Power plant rainfall calculation method and device, electronic equipment and storage medium

Abstract

The invention relates to a power plant rainfall calculation method and device, electronic equipment and a storage medium, and the method comprises the steps: taking a preset spatial resolution as a condition, and obtaining an actual remote sensing image map of a power plant; generating a land utilization category map of the power plant according to the actual remote sensing image map; on the basis of the land utilization category map, obtaining localization parameters meeting preset conditions, optimizing a preset rainfall runoff model according to the localization parameters, and constructing a rainfall runoff model of the power plant according to an optimization result; and based on the rainfall runoff model, multiplying the runoff volume generated by the power plant on the earth surface category by the land category area corresponding to the power plant to generate a space map layer of the rainfall of the power plant, and calculating the actual rainfall of the power plant according to the space map layer. Therefore, the problems that a rainfall capacity calculation method capable of covering each land utilization category of the power plant and reflecting the rainfall characteristic difference of each power plant is lacked in the related technology, the accuracy calculation of rainfall data and the difference between different power plants cannot be guaranteed and the like are solved.

Description

Technical Field

[0001] This application relates to the technical field of environmental protection, and particularly relates to a method, device, electronic device and storage medium for calculating rainfall in a power plant. Background Art

[0002] The collection and reuse of rainwater can reduce the use of conventional water resources, which is of great significance for power plants with insufficient annual water intake quotas and for completely achieving rain and sewage diversion in power plants. Therefore, the accuracy of front-end rainfall calculation will directly affect the back-end treatment and reuse methods and effects.

[0003] Currently, rainfall data is mostly based on local rainfall observation stations, generally annual average data or data covering a large spatial scale, and the spatio-temporal characteristics of the data are poor, unable to reflect the rainfall characteristics within a relatively short time or a small area. Existing rainfall calculations also mostly focus on a large-scale spatial area, and can construct a hydraulic model of the rainwater pipe network in the target area, and obtain the cumulative rainfall volume within a certain period of time according to the relationship curve between the water depth and time at the rainwater well nodes. The calculation spatial scale is the city.

[0004] However, the related technology lacks a rainfall calculation method that can cover various land use categories of power plants and reflect the differences in rainfall characteristics of each power plant, and cannot guarantee the accurate calculation of rainfall data and the differences between different power plants, which urgently needs to be improved. Summary of the Invention

[0005] This application provides a method, device, electronic device and storage medium for calculating rainfall in a power plant to solve the problems that the related technology lacks a rainfall calculation method that can cover various land use categories of power plants and reflect the differences in rainfall characteristics of each power plant, and cannot guarantee the accurate calculation of rainfall data and the differences between different power plants.

[0006] The first aspect of the embodiments of this application provides a method for calculating rainfall in a power plant, including the following steps: obtaining the actual remote sensing image of the power plant under the condition of a preset spatial resolution; generating the land use category map of the power plant according to the actual remote sensing image; based on the land use category map, obtaining the localized parameters that meet the preset conditions, and optimizing the preset rainfall runoff model according to the localized parameters to generate an optimization result, and constructing the rainfall runoff model of the power plant according to the optimization result; based on the rainfall runoff model, multiplying the runoff volume generated by the power plant on the surface category by the area of the corresponding land use category of the power plant to generate a spatial layer of the rainfall in the power plant, and calculating the actual rainfall of the power plant according to the spatial layer.

[0007] The above technical solution can obtain the actual remote sensing image map of the power plant, generate a land use category map, optimize the rainfall runoff model, and calculate the actual rainfall of the power plant based on the spatial layer, so as to accurately identify different land use categories inside the power plant within the small-scale space of the power plant, construct rainfall models suitable for different power plants, and then improve the accuracy, fineness and specificity of rainfall calculation in the power plant, providing a scientific basis for water resource management, flood control planning, etc.

[0008] Optionally, in an embodiment of the present application, the generating the land use category map of the power plant according to the actual remote sensing image map includes: performing at least one preprocessing of radiometric calibration, atmospheric correction, orthorectification, image fusion, image color homogenization mosaicking and image cropping on the remote sensing image map of the power plant to generate a preprocessed image; formulating an interpretation key corresponding to the power plant according to the online map of the power plant, and verifying whether the land use category on the remote sensing image is consistent with the actual land use category of the online map according to the interpretation key to generate a verification result; based on the verification result, optimizing the training samples to generate optimized samples, and supervised classifying the preprocessed image according to the optimized samples to generate the land use category map.

