A method and system for calculating nodes in power grid path planning based on road network

Abstract

The present invention discloses a node space calculation method for power grid path planning based on a road network, comprising the following steps: based on the data characteristics of multiple road network open resources, obtaining multiple corresponding power grid path data sets; preprocessing the multiple power grid path data sets to obtain multiple corresponding preprocessed data sets; performing road network node calculation processing on the preprocessed data sets, and obtaining unit attribute assignment based on the minimum distribution network ring network unit; based on the unit attribute assignment, graphically displaying the obtained calculation results; compared with the prior art, the present invention proposes a distribution network power supply area analysis technology by using spatial geographic information data to assist installed capacity and load data values, and realizes a power grid space area analysis calculation method with road network nodes as the minimum unit, so that distribution network planning is more reasonable and planning efficiency is greatly improved.

Description

Technical Field

[0001] The invention belongs to the field of computers, and in particular relates to a calculation method and system for power grid path planning nodes. Background Art

[0002] The development of society and cities is inseparable from the supply of power resources. In order to better meet the sustainable social and economic development of the region, specific regions need to carry out detailed and comprehensive power planning to ensure the power supply needs of the region in the later stage of development and improve the power supply efficiency of the regional power grid. To this end, it is necessary to carry out spatial calculation of distribution network service nodes, analyze the power supply capacity of the planning area from the perspective of geographic space, and meet the power supply needs that meet the regional economic development.

[0003] In the process of calculating the service nodes of the distribution network, the actual situation of the power supply area is often compared and analyzed through the calculated installed capacity and load value table, ignoring the spatial geographic information of the planning area, which makes the node analysis and calculation unreasonable and affects the distribution of power supply capacity demand in the planning area.

[0004] In recent years, with the rapid development of spatial geographic information computing capabilities, the calculation and analysis methods for distribution network planning have gradually become richer. For example, the CN104463428B patent automatically obtains corresponding business data and spatial data through the intranet of the power grid company, and then processes the data for use in the practical process of power grid planning data processing. Another example is the distribution network node importance evaluation method, system, storage medium and computing device disclosed in the CN112365180A patent document, which abstracts the distribution network into a plane topology map, and then uses the weighted betweenness centrality as the basis to realize the evaluation of the importance of the distribution network nodes. In the practice of calculating the service nodes of the distribution network, there is still a lack of effective and reasonable application of spatial geographic information to the node calculation and processing of the distribution network path. Summary of the invention

[0005] In view of the defects existing in the above-mentioned prior art, the present invention proposes a method for quickly dividing the planning area into nodes by utilizing spatial geographic road network information, taking geographic spatial images and distribution network ring network CAD path distribution maps as processing data sources, combining installed capacity and load table data, and taking the minimum distribution network ring network unit as the basis to form unit attribute assignment.

[0006] A method for calculating node space of power grid path planning based on a road network comprises the following steps:

[0007] Based on the data characteristics of multiple road network open resources, multiple corresponding power grid path data sets are obtained;

[0008] Preprocessing the plurality of power grid path data sets to obtain a plurality of corresponding preprocessed data sets;

[0009] Performing network node calculation processing on the preprocessed data set, and obtaining unit attribute assignment based on the minimum distribution network ring network unit;

[0010] Based on the cell attribute assignment, the obtained calculation results are displayed graphically.

[0011] Further, the power grid path dataset includes a geospatial image dataset and a distribution network ring network CAD path distribution dataset;

[0012] Based on the data characteristics of multiple road network open source resources, multiple corresponding power grid path data sets are obtained, including:

[0013] The image data of the distribution network planning area is obtained from the open source resources of geospatial images to obtain the geospatial image dataset, and the distribution network ring network CAD path distribution dataset is obtained from the planning CAD open resources of the distribution network planning area.

