Method and system for dynamic assessment of wind-solar resources based on multi-source remote sensing data

Abstract

The present application relates to the technical field of new energy resource evaluation, in particular to a wind and light resource dynamic evaluation method and system based on multi-source remote sensing data, comprising the following steps: inputting regional remote sensing images, calculating boundary confidence coefficients, generating interference classification marks, reconstructing resource prediction output values, predicting trend analysis results, evaluating development suitability grades, and generating regional resource evaluation results.In the present application, through the collaborative judgment of the boundary structure and the reflected light intensity distribution of the remote sensing images, the effective quantification of the boundary stability and the data confidence is realized, based on the terrain slope and the wind direction angle, the wind flow deviation and the regional shielding are carefully identified, according to the spatial grid connection combination and the neighborhood change trend, the wind speed and radiation data are accurately interpolated, at the same time, the climate factors and the resource change trend are accurately coupled, and the actual path connectivity is determined combined with the terrain and the land property, which effectively improves the spatial adaptability and the development accuracy of the wind and light resources.

Description

Technical Field

[0001] This invention relates to the field of new energy resource assessment technology, and in particular to a method and system for dynamic assessment of wind and solar resources based on multi-source remote sensing data. Background Technology

[0002] The field of new energy resource assessment technology encompasses the analysis, modeling, and prediction of the spatial distribution, reserve potential, and temporal changes of renewable energy sources such as solar, wind, hydro, and biomass energy. It aims to provide scientific site selection criteria and feasibility assessments for new energy projects through quantitative methods. This includes utilizing multi-source data such as remote sensing imagery, meteorological observations, and geographic information systems, combined with statistical modeling, spatial analysis, and historical trend analysis, to assess the exploitability and dynamic characteristics of new energy resources within a region. Specifically, the dynamic assessment method for wind and solar resources based on multi-source remote sensing data refers to using multiple data sources, such as remote sensing satellite data, ground meteorological station data, and UAV high-altitude imagery, to assess wind and solar energy resources within a specific region. The joint assessment of spatial distribution and temporal changes covers automatic acquisition and synchronous processing of data sources, spatial reconstruction of wind speed and solar radiation parameters, correction of correspondence between observed and measured data, dynamic sequence interpolation, and generation of time-series resource maps. This includes extracting wind speed-sensitive areas and high-light areas from remote sensing images using rule templates, applying ground observation data for fixed-point and timing error correction, dynamically partitioning and interpolating resource data using hierarchical time series, and outputting charts and statistical data that can be used for resource analysis in geographic grid units. The process relies on specific remote sensing image processing workflows, meteorological data correction mechanisms, and time series interpolation methods to achieve accurate quantification and structured expression of the spatial and temporal dimensions of wind and solar resources.

[0003] Traditional wind and solar resource assessment techniques tend to focus on the spatial distribution characteristics of a single data source when processing remote sensing data. They are insufficient in assessing the consistency between boundary structure and light reflection distribution, and fail to clearly identify wind disturbances caused by the angle between topographic slope and wind direction. When faced with missing data areas, they only perform coarse interpolation to fill the gaps, ignore the correlation between climate factors and resource trends, and do not fully consider the impact of topography and land combination structure on the suitability of resource development. As a result, the wind and solar resource assessment results are prone to local distortion and misjudgment of development. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a method and system for dynamic assessment of wind and solar resources based on multi-source remote sensing data. The technical solution is as follows:

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a dynamic assessment method for wind and solar resources based on multi-source remote sensing data, comprising the following steps:

[0006] S1: Input a remote sensing image of the region, analyze the degree of closure of the image contour boundary and the distribution of reflected light intensity, calculate the boundary continuity and the degree of reflection overlap, determine the consistency of the boundary structure between the image and the reference image, combine the main direction slope and morphological similarity, evaluate the image confidence level, and generate the boundary confidence coefficient;

[0007] S2: Using the boundary confidence coefficient, the area is divided into multiple grids, the angle between the grid slope angle and the prevailing wind direction is calculated, the influence of terrain on wind flow deviation is determined, and the shading area and shading path are analyzed in combination with elevation and slope characteristics to generate interference classification labels.

[0008] S3: Using the interference classification identifier, identify missing segments of remote sensing grids, calculate the direction and magnitude of wind speed and radiation changes in adjacent grids, construct a spatial interpolation path and extend the trend of observed data, reconstruct the wind speed and radiation distribution pattern in the missing area, and generate resource prediction output values.

[0009] S4: Using the resource prediction output values, analyze the wind speed and radiation change trajectories, calculate the corresponding offset directions of various climate factors between each region and the changes in wind and solar resources, construct the factor response dominant relationship sequence, predict the changing trends of wind and solar resources in multiple regions, and generate trend analysis results.

[0010] As a further aspect of the present invention, the boundary confidence coefficient includes boundary stability level, reflection structure integrity index, and slope main direction matching mark; the interference classification identifier specifically includes wind direction shift response level, shading propagation direction encoding, and interference area distribution label; the resource prediction output value includes wind speed change trend, radiation extension curve, and data interpolation coverage; and the trend analysis result includes factor shift direction vector, resource response synchronization index, and trend turning point spatial node.

[0011] As a further aspect of the present invention, the step of obtaining the boundary confidence coefficient specifically includes:

[0012] S101: Acquire regional remote sensing images, analyze the spatial closure of contour boundaries in the images, detect the distribution range of reflected light intensity, calculate the spatial continuity between boundary points and the degree of overlap of the coverage area of ​​the main reflection peak region, and generate boundary continuity coverage intervals.

[0013] S102: Based on the boundary continuity coverage area, determine the degree of consistency between the boundary structure in the current image and the reference image in terms of position and shape. Combine the slope distribution of the main boundary direction, analyze the changing trend of the main boundary direction and the similarity level of the geometric shape to obtain the boundary structure consistency index.

[0014] The specific formula for the similarity level between the changing trend of the main direction of the analysis boundary and the geometric shape is as follows:

[0015] ;

[0016] Calculate the boundary structure comparison parameters;

[0017] in, For boundary structure comparison parameters, For the current image, the first The normalized value of the slope of the principal direction of the segment boundary. For the reference image, the first The normalized value of the slope of the principal direction of the segment boundary. For the current image, the first The normalized value of the segment boundary length. For the reference image, the first The normalized value of the segment boundary length. For the current image, the first The normalized value of the variance of the distribution at the segment boundary points. For the reference image, the first The normalized value of the variance of the distribution at the segment boundary points. For the first Segment boundary structure noise influence factor For boundary segment index, The total number of boundary segments participating in the comparison;

[0018] S103: Based on the boundary structure consistency index, evaluate the image confidence level, establish an image availability grading sequence, filter each grading category in the confidence sequence, and obtain the boundary confidence coefficient.

