Solar energy resource monitoring method, device and electronic equipment

Abstract

The present application provides a solar energy resource monitoring method, device and electronic device, relating to the technical field of monitoring, and solving the technical problem of low accuracy of current solar energy resource monitoring data. The method includes: establishing a radiation terrain correction model by using a spectral radiation transfer model based on radiation observation data and target terrain data at a specified high resolution; performing terrain correction processing on the resampled first radiation data and the target terrain data through the radiation terrain correction model to obtain target radiation data after terrain correction processing; performing spatio-temporal consistency alignment processing and multi-source collaborative quality control processing on the radiation observation data, the target radiation data and the target terrain data to obtain target observed multi-source data with aligned horizontal spatial grid points and time grid points; and obtaining solar energy resource monitoring data through a multi-grid variational fusion method based on the target observed multi-source data and using the target radiation data as a background field.

Description

Technical Field

[0001] The present application relates to the field of monitoring technologies, and in particular, to a solar energy resource monitoring method, apparatus, and electronic device. Background Art

[0002] Solar radiant energy is the most important energy source on the earth. Solar energy is the energy generated by continuous nuclear fusion reactions inside the sun. Although the energy radiated by the sun to the outer boundary of the earth's atmosphere is only one two-billionth of its total radiation energy (about 3.75×10^14 tw), its radiation flux has reached as high as 1.73×10^5 tw, that is, the energy projected by the sun onto the earth per second is equivalent to 5.9×10^6 tons of coal. Most of the energy on the earth comes from solar energy. Wind energy, water energy, biomass energy, ocean thermal energy, wave energy, and tidal energy all originate from the sun. However, the accuracy of current solar energy resource monitoring data is relatively low. Summary of the Invention

[0003] The purpose of the present invention is to provide a solar energy resource monitoring method, apparatus, and electronic device to solve the technical problem of relatively low accuracy of current solar energy resource monitoring data.

[0004] In a first aspect, the present application provides a solar energy resource monitoring method, and the method includes:

[0005] Obtain the radiation data to be processed, and convert the original resolution data of the radiation data to be processed into a radiation element field with a specified spatial resolution to obtain the first radiation data after resampling; wherein, the radiation data to be processed includes satellite full-disk ground solar radiation data or numerical simulation radiation data;

[0006] Based on the radiation observation data and the target terrain data at a specified high resolution, establish a radiation terrain correction model by using a spectral radiation transfer model; wherein, the radiation observation data includes discrete radiation observation data of multiple different spatio-temporal scales;

[0007] Perform terrain correction processing on the first radiation data after resampling and the target terrain data through the radiation terrain correction model to obtain the target radiation data after terrain correction processing;

[0008] Perform spatio-temporal consistency alignment processing and multi-source collaborative quality control processing on the radiation observation data, the target radiation data, and the target terrain data to obtain the target observed multi-source data with both horizontal spatial grid points and time grid points aligned; wherein, the target terrain data includes underlying surface data, terrain elevation data, slope data, and aspect data;

[0009] Based on the aligned target observed multi-source data and using the target radiation data as the background field, solar resource monitoring data is obtained through the multi-grid variational fusion method.

[0010] In a possible implementation, after obtaining the solar resource monitoring data based on the aligned target observed multi-source data and using the target radiation data as the background field through the multi-grid variational fusion method, it further includes:

[0011] Taking the newly collected photometric data as new observed data, combining the new observed data and historical observed data to obtain an updated observed data set, and using the updated observed data set as the background field to perform multi-grid variational fusion processing to achieve the update and optimization of the solar resource monitoring data.

[0012] In a possible implementation, the method further includes:

[0013] Obtaining the observed data of photovoltaic enterprises and newly built radiation stations, taking the observed data of photovoltaic enterprises and newly built radiation stations as new radiation observed data, using the solar resource evaluation method to perform quality control on the new radiation observed data, obtaining a radiation observed data set that meets the specified quality conditions, and using the radiation observed data set as the test and evaluation data source for the solar resource monitoring data;

[0014] Based on the site longitude and latitude information of the radiation observed data set, using the nearest distance method to extract the corresponding grid point solar resource extraction values of the solar resource monitoring data, and calculating the corresponding test and evaluation indicators based on the corresponding grid point data: root mean square error RMSE / mean bias BIAS / correlation coefficient R; where, the linear correlation relationship between the single-station time series observed value of the site of the radiation observed data set and the corresponding grid point solar resource extraction value is represented by the following first formula of Pearson correlation coefficient: ; where, is the average value of the analysis value of site j extracted from the solar resource data set in the analysis area and time period; is the radiation value at time j of site i extracted from the solar resource data set; is the average value of the observed values of radiation observation site j in the analysis area and time period; is the radiation observation value at time j of radiation observation station i; is the Pearson correlation coefficient; the average value of the difference between the radiation elements of the solar resource monitoring data and the actual radiation observation elements is determined by the following second formula: ; where, is the average value of the difference between the radiation elements of the solar resource monitoring data and the actual radiation observation elements, The radiation value at time j of site i extracted from the solar energy resource dataset; The radiation observation value at time j of radiation observation station i The root mean square value of the sum of squared differences between the radiation elements of the solar energy resource monitoring data and the actual radiation observation elements is determined by the following third formula: ; where N is the number of statistical sites, The radiation value at time j of site i extracted from the solar energy resource dataset; The radiation observation value at time j of radiation observation station i; The root mean square value of the sum of squared differences;

[0015] Calculate according to the first formula, the second formula and the third formula for all times and all sites to obtain the time series of the test index, and analyze the test index using the time series of the test index to obtain the overall characteristics and spatio-temporal distribution characteristics;

[0016] Calculate the accuracy of the percentage threshold based on the time series of the test index to reflect the probability distribution characteristics of the error test index, process the samples of the time series of the test index, reorder the samples of the time series of the test index according to the ascending order of the deviation value, and calculate the accuracy of each percentage threshold according to the following fourth formula: ; where F is the percentage threshold; n is the total number of the time series; m is the serial number of the deviation index corresponding to the calculated percentage threshold. If m is rounded to an integer, the deviation index corresponding to the mth serial number is the accuracy of each percentage threshold.

