Intelligent line selection method and system for urban power pipe network

Abstract

The invention relates to an intelligent line selection scheme for power planning and design. The intelligent line selection scheme comprises the following steps: acquiring required basic data from each data source; and carrying out data topology check on the collected basic data. And constructing an electric power pipe network by utilizing a spatial analysis function of the hypergraph. Setting an intelligent line selection analysis environment: configuring line selection analysis basic parameters; configuring a line selection analysis result; and acquiring parameters required by the intelligent line selection scheme. Based on the set intelligent line selection analysis environment, intelligent line selection is achieved by executing optimal path analysis, and the method comprises the steps that an analysis area, a starting point and an ending point related to intelligent line selection are set; filtering and screening pipe sections in the electric power pipe network according to the parameters required by the obtained intelligent line selection scheme; optimal path analysis is performed by configuring corresponding weight fields according to different schemes to generate a corresponding shortest path.

Description

Technical Field

[0001] The present application relates to the field of urban pipeline network design and construction, and specifically, to an intelligent line selection solution for power planning and design. Background Art

[0002] With the gradual improvement of urban construction, a large number of power pipelines have been pre-buried in the city, forming a huge and complex network. At the same time, the rivers, rail transit, viaducts and engineering projects under construction in the city will greatly affect the economic and time costs of power pipeline projects. Therefore, in the planning and design of power pipelines, how to most effectively use the existing pre-buried pipelines to find a reasonable route and comprehensively foresee the situations and problems that may be encountered in the entire project are the core requirements of power line selection planning.

[0003] However, in traditional power planning and design schemes, manual line selection is usually adopted, which places extremely high professional requirements on business personnel. The only way is to simplify the pre-buried pipes into the center line of the road and select the appropriate routing path based on the experience and judgment of municipal planning designers. The results obtained by this method are relatively rough, and it is difficult to provide accurate and quantitative calculation results to verify whether it is the optimal design, and it takes a lot of time to further perform manual calculations. Moreover, it is time-consuming and laborious to comprehensively process the information of various influencing factors and provide feasible solutions. These factors make the intelligent line selection of urban power pipelines a very complicated task.

[0004] At the same time, with the continuous development of GIS and BIM technologies, and the continuous improvement of urban twin data, people hope to build an intelligent system to process and analyze the various factor data of the pipeline network to achieve fast and intelligent line selection solutions. Summary of the invention

[0005] This application aims to build a multi-factor spatial GIS database of urban power pipeline networks, use GIS network analysis algorithms, and combine actual business experience to simulate various situations that may be encountered in real power projects, realize the selection of starting and ending points in any area of ​​the city, and intelligently provide a variety of feasible line selection plans to provide decision support.

[0006] According to a first aspect of the present application, there is provided an intelligent line selection method for power planning and design, comprising:

[0007] Collect the required basic data from various data sources;

[0008] Performing a data topology check on the collected basic data;

[0009] Use the spatial analysis function of hypergraph to construct the power pipeline network;

[0010] Set up the intelligent line selection analysis environment, including:

[0011] Configure basic parameters for line selection analysis;

[0012] Configure line selection analysis results;

[0013] Obtain the parameters required for the intelligent line selection solution; and

[0014] Based on the set intelligent line selection analysis environment, intelligent line selection is achieved by performing optimal path analysis, including:

[0015] Set the analysis area, start point, and end point involved in intelligent line selection;

[0016] Filtering and selecting the pipe sections in the power pipe network according to the parameters required by the acquired intelligent line selection scheme;

[0017] The optimal path analysis is performed by configuring the corresponding weight fields according to different schemes to generate the corresponding shortest path.

[0018] According to a second aspect of the present application, there is provided an intelligent line selection system for power planning and design, comprising a device for executing the intelligent line selection method as described in the first aspect.

