Pumped storage reservoir intelligent site selection method and system based on python

Abstract

The invention provides a python-based pumped storage reservoir intelligent site selection method and system. The method comprises the following steps: acquiring Dem basic topographic data and a remote sensing image of a pre-selected region; filling pits; the river reach identification sub-module carries out river reach identification; the sub-basin identification sub-module carries out sub-basin identification; generating a contour line; preliminarily determining a plurality of reservoirs in the pre-selected area by adopting a reservoir identification module; and a pumped storage power station reservoir matching identification module is adopted to carry out upper and lower reservoir matching on each preliminarily determined reservoir, all reservoir combinations meeting constraint conditions are identified, and multiple different reservoir intelligent site selection combination schemes are formed. According to the method, the reservoir site selection efficiency can be effectively improved, and the automation and intelligence of the reservoir site selection process are realized; besides, the method can also reduce traditional human input and experience errors, and improve the accuracy and reliability of site selection results, thereby effectively supporting large-scale multi-region pumped storage power station site selection and engineering decision.

Description

Technical Field

[0001] The invention belongs to the technical field of water conservancy and hydropower engineering, and specifically relates to a Python-based pumped storage reservoir intelligent site selection method and system. Background Art

[0002] At present, the development of pumped storage power stations has ushered in a construction peak. In the early stage of engineering construction, large-scale site selection work needs to be carried out, and the site selection of pumped storage power stations is crucial. Specifically, the site selection of pumped storage power stations needs to consider water source conditions, water head, reservoir capacity, distance-to-height ratio between upper and lower reservoirs, geographical location and geological conditions. It is a work with many influencing factors, time-consuming and labor-intensive, and huge workload. At present, the main method of manual site selection on topographic maps is to obtain the largest possible reservoir capacity and the smallest distance-to-height ratio between upper and lower reservoirs as the site selection criteria based on the direction of the terrain. Manual site selection has the problems of long time, errors caused by experience judgment, and insufficient screening range. Therefore, it is urgent to improve the efficiency of site selection, as well as the accuracy and reliability of site selection results. Summary of the invention

[0003] In view of the defects of the prior art, the present invention provides a Python-based pumped storage reservoir intelligent site selection method and system, which can effectively solve the above problems.

[0004] The technical solution adopted by the present invention is as follows:

[0005] The first aspect of the present invention provides a Python-based pumped storage reservoir intelligent site selection method, comprising the following steps:

[0006] Step S1, obtaining Dem basic terrain data and remote sensing images of a pre-selected area;

[0007] Step S2, based on the Dem basic terrain data, using a data preprocessing module to perform depression-filling processing on the remote sensing image to obtain a vector layer of the remote sensing image after the depression-filling processing;

[0008] Step S3, using the river section identification submodule in the water system identification module, based on preset river section identification related parameters, to identify and extract the river sections in the remote sensing image vector layer, thereby generating a river section distribution vector layer of the preselected area;

[0009] Step S4, using the sub-watershed identification submodule in the water system identification module, based on pre-set sub-watershed identification related parameters, to identify and extract the sub-watersheds in the river section distribution vector layer of the pre-selected area, thereby generating a sub-watershed distribution vector layer of the pre-selected area;

[0010] Step S5, generating contour lines in the sub-watershed distribution vector layer of the pre-selected area to obtain a sub-watershed distribution vector layer with contour lines;

[0011] Step S6, using a reservoir identification module to perform reservoir site selection analysis on the sub-basin distribution vector layer with contour lines, and preliminarily determine a number of reservoirs in the pre-selected area;

[0012] Step S7, using the pumped storage power station reservoir matching and identification module, matches the upper and lower reservoirs of each initially determined reservoir, identifies all reservoir combinations that meet the constraint conditions, and forms a variety of different reservoir intelligent site selection combination schemes.

[0013] Preferably, step S3 specifically comprises:

[0014] Step S3.1, the preset parameters related to the river section identification include a river section identification scale threshold;

[0015] Step S3.2, preliminarily identifying a number of river sections that meet the river section identification scale threshold in the remote sensing image vector layer;

[0016] Step S3.3, presetting river section screening conditions including the elevation range of the downstream outlet of the river section and the elevation difference range of the river section; using the river section screening conditions, filtering each of the initially identified river sections, filtering out river sections that do not meet the river section screening conditions, and obtaining a plurality of filtered river sections;

[0017] Step S3.4, for each river section after filtering, perform node segmentation:

[0018] For each filtered river section, take the downstream end point of the river section as the first node, and mark multiple nodes in sequence along the river section from downstream to upstream according to the preset node segmentation scale;

[0019] Step S3.5, generate a river section distribution vector layer of the pre-selected area for all river sections after the marked nodes.

[0020] Preferably, step S4 is specifically:

[0021] Step S4.1, using the sub-basin identification sub-module in the water system identification module, using the Dem basic terrain data and the D8 algorithm, analyzing the flow direction of each river section in the river section distribution vector layer to determine the flow direction of each river section;

[0022] Step S4.2, according to the flow direction of each river section, analyze and determine a number of water system intersections; take each water system intersection as the discharge outlet, determine the river sections associated with the water system intersection, and thus divide and form a number of sub-basins;

[0023] Step S4.3, extracting each sub-watershed of the pre-selected area, thereby generating a sub-watershed distribution vector layer of the pre-selected area.