[0017] In a possible implementation, the establishing a radiation terrain correction model by using the fractional spectral radiation transfer model based on the radiation observation data and the target terrain data at a specified high resolution includes:

[0018] Based on the radiation observation data and the target terrain data at a specified high resolution, calculate the surface solar radiation data under different terrain parameters by using the fractional spectral radiation transfer model, construct a relationship model between each radiation element and the terrain parameters through the surface solar radiation data, and establish a radiation terrain correction model based on the relationship model; where the terrain parameters include terrain elevation, slope, aspect and underlying surface data.

[0019] In a possible implementation, the target terrain data is terrain grid data clustered from 90m to 1km, and the resampled first radiation data is 1km first radiation data;

[0020] Performing terrain correction processing on the resampled first radiation data and the target terrain data through the radiation terrain correction model to obtain the target radiation data after terrain correction processing, including:

[0021] Using the terrain grid data to perform terrain correction processing on the 1km first radiation data through the radiation terrain correction model to obtain the hourly regional refined ground solar radiation data, and using the regional refined ground solar radiation data as the target radiation data after satellite terrain correction processing;

[0022] Among them, the relationship model between each radiation element and terrain parameters includes: GHI = F(S); where GHI is solar radiation, S is terrain parameters, and the terrain parameters include terrain elevation, slope, aspect, and underlying surface data;

[0023] The radiation terrain correction model includes: GHI 订正 = GHI 重采样 ×(F(S 细网格 ) / F(S 粗网格 )); where GHI 订正 is the target radiation data after the terrain correction processing, GHI 重采样 is the resampled first radiation data, S 细网格 is the terrain parameter at a fine grid spatio-temporal resolution of 1km, S 粗网格 is the terrain parameter at a coarse grid spatio-temporal resolution of 4km or 9km. The terrain parameter with a 4km spatial resolution corresponds to the satellite full-disk ground solar radiation data, and the terrain parameter with a 9km spatial resolution corresponds to the numerical simulation radiation data.

[0024] In a possible implementation, performing spatio-temporal consistency alignment processing and multi-source collaborative quality control processing on the radiation observation data, the target radiation data, and the target terrain data to obtain the target observed multi-source data with aligned horizontal spatial grid and time grid, including:

[0025] Clustering the underlying surface data at the specified high resolution from 90m to 1km and keeping the spatial grid consistent with the target terrain data, and converting the radiation observation data of the station to the spatial grid corresponding to the target terrain data to achieve spatial consistency processing;

[0026] Aligning the time grid of the radiation observation data of the station with the target radiation data after satellite terrain correction processing to be hourly data at whole points to achieve time consistency processing;

[0027] Compare and analyze the radiation observation data and the target radiation data to obtain a comparative analysis result of the difference between the radiation observation data and the target radiation data; perform outlier detection on the radiation observation data and the target radiation data to obtain an outlier detection result; perform data correction processing based on the comparative analysis result and the outlier detection result to eliminate the difference between the radiation observation data and the target radiation data and achieve multi-source collaborative quality control processing.

[0028] In a possible implementation, the radiation element field with the specified spatial resolution includes: a radiation element field with a spatial resolution less than or equal to 1 km; the target observation multi-source data is multi-source data matching the corresponding grid with a resolution less than or equal to 1 km per hour; the solar energy resource monitoring data is solar energy resource monitoring data with a resolution less than or equal to 1 km per hour.

[0029] In a second aspect, the present application provides a solar energy resource monitoring device, including:

[0030] An acquisition module, configured to acquire radiation data to be processed, and convert the original resolution data of the radiation data to be processed into a radiation element field with a specified spatial resolution to obtain first radiation data after resampling; wherein, the radiation data to be processed includes satellite full-disk ground solar radiation data or numerical simulation radiation data;

[0031] A establishment module, configured to establish a radiation terrain correction model by using a spectral radiation transfer model based on radiation observation data and target terrain data at a specified high resolution; wherein, the radiation observation data includes discrete radiation observation data of multiple different spatio-temporal scales;

[0032] A correction module, configured to perform terrain correction processing on the first radiation data after resampling and the target terrain data through the radiation terrain correction model to obtain target radiation data after terrain correction processing;

[0033] A processing module, configured to perform spatio-temporal consistency alignment processing and multi-source collaborative quality control processing on the radiation observation data, the target radiation data, and the target terrain data to obtain target observation multi-source data with both horizontal spatial grid points and time grid points aligned; wherein, the target terrain data includes underlying surface data, terrain elevation data, slope data, and aspect data;

[0034] A fusion module, configured to obtain solar energy resource monitoring data through a multi-grid variational fusion method based on the aligned target observation multi-source data and using the target radiation data as a background field.