[0019] This Summary is provided to introduce some concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to describe the manner in which the above and other advantages and features of the present invention can be obtained, a more particular description of the invention briefly described above will be presented by reference to specific embodiments of the invention shown in the accompanying drawings. Understanding that these drawings depict only typical embodiments of the invention and are not therefore to be considered limiting of its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings, in which:

[0021] Figure 1 A schematic flow chart of an intelligent line selection method for power planning and design according to an embodiment of the present application is shown.

[0022] Figure 2 A schematic diagram of a completed power pipeline network in an application example is shown.

[0023] FIG3( a ) shows a schematic diagram of the shortest path calculated by taking the length W1 as the weight of each edge of the power network.

[0024] FIG3( b ) shows a schematic diagram of a path with the lowest time cost calculated by taking the time cost W2 as the weight of each edge of the power network.

[0025] FIG3( c ) shows a schematic diagram of a path with the lowest economic cost calculated by taking the economic cost W3 as the weight of each edge of the power network.

[0026] FIG3( d ) shows a schematic diagram of a randomly generated path without using the shortest path algorithm. DETAILED DESCRIPTION

[0027] This application aims to build a multi-factor spatial GIS database of urban power pipeline networks, use GIS network analysis algorithms, and combine actual business experience to simulate various situations that may be encountered in real power projects, so as to select the starting and ending points in any area of ​​the city, and intelligently provide a variety of feasible line selection plans to provide decision support.

[0028] Generally speaking, this application constructs a city power pipeline network in a computer, performs spatial topological analysis on the spatial range of influences generated by the network edges (i.e., pipeline segments) and urban rivers, rail transit, viaducts, and projects under construction, and identifies which network pipeline segments will be affected by these influences. Then, based on actual conditions and relevant work experience, quantitative resistance values ​​such as "length", "economic cost", and "time cost" are assigned to each pipeline segment in the network, i.e., weights, thereby truly simulating the costs incurred in laying power pipelines.

[0029] Among them, power pipes refer to pipes that have been pre-buried underground. Each pipe section contains multiple pipe holes, some of which are occupied and some are free for new cables. The pipe section with power pipes does not need to be excavated again. It only needs to directly pass new cables through the unoccupied pipe holes in the power well. The time and economic cost required are greatly reduced compared to the direct cable burial method.

[0030] Direct buried cable: refers to the situation where there is no pre-buried power pipe in the pipe section, or the pipe holes in the pipe are all occupied or insufficient, then the road surface needs to be excavated and new cables buried. The time and economic cost required are greatly increased compared to the power pipe method.

[0031] Then, by using the shortest path algorithm in topological network analysis (such as the CH (Contraction Hierarchies) algorithm), the required length, economic cost and time cost are used as assignment weights respectively, and multiple calculations are performed to generate multiple optimal path solutions such as the shortest total length path, the path with the lowest economic cost, the path with the lowest time cost, etc., to achieve intelligent recommendation of multiple types of solutions.

[0032] At the same time, the current system can also exclude the pre-buried pipes with insufficient number of pipe holes from the analysis network by inputting the number of pipe holes required to be occupied. Or by specifying options such as avoiding rivers and projects under construction, multiple solutions that meet the restrictions can be output.

[0033] Therefore, the solution constructs a complete digital twin network of power pipelines containing various urban elements, truly simulates various situations that may be encountered when laying urban power pipelines, and quantitatively simulates the cost of laying each pipe section by assigning resistance values. In addition, a topological network analysis model is used to generate multiple optimal path solutions, providing efficient, accurate, and intelligent decision support for power pipeline planning and design.

[0034] Combine the following Figure 1 A schematic flow chart of an intelligent line selection method for power planning and design according to an embodiment of the present application is specifically introduced.

[0035] First, in step 102, the required basic data is collected from various data sources. The basic data may include river data, elevated roads, rail transit, planned roads, construction projects, railway lines, power wells, power pipe terminals, power pipes, direct buried cables, etc. These data can be obtained from relevant municipal departments, cable manufacturers, urban planning and design institutes, and other institutions and websites.