[0014] Furthermore, the geospatial image dataset is preprocessed, including:

[0015] Through intelligent recognition technology and semantic segmentation technology, sample data of different levels of objects of interest are extracted from the road network objects in the target to obtain a geospatial image preprocessing data set.

[0016] Furthermore, the distribution network ring network CAD path distribution data set is preprocessed, including:

[0017] Data space benchmark unification, data type classification and multi-source data fusion. Data space benchmark unification is achieved through data space benchmark conversion; intelligent recognition technology and semantic segmentation technology complete data type classification and multi-source data fusion goals.

[0018] Furthermore, the pre-processed data set is subjected to a network node calculation process, and a unit attribute assignment is obtained based on a minimum distribution network ring network unit, specifically including:

[0019] Linearly abstract the road network surface object data in the acquired geospatial image preprocessing data set to obtain linear abstract data, wherein the linear abstraction is to process the road network surface data, extract the surface data center points, connect the various center points, form the surface data center line, and realize the linear abstraction process of converting the road network surface data into linear data;

[0020] Integrate the linear abstract data to form linear road network data, wherein the linear abstract data integration is to integrate all processed linear abstract data in the planning area and eliminate erroneous and messy data;

[0021] For the linear road network data, the coordinates of the intersection nodes are calculated by using the GIS spatial analysis function, and each node is taken as an object to form the smallest unit of the road network;

[0022] Based on the coordinates of the road network nodes, the pre-processed CAD distribution data set of the distribution network ring network path is superimposed to cut the distribution network ring network path. The minimum unit of the distribution network ring network is formed by the minimum unit of the road network. The specific cutting of the power grid ring network path is based on the GIS spatial analysis technology. The line is partitioned and cut according to the point coordinates to form the minimum unit of the distribution network ring network.

[0023] The numerical attributes of the installed capacity and load of each section of the distribution network ring are loaded, and the unit attribute assignment is obtained based on the minimum distribution network ring unit.

[0024] Furthermore, the linear abstract data integration is implemented based on arcpy and gdal modules in Python.

[0025] Furthermore, the method also includes: the calculation result display is graphically displayed based on Python.

[0026] Furthermore, the method also includes: providing an external achievement call output interface.

[0027] Furthermore, the method also includes: the external achievement calling output interface outputs the node information after calculation and processing, wherein the node information includes node number, coordinates, adjacent connections and corresponding attribute relationship values.

[0028] In another aspect of the present invention, a power grid planning node calculation system based on the above method is proposed, comprising:

[0029] A data acquisition module, which acquires corresponding multiple power grid path data sets based on the data characteristics of multiple road network open resources;

[0030] A data preprocessing module, which preprocesses the plurality of power grid path data sets to obtain a corresponding plurality of preprocessed data sets;

[0031] A data calculation and processing module performs network node calculation processing on the pre-processed data set, and obtains unit attribute assignment based on the minimum distribution network ring network unit;

[0032] The data display module graphically displays the obtained calculation results based on the unit attribute assignment.

[0033] Compared with the prior art, the present invention aims at the defects existing in the above-mentioned prior art, targets the installed capacity and load data of the distribution network provided by the distribution network, and semantically segments the road network objects in the target through intelligent recognition technology based on the geographic space image and the distribution network ring network CAD path distribution map, loads the numerical attributes of the installed capacity and load of the distribution network ring network, forms unit attribute assignment based on the minimum distribution network ring network unit, combines with the graphic display module, graphically displays the calculation results, and realizes the node calculation display function.