[0009] The above technical solution can preprocess the remote sensing image map and further perform supervised classification and interpretation on the preprocessed image, which is conducive to generating a high-precision land use category map and improving the accuracy, fineness and specificity of rainfall calculation in the power plant.

[0010] Optionally, in an embodiment of the present application, the obtaining the localized parameters that meet the preset conditions based on the land use category map includes: based on the land use category map, performing natural rainfall monitoring or conducting simulated rainfall experiments on each land use category in the power plant to obtain the localized parameters that meet the preset conditions.

[0011] The above technical solution can effectively obtain the localized parameters of the surface categories by performing natural rainfall monitoring or simulated rainfall experiments on different land use categories in the power plant, which not only helps to construct a more accurate rainfall runoff model for the power plant, but also provides a scientific basis for water resource management in the power plant.

[0012] Optionally, in an embodiment of the present application, the construction formula of the rainfall runoff model is:

[0013]

[0014] where p is the rainfall, i a is the initial rainfall loss value, and S is the maximum infiltration capacity.

[0015] The above technical solution can further improve the accuracy of rainfall calculation according to the conversion formula of the rainfall runoff model, and improve the accuracy, fineness and specificity of rainfall calculation in power plants.

[0016] Optionally, in an embodiment of the present application, before multiplying the runoff generated by the power plant on the surface category by the area of the land use category corresponding to the power plant to generate a spatial layer of the power plant rainfall, it further includes: obtaining the initial rainfall loss value in the rainfall runoff model; generating rainfall data of the SCS-CN rainfall runoff curve based on the daily rainfall data of the rainfall observation station to which the power plant belongs; generating the runoff generated by the power plant on the surface category according to the initial rainfall loss value and the rainfall data.

[0017] The above technical solution can generate the runoff generated by the power plant on the surface category according to the initial rainfall loss value and the rainfall data, which not only improves the accuracy and fineness of rainfall calculation, but also can effectively reflect the differences in rainfall characteristics between different power plants due to factors such as geographical location and land use methods.

[0018] An embodiment of the second aspect of the present application provides a power plant rainfall calculation device, including: an acquisition module, configured to acquire the actual remote sensing image of the power plant under the condition of a preset spatial resolution; a generation module, configured to generate the land use category map of the power plant according to the actual remote sensing image; a construction module, configured to obtain localized parameters that meet the preset conditions based on the land use category map, optimize the preset rainfall runoff model according to the localized parameters to generate an optimization result, and construct the rainfall runoff model of the power plant according to the optimization result; a calculation module, configured to multiply the runoff generated by the power plant on the surface category by the area of the land use category corresponding to the power plant based on the rainfall runoff model to generate a spatial layer of the power plant rainfall, and calculate the actual rainfall of the power plant according to the spatial layer.

[0019] Optionally, in an embodiment of the present application, the generation module includes: a preprocessing unit, configured to perform at least one of radiometric calibration, atmospheric correction, orthorectification, image fusion, image color homogenization mosaicking, and image cropping on the remote sensing image of the power plant to generate a preprocessed image; a verification unit, configured to formulate an interpretation mark corresponding to the power plant according to the online map of the power plant, and verify whether the land use category on the remote sensing image is consistent with the actual land use category of the online map according to the interpretation mark to generate a verification result; an optimization unit, configured to optimize the training samples based on the verification result to generate optimized samples, and perform supervised classification on the preprocessed image according to the optimized samples to generate the land use category map.

[0020] Optionally, in an embodiment of the present application, the building module includes: a monitoring unit configured to perform natural rainfall monitoring or conduct simulated rainfall experiments on each land use category within the power plant based on the land use category map to obtain the localized parameters that meet the preset conditions.

[0021] Optionally, in an embodiment of the present application, the construction formula of the rainfall runoff model is:

[0022]

[0023] where p is the rainfall, i a is the initial rainfall loss value, and S is the maximum infiltration capacity.

[0024] Optionally, in an embodiment of the present application, it further includes: a data acquisition module configured to obtain the initial rainfall loss value in the rainfall runoff model before multiplying the runoff volume generated by the power plant on the surface category by the area of the land use category corresponding to the power plant to generate a spatial layer of the power plant rainfall; a data generation module configured to generate rainfall data of the SCS-CN rainfall runoff curve based on the daily rainfall data of the rainfall observation station to which the power plant belongs; and a runoff volume generation module configured to generate the runoff volume generated by the power plant on the surface category according to the initial rainfall loss value and the rainfall data.