[0019] As a further aspect of the present invention, the step of obtaining the interference classification identifier specifically includes:

[0020] S201: Obtain the boundary confidence coefficient, divide the area into multiple grids, calculate the slope aspect angle of each grid, collect the prevailing wind direction data of the area, calculate the angle difference between the slope aspect and the prevailing wind direction of each grid, and establish the slope wind angle distribution range based on the angle difference.

[0021] S202: Based on the slope-wind angle distribution range, determine the influence of terrain contour on wind flow offset, compare the spatial elevation position and relative slope characteristics of each grid, analyze the elevation differences and slope changes between each grid, and obtain spatial undulation characteristic factors.

[0022] S203: Based on the spatial undulation characteristic factor, analyze the occlusion area of ​​each grid in the horizontal direction, calculate the shading path range, construct the interference level index of the grid, and generate an interference classification label by combining the interference level of each grid in the area.

[0023] As a further aspect of the present invention, the step of obtaining the resource prediction output value specifically includes:

[0024] S301: Obtain the interference classification identifier, identify the missing grid segments in the remote sensing layer, analyze the number of connections of each grid in the spatial direction, determine the spatial arrangement order and the grid missing measurement distribution state, and establish the spatial missing measurement arrangement coefficient.

[0025] S302: Based on the spatial missing measurement arrangement coefficient, calculate the direction of change of wind speed and radiation between adjacent grids, statistically analyze the continuous amplitude of change of each grid in the time series, determine the distribution concentration and change frequency of the missing measurement segments, and obtain the dynamic trend characteristics of wind and light.

[0026] S303: Based on the dynamic trend characteristics of wind and solar energy, construct a spatial interpolation path, extend the trend of observed data changes, reconstruct the wind speed and radiation distribution pattern of the missing area, and generate resource prediction output values.

[0027] As a further aspect of the present invention, the steps for obtaining the trend analysis results are specifically as follows:

[0028] S401: Obtain the resource prediction output value, analyze the change trajectory of wind speed and solar radiation layers in the time series, calculate the change direction of wind speed and radiation under each spatial grid, and generate a resource change trend sequence by combining the monitoring data of multiple climate factors in the region.

[0029] S402: Based on the resource change trend sequence, determine the trend consistency of each climate factor with wind speed and radiation changes in multiple regions, and screen the main climate factors according to the degree of trend synchronization, calculate the distribution of the superposition effect of the target factors under the spatial grid structure, and obtain the spatial sequence of the main factors.

[0030] S403: Combining the spatial sequence of the main control factors, construct the factor response dominance relationship, and predict the changes in wind and solar resources in each region based on climate change trends, and obtain trend analysis results.

[0031] As a further aspect of the present invention, the specific formula for calculating the distribution of the superposition effect of the target factor under the spatial grid structure is as follows:

[0032] ;

[0033] Calculate the response intensity index of the main control factor;

[0034] in, For the first The response intensity index of each climate factor across all spatial grids For the first The climate factor in the first Normalized trend change values ​​within the time series of individual grid cells The normalized superposition mean of wind speed trend and radiation trend is the value at the th... The values ​​taken in each grid cell, For the first The climate factor in the first The normalized value of the variance of trend fluctuation within each grid cell. For the first The climate factor in the first The normalized importance weights corresponding to each grid cell For the index of spatial grid cells, This represents the total number of spatial grid cells. This refers to the sequence number of the climate factor.

[0035] As a further aspect of the present invention, the method further includes:

[0036] S5: Call the trend analysis results to analyze the variation range of terrain slope and land attribute combination structure between resource areas and substation nodes, calculate the elevation difference distribution and slope superposition range in continuous path segments, analyze the path connectivity and accessibility level of multiple locations, combine the wind and light resource level of the target location, evaluate the development suitability level of multiple locations, and generate regional resource assessment results.

[0037] The regional resource assessment results include path connectivity evaluation coefficient, resource accessibility matching ratio, and suitability level classification label.

[0038] As a further aspect of the present invention, the steps for obtaining the regional resource assessment results are specifically as follows:

[0039] S501: Call the trend analysis results to analyze the change range of terrain slope between the resource area and the substation node, and combine the land attribute combination structure to calculate the elevation distribution and slope combination in the continuous segment of the path to obtain the terrain accessibility feature interval.

[0040] S502: Based on the terrain accessibility feature range, analyze the path connectivity and accessibility level of multiple locations, combine the wind and light resource level of each target location, evaluate the development suitability level of multiple locations, and obtain suitable development judgment parameters.

[0041] S503: Based on the appropriate development judgment parameters, filter the development priority level of each resource in multiple locations in the region and match the level labels to generate regional resource assessment results.

[0042] On the other hand, a dynamic assessment system for wind and solar resources based on multi-source remote sensing data is provided. This system is applied to the dynamic assessment method for wind and solar resources based on multi-source remote sensing data. The system includes:

[0043] The boundary confidence analysis module takes a remote sensing image of the region as input, analyzes the degree of closure of the image contour boundaries and the distribution of reflected light intensity, calculates the continuity of the boundaries and the degree of reflection overlap, judges the consistency of the boundary structure between the image and the reference image, and evaluates the image confidence level by combining the main direction slope and morphological similarity, and generates the boundary confidence coefficient.

[0044] The terrain interference calculation module uses the boundary confidence coefficient to divide the area into multiple grids, calculates the angle between the grid slope angle and the prevailing wind direction, determines the impact of terrain on wind flow deviation, and analyzes the shading area and shading path by combining elevation and slope characteristics to generate interference classification labels.

[0045] The missing data completion module uses the interference classification identifier to identify missing segments of remote sensing grids, calculate the direction and magnitude of wind speed and radiation changes in adjacent grids, construct a spatial interpolation path and extend the trend of observed data, reconstruct the wind speed and radiation distribution pattern in the missing area, and generate resource prediction output values.

[0046] The climate trend linkage module uses the resource prediction output value to analyze the wind speed and radiation change trajectory, calculate the corresponding offset direction of various climate factors between each region and the change of wind and solar resources, construct the factor response dominant relationship sequence, predict the change trend of wind and solar resources in multiple regions, and generate trend analysis results.

[0047] The resource suitability assessment module calls upon the trend analysis results to analyze the variation range of terrain slope and land attribute combination structure between resource areas and substation nodes, calculates the elevation difference distribution and slope superposition range in continuous path segments, analyzes the path connectivity and accessibility levels of multiple locations, and assesses the development suitability level of multiple locations in conjunction with the wind and solar resource level of the target location, generating regional resource assessment results.