[0034] Such a calculation method for power grid planning nodes uses geographic space images and distribution network ring network CAD path distribution maps as basic data sources, and forms a road network object in the target by intelligently identifying and semantically segmenting data such as image maps of distribution network planning areas and planning CAD drawings of distribution network planning areas, and obtains corresponding sample data. At the same time, the distribution network ring network path in the CAD graphics is processed, and road network nodes are formed through spatial operations. The distribution network ring network CAD path distribution map is superimposed, and the distribution network ring network path is cut. The distribution network ring network minimum unit is formed from the road network minimum unit, and then the unit assignment is obtained by loading the installed capacity and load numerical attributes of each section of the distribution network ring network. The present invention effectively solves the above-mentioned technical defects and achieves the following beneficial technical effects:

[0035] (1) Aiming at the actual distribution network ring network path planning, the present invention proposes a geographic space image intelligent recognition semantic segmentation preprocessing technology and a road network node spatial calculation technology, and optimizes the method of calculating the service range of the service node, which has good actual market promotion value.

[0036] (2) Compared with the existing technology, it can combine geographic spatial data to improve the accuracy of the installed capacity and load numerical calculation results of the distribution network ring network, make the distribution network planning more reasonable with a higher degree of fit with the actual situation of the power grid spatial planning area, and greatly improve the efficiency of distribution network spatial planning.

[0037] (3) The present invention displays and outputs in a graphical manner to greatly optimize the utilization efficiency of data results, making the display more direct and user-friendly. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present disclosure will become readily understood. In the accompanying drawings, several embodiments of the present disclosure are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0039] Figure 1 is a flow chart showing a method for calculating nodes in power grid path planning according to an embodiment of the present invention;

[0040] Figure 2It is a schematic diagram showing a method for calculating nodes in power grid path planning according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] The optional embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0043] Embodiment 1:

[0044] like Figure 1 As shown, the present invention discloses a method for calculating node space of power grid path planning based on a road network. The method comprises the following steps:

[0045] Step 101, based on the data characteristics of multiple road network open resources, obtain corresponding multiple power grid path data sets;

[0046] Step 102, preprocessing the plurality of power grid path data sets to obtain a plurality of corresponding preprocessed data sets;

[0047] Step 103, performing network node calculation processing on the preprocessed data set, and obtaining unit attribute assignment based on the minimum distribution network ring network unit;

[0048] Step 104: Based on the cell attribute assignment, the obtained calculation results are graphically displayed.

[0049] Further, the power grid path dataset includes a geospatial image dataset and a distribution network ring network CAD path distribution dataset;

[0050] Based on the data characteristics of multiple road network open source resources, multiple corresponding power grid path data sets are obtained, including:

[0051] The image data of the distribution network planning area is obtained from the open source resources of geospatial images to obtain the geospatial image dataset, and the distribution network ring network CAD path distribution dataset is obtained from the planning CAD open resources of the distribution network planning area.

[0052] Furthermore, the data acquisition is performed by extracting the spatial intersection of the distribution network ring network CAD path distribution data set and the open source road network data (OpenStreetmap) to obtain the multiple power grid path data sets.

[0053] Furthermore, the geospatial image dataset is preprocessed, including:

[0054] Through intelligent recognition technology and semantic segmentation technology, sample data of different levels of objects of interest are extracted from the road network objects in the target to obtain a geospatial image preprocessing data set.

[0055] Furthermore, the distribution network ring network CAD path distribution data set is preprocessed, including:

[0056] Data space benchmark unification, data type classification and multi-source data fusion. Data space benchmark unification is achieved through data space benchmark conversion; intelligent recognition technology and semantic segmentation technology complete data type classification and multi-source data fusion goals.

[0057] Embodiment 2:

[0058] In this embodiment, Python language and database technology are used for program development. Figure 2 As shown, the specific implementation method of the node space calculation method for power grid path planning based on the road network in this embodiment is as follows:

[0059] Data input:

[0060] The geospatial image and the distribution network ring network CAD path distribution map are used as the processing data source. The information mainly includes the image map of the distribution network planning area, the planning CAD drawings of the distribution network planning area and other data sources;

[0061] Data preprocessing:

[0062] The geospatial image dataset is preprocessed, and sample data of different levels of objects of interest are extracted from the road network objects in the target through intelligent recognition technology and semantic segmentation technology to obtain a geospatial image preprocessing dataset; the distribution network ring network CAD path distribution dataset is preprocessed to obtain a distribution network ring network CAD path distribution dataset preprocessing dataset, and the distribution network ring network CAD preprocessing mainly includes three parts: data space benchmark unification, data type classification and multi-source data fusion. The data space benchmark unification is achieved through data space benchmark conversion; the intelligent recognition technology and semantic segmentation technology can achieve the data type classification and multi-source data fusion goals.