[0025] An embodiment of the third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the power plant rainfall calculation method as described in the above embodiment.

[0026] An embodiment of the fourth aspect of the present application provides a computer-readable storage medium storing a computer program, and when the program is executed by a processor, it implements the power plant rainfall calculation method as above.

[0027] An embodiment of the fifth aspect of the present application provides a computer program product storing a computer program, and when the program is executed by a processor, it implements the power plant rainfall calculation method as above.

[0028] The embodiments of the present application can obtain the actual remote sensing image map of a power plant, generate a land use category map, optimize the rainfall runoff model, and calculate the actual rainfall of the power plant based on the spatial layer, so as to accurately identify different land use categories inside the power plant within the small-scale space of the power plant, construct rainfall models suitable for different power plants, and further improve the accuracy, fineness, and specificity of rainfall calculation in the power plant, providing a scientific basis for water resource management, flood control planning, etc. Thus, the problems in the related art that lack a rainfall calculation method that can cover all land use categories of the power plant and reflect the differences in rainfall characteristics of each power plant, and cannot guarantee the accurate calculation of rainfall data and the differences between different power plants are solved.

[0029] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0031] Figure 1 is a flowchart of a method for calculating rainfall in a power plant according to an embodiment of the present application;

[0032] Figure 2 is a remote sensing image map of a power plant area according to an embodiment of the present application;

[0033] Figure 3 is an interpreted land use category map according to an embodiment of the present application;

[0034] Figure 4 is a schematic structural diagram of a device for calculating rainfall in a power plant according to an embodiment of the present application;

[0035] Figure 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, in which the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application and should not be construed as limiting the present application.

[0037] The following describes the power plant rainfall calculation method, device, electronic device, and storage medium according to the embodiments of the present application with reference to the accompanying drawings. In view of the problem in the related art mentioned in the above background technology that there is a lack of a rainfall calculation method that can cover various land use categories of power plants and reflect the differences in rainfall characteristics of different power plants, and it is impossible to ensure the accurate calculation of rainfall data and the differences between different power plants, the present application provides a power plant rainfall calculation method. In this method, the actual remote sensing image of the power plant can be obtained, a land use category map can be generated, the rainfall runoff model can be optimized, and the actual rainfall of the power plant can be calculated according to the spatial layer, so as to accurately identify different land use categories inside the power plant within the small-scale space range of the power plant, construct a rainfall model suitable for different power plants, and then improve the accuracy, fineness, and specificity of the rainfall calculation of the power plant, providing a scientific basis for water resource management, flood control planning, etc. Thus, the problems in the related technology that there is a lack of a rainfall calculation method that can cover various land use categories of power plants and reflect the differences in rainfall characteristics of different power plants, and it is impossible to ensure the accurate calculation of rainfall data and the differences between different power plants are solved.

[0038] Specifically, Figure 1 FIG. is a flowchart of a power plant rainfall calculation method provided by an embodiment of the present application.

[0039] As Figure 1 shown, the power plant rainfall calculation method includes the following steps:

[0040] In step S101, the actual remote sensing image of the power plant is obtained under the condition of a preset spatial resolution.

[0041] It can be understood that the preset spatial resolution in the embodiments of the present application can be the required spatial resolution, that is, the resolution value determined according to specific requirements and application scenarios.

[0042] Among them, the embodiments of the present application can calculate the required spatial resolution, and obtain the remote sensing image of the power plant under the condition of the required spatial resolution. For example, according to the calculated required resolution, open-source database images or commercial remote sensing images such as 5m×5m and 10m×10m are selected as the subsequent land use category map.

[0043] For example, when the total area of the power plant in the embodiments of the present application is about 870,000 square meters, the power plant is classified as a large-scale power plant according to the floor area. By using a remote sensing image with a resolution of 30m×30m, the demand for interpreting various land use categories can be met after the power plant in this area segment. Therefore, the embodiments of the present application can select a remote sensing image with a resolution of 30m×30m.

[0044] It should be noted that the preset spatial resolution can be set by those skilled in the art according to the actual situation, and no specific limitation is made here.

[0045] In step S102, a land use category map of the power plant is generated based on the actual remote sensing image map.