[0048] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0049] By collaboratively judging the boundary structure and reflected light intensity distribution of remote sensing images, the stability of boundaries and data confidence can be effectively quantified. Based on the terrain slope and wind direction angle, wind flow shift and regional occlusion are meticulously identified. In the missing measurement area, wind speed radiation data is accurately interpolated according to the spatial grid connection combination and the neighborhood change trend. At the same time, climate factors and resource change trends are accurately coupled, and the actual path connectivity is determined by combining terrain and land attributes, which effectively improves the spatial adaptability and development accuracy of wind and solar resources. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a schematic diagram of the workflow of the present invention;

[0052] Figure 2 This is a system flowchart of the present invention. Detailed Implementation

[0053] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0054] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0055] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0056] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0057] Please see Figure 1 This invention provides a technical solution for a dynamic assessment method of wind and solar resources based on multi-source remote sensing data, comprising the following steps:

[0058] S1: Input a remote sensing image of the region, analyze the degree of closure of the image contour boundary and the distribution of reflected light intensity, calculate the boundary continuity and the degree of reflection overlap, determine the consistency of the boundary structure between the image and the reference image, combine the main direction slope and morphological similarity, evaluate the image confidence level, and generate the boundary confidence coefficient;

[0059] S2: Using the boundary confidence coefficient, the area is divided into multiple grids, the angle between the grid slope angle and the prevailing wind direction is calculated, the influence of terrain on wind flow shift is determined, and the shading area and shading path are analyzed in combination with elevation and slope characteristics to generate interference classification labels.

[0060] S3: Using interference classification markers, identify missing segments of remote sensing grids, calculate the direction and magnitude of wind speed and radiation changes in adjacent grids, construct spatial interpolation paths and extend the trends of observed data, reconstruct the wind speed and radiation distribution patterns in the missing areas, and generate resource prediction output values.

[0061] S4: Using the resource prediction output, analyze the trajectory of wind speed and radiation changes, calculate the corresponding offset direction of various climate factors between each region and the changes in wind and solar resources, construct the factor response dominant relationship sequence, predict the changing trend of wind and solar resources in multiple regions, and generate trend analysis results.

[0062] S5: Call the trend analysis results to analyze the variation range of terrain slope and land attribute combination structure between resource areas and substation nodes, calculate the elevation difference distribution and slope superposition range in continuous path segments, analyze the path connectivity and accessibility level of multiple locations, combine the wind and light resource level of the target location, evaluate the development suitability level of multiple locations, and generate regional resource assessment results.

[0063] Boundary confidence coefficients include boundary stability level, reflective structure integrity index, and slope principal direction matching marker. Interference classification identifiers specifically include wind direction shift response level, shading propagation direction coding, and interference area distribution label. Resource prediction output values ​​include wind speed change trend, radiation extension curve, and data interpolation coverage. Trend analysis results include factor shift direction vector, resource response synchronization index, and trend turning point spatial nodes. Regional resource assessment results include path connectivity evaluation coefficient, resource accessibility matching ratio, and suitability level classification label.

[0064] The specific steps for obtaining the boundary confidence coefficient are as follows:

[0065] S101: Acquire regional remote sensing images, analyze the spatial closure of contour boundaries in the images, detect the distribution range of reflected light intensity, calculate the spatial continuity between boundary points and the degree of overlap of the coverage area of ​​the main reflection peak region, and generate boundary continuity coverage intervals.

[0066] Acquire regional remote sensing images, select satellite remote sensing image data and import it into the analysis platform. Collect image data for a specified area. Assuming the image resolution is 10 meters, select all pixels within the analysis area. For each pixel, use a gray-level difference algorithm to identify the locations of gray-level abrupt changes between pixels and extract the coordinate information of all boundary pixels. Draw a set of boundary line contours. Use spatial distance to calculate the closure of the boundary. If the distance between the start and end points of the boundary is less than the length of 3 pixels, the boundary is considered to have closure characteristics. Record the pixel number of the closed boundary. Number and classify the internal and external pixels of all closed boundaries. Then, for the interior of each closed boundary, use the light intensity data of the pixels to statistically determine the main reflection peak interval. Calculate the average reflectance of the pixels in the main peak interval. Select the continuous area with a reflectance greater than 0.7 as the main peak area. Mark the position coordinates of the main peak interval within the boundary. Collect the length of the main peak interval and calculate its ratio with the total length of the boundary. The ratio of the main peak area length to the boundary length is set as... If the ratio is greater than 0.4, the main peak area is considered to have sufficient coverage; otherwise, it is considered to have partial coverage. The length of the main peak interval For the full length of the boundary, continue to compare the coordinate order of each boundary point, calculate the Euclidean distance between each pair of adjacent points, determine the uniformity of the distance distribution between all boundary points, calculate its standard deviation, if the standard deviation is less than 0.5 meters, then it is determined that the spatial continuity between the boundary points is in the uniform range, and finally output the statistical results of the continuity coverage range between the boundary points. Combined with the reflection main peak range marking, the boundary continuity coverage range is finally established.

[0067] S102: Based on the boundary continuity coverage area, determine the degree of consistency between the boundary structure in the current image and the reference image in terms of position and shape. Combine the slope distribution of the main boundary direction to analyze the changing trend of the main boundary direction and the similarity level of the geometric shape, and obtain the boundary structure consistency index.

[0068] The specific formula for analyzing the similarity level between the trend of change in the principal direction of the boundary and the geometric shape is as follows:

[0069] ;

[0070] Calculate the boundary structure comparison parameters;

[0071] in, For boundary structure comparison parameters, For the current image, the first The normalized value of the slope of the principal direction of the segment boundary. For the reference image, the first The normalized value of the slope of the principal direction of the segment boundary. For the current image, the first The normalized value of the segment boundary length. For the reference image, the first The normalized value of the segment boundary length. For the current image, the first The normalized value of the variance of the distribution at the segment boundary points. For the reference image, the first The normalized value of the variance of the distribution at the segment boundary points. For the first Segment boundary structure noise influence factor For boundary segment index, This represents the total number of boundary segments participating in the comparison.

[0072] A boundary structure consistency index is established to quantitatively evaluate the boundary structure between remote sensing images and reference images. The consistency index... The following formula is used for calculation: The formula first calculates the absolute values ​​of the differences between the current image and the reference image in terms of the slope and length in the principal direction, and then sums them to obtain the spatial difference of the boundary structure. Subsequently, a boundary structure noise influence factor is introduced. Weighting is applied to reflect the degree of local imaging interference in remote sensing images; finally, normalization is performed by taking the square root of the sum of the variances of the boundary point distributions to obtain a uniform index for further consistency assessment. The parameters in the formula are explained below: These are dimensionless parameters used for boundary structure comparison. For the current image, the first The normalized value of the slope of the principal direction of the boundary segment is obtained by normalizing the maximum and minimum values ​​after measuring the actual slope of each boundary segment using remote sensing image analysis software. The specific normalization method is as follows: [The text abruptly ends here, so the translation stops as well.] The actual slope value of the segment is subtracted from the minimum slope value of all slope values ​​in the remote sensing image, and then divided by the difference between the maximum slope and the minimum slope. For reference image number The normalized value of the slope of the main direction of the segment boundary is obtained by the same method as above, and the slope is measured through a standard geographic information system (GIS) platform. For the current image, the first The normalized value of the segment boundary length is obtained in the image analysis software after the length measurement is corrected by image scale, and then normalized according to the above method; For reference image number The normalized value of the segment boundary length; the length measurement is the same as above. For the current image, the first The normalized value of the variance of the position distribution of the segment boundary points is obtained by normalizing the deviation between the coordinates of the actual measured position points on the boundary line and the coordinates of the fitted boundary line after statistical calculation. For reference image number The normalized value of the variance of the distribution of segment boundary points is calculated in the same way as above; For the first The noise impact factor of the segment boundary structure, with a numerical range of 0 to 1, is obtained by quantitatively analyzing the texture blurring degree of each segment boundary region. The blurring degree is divided into 5 levels: very clear (0-0.2), clear (0.2-0.4), moderate (0.4-0.6), blurry (0.6-0.8), and extremely blurry (0.8~1.0). This represents the total number of boundary segments participating in the comparison.

