5.6;
FIG. 6 is a diagram showing the structure of each sub-step of step 5.6
FIG. 7 is a structural diagram of each sub-step of step 5.6.2;
fig. 8 is a diagram showing the structure of each sub-step of step 5.6.3.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
As shown in fig. 1, the overall flow structure diagram of the profile analysis algorithm has five logic steps: step 1, inputting a pipeline table name (marked as pipeline table) and numbers (marked as pipeline1 and pipeline 2) of two sections of pipelines to be inquired; step 2, the number of the serial numbers of the two sections of pipelines in a pipeline table is inquired by sql, if the serial numbers of the two sections of pipelines are equal to 2, the step 3 is entered, and if the serial numbers of the two sections of pipelines are not equal to 2, the step exits and returns to a null record; step 3, judging whether the two pipeline segments are adjacent according to the numbers of the starting point and the end point of the pipeline1 (marked as line1_ s _ point), the end point (marked as line1_ e _ point), the starting point of the pipeline2 (marked as line2_ s _ point) and the end point (marked as line2_ e _ point), entering the step 4 when the two pipeline segments are adjacent, and entering the step 5 when the two pipeline segments are not adjacent; step 4, sql queries the pipeline table for the Objson object with pipeline number pipeline1 (as in fig. 2) and returns the Objson object with pipeline number pipeline2 (as in fig. 2);
step 5, as shown in fig. 3, is an overall flowchart of step 5. As shown in fig. 4 as a block diagram of the algorithm steps in fig. 3, the sub-steps are as follows:
step 5.1: variables are initialized.
Adding line1_ s _ point and line1_ e _ point, and recording all queried node arrays (denoted as all _ check _ point _ array); adding an Objson object (the structure of which is shown in fig. 2) character string of pipeline1, and a path array (marked as point _ path _ array), wherein a firstPoint in the Objson is line1_ s _ point, and a lastPoint is line1_ e _ point; adding an Objson object character string of pipeline1, and entering a path array (marked as point _ path _ array), wherein a firstPoint in the Objson is line1_ e _ point, and a lastPoint is line1_ s _ point; adding line1_ s _ point and line1_ e _ point, and traversing the node array (marked as current _ check _ point _ array);
step 5.2: and circularly judging whether the condition is that current _ check _ point _ array is larger than 0, if so, entering the step 5.3, and otherwise, returning to the empty record.
Step 5.3: identification (marked as init _ source), initializing cursor identification init _ source =0, action: identify the opportunity to clear current _ check _ point _ array.
Step 5.4: circularly traversing the records with the starting point or the end point equal to current _ check _ point _ array in the pipeline table, recording the duplicate, and traversing each record (the record is recorded as temprow)
Step 5.5: judging whether temprow reaches the tail of the cycle, if yes, entering step 5.2, and if not, entering step 5.6
Step 5.6: as in fig. 5, as step 5.6 the overall flowchart. As shown in fig. 6 as a block diagram of the algorithm steps in fig. 5, the sub-steps are as follows:
step 5.6.1: acquiring a current traversal pieTable record temprow; meanwhile, whether init _ source is equal to 0 is judged, and if so, current _ check _ point _ array is assigned to null; then the init _ source is assigned to be 1 again; according to the judgment of the starting point and the ending point of temprow (denoted as temprow.s _ point, temprow.e _ point), if all _ check _ point _ array contains temprow.e _ point and does not contain temprow.s _ point, step 5.6.2 is entered, and if all _ check _ point _ array contains temprow.s _ point and does not contain temprow.e _ point, step 5.6.3 is entered into step 5.6.3
Step 5.6.2: as shown in fig. 7 as an overall flowchart of step 5.6.2, the sub-steps are as follows:
step 5.6.2.1: judging that all _ check _ point _ array contains temprow.e _ point and does not contain temprow.s _ point, if the condition is satisfied, go to step 5.6.2.2
Step 5.6.2.2: if temprow.s _ point is equal to line2_ e _ point or line2_ s _ point, the process proceeds to step 5.6.2.3 if the condition is satisfied, and proceeds to step 5.6.2.4 if the condition is not satisfied
Step 5.6.2.3: traversing each piece of data in point _ path _ array, inquiring json data of which the last lastpoint of the current json data is equal to temprow.e _ point, splicing the Objson object of temprow and the Objson object of line2 after the data, returning records, and exiting from two-layer loop
Step 5.6.2.4: constructing an Objson object according to temprow, traversing each piece of data in point _ path _ array, screening out all the last data with lastpoint equal to temprow.e _ point, respectively splicing each piece of data with the Objson object to generate new data, adding each piece of new data to a point _ path _ array, and then entering step 5.6.2.5