[0046] It can be understood that through on-site research on the power plant in the embodiments of the present application, the land use categories involved in the power plant can be classified into 8 categories, namely forest land, grassland, commercial service land, industrial and mining storage land, residential land, transportation land, water area and idle land. According to the actual land use categories of each power plant, it is possible to conduct supervised classification and interpretation on the land use categories involved in the power plant among these 8 land use categories subsequently.

[0047] In the actual implementation process, the embodiments of the present application can generate a land use category map of the power plant based on the actual remote sensing image map, so as to provide support for constructing a rainfall runoff model of the power plant subsequently based on the land use category map, and further help to accurately estimate the rainfall of each land use category of the power plant.

[0048] Optionally, in an embodiment of the present application, generating a land use category map of the power plant based on the actual remote sensing image map includes: performing at least one of preprocessing such as radiometric calibration, atmospheric correction, orthorectification, image fusion, image homogenization mosaicking, and image cropping on the remote sensing image map of the power plant to generate a preprocessed image; formulating an interpretation key corresponding to the power plant according to the online map of the power plant, and verifying whether the land use category on the remote sensing image is consistent with the actual land use category of the online map according to the interpretation key to generate a verification result; based on the verification result, optimizing the training samples to generate optimized samples, and performing supervised classification on the preprocessed image according to the optimized samples to generate a land use category map.

[0049] It can be understood that the preprocessing of the remote sensing image map in the embodiments of the present application can be carried out in ENVI software.

[0050] Among them, as Figure 2 shown, the embodiments of the present application can preprocess the remote sensing image map in ENVI software through steps such as radiometric calibration, atmospheric correction, orthorectification, image fusion, image homogenization mosaicking, and image cropping to generate a preprocessed image, and preprocess the remote sensing image map to serve as the basis for subsequent calculation of rainfall.

[0051] The embodiments of the present application can first perform visual interpretation on the preprocessed image, that is, manually distinguish 6 land use categories through the online map for further supervised classification and interpretation. The embodiments of the present application can perform further supervised classification and interpretation on the preprocessed image, and the specific steps include:

[0052] (1) Develop interpretation signs with reference to online maps: Open the area where the power plant is located on the online map. Taking grasslands and vacant lands as examples, the grassland sign is selected as light green plots, appearing in patches and surrounded by buildings, and the vacant land sign is selected as large brown areas without object occlusion around. For other land use categories, corresponding interpretation signs are developed according to the actual visual situation.

[0053] (2) According to the developed interpretation signs, randomly select plots on the remote sensing image for judgment, and finally verify through the online map to generate verification results: Randomly select different category plots in Figure 2 to manually judge what kind of land use category they belong to, and compare whether it is consistent with the actual land use category shown on the online map.

[0054] (3) Select training samples on the remote sensing image: Open Figure 2 in the ENVI software, and select 2 - 3 typical samples for each land use category respectively as the basis for model learning and performing supervised classification of land use categories.

[0055] (4) Optimize and purify the samples, and perform supervised classification according to steps (1) and (2) to generate a land use category map;

[0056] (5) For the land use category map obtained from the supervised classification, combined with the online map, perform manual visual interpretation adjustment: Manually visually search and identify the error areas and extract the error areas, re - perform supervised classification on the error areas and mosaic them into the output image, and finally obtain the interpreted land use category map, as shown in Figure 3 .

[0057] The embodiments of the present application can pre - process the remote sensing image map, and further perform supervised classification and interpretation on the pre - processed image, which is beneficial to generating a high - precision land use category map, and improving the accuracy, fineness and specificity of the power plant rainfall calculation.

[0058] In step S103, based on the land use category map, obtain the localized parameters that meet the preset conditions, optimize the preset rainfall - runoff model according to the localized parameters to generate an optimization result, and construct the rainfall - runoff model of the power plant according to the optimization result.

[0059] It can be understood that the preset rainfall - runoff model in the embodiments of the present application can be a basic rainfall - runoff model; the localized parameters of the preset conditions in the embodiments of the present application can be the localized parameters required for the rainfall - runoff model.

[0060] In the actual implementation process, the embodiments of the present application can generate localized parameters of surface categories based on the land use category map, and correct the basic rainfall runoff model according to the localized parameters to make it better reflect the actual situation of the research area. For example, for areas with high vegetation coverage, it may be necessary to adjust the evapotranspiration coefficient in the model; while for areas with more hardened ground, it may be necessary to adjust the runoff coefficient, etc. The embodiments of the present application can optimize the basic rainfall runoff model to generate an optimization result, that is, the optimized basic rainfall runoff model, and construct the rainfall runoff model of the power plant according to the optimized basic rainfall runoff model.