[0036] In this embodiment, the target is the urban area, so the basic data collected is within the urban area. However, it should be understood that this solution is not limited to the urban area, and can also be applied to the selection of power pipes in other administrative areas such as counties, districts, towns, etc. Correspondingly, the collection of the basic data can also be carried out in administrative areas such as counties, districts, towns, etc. These all fall within the scope of protection required by this application.

[0037] As for the basic data collected, they can be further divided into the following three categories:

[0038] Vector line data, such as power pipes and direct buried cables;

[0039] Vector point data, such as power wells and power pipe terminals;

[0040] Vector surface data, such as river surfaces, elevated road surfaces, rail transit surfaces, and construction project surfaces.

[0041] The data structure of the basic data includes, in addition to common system fields, different non-system fields for line selection related data processing.

[0042] Examples of the newly added non-system fields are as follows:

[0043] 1. Added Hole field and UsedHole field to determine whether the power pipe is available.

[0044] 2. Added the EconCost (economic cost) field to calculate the path with the lowest economic cost.

[0045] 3. Added the TimeCost field to calculate the path with the lowest time cost.

[0046] 4. A new direct burial shortest field is added, which is the direct burial cable length × expansion factor, which is used to calculate the shortest direct burial path.

[0047] 5. Added the Name field to identify the name of each route segment, such as rivers such as Zhongyang Lake and Pengyuepu Lake, and elevated roads such as the North-South Elevated Road and the Central Ring Elevated Road.

[0048] 6. Added fields for projects under construction, elevated roads, rail transit, and rivers. A field value of 1 indicates that the route segment has an intersection with projects under construction, elevated roads, rail transit, and rivers. A field value of 0 indicates that there is no intersection.

[0049] Then, in step 104, the collected basic data is subjected to a data topology check to prevent false nodes, redundant nodes, hanging lines, duplicate lines and other erroneous data. Common check contents are as follows:

[0050] 1. Check the connection between nodes and lines, and between lines and surfaces.

[0051] 2. Check whether the same node or the same line has been digitized multiple times.

[0052] 3. Check whether there are cracks or intersections between adjacent surface geometric objects.

[0053] 4. Check whether the polygon is closed.

[0054] 5. Check whether the edges of the polygon are dangling lines.

[0055] The topological processing is completed by removing false nodes, short hanging lines, duplicate lines, redundant points, etc. The processing is provided by corresponding functions in many GIS software.

[0056] Next, in step 106, the spatial analysis function of Supermap is used to construct the power pipeline network. For example, the network construction function is used to combine the power well, the end of the power pipe, the power pipe, and the direct buried cable to construct a network. Since the power pipe and the direct buried cable are not connected, the construction rule is set to not automatically break the line at the point. This is because the power pipe and the direct buried cable are two sets of networks in reality, and they are only connected at the power pipe well and the end of the pipe. Because the GIS model is a plane, the two sets of networks are topologically intersecting. If you choose to automatically break the line when the supermap constructs the network, the tool will automatically break the topologically intersecting lines according to the intersection points and connect them. Therefore, when building a network, you need to choose not to automatically break the line at the point.

[0057] Then, at step 108, the intelligent line selection analysis environment is set. This can be achieved by filling in values ​​or checking corresponding setting items in the SuperMap software.

[0058] Specifically, first, configure the basic parameters of line selection analysis, that is, configure the line selection network dataset, weight field, node identification field, arc identification field, start node identification field, end node identification field, arc filtering expression and other parameters in the backend code, and call functions.

[0059] Secondly, configure the line selection analysis results, that is, configure the result data source, node information field, and arc information field in the backend code.

