A rapid prediction method, terminal and medium for flooding of power distribution terminal equipment.

Abstract

This invention discloses a rapid prediction method, terminal, and medium for power distribution terminal equipment inundated by rainwater. The method constructs a two-dimensional hydrodynamic model based on geographical information and extreme rainfall, performs rainfall inundation simulation to obtain a complete urban simulation area rainwater distribution map. Based on the water depth, a core simulation area is selected from the complete simulation area and subjected to inundation simulation to obtain its rainwater distribution map. The maximum error between the water depth of water accumulation points in the core simulation area and the corresponding water accumulation points in the complete simulation area is determined, and it is judged whether the maximum error is less than or equal to a threshold Y. If not, the core simulation area is expanded or the simulation area is extended to form a new extended simulation area. The extended simulation area that meets the threshold requirement is taken as the optimal simulation area. In future rainstorm warnings, inundation simulation is performed only on each optimal simulation area, quickly predicting dangerous and high-risk flooded power outage equipment. Compared with complete area simulation, this method ensures simulation accuracy while significantly reducing simulation time, meeting the timeliness requirements of power grid companies for flood prevention and emergency response.

Description

Technical Field

[0001] This invention relates to the field of disaster early warning technology for power grid distribution equipment, specifically to a rapid prediction method, terminal, and medium for power distribution terminal equipment that is flooded by rain. Background Technology

[0002] Under the trend of global warming, rising global temperatures lead to increased surface evaporation and atmospheric water content, intensifying the global water cycle and resulting in a trend of increased intensity of extreme precipitation events. Urban flooding caused by torrential rains is becoming increasingly prominent, damaging not only the ecological environment but also posing a serious threat to human property. Urban power distribution networks directly serve end users and are closely related to the production and lives of the general public. With urban development, the number of power distribution terminal devices in urban areas is increasing, but the flexibility of their site selection is limited. As extreme precipitation events increase nationwide, accidents caused by torrential rains flooding power distribution terminal devices, resulting in equipment damage and power outages for users, are becoming more frequent. Therefore, there is an urgent need to study rapid prediction methods for power distribution equipment flooding.

[0003] In the field of meteorology, it is now possible to predict rainfall over a period of time. Using this predicted rainfall data to build hydrodynamic models for flood simulation can clearly identify the flood risk to power distribution equipment under predicted rainfall. Currently, many scholars use hydrodynamic models to simulate urban flooding. However, hydrodynamic models take a long time to compute on a large scale. They are often based on hydrodynamic principles and consider the eight surrounding cells of a grid, calculating the impact on each of the eight surrounding cells. Therefore, as the simulation area expands, the computational load increases proportionally to the square of the simulation area. For example, simulating rainfall across an entire city can take tens of hours, which is insufficient to meet the timeliness requirements of power grid companies' daily flood prevention efforts. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention proposes a rapid prediction method, terminal, and medium for power distribution equipment to be flooded by rainwater. The prediction speed is fast and accurate, which can improve the emergency response capability of power distribution equipment to rainwater disasters.

[0005] The first objective of this invention is achieved by the following technical solution:

[0006] The rapid prediction method for flooding of power distribution terminal equipment includes the following steps:

[0007] Step 1: Based on the minimum safe heights h1 and h2 of the power distribution terminal equipment cabinet and internal cable joints relative to the ground surface, classify the flooding power outage risk levels as no risk, medium risk, and high risk, and the corresponding water depth h should meet the following conditions:

[0008] ​Step 2: Based on urban geographic information data and extreme rainfall data, construct a two-dimensional hydrodynamic simulation model, perform rainfall inundation simulation on the entire urban area, and obtain a rain and flood distribution map of the complete simulation area;

[0009] Step 3: Based on the rainwater distribution map of the complete simulation area, and according to the water depth h ≥ h c Multiple core simulation regions are selected from the complete simulation region, with a critical depth h. c ≥h1, h is preferred c =h2, denoted as the core simulation region. The area, recorded in the complete simulation area, is related to... The simulation area corresponding to the area is k = 1, 2, ..., K, where K represents the number of core simulation areas;