[0035] In a third aspect, the present application further provides an electronic device, including a memory and a processor. A computer program that can run on the processor is stored in the memory. When the processor executes the computer program, the method described in the first aspect above is implemented.

[0036] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and run by a processor, the computer-executable instructions cause the processor to run the method described in the first aspect above.

[0037] The present application brings the following beneficial effects:

[0038] A solar resource monitoring method, device, and electronic device provided by the present application can convert the original resolution data of the radiation data to be processed into a radiation element field with a specified spatial resolution, and obtain the first radiation data after resampling. Among them, the radiation data to be processed includes satellite full-disk ground solar radiation data or numerical simulation radiation data. Based on the radiation observation data and the target terrain data at a specified high resolution, a radiation terrain correction model is established using a spectral radiation transfer model. Among them, the radiation observation data includes discrete radiation observation data of multiple different spatio-temporal scales. Based on the resampled first radiation data and the target terrain data, terrain correction processing is performed through the radiation terrain correction model to obtain the target radiation data after terrain correction processing. Through spatio-temporal consistency alignment processing and multi-source collaborative quality control processing of the radiation observation data, the target radiation data, and the target terrain data, the target observed multi-source data with aligned horizontal spatial grid points and time grid points is obtained. Among them, the target terrain data includes underlying surface data, terrain elevation data, slope data, and aspect data. Based on the aligned target observed multi-source data and using the target radiation data as the background field, solar resource monitoring data is obtained through the multi-grid variational fusion method. In this solution, the refined monitoring of solar resources is centered on the core algorithm of multi-grid variational fusion, i.e., STMAS. On the basis of satellite inversion or numerical simulation radiation, discrete radiation observation data of various different spatio-temporal scales are comprehensively fused, making the data set closer to the observation and of higher quality, obtaining solar resource monitoring with high monitoring accuracy and high time and space resolution, and solving the technical problem of the low accuracy of current solar resource monitoring data.

[0039] To make the above objects, features, and advantages of the present application more obvious and understandable, the following specific preferred embodiments are given below and described in detail in conjunction with the accompanying drawings. Description of the Drawings

[0040] To more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0041] Figure 1 Schematic flowchart of the solar energy resource monitoring method provided by an embodiment of the present application;

[0042] Figure 2 Schematic diagram of the resampling principle in the solar energy resource monitoring method provided by an embodiment of the present application;

[0043] Figure 3 An example of the change of the percentile threshold curve of the wind speed accuracy rate in the solar energy resource monitoring method provided by an embodiment of the present application;

[0044] Figure 4 Schematic structural diagram of a solar energy resource monitoring device provided by an embodiment of the present application;

[0045] Figure 5 Shows the schematic structural diagram of an electronic device provided by an embodiment of the present application. Specific embodiments

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions of the present application with reference to the drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0047] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes other steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products, or devices.

[0048] Currently, the accuracy rate of solar energy resource monitoring data is relatively low. Based on this, the embodiments of the present application provide a solar energy resource monitoring method, device, and electronic device, which can solve the technical problem of the relatively low accuracy rate of current solar energy resource monitoring data.

[0049] The following further introduces the embodiments of the present invention with reference to the drawings.

[0050] Figure 1 This is a schematic flowchart of a solar resource monitoring method provided by an embodiment of the present application. As Figure 1 shown, the method includes:

[0051] Step S110: Obtain the radiation data to be processed, and convert the original resolution data of the radiation data to be processed into a radiation element field with a specified spatial resolution, so as to obtain the first radiation data after resampling.

[0052] Among them, the radiation data to be processed includes satellite full-disk ground solar radiation data or numerical simulation radiation data.

[0053] As a possible implementation manner, obtain Fengyun satellite full-disk ground solar radiation data or numerical simulation data, and resample and process it into a radiation element field with a spatial resolution of 1 km or (less than 1 km or 100 meters). Exemplarily, as Figure 2 shown in the schematic diagram of the resampling principle, process the data with the original resolution into 1 km data.

[0054] Step S120: Based on the radiation observation data and the target terrain data at a specified high resolution, establish a radiation terrain correction model by using a spectral radiation transfer model.

[0055] Among them, the radiation observation data includes discrete radiation observation data of multiple different spatio-temporal scales. Exemplarily, based on a spectral radiation transfer model, meteorological station observation data, and high-resolution terrain data, establish a radiation terrain spatial downscaling correction model.

[0056] As an optional implementation manner, this step S120 may specifically include the following steps: Based on the radiation observation data, the target terrain data at a specified high resolution, and other element observation data of the meteorological station, use a spectral radiation transfer model to calculate the surface solar radiation data under different terrain parameters (terrain elevation, slope, aspect, etc.), construct a relationship model between each radiation element and the terrain parameters through the surface solar radiation data, and establish a radiation terrain correction model based on the relationship model; among them, the terrain parameters include terrain elevation, slope, aspect, and underlying surface data. In this way, the establishment efficiency of the radiation terrain correction model can be improved, and the data accuracy of the established radiation terrain correction model can also be improved.

[0057] Step S130: Perform terrain correction processing on the first radiation data after resampling and the target terrain data through the radiation terrain correction model to obtain the target radiation data after terrain correction processing.