[0060] Next, the parameters required for the intelligent line selection scheme are obtained, among which the parameters manually set by the user from the software front end are: whether to avoid engineering, whether to avoid rivers, whether to avoid rail transit, whether to avoid elevated roads, starting nodes, ending nodes, and the number of holes required. The parameters automatically obtained from the attribute table of the network data set (elevated roads, rail transit, planned roads, construction projects, power pipes, power wells, power pipe ends, and direct buried cables) are: Hole (number of holes) field, UsedHole (number of used holes) field, EconCost (economic cost) field, TimeCost (time cost) field, SmLength (segment length) field, engineering, rivers, rail transit, elevated roads, and other parameters, among which:

[0061] 1. Number of holes (only for power pipe): the total number of holes in each section of the power pipe (fill in based on the current situation)

[0062] 2. Number of used holes (only for power distribution pipes): The number of occupied holes in each pipe section of the power distribution pipes. The number of unoccupied holes is the total number of holes minus the number of occupied holes (fill in based on the current situation).

[0063] 3. (Under construction) project: Determine whether the pipeline section passes through a project under construction (determined by topological analysis). The project under construction will greatly affect the time and economic costs of pipeline construction, and it may even be necessary to wait until the project under construction is completed before pipeline construction can be carried out.

[0064] 4. Elevated: Determine whether the pipe section passes through an elevated route (determined through topological analysis). Passing through an elevated route will slightly increase the time and economic costs of laying the power pipes, and significantly increase the time and economic costs of direct buried cables.

[0065] 5. Rail transit: Determine whether the pipe section passes through the river (through topological analysis). Passing through the rail transit will slightly increase the time and economic costs of laying the power pipes, and significantly increase the time and economic costs of direct buried cables.

[0066] 6. River channel: Determine whether the pipe section passes through the river channel (determined by topological analysis). Passing through the river channel will slightly increase the time cost and economic cost of the power pipe laying, and significantly increase the time cost and economic cost of the direct buried cable.

[0067] 7. Time cost: The construction time required when the newly planned power pipeline passes through this section (set by the user based on actual conditions, affected by fields 3, 4, 5, and 6).

[0068] 8. Economic cost: The amount of construction cost required when the newly planned power pipeline passes through this section (set by the user based on actual conditions, affected by fields 3, 4, 5, and 6).

[0069] 9. Length: length of pipe construction and direct burial construction (automatically obtained from GIS model).

[0070] After configuring and obtaining the above parameter fields, the configuration of the intelligent line selection analysis environment is completed.

[0071] Then, in step 110, based on the set intelligent line selection analysis environment, intelligent line selection is realized by performing optimal path analysis. The so-called "optimal path analysis" refers to using the optimal path analysis component in the hypergraph to calculate multiple shortest path result routes between two sites by configuring the field identifying the network arc segment ID, the field identifying the network node ID, the field identifying the arc segment start node ID, the field identifying the arc segment end node ID, and the arc segment filtering expression. And different weight fields.

[0072] Specifically:

[0073] First, set the analysis area, starting point, end point and waypoint (if necessary) involved in intelligent line selection. For example, you can click the "Set Analysis Area" icon in the upper operation bar of the SuperMap software and select a rectangular area on the map as the analysis area. Then, click the "Set Start Point" and "Set End Point" icons and click on the corresponding positions on the map to set the starting point and end point. If the designer has a need, you can also click the "Set Waypoint" icon and select points on the map. If there are multiple waypoints, you can perform the above operations multiple times to set the multiple waypoints.

[0074] The start node and end node manually drawn by the user on the map are set as the start node identification field and end node identification field required for line selection analysis.

[0075] Next, each pipe section in the power pipe network is filtered and selected according to the parameters required by the acquired intelligent line selection scheme. That is:

[0076] First, according to parameters such as whether to avoid construction projects, whether to avoid river channels, whether to avoid rail transit, and whether to avoid elevated roads manually selected by the user, corresponding filtering conditions are generated to filter and screen data items in the power grid network. Subsequently, the filtering conditions are applied to each basic data belonging to the selected analysis area for filtering. For example, if the obstacle avoidance plan of avoiding construction projects is selected, then when generating a path, if the value of the "Construction Project" field corresponding to a certain section of the route in the network dataset attribute table is 1, indicating that this section of the route belongs to an under-construction project, then this section of the route is excluded from the calculation and other routes need to be searched again.