[0010] Step 4: Based on urban geographic information data and extreme rainfall data, construct a two-dimensional hydrodynamic simulation model, conduct rainfall inundation simulation on the core simulation area, obtain the rainwater distribution map of the core simulation area, and determine each Waterlogging points in the area and The maximum error in the water depth at the corresponding water accumulation point within the area;

[0011] Step 5, Judgment The maximum error in the water depth of the area is checked to see if it is less than or equal to a threshold Y, where Y is typically taken as 0.1m. If so, this core simulation area is considered an optimal simulation area suitable for rapid prediction, and this optimal simulation area is denoted as... If the conditions are not met, the core simulation area is expanded to obtain an extended simulation area. Rainfall inundation simulation is then performed on the extended simulation area to obtain a rainwater distribution map. The values ​​of the elements in the extended simulation area that are related to the core simulation area are recorded. The simulation area corresponding to the area is The region is defined as m, where m is the expansion number + 1. After expanding the simulation region, rainfall inundation simulation is performed on the expanded simulation region to obtain the rainfall and flood distribution map of the expanded simulation region, thus determining... The depth of water accumulation at water accumulation points within the area and the corresponding The maximum error in the water depth at the corresponding water accumulation point within the area is used for error assessment again; if the requirement is not met, the simulation area is further expanded until an expanded simulation area that meets the threshold requirement is selected as the appropriate area for rapid prediction. district;

[0012] Step 6: Obtain rainfall forecast data for each Rainfall inundation simulations were conducted in different districts to obtain corresponding results. The district's rain and flood distribution map will Corresponding in the area The power distribution terminal equipment in the area at medium and high risk of flooding and power outage is predicted to be dangerous flooding power outage equipment and high-risk flooding power outage equipment, respectively.

[0013] As a preferred solution, in step 1, the bottom of the cabinet of the distribution terminal equipment is generally 0.3 - 0.5 m higher than the ground surface. Generally, h1 = 0.3 m is taken. The cable joint inside the distribution terminal equipment is generally about 0.2 m higher than the bottom of the distribution terminal equipment. Generally, h2 = 0.2 + 0.3 = 0.5 m is taken. Therefore, when the accumulated water depth h < h1, that is, h < 0.3 m, there is no risk. When the accumulated water depth reaches h1 ≤ h < h2, that is, 0.3 m ≤ h < 0.5 m, it is possible that the cabinet of the distribution terminal equipment has been reached. Therefore, it is a medium risk. When the accumulated water depth h ≥ h2, that is, h ≥ 0.5 m, the cable joint may be flooded. Therefore, it is a high risk.

[0014] As a preferred solution, in step 2, the geographic information data includes digital elevation model data, land cover data, surface roughness data, and surface runoff coefficient. Before constructing the two-dimensional hydrodynamic model, according to the roughness and runoff coefficient corresponding to different land types, the resampling technology of GIS is used to resample the land cover data (i.e., land use classification data) to generate surface roughness data and surface runoff coefficient.

[0015] As a preferred solution, in step 2, according to the rainstorm design model, the rainstorm intensity calculated by formula (1) is distributed to the entire urban area to obtain extreme rainfall data:

[0016] <​​​​​​​​​​​​​​​​​​​​​​​​The maximum error in the water depth at the corresponding water accumulation point within the area, or The depth of water accumulation at water accumulation points within the area and The maximum error in the water depth at the corresponding water accumulation point within the area is determined according to formulas (2) and (3):

[0021] Δ max =maxΔ ij (2)

[0022] Δ ij =x ij -y ij ,i∈[0,a],j∈[0,b](3)

[0023] In the formula: Δ max The maximum error in water depth is Δ. ij for District or The water depth at any water accumulation point within the area and the corresponding The errors in the water depth at the corresponding water accumulation points within the area, a and b, are respectively District or District or Total number of rows and columns of waterlogged points within the area, x ij for District or The depth of water accumulation at any point within the area, y ij In response The water depth at the corresponding water accumulation points within the area is simulated using grid points.

