Wind energy resource monitoring method and device

Abstract

The invention provides a wind energy resource monitoring method and device.The wind energy resource monitoring method comprises the steps that atmospheric analysis data and multi-source observation data in a target area are obtained, and the multi-source observation data comprise wind energy resource data observed by multiple observation sources; determining a mesoscale mode of the atmospheric analysis data in the target area; according to the atmospheric analysis data and the mesoscale mode, downscaling the atmospheric analysis data to generate first wind energy resource monitoring data corresponding to an expected resolution, the expected resolution being a spatial resolution expected to be reached by the wind energy resource monitoring data generated based on the atmospheric analysis data; and fusing the multi-source observation data and the first wind energy resource monitoring data to obtain second wind energy resource monitoring data corresponding to the expected resolution. The resolution and accuracy of atmospheric analysis data are improved.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a wind energy resource monitoring method, a wind energy resource monitoring device, an electronic device, and a computer-readable medium. Background Art

[0002] Wind energy is a clean, renewable resource with large reserves. At present, the development of wind energy resources is gradually increasing, the wind power market is developing rapidly, and wind energy resources have become the main power source in practical applications. Through wind energy resource monitoring, the potential of wind energy resources in different regions can be evaluated, providing a scientific basis for the planning and construction of wind farms.

[0003] The wind energy monitoring methods currently implemented generally have the problems of low accuracy and low resolution in wind energy resource monitoring. Summary of the invention

[0004] To this end, the present invention provides a wind energy resource monitoring method, a wind energy resource monitoring device, an electronic device and a readable medium to solve the problems of low accuracy and low resolution of wind energy resource monitoring in the prior art.

[0005] In order to achieve the above-mentioned objectives, the first aspect of the present invention provides a wind energy resource monitoring method, comprising: obtaining atmospheric analysis data and multi-source observation data in a target area, wherein the multi-source observation data includes wind energy resource data observed by multiple observation sources; determining the mesoscale pattern of the atmospheric analysis data in the target area; downscaling the atmospheric analysis data according to the atmospheric analysis data and the mesoscale pattern to generate first wind energy resource monitoring data corresponding to an expected resolution, wherein the expected resolution is the spatial resolution expected to be achieved by the wind energy resource monitoring data generated based on the atmospheric analysis data; fusing the multi-source observation data and the first wind energy resource monitoring data to obtain second wind energy resource monitoring data corresponding to the expected resolution.

[0006] Optionally, determining the mesoscale model of the atmospheric analysis data in the target area includes: obtaining driving data of a first mesoscale model of the atmospheric analysis data based on a preprocessing system of a mesoscale meteorological numerical model, and obtaining a plurality of parameterization schemes corresponding to the first mesoscale model; determining a target parameterization scheme among the plurality of parameterization schemes; debugging key parameters in the target parameterization scheme according to the driving data to obtain the debugged key parameters; generating a second mesoscale model according to the target parameterization scheme and the debugged key parameters, and determining the second mesoscale model as the mesoscale model of the atmospheric analysis data in the target area.

[0007] Optionally, among the multiple parameterization schemes, determining a target parameterization scheme includes: determining the climatic and geographical characteristics of the target area; screening a plurality of candidate parameterization schemes from the multiple parameterization schemes based on the climatic and geographical characteristics; respectively running the plurality of candidate parameterization schemes based on the driving data to obtain scheme running results corresponding to the plurality of candidate parameterization schemes; and determining a target parameterization scheme from the plurality of candidate parameterization schemes based on the plurality of scheme running results.

[0008] Optionally, the atmospheric analysis data are downscaled according to the atmospheric analysis data and the mesoscale model to generate first wind energy resource monitoring data corresponding to the expected resolution, including: generating third wind energy resource monitoring data corresponding to an intermediate resolution according to the atmospheric analysis data and the mesoscale model, wherein the intermediate resolution is lower than the expected resolution; and based on a spatial downscaling model, the third wind energy resource monitoring data are downscaled to generate first wind energy resource monitoring data corresponding to the expected resolution.

[0009] Optionally, the multi-source observation data and the first wind energy resource monitoring data are fused to obtain second wind energy resource monitoring data corresponding to the expected resolution, including: taking the first wind energy resource monitoring data as the background field, fusing the multi-source observation data and the first wind energy resource monitoring data based on a multi-grid variational fusion algorithm to obtain the second wind energy resource monitoring data corresponding to the expected resolution.

[0010] Optionally, obtaining multi-source observation data within the target area includes: obtaining meteorological site observation data, radar observation data, satellite observation data and first wind measurement data within the target area; and processing the meteorological site observation data, the radar observation data, the satellite observation data and the first wind measurement data into multi-source observation data corresponding to the desired resolution.

[0011] Optionally, after obtaining the second wind energy resource monitoring data corresponding to the expected resolution, the method further includes: when obtaining the second wind measurement data, fusing the second wind measurement data, the multi-source observation data and the first wind energy resource monitoring data to obtain the fourth wind energy resource monitoring data.

[0012] A second aspect of the present invention provides a wind energy resource monitoring device, comprising: an acquisition module, configured to acquire atmospheric analysis data and multi-source observation data in a target area, wherein the multi-source observation data includes wind energy resource data observed by multiple observation sources; a determination module, configured to determine the mesoscale pattern of the atmospheric analysis data in the target area; a generation module, configured to downscale the atmospheric analysis data according to the atmospheric analysis data and the mesoscale pattern, and generate first wind energy resource monitoring data corresponding to an expected resolution, wherein the expected resolution is the spatial resolution expected to be achieved by the wind energy resource monitoring data generated based on the atmospheric analysis data; a first fusion module, configured to fuse the multi-source observation data and the first wind energy resource monitoring data, and obtain second wind energy resource monitoring data corresponding to the expected resolution.

[0013] The third aspect of the present invention provides an electronic device, comprising: one or more processors; a memory on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned wind energy resource monitoring method; one or more I / O interfaces, connected between the processor and the memory, configured to implement information interaction between the processor and the memory.