[0061] By introducing specific surface parameters, the embodiments of the present application enable the rainfall runoff model to more truly reflect the actual situation, improve the prediction accuracy, and the accurate rainfall runoff model provides a scientific basis for the safe operation of power facilities.

[0062] It should be noted that the preset conditions can be set by those skilled in the art according to the actual situation, and no specific limitation is made here.

[0063] Optionally, in an embodiment of the present application, generating the localized parameters of surface categories based on the land use category map includes: based on the land use category map, conducting natural rainfall monitoring or carrying out simulated rainfall experiments on each land use category within the power plant to generate the localized parameters of surface categories.

[0064] It can be understood that the natural rainfall monitoring in the embodiments of the present application can record data such as the precipitation amount and the initial rainfall loss value in each rainfall event; the simulated rainfall experiment in the embodiments of the present application can be to conduct experiments on a flat ground without building and vegetation obstruction using a special simulated rainfall device.

[0065] In the actual implementation process, the embodiments of the present application can conduct natural rainfall monitoring on each land use category within the power plant. To verify the reliability and stability of the natural rainfall monitoring data, simulated rainfall experiments can be carried out when necessary to generate the localized parameters of surface categories.

[0066] By conducting natural rainfall monitoring or simulated rainfall experiments on different land use categories within the power plant, the embodiments of the present application can effectively obtain the localized parameters of surface categories, which not only helps to construct a more accurate rainfall runoff model for the power plant, but also provides a scientific basis for the water resource management of the power plant.

[0067] Among them, in an embodiment of the present application, the construction formula of the rainfall runoff model is:

[0068]

[0069] Among them, p is the rainfall amount, i a is the initial rainfall loss value, and S is the maximum infiltration capacity.

[0070] Specifically, the embodiments of the present application can utilize the SCS-CN rainfall-runoff curve to establish rainfall-runoff models for different land categories of the power plant. Among them, q is the runoff volume, with the unit of mm, p is the rainfall amount, with the unit of mm, i a is the initial rainfall loss value, with the unit of mm, and S is the maximum infiltration capacity, with the unit of mm. Among them, i a is in a direct proportional relationship with S, that is, i a = λS. Here, the proportional value is 0.2, that is, i a = 0.2S, then the model can be transformed into:

[0071]

[0072] Optionally, in an embodiment of the present application, before multiplying the runoff volume generated by the power plant on the surface category by the area of the land use category corresponding to the power plant to generate the spatial layer of the rainfall amount of the power plant, it further includes: obtaining the initial rainfall loss value in the rainfall-runoff model; generating the rainfall amount data of the SCS-CN rainfall-runoff curve based on the daily rainfall amount data of the rainfall observation station to which the power plant belongs; generating the runoff volume generated by the power plant on the surface category according to the initial rainfall loss value and the rainfall amount data.

[0073] It can be understood that since the area where the power plant is located and the season during the calculation in the embodiments of the present application are abundant in rain, the natural rainfall observation method can be used to obtain the initial rainfall loss value in the rainfall-runoff model.

[0074] In the actual execution process, taking the initial rainfall loss value of the grassland category as an example in the embodiments of the present application, first, a flat grassland ground with only grassland as the land use category and no other objects blocking around is selected in the factory. Before the rainfall starts, the rain gauge is placed on this plot. When the rainfall starts and continues until the plot produces runoff, the reading of the rain gauge is read, and the initial rainfall loss value of the grassland category of this power plant is obtained as 2.55 mm. Using the same method, the initial rainfall loss values of the other 5 land use categories are obtained respectively. Table 1 is the table of the initial rainfall loss values of different land use categories of the power plant. Among them, as shown in Table 1:

[0075] Table 1

[0076] Land use category Initial rainfall loss value Ia (mm) Grassland 2.55 Commercial service land 0.35 Industrial and mining storage land 0.25 Residential land 0.45 Transportation land 0.50 Idle land 0.20

[0077] Furthermore, the rainfall amount data in the SCS-CN runoff curve adopts the daily rainfall amount data of the rainfall observation station in the street to which the power plant belongs in the current season, with the value of 7.6 mm. After obtaining the initial rainfall loss values and the daily rainfall amount data of each land use category, through model calculation, the runoff volume of each land use category of this power plant can be obtained. Table 2 is the table of the runoff volumes of different land use categories of the power plant. Among them, as shown in Table 2:

[0078] Table 2

[0079] Land use category Runoff q (mm) Grassland 1.43 Commercial service land 5.84 Industrial and mining storage land 6.28 Residential land 5.44 Transportation land 5.25 Idle land 6.52

[0080] The embodiments of the present application can generate the runoff generated by a power plant on the surface category according to the initial rainfall loss value and rainfall data, which not only improves the accuracy and fineness of rainfall calculation, but also can effectively reflect the differences in rainfall characteristics caused by factors such as geographical location and land use mode between different power plants.