[0024] As a preferred option, in step 5, the expanded simulation area is obtained as follows:

[0025] Based on the core simulation area, each contour of the core simulation area is synchronously expanded outward with step sizes of d3 and d4 to obtain the extended simulation area; or based on the extended simulation area, a new extended simulation area is obtained by expanding it in the same way as above; d3 is longitude, d4 is latitude, and d3 and d4 are 0.2"-2", preferably 0.5"-1".

[0026] As a preferred option, in step 6, the rainfall forecast data is the rainfall forecast data for a future period of time released by the national meteorological department.

[0027] The second objective of this invention is achieved by the following technical solution: an electronic terminal, including a processor and a memory for storing processor-executable programs, wherein when the processor executes the program stored in the memory, it implements the rapid prediction method for rainwater flooding distribution terminal equipment described in the first objective of this invention.

[0028] The third objective of this invention is achieved by the following technical solution: a computer-readable storage medium storing a program, which, when executed by a processor, implements the rapid prediction method for flood-prone power distribution terminal equipment as described in the first objective of this invention.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1) Due to the influence of micro-topography, the inundation range during rainstorms is often fixed and concentrated in a limited number of locations. Current rainstorm inundation calculation methods based on hydrodynamic models, both domestically and internationally, have long calculation times on a large scale, sometimes reaching ten or even tens of hours, which is difficult to meet the timeliness requirements of power grid companies for daily flood prevention. This invention addresses the fact that rainstorm inundation ranges are often small and the affected areas are often fixed. It selects areas that are easily inundated to dangerous depths as the core simulation area and proposes a simulation method that is fast and accurate. During rainstorm warnings, inundation calculations are performed only on the selected optimal inundation simulation area to clearly identify the inundation and power outage risks of distribution terminal equipment at different locations in the core simulation area. This allows for rapid prediction of dangerous and high-risk flooded power outage equipment, improving the early warning capabilities of power grid companies.

[0031] 2) This invention involves four types of regions: the first is the complete urban simulation region, the second is the core simulation region, the third is the extended simulation region, and the fourth is the optimal simulation region. The core simulation region is denoted as... The area, recorded within the extended simulation area, is related to... The area corresponding to the district is The region (m represents the expansion number + 1) is defined as the region within the complete simulation area. The area corresponding to the district is The optimal simulation region is denoted as region. district; The region is based on h≥h c The core simulation zone, easily submerged to a dangerous depth, was selected from the complete simulation zone. However, accurately simulating the inflow and outflow of water at the boundary of the submerged simulation zone is difficult. Therefore, performing submersion simulation only on the core simulation zone would result in significant errors at the boundary, potentially failing to meet the required error tolerance. The depth of water accumulation in the area and The water depth within the area may vary considerably. The expanded simulation area is obtained by gradually enlarging the outline of the core simulation area. District or The depth of water accumulation at the water accumulation point in the area and The water depth error is obtained by comparing the water depth at corresponding water accumulation points in the area. When the maximum error in water depth does not exceed the threshold Y (Y = 0.1m), the corresponding core simulation area or extended simulation area is taken as the correct area. If the area exceeds a threshold, the core simulation area or the extended simulation area will be expanded; because The total area of ​​the district is significantly smaller than that of the complete urban simulation area. Therefore, when conducting rainstorm and flood inundation early warning for power distribution terminal equipment in the future, the flood simulation speed can be significantly accelerated, and the simulation results of the core simulation area will not produce significant deviations, which can meet the timeliness requirements of the power grid company's daily flood prevention.

[0032] 3) The present invention also provides an electronic terminal and a computer-readable storage medium, which can help realize the rapid prediction of flooding of power distribution terminal equipment and has good application value. Attached Figure Description

[0033] Figure 1 A flowchart of a rapid prediction method for flooding of power distribution terminal equipment.

[0034] Figure 2 This is a map showing the distribution of rain and flooding in parts of Changsha City in Example 1.

[0035] Figure 3 This is a schematic diagram of the three core simulation areas in Example 1.