[0077] Then, according to the required number of holes set by the user (which is also the parameter obtained in the previous step), an arc filtering expression required for route selection analysis is set. For example, if the input required number of holes is n, and when generating a path, the UsedHole (number of used holes) field and Hole (number of holes) field in the network dataset of each section of the route will be automatically obtained, then if Hole - UsedHole < n, that is, (number of holes - number of used holes) < hole limit, then this section of the route will be excluded, that is, it will not participate in the calculation.

[0078] After the above preparations, the optimal path analysis is started. That is, the optimal path analysis is performed by configuring corresponding weight fields according to different schemes to generate the corresponding shortest path.

[0079] The optimal path analysis can adopt, for example, the CH (Contraction Hierarchies) algorithm.

[0080] The CH algorithm is an efficient algorithm for finding the shortest path in a network graph. This algorithm preprocesses the graph and decomposes it into a hierarchical structure, thereby reducing the search space and improving the calculation speed. The CH algorithm is based on the idea of a directed acyclic graph (DAG). When constructing the hierarchical structure, the vertices are contracted according to their priorities, and some important information in the original graph is retained.

[0081] Since the CH algorithm itself is a well-known shortest path algorithm in the computer field, therefore, its definition and content will not be specifically introduced here.

[0082] In the algorithm, the generated shortest path can be adjusted by setting the resistance value (i.e., weight) of each edge, thereby realizing complex scheme recommendations. In other words, according to different required schemes, different weights, such as length, time, and economy, can be configured for each route respectively. In this way, the optimal path analysis of different types of path schemes can be realized.

[0083] Specifically, according to the characteristics of the algorithm, the optimal path analysis can provide three different types of shortest path solutions based on three different parameters:

[0084] 1. First, configure the weight field to SmLength (segment length, which usually indicates the length of the power pipe) to set the weight of each edge in the power pipe network to the length, and then apply the algorithm to obtain solution 1: the shortest path of the sum of paths.

[0085] 2. First, configure the weight field to TimeCost (time cost, which is a subjectively defined value, usually understood as how much time it takes to build a 1-meter pipe section) to set the weight of each edge in the power pipe network to the time cost, and then apply the algorithm to obtain Solution 2: the path with the lowest time cost.

[0086] 3. By first configuring the weight field to EconCost (economic cost, which is a subjectively defined value, usually understood as how much money is needed to build a 1-meter pipe section), the weight of each edge in the power pipeline network is set to the economic cost, and then applying the algorithm to obtain solution three: the path with the lowest economic cost.

[0087] Since the CH algorithm is a commonly used shortest path algorithm in the computer field, its specific algorithm flow will not be repeated here. However, in order to facilitate the understanding of technicians, this solution provides a specific application example for illustration.

[0088] exist Figure 2 The figure shows a schematic diagram of a completed power pipeline network, in which the solid grey lines represent pre-buried power pipes; the dotted grey lines represent directly buried cables that need to be excavated; the light blue area represents the river; the starting point O represents the substation; the end point D represents a project that needs new power supply; the points represented by the remaining letters are power wells.

[0089] According to the above method of the present application, by establishing a network data set model and calling the shortest path algorithm (usually using the CH algorithm) after selecting different edge weights W according to different requirements, the optimal solution under the requirements is calculated and generated.

[0090] according to Figure 2 The network dataset model established for the power pipe network shown includes the following data tables:

[0091] Figure 2 Attribute data table of each pipe segment

[0092] The above table shows that different pipeline types, whether they cross the river, and other attributes have different effects on different edge weights W. The length of each pipe segment can be calculated based on the coordinates of each point, and the time cost and economic cost can be derived and filled in based on previous construction experience and the actual working conditions of the on-site survey, or can be automatically generated by training the corresponding cost calculation model using artificial intelligence technology.