[0024] Preferably, step S6 specifically comprises:

[0025] Step S6.1, determining the relevant constraint parameters for reservoir identification, including the preset threshold value of reservoir characteristic parameters and the scanning section; the scanning section is the target length ratio of the river section from downstream to upstream for identifying the reservoir along the river section;

[0026] Step S6.2, in the sub-basin distribution vector layer with contour lines, performing reservoir site selection analysis on each sub-basin in parallel;

[0027] Among them: For each sub-basin, the reservoir site selection analysis method is: from downstream to upstream, traverse each river section of the sub-basin in turn. For the current traversed river section, the reservoir site selection analysis method is:

[0028] Step S6.2.1, determining the target length interval of the current river section according to the scanned section;

[0029] Step S6.2.2, in the target length interval of the river section, the second node of the river section is used as the scanning starting node, and at the second node, an initial dam line is given in a direction perpendicular to the river section, and then, it is determined whether the initial dam line intersects with the contour line of the river section to form a closed area. If so, step S6.2.3 is executed; if not, the dam line is moved along the river section nodes with the divided nodes as the step length, so that the dam line is moved to the third node, and a dam line perpendicular to the direction of the river section is formed at the third node, and then, it is determined whether the dam line intersects with the contour line of the river section to form a closed area. If so, step S6.2.3 is executed; if not, the dam line is continued to be moved along the river section nodes until it moves to the last node of the target length interval of the river section, which means that the river section is not suitable for reservoir site selection, and the analysis of the current river section is terminated;

[0030] Step S6.2.3, determining the nearest intersection point between the current dam line and the left and right sides of the river section, and the line connecting the two intersection points forms a dam line segment; the dam line segment and the river section contour line form a closed polygonal area, which is initially used as the determined reservoir surface;

[0031] Step S6.2.4, using the area Dem of the reservoir surface, adopting the surface volume method, calculate the reservoir characteristic parameters;

[0032] Step S6.2.5, determine whether the reservoir characteristic parameters meet the preset threshold values ​​of the reservoir characteristic parameters. If so, retain the currently analyzed reservoir site; if not, continue scanning the next node of the current river section until the last node of the target length interval of the river section is scanned, indicating that the river section is not suitable as a reservoir site, and end the analysis of the current river section.

[0033] Preferably, the preset thresholds of the reservoir characteristic parameters include preset thresholds of dam length, dam height, dam top elevation, dam bottom elevation, reservoir surface area and reservoir capacity, as well as adjacent multipliers.

[0034] Preferably, when conducting reservoir site selection analysis for each river section, the following is also included:

[0035] In the process of identifying and calculating the reservoir capacity, the reservoir capacity range threshold is used as a constraint. When the calculated reservoir capacity meets the reservoir capacity range threshold, the river section where the current reservoir is located is jumped out, and the river section is extended from the current reservoir dam line position by the maximum length of the diagonal of the current reservoir area multiplied by the length of the adjacent multiplier, and the river section is automatically jumped to the end of the extended river section to continue the reservoir site analysis and identification.

[0036] Preferably, step S7 specifically includes:

[0037] The constraints are determined to be the distance-to-height ratio range and the reservoir capacity assessment coefficient range;

[0038] The distance-to-height ratio is the horizontal distance between the center points of the dam sites of the two reservoirs / the normal water level elevation difference between the upper and lower reservoirs;

[0039] The storage capacity assessment coefficient is calculated by the following formula:

[0040]

[0041] Where: S is the reservoir capacity assessment coefficient; v is the reservoir capacity; L is the dam length; H is the dam height.

[0042] The second aspect of the present invention provides a Python-based pumped storage reservoir intelligent site selection system, comprising:

[0043] Acquisition module, used to obtain Dem basic terrain data and remote sensing images of the pre-selected area;

[0044] A depression filling processing module is used to perform depression filling processing on the remote sensing image based on the Dem basic terrain data using a data preprocessing module to obtain a vector layer of the remote sensing image after the depression filling processing;

[0045] Water system identification module, including river section identification submodule and sub-basin identification submodule;

[0046] The river section identification submodule is used to identify and extract the river sections in the remote sensing image vector layer based on the preset river section identification related parameters, so as to generate the river section distribution vector layer of the pre-selected area;

[0047] The sub-watershed identification submodule is used to identify and extract the sub-watersheds in the river section distribution vector layer of the pre-selected area based on pre-set sub-watershed identification related parameters, so as to generate the sub-watershed distribution vector layer of the pre-selected area;

[0048] A contour line generation module, used for generating contour lines in the sub-basin distribution vector layer of the pre-selected area to obtain a sub-basin distribution vector layer with contour lines;

[0049] A reservoir identification module is used to perform reservoir site selection analysis on the sub-basin distribution vector map layer with contour lines, and preliminarily determine a number of reservoirs in the pre-selected area;

[0050] The reservoir matching and identification module of the pumped storage power station is used to match the upper and lower reservoirs of each initially determined reservoir, identify all reservoir combinations that meet the constraint conditions, and form a variety of different reservoir intelligent site selection combination plans.

[0051] The third aspect of the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the Python-based pumped-storage reservoir intelligent site selection method by executing the computer instructions.

[0052] A fourth aspect of the present invention provides a computer-readable storage medium, in which computer instructions are stored, and the computer instructions are used to enable a computer to execute the Python-based intelligent site selection method for pumped-storage reservoirs.