[0014] A fourth aspect of the present invention provides a computer-readable medium having a computer program stored thereon, wherein the program implements the above-mentioned wind energy resource monitoring method when executed by a processor.

[0015] The present invention has the following advantages:

[0016] The embodiment provided by the present invention realizes that after obtaining the atmospheric analysis data in the target area, by determining the mesoscale pattern of the atmospheric analysis data, and downscaling the atmospheric analysis data according to the atmospheric analysis data and the mesoscale pattern, the first wind energy resource monitoring data corresponding to the desired resolution can be generated, thereby improving the resolution of the atmospheric analysis data; after obtaining the first wind energy resource monitoring data, the multi-source observation data is combined with the acquired multi-source observation data, and the multi-source observation data is fused with the first wind energy resource monitoring data, so that the second wind energy resource monitoring data corresponding to the desired resolution can be obtained, so that the second wind energy resource monitoring data is closer to the multi-source observation data, thereby improving the accuracy of the second wind energy resource monitoring data. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present invention, but do not constitute a limitation of the present invention.

[0018] Figure 1 is a flow chart of a wind energy resource monitoring method provided by an embodiment of the present invention;

[0019] Figure 2 is a processing flow chart of a wind energy resource monitoring method provided by an embodiment of the present invention;

[0020] Figure 3 is a structural schematic diagram of a wind energy resource monitoring device provided by an embodiment of the present invention;

[0021] Figure 4 It is a block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0023] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0024] The terms used in the present invention are only used to describe specific embodiments and are not intended to limit the present invention. As used in the present invention, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

[0025] When the terms “comprising” and / or “made of…” are used in the present invention, it specifies the existence of the stated features, integers, steps, operations, elements and / or components, but does not exclude the existence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0026] Unless otherwise defined, all terms (including technical and scientific terms) used in the present invention have the same meaning as those commonly understood by those of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present invention, and will not be interpreted as having an idealized or overly formal meaning unless the present invention clearly defines it as such.

[0027] Wind energy is a clean, renewable resource with large reserves. At present, the development of wind energy resources is gradually increasing, the wind power market is developing rapidly, and wind energy resources have become the main power source in practical applications. Through wind energy resource monitoring, the potential of wind energy resources in different regions can be evaluated, providing a scientific basis for the planning and construction of wind farms. Due to the influence of atmospheric circulation, daily variation cycle of solar radiation and terrain blocking, the wind speed near the ground layer has the natural properties of fast temporal changes and uneven spatial distribution. In current practical applications, there is little observation data on wind near the ground layer, and wind energy resource monitoring is difficult and challenging.

[0028] At present, numerical simulation methods are usually used to monitor wind energy resources. However, due to the imperfect parameterization scheme of numerical simulation methods, large parameter uncertainty and low resolution, there are large errors in the simulation results.

[0029] Based on this, embodiments of the present invention provide a wind energy resource monitoring method, a wind energy resource monitoring device, an electronic device, and a computer-readable medium, which are described in detail one by one in the following embodiments.

[0030] In a first aspect, an embodiment of the present invention provides a method for monitoring wind energy resources. Figure 1 , Figure 1 A flow chart of a wind energy resource monitoring method provided according to an embodiment of the present invention is shown, which specifically includes the following steps:

[0031] Step 102: Acquire atmospheric analysis data and multi-source observation data in the target area.

[0032] The target area refers to any area where wind energy resources monitoring is required. Atmospheric analysis data includes but is not limited to resource data such as wind speed, wind direction, air density, temperature, humidity, and air pressure in the target area. In practical applications, the atmospheric analysis data can be a long sequence of atmospheric reanalysis data within a historical time interval, or it can be the atmospheric real-time analysis data at the current time, which can be determined according to the actual application situation.

[0033] For example, the atmospheric analysis data may be global atmospheric reanalysis data for the past year, or real-time atmospheric analysis data at the current time.

[0034] Multi-source observation data includes wind energy resource data observed by multiple observation sources, including meteorological station observation data, radar observation data, satellite observation data, and first wind measurement data. Radar observation data can be wind profile radar data, laser wind radar data, etc. Satellite observation data can be satellite ocean surface wind data. First wind measurement data can be enterprise wind farm wind measurement data and newly built wind measurement data that can be obtained at the current time.

[0035] Specifically, by acquiring atmospheric analysis data in the target area and multi-source observation data observed by multiple observation sources, the atmospheric analysis data is processed in a subsequent process to obtain higher-resolution wind energy resource monitoring data.

[0036] In a specific embodiment of the present invention, obtaining multi-source observation data in a target area includes:

[0037] Acquire meteorological station observation data, radar observation data, satellite observation data and first wind measurement data within the target area;

[0038] The meteorological site observation data, the radar observation data, the satellite observation data and the first wind measurement data are processed into multi-source observation data corresponding to the desired resolution.

[0039] Among them, meteorological station observation data refers to wind energy resource data based on meteorological station observations; radar observation data refers to wind energy resource data based on radar observations; satellite observation data refers to wind energy resource data based on satellite observations. Expected resolution refers to the spatial resolution that is expected to be achieved by wind energy resource monitoring data generated based on atmospheric analysis data. The expected resolution can be customized according to actual needs, for example, 2 km, 1 km, etc.

[0040] Specifically, meteorological station observation data based on meteorological station observations, radar observation data based on radar observations, and satellite observation data based on satellite observations are obtained, and wind farm wind measurement data of each enterprise can be further obtained. The meteorological station observation data, radar observation data, satellite observation data, and the enterprise's wind farm wind measurement data (i.e., the first wind measurement data) are unified in terms of temporal resolution and spatial resolution, so that the meteorological station observation data, radar observation data, satellite observation data, and the first wind measurement data have temporal and spatial consistency, and the spatial resolutions of the meteorological station observation data, radar observation data, satellite observation data, and the first wind measurement data are all the expected resolution.