[0063] Spatial calculation of road network nodes:

[0064] The extracted road network surface objects are linearly abstracted; the linear abstraction is a linear abstraction process of processing the road network surface data, extracting the surface data center points, connecting the various center points, forming the surface data center line, and realizing the transformation of the road network surface data into linear data;

[0065] Abstract linear integration is used to form linear road network data (based on arcpy and gdal modules in python). The linear abstract data integration is to integrate all processed linear abstract data in the planning area and eliminate erroneous and messy data;

[0066] For the linear road network data, the coordinates of the intersection nodes are calculated through the GIS spatial analysis function, and each node is used as the object to form the smallest unit of the road network. Specifically, the opencv library in python is used to read the image, and then the conventional grayscale conversion, Gaussian blur, edge detection, and Hough transform are used before straight line detection.

[0067] Specifically, the Opencv library provides the HoughLines function to implement the Hough transform. The HoughLines function stores the output polar coordinate vector of the detected straight line in the data container. The straight line can be set to intersect the coordinate axis at points M and N respectively. In the polar coordinate system, the straight line MN can be expressed as:

[0068] ρ = x cos θ + y sin θ;

[0069] Among them, ρ and θ are the two coordinate dimensions of the polar coordinate system; from this, the polar coordinates of the intersection of points M and N can be calculated as and Therefore, the original straight line can be restored and the detection is completed.

[0070] Based on the coordinates of the road network nodes, the pre-processed CAD distribution data set of the distribution network ring network path is superimposed to cut the distribution network ring network path. The minimum unit of the distribution network ring network is formed by the minimum unit of the road network. The specific cutting of the power grid ring network path is based on the GIS spatial analysis technology. The line is partitioned and cut according to the point coordinates to form the minimum unit of the distribution network ring network.

[0071] The numerical attributes of the installed capacity and load of each section of the distribution network ring network are loaded, and the unit attribute assignment is formed based on the minimum distribution network ring network unit.

[0072] The calculation results are displayed graphically through the Python graphics display module.

[0073] Output of results: Output the calculated node information, including node number, coordinates, adjacent connections, and corresponding attribute relationship values.

[0074] The method provided in the above embodiment achieves the following beneficial technical effects:

[0075] (1) Aiming at the actual distribution network ring network path planning, this paper proposes the semantic segmentation preprocessing technology of geospatial image intelligent recognition and the spatial operation technology of road network nodes, and optimizes the method of calculating the service range of service nodes, which has great practical market promotion value.

[0076] (2) Compared with the existing technology, it can combine geographic spatial data to improve the accuracy of the installed capacity and load numerical calculation results of the distribution network ring network, make the distribution network planning more reasonable with a higher degree of fit with the actual situation of the power grid spatial planning area, and greatly improve the efficiency of distribution network spatial planning.

[0077] (3) The present invention displays and outputs in a graphical manner to greatly optimize the utilization efficiency of data results, making the display more direct and user-friendly.

[0078] Embodiment three:

[0079] In addition, the present invention also proposes a network path planning node space calculation system based on the method shown in the above embodiment, which includes:

[0080] A data acquisition module, which acquires corresponding multiple power grid path data sets based on the data characteristics of multiple road network open resources;

[0081] A data preprocessing module, which preprocesses the plurality of power grid path data sets to obtain a corresponding plurality of preprocessed data sets;

[0082] A data calculation and processing module performs network node calculation processing on the pre-processed data set, and obtains unit attribute assignment based on the minimum distribution network ring network unit;

[0083] The data display module graphically displays the obtained calculation results based on the unit attribute assignment.