[0036] Figure 4 This is an enlarged view of the optimal simulation area in Example 1. Detailed Implementation

[0037] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0038] Example 1;

[0039] See Figure 1-4 This embodiment takes a portion of Changsha City (Yuelu District, Furong District, northern Yuhua District, northern Tianxin District, eastern Changsha County, and southern Wangcheng District, with an area of ​​approximately 22"×13") as an example. The optimal simulation area is determined using the rapid prediction method for flooded power distribution equipment described in this invention. This method includes the following steps:

[0040] Step 1: Based on the minimum safe heights h1 and h2 of the power distribution terminal equipment cabinet and internal cable joints relative to the ground surface, classify the flooding power outage risk levels as no risk, medium risk, and high risk, and the corresponding water depth h should meet the following conditions:

[0041] ​The bottom of the basic cabinet of the distribution terminal equipment is generally 0.3 - 0.5 m above the floor level. Generally, h1 = 0.3 m is taken. The cable joints inside the distribution terminal equipment are generally about 0.2 m above the bottom of the distribution terminal equipment. Generally, h2 = 0.2 + 0.3 = 0.5 m is taken. Therefore, when the accumulated water depth h < h1, that is, h < 0.3 m, there is no risk. When the accumulated water depth reaches h1 ≤ h < h2, that is, 0.3 m ≤ h < 0.5 m, it is possible that the cabinet of the distribution terminal equipment has been reached. Therefore, it is a medium risk. When the accumulated water depth h ≥ h2, that is, h ≥ 0.5 m, the cable joints may be submerged. Therefore, it is a high risk.

[0042] Step 2: According to the urban geographic information data and extreme rainfall data, construct a two-dimensional hydrodynamic simulation model, conduct rainfall inundation simulation on the complete urban area, and obtain the rainwaterlogging distribution map of the complete simulation area;

[0043] Collect the geographic information data of Changsha City. The geographic information data includes DEM (Digital Elevation Model) data, surface cover data, surface roughness data, and surface runoff coefficient; before constructing the two-dimensional hydrodynamic model, according to the roughness and runoff coefficient corresponding to different land types, use the resampling technology of GIS to resample the surface cover data (i.e., land use classification data) to generate surface roughness data and surface runoff coefficient.

[0044] According to the rainstorm design model, distribute the rainstorm intensity calculated by Equation (1) to the complete urban area to obtain the extreme rainfall data:

[0045]

[0046] In the formula: q is the rainstorm intensity (unit: L / S·104m 2 ); P is the design return period (unit: year). For the distribution terminal equipment, P is not less than 50 years; t is the rainfall duration (unit: min); A1, b, C, n are relevant parameters affected by the local urban rainstorm characteristics, and the values in different regions are often different and can be determined by looking up information.

[0047] [[ID=二十]]For the distribution terminal equipment, the design return period cannot be less than 50 years. In this embodiment, the design return period P is taken as 50 years, and the rainfall duration t is taken as 3 h, that is, 180 min. The rainstorm intensity parameters of Changsha City are found to be A1 = 6.84, b = 8.277, C = 0.54, n = 0.5127. The rainstorm intensity of Changsha City once in 50 years for 3 hours calculated is 161.58 mm; according to the local standard DB43 / T 1628 - 2019 of Hunan Province, the recommended short-duration rainstorm design model in Hunan Province is the Chicago rain type. Therefore, distribute the rainfall amount ratio per hour according to the Chicago rain type to the entire Changsha City area;

[0048] Then, the two-dimensional hydrodynamic software was launched, and the geographic information data and the above extreme rainfall data were input to simulate the rainfall inundation of the entire Changsha area. High-risk and medium-risk areas were marked in black and gray respectively, and finally, a complete simulation map of the rain and flood distribution of Changsha was obtained, which was used as the benchmark for subsequent comparisons.

[0049] Step 3: Based on the rainwater distribution map of the complete simulation area in Step 2, calculate the water accumulation depth h ≥ h c Multiple core simulation regions are selected from the complete simulation region, with a critical depth h. c ≥h1, h is preferred c =h2, denoted as the core simulation region. The area, recorded in the complete simulation area, is related to... The simulation area corresponding to the area is k = 1, 2, ..., K, where K represents the number of core simulation areas;

[0050] The core simulation area is selected using the following method:

[0051] Using a rectangular outline, multiple core simulation areas are extracted with step sizes d1 and d2, where d1 is longitude and d2 is latitude, and d1 and d2 are taken as 0.2"-2". In this embodiment, d1 and d2 are taken as 1" (d2 = 1" ≈ 1.85km). The core simulation area must contain water depth h ≥ h in the complete simulation area. c In this embodiment, h is selected as a partial area. c =h2=0.5m, and satisfies: the water depth h in the core simulation area ≥ h c The total area of ​​the region is greater than or equal to 300m² 2 Preferably, it is greater than or equal to 2000m 2 In this embodiment, the water depth h ≥ h c The threshold for the total area of ​​the region is 2000m². 2 Three core simulation regions were obtained. The location and size of these core simulation regions are shown in Table 1. The optimal simulation region corresponding to this core simulation region will be determined by taking region ① (the area near the Hexi Lugu region) as an example.

[0052] Table 1. Location and Size of the Core Simulation Area

[0053]

[0054] Step 4: Based on urban geographic information data and extreme rainfall data, construct a two-dimensional hydrodynamic simulation model, conduct rainfall inundation simulation on the core simulation area, obtain the rainwater distribution map of the core simulation area, and determine each The depth of water accumulation at water accumulation points within the area and The maximum error in the water depth at the corresponding water accumulation point within the area;

[0055] Step 5: Determine whether the maximum error in the water depth is less than or equal to a threshold Y, typically 0.1m. If so, this core simulation area is considered an optimal simulation area suitable for rapid prediction, denoted as ______. If the conditions are not met, the core simulation area is expanded to obtain an extended simulation area. Rainfall inundation simulation is then performed on the extended simulation area to obtain a rainwater distribution map. The values ​​of the elements in the extended simulation area that are related to the core simulation area are recorded. The simulation area corresponding to the area is The region is defined as m, where m represents the expansion increment + 1. After expanding the simulation region, rainfall inundation simulation is performed on the expanded simulation region to obtain the rainfall and flood distribution map of the expanded simulation region, thus determining... The depth of water accumulation at water accumulation points within the area and the corresponding The maximum error in the water depth at the corresponding water accumulation point within the area is used for error assessment again; if the requirement is not met, the simulation area is further expanded until an expanded simulation area that meets the threshold requirement is selected as the appropriate area for rapid prediction. district.

[0056] The maximum error in water depth is The depth of water accumulation at water accumulation points within the area and The maximum error in the water depth at the corresponding water accumulation point within the area, or The depth of water accumulation at water accumulation points within the area and The maximum error in the water depth at the corresponding water accumulation point within the area is determined according to formulas (2) and (3):

[0057] Δ max =maxΔ ij (2)

[0058] Δ ij =x ij -y ij ,i∈[0,a],j∈[0,b](3)

[0059] In the formula: Δ max The maximum error in water depth is Δ. ij for District or The water depth at any water accumulation point within the area and the corresponding The errors in the water depth at the corresponding water accumulation points within the area, a and b, are respectively District or District or Total number of rows and columns of waterlogged points within the area, x ij for District or The depth of water accumulation at any point within the area, y ij In response The water depth at the corresponding water accumulation points within the area is simulated using grid points.

[0060] In this embodiment, flooding simulation is performed on the 5"×5" core simulation area ① selected in step 3 according to the method described in step 4, resulting in a rainwater distribution map of the core simulation area. This core simulation area is denoted as... The area, the complete simulation area and The simulation area corresponding to the area is denoted as calculate The water depth at the water accumulation points within the area is similar to that in step 2. The error between the water depths at the corresponding water accumulation points is shown in Table 1; the error range is calculated in step 4. The error range of the area is [-0.033, 0.131], and the maximum error exceeds the threshold Y = 0.1m. At this time, if only the 5"×5" core simulation area ① is used as the optimal simulation area, the water depth is much different from the complete simulation area, which will have a great impact on the prediction of flooding and power outage equipment, and is prone to misjudgment or omission. Therefore, it is necessary to expand the core simulation area to form an extended simulation area.