[0041] In order to improve the quality of multi-source observation data, the acquired multi-source observation data can also be collaboratively quality controlled. Specifically, after obtaining the meteorological station observation data, radar observation data, satellite observation data and enterprise wind farm wind measurement data in the target area, refer to the "Technical Specifications for Review, Interpolation and Correction of Meteorological Observation Data of Wind Farms" (GB / T 37523-2019), "Quality Control of Ground Meteorological Observation Data" (QX / T 118-2010) and other standards and specifications to perform integrity, rationality and consistency quality control on various types of observation data to obtain outlier detection results; compare and analyze the meteorological station observation data, radar observation data, satellite observation data, enterprise wind farm wind measurement data and wind energy resource monitoring data generated by spatial downscaling of atmospheric analysis data in the target area, and obtain comparative analysis results of the differences between multi-source observation data and wind energy resource monitoring data generated by spatial downscaling of atmospheric analysis data; based on the comparative analysis results and outlier detection results, the outliers of the multi-source observation data are eliminated and marked, so as to form multi-source observation data with better quality (availability greater than 90%).

[0042] Since the multi-source observation data has a high accuracy, after obtaining the multi-source observation data, the accuracy of the wind energy resource monitoring data of the atmospheric analysis data can be improved based on the multi-source observation data in the subsequent process.

[0043] Step 104: Determine the mesoscale pattern of the atmospheric analysis data in the target area.

[0044] After obtaining the atmospheric analysis data in the target area, since the atmospheric analysis data at this time has not been processed, its spatial resolution is low. Therefore, the spatial resolution of the atmospheric analysis data can be improved based on the mesoscale model to obtain higher-resolution wind energy resource monitoring data.

[0045] Based on this, in a specific implementation manner provided by the present invention, determining the mesoscale pattern of the atmospheric analysis data in the target area includes:

[0046] Based on the preprocessing system of the mesoscale meteorological numerical model, driving data of the first mesoscale model of the atmospheric analysis data is obtained, and multiple parameterization schemes corresponding to the first mesoscale model are obtained;

[0047] Determining a target parameterization scheme among the multiple parameterization schemes;

[0048] Based on the driving data, debugging key parameters in the target parameterization scheme to obtain debugged key parameters;

[0049] A second mesoscale model is generated according to the target parameterization scheme and the debugged key parameters, and the second mesoscale model is determined as the mesoscale model of the atmospheric analysis data in the target area.

[0050] Among them, the mesoscale meteorological numerical model is specifically the WRF (Weather Research and Forecasting Model) model. The WRF model is suitable for mesoscale weather research and contains a variety of physical parameterization schemes. The preprocessing system, specifically the preprocessing system of the WRF model, that is, the WPS (WRF Preprocessing System) system, is a system that prepares input data for the WRF model. The first mesoscale model is used to debug and determine the parameterization scheme and key parameters based on the driving data, and generate the initial field, boundary field and scheme operation results of the mesoscale model. Driving data, specifically refers to the meteorological grid data that can be used for the operation of the first mesoscale model generated by the atmospheric analysis data processed by the preprocessing system of the mesoscale meteorological numerical model, and also includes underlying surface data and geographic data. Different parameterization schemes of the mesoscale meteorological numerical model are suitable for different physical processes.

[0051] The target parameterization scheme refers to the optimal parameterization scheme determined among multiple parameterization schemes. Key parameters refer to the parameters that are critical to wind field simulation based on atmospheric analysis data, such as model vertical coordinate parameters, model operation step size, terrain parameters, etc. The second mesoscale model refers to the mesoscale model generated based on the target parameterization scheme and the key parameters determined after debugging. The second mesoscale model is used to downscale the atmospheric analysis data.

[0052] Specifically, the acquired atmospheric analysis data is input into the preprocessing system of the mesoscale meteorological numerical model, the driving data processed by the preprocessing system can be obtained, and a plurality of parameterization schemes corresponding to the first mesoscale model can be obtained. Since different parameterization schemes are suitable for different physical processes, it is necessary to determine a better target parameterization scheme among a plurality of parameterization schemes. After determining the target parameterization scheme, the key parameters in the target parameterization scheme are debugged according to the driving data to obtain better key parameters. After the key parameters are debugged, the second mesoscale model of the atmospheric analysis data can be generated according to the target parameterization scheme and the debugged key parameters, and the atmospheric analysis data is downscaled based on the second mesoscale model in the subsequent process.

[0053] The embodiment provided by the present invention can obtain the driving data of the first mesoscale model of atmospheric analysis data through the preprocessing system of the mesoscale meteorological numerical model, and obtain multiple parameterization schemes corresponding to the first mesoscale model. A target parameterization scheme is determined among the multiple parameterization schemes, and the key parameters in the target parameterization scheme are debugged to obtain the debugged key parameters. A second mesoscale model can be generated according to the target parameterization scheme and the key parameters, thereby improving the accuracy of the second mesoscale model and further improving the accuracy of downscaling the atmospheric analysis data based on the second mesoscale model.

[0054] The following is an explanation of the specific implementation process of determining a target parameterization scheme among multiple parameterization schemes.

[0055] In a specific embodiment provided by the present invention, determining a target parameterization scheme among the multiple parameterization schemes includes:

[0056] Determine the climatic and geographical characteristics of the target area;

[0057] According to the climate and geographical characteristics, screening a plurality of candidate parameterization schemes from the plurality of parameterization schemes;

[0058] According to the driving data, respectively running the multiple candidate parameterization schemes, and obtaining scheme running results corresponding to the multiple candidate parameterization schemes;

[0059] According to the running results of the multiple schemes, a target parameterization scheme is determined from the multiple candidate parameterization schemes.

[0060] Among them, climate and geographical characteristics refer to the climate characteristics and geographical characteristics of the target area.