[0093] Before calculating the shortest path, the user needs to set up the intelligent line selection analysis environment, such as "whether to avoid engineering", "whether to avoid rivers", "whether to avoid rail transit", "whether to avoid elevated roads", etc. In this example, as shown in the above data table, for the sake of simplicity, only the "river" factor is taken into account, and it is assumed that the user has set "not passing through the river" in the configuration stage (for example, the checkbox in front of this option is checked). Therefore, the IL and LD pipe sections crossing the river are excluded from the subsequent shortest path calculation process, thereby forming a new path planning scheme.

[0094] In addition, the application example does not take into account the "number of holes". If the user sets a limit on the number of holes, the calculation of the shortest path algorithm will exclude the pipe segments that do not meet the limit on the number of holes.

[0095] As mentioned above, according to the above configuration settings, corresponding arc segment filtering expressions can be generated to filter and screen each pipe segment in the data table, excluding pipe segments that do not meet user requirements to reduce unnecessary calculations.

[0096] After filtering the data table in the network dataset model according to the user configuration settings, the corresponding shortest path calculation is performed according to the weight previously configured by the user for the weight field. As shown in the data table, three weights are configured for each edge (pipeline) in this application example, namely, length W1, time cost W2, and economic cost W3.

[0097] Solution 1: When the user configures the weight field as SmLength (segment length), the CH algorithm uses the length W1 weight as the weight of each edge to calculate the optimal solution for the shortest path, i.e., FIG. 3(a) and Table 1.

[0098] Table 1

[0099]

[0100] As shown in Figure 3(a) and the corresponding Table 1, the preferred shortest path is OC->CG->GI->IL->LD. When this path is used, the total length is 52+77+13+37+55=234 meters, which is shorter than other path solutions. Therefore, it is selected as the optimal solution of "the shortest path sum". In Table 1, the total time cost and total economic cost are also added to facilitate users to make multi-faceted comparisons and trade-offs.

[0101] Solution 2: When the user configures the weight field as TimeCost, the CH algorithm uses the time cost W2 weight as the weight of each edge to calculate the optimal solution for the path with the lowest time cost, i.e., FIG. 3(b) and Table 2.

[0102] Table 2

[0103]

[0104] As shown in Figure 3(b) and the corresponding Table 2, the preferred shortest path is OA->AB->BE->EK->KM->MD. When this path is used, the total time cost is 1+1.5+1.5+2+2+4.2=12.2 hours, which is shorter than other paths. Therefore, it is selected as the optimal solution for the "path with the lowest time cost". In Table 2, the total length and total economic cost are also added to facilitate users to make multi-faceted comparisons and trade-offs.

[0105] Solution 3: When the user configures the weight field as EconCost (economic cost), the CH algorithm uses the economic cost W3 weight as the weight of each edge to calculate the optimal solution for the path with the lowest economic cost, namely Figure 3(c) and Table 3.

[0106] Table 3

[0107]

[0108] As shown in Figure 3(c) and the corresponding Table 3, the preferred shortest path is OA->AB->BJ->JN->KN->KM->MD. When this path is adopted, the total economic cost is 0.15+0.35+

[0109] 0.86+0.23+0.11+0.68+3=83,800 yuan. The economic cost of this path is lower than that of other paths. Therefore, it is selected as the optimal solution of "the path with the lowest economic cost". In Table 3, the total length and total time cost are also added to facilitate users to make multi-faceted comparisons and trade-offs.

[0110] Solution 4: Finally, for comparison, FIG3(d) shows an example of a randomly generated path without using the shortest path algorithm and its corresponding Table 4.

[0111] Table 4

[0112]

[0113] As shown in Figure 3(d) and the corresponding Table 4, the selected path is AB->BJ->JN->KN->HK->HI->IL->LD. In this scheme, the time-consuming and labor-intensive IL and LD sections were selected because the "river" factor was not considered. Therefore, when this path is adopted, it is not the optimal solution compared with the previous three shortest path schemes 1-3 in terms of total length, time cost or economic cost. Therefore, this scheme is not the optimal solution in all aspects and cannot meet the needs of users.