[0073] The following table, using remote sensing imagery of a certain area as an example, shows the actual measured data:

[0074] Table 1 Measurement data of boundary structure parameters

[0075]

[0076] The parameters in Table 1 are all results after normalization of the measured data. For the method of obtaining each parameter, please refer to the above description.

[0077] Input data and calculate:

[0078] ;

[0079] ;

[0080] ;

[0081] A smaller value indicates a more consistent boundary structure between the current image and the reference image. Generally, a consistency evaluation benchmark of 0-0.3 is considered high consistency, 0.3-0.6 medium consistency, and above 0.6 low consistency. The calculated result in this case is 0.7169, indicating poor consistency between the current image and the reference image's boundary structure. Therefore, it needs to be marked as low consistency, and the image usability ranking needs further confirmation. The formula incorporates a structural noise factor. It effectively amplifies the representation of differences in image structure and uses a normalization method to unify data units, making the calculation results more objective and reliable, and enhancing the practicality of the image boundary consistency index in remote sensing image quality assessment.

[0082] S103: Based on the boundary structure consistency index, assess the image confidence level, establish an image availability grading sequence, filter each grading category in the confidence sequence, and obtain the boundary confidence coefficient;

[0083] Based on the boundary structure consistency index, for each remote sensing image, the number of boundaries with a high consistency index in the previous alignment is counted across all boundaries in the entire region. Using a grading standard, the boundary consistency index is divided into four levels: 0.7-1, 0.5-0.7, 0.3-0.5, and 0-0.3. Within each grading category, the number of corresponding boundaries is counted, and confidence level labels are assigned to each category: Category I, Category II, Category III, and Category IV. Images with a large number of boundaries within Category I are marked as high-confidence sequences, Categories II and III as medium-confidence sequences, and Category IV as low-confidence sequences. Representative boundaries within each sequence are selected as benchmarks, and weighting coefficients are assigned. Assuming a confidence weight of 1 for Category I, 0.8 for Category II, 0.5 for Category III, and 0.2 for Category IV, a weighted sum is calculated for each image. Assuming an image has 6 boundaries in Category I and 5 boundaries in Category II, the confidence score of that image is... ,in, As a confidence score, sort all image confidence scores in this way to establish an image availability grading sequence, filter the boundary numbers corresponding to the grading categories, assign boundary confidence coefficients to the boundaries within each grading category, and finally output the confidence coefficients of each boundary.

[0084] The specific steps for obtaining interference classification identifiers are as follows:

[0085] S201: Obtain the boundary confidence coefficient, divide the area into multiple grids, calculate the slope aspect angle of each grid, collect the prevailing wind direction data of the area, calculate the angle difference between the slope aspect and the prevailing wind direction of each grid, and establish the slope wind angle distribution range based on the angle difference.

[0086] After obtaining the boundary confidence coefficients, the study area was divided into 100 × 100 grids using a 0.01° × 0.01° latitude and longitude grid. The center point coordinates of each grid were calculated, and the slope aspect angle was extracted using SRTM digital elevation data with a resolution of 30 meters. Assuming the center point coordinates of grid A are (35.520N, 110.130E), the extracted slope aspect is 75°. Next, the prevailing wind direction data for the entire year of 2023 was obtained from the regional meteorological station. Statistically, the prevailing wind direction was southeast-east, with a wind direction angle of approximately 110°. The difference between the slope aspect of grid A (75°) and the prevailing wind direction (110°) was calculated, resulting in an angle of 35°, which was categorized into the 30°–60° range. After processing all grids in the same way, the resulting grids are as follows: 1860 grids in the 0°–30° range, 2740 in the 30°–60° range, 2600 in the 60°–90° range, 1800 in the 90°–120° range, and approximately 1000 other grids. Based on this, a complete slope-wind angle distribution table is generated for subsequent yaw disturbance modeling, yielding the slope-wind angle distribution ranges.

[0087] S202: Based on the distribution range of slope wind angle, determine the influence of terrain contour on wind flow offset, compare the spatial elevation position and relative slope characteristics of each grid, analyze the elevation differences and slope changes between each grid, and obtain spatial undulation characteristic factors.

[0088] Based on the aforementioned slope-wind angle zoning, the elevation data of each grid cell and its eight adjacent grid cells are extracted. For example, grid B has an elevation of 1120m, its north grid cell is at 1115m, its east grid cell is at 1112m, its southwest grid cell is at 1130m, and the elevations in the other five directions are 1121m, 1119m, 1124m, 1132m, and 1118m respectively. The average elevation difference between adjacent grid cells of grid B is calculated to be 7.1m, and the range is 17m. Such grid cells are marked as high-undulation areas, and the percentage of grid cells in their respective slope-wind angle groups that meet the criteria of an average elevation difference greater than 6m and a range greater than 15m is found to be 32%. Next, the slope value of grid B is extracted as 12°, the average slope within its group is 9.5°, and the standard deviation is 2.1°. Using all grid cells as samples, the average elevation difference, range, and average slope value of each group are output to form a three-factor data table of undulation characteristics. The judgment rule is: if the average elevation difference is greater than 6m and the standard deviation of the slope is greater than 1.8°, then the group is regarded as a strong undulation zone, and finally the table data corresponding to the number is formed to obtain the spatial undulation characteristic factor.

[0089] S203: Based on the spatial undulation characteristic factors, analyze the shading area of ​​each grid in the horizontal direction, calculate the shading path range, construct the interference level index of the grid, and generate interference classification labels by combining the interference level of each grid in the region.

[0090] Based on the spatial undulation characteristic factor, the shading area of ​​each grid cell on its leeward side is calculated, using a solar altitude angle of 45° as the criterion. For example, if grid C has an elevation of 1180m and its leeward side grid cell has an elevation of 1155m, with a horizontal distance of 150m between them, the calculated shading angle corresponding to a height difference of 25m is 9.5°. If the shading angle is greater than the solar altitude angle, it is considered to be shading. The shading effect of grid C is marked on the two leeward sides grids D and E, and their shading angles are recorded as 9.5° and 6.2°, respectively. Simultaneously, considering the original slope aspect of D and E, it is determined whether the angle between their reflected light direction and the prevailing wind direction is less than 45°. If so, it is considered to be subject to double interference and is recorded as a double interference zone. The number of grid cells in the area that meet the criteria of shading angle exceeding the solar altitude angle and slope angle less than 45° is 890. Their interference level is set to 3, and the rest are set to 2, 1 or 0 respectively. Finally, a grid interference level map of the whole area is constructed, and interference labels of 0-3 are generated according to the interference level to obtain interference classification labels.