[0061] Specifically, since different parameterization schemes are applicable to different physical processes, in practical applications, the corresponding physical process can be determined according to the climatic and geographical characteristics of the target area, thereby determining the applicable parameterization scheme according to the physical process. Therefore, before determining the target parameterization scheme, the climatic and geographical characteristics of the target area can be determined, and the applicable candidate parameterization schemes can be screened from a variety of parameterization schemes according to the climatic and geographical characteristics of the target area. The driving data is input into the candidate parameterization schemes, and multiple candidate parameterization schemes are respectively run to obtain the scheme running results corresponding to the multiple candidate parameterization schemes. Among the multiple scheme running results, the scheme with better running results is determined, and the candidate parameterization scheme with better scheme running results is determined as the target parameterization scheme.

[0062] For example, after determining the climate and geographical characteristics of the target area, it is determined that the target area contains near-surface winds according to the climate and geographical characteristics. For near-surface winds, the physical process of the boundary layer is relatively important. Therefore, several boundary layer parameterization schemes can be selected from multiple parameterization schemes. Further, taking the selected boundary layer parameterization schemes as boundary layer parameterization scheme 1, boundary layer parameterization scheme 2, and boundary layer parameterization scheme 3 as examples, the driving data is input into the above three boundary layer parameterization schemes, and the boundary layer parameterization schemes are respectively run to obtain the scheme running result 1 corresponding to the boundary layer parameterization scheme 1, the scheme running result 2 corresponding to the boundary layer parameterization scheme 2, and the scheme running result 3 corresponding to the boundary layer parameterization scheme 3. After testing and evaluating the scheme running results 1, 2, and 3, it is determined that the scheme running result 1 is better than the scheme running results 2 and 3. Therefore, the boundary layer parameterization scheme 1 corresponding to the scheme running result 1 is determined as the target parameterization scheme.

[0063] The embodiment provided by the present invention can, after obtaining multiple parameterization schemes corresponding to the first mesoscale model, realize preliminary screening of candidate parameterization schemes from multiple parameterization schemes based on the climatic and geographical characteristics of the target area, so as to reduce the amount of calculation for subsequent operation of the parameterization schemes; after screening out the candidate parameterization schemes, respectively run multiple candidate parameterization schemes according to the driving data, and after obtaining the scheme operation results, further determine the target parameterization scheme from the candidate parameterization schemes according to the scheme operation results, so as to improve the accuracy and reliability of determining the target parameterization scheme.

[0064] Step 106: Downscaling the atmospheric analysis data according to the atmospheric analysis data and the mesoscale model to generate first wind energy resource monitoring data corresponding to a desired resolution.

[0065] After determining the mesoscale pattern of the atmospheric analysis data, the atmospheric analysis data can be downscaled based on the mesoscale pattern to improve the resolution of the atmospheric analysis data.

[0066] The expected resolution is the spatial resolution of the wind energy resource monitoring data generated based on the atmospheric analysis data; the first wind energy resource monitoring data refers to the wind energy resource monitoring data obtained after downscaling the atmospheric analysis data twice, and the spatial resolution of the first wind energy resource monitoring data is the expected resolution. The specific implementation process of downscaling the atmospheric analysis data to obtain the first wind energy resource monitoring data is as follows:

[0067] In a specific implementation manner provided by the present invention, downscaling the atmospheric analysis data is performed according to the atmospheric analysis data and the mesoscale model to generate first wind energy resource monitoring data corresponding to the desired resolution, including:

[0068] generating third wind energy resource monitoring data corresponding to an intermediate resolution according to the atmospheric analysis data and the mesoscale model, wherein the intermediate resolution is lower than the expected resolution;

[0069] Based on the spatial downscaling model, the third wind energy resource monitoring data is downscaled to generate the first wind energy resource monitoring data corresponding to the expected resolution.

[0070] Among them, the intermediate resolution refers to the spatial resolution of the wind energy resource monitoring data after the atmospheric analysis data is downscaled once. The intermediate resolution can be set in advance, and the intermediate resolution is lower than the expected resolution. The third wind energy resource monitoring data refers to the wind energy resource monitoring data obtained by downscaling the atmospheric analysis data once, and the spatial resolution of the third wind energy resource monitoring data is the intermediate resolution. The spatial downscaling model refers to a model used to perform statistical downscaling on the third wind energy resource monitoring data. In the process of processing the third wind energy resource monitoring data, the spatial downscaling model can consider the terrain and underlying surface factors of the target area.

[0071] Specifically, after determining the mesoscale model of the atmospheric analysis data, the atmospheric analysis data is input into the mesoscale model, and the mesoscale model performs dynamic downscaling on the atmospheric analysis data, so as to generate the third wind energy resource monitoring data corresponding to the intermediate resolution. Since the downscaling process based on the mesoscale model has a large amount of calculation and consumes more computing resources and memory space, the spatial downscaling model can be used to continue to downscale the third wind energy resource monitoring data. Based on this, the third wind energy resource monitoring data is input into the spatial downscaling model, and the spatial downscaling model performs statistical downscaling on the third wind energy resource monitoring data, so as to generate the first wind energy resource monitoring data corresponding to the desired resolution.

[0072] For example, hourly atmospheric analysis data with a spatial resolution of 10 kilometers in the horizontal direction and 2 altitude layers below 300 meters in the vertical direction are obtained. Based on the mesoscale model, the atmospheric analysis data is dynamically downscaled to generate hourly wind energy resource monitoring data with a spatial resolution of 3 kilometers in the horizontal direction and at least 6 altitude layers below 300 meters in the vertical direction. Based on the spatial downscaling model, the hourly wind energy resource monitoring data with a spatial resolution of 3 kilometers in the horizontal direction and at least 6 altitude layers below 300 meters in the vertical direction are further downscaled to generate hourly wind energy resource monitoring data with a spatial resolution of 1 kilometer in the horizontal direction and 25 altitude layers below 300 meters in the vertical direction.