[0053] The Python-based pumped storage reservoir intelligent site selection method and system provided by the present invention has the following advantages:

[0054] The present invention can effectively improve the efficiency of reservoir site selection and realize the automation and intelligence of the reservoir site selection process; in addition, the present invention can also reduce traditional manpower input and experience errors, improve the accuracy and reliability of site selection results, and thus effectively support large-scale and multi-regional pumped-storage power station site selection and engineering decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The drawings constituting a part of the specification of the present application are used to provide a further understanding of the present application. The illustrative embodiments of the present application and their descriptions are only used to explain the present application and do not constitute an improper limitation on the present application.

[0056] Figure 1 It is a schematic diagram of the process flow of the intelligent site selection method for pumped storage reservoirs described in an embodiment of the present invention;

[0057] Figure 2 It is a schematic diagram of reservoir identification and matching results of the intelligent site selection system for pumped storage reservoirs described in an embodiment of the present invention;

[0058] Figure 3 It is a schematic diagram of the process of the intelligent site selection method for pumped storage reservoirs described in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0060] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The described embodiments are part of the embodiments of the present invention, not all of the embodiments. The specific implementation methods described are only used to explain the present invention and are not used to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0061] The present invention provides a method and system for intelligent site selection of pumped-storage reservoirs that is simple to operate, flexible, efficient and stable, and can realize automatic identification of water system reservoirs in a large-scale regional range and automatic matching of upper and lower reservoirs. The present invention can effectively improve the efficiency of reservoir site selection and realize the automation and intelligence of the reservoir site selection process; in addition, the present invention can also reduce traditional manpower input and experience errors, improve the accuracy and reliability of site selection results, and thus effectively support the site selection and engineering decision-making of large-scale and multi-regional pumped-storage power stations.

[0062] See also Figure 1 The present invention provides a Python-based pumped storage reservoir intelligent site selection method, comprising the following steps:

[0063] Step S1, obtaining Dem basic terrain data and remote sensing images of a pre-selected area;

[0064] Step S2, based on the Dem basic terrain data, using a data preprocessing module to perform depression-filling processing on the remote sensing image to obtain a vector layer of the remote sensing image after the depression-filling processing;

[0065] Step S3, using the river section identification submodule in the water system identification module, based on preset river section identification related parameters, to identify and extract the river sections in the remote sensing image vector layer, thereby generating a river section distribution vector layer of the preselected area;

[0066] Step S3 is specifically as follows:

[0067] Step S3.1, the preset parameters related to the river section identification include a river section identification scale threshold;

[0068] Step S3.2, preliminarily identifying a number of river sections that meet the river section identification scale threshold in the remote sensing image vector layer;

[0069] Step S3.3, presetting river section screening conditions including the elevation range of the downstream outlet of the river section and the elevation difference range of the river section; using the river section screening conditions, filtering each of the initially identified river sections, filtering out river sections that do not meet the river section screening conditions, and obtaining a plurality of filtered river sections;

[0070] Step S3.4, for each river section after filtering, perform node segmentation:

[0071] For each filtered river section, take the downstream end point of the river section as the first node, and mark multiple nodes in sequence along the river section from downstream to upstream according to the preset node segmentation scale;

[0072] Step S3.5, generate a river section distribution vector layer of the pre-selected area for all river sections after the marked nodes.

[0073] Step S4, using the sub-watershed identification submodule in the water system identification module, based on pre-set sub-watershed identification related parameters, to identify and extract the sub-watersheds in the river section distribution vector layer of the pre-selected area, thereby generating a sub-watershed distribution vector layer of the pre-selected area;

[0074] Step S4 is specifically as follows:

[0075] Step S4.1, using the sub-basin identification sub-module in the water system identification module, using the Dem basic terrain data and the D8 algorithm, analyzing the flow direction of each river section in the river section distribution vector layer to determine the flow direction of each river section;

[0076] Step S4.2, according to the flow direction of each river section, analyze and determine a number of water system intersections; take each water system intersection as the discharge outlet, determine the river sections associated with the water system intersection, and thus divide and form a number of sub-basins;

[0077] Step S4.3, extracting each sub-watershed of the pre-selected area, thereby generating a sub-watershed distribution vector layer of the pre-selected area.

[0078] Step S5, generating contour lines in the sub-watershed distribution vector layer of the pre-selected area to obtain a sub-watershed distribution vector layer with contour lines;

[0079] Step S6, using a reservoir identification module to perform reservoir site selection analysis on the sub-basin distribution vector layer with contour lines, and preliminarily determine a number of reservoirs in the pre-selected area;

[0080] Step S6 is specifically as follows:

[0081] Step S6.1, determine the constraint parameters related to reservoir identification, including preset thresholds and scanning sections of reservoir characteristic parameters; the scanning section is the target length ratio of the river section from downstream to upstream for identifying the reservoir along the river section; the preset thresholds of reservoir characteristic parameters include preset thresholds of dam length, dam height, dam top elevation, dam bottom elevation, reservoir surface area and reservoir capacity, and adjacent multipliers.