[0081] In step S104, based on the rainfall runoff model, multiply the runoff generated by the power plant on the surface category by the area of the corresponding land use category of the power plant to generate a spatial layer of the rainfall of the power plant, and calculate the actual rainfall of the power plant according to the spatial layer.

[0082] It can be understood that the areas of different land use categories of the power plant in the embodiments of the present application can be statistically analyzed for the element of "area" through the "Statistics" in the Arcgis software; the actual rainfall generated by the power plant in the embodiments of the present application can be the total rainfall of the power plant, which can be obtained by multiplying and overlaying the two spatialized layers of "runoff" × "land use area" in the Arcgis software using a raster calculator.

[0083] In the actual execution process, the embodiments of the present application can multiply the runoff generated by the power plant on each surface category by the corresponding land use category area based on the rainfall runoff model using the Arcgis software to obtain a spatial layer of the rainfall of the power plant, and calculate the actual rainfall of the power plant according to the spatial layer.

[0084] For example, the embodiments of the present application can link the runoff of different land use categories to the land use category layer to generate spatial layer 1, link the areas of different land use categories to the land use category layer to generate spatial layer 2, multiply and overlay spatial layer 1 and spatial layer 2 using a raster calculator to obtain the spatial layer of the rainfall of the power plant, and finally obtain the daily rainfall data of the power plant as 5134.17m 3 。

[0085] The embodiments of the present application calculate the actual rainfall of the power plant according to the spatial layer, which can not only accurately calculate the actual rainfall of the power plant, but also intuitively show the influence of different surface types on rainfall runoff, helping to further optimize the water resource management of the power plant.

[0086] The method for calculating the rainfall of a power plant proposed according to the embodiments of the present application can obtain the actual remote sensing image of the power plant, generate a land use category map, optimize the rainfall runoff model, and calculate the actual rainfall of the power plant based on the spatial layer, so as to accurately identify different land use categories inside the power plant within the small-scale space of the power plant, construct a rainfall model suitable for different power plants, and then improve the accuracy, fineness and specificity of the rainfall calculation of the power plant, providing a scientific basis for water resource management, flood control planning, etc. Thus, it solves the problem that the related technology lacks a rainfall calculation method that can cover all land use categories of power plants and reflect the differences in rainfall characteristics of different power plants, and cannot guarantee the accurate calculation of rainfall data and the differences between different power plants.

[0087] Next, the device for calculating the rainfall of a power plant proposed according to the embodiments of the present application will be described with reference to the accompanying drawings.

[0088] Figure 4 It is a schematic structural diagram of the device for calculating the rainfall of a power plant according to the embodiments of the present application.

[0089] As Figure 4 shown, the device 10 for calculating the rainfall of a power plant includes: an acquisition module 100, a generation module 200, a construction module 300, and a calculation module 400.

[0090] Specifically, the acquisition module 100 is configured to obtain the actual remote sensing image of the power plant on the condition of a preset spatial resolution.

[0091] The generation module 200 is configured to generate a land use category map of the power plant according to the actual remote sensing image.

[0092] The construction module 300 is configured to generate localized parameters of the surface category based on the land use category map, optimize the preset rainfall runoff model according to the localized parameters to generate an optimization result, and construct a rainfall runoff model of the power plant according to the optimization result.

[0093] The calculation module 400 is configured to multiply the runoff generated by the power plant on the surface category by the area of the corresponding land use category of the power plant based on the rainfall runoff model to generate a spatial layer of the rainfall of the power plant, and calculate the actual rainfall of the power plant according to the spatial layer.

[0094] Optionally, in an embodiment of the present application, the generation module 200 includes: a preprocessing unit, a verification unit, and an optimization unit.

[0095] Among them, the preprocessing unit is configured to perform at least one preprocessing of radiometric calibration, atmospheric correction, orthorectification, image fusion, image color homogenization mosaicking, and image cropping on the remote sensing image of the power plant to generate a preprocessed image.