[0058] Exemplarily, based on the resampled satellite radiation data and high-resolution terrain data, regional refined ground solar radiation data (hourly at 1 km) are obtained using a radiation-terrain spatial downscaling correction model.

[0059] As an alternative implementation, the target terrain data are terrain grid data clustered from 90 m to 1 km, and the resampled first radiation data are 1 km first radiation data; this step S130 may specifically include the following steps: using the terrain grid data to perform terrain correction processing on the 1 km first radiation data through a radiation-terrain correction model to obtain regional refined ground solar radiation data hourly at 1 km, and taking the regional refined ground solar radiation data as the target radiation data after terrain correction processing.

[0060] Among them, the radiation-terrain correction model includes: GHI 订正 =GHI 重采样 ×(F(S 细网格 ) / F(S 粗网格 )); where GHI 订正 is the target radiation data after terrain correction processing, GHI 重采样 is the resampled first radiation data, S 细网格 is the terrain parameter when the fine grid spatio-temporal resolution is 1 km, S 粗网格 is the terrain parameter when the coarse grid spatio-temporal resolution is 4 km or 9 km. The terrain parameter with a 4 km spatial resolution corresponds to the satellite full-disk ground solar radiation data, and the terrain parameter with a 9 km spatial resolution corresponds to the numerical simulation radiation data.

[0061] Exemplarily, using a spectral radiation transfer model (Simple Model for Atmospheric Transmission of Sunshine, SMARTS), calculate the surface solar radiation under terrain parameters based on meteorological station observation data, construct a relationship model between each radiation element and terrain parameters, and thus establish a radiation-terrain correction model. Based on the terrain grid data with a 1 km resolution in the region (clustered from 90 m to 1 km), perform terrain correction on the resampled 1 km satellite radiation data to obtain regional refined ground solar radiation data (hourly at 1 km).

[0062] In practical applications, the relationship model between each radiation element and terrain parameters includes: GHI = F(S); where GHI is solar radiation, S is the terrain parameter, and the terrain parameter includes terrain elevation, slope, aspect, and underlying surface data; the radiation-terrain correction model includes: GHI 订正 =GHI 重采样 ×(F(S 细网格 ) / F(S 粗网格 )); where GHI订正 is the target radiation data after terrain correction processing, GHI 重采样 is the first radiation data after resampling, S 细网格 is the terrain parameter when the fine grid spatio-temporal resolution is 1 km, S 粗网格 is the terrain parameter when the coarse grid spatio-temporal resolution is 4 km or 9 km. The terrain parameter with a 4 km spatial resolution corresponds to the satellite full-disc ground solar radiation data, and the terrain parameter with a 9 km spatial resolution corresponds to the numerical simulation radiation data.

[0063] Step S140, perform spatio-temporal consistency alignment processing and multi-source collaborative quality control processing on the radiation observation data, target radiation data, and target terrain data to obtain the target observed multi-source data with aligned horizontal spatial grid points and time grid points.

[0064] Among them, the target terrain data includes underlying surface data, terrain elevation data, slope data, and aspect data. Exemplarily, through the spatio-temporal consistency and multi-source collaborative quality control of meteorological station radiation observation data (i.e., the above-mentioned meteorological station observation data), satellite terrain-corrected regional refined ground solar radiation data, and high-resolution terrain underlying surface data (underlying surface data and the above-mentioned high-resolution terrain data), multi-source data with a unified horizontal spatial grid point and time grid point of 1 km per hour are obtained.

[0065] It should be noted that the target observed multi-source data includes various data such as meteorological radiation station observation data, photovoltaic power station observation data, and newly built or self-built radiation station observation data.

[0066] As an optional implementation manner, this step S140 may specifically include the following steps: cluster the terrain underlying surface data at a specified high resolution from 90 m to 1 km and keep the spatial grid consistent with the target terrain data, and convert the radiation observation data of the site to the spatial grid corresponding to the target terrain data to achieve spatial consistency processing; align the time grid of the radiation observation data of the site with the target radiation data after satellite terrain correction processing into hourly data at the whole hour to achieve time consistency processing; perform a comparative analysis between the radiation observation data and the target radiation data to obtain the comparative analysis result of the difference between the radiation observation data and the target radiation data; perform outlier detection on the radiation observation data and the target radiation data to obtain the outlier detection result; perform data correction processing based on the comparative analysis result and the outlier detection result to eliminate the difference between the radiation observation data and the target radiation data and achieve multi-source collaborative quality control processing.

[0067] Regarding the above spatio-temporal consistency, it should be noted that spatio-temporal consistency means ensuring that the spatial grids and time grids of all data are consistent. For the processing of spatial consistency, it should be noted that through the above steps S120 and S130, the spatial grids of the regional refined ground solar radiation data and high-resolution terrain data after satellite terrain correction are consistent. At this time, it is also necessary to cluster the high-resolution underlying surface data from 90m to 1km and ensure that the spatial grid is consistent with the target terrain data. In addition, the radiation observation data of the stations also need to be transformed to the corresponding spatial grid of the target terrain data. For the processing of time consistency, it should be noted that the time grids of the station radiation observation data and the first radiation data after terrain correction are aligned, both being hourly data at the whole hour.

[0068] Step S150, based on the aligned target observed multi-source data and using the target radiation data as the background field, obtain the solar energy resource monitoring data through the multi-grid variational fusion method.