[0073] In the embodiment provided by the present invention, the atmospheric analysis data is first downscaled based on the mesoscale model to obtain third wind energy resource monitoring data corresponding to the intermediate resolution, and then the third wind energy resource monitoring data is second downscaled based on the spatial downscaling model to obtain first wind energy resource monitoring data corresponding to the desired resolution. By downscaling the atmospheric analysis data twice, the resolution of the atmospheric analysis data can be improved. By downscaling the atmospheric analysis data in two different downscaling processing modes, the mesoscale model and the spatial downscaling model, the atmospheric analysis data is downscaled, and the computing resources and memory resources consumed by the downscaling processing can be reduced on the basis of improving the resolution of the atmospheric analysis data.

[0074] Step 108: Fusing the multi-source observation data and the first wind energy resource monitoring data to obtain second wind energy resource monitoring data corresponding to the desired resolution.

[0075] After generating the first wind energy resource monitoring data corresponding to the expected resolution, although the resolution of the first wind energy resource monitoring data can be significantly improved, its accuracy needs to be further improved. Based on this, the acquired multi-source observation data can be fused with the first wind energy resource monitoring data to make the first wind energy resource monitoring data closer to the multi-source observation data to improve the accuracy of the first wind energy resource monitoring data.

[0076] Among them, the second wind energy resource monitoring data refers to the wind energy resource monitoring data obtained by integrating multi-source observation data and the first wind energy resource monitoring data.

[0077] In a specific implementation manner provided by the present invention, fusing the multi-source observation data and the first wind energy resource monitoring data to obtain the second wind energy resource monitoring data corresponding to the desired resolution includes:

[0078] Taking the first wind energy resource monitoring data as a background field, the multi-source observation data and the first wind energy resource monitoring data are fused based on a multi-grid variational fusion algorithm to obtain second wind energy resource monitoring data corresponding to the desired resolution.

[0079] Among them, the multi-grid variational fusion algorithm can specifically be the STMAS (Space Time Multiscale Analysis System) algorithm.

[0080] Specifically, after obtaining the first wind energy resource monitoring data, in order to improve the accuracy of the first wind energy resource monitoring data, the first wind energy resource monitoring data can be used as the background field, and based on the multi-grid variational fusion algorithm, the first wind energy resource monitoring data and the multi-source observation data are fused, that is, the first wind energy resource monitoring data, the meteorological station observation data, the radar observation data, the satellite observation data and the first wind measurement data are fused. The multi-grid variational fusion algorithm can perform multi-scale corrections on the first wind energy resource monitoring data according to the multi-source observation data on the basis of maintaining the expected resolution of the first wind energy resource monitoring data. For example, the information of the multi-source observation data is extracted at multiple scales such as 1 km, 2 km, 4 km, 8 km, and 16 km, and the first wind energy resource monitoring data is corrected, so as to obtain the fused second wind energy resource monitoring data, and the spatial resolution of the second wind energy resource monitoring data is the expected resolution.

[0081] Continuing with the above example, by integrating multi-source observation data and the hourly first wind energy resource monitoring data of 25 height layers at 1 km in the horizontal direction and below 300 meters (60-300 meters) in the vertical direction, we can generate the hourly second wind energy resource monitoring data of 1 km in the horizontal direction and 60-300 meters every 10 meters in the vertical direction.

[0082] It should be noted that since the STMAS algorithm does not have a fusion analysis function for wind elements below 300 meters near the ground, in the embodiments provided by the present invention, the core algorithm will be applied to perform adaptive algorithm design and function development for wind elements below 300 meters near the ground and the characteristics of observation data, including background field data design, key sensitive parameter design of the variational algorithm (for the distribution characteristics and errors of wind elements and observation data below 300 meters near the ground) and design of a fusion analysis function for three-dimensional wind elements near the ground.

[0083] The embodiment provided by the present invention fuses the first wind energy resource monitoring data and the multi-source observation data based on a multi-grid variational fusion algorithm, so that the second wind energy resource monitoring data can further improve the accuracy of the second wind energy resource monitoring data while maintaining the desired resolution.

[0084] Furthermore, the generated second wind energy resource monitoring data can provide a scientific basis for the site selection and planning of wind farms in practical applications. In practical applications, as time goes by, the enterprise wind farm wind measurement data, newly built wind measurement data, etc. may be updated. For example, the number of enterprise wind farm wind measurement data and newly built wind measurement data increases. After quality control of the enterprise wind farm wind measurement data and newly built wind measurement data, higher quality enterprise wind farm wind measurement data and newly built wind measurement data are obtained. On this basis, in order to ensure the accuracy of the wind energy resource monitoring data in the target area, the updated enterprise wind farm wind measurement data, newly built wind measurement data, etc. can be obtained, and the obtained first wind energy resource monitoring data can be again subjected to variational fusion to achieve the optimization of the wind energy resource monitoring data.

[0085] Based on this, in a specific implementation manner provided by the present invention, after obtaining the second wind energy resource monitoring data corresponding to the desired resolution, the method further includes:

[0086] When the second wind measurement data is obtained, the second wind measurement data, the multi-source observation data and the first wind energy resource monitoring data are integrated to obtain fourth wind energy resource monitoring data.

[0087] Among them, the second wind measurement data, specifically including the enterprise wind farm wind measurement data and the newly built wind measurement data, can be used as new observation data for multi-grid variational fusion of the first wind energy resource monitoring data. The second wind measurement data and the first wind measurement data are the enterprise wind farm wind measurement data and the newly built wind measurement data obtained at different times.

[0088] Specifically, after performing multi-grid variational fusion on the first wind energy resource monitoring data based on multi-source observation data (specifically including meteorological station observation data, radar observation data, satellite observation data and first wind measurement data) and obtaining the corresponding second wind energy resource monitoring data, if new second wind measurement data is obtained (such as new enterprise wind farm wind measurement data, newly built wind measurement data, etc.), the second wind measurement data can be multi-grid variationally fused with the multi-source observation data and the first wind energy resource monitoring data to obtain fourth wind energy resource monitoring data with higher accuracy.