[0091] The specific steps for obtaining the resource prediction output value are as follows:

[0092] S301: Obtain interference classification labels, identify missing raster segments in the remote sensing layer, analyze the number of connections of each raster in the spatial direction, determine the spatial arrangement order and the distribution state of missing raster measurements, and establish spatial missing measurement arrangement coefficients.

[0093] To obtain interference classification labels, firstly, the interference level label of each raster in the entire remote sensing layer is read according to its number. All raster numbers containing missing pixels are listed. Through raster connectivity analysis, the number of connections between each missing raster and its adjacent raster is indicated. For example, missing raster A has 4 directly adjacent missing raster cells, while missing raster B has only 1. The spatial arrangement of all missing raster cells is classified by statistically analyzing the actual connections of their four- or eight-neighbor areas (top, bottom, left, and right). Isolated missing raster cells and patchy missing raster cells are labeled separately. The area, length, and width of the minimum envelope rectangle for each missing segment are calculated. Furthermore, the location of the missing segments across the entire area is compared. For example, the horizontal length of missing segment C within the area is... The grid consists of 5 grids, with a vertical width of 3 grids, arranged in a strip structure. Number D represents a single-point scattering, while number E represents a ring-shaped concentrated distribution. For the missing data arrangement, the spatial arrangement parameters of each group of missing data segments are calculated. Combining the total number of grids and the total number of missing data segments, the concentration of the arrangement distribution is determined. The concentration is divided into three intervals: high (greater than 0.7), medium (0.3-0.7), and low (less than 0.3). The concentration is the ratio of the number of grids in the missing data segment to the total number of missing data segments. If a missing data segment contains 20 grids, accounting for 0.5 of the total number of missing data segments (40), it is classified as a medium interval. Based on the above statistics, the spatial arrangement sequence number, concentration value, and arrangement type of each group are generated and sorted by spatial number. Finally, the spatial missing data arrangement coefficient is established.

[0094] S302: Based on the spatial missing data arrangement coefficient, calculate the direction of change of wind speed and radiation between adjacent grids, statistically analyze the continuous range of change of each grid in the time series, determine the distribution concentration and change frequency of missing data segments, and obtain the dynamic trend characteristics of wind and light.

[0095] Based on the spatial missing data arrangement coefficient, wind speed and radiation observation sequences of adjacent grids of the missing data segment are retrieved. Wind speed and radiation data of normal grids at the boundary of each missing data segment are statistically analyzed. For each pair of adjacent grids, the wind speed change is determined to be positive (increasing), negative (decreasing), or unchanged. The direction of change for each segment is encoded. Simultaneously, the wind speed change amplitude over five consecutive days in the time series is statistically analyzed. For example, if the five-day data for the normal grid at the boundary are 6.1, 6.2, 6.0, 5.8, and 6.3 m / s, the amplitude is the maximum 6.3 minus the minimum 5.8, which is 0.5 m / s. The continuous change amplitude for each grid is statistically analyzed, and the mean and variance are categorized. For all missing data segments, wind speed changes are recorded as either predominantly increasing (positive mean) or decreasing (negative mean). Changes less than 0.3 m / s are categorized as low-frequency, those greater than 0.7 m / s as high-frequency, and the rest as mid-frequency. Based on the magnitude and direction of change, combined with the spatial arrangement coefficient, the missing segment is comprehensively determined to be either a concentrated high-variable, medium-variable, low-variable, or discrete high-variable segment. For example, missing segment number F has an average wind speed increase of 0.9 m / s and a spatial arrangement concentration of 0.6, so it is classified as a concentrated high-variable segment. This yields the dynamic trend characteristics of the wind and light in all missing segments.

[0096] S303: Based on the dynamic trend characteristics of wind and solar energy, construct a spatial interpolation path, extend the change trend of observed data, reconstruct the wind speed and radiation distribution pattern of the missing area, and generate resource prediction output values;

[0097] Based on the dynamic trends of wind and solar radiation, for each missing segment, the spatial sequence data of wind speed and radiation in the normal grid at the boundary are analyzed. Linear extrapolation or multi-point averaging is used to assign interpolation values ​​to the missing segment. For example, for segment G, the boundary grid wind speeds are 5.9, 6.0, and 6.2 m / s, and the radiation data are 410, 425, and 430 W / m², respectively. Therefore, all grids within the missing segment are interpolated based on the boundary average wind speed of 6.03 m / s and the radiation of 421.7 W / m². If a segment has a zonal structure and the wind speed is higher in the east and lower in the west, the interpolation value order decreases sequentially from east to west. Interpolation path numbers are automatically generated for all missing segments based on spatial arrangement and wind and solar radiation dynamic trends. After interpolation, the mean square error between the new wind speed and radiation data of the interpolated grids in all missing segments and the surrounding normal data is calculated. If the mean square error is less than 0.3, the interpolation path is consistent with the actual observation. Finally, the reconstructed wind speed and radiation distribution of all missing blocks are output, and a complete set of resource prediction output values ​​is generated.

[0098] The specific steps for obtaining trend analysis results are as follows:

[0099] S401: Obtain resource prediction output values, analyze the change trajectory of wind speed and solar radiation layers in the time series, calculate the change direction of wind speed and radiation under each spatial grid, and generate a resource change trend sequence by combining monitoring data of multiple climate factors in the region.

[0100] To obtain resource forecast output values, first, import the daily wind speed and solar radiation forecast data for the period 2020 to 2024 within the regional grid. Iterate through each spatial grid, arranging the daily wind speed series in ascending order by time. Use the difference in wind speed between adjacent days to determine positive or negative trends. Count the number of days with rising and falling wind speeds each year. If the number of days with rising wind speeds exceeds the number of days with falling wind speeds throughout the year, the wind speed trend for that grid is considered positive; otherwise, it is considered negative. Similarly, perform the same processing on the solar radiation series for each grid, counting the number of days with varying radiation throughout the year. Further, analyze the climate factors such as temperature, air pressure, and relative humidity within the same region. Sub-monitoring data is matched into each spatial grid on a daily basis to construct a joint sequence of daily wind speed, radiation, temperature, air pressure, and humidity. The direction of daily wind speed change is compared with that of each climate factor. If the wind speed changes in the same direction as temperature, humidity, and air pressure on the same day, it is counted as a synchronization. The number of synchronizations throughout the year is summarized and the proportion is calculated. Point sets with a synchronization ratio of more than 0.7 in the entire region are screened. At the same time, monthly average wind speed and radiation trends are fitted for the spatial grids. The interannual change rate of each grid is compared. The spatial distribution pattern is collected according to the grid number, and finally, a multi-year, multi-factor resource change trend sequence is generated.