[0082] Step S6.2, in the sub-basin distribution vector layer with contour lines, performing reservoir site selection analysis on each sub-basin in parallel;

[0083] Among them: For each sub-basin, the reservoir site selection analysis method is: from downstream to upstream, traverse each river section of the sub-basin in turn. For the current traversed river section, the reservoir site selection analysis method is:

[0084] Step S6.2.1, determining the target length interval of the current river section according to the scanned section;

[0085] Step S6.2.2, in the target length interval of the river section, the second node of the river section is used as the scanning starting node, and at the second node, an initial dam line is given in a direction perpendicular to the river section, and then, it is determined whether the initial dam line intersects with the contour line of the river section to form a closed area. If so, step S6.2.3 is executed; if not, the dam line is moved along the river section nodes with the divided nodes as the step length, so that the dam line is moved to the third node, and a dam line perpendicular to the direction of the river section is formed at the third node, and then, it is determined whether the dam line intersects with the contour line of the river section to form a closed area. If so, step S6.2.3 is executed; if not, the dam line is continued to be moved along the river section nodes until it moves to the last node of the target length interval of the river section, which means that the river section is not suitable for reservoir site selection, and the analysis of the current river section is terminated;

[0086] Step S6.2.3, determining the nearest intersection point between the current dam line and the left and right sides of the river section, and the line connecting the two intersection points forms a dam line segment; the dam line segment and the river section contour line form a closed polygonal area, which is initially used as the determined reservoir surface;

[0087] Step S6.2.4, using the area Dem of the reservoir surface, adopting the surface volume method, calculate the reservoir characteristic parameters;

[0088] Step S6.2.5, determine whether the reservoir characteristic parameters meet the preset threshold values ​​of the reservoir characteristic parameters. If so, retain the currently analyzed reservoir site; if not, continue scanning the next node of the current river section until the last node of the target length interval of the river section is scanned, indicating that the river section is not suitable as a reservoir site, and end the analysis of the current river section.

[0089] In practical applications, when analyzing the reservoir site selection for each river section, it also includes:

[0090] In the process of identifying and calculating the reservoir capacity, the reservoir capacity range threshold is used as a constraint. When the calculated reservoir capacity meets the reservoir capacity range threshold, the river section where the current reservoir is located is jumped out, and the river section is extended from the current reservoir dam line position by the maximum length of the diagonal of the current reservoir area multiplied by the length of the adjacent multiplier, and the river section is automatically jumped to the end of the extended river section to continue the reservoir site analysis and identification.

[0091] Step S7, using the pumped storage power station reservoir matching and identification module, matches the upper and lower reservoirs of each initially determined reservoir, identifies all reservoir combinations that meet the constraint conditions, and forms a variety of different reservoir intelligent site selection combination schemes.

[0092] In this step, the constraints are determined to be the distance-to-height ratio range and the reservoir capacity assessment coefficient range;

[0093] The distance-to-height ratio is the horizontal distance between the center points of the dam sites of the two reservoirs / the normal water level elevation difference between the upper and lower reservoirs;

[0094] The storage capacity assessment coefficient is calculated by the following formula:

[0095]

[0096] Where: S is the reservoir capacity assessment coefficient; v is the reservoir capacity; L is the dam length; H is the dam height.

[0097] The present invention also provides a Python-based pumped storage reservoir intelligent site selection system, comprising:

[0098] Acquisition module, used to obtain Dem basic terrain data and remote sensing images of the pre-selected area;

[0099] A depression filling processing module is used to perform depression filling processing on the remote sensing image based on the Dem basic terrain data using a data preprocessing module to obtain a vector layer of the remote sensing image after the depression filling processing;

[0100] Water system identification module, including river section identification submodule and sub-basin identification submodule;

[0101] The river section identification submodule is used to identify and extract the river sections in the remote sensing image vector layer based on the preset river section identification related parameters, so as to generate the river section distribution vector layer of the pre-selected area;

[0102] The sub-watershed identification submodule is used to identify and extract the sub-watersheds in the river section distribution vector layer of the pre-selected area based on pre-set sub-watershed identification related parameters, so as to generate the sub-watershed distribution vector layer of the pre-selected area;

[0103] A contour line generation module, used for generating contour lines in the sub-basin distribution vector layer of the pre-selected area to obtain a sub-basin distribution vector layer with contour lines;

[0104] A reservoir identification module is used to perform reservoir site selection analysis on the sub-basin distribution vector map layer with contour lines, and preliminarily determine a number of reservoirs in the pre-selected area;

[0105] The reservoir matching and identification module of the pumped storage power station is used to match the upper and lower reservoirs of each initially determined reservoir, identify all reservoir combinations that meet the constraint conditions, and form a variety of different reservoir intelligent site selection combination plans.

[0106] The present invention also provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the aforementioned Python-based pumped storage reservoir intelligent site selection method by executing the computer instructions.

[0107] The present invention also provides a computer-readable storage medium, in which computer instructions are stored, and the computer instructions are used to enable a computer to execute the aforementioned Python-based pumped storage reservoir intelligent site selection method.

[0108] Two embodiments are described below:

[0109] Embodiment 1:

[0110] The embodiment of the present invention discloses a Python-based intelligent site selection method for pumped storage reservoirs, including modules such as automatic identification of water systems, automatic division of catchment areas (sub-basins), automatic identification of reservoirs, and automatic identification of pumped storage matching. The main steps are as follows:

[0111] S1: Obtain vector data such as DEM and remote sensing images of the pre-selected area; load the acquired DEM and remote sensing images into the intelligent site selection system so that the intelligent site selection system can intelligently select the site for the pumped storage reservoir based on the DEM basic terrain data. Among them, the DEM basic terrain data can be downloaded from the website channel. The developed system supports DEM data of UTM projection (WGS1984) and Gauss Kruger projection (CGCS2000).