Step 5.6.2.5: add temprow.s _ point to the all _ check _ point _ array and current _ check _ point _ array arrays, then go to step 5.5 again
Step 5.6.3: as shown in fig. 8 as an overall flowchart of step 5.6.3, the sub-steps are as follows:
step 5.6.3.1: judging that the all _ check _ point _ array contains temprow.s _ point and does not contain temprow.e _ point, if the condition is satisfied, the flow proceeds to step 5.6.3.2
Step 5.6.3.2: if the temprow.e _ point is equal to line2_ e _ point or line2_ s _ point, the process proceeds to step 5.6.3.3 if the condition is satisfied, and proceeds to step 5.6.3.4 if the condition is not satisfied
Step 5.6.3.3: traversing each piece of data in point _ path _ array, querying json data of which the last lastpoint of the current json data is equal to temprow.s _ point, splicing the Objson object of temprow and the Objson object of line2 after the data, returning records, and exiting two-layer loop
Step 5.6.3.4: constructing an Objson object according to temprow, traversing each piece of data in point _ path _ array, screening out all the last data with lastpoint equal to temprow.s _ point, respectively splicing each piece of data with the Objson object to generate new data, adding each piece of new data to a point _ path _ array, and then entering step 5.6.3.5
Step 5.6.3.5: e _ point is added to the all _ check _ point _ array and current _ check _ point _ array arrays, and then step 5.5 is entered again.
In the above flow, step 2, step 4, step 5.2, step 5.6.2.3, and step 5.6.3.3 are all outputs. Step 5.2 is a first tier cycle and step 5.5 is a second tier cycle.
Claims (7)
1. A underground pipe network longitudinal section analysis method based on POSTGRESQL is characterized by comprising the following steps:
step 1, inputting a pipeline table name and serial numbers of two sections of pipelines to be inquired;
step 2, the number of the serial numbers of the two sections of pipelines in a pipeline table is inquired by sql, if the serial numbers of the two sections of pipelines are equal to 2, the step 3 is entered, and if the serial numbers of the two sections of pipelines are not equal to 2, the step exits and returns to a null record;
step 3, judging whether the two pipe sections are adjacent according to the starting and ending point numbers of the two pipe sections, entering the step 4 when the two pipe sections are adjacent, and entering the step 5 when the two pipe sections are not adjacent;
step 4, the sql inquires the serial numbers of two pipe sections in the pipeline table, the lengths of the pipe sections, the horizontal projection distance and other attribute information in the table to return;
and 5, the two pipe sections are not adjacent, all the path paths from the starting pipe section to the ending pipe section are inquired, the serial number, the length of the pipe section, the horizontal projection distance and other attribute information in the table are recorded in the traversing process, if the traversing reaches the ending pipe section, the input serial number, the length of the pipe section, the horizontal projection distance and the other attribute information in the table are used as a longitudinal section result to be returned, and meanwhile, the traversing is ended.
2. The underground pipe network profile analysis method based on POSTGRESQL as claimed in claim 1, wherein the step 5 is as follows:
step 5.1: initializing each variable:
adding line1_ s _ point and line1_ e _ point, and further recording all queried node arrays all _ check _ point _ array; adding an Objson object character string of pipeline1, carrying out a path array point _ path _ array, wherein a firstPoint in the Objson is line1_ s _ point, and a lastPoint is line1_ e _ point; adding an Objson object character string of pipeline1, carrying out a path array point _ path _ array, wherein a firstPoint in the Objson is line1_ e _ point, and a lastPoint is line1_ s _ point; adding line1_ s _ point and line1_ e _ point, and traversing node array current _ check _ point _ array;
step 5.2: circularly judging whether the current _ check _ point _ array is greater than 0 or not, if so, entering the step 5.3, and otherwise, returning to the empty record;
step 5.3: identify init _ source, initialize cursor identify init _ source =0, function: identifying an opportunity to empty current _ check _ point _ array;
step 5.4: circularly traversing records with starting points or end points equal to current _ check _ point _ array in the pipeline table, recording duplication removal, and traversing each record temprow;
step 5.5: and (5) judging whether temprow reaches the tail of the cycle, if so, entering the step 5.2, and if not, entering the step 5.6.