[0101] S402: Based on the resource change trend sequence, determine the trend consistency of each climate factor with wind speed and radiation changes in multiple regions, and screen the main climate factors according to the degree of trend synchronization. Calculate the distribution of the superposition effect of the target factors under the spatial grid structure to obtain the spatial sequence of the main factors.

[0102] The specific formula for calculating the distribution of the superposition effect of the target factor under the spatial grid structure is as follows:

[0103] ;

[0104] Calculate the response intensity index of the main control factor;

[0105] in, For the first The response intensity index of each climate factor across all spatial grids For the first The climate factor in the first Normalized trend change values ​​within the time series of individual grid cells The normalized superposition mean of wind speed trend and radiation trend is the value at the th... The values ​​taken in each grid cell, For the first The climate factor in the first The normalized value of the variance of trend fluctuation within each grid cell. For the first The climate factor in the first The normalized importance weights corresponding to each grid cell For the index of spatial grid cells, This represents the total number of spatial grid cells. This refers to the sequence number of the climate factor.

[0106] The specific formula for calculating the distribution of the superposition effect of the target factor under the spatial grid structure is as follows: ;in, For the first The response intensity index of each climate factor across all spatial grids reflects the overall degree of the combined effect of climate factors in different regions; summation sign This represents the total number of grid cells in the space (totaling 100,000). The results are summed item by item. This represents the absolute value of the difference in trend-normalized values, characterizing the trend differences between climate factors and wind and solar resources; 1+ A correction factor representing the degree of trend fluctuation, used to reduce the weighting impact of areas of high volatility; This represents the importance weight of climate factors, adjusting the contribution of each grid. The following section uses actual monitoring data as an example to explain in detail how to obtain the formula parameters and the calculation process:

[0107] Based on meteorological station monitoring data from the past five years, taking air pressure factor as an example, the trend change rate of monthly average air pressure in three grids of a certain region from 2020 to 2024 was normalized to obtain the following data: Grid 1: 0.62; Grid 2: 0.59; Grid 3: 0.64.

[0108] Based on wind speed and radiation observation data, the arithmetic mean is calculated after normalizing the trend changes. For example, if the normalized wind speed trend value for grid 1 is 0.60 and the normalized radiation trend value is 0.58, then... If the wind speed trend normalization value in grid 2 is 0.61 and the radiation trend normalization value is 0.62, then... If the wind speed trend normalization value in grid 3 is 0.63 and the radiation trend normalization value is 0.61, then... .

[0109] This is achieved by calculating the variance of the trend data for each grid cell and then normalizing it. For example, if grid 1 has an original trend fluctuation variance of 0.02 and a maximum fluctuation variance of 0.08, then... Grid 2 is 0.30, and Grid 3 is 0.22.

[0110] Non-numerical data quantification method: Expert scoring is performed using the Analytic Hierarchy Process (AHP), and the average score of experts on the barometric factor is used as the weight. Specific scoring examples are as follows: Expert scores are 0.32, 0.34, 0.33, 0.35, and 0.33. The average weight value is calculated as follows. .

[0111] The parameters calculated above are summarized below (see Table 2):

[0112] Table 2 Numerical Table of Climate Factor Parameters

[0113]

[0114] As shown in Table 2, each item is substituted into the formula to calculate the value of each grid cell. :

[0115] Grid 1: ;

[0116] Grid 2: ;

[0117] Grid 3: .

[0118] Cumulative summation across three grids: .

[0119] This result, compared with the preset response intensity threshold of 0.015, shows that... The results indicate that the air pressure factor has a significant impact on regional wind and solar resources and can be identified as the main controlling factor. The innovation of the formula lies in its ability to accurately reflect the contribution of the differences between each grid unit to the overall trend judgment through the integrated calculation of trend differences, fluctuation correction, and expert weights. This effectively improves the accuracy of regional factor selection and provides a precise basis for the development of wind and solar resources.

[0120] S403: By combining the spatial sequence of the main control factors, construct the dominant relationship of factor response, and predict the changes in wind and solar resources in each region based on climate change trends, and obtain trend analysis results;

[0121] Combining the spatial sequence of the main control factors, for each type of main control factor-dominated area, the historical changes of climate factors and wind and solar resources over the past five years are traced back. First, the trends of temperature, humidity, and air pressure changes in each grid are grouped in pairs with the trends of wind speed and radiation changes according to spatial number. Blocks with consistent trends in the spatial sequence are analyzed to construct a factor response dominance relationship chain. Further, for each main control factor block, the trend change direction code is calculated based on the synchronicity of the predicted climate factor trend with the wind speed and radiation trend in the next year, and it is determined whether the response is positive or negative. The interannual change direction and magnitude of the predicted wind and solar resources in the dominant areas of various main control factors in the whole region are statistically analyzed to form a spatial distribution table. For example, for grid X, if the temperature trend rises and the wind speed rises simultaneously, then the trend code for grid X is a positive response. For grid Y, if the humidity rises and the radiation decreases, then it is a negative response. Finally, the predicted distribution of wind and solar resources trends in all spatial grids is coded and summarized, and the trend analysis results are output.

[0122] The specific steps for obtaining regional resource assessment results are as follows:

[0123] S501: Call the trend analysis results to analyze the change in terrain slope between resource areas and substation nodes. Combine the land attribute combination structure to calculate the elevation distribution and slope combination in the continuous segment of the path and obtain the terrain accessibility feature interval.

[0124] After calling the trend analysis results, the elevation data and slope information between the resource area and the substation node are first extracted. The elevation changes between the two points are statistically segmented, and each path segment is marked with a 50-meter grid. The elevation difference between the start and end points of each segment and the slope inclination direction are calculated. Then, the combined slope types are divided according to the average slope interval. At the same time, the land use code of the corresponding path area in the land attribute layer is extracted, and the land use combination pattern is determined according to the land use classification standard. On this basis, combined with the number of grid nodes, the segmented slope types and the land distribution types, the average slope combination distribution rate of each type of combined path is calculated. All path segments are matched under the terrain slope structure and land use distribution attributes. The path type is marked after the continuous combination of adjacent path segments. The elevation difference distribution, slope direction and land use structure are integrated into a unified matrix. Then, the passability level of each path segment is marked according to the statistical density and the continuity of the path distribution, and the terrain accessibility feature interval is generated.

[0125] S502: Based on terrain accessibility features, analyze the path connectivity and accessibility levels of multiple locations, combine the scenic resource level of each target location, assess the development suitability level of multiple locations, and obtain suitable development judgment parameters.