[0089] When the second wind measurement data is obtained, the first wind energy resource monitoring data can be fused with multi-grid variational methods again through the second wind measurement data, so as to improve the accuracy of the wind energy resource monitoring data. The second wind measurement data can be used as new multi-source observation data to enrich the diversity of the multi-source observation data.

[0090] The wind energy resource monitoring method provided by the present invention includes: acquiring atmospheric analysis data and multi-source observation data in a target area, wherein the multi-source observation data includes wind energy resource data observed by multiple observation sources; determining the mesoscale pattern of the atmospheric analysis data in the target area; downscaling the atmospheric analysis data according to the atmospheric analysis data and the mesoscale pattern to generate first wind energy resource monitoring data corresponding to an expected resolution, wherein the expected resolution is the spatial resolution that the wind energy resource monitoring data generated based on the atmospheric analysis data is expected to achieve; fusing the multi-source observation data and the first wind energy resource monitoring data to obtain second wind energy resource monitoring data corresponding to the expected resolution.

[0091] The embodiment provided by the present invention realizes that after obtaining the atmospheric analysis data in the target area, by determining the mesoscale pattern of the atmospheric analysis data, the atmospheric analysis data is downscaled according to the atmospheric analysis data and the mesoscale pattern, so that the first wind energy resource monitoring data corresponding to the desired resolution can be generated, thereby improving the resolution of the atmospheric analysis data; after obtaining the first wind energy resource monitoring data, the multi-source observation data is combined with the obtained multi-source observation data, and the multi-source observation data is multi-grid variationally fused with the first wind energy resource monitoring data, so that the second wind energy resource monitoring data corresponding to the desired resolution can be obtained, so that the second wind energy resource monitoring data is closer to the multi-source observation data, thereby improving the accuracy of the second wind energy resource monitoring data; and when the second wind measurement data is obtained, the first wind energy resource monitoring data is again multi-grid variationally fused in combination with the second wind measurement data and the multi-source observation data, so as to further ensure the accuracy of the wind energy resource monitoring data.

[0092] The specific implementation details of the above embodiment are described below with an example.

[0093] The following combination Figure 2 , the wind energy resource monitoring method provided by the embodiment of the present invention is further explained. Figure 2 A processing flow chart of a wind energy resource monitoring method provided according to an embodiment of the present invention is shown, taking the acquired atmospheric analysis data as global atmospheric reanalysis data within the past year as an example for explanation. Figure 2 As shown in Figure 2, the WPS preprocessing system based on the WRF model provides the initial and boundary fields for the first mesoscale model of the global atmospheric reanalysis data ( Figure 2 (not shown), the parameterization scheme and key parameters of the first mesoscale model are debugged, and after the debugging is completed, the second mesoscale model can be obtained, and the second mesoscale model is used as the mesoscale model for downscaling the global atmospheric reanalysis data. By setting the altitude layer and the intermediate resolution, the global atmospheric reanalysis data is dynamically downscaled based on the mesoscale model, and the third wind energy resource monitoring data corresponding to the intermediate resolution can be generated. For example, the third wind energy resource monitoring data is hourly three-dimensional atmospheric reanalysis data of at least 6 altitude layers below 3 kilometers in the horizontal direction and 300 meters in the vertical direction.

[0094] Based on the spatial downscaling model considering the refined terrain and underlying surface, the third wind energy resource monitoring data is statistically downscaled to obtain the first wind energy resource monitoring data corresponding to the expected resolution. For example, the first wind energy resource monitoring data is the hourly atmospheric reanalysis grid data of 25 height layers below 1 km in the horizontal direction and 300 meters in the vertical direction.

[0095] Obtain meteorological station observation data, wind profiler radar data, satellite ocean surface wind data and enterprise wind farm wind measurement data, and process the meteorological station observation data, wind profiler radar data, satellite ocean surface wind data and enterprise wind farm wind measurement data so that the time and space grids of the meteorological station observation data, wind profiler radar data, satellite ocean surface wind data and enterprise wind farm wind measurement data are consistent and have spatiotemporal consistency, and obtain multi-source observation data with spatiotemporal consistency. After multi-source collaborative quality control of the multi-source observation data and the first wind energy resource monitoring data, based on the multi-grid variational fusion algorithm, the multi-source observation data and the first wind energy resource monitoring data are fused to generate the second wind energy resource monitoring data corresponding to the desired resolution. For example, the second wind energy resource monitoring data is hourly wind energy resource monitoring data of 1 km in the horizontal direction and 60-300 meters in the vertical direction, 10 meters at a time.

[0096] Furthermore, when new enterprise wind farm wind measurement data and newly built wind measurement data are obtained, the first wind energy resource monitoring data can be combined with the newly obtained enterprise wind farm wind measurement data, the newly built wind measurement data and the multi-source observation data to perform multi-grid variational fusion again to obtain fourth wind energy resource monitoring data with higher accuracy, thereby further ensuring the accuracy of the wind energy resource monitoring data. Since the resolution of the first wind energy resource monitoring data is not processed during the multi-grid variational fusion process, the resolution of the fourth wind energy resource monitoring data is still the expected resolution.

[0097] An embodiment of the present invention realizes that after obtaining global atmospheric reanalysis data, a mesoscale model of global atmospheric reanalysis data is created, and according to the global atmospheric reanalysis data and the mesoscale model, the global atmospheric reanalysis data is dynamically downscaled to generate third wind energy resource monitoring data corresponding to the intermediate resolution. Further, based on the spatial downscaling model, the third wind energy resource monitoring data is statistically downscaled to generate first wind energy resource monitoring data corresponding to the desired resolution, thereby improving the resolution of the global atmospheric reanalysis data. After obtaining the first wind energy resource monitoring data, the multi-source observation data is combined with the obtained multi-source observation data, and the multi-source observation data is multi-grid variationally fused with the first wind energy resource monitoring data, so that the second wind energy resource monitoring data is closer to the multi-source observation data, thereby improving the accuracy of the second wind energy resource monitoring data. And further, after obtaining new wind measurement data, the first wind energy resource monitoring data is multi-grid variationally fused again based on all existing wind measurement data to obtain fourth wind energy resource monitoring data with higher accuracy. Thus, an important basis is provided for the planning and site selection of wind farms.