[0069] It should be noted that the multi-grid variational fusion method is the core algorithm of the Spatio-Temporal Multi-Scale Analysis System (STMAS). Essentially, it is a multi-grid three-dimensional variational assimilation method, which can also be understood as the deep combination of multi-grid and 3DVAR. Among them, STMAS is a new generation of fusion system developed under the framework of LAPS.

[0070] In an optional implementation manner, since the STMAS system does not have the fusion analysis function for solar radiation elements, in the embodiments of the present application, the core algorithm will be applied to perform adaptive algorithm design and function development for the solar radiation elements and the characteristics of the observation data, including the design of background field data, the design of key sensitive parameters of the variational algorithm (for the distribution characteristics and errors of radiation elements and observation data), and the design of the fusion analysis function for radiation elements.

[0071] As an optional implementation manner, the radiation element field with a specified spatial resolution includes: a radiation element field with a spatial resolution less than or equal to 1km; the target observed multi-source data is multi-source data matching the corresponding grid with a resolution less than or equal to 1km per hour; the solar energy resource monitoring data is solar energy resource monitoring data with a resolution less than or equal to 1km per hour.

[0072] Exemplarily, in this step, the radiation grid data (the data after the above model correction) inverted by the satellite with spatial downscaling is used as the background field, and based on the spatio-temporally consistent radiation observation data and the multi-grid variational fusion method, a solar energy resource monitoring product with a resolution less than or equal to 1km per hour is obtained.

[0073] In the embodiments of the present application, the refined monitoring of solar energy resources takes the technology of multi-grid variational fusion (the core algorithm of STMAS) as the core. Based on satellite inversion or numerical simulation of radiation, various discrete radiation observation data of different spatio-temporal scales are comprehensively fused, making the data set closer to the observation and of higher quality, obtaining solar energy resource monitoring with higher data accuracy and higher spatial resolution, thus solving the problems of low monitoring accuracy and low spatial resolution of solar energy resources in the prior art.

[0074] In some embodiments, after step S150, the method may further include the following steps:

[0075] Taking the newly collected photometric data as new observation data, combining the new observation data and historical observation data to obtain an updated observation data set, and using the updated observation data set as the background field for multi-grid variational fusion processing with the target radiation data to realize the update and optimization of the solar energy resource monitoring data.

[0076] Exemplarily, combining the newly collected photometric data with the historical observation data to obtain more observation data, and performing multi-grid variational fusion based on these more observation data, thereby realizing the continuous iteration and optimization of the solar energy resource monitoring product, and continuously updating the product version.

[0077] In some embodiments, the method may further include the following steps:

[0078] Obtaining the observation data of photovoltaic enterprises and newly built radiation stations, taking the observation data of photovoltaic enterprises and newly built radiation stations as new radiation observation data, using the solar energy resource evaluation method to perform quality control on the new radiation observation data, obtaining a radiation observation data set that meets the specified quality conditions, and taking the radiation observation data set as the test and evaluation data source for solar energy resource monitoring data;

[0079] Based on the site longitude and latitude information of the radiation observation data set, using the nearest distance method to extract the corresponding grid point solar energy resource extraction value of the solar energy resource monitoring data, and calculating the corresponding test and evaluation indexes based on the corresponding grid point data: root mean square error RMSE / mean bias BIAS / correlation coefficient R; among them, the linear correlation relationship between the single-site time series observation value of the site of the radiation observation data set and the corresponding grid point solar energy resource extraction value is represented by the following first formula of Pearson correlation coefficient: ; where is the average value of the analysis value of site j extracted from the solar energy resource data set in the analysis area and time period; is the radiation value at time j of site i extracted from the solar energy resource data set; is the average value of the observation values of radiation observation site j in the analysis area and time period; is the radiation observation value at time j of radiation observation station i; is the Pearson correlation coefficient; The average value of the difference between the radiation element of the solar energy resource monitoring data and the actual radiation observation element is determined by the following second formula: ; where is the average value of the difference between the radiation element of the solar energy resource monitoring data and the actual radiation observation element, is the radiation value at time j of site i extracted from the solar energy resource dataset; is the radiation observation value at time j of radiation observation station i The root mean square value of the sum of squares of the differences between the radiation element of the solar energy resource monitoring data and the actual radiation observation element is determined by the following third formula: ; where N is the number of statistical sites, is the radiation value at time j of site i extracted from the solar energy resource dataset; is the radiation observation value at time j of radiation observation station i; is the root mean square value of the sum of squares of the differences;

[0080] Calculate according to the first formula, the second formula and the third formula for all times and all sites to obtain the time series of the test index, and use the time series of the test index to analyze the test index to obtain the overall characteristics and spatio-temporal distribution characteristics;

[0081] Calculate the accuracy rate of the percentage threshold based on the time series of the test index to reflect the probability distribution characteristics of the error test index, process the samples of the time series of the test index, reorder the samples of the time series of the test index according to the ascending order of the deviation value, and calculate the accuracy rate of each percentage threshold according to the following fourth formula: ; where F is the percentage threshold; n is the total number of the time series; m is the serial number of the deviation index corresponding to the calculated percentage threshold. If m is rounded to an integer, the deviation index corresponding to the mth serial number is the accuracy rate of each percentage threshold.

[0082] Among them, for the specific evaluation criteria of the above solar energy resource evaluation method, exemplarily, the evaluation criteria can be the criteria of "Solar Energy Resource Assessment Method GB / T 37526—2019".