[0061] Therefore, based on the core simulation area ①, each contour of the core simulation area is synchronously expanded outward in steps d3 and d4, where d3 is longitude and d4 is latitude. d3 and d4 are taken as 0.2"-2", preferably 0.5"-1". In this embodiment, d3 and d4 are taken as 1", resulting in a new extended simulation area of ​​7"×7". The simulation area corresponding to the area is denoted as The area was calculated. Waterlogging points in the area and The error range of the water depth at the corresponding water accumulation point in the area is [-0.021, 0.053]. The maximum error is 0.053, which is less than the threshold Y = 0.1, thus meeting the threshold requirement. The simulation time is 20 minutes and 29 seconds. Therefore, the 7"×7" extended simulation area corresponding to core simulation area ① is selected as the optimal simulation area suitable for rapid prediction (denoted as...). district).

[0062] To better demonstrate the superiority of the optimal simulation area selected by this method, the flooding simulation area was further expanded to a new extended simulation area of ​​9"×9" with an error range of [-0.017, 0.051]. Although the error met the threshold requirement, the simulation time was 32 minutes and 47 seconds, which is about 60.0% longer than the extended flooding simulation area of ​​7"×7". However, the error range did not decrease significantly, which obviously does not meet the requirements of rapid emergency prediction. Extended simulation areas larger than 9"×9" will only increase the simulation time and will not significantly reduce the error range.

[0063] Table 1 Comparison of Simulation Results Parameters

[0064]

[0065] As shown in Table 1, when the simulation area size is 7"×7", the maximum error within the error range is 0.053m, which is less than 0.1m. However, the difference in water depth between high-risk and medium-risk areas in the flooding power outage risk classification is h2-h1=0.2m>0.053m. At this point, the impact of the error on the risk classification is already relatively small. Furthermore, as the simulation area increases further, the trend of error reduction becomes less pronounced, while the simulation time increases significantly. Therefore, choosing a 7"×7" simulation area as the optimal simulation area is more reasonable. middle District Figure 4 .

[0066] Step 6: Obtain rainfall forecast data for each Rainfall inundation simulation was conducted in the area, and corresponding results were obtained. The district's rain and flood distribution map will Corresponding in the area The power distribution terminal equipment in the area that is at medium to high risk of flooding and power outage is predicted to be dangerous and high-risk flooding and power outage equipment, respectively.

[0067] Rainfall forecast data is the prediction data of rainfall over a future period issued by the national meteorological department. Under a rainstorm scenario, most areas prone to flooding are fixed; therefore, the focus can be on predicting the inundation of power distribution equipment in flood-prone areas, reducing the size of the simulation area and accelerating the simulation time. When a rainstorm warning is issued in the future, only the optimal simulation areas determined in step 5 need to be analyzed. Rainfall inundation simulation can predict the risk of power outages due to flooding for most power distribution terminal equipment in different locations within a short period of time, and then... Corresponding in the area Within the area, select power distribution terminal equipment at medium and high risk levels and mark their locations. These equipment will be pre-reported as dangerous or high-risk flood-prone power outage equipment, respectively. Then, take targeted rain and flood prevention measures for these power distribution terminal equipment as early as possible, such as increasing the minimum safe height h1 and h2 of the power distribution terminal equipment cabinet and internal cable joints relative to the ground surface.

[0068] In other embodiments of the present invention, the storm design model can use other models in the "Outdoor Drainage Design Standard" (GB 50014-2021) to determine the extreme rainfall; when selecting the core simulation area, the step sizes d1 and d2 can take other values, such as 0.5"; when expanding the core simulation area or extending the simulation area, the step sizes d3 and d4 can also take other values, such as 0.5". In actual selection, it can be based on the size of the city area and the water depth h ≥ h in the simulation area. cThe optimal simulation area is determined by the density of the region. The smaller the step size, the more accurate the optimal simulation area, but the more iterations are required. Conversely, the larger the step size, the coarser the optimal simulation area, but the fewer iterations are required. The threshold Y for the water depth error can also be other than 0.1. Decreasing the threshold will improve the prediction accuracy of flooded power outage equipment, while increasing the threshold will decrease the prediction accuracy.