[0126] Based on terrain accessibility features, multiple candidate target locations are traversed, and accessibility level sequences are retrieved from all path segments between them and substation nodes. The average level and continuous distribution ratio of accessibility level segments are determined. Simultaneously, wind and solar resource intensity values ​​corresponding to each location are retrieved, including annual average wind speed, cumulative radiation value, diurnal wind speed variation, and peak sunshine duration. Each resource item is standardized in turn, and the standardized wind speed and radiation values ​​are mapped one-to-one with the path accessibility level. Three types of accessibility intervals are set: discontinuous paths (average accessibility level below 3), semi-continuous paths (level between 4 and 6), and continuous paths (level above 6). The distribution mean values ​​of wind speed and radiation in the three types of paths are then weighted to obtain the suitability level evaluation score of each location under the joint evaluation of resources and accessibility. A suitable development level matrix is ​​constructed, and target locations with level values ​​falling in the upper 20% of the scoring interval are selected to generate suitable development judgment parameters.

[0127] S503: Based on the appropriate development judgment parameters, filter the development priority level of each resource in multiple locations in the region and match the level labels to generate regional resource assessment results;

[0128] Based on the suitable development criteria, the target areas are first grouped according to wind and solar resource types. Then, the suitability level values ​​in each group are standardized by the maximum and minimum values. Areas with a normalized value higher than 0.75 are selected as priority areas, and priority labels are set, such as Priority I, Priority II, and Feasibility Study Area. Based on the resource type corresponding to each label, the areas are reclassified to form three categories: wind energy priority, solar energy priority, and dual-resource composite priority. Subsequently, all labels and level distributions are mapped to a geographic grid, and priority rules are set for overlapping areas. The priority rules are: wind energy priority is defined as wind energy intensity exceeding 65%, solar energy priority is defined as cumulative sunshine duration exceeding 2600 hours, and other areas are classified as composite priority areas. Finally, the labels and resource level tables are merged, and four fields are output: location name, resource type, development level, and priority label, generating the regional resource assessment results.

[0129] Please see Figure 2 A dynamic assessment system for wind and solar resources based on multi-source remote sensing data is used to execute the aforementioned dynamic assessment method for wind and solar resources based on multi-source remote sensing data. The system includes:

[0130] The boundary confidence analysis module takes a remote sensing image of the region as input, analyzes the degree of closure of the image contour boundaries and the distribution of reflected light intensity, calculates the continuity of the boundaries and the degree of reflection overlap, judges the consistency of the boundary structure between the image and the reference image, and evaluates the image confidence level by combining the main direction slope and morphological similarity, and generates the boundary confidence coefficient.

[0131] The terrain interference calculation module uses the boundary confidence coefficient to divide the area into multiple grids, calculates the angle between the grid slope angle and the prevailing wind direction, determines the impact of terrain on wind flow deviation, and analyzes the shading area and shading path by combining elevation and slope characteristics to generate interference classification labels.

[0132] The missing data completion module uses interference classification markers to identify missing segments of remote sensing grids, calculates the direction and magnitude of wind speed and radiation changes in adjacent grids, constructs spatial interpolation paths and extends the trends of observed data, reconstructs the wind speed and radiation distribution patterns in the missing data area, and generates resource prediction output values.

[0133] The climate trend linkage module uses resource prediction output values ​​to analyze the trajectory of wind speed and radiation changes, calculates the corresponding offset direction of various climate factors between each region and wind and solar resource changes, constructs the factor response dominant relationship sequence, predicts the changing trend of wind and solar resources in multiple regions, and generates trend analysis results.

[0134] The resource suitability assessment module calls upon trend analysis results to analyze the variation range of terrain slope and land attribute combination structure between resource areas and substation nodes, calculates the elevation difference distribution and slope superposition range in continuous path segments, analyzes the path connectivity and accessibility levels of multiple locations, and assesses the development suitability level of multiple locations in conjunction with the wind and solar resource levels of the target location, generating regional resource assessment results.

[0135] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0136] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0137] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0138] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0139] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0140] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0141] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0142] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0143] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0144] If the aforementioned functions are implemented as 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 invention, or the part that contributes to the prior art, or a part of the 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0145] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A dynamic assessment method for wind and solar resources based on multi-source remote sensing data, characterized in that, The method includes: S1: Input a remote sensing image of the region, analyze the degree of closure of the image contour boundary and the distribution of reflected light intensity, calculate the boundary continuity and the degree of reflection overlap, determine the consistency of the boundary structure between the image and the reference image, combine the main direction slope and morphological similarity, evaluate the image confidence level, and generate the boundary confidence coefficient; S2: Using the boundary confidence coefficient, the area is divided into multiple grids, the angle between the grid slope angle and the prevailing wind direction is calculated, the influence of terrain on wind flow deviation is determined, and the shading area and shading path are analyzed in combination with elevation and slope characteristics to generate interference classification labels. S3: Using the interference classification identifier, identify missing segments of remote sensing grids, calculate the direction and magnitude of wind speed and radiation changes in adjacent grids, construct a spatial interpolation path and extend the trend of observed data, reconstruct the wind speed and radiation distribution pattern in the missing area, and generate resource prediction output values. S4: Using the resource prediction output value, analyze the wind speed and radiation change trajectory, calculate the corresponding offset direction of various climate factors between each region and the change of wind and solar resources, construct the factor response dominant relationship sequence, predict the change trend of wind and solar resources in multiple regions, and generate trend analysis results. S5: Call the trend analysis results to analyze the variation range of terrain slope and land attribute combination structure between resource areas and substation nodes, calculate the elevation difference distribution and slope superposition range in continuous path segments, analyze the path connectivity and accessibility level of multiple locations, combine the wind and light resource level of the target location, evaluate the development suitability level of multiple locations, and generate regional resource assessment results.

2. The method for dynamic assessment of wind and solar resources based on multi-source remote sensing data according to claim 1, characterized in that, The boundary confidence coefficient includes boundary stability level, reflection structure integrity index, and slope main direction matching mark. The interference classification identifier specifically includes wind direction shift response level, shading propagation direction encoding, and interference area distribution label. The resource prediction output value includes wind speed change trend, radiation extension curve, and data interpolation coverage. The trend analysis results include factor shift direction vector, resource response synchronization index, and trend turning point spatial node. The regional resource assessment results include path connectivity evaluation coefficient, resource accessibility matching ratio, and suitability level classification label.

3. The method for dynamic assessment of wind and solar resources based on multi-source remote sensing data according to claim 1, characterized in that, The specific steps for obtaining the boundary confidence coefficient are as follows: S101: Acquire regional remote sensing images, analyze the spatial closure of contour boundaries in the images, detect the distribution range of reflected light intensity, calculate the spatial continuity between boundary points and the degree of overlap of the coverage area of ​​the main reflection peak region, and generate boundary continuity coverage intervals. S102: Based on the boundary continuity coverage area, determine the degree of consistency between the boundary structure in the current image and the reference image in terms of position and shape. Combine the slope distribution of the main boundary direction, analyze the changing trend of the main boundary direction and the similarity level of the geometric shape to obtain the boundary structure consistency index. The specific formula for the similarity level between the changing trend of the main direction of the analysis boundary and the geometric shape is as follows: ； Calculate the boundary structure comparison parameters; in, For boundary structure comparison parameters, For the current image, the first The normalized value of the slope of the principal direction of the segment boundary. For the reference image, the first The normalized value of the slope of the principal direction of the segment boundary. For the current image, the first The normalized value of the segment boundary length. For the reference image, the first The normalized value of the segment boundary length. For the current image, the first The normalized value of the variance of the distribution at the segment boundary points. For the reference image, the first The normalized value of the variance of the distribution at the segment boundary points. For the first Segment boundary structure noise influence factor For boundary segment index, The total number of boundary segments participating in the comparison; S103: Based on the boundary structure consistency index, evaluate the image confidence level, establish an image availability grading sequence, filter each grading category in the confidence sequence, and obtain the boundary confidence coefficient.