[0083] As an example, the inspection and evaluation process of the high-resolution solar energy resource dataset can specifically include the following steps:

[0084] 1. Organize and obtain the observed data of optoelectronic enterprises and newly built radiation stations, and use the national standard GB / T 37526-2019 (Solar Energy Resource Evaluation Method) to control the quality of radiation observation data, obtaining a reliable radiation observation data set as the test and evaluation data source for the high spatio-temporal resolution solar energy resource data set;

[0085] 2. Based on the longitude and latitude information of the stations in the radiation observation data, use the nearest distance method to extract the corresponding grid point data of the high spatio-temporal resolution solar energy resource data set, and calculate the corresponding test and evaluation indicators: RMSE (Root Mean Square Error) / BIAS (Mean Bias) / R (Correlation Coefficient):

[0086] The linear correlation between the single-station time series observation values of the radiation observation station and the extracted values of the solar energy resource is described by the Pearson correlation coefficient R:

[0087] (1);

[0088] Among them, is the average value of the analysis values of station j extracted from the solar energy resource data set within the analysis area and time period; is the average value of the observed values of radiation observation station j within the analysis area and time period.

[0089] Mean Bias: The average value of the difference between the radiation elements of the solar energy resource data set and the actual radiation observation elements.

[0090] (2);

[0091] Root Mean Square Error: The root mean square value of the sum of the squares of the differences between the radiation elements of the solar energy resource data set and the actual radiation observation elements.

[0092] (3);

[0093] Among them, N is the number of statistical stations, is the radiation value at time j of station i extracted from the solar energy resource data set, is the radiation observation value at time j of radiation observation station i.

[0094] 3. Calculate according to the above formulas (1), (2), and (3) for all times and all stations, obtaining the time series of the test indicators, and the overall characteristics and spatio-temporal distribution characteristics can be analyzed from the test indicators.

[0095] 4. Calculate the percentage threshold accuracy based on the time series of the test indicators. This test method can reflect the probability distribution characteristics of the error test indicators. Process the time series samples of the test indicators, reorder the samples according to the ascending order of the deviation values, and then calculate the accuracy of each percentile threshold according to formula (4).

[0096] (4);

[0097] Among them, F is the percentage threshold (such as 5%, 10%, etc.), n is the total number of time series data, m is the serial number of the deviation index corresponding to the calculated percentage threshold, round m to the nearest integer, and the deviation index corresponding to the m-th serial number is the accuracy rate of each sub-threshold. Exemplarily, as Figure 3 shown in the variation curve of the percentile threshold of wind speed accuracy.

[0098] Through the above four steps, a more comprehensive inspection and evaluation of the quality of the solar energy resource dataset can be carried out. Through the above data processing method, continuous iteration and optimization of the solar energy resource monitoring data can be achieved, thereby continuously updating the solar energy resource monitoring products.

[0099] Figure 4 A structural schematic diagram of a solar energy resource monitoring device is provided. As Figure 4 shown, the solar energy resource monitoring device 400 includes:

[0100] An acquisition module 401, configured to acquire the radiation data to be processed, and convert the original resolution data of the radiation data to be processed into a radiation element field with a specified spatial resolution, so as to obtain the first radiation data after resampling; among them, the radiation data to be processed includes satellite full-disk ground solar radiation data or numerical simulation radiation data;

[0101] A building module 402, configured to establish a radiation terrain correction model by using a spectral radiation transfer model based on radiation observation data and target terrain data at a specified high resolution; among them, the radiation observation data includes discrete multi-class radiation observation data with different spatio-temporal scales;

[0102] A correction module 403, configured to perform terrain correction processing on the resampled first radiation data and the target terrain data through the radiation terrain correction model to obtain the target radiation data after terrain correction processing;

[0103] A processing module 404, configured to perform spatio-temporal consistency alignment processing and multi-source collaborative quality control processing on the radiation observation data, the target radiation data, and the target terrain data to obtain the target observed multi-source data with both horizontal spatial grid points and time grid points aligned; among them, the target terrain data includes underlying surface data, terrain elevation data, slope data, and aspect data;

[0104] A fusion module 405, configured to obtain solar energy resource monitoring data through a multi-grid variational fusion method based on the aligned target observed multi-source data and using the target radiation data as the background field.

[0105] The solar resource monitoring device provided by the embodiment of the present application has the same technical features as the solar resource monitoring method provided by the above embodiment, so it can also solve the same technical problems and achieve the same technical effects.

[0106] An electronic device provided by an embodiment of the present application, such as Figure 5 As shown, the electronic device 500 includes a processor 502 and a memory 501. A computer program that can run on the processor is stored in the memory. When the processor executes the computer program, the steps of the method provided by the above embodiment are implemented.

[0107] See Figure 5 , the electronic device further includes: a bus 503 and a communication interface 504. The processor 502, the communication interface 504, and the memory 501 are connected through the bus 503. The processor 502 is used to execute an executable module stored in the memory 501, such as a computer program.

[0108] Among them, the memory 501 may include a high-speed random access memory (Random Access Memory, abbreviated as RAM), and may also include a non-volatile memory, such as at least one disk memory. Through at least one communication interface 504 (which can be wired or wireless), a communication connection between the system network element and at least one other network element can be realized, and the Internet, wide area network, local area network, metropolitan area network, etc. can be used.

[0109] The bus 503 may be an ISA bus, a PCI bus, an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of simplicity of representation, Figure 5 only a bidirectional arrow is used in

[0110] to represent, but it does not mean that there is only one bus or one type of bus.