[0069] In other embodiments of the present invention, when determining the core simulation area and the optimal simulation area, other geometric contours with regular shapes besides rectangular contours can also be used, such as circles, rhombuses, trapezoids, etc. The expansion of the core simulation area or the extended simulation area is similar to that when using a rectangular contour; when selecting the core simulation area, taking a rectangular contour as an example, the water depth h ≥ h c The total area threshold of the region can also be changed, for example, 500m. 2 100m 2 Specifically, based on the water depth h ≥ h c The accuracy of predicting flood-related power outages will be improved as the total area decreases, based on the distribution and density of the regions.

[0070] Compared with the prior art, the present invention has the following technical effects:

[0071] In existing technologies, rainfall inundation simulations use a grid-like approach to represent the ground, with each grid surrounded by eight other grids. The simulation requires calculating the inflow from the surrounding eight grids for each grid. Therefore, the computational complexity of inundation simulations is approximately proportional to the square of the simulation area. For example, for an urban area of ​​900 km²... 2 The city's area is 10 times that of the urban area, or 9000 km². 2 For cities, the computation time required for flood simulation of only the core urban area is 1 / 100 of that for city-wide simulation. However, the optimal simulation area in this invention is determined by setting the severely flooded local area in the flood simulation as the core simulation area, and then gradually expanding the outline of the core simulation area to determine the optimal simulation area. Rainfall flood simulation is then performed on the selected optimal simulation area that meets the threshold conditions. Compared with the simulation of the entire city, this significantly reduces the size of the simulation area, greatly reduces the number of grids during simulation, and reduces the simulation time. At the same time, it has high prediction accuracy and does not lose accuracy, making it suitable for rapid emergency prediction of flooding of power distribution terminal equipment.

[0072] Example 2;

[0073] This embodiment discloses a terminal device, including a processor and a memory for storing processor-executable programs. When the processor executes the program stored in the memory, it implements a rapid prediction method for a power distribution terminal device subjected to rain and flooding as described in Embodiment 1. The method steps and the specific implementation process of each step are described in Embodiment 1, and will not be repeated here.

[0074] In this embodiment, the terminal device can be a desktop computer, a laptop computer, a smartphone, a PDA handheld terminal, a tablet computer, or other terminal devices.

[0075] Example 3;

[0076] This embodiment discloses a computer-readable storage medium storing a program. When the program is executed by a processor, it implements the rapid prediction method for flooded power distribution terminal equipment as described in the first objective of this invention. The method steps and the specific implementation process of each step are described in Embodiment 1, and will not be repeated here.

[0077] In this embodiment, the readable storage medium can be a disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), USB flash drive, portable hard drive, etc.

[0078] Matters not covered in this invention are existing technologies.

[0079] The above embodiments are only for illustrating the technical concept and features of the present invention. Their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be used to limit the scope of protection of the present invention. All equivalent changes or modifications made in accordance with the spirit and essence of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A rapid prediction method for flooding of power distribution terminal equipment, characterized in that: Includes the following steps: Step 1: Based on the minimum safe heights h1 and h2 of the power distribution terminal equipment cabinet and internal cable joints relative to the ground surface, classify the flooding power outage risk levels as no risk, medium risk, and high risk, and the corresponding water depth h should meet the following conditions: <h1、h1≤h<h2、h≥h2; Step 2: Based on urban geographic information data and extreme rainfall data, construct a two-dimensional hydrodynamic simulation model, perform rainfall inundation simulation on the entire urban area, and obtain a rain and flood distribution map of the complete simulation area; Step 3: Based on the rainwater distribution map of the complete simulation area, and according to the water depth h ≥ h c Multiple core simulation regions are selected from the complete simulation region, with a critical depth h. c ≥h1, denoted as the core simulation region. The area, recorded in the complete simulation area, is related to... The simulation area corresponding to the area is K represents the number of core simulation areas; Step 4: Based on urban geographic information data and extreme rainfall data, construct a two-dimensional hydrodynamic simulation model, conduct rainfall inundation simulation on the core simulation area, obtain the rainwater distribution map of the core simulation area, and determine each The depth of water accumulation at water accumulation points within the area and The maximum error in the water depth at the corresponding water accumulation point within the area; Step 5: Determine whether the maximum error in the water depth is less than or equal to the threshold Y. If it is, then this core simulation area is taken as the optimal simulation area, and the optimal simulation area is denoted as Y. If the conditions are not met, the core simulation area is expanded to obtain an extended simulation area. Rainfall inundation simulation is then performed on the extended simulation area to obtain a rainwater distribution map. The values ​​of the elements in the extended simulation area that are related to the core simulation area are recorded. The simulation area corresponding to the area is The region is defined as m, where m is the expansion number + 1. After expanding the simulation region, rainfall inundation simulation is performed on the expanded simulation region to obtain the rainfall and flood distribution map of the expanded simulation region, thus determining... The depth of water accumulation at water accumulation points within the area and the corresponding The maximum error in the water depth at the corresponding water accumulation point within the area is used to determine the error again; if it does not meet the requirements, the simulation area is further expanded until an expanded simulation area that meets the threshold requirement is selected as the corresponding area. district; Step 6: Obtain rainfall forecast data for each Rainfall inundation simulations were conducted in different districts to obtain corresponding results. The district's rain and flood distribution map will Corresponding in the area The power distribution terminal equipment in the area at medium and high risk of flooding and power outage is predicted to be dangerous flooding power outage equipment and high-risk flooding power outage equipment, respectively.