[0084] Specifically, the power grid path data set includes a geospatial image data set and a distribution network ring network CAD path distribution data set; the data acquisition module acquires multiple corresponding power grid path data sets based on the data characteristics of multiple road network open source resources, specifically including:

[0085] The image data of the distribution network planning area is obtained from the open source resources of geospatial images to obtain the geospatial image dataset, and the distribution network ring network CAD path distribution dataset is obtained from the planning CAD open resources of the distribution network planning area.

[0086] The data preprocessing module preprocesses the geospatial image dataset, including:

[0087] Through intelligent recognition technology and semantic segmentation technology, sample data of different levels of objects of interest are extracted from the road network objects in the target to obtain a geospatial image preprocessing data set.

[0088] The data preprocessing module preprocesses the distribution network ring network CAD path distribution data set, including:

[0089] Data space benchmark unification, data type classification and multi-source data fusion. Data space benchmark unification is achieved through data space benchmark conversion; intelligent recognition technology and semantic segmentation technology complete data type classification and multi-source data fusion goals.

[0090] The data calculation and processing module performs network node calculation processing on the pre-processed data set, and obtains unit attribute assignment based on the minimum distribution network ring network unit, specifically including:

[0091] Linearly abstract the road network surface object data in the acquired geospatial image preprocessing data set to obtain linear abstract data, wherein the linear abstraction is to process the road network surface data, extract the surface data center points, connect the various center points, form the surface data center line, and realize the linear abstraction process of converting the road network surface data into linear data;

[0092] Integrate the linear abstract data to form linear road network data, wherein the linear abstract data integration is to integrate all processed linear abstract data in the planning area and eliminate erroneous and messy data;

[0093] For the linear road network data, the coordinates of the intersection nodes are calculated by using the GIS spatial analysis function, and each node is taken as an object to form the smallest unit of the road network;

[0094] Based on the coordinates of the road network nodes, the pre-processed CAD distribution data set of the distribution network ring network path is superimposed to cut the distribution network ring network path. The minimum unit of the distribution network ring network is formed by the minimum unit of the road network. The specific cutting of the power grid ring network path is based on the GIS spatial analysis technology. The line is partitioned and cut according to the point coordinates to form the minimum unit of the distribution network ring network.

[0095] The numerical attributes of the installed capacity and load of each section of the distribution network ring are loaded, and the unit attribute assignment is obtained based on the minimum distribution network ring unit.

[0096] The linear abstract data integration is implemented based on arcpy and gdal modules in Python.

[0097] The data display module is based on Python for graphical display.

[0098] Embodiment 4:

[0099] The present disclosure provides a non-volatile computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions can execute the method steps described in the above embodiment. For example, the method steps include:

[0100] Based on the data characteristics of multiple road network open resources, multiple corresponding power grid path data sets are obtained;

[0101] Preprocessing the plurality of power grid path data sets to obtain a plurality of corresponding preprocessed data sets;

[0102] Performing network node calculation processing on the preprocessed data set, and obtaining unit attribute assignment based on the minimum distribution network ring network unit;

[0103] Based on the cell attribute assignment, the obtained calculation results are displayed graphically.

[0104] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. The above-mentioned computer-readable medium may be contained in the above-mentioned electronic device; or it may exist alone without being assembled into the electronic device.

[0105] The above introduces the preferred embodiments of the present invention, which is intended to make the spirit of the present invention clearer and easier to understand, but is not intended to limit the present invention. All modifications, substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection outlined by the claims attached to the present invention.

[0106] The present invention has been disclosed above in terms of preferred embodiments, which are not intended to limit the present invention. Any technical solutions obtained by equivalent replacement or equivalent transformation shall fall within the protection scope of the present invention.