[0111] The processor 502 may be an integrated circuit chip with the ability to process signals. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor 502 or the instructions in the form of software. The above-mentioned processor 502 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by a hardware decoding processor, or executed and completed by a combination of hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 501, and the processor 502 reads the information in the memory 501 and combines its hardware to complete the steps of the above method.

[0112] Corresponding to the above solar resource monitoring method, an embodiment of the present application further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and run by a processor, the computer-executable instructions cause the processor to run the steps of the above solar resource monitoring method.

[0113] The solar resource monitoring device provided by the embodiments of the present application may be specific hardware on a device or software or firmware installed on the device, etc. For the device provided by the embodiments of the present application, the implementation principle and the technical effects generated are the same as those of the foregoing method embodiments. For the sake of brief description, for the parts not mentioned in the device embodiments, reference may be made to the corresponding content in the foregoing method embodiments. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can all refer to the corresponding processes in the above method embodiments, and will not be repeated here.

[0114] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical or other forms.

[0115] For another example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment or a part of code, and the part of the module, the program segment or the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0116] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0117] In addition, the functional units in the embodiments provided in the present application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0118] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the solar resource monitoring method described in various embodiments of this application. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0119] It should be noted that: similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", "third", etc. are only used for descriptive distinction and cannot be understood as indicating or implying relative importance.

[0120] Finally, it should be noted that: the above-mentioned embodiments are only specific implementation manners of this application, used to illustrate the technical solution of this application, rather than limiting it. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed in this application can still modify the technical solutions recorded in the foregoing embodiments, or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes, or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A solar resource monitoring method, characterized in that, The method includes: Obtaining the radiation data to be processed, and converting the original resolution data of the radiation data to be processed into a radiation element field with a specified spatial resolution to obtain the first radiation data after resampling; wherein, the radiation data to be processed includes satellite full-disk ground solar radiation data or numerical simulation radiation data; Based on the radiation observation data and the target terrain data at a specified high resolution, establishing a radiation terrain correction model by using a spectral radiation transfer model; wherein, the radiation observation data includes discrete radiation observation data of multiple different spatio-temporal scales; Performing terrain correction processing on the first radiation data after resampling and the target terrain data through the radiation terrain correction model to obtain the target radiation data after terrain correction processing; Performing spatio-temporal consistency alignment processing and multi-source collaborative quality control processing on the radiation observation data, the target radiation data, and the target terrain data to obtain the target observed multi-source data with both horizontal spatial grid points and time grid points aligned; wherein, the target terrain data includes underlying surface data, terrain elevation data, slope data, and aspect data; Based on the aligned target observed multi-source data and using the target radiation data as the background field, obtaining solar energy resource monitoring data through a multi-grid variational fusion method.

2. The method according to claim 1, characterized in that, After obtaining the solar energy resource monitoring data through the multi-grid variational fusion method based on the aligned target observed multi-source data and using the target radiation data as the background field, it further includes: Taking the newly collected photometric data as new observation data, combining the new observation data and the historical observation data to obtain an updated observation data set, and using the updated observation data set as the background field with the target radiation data to perform multi-grid variational fusion processing to realize the update and optimization of the solar energy resource monitoring data.

3. The method according to claim 2, wherein It further includes: Obtaining the observation data of photovoltaic enterprises and newly built radiation stations, and taking the observation data of photovoltaic enterprises and newly built radiation stations as new radiation observation data, using a solar energy resource evaluation method to perform quality control on the new radiation observation data to obtain a radiation observation data set meeting the specified quality conditions, and taking the radiation observation data set as the test and evaluation data source of the solar energy resource monitoring data; Based on the longitude and latitude information of the stations in the radiation observation dataset, the corresponding grid point solar resource extraction values of the solar resource monitoring data are extracted using the nearest distance method, and the corresponding test and evaluation indicators are calculated based on the corresponding grid point data: root mean square error RMSE / mean bias BIAS / correlation coefficient R; among them, the linear correlation relationship between the single-station time series observation values of the stations in the radiation observation dataset and the corresponding grid point solar resource extraction values is represented by the following first formula of Pearson correlation coefficient: ; among them, is the average value of the analysis values of station j extracted from the solar resource dataset within the analysis area and time period; is the radiation value at time j of station i extracted from the solar resource dataset; is the average value of the observation values of radiation observation station j within the analysis area and time period; is the radiation observation value at time j of radiation observation station i; is the Pearson correlation coefficient; the average value of the difference between the radiation elements of the solar resource monitoring data and the actual radiation observation elements is determined by the following second formula: ; among them, is the average value of the difference between the radiation elements of the solar resource monitoring data and the actual radiation observation elements, is the radiation value at time j of station i extracted from the solar resource dataset; is the radiation observation value at time j of radiation observation station i The root mean square value of the sum of squares of the differences between the radiation elements of the solar resource monitoring data and the actual radiation observation elements is determined by the following third formula: ; where N is the number of statistical stations, is the radiation value at time j of station i extracted from the solar resource dataset; is the radiation observation value at time j of radiation observation station i; is the root mean square value of the sum of squares of the differences; Calculating according to the first formula, the second formula, and the third formula for all times and all stations to obtain a time series of test indicators, and using the time series of test indicators to analyze the test indicators to obtain the overall characteristics and spatio-temporal distribution characteristics; Calculate the accuracy rate of the percentage threshold based on the time series of the inspection indicators to reflect the probability distribution characteristics of the error inspection indicators. Process the samples of the time series of the inspection indicators, reorder the samples of the time series of the inspection indicators in ascending order of the deviation value, and calculate the accuracy rate of each percentage threshold according to the following fourth formula: ; where F is the percentage threshold; n is the total number of the time series; m is the serial number of the deviation index corresponding to the calculated percentage threshold. If m is rounded to an integer, the deviation index corresponding to the m-th serial number is the accuracy rate of each percentage threshold.