2. The rapid prediction method for flood-prone power distribution terminal equipment as described in claim 1, characterized in that: In step 2, the geographic information data includes digital elevation model data, land cover data, land surface roughness data, and land surface runoff coefficient.

3. The rapid prediction method for flooded power distribution terminal equipment as described in claim 1, characterized in that: In step 2, based on the rainstorm design model, the rainstorm intensity calculated according to equation (1) is distributed to the entire urban area to obtain extreme rainfall data: In the formula: q is the intensity of the rainstorm; P is the design return period; A1, b, C, and n are relevant parameters affected by the characteristics of local urban rainstorms.

4. The rapid prediction method for flooded power distribution terminal equipment as described in claim 1, characterized in that: In step 3, the core simulation area is selected as follows: Using a rectangular contour, multiple core simulation regions are extracted with step sizes d1 and d2, where d1 represents longitude and d2 represents latitude, and d1 and d2 are both 0.2"-2". Each core simulation region must contain water depth h ≥ h in the complete simulation region. c In a portion of the core simulation area, the water depth h ≥ h c The total area of ​​the region is greater than or equal to 300m² 2 .

5. The rapid prediction method for flooded power distribution terminal equipment as described in claim 1, characterized in that: In steps 4 and 5, the maximum error in the water depth is determined according to equations (2) and (3): Δ max =maxΔ ij (2) D ij =x ij -y ij ,i∈[0,a],j∈[0,b] (3) In the formula: Δ max The maximum error in water depth is Δ. ij for District or The water depth at any water accumulation point within the area and the corresponding The errors in the water depth at the corresponding water accumulation points within the area, a and b, are respectively District or District or Total number of rows and columns of waterlogged points within the area, x ij for District or The depth of water accumulation at any point within the area, y ij In response The depth of water accumulation at the corresponding water accumulation points within the area.

6. The rapid prediction method for flooded power distribution terminal equipment as described in claim 1, characterized in that: In step 5, the expanded simulation area is obtained as follows: Based on the core simulation area, each contour of the core simulation area is synchronously expanded outward with step sizes of d3 and d4 to obtain the extended simulation area; or based on the extended simulation area, a new extended simulation area is obtained by expanding it in the same way as above; d3 is longitude, d4 is latitude, and d3 and d4 are 0.2"-2".

7. The rapid prediction method for flooded power distribution terminal equipment as described in claim 1, characterized in that: The threshold Y is set to 0.1m.

8. A rapid prediction method for flood-prone power distribution terminal equipment as described in any one of claims 1-7, characterized in that: Critical depth h c =h2.

9. An electronic terminal, comprising a processor and a memory for storing a processor-executable program, wherein when the processor executes the program stored in the memory, it implements a rapid prediction method for a power distribution terminal device subject to rain and flooding as described in any one of claims 1-8.

10. A computer-readable storage medium storing a program that, when executed by a processor, implements a rapid prediction method for flood-prone power distribution terminal equipment as described in any one of claims 1-8.

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