[0096] A verification unit, configured to formulate an interpretation flag corresponding to a power plant according to an online map of the power plant, and verify whether the land use category on a remote sensing image is consistent with the actual land use category on the online map according to the interpretation flag, so as to generate a verification result.

[0097] An optimization unit, configured to optimize training samples based on the verification result to generate optimized samples, and supervise and classify the preprocessed image according to the optimized samples to generate a land use category map.

[0098] Optionally, in an embodiment of the present application, the construction module 300 includes: a monitoring unit.

[0099] Wherein, the monitoring unit is configured to perform natural rainfall monitoring or conduct a simulated rainfall experiment on each land use category in the power plant based on the land use category map, so as to generate localized parameters of the surface category.

[0100] Optionally, in an embodiment of the present application, the construction formula of the rainfall runoff model is:

[0101]

[0102] Wherein, p is the rainfall, i a is the initial rainfall loss value, and S is the maximum infiltration capacity.

[0103] Optionally, in an embodiment of the present application, the power plant rainfall calculation device 10 further includes: a data acquisition module, a data generation module, and a runoff generation module.

[0104] Wherein, the data acquisition module is configured to acquire the initial rainfall loss value in the rainfall runoff model before multiplying the runoff generated by the power plant on the surface category by the area of the land use category corresponding to the power plant to generate a spatial layer of the power plant rainfall.

[0105] The data generation module is configured to generate rainfall data of the SCS-CN rainfall runoff curve based on the daily rainfall data of the rainfall observation station to which the power plant belongs.

[0106] The runoff generation module is configured to generate the runoff generated by the power plant on the surface category according to the initial rainfall loss value and the rainfall data.

[0107] It should be noted that the foregoing explanation of the embodiment of the power plant rainfall calculation method also applies to the power plant rainfall calculation device of this embodiment, and will not be elaborated here.

[0108] The power plant rainfall calculation device proposed according to the embodiments of the present application can obtain the actual remote sensing image of the power plant, generate a land use category map, optimize the rainfall runoff model, and calculate the actual rainfall of the power plant according to the spatial layer, so as to accurately identify different land use categories inside the power plant within the small-scale space of the power plant, construct a rainfall model suitable for different power plants, and further improve the accuracy, fineness and specificity of rainfall calculation in the power plant, providing a scientific basis for water resource management, flood control planning, etc. Thus, it solves the problem that the related technology lacks a rainfall calculation method that can cover various land use categories of power plants and reflect the differences in rainfall characteristics of different power plants, and cannot guarantee the accurate calculation of rainfall data and the differences between different power plants.

[0109] Figure 5 The following is a schematic structural diagram of the electronic device provided by the embodiments of the present application. The electronic device may include:

[0110] A memory 501, a processor 502, and a computer program stored on the memory 501 and executable on the processor 502.

[0111] When the processor 502 executes the program, it implements the power plant rainfall calculation method provided in the above embodiments.

[0112] Furthermore, the electronic device further includes:

[0113] A communication interface 503 for communication between the memory 501 and the processor 502.

[0114] The memory 501 is used to store a computer program executable on the processor 502.

[0115] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0116] If the memory 501, the processor 502, and the communication interface 503 are independently implemented, the communication interface 503, the memory 501, and the processor 502 can be interconnected through a bus and complete communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 5 only a thick line is shown in the figure, but it does not mean that there is only one bus or one type of bus.

[0117] Optionally, in a specific implementation, if the memory 501, the processor 502, and the communication interface 503 are integrated on a single chip, the memory 501, the processor 502, and the communication interface 503 can communicate with each other through an internal interface.

[0118] The processor 502 may be a central processing unit (CPU for short), or an application specific integrated circuit (ASIC for short), or one or more integrated circuits configured to implement the embodiments of the present application.

[0119] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the above-mentioned power plant rainfall calculation method is implemented.

[0120] This application embodiment also provides a computer program product, on which a computer program is stored. When the program is executed by a processor, the above-mentioned power plant rainfall calculation method is implemented.

[0121] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, without conflict, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples.

[0122] In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0123] Any process or method description represented in a flowchart or otherwise described herein can be understood to represent a module, segment, or portion of code including one or N executable instructions for implementing a customized logical function or process. The scope of the preferred embodiments of this application includes additional implementations, where functions may be executed in a substantially simultaneous manner or in the reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the technical field to which the embodiments of this application pertain.