Claims

1. A method for calculating node space of power grid path planning based on road network, characterized in that: The following steps are involved: Based on the data characteristics of multiple road network open resources, multiple corresponding power grid path data sets are obtained; Preprocessing the plurality of power grid path data sets to obtain a plurality of corresponding preprocessed data sets; Performing network node calculation processing on the preprocessed data set, and obtaining unit attribute assignment based on the minimum distribution network ring network unit; Based on the unit attribute assignment, the obtained calculation results are graphically displayed; Wherein, the power grid path dataset includes a geospatial image dataset and a distribution network ring network CAD path distribution dataset; Based on the data characteristics of multiple road network open resources, multiple corresponding power grid path data sets are obtained, specifically including: obtaining image map data of the distribution network planning area from the geospatial image open source resources to obtain the geospatial image data set, and obtaining the distribution network ring network CAD path distribution data set from the planning CAD open resources of the distribution network planning area; The preprocessed data set is subjected to road network node calculation processing, and unit attribute assignment is obtained based on the minimum distribution network ring network unit, specifically including: linearly abstracting the road network surface object data in the acquired geospatial image preprocessed data set to obtain linear abstract data; integrating the linear abstract data to form linear road network data; for the linear road network data, calculating the coordinates of the cross nodes through the GIS spatial analysis function, and forming the minimum unit of the road network with each node as the object; based on the road network node coordinates, superimposing the preprocessed distribution network ring network path CAD distribution data set, cutting the distribution network ring network path, and forming the minimum unit of the distribution network ring network from the minimum unit of the road network; loading the numerical attributes of the installed capacity and load of each section of the distribution network ring network, and obtaining the unit attribute assignment based on the minimum distribution network ring network unit.

2. The method according to claim 1, characterized in that: Preprocessing of geospatial image datasets, including: Through intelligent recognition technology and semantic segmentation technology, sample data of different levels of objects of interest are extracted from the road network objects in the target to obtain a geospatial image preprocessing data set.

3. The method according to claim 1, characterized in that: Preprocess the distribution network ring network CAD path distribution data set, including: Data space benchmark unification, data type classification and multi-source data fusion. Data space benchmark unification is achieved through data space benchmark conversion; intelligent recognition technology and semantic segmentation technology complete data type classification and multi-source data fusion goals.

4. The method according to any one of claims 1 to 3, characterized in that: The linear abstraction is a linear abstraction process of processing the road network surface data, extracting the surface data center points, connecting the various center points, forming the surface data center line, and realizing the transformation of the road network surface data into linear data; The linear abstract data integration is to integrate all processed linear abstract data in the planning area and eliminate erroneous and messy data; The distribution network ring path is cut based on GIS spatial analysis technology, and the line is partitioned and cut according to the point coordinates to form the smallest unit of the distribution network ring.

5. The method according to claim 4, characterized in that The linear abstract data integration is implemented based on arcpy and gdal modules in Python.

6. The method according to claim 1, characterized in that The method also includes: the calculation result display is graphically displayed based on Python.

7. The method according to claim 1, characterized in that The method also includes: providing an external achievement calling output interface.

8. The method according to claim 7, characterized in that The method further comprises: the external achievement calling output interface outputs the node information after calculation and processing, wherein the node information comprises node number, coordinates, adjacent connections and corresponding attribute relationship values.

9. A power grid planning node calculation system according to any one of the methods of claims 1-8, characterized in that: include: A data acquisition module, which acquires corresponding multiple power grid path data sets based on the data characteristics of multiple road network open resources; A data preprocessing module, which preprocesses the plurality of power grid path data sets to obtain a corresponding plurality of preprocessed data sets; A data calculation and processing module performs network node calculation processing on the pre-processed data set, and obtains unit attribute assignment based on the minimum distribution network ring network unit; The data display module graphically displays the obtained calculation results based on the unit attribute assignment.

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