4. The method for dynamic assessment of wind and solar resources based on multi-source remote sensing data according to claim 1, characterized in that, The specific steps for obtaining the interference classification identifier are as follows: S201: Obtain the boundary confidence coefficient, divide the area into multiple grids, calculate the slope aspect angle of each grid, collect the prevailing wind direction data of the area, calculate the angle difference between the slope aspect and the prevailing wind direction of each grid, and establish the slope wind angle distribution range based on the angle difference. S202: Based on the slope-wind angle distribution range, determine the influence of terrain contour on wind flow offset, compare the spatial elevation position and relative slope characteristics of each grid, analyze the elevation differences and slope changes between each grid, and obtain spatial undulation characteristic factors. S203: Based on the spatial undulation characteristic factor, analyze the occlusion area of ​​each grid in the horizontal direction, calculate the shading path range, construct the interference level index of the grid, and generate an interference classification label by combining the interference level of each grid in the area.

5. The method for dynamic assessment of wind and solar resources based on multi-source remote sensing data according to claim 1, characterized in that, The specific steps for obtaining the resource prediction output value are as follows: S301: Obtain the interference classification identifier, identify the missing grid segments in the remote sensing layer, analyze the number of connections of each grid in the spatial direction, determine the spatial arrangement order and the grid missing measurement distribution state, and establish the spatial missing measurement arrangement coefficient. S302: Based on the spatial missing measurement arrangement coefficient, calculate the direction of change of wind speed and radiation between adjacent grids, statistically analyze the continuous amplitude of change of each grid in the time series, determine the distribution concentration and change frequency of the missing measurement segments, and obtain the dynamic trend characteristics of wind and light. S303: Based on the dynamic trend characteristics of wind and solar energy, construct a spatial interpolation path, extend the trend of observed data changes, reconstruct the wind speed and radiation distribution pattern of the missing area, and generate resource prediction output values.

6. The method for dynamic assessment of wind and solar resources based on multi-source remote sensing data according to claim 1, characterized in that, The specific steps for obtaining the trend analysis results are as follows: S401: Obtain the resource prediction output value, analyze the change trajectory of wind speed and solar radiation layers in the time series, calculate the change direction of wind speed and radiation under each spatial grid, and generate a resource change trend sequence by combining the monitoring data of multiple climate factors in the region. S402: Based on the resource change trend sequence, determine the trend consistency of each climate factor with wind speed and radiation changes in multiple regions, and screen the main climate factors according to the degree of trend synchronization, calculate the distribution of the superposition effect of the target factors under the spatial grid structure, and obtain the spatial sequence of the main factors. S403: Combining the spatial sequence of the main control factors, construct the factor response dominance relationship, and predict the changes in wind and solar resources in each region based on climate change trends, and obtain trend analysis results.

7. The method for dynamic assessment of wind and solar resources based on multi-source remote sensing data according to claim 6, characterized in that, The specific formula for calculating the distribution of the superposition effect of the target factor under the spatial grid structure is as follows: ； Calculate the response intensity index of the main control factor; in, For the first The response intensity index of each climate factor across all spatial grids For the first The climate factor in the first Normalized trend change values ​​within the time series of individual grid cells The normalized superposition mean of wind speed trend and radiation trend is the value at the th... The values ​​taken in each grid cell, For the first The climate factor in the first The normalized value of the variance of trend fluctuation within each grid cell. For the first The climate factor in the first The normalized importance weights corresponding to each grid cell For the index of spatial grid cells, This represents the total number of spatial grid cells. This refers to the sequence number of the climate factor.

8. The method for dynamic assessment of wind and solar resources based on multi-source remote sensing data according to claim 1, characterized in that, The specific steps for obtaining the regional resource assessment results are as follows: S501: Call the trend analysis results to analyze the change range of terrain slope between the resource area and the substation node, and combine the land attribute combination structure to calculate the elevation distribution and slope combination in the continuous segment of the path to obtain the terrain accessibility feature interval. S502: Based on the terrain accessibility feature range, analyze the path connectivity and accessibility level of multiple locations, combine the wind and light resource level of each target location, evaluate the development suitability level of multiple locations, and obtain suitable development judgment parameters. S503: Based on the appropriate development judgment parameters, filter the development priority level of each resource in multiple locations in the region and match the level labels to generate regional resource assessment results.

9. A dynamic assessment system for wind and solar resources based on multi-source remote sensing data, characterized in that, The system is used to implement the dynamic assessment method for wind and solar resources based on multi-source remote sensing data as described in any one of claims 1-8, and the system comprises: The boundary confidence analysis module takes a remote sensing image of the region as input, analyzes the degree of closure of the image contour boundaries and the distribution of reflected light intensity, calculates the continuity of the boundaries and the degree of reflection overlap, judges the consistency of the boundary structure between the image and the reference image, and evaluates the image confidence level by combining the main direction slope and morphological similarity, and generates the boundary confidence coefficient. The terrain interference calculation module uses the boundary confidence coefficient to divide the area into multiple grids, calculates the angle between the grid slope angle and the prevailing wind direction, determines the impact of terrain on wind flow deviation, and analyzes the shading area and shading path by combining elevation and slope characteristics to generate interference classification labels. The missing data completion module uses the interference classification identifier to identify missing segments of remote sensing grids, calculate the direction and magnitude of wind speed and radiation changes in adjacent grids, construct a spatial interpolation path and extend the trend of observed data, reconstruct the wind speed and radiation distribution pattern in the missing area, and generate resource prediction output values. The climate trend linkage module uses the resource prediction output value to analyze the wind speed and radiation change trajectory, calculate the corresponding offset direction of various climate factors between each region and the change of wind and solar resources, construct the factor response dominant relationship sequence, predict the change trend of wind and solar resources in multiple regions, and generate trend analysis results. The resource suitability assessment module calls upon the trend analysis results to analyze the variation range of terrain slope and land attribute combination structure between resource areas and substation nodes, calculates the elevation difference distribution and slope superposition range in continuous path segments, analyzes the path connectivity and accessibility levels of multiple locations, and assesses the development suitability level of multiple locations in conjunction with the wind and solar resource level of the target location, generating regional resource assessment results.

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