[0114] And as Figure 3(a)-3(c) As shown in Tables 1-3, the shortest path algorithm of the present application can flexibly set the weight of the pipe segment in each shortest path calculation by configuring the weight field to achieve the switching of the shortest path for different needs. Therefore, it can be understood that the optional weights are not limited to the above three types, and the optimal solution is not limited to the three aspects of length, time cost and economic cost. Other parameters can also be used as weights for optimal path analysis, which also fall within the scope of protection of this application. It is only for space considerations that I will not repeat them here.

[0115] Furthermore, the workload can be reduced by allowing users to set up the analysis environment during the configuration phase to filter out pipelines that do not meet the requirements.

[0116] In addition to the CH algorithm, other shortest path algorithms may be used to implement the above process, such as Dijkstra's method, which will not be described in detail here.

[0117] In addition, it should be understood that although in the above application examples, the optimal paths obtained by the three schemes of "shortest path sum", "path with lowest time cost" and "path with lowest economic cost" are different, in fact, there is a situation where the optimal paths finally calculated by two or even three of the schemes are the same path, and this situation is entirely possible.

[0118] Finally, in step 110, the shortest path after the optimal path analysis is output on the map, for example, the above three shortest path solutions 1-3 are summarized and displayed on the map. For example, the direct buried pipeline is marked with red color, and the existing power pipe is marked with green color, and then displayed on the map, and the solution number and total length are marked. When there is overlap among the three shortest paths, the same solution can be automatically merged.

[0119] Of course, buttons may also be provided to allow the user to choose to display only one or two of the three solutions, or to switch back and forth among the three solutions for display comparison.

[0120] In addition, if necessary, the calculated optimal path plan can be exported, for example, as a .shp file. The exported data can include the total length of the path, the length of the pipes, the length of the direct burial, the total investment, the construction period, whether it passes through the elevated road, whether it passes through the rail transit, whether it passes through the river, whether it passes through the construction project, etc., and each field can also be sorted by size.

[0121] In a preferred embodiment, basic map elements such as planned roads and railway lines can also be displayed on the map at the same time to enrich the map data and help users better understand the planning drawings.

[0122] It can be seen that the intelligent line selection solution of the present application can provide the following advantages:

[0123] 1. The system has built a spatial GIS database for urban power pipeline networks, and established a unified spatial model for multiple factors that may affect power line selection. It also simulates the scenarios encountered in actual power pipeline projects as realistically as possible.

[0124] 2. The system can select the starting and ending points in any area of ​​the model and generate multiple route selection schemes. Each scheme has its own advantages and disadvantages. You can switch and compare between multiple schemes and view the length, economic and time costs of each scheme; whether it crosses the river; how long the drainage pipe is crossed, and other information.

[0125] 3. The system provides a variety of rule options, including the number of holes required for this project. If the number of holes required exceeds the number of holes available for the power pipe, the power pipe cannot be used. It also provides options for whether to pass through rivers, projects under construction, etc. If selected, the solution will actively avoid these factors.

[0126] Although in the above embodiments, the scheme of the present application is described by taking the electric power line selection planning as an example, it should be understood that the scheme is also applicable to the line selection planning of other types of pipelines such as hydraulic, communication, and gas after modifying some attributes.

[0127] Although various embodiments are described above, it should be understood that they are intended to be exemplary rather than limiting. Those skilled in the relevant art(s) will appreciate that various modifications may be made in form and detail without departing from the spirit and scope of the present invention as defined in the appended claims. Therefore, the breadth and scope of the present invention disclosed herein should not be limited by the exemplary embodiments disclosed above, but should only be defined in accordance with the appended claims and their equivalents.

Claims

1. An intelligent line selection method for power planning and design, comprising: Collect the required basic data from various data sources; Performing a data topology check on the collected basic data; Use the spatial analysis function of hypergraph to construct the power pipeline network; Set up the intelligent line selection analysis environment, including: Configure basic parameters for line selection analysis; Configure line selection analysis results; Obtain the parameters required for the intelligent line selection solution; and Based on the set intelligent line selection analysis environment, intelligent line selection is achieved by performing optimal path analysis, including: Set the analysis area, start point, and end point involved in intelligent line selection; Filtering and selecting the pipe sections in the power pipe network according to the parameters required by the acquired intelligent line selection scheme; The optimal path analysis is performed by configuring the corresponding weight fields according to different schemes to generate the corresponding shortest path.