[0112] S2: Use the data preprocessing module to fill the dem terrain data to eliminate the impact of potholes on the system model operation.

[0113] S3: Automatically identify river sections in pre-selected areas.

[0114] By using the system's water system identification module and setting the relevant parameters for water system identification, automatic water system identification is performed in the entire domain with one click, and river section feature data such as the number of river sections and river section length are extracted, and a vector layer is generated.

[0115] Specifically, when automatically identifying river sections, the river network is determined and extracted based on the threshold of the river section identification size scale. The smaller the threshold, the more river sections are automatically generated. The water system automatic identification module contains a river section filtering model, which can filter the river section length, downstream outlet elevation of the river section, and river section elevation difference. River sections that do not meet the screening conditions will be automatically deleted, and the target river sections will be retained, thereby ensuring the quality of the automatically generated river network.

[0116] S4: Divide the watershed area (sub-basin) based on the generated global water system.

[0117] The sub-basin identification module of the system is used to automatically divide several sub-basins with the intersection of water systems as the discharge outlet. The sub-basin characteristic data such as the number and area of ​​sub-basins are extracted and a vector layer is generated.

[0118] Specifically, when the catchment area (sub-basin) is automatically identified, the system uses the D8 algorithm using the dem grid data to perform flow direction analysis and basin extraction. When identifying the basin, the second node of each river section is used to start the identification to avoid node intersection. And each sub-basin is identified in parallel in the whole domain. When parallel computing, the waiting interval for each sub-basin calculation is 5 seconds.

[0119] S5: Use dem to generate contour lines in the entire domain and extract the contour lines of each sub-basin.

[0120] S6: Based on the above steps, automatic identification of reservoirs in the entire region is carried out, and basic parameter data such as dam length, dam height, dam top and bottom elevations, dam top and bottom coordinates, reservoir water surface area, and storage capacity assessment coefficient of each reservoir are calculated, identified and extracted.

[0121] Specifically, for automatic reservoir identification, the relevant constraint parameters of reservoir identification calculation must be determined before the identification calculation, including storage capacity range, scanning section, adjacent magnification, maximum dam height, maximum dam length, etc.; wherein, the scanning section is the length ratio of the river section from upstream to downstream or from downstream to upstream for identifying the site of the reservoir.

[0122] The identification process is as follows: the system adopts multi-segment node identification technology, based on the river section of each sub-basin, automatically takes the second node as the scanning starting node, and automatically scans and calculates the reservoir from downstream to upstream along the river section. The initial dam line is given in a direction perpendicular to the river section, and the subdivided nodes are used as the step length. The dam line is gradually moved along the river section nodes until a closed area intersecting with the contour lines in the basin is found, and the intersection points closest to the left and right sides of the river section are taken as the determined dam line. The dam line and the contour lines form a closed polygon, which is initially used as the identified reservoir surface. Using the reservoir surface area dem, the surface volume method is used to calculate the reservoir characteristic value and other related parameters, including dam length, dam height, dam top and bottom elevation, reservoir surface area, storage capacity, etc. It is judged whether the parameters meet the preset threshold requirements of the reservoir characteristic parameters. If they meet the requirements, the reservoir is retained, otherwise the reservoir is continued to be searched downward until the current sub-basin is fully identified. After the current sub-basin is identified, the system will automatically jump to the next sub-basin and continue to search for reservoirs in the same way until all sub-basins have completed reservoir identification. At this point, the system realizes automatic identification and calculation of reservoirs across the entire region with one click.

[0123] S7: After all reservoirs in the whole region are identified, the upper and lower reservoirs are automatically matched and identified. With the distance-to-height ratio, the head difference between the upper and lower reservoirs, and the horizontal distance between the upper and lower reservoirs as constraints and screening conditions, all reservoirs are automatically paired and combined, and all combinations that meet the constraints are identified as the final results of the pumped storage matching identification, completing the final site selection.

[0124] Specifically, for automatic identification of pumped-storage matching, one of its main screening conditions is the distance-to-height ratio: the horizontal distance between the center points of the dam sites of the two reservoirs / the difference in normal water level elevation between the upper and lower reservoirs.

[0125] This embodiment also provides a Python-based pumped storage reservoir intelligent site selection system, including the following modules:

[0126] The geographic image acquisition and processing module is used to acquire and load terrain elevation data, establish a geographic information system database, and process the terrain to provide basic spatial data for the intelligent site selection of pumped storage reservoirs.

[0127] The modeling module is used to construct a water system automatic identification model, a catchment area automatic identification model, a reservoir automatic identification model, an upper and lower reservoir matching model, etc. during the intelligent site selection process, and finally screen out an ideal reservoir site combination in the region.

[0128] In this system, multiple pumped storage site selection calculation model function libraries have been independently developed based on Python, including Contour.py, ContourClip.py, DeleteResv.py, DEM_Fill.py, Rch_Detect.py, Wsh_Detect.py, WSmainM.py, WSmainS.py, etc., and key application program interfaces have been encapsulated. The function library developed using Python mainly includes: data processing module (for loading and processing various data), hydrological calculation module, optimization module (implementing multi-objective optimization algorithm), and result visualization module (using libraries such as matplotlib or seaborn for visualization of results). In addition, the MapWinGIS open source engine is used to fully realize map interaction.