[0098] The step division of the various methods above is only for clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the protection scope of the present invention. Adding insignificant modifications or introducing insignificant designs to the algorithm or process without changing the core design of the algorithm and process are all within the protection scope of the invention.

[0099] In a second aspect, an embodiment of the present invention provides a wind energy resource monitoring device. Figure 3 FIG. 4 shows a schematic diagram of the structure of a wind energy resource monitoring device provided according to an embodiment of the present invention. Figure 3 As shown, the wind energy resource monitoring device comprises:

[0100] An acquisition module 302 is configured to acquire atmospheric analysis data and multi-source observation data in a target area, wherein the multi-source observation data includes wind energy resource data observed by multiple observation sources;

[0101] A determination module 304 is configured to determine a mesoscale pattern of the atmospheric analysis data in the target area;

[0102] A generating module 306 is configured to downscale the atmospheric analysis data according to the atmospheric analysis data and the mesoscale model to generate first wind energy resource monitoring data corresponding to an expected resolution, wherein the expected resolution is a spatial resolution that the wind energy resource monitoring data generated based on the atmospheric analysis data is expected to achieve;

[0103] The first fusion module 308 is configured to fuse the multi-source observation data and the first wind energy resource monitoring data to obtain second wind energy resource monitoring data corresponding to the desired resolution.

[0104] Optionally, the determining module 304 is further configured to:

[0105] Based on the preprocessing system of the mesoscale meteorological numerical model, driving data of the first mesoscale model of the atmospheric analysis data is obtained, and multiple parameterization schemes corresponding to the first mesoscale model are obtained;

[0106] Determining a target parameterization scheme among the multiple parameterization schemes;

[0107] Debugging key parameters in the target parameterization scheme according to the driving data to obtain debugged key parameters;

[0108] A second mesoscale model is generated according to the target parameterization scheme and the debugged key parameters, and the second mesoscale model is determined as the mesoscale model of the atmospheric analysis data in the target area.

[0109] Optionally, the determining module 304 is further configured to:

[0110] Determine the climatic and geographical characteristics of the target area;

[0111] According to the climate and geographical characteristics, screening a plurality of candidate parameterization schemes from the plurality of parameterization schemes;

[0112] According to the driving data, respectively running the multiple candidate parameterization schemes, and obtaining scheme running results corresponding to the multiple candidate parameterization schemes;

[0113] According to the running results of the multiple schemes, a target parameterization scheme is determined from the multiple candidate parameterization schemes.

[0114] Optionally, the generating module 306 is further configured to:

[0115] generating third wind energy resource monitoring data corresponding to an intermediate resolution according to the atmospheric analysis data and the mesoscale model, wherein the intermediate resolution is lower than the expected resolution;

[0116] Based on the spatial downscaling model, the third wind energy resource monitoring data is downscaled to generate the first wind energy resource monitoring data corresponding to the expected resolution.

[0117] Optionally, the fusion module 308 is further configured to:

[0118] Taking the first wind energy resource monitoring data as a background field, the multi-source observation data and the first wind energy resource monitoring data are fused based on a multi-grid variational fusion algorithm to obtain second wind energy resource monitoring data corresponding to the desired resolution.

[0119] Optionally, the acquisition module 302 is further configured to:

[0120] Acquire meteorological station observation data, radar observation data, satellite observation data and first wind measurement data within the target area;

[0121] The meteorological site observation data, the radar observation data, the satellite observation data and the first wind measurement data are processed into multi-source observation data corresponding to the desired resolution.

[0122] Optionally, the device further includes a second fusion module configured to:

[0123] When the second wind measurement data is obtained, the second wind measurement data, the multi-source observation data and the first wind energy resource monitoring data are integrated to obtain fourth wind energy resource monitoring data.

[0124] The wind energy resource monitoring device provided by the present invention includes: an acquisition module, configured to acquire atmospheric analysis data and multi-source observation data in a target area, wherein the multi-source observation data includes wind energy resource data observed by multiple observation sources; a determination module, configured to determine the mesoscale pattern of the atmospheric analysis data in the target area; a generation module, configured to downscale the atmospheric analysis data according to the atmospheric analysis data and the mesoscale pattern, and generate first wind energy resource monitoring data corresponding to an expected resolution, wherein the expected resolution is the spatial resolution expected to be achieved by the wind energy resource monitoring data generated based on the atmospheric analysis data; a first fusion module, configured to fuse the multi-source observation data and the first wind energy resource monitoring data, and obtain second wind energy resource monitoring data corresponding to the expected resolution.

[0125] An embodiment of the present invention realizes that, after acquiring atmospheric analysis data in a target area, by determining the mesoscale pattern of the atmospheric analysis data, and downscaling the atmospheric analysis data according to the atmospheric analysis data and the mesoscale pattern, first wind energy resource monitoring data corresponding to a desired resolution can be generated, thereby improving the resolution of the atmospheric analysis data; after obtaining the first wind energy resource monitoring data, the multi-source observation data is combined with the acquired multi-source observation data, and the multi-source observation data is fused with the first wind energy resource monitoring data, so that the second wind energy resource monitoring data corresponding to the desired resolution can be obtained, so that the second wind energy resource monitoring data is closer to the multi-source observation data, thereby improving the accuracy of the second wind energy resource monitoring data.

[0126] The functions or modules included in the device provided in the embodiment of the present invention can be used to execute the method described in the first aspect method embodiment above. Its specific implementation and technical effects can refer to the description of the above method embodiment. For the sake of brevity, they will not be repeated here.

[0127] It should be noted that all modules involved in this embodiment are logic modules. In practical applications, a logic unit may be a physical unit, or a part of a physical unit, or may be implemented as a combination of multiple physical units. In addition, in order to highlight the innovative part of the present invention, this embodiment does not introduce units that are not closely related to solving the technical problem proposed by the present invention, but this does not mean that there are no other units in this embodiment.