2. The intelligent line selection method according to claim 1, characterized in that: The basic data includes newly added non-system fields, and the non-system fields include: Hole: hole number field, and UsedHole: used hole number field, used to determine whether the power pipe is available; EconCost: economic cost field, used to calculate the path with the lowest economic cost; TimeCost: time cost field, used to calculate the path with the lowest time cost; The shortest direct burial field is the direct burial cable length × expansion factor, which is used to calculate the shortest direct burial path; Name: Name field, used to identify the name of each route; The fields for projects under construction, elevated roads, rail transit, and rivers. When the field value is 1, it means that this route segment has an intersection with the projects under construction, elevated roads, rail transit, and rivers. When the field value is 0, it indicates that there is no intersection.

3. The intelligent line selection method according to claim 1, characterized in that: The performing data topology check on the collected basic data includes: Check the connection between nodes and lines, and between lines and surfaces; Check whether the same node or line is digitized multiple times; Check whether adjacent surface geometric objects have cracks or intersections; Check if the polygon is closed; Checks if the edges of the polygon are dangling lines.

4. The intelligent line selection method according to claim 2, characterized in that: in Configuring the basic parameters of the line selection analysis includes configuring the line selection network data set, weight field, node identification field, arc identification field, start node identification field, end node identification field, arc filtering expression and other parameters, and calling functions in the backend code; The configuring the line selection analysis result includes configuring a result data source, a node information field, and an arc information field in the backend code; The parameters required for obtaining the intelligent line selection plan include obtaining the following parameters from the software front end: whether to avoid engineering, whether to avoid rivers, whether to avoid rail transit, whether to avoid elevated roads, starting nodes, ending nodes, and the required number of holes; wherein the following parameters are automatically obtained from the attribute table of the network data set: the number of holes Hole field, the number of used holes UsedHole field, the economic cost EconCost field, the time cost TimeCost field, the pipe section length SmLength field, engineering, rivers, rail transit, elevated roads and other parameters.

5. The intelligent line selection method according to claim 4, characterized in that: The filtering and screening of the pipe sections in the power pipe network according to the acquired parameters required by the intelligent line selection scheme includes: Generate corresponding filtering conditions to filter and screen the data in the power pipe network according to whether to avoid engineering, whether to avoid rivers, whether to avoid rail transit, whether to avoid elevated roads and other parameters; and The arc segment filtering expression required for line selection analysis is set according to the number of holes set by the user.

6. The intelligent line selection method according to claim 5, characterized in that: The optimal path analysis adopts the CH algorithm.

7. The intelligent line selection method according to claim 6, characterized in that: The optimal path analysis provides three different types of shortest path solutions based on the following three different parameters: 1) first configure the weight field to SmLength to set the weight of each edge in the power network to the length, and then apply the CH algorithm to obtain solution 1: the shortest path of the sum of paths; 2) first configuring the weight field as TimeCost to set the weight of each edge in the power network as the time cost, and then applying the CH algorithm to obtain solution 2: the path with the lowest time cost; 3) By first configuring the weight field to EconCost to set the weight of each edge in the power network to the economic cost, and then applying the CH algorithm to obtain solution three: the path with the lowest economic cost.

8. The intelligent line selection method according to claim 1, characterized in that: The intelligent line selection method further includes: In addition to the starting point and the end point, you can also set waypoints.

9. The intelligent line selection method according to claim 1, characterized in that: The intelligent line selection method further includes: Outputting the shortest path after the optimal path analysis on a map; and Allows the user to export the shortest path as a data file.

10. An intelligent line selection system for electric power planning and design, comprising a device for executing the intelligent line selection method according to any one of claims 1 to 9.

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