[0129] Embodiment 2:

[0130] This embodiment takes a pumped storage reservoir pre-selected area of ​​the present invention as an example and further explains with reference to the accompanying drawings:

[0131] According to the pumped storage site selection requirements, determine the pre-selected area for the construction of the pumped storage power station.

[0132] Get the dem data of the pre-selected area. The pre-selected site area of ​​a pumped storage reservoir is 413km 2 , the DEM data with an accuracy of 7.26m and a projection coordinate system of CGCS2000 is used as the basic terrain data for intelligent site selection.

[0133] After loading the dem data into this system, the preprocessing module is used to fill the dem data. The purpose of this step is to eliminate the influence of potholes on the system model operation.

[0134] The main process of the pumped storage reservoir site selection embodiment of the present invention is:

[0135] Automatic identification of water systems: The system obtains topographic and geological characteristics and water system characteristics based on DEM data, and uses the water system identification module to identify and extract the water system distribution and basic data of the pre-selected area, including the number of river sections, river section length, river distribution, etc.

[0136] In the water system identification process of the embodiment, the threshold of the river segment identification scale must be determined first. The smaller the threshold, the denser the identified river network and the more river segments are generated. In this embodiment, when the threshold is 30,000, 113 river segments are generated.

[0137] Automatic identification of sub-basins: Automatically divide the catchment area (sub-basin) based on the generated global water system. Using the system's sub-basin identification tool, the system uses the water system intersection as the discharge outlet, automatically divides the sub-basin in the pre-selected area, and extracts the sub-basin quantity, area, distribution and other characteristic data and vector layers.

[0138] In the sub-watershed identification process of the embodiment, the system adopts parallel computing to significantly improve the computing and identification speed. In this embodiment, based on the above-mentioned water system identification, a total of 110 sub-watersheds are generated.

[0139] Automatic identification of reservoirs. Before identifying a reservoir, the pre-processing tool of the reservoir identification module is used to generate contour lines in the entire domain using dem, and the contour lines of each sub-basin to be identified are extracted. After the contour lines are extracted, the reservoir identification tool is used to adopt the multi-segment node identification technology. Based on the selected river section of each sub-basin, the system automatically uses the second node as the scanning start node, and automatically scans and calculates the reservoir from downstream to upstream along the river section. The preset initial dam line is given in the direction perpendicular to the river section, and the dam line is gradually moved along the nodes on the selected river section with the node interval as the step length until a closed area intersecting with the contour lines in the basin is found, and the intersection closest to the left and right sides of the river section is taken as the determined dam line. When the dam line and the contour lines form a closed area, it is used as the initially identified reservoir surface. Using the reservoir surface area dem, the surface volume method is used to calculate the reservoir characteristic value and other related parameters, including dam length, dam height, dam top and bottom elevation, reservoir surface area, and reservoir capacity. Determine whether the parameters meet the preset threshold requirements of the reservoir characteristic parameters. If they meet the requirements, keep this reservoir. Otherwise, continue to search for and calculate reservoirs until all sub-basins are identified. After the current sub-basin is identified, the system will automatically jump to the next sub-basin to continue searching for reservoirs until all sub-basins have completed reservoir identification. At this point, the system realizes automatic identification and calculation of reservoirs in the entire domain with one click.

[0140] Before the reservoir identification process in the described embodiment, it is necessary to perform node segmentation on the generated river network in the water system identification stage, and then when identifying the reservoir, on the selected river section of each river basin to be identified, the dam line is gradually moved along the nodes on the selected river section with the node interval as the step length to carry out the reservoir identification calculation process.

[0141] In the automatic reservoir identification stage of the embodiment, it is necessary to set the relevant constraint parameters for reservoir identification calculation before identification, that is, preset thresholds for reservoir characteristic parameters, including storage capacity range, scanning section, maximum dam height, maximum dam length, adjacent multiples, etc.; wherein, the scanning section is the length ratio of the river section from upstream to downstream or from downstream to upstream to identify the reservoir. In the process of identifying and calculating storage capacity, the storage capacity range threshold is used as a constraint. When the calculated reservoir capacity meets the threshold requirement, the river section where the current reservoir is located is jumped out, and from the current reservoir dam line position, along the selected river section, the river section is extended by the maximum length of the diagonal of the current reservoir area multiplied by the adjacent multiples, and the identification calculation is automatically jumped to the end of the extended river section. Continue. Until the identification of reservoirs in the basin is completed. In this embodiment, the reservoir selection range is 10-20 million m 3 The scanning section is 100% scanning from downstream to upstream, the maximum dam height is 200m, and the maximum dam length is 1500m. After identification, a total of 77 reservoirs that meet the reservoir conditions are identified in this embodiment.

[0142] The reservoir automatic identification module of the embodiment adopts parallel computing. The parallel computing is to build a computing cluster system in the system, including a main controller and multiple computing nodes. The main controller can be implemented by a main control computer, and the main control computer includes an engine management center. The whole system is managed by the main program for the number of engines and the execution of task functions. The main controller automatically splits the computing tasks of each stage into several subtasks, automatically distributes them to each computing cluster node, and recovers the computing results of each node in real time. This parallel computing method greatly improves the efficiency of reservoir site selection, shortens the time of reservoir site selection, and solves the problem of large computing workload and extremely low efficiency of reservoir site selection.