3. The underground pipe network profile analysis method realized based on POSTGRESQL according to claim 2, wherein the step 5.6 is as follows:
step 5.6.1: acquiring a current traversal pieTable record temprow; meanwhile, judging whether init _ source is equal to 0 or not, and if so, assigning current _ check _ point _ array to null; then the init _ source is assigned to be 1 again; according to the starting and ending point temprow.s _ point of temprow, temprow.e _ point, if all _ check _ point _ array contains temprow.e _ point and does not contain temprow.s _ point, go to step 5.6.2, and if all _ check _ point _ array contains temprow.s _ point and does not contain temprow.e _ point, go to step 5.6.3.
4. The underground pipe network profile analysis method realized based on POSTGRESQL according to claim 3, wherein the step 5.6.2 is as follows:
step 5.6.2.1: judging that the all _ check _ point _ array contains temprow.e _ point and does not contain temprow.s _ point, and entering step 5.6.2.2 if the condition is met;
step 5.6.2.2: if temprow.s _ point is equal to line2_ e _ point or line2_ s _ point, the process proceeds to step 5.6.2.3 if the condition is satisfied, and proceeds to step 5.6.2.4 if the condition is not satisfied
Step 5.6.2.3: traversing each piece of data in point _ path _ array, inquiring json data of which the last lastpoint of the current json data is equal to temprow.e _ point, splicing the Objson object of temprow and the Objson object of line2 after the data, returning records, and exiting from two-layer loop
Step 5.6.2.4: constructing an Objson object according to temprow, traversing each piece of data in point _ path _ array, screening out all the last piece of data with lastpoint equal to temprow.e _ point, respectively splicing each piece of data with the Objson object to generate new data, adding each piece of new data to a point _ path _ array, and then entering step 5.6.2.5;
step 5.6.2.5: add temprow.s _ point to the array all _ check _ point _ array and current _ check _ point _ array, and then go to step 5.5 again.
5. The underground pipe network profile analysis method realized on the basis of POSTGRESQL according to claim 3, wherein the step 5.6.3 is as follows:
step 5.6.3.1: judging that the all _ check _ point _ array contains temprow.s _ point and does not contain temprow.e _ point, and if the condition is met, entering step 5.6.3.2;
step 5.6.3.2: if the temperature _ point is equal to line2_ e _ point or line2_ s _ point, the process goes to step 5.6.3.3 if the condition is satisfied, and the process goes to step 5.6.3.4 if the condition is not satisfied;
step 5.6.3.3: traversing each piece of data in the point _ path _ array, inquiring json data of which the last lastpoint of the current json data is equal to temprow.s _ point, splicing an Objson object of temprow and an Objson object of line2 after the data, returning to records, and exiting from two-layer circulation;
step 5.6.3.4: constructing an Objson object according to temprow, traversing each piece of data in point _ path _ array, screening out all the last piece of data with lastpoint equal to temprow.s _ point, respectively splicing each piece of data with the Objson object to generate new data, adding each piece of new data to a point _ path _ array, and then entering step 5.6.3.5;
step 5.6.3.5: e _ point is added to the all _ check _ point _ array and current _ check _ point _ array arrays, and then step 5.5 is entered again.
6. The underground pipe network profile analysis method based on POSTGRESQL as claimed in claims 1, 2, 4 and 5, wherein the steps 2, 4, 5.2, 5.6.2.3 and 5.6.3.3 are all outputs.
7. The underground pipe network profile analysis method based on POSTGRESQL as claimed in claim 2, wherein the step 5.2 is a first layer cycle, and the step 5.5 is a second layer cycle.
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| US20170195188A1 (en) * | 2016-01-05 | 2017-07-06 | Schneider Electric Usa Inc. | System and methods for creating a geospatial network model in a client environment |
| CN107958104A (en) * | 2017-11-08 | 2018-04-24 | 天津大学 | By open GIS and the management of database sharing water supply network and hydraulic model method |
| CN108021709A (en) * | 2017-12-28 | 2018-05-11 | 广州中工水务信息科技有限公司 | A kind of drainage pipeline networks retrospective analysis method generally changed based on pipeline and search plain engine |
| WO2022100447A1 (en) * | 2020-11-13 | 2022-05-19 | 华南理工大学 | Automatic parsing and path analysis method for unit test code structure |
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