[0128] Reference Figure 4 , an embodiment of the present invention provides an electronic device 400, which includes:

[0129] One or more processors 401;

[0130] A memory 402 having one or more programs stored thereon, when the one or more programs are executed by one or more processors, the one or more processors implement the above-mentioned wind energy resource monitoring method;

[0131] One or more I / O interfaces 403 are connected between the processor and the memory and are configured to implement information exchange between the processor and the memory.

[0132] Among them, the processor 401 is a device with data processing capabilities, including but not limited to a central processing unit (CPU) and the like; the memory 402 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory (FLASH); the I / O interface (read-write interface) 403 is connected between the processor 401 and the memory 402, and can realize information interaction between the processor 401 and the memory 402, including but not limited to a data bus (Bus) and the like.

[0133] In some embodiments, the processor 401 , the memory 402 , and the I / O interface 403 are connected to each other through a bus, and further connected to other components of the computing device.

[0134] This embodiment also provides a computer-readable medium on which a computer program is stored. When the program is executed by a processor, the wind energy resource monitoring method provided in this embodiment is implemented. To avoid repeated description, the specific steps of the wind energy resource monitoring method are not repeated here.

[0135] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods invented above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or transient medium). As is well known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0136] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0137] Those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features from different embodiments is meant to be within the scope of the present embodiment and to form different embodiments.

[0138] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present invention, but the present invention is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A wind energy resource monitoring method, characterized in that: include: Acquiring atmospheric analysis data and multi-source observation data in a target area, wherein the multi-source observation data includes wind energy resource data observed by multiple observation sources; determining a mesoscale pattern of the atmospheric analysis data in the target area; Downscaling the atmospheric analysis data according to the atmospheric analysis data and the mesoscale model to generate first wind energy resource monitoring data corresponding to an expected resolution, wherein the expected resolution is a spatial resolution that the wind energy resource monitoring data generated based on the atmospheric analysis data is expected to achieve; The multi-source observation data and the first wind energy resource monitoring data are integrated to obtain second wind energy resource monitoring data corresponding to the desired resolution.

2. The method according to claim 1, characterized in that Determining a mesoscale pattern of the atmospheric analysis data in the target area includes: Based on the preprocessing system of the mesoscale meteorological numerical model, driving data of the first mesoscale model of the atmospheric analysis data is obtained, and multiple parameterization schemes corresponding to the first mesoscale model are obtained; Determining a target parameterization scheme among the multiple parameterization schemes; Debugging key parameters in the target parameterization scheme according to the driving data to obtain debugged key parameters; A second mesoscale model is generated according to the target parameterization scheme and the debugged key parameters, and the second mesoscale model is determined as the mesoscale model of the atmospheric analysis data in the target area.

3. The method according to claim 2, characterized in that Among the multiple parameterization schemes, a target parameterization scheme is determined, including: Determine the climatic and geographical characteristics of the target area; According to the climate and geographical characteristics, screening a plurality of candidate parameterization schemes from the plurality of parameterization schemes; According to the driving data, respectively running the multiple candidate parameterization schemes, and obtaining scheme running results corresponding to the multiple candidate parameterization schemes; According to the running results of the multiple schemes, a target parameterization scheme is determined from the multiple candidate parameterization schemes.

4. The method according to claim 1, characterized in that According to the atmospheric analysis data and the mesoscale model, downscaling the atmospheric analysis data to generate first wind energy resource monitoring data corresponding to the desired resolution includes: generating third wind energy resource monitoring data corresponding to an intermediate resolution according to the atmospheric analysis data and the mesoscale model, wherein the intermediate resolution is lower than the expected resolution; Based on the spatial downscaling model, the third wind energy resource monitoring data is downscaled to generate the first wind energy resource monitoring data corresponding to the expected resolution.

5. The method according to claim 1, characterized in that The multi-source observation data and the first wind energy resource monitoring data are integrated to obtain second wind energy resource monitoring data corresponding to the desired resolution, including: Taking the first wind energy resource monitoring data as a background field, the multi-source observation data and the first wind energy resource monitoring data are fused based on a multi-grid variational fusion algorithm to obtain second wind energy resource monitoring data corresponding to the desired resolution.

6. The method according to claim 1, characterized in that Acquire multi-source observation data in the target area, including: Acquire meteorological station observation data, radar observation data, satellite observation data and first wind measurement data within the target area; The meteorological site observation data, the radar observation data, the satellite observation data and the first wind measurement data are processed into multi-source observation data corresponding to the desired resolution.

7. The method according to claim 1, characterized in that After obtaining the second wind energy resource monitoring data corresponding to the desired resolution, the method further includes: When the second wind measurement data is obtained, the second wind measurement data, the multi-source observation data and the first wind energy resource monitoring data are integrated to obtain fourth wind energy resource monitoring data.

8. A wind energy resource monitoring device, characterized in that: include: An acquisition module is configured to acquire atmospheric analysis data and multi-source observation data in a target area, wherein the multi-source observation data includes wind energy resource data observed by multiple observation sources; A determination module, configured to determine a mesoscale pattern of the atmospheric analysis data in the target area; A generating module is configured to downscale the atmospheric analysis data according to the atmospheric analysis data and the mesoscale model to generate first wind energy resource monitoring data corresponding to an expected resolution, wherein the expected resolution is a spatial resolution that the wind energy resource monitoring data generated based on the atmospheric analysis data is expected to achieve; The first fusion module is configured to fuse the multi-source observation data and the first wind energy resource monitoring data to obtain second wind energy resource monitoring data corresponding to the expected resolution.

9. An electronic device, characterized in that: include: one or more processors; A memory having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7; One or more I / O interfaces are connected between the processor and the memory and are configured to implement information interaction between the processor and the memory.

10. A computer-readable medium, characterized in that A computer program is stored thereon, and when the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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