[0143] Pumping matching recognition:

[0144] In this embodiment, the maximum distance-to-height ratio is 18, the head difference between the upper and lower reservoirs is 50-750m, and the maximum horizontal distance between the upper and lower reservoirs is 10km as the constraints and screening conditions. Using the pumped storage matching identification tool, all reservoirs are automatically paired and combined. Combinations that do not meet the screening range are automatically filtered out, and all combinations that meet the constraints are retained as the pumped storage matching identification results to complete the preliminary site selection work.

[0145] After pumping and storage matching identification, this embodiment screened out 12 pairs of reservoir combinations that meet the pumping and storage conditions.

[0146] The pumped storage matching identification module of the embodiment can effectively solve the survey and planning problems of the pumped storage reservoir combination, reduce the workload of artificial initial matching reservoir calculation and survey site selection, improve the site selection speed, and help to obtain the preferred site combination more quickly and accurately. This intelligent site selection system and method is used as technical support and assistance in the early site selection stage of the actual project. In the future, the pumped storage project construction can be further carried out in combination with the specific design requirements and kinetic energy parameter requirements of the pumped storage power station.

[0147] The above embodiment provides a Python-based pumped storage reservoir intelligent site selection method and system, which has broad application prospects.

[0148] The present invention provides a method and system for intelligent site selection of pumped storage reservoirs based on Python. The method and system are not limited by diversified terrain and regional scope. It only needs to provide DEM data. Based on DEM data, a multi-terrain reservoir intelligent site selection model is studied and adopted to realize the main functions of automatic identification of water systems, automatic identification of reservoirs, and pumped storage matching identification in the site selection area, and realize the automation and intelligence of the site selection process. This system integrates a variety of data analysis technologies, greatly improves the site selection efficiency, can reduce the manpower input and experience errors of traditional manual site selection, and improve the accuracy and reliability of site selection results, which can provide scientific and reliable site selection basis for engineering decision makers and optimize the decision-making process of pumped storage project construction.

[0149] The Python-based intelligent site selection method and system for pumped-storage reservoirs described in the present invention proposes and adopts multi-segment node recognition technology in the intelligent identification calculation of reservoirs, realizes rapid calculation and identification of reservoirs on each sited river section, and significantly improves the reservoir identification speed.

[0150] The Python-based pumped storage reservoir intelligent site selection method and system described in the present invention calculates the reservoir capacity curve based on DEM data using the grid method, which has higher calculation accuracy than the traditional contour method.

[0151] The embodiments described above are merely examples of the present invention. The protection scope of the present invention is not limited by the embodiments. The present invention may have various changes and variations. Any modification, equivalent substitution, improvement, etc. made to the same or similar design as the present invention within the spirit and principle of the present invention application shall fall within the protection scope of the present invention.

Claims

1. A Python-based pumped storage reservoir intelligent site selection method, characterized in that: The following steps are involved: Step S1, obtaining Dem basic terrain data and remote sensing images of a pre-selected area; Step S2, based on the Dem basic terrain data, using a data preprocessing module to perform depression-filling processing on the remote sensing image to obtain a vector layer of the remote sensing image after the depression-filling processing; Step S3, using the river section identification submodule in the water system identification module, based on preset river section identification related parameters, to identify and extract the river sections in the remote sensing image vector layer, thereby generating a river section distribution vector layer of the preselected area; Step S4, using the sub-watershed identification submodule in the water system identification module, based on pre-set sub-watershed identification related parameters, to identify and extract the sub-watersheds in the river section distribution vector layer of the pre-selected area, thereby generating a sub-watershed distribution vector layer of the pre-selected area; Step S5, generating contour lines in the sub-watershed distribution vector layer of the pre-selected area to obtain a sub-watershed distribution vector layer with contour lines; Step S6, using a reservoir identification module to perform reservoir site selection analysis on the sub-basin distribution vector layer with contour lines, and preliminarily determine a number of reservoirs in the pre-selected area; Step S7, using the pumped storage power station reservoir matching and identification module, matches the upper and lower reservoirs of each initially determined reservoir, identifies all reservoir combinations that meet the constraint conditions, and forms a variety of different reservoir intelligent site selection combination schemes.

2. According to claim 1, a Python-based pumped storage reservoir intelligent site selection method is characterized in that: Step S3 is specifically as follows: Step S3.1, the preset parameters related to the river section identification include a river section identification scale threshold; Step S3.2, preliminarily identifying a number of river sections that meet the river section identification scale threshold in the remote sensing image vector layer; Step S3.3, presetting river section screening conditions including the elevation range of the downstream outlet of the river section and the elevation difference range of the river section; using the river section screening conditions, filtering each of the initially identified river sections, filtering out river sections that do not meet the river section screening conditions, and obtaining a plurality of filtered river sections; Step S3.4, for each river section after filtering, perform node segmentation: For each filtered river section, take the downstream end point of the river section as the first node, and mark multiple nodes in sequence along the river section from downstream to upstream according to the preset node segmentation scale; Step S3.5, generate a river section distribution vector layer of the pre-selected area for all river sections after the marked nodes.

3. According to claim 1, a Python-based pumped storage reservoir intelligent site selection method is characterized in that: Step S4 is specifically as follows: Step S4.1, using the sub-basin identification sub-module in the water system identification module, using the Dem basic terrain data and the D8 algorithm, analyzing the flow direction of each river section in the river section distribution vector layer to determine the flow direction of each river section; Step S4.2, according to the flow direction of each river section, analyze and determine a number of water system intersections; take each water system intersection as the discharge outlet, determine the river sections associated with the water system intersection, and thus divide and form a number of sub-basins; Step S4.3, extracting each sub-watershed of the pre-selected area, thereby generating a sub-watershed distribution vector layer of the pre-selected area.