[0124] Logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered a sequenced list of executable instructions for implementing a logical function, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with such instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or N wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, a computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0125] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0126] Those of ordinary skill in the art can understand that all or part of the steps carried out in the method of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0127] In addition, in each of the embodiments of the present application, the functional units can be integrated in a processing module, or each unit can exist physically alone, or two or more units can be integrated in a module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the above integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0128] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for calculating rainfall in a power plant, characterized in that, It includes the following steps: Obtain the actual remote sensing image of the power plant on the condition of a preset spatial resolution; Generate the land use category map of the power plant according to the actual remote sensing image; Based on the land use category map, obtain the localization parameters that meet the preset conditions, optimize the preset rainfall runoff model according to the localization parameters, generate the optimization result, and construct the rainfall runoff model of the power plant according to the optimization result; Based on the rainfall runoff model, multiply the runoff generated by the power plant on the surface category by the area of the land use category corresponding to the power plant to generate a spatial layer of the rainfall of the power plant, and calculate the actual rainfall of the power plant according to the spatial layer; 2. The method according to claim 1, characterized in that, The generating the land use category map of the power plant according to the actual remote sensing image includes: Perform at least one preprocessing of radiometric calibration, atmospheric correction, orthorectification, image fusion, image homogenization mosaicking, and image cropping on the remote sensing image of the power plant to generate a preprocessed image; Formulate the interpretation marks corresponding to the power plant according to the online map of the power plant, and verify whether the land use category on the remote sensing image is consistent with the actual land use category of the online map according to the interpretation marks to generate a verification result; Based on the verification result, optimize the training samples to generate optimized samples, and supervise and classify the preprocessed image according to the optimized samples to generate the land use category map; 3. The method according to claim 1, wherein The obtaining the localization parameters that meet the preset conditions based on the land use category map includes: Based on the land use category map, conduct natural rainfall monitoring or carry out simulated rainfall experiments on each land use category in the power plant to obtain the localization parameters that meet the preset conditions; 4. The method according to claim 1, wherein The construction formula of the rainfall runoff model is: Among them, p is the rainfall, i a is the initial rainfall loss value, and S is the maximum infiltration capacity.

5. The method according to claim 1, wherein Before multiplying the runoff generated by the power plant on the surface category by the area of the land use category corresponding to the power plant to generate a spatial layer of the rainfall of the power plant, it further includes: Obtain the initial rainfall loss value in the rainfall runoff model; Generate the rainfall data of the SCS-CN rainfall runoff curve based on the daily rainfall data of the rainfall observation station to which the power plant belongs; Generate the runoff generated by the power plant on the surface category according to the initial rainfall loss value and the rainfall data; 6. A rainfall calculation device for a power plant, characterized in that, It includes: An obtaining module, configured to obtain the actual remote sensing image of the power plant on the condition of a preset spatial resolution; A generating module, configured to generate the land use category map of the power plant according to the actual remote sensing image; A constructing module, configured to obtain the localization parameters that meet the preset conditions based on the land use category map, optimize the preset rainfall runoff model according to the localization parameters, generate the optimization result, and construct the rainfall runoff model of the power plant according to the optimization result; A calculating module, configured to multiply the runoff generated by the power plant on the surface category by the area of the land use category corresponding to the power plant based on the rainfall runoff model to generate a spatial layer of the rainfall of the power plant, and calculate the actual rainfall of the power plant according to the spatial layer; 7. The device according to claim 6, characterized in that The generating module includes: A preprocessing unit for performing at least one preprocessing operation on the remote sensing image map of the power plant, including radiometric calibration, atmospheric correction, orthorectification, image fusion, image color homogenization mosaicking, and image cropping, to generate a preprocessed image; A verification unit for formulating an interpretation key corresponding to the power plant based on the online map of the power plant, and verifying whether the land use categories on the remote sensing image are consistent with the actual land use categories on the online map according to the interpretation key, to generate a verification result; An optimization unit for optimizing training samples based on the verification result to generate optimized samples, and performing supervised classification on the preprocessed image according to the optimized samples to generate the land use category map.

8. An electronic device, characterized in that, Comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the power plant rainfall calculation method according to any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to be used for implementing the power plant rainfall calculation method according to any one of claims 1-5.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed to be used for implementing the power plant rainfall calculation method according to any one of claims 1-5.

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