4. The method according to claim 1, characterized in that, The establishing of the radiation terrain correction model by using a spectral radiation transfer model based on the radiation observation data and the target terrain data at a specified high resolution includes: Based on the radiation observation data and the target terrain data at a specified high resolution, using a spectral radiation transfer model to calculate the surface solar radiation data under different terrain parameters, constructing a relationship model between each radiation element and the terrain parameters through the surface solar radiation data, and establishing a radiation terrain correction model based on the relationship model; wherein, the terrain parameters include terrain elevation, slope, aspect, and underlying surface data.

5. The method according to claim 4, wherein The target terrain data is terrain grid data clustered from 90m to 1km, and the resampled first radiation data is 1km first radiation data; Performing terrain correction processing on the resampled first radiation data and the target terrain data through the radiation terrain correction model to obtain the target radiation data after terrain correction processing, including: Using the terrain grid data to perform terrain correction processing on the 1km first radiation data through the radiation terrain correction model to obtain the hourly regional refined ground solar radiation data, and using the regional refined ground solar radiation data as the target radiation data after terrain correction processing; Among them, the relationship model between each radiation element and terrain parameters includes: GHI = F(S); where GHI is solar radiation, S is terrain parameters, and the terrain parameters include terrain elevation, slope, aspect, and underlying surface data; The radiation terrain correction model includes: GHI 订正 =GHI 重采样 ×(F(S 细网格 ) / F(S 粗网格 )); where, GHI 订正 is the target radiation data after the terrain correction process, GHI 重采样 is the first radiation data after the resampling, S 细网格 is the terrain parameter when the fine grid spatio-temporal resolution is 1 km, S 粗网格 is the terrain parameter when the coarse grid spatio-temporal resolution is 4 km or 9 km. The terrain parameter with a 4 km spatial resolution corresponds to the satellite full-disk ground solar radiation data, and the terrain parameter with a 9 km spatial resolution corresponds to the numerical simulation radiation data.

6. The method according to claim 1, wherein Performing spatio-temporal consistency alignment processing and multi-source collaborative quality control processing on the radiation observation data, the target radiation data, and the target terrain data to obtain the target observed multi-source data with aligned horizontal spatial grid and time grid, including: Clustering the underlying surface data at the specified high resolution from 90m to 1km and keeping the spatial grid consistent with the target terrain data, and converting the radiation observation data of the station to the spatial grid corresponding to the target terrain data to achieve spatial consistency processing; Aligning the time grid of the radiation observation data of the station with the target radiation data after satellite terrain correction processing into hourly data at the whole hour to achieve time consistency processing; Performing comparative analysis between the radiation observation data and the target radiation data to obtain the comparative analysis result of the difference between the radiation observation data and the target radiation data; performing outlier detection on the radiation observation data and the target radiation data to obtain the outlier detection result; performing data correction processing based on the comparative analysis result and the outlier detection result to eliminate the difference between the radiation observation data and the target radiation data and achieve multi-source collaborative quality control processing.

7. The method according to claim 5, characterized in that The radiation element field with the specified spatial resolution includes: the radiation element field with a spatial resolution less than or equal to 1km; the target observed multi-source data is the multi-source data matching the corresponding grid with an hourly resolution less than or equal to 1km; the solar energy resource monitoring data is the solar energy resource monitoring data with a resolution less than or equal to 1km per hour.

8. A solar resource monitoring device, characterized in that, Including: An acquisition module, configured to acquire the radiation data to be processed, and convert the original resolution data of the radiation data to be processed into a radiation element field with a specified spatial resolution to obtain the resampled first radiation data; where the radiation data to be processed includes satellite full-disk ground solar radiation data or numerical simulation radiation data; A building module, configured to establish a radiation terrain correction model based on the radiation observation data and the target terrain data at the specified high resolution by using the spectral radiation transfer model; where the radiation observation data includes discrete multi-class radiation observation data with different spatio-temporal scales; A correction module, configured to perform terrain correction processing on the resampled first radiation data and the target terrain data through the radiation terrain correction model to obtain the target radiation data after terrain correction processing; A processing module, configured to perform spatio-temporal consistency alignment processing and multi-source collaborative quality control processing on the radiation observation data, the target radiation data, and the target terrain data to obtain the target observation multi-source data with aligned horizontal spatial grid points and time grid points; wherein, the target terrain data includes underlying surface data, terrain elevation data, slope data, and aspect data; A fusion module, configured to obtain solar resource monitoring data through a multi-grid variational fusion method based on the aligned target observation multi-source data with the target radiation data as the background field.

9. An electronic device, comprising a memory and a processor, wherein a computer program that can run on the processor is stored in the memory, and is characterized in that When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 above are implemented.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and run by the processor, the computer-executable instructions cause the processor to run the method according to any one of claims 1 to 7.

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