4. According to claim 1, a Python-based pumped storage reservoir intelligent site selection method is characterized in that: Step S6 is specifically as follows: Step S6.1, determining the constraint parameters related to reservoir identification, including the preset threshold of reservoir characteristic parameters and the scanning section; the scanning section is the target length ratio of the river section from downstream to upstream to identify the reservoir along the river section; Step S6.2, in the sub-basin distribution vector layer with contour lines, performing reservoir site selection analysis on each sub-basin in parallel; Among them: For each sub-basin, the reservoir site selection analysis method is: from downstream to upstream, traverse each river section of the sub-basin in turn. For the current traversed river section, the reservoir site selection analysis method is: Step S6.2.1, determining the target length interval of the current river section according to the scanned section; Step S6.2.2, in the target length interval of the river section, the second node of the river section is used as the scanning starting node, and at the second node, an initial dam line is given in a direction perpendicular to the river section, and then, it is determined whether the initial dam line intersects with the contour line of the river section to form a closed area. If so, step S6.2.3 is executed; if not, the dam line is moved along the river section nodes with the divided nodes as the step length, so that the dam line is moved to the third node, and a dam line perpendicular to the direction of the river section is formed at the third node, and then, it is determined whether the dam line intersects with the contour line of the river section to form a closed area. If so, step S6.2.3 is executed; if not, the dam line is continued to be moved along the river section nodes until it moves to the last node of the target length interval of the river section, which means that the river section is not suitable for reservoir site selection, and the analysis of the current river section is terminated; Step S6.2.3, determining the nearest intersection point between the current dam line and the left and right sides of the river section, and the line connecting the two intersection points forms a dam line segment; the dam line segment and the river section contour line form a closed polygonal area, which is initially used as the determined reservoir surface; Step S6.2.4, using the area Dem of the reservoir surface, adopting the surface volume method, calculate the reservoir characteristic parameters; Step S6.2.5, determine whether the reservoir characteristic parameters meet the preset threshold values ​​of the reservoir characteristic parameters. If so, retain the currently analyzed reservoir site; if not, continue scanning the next node of the current river section until the last node of the target length interval of the river section is scanned, indicating that the river section is not suitable as a reservoir site, and end the analysis of the current river section.

5. According to claim 4, a Python-based pumped storage reservoir intelligent site selection method is characterized in that: The preset thresholds of the reservoir characteristic parameters include preset thresholds of dam length, dam height, dam top elevation, dam bottom elevation, reservoir surface area and reservoir capacity, as well as adjacent multiples.

6. According to claim 4, a Python-based pumped storage reservoir intelligent site selection method is characterized in that: When analyzing the reservoir site selection for each river section, the following are also included: In the process of identifying and calculating the reservoir capacity, the reservoir capacity range threshold is used as a constraint. When the calculated reservoir capacity meets the reservoir capacity range threshold, the river section where the current reservoir is located is jumped out, and the river section is extended from the current reservoir dam line position by the maximum length of the diagonal of the current reservoir area multiplied by the length of the adjacent multiplier, and the river section is automatically jumped to the end of the extended river section to continue the reservoir site analysis and identification.

7. According to claim 1, a Python-based pumped storage reservoir intelligent site selection method is characterized in that: Step S7 is specifically as follows: The constraints are determined to be the distance-to-height ratio range and the reservoir capacity assessment coefficient range; The distance-to-height ratio is the horizontal distance between the center points of the dam sites of the two reservoirs / the normal water level elevation difference between the upper and lower reservoirs; The storage capacity assessment coefficient is calculated by the following formula: Where: S is the reservoir capacity assessment coefficient; v is the reservoir capacity; L is the dam length; H is the dam height.

8. A Python-based pumped storage reservoir intelligent site selection system, characterized in that: include: Acquisition module, used to obtain Dem basic terrain data and remote sensing images of the pre-selected area; A depression filling processing module is used to perform depression filling processing on the remote sensing image based on the Dem basic terrain data using a data preprocessing module to obtain a vector layer of the remote sensing image after the depression filling processing; Water system identification module, including river section identification submodule and sub-basin identification submodule; The river section identification submodule is used to identify and extract the river sections in the remote sensing image vector layer based on the preset river section identification related parameters, so as to generate the river section distribution vector layer of the pre-selected area; The sub-watershed identification submodule is used to identify and extract the sub-watersheds in the river section distribution vector layer of the pre-selected area based on pre-set sub-watershed identification related parameters, so as to generate the sub-watershed distribution vector layer of the pre-selected area; A contour line generation module, used to generate contour lines in the sub-basin distribution vector layer of the pre-selected area to obtain a sub-basin distribution vector layer with contour lines; A reservoir identification module is used to perform reservoir site selection analysis on the sub-basin distribution vector map layer with contour lines, and preliminarily determine a number of reservoirs in the pre-selected area; The reservoir matching and identification module of the pumped storage power station is used to match the upper and lower reservoirs of each initially determined reservoir, identify all reservoir combinations that meet the constraint conditions, and form a variety of different reservoir intelligent site selection combination plans.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the Python-based intelligent site selection method for pumped-storage reservoirs as described in any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute a Python-based intelligent site selection method for pumped-storage reservoirs as described in any one of claims 1 to 7.

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