Method for quantitatively evaluating influence of weather station observation environment occlusion based on three-dimensional point cloud

Abstract

The application provides a weather station observation environment sheltering influence quantification evaluation method based on three-dimensional point cloud. The method obtains three-dimensional point cloud data of the observation environment around the target weather station and the spatial position of the observation instrument sensing element, extracts the sheltering point cloud data in the preset evaluation range, establishes a local space coordinate system with the sensing element as the origin, determines the azimuth angle, elevation angle, horizontal distance and effective sheltering height of the point cloud points, divides the sky hemisphere region into grids, determines the effective sheltering points of each sky grid, calculates the sheltering influence degree based on the height ratio and the attenuation coefficient, comprehensively processes the sheltering influence degrees of each sky grid, obtains the comprehensive sheltering influence degree, and generates the quantification evaluation result. The scheme can improve the accuracy and stability of the weather station observation environment sheltering evaluation.

Description

Technical Field

[0001] This invention relates to the field of meteorological observation environment assessment and three-dimensional point cloud data processing technology, and in particular to a method for quantitatively evaluating the occlusion impact of meteorological station observation environment based on three-dimensional point clouds. Background Technology

[0002] Meteorological stations are used to continuously observe meteorological elements such as wind speed, wind direction, temperature, humidity, radiation, and precipitation. Fixed obstructions such as buildings, structures, walls, and poles surrounding the meteorological station can alter ventilation conditions, solar radiation conditions, and the local spatial environment, thus affecting the representativeness and accuracy of the observed data. Therefore, the impact of obstructions on the surrounding observation environment needs to be evaluated during the construction, relocation, operation and maintenance assessment, and observation data quality control of meteorological stations.

[0003] Current methods for evaluating the observation environment of meteorological stations typically rely on manual on-site measurements, manual inspections, or two-dimensional planar distance judgments. These methods have the following problems: First, they are difficult to accurately reflect the true shading relationship of obstructions relative to the sensing elements of the observation instruments in three-dimensional space; second, when there are multiple buildings or structures around the meteorological station, these obstructions may overlap. If the evaluation is based directly on all spatial points or all obstructions, obstructions that do not actually contribute to shading may be counted repeatedly, leading to a distorted evaluation of the shading impact. Summary of the Invention

[0004] This invention aims to solve the problem in the prior art that it is difficult to accurately identify effective occlusion points in the meteorological station observation environment based on three-dimensional point cloud data, which makes the evaluation of occlusion impact easily affected by redundant point clouds, overlapping occlusion objects, and abnormal occlusion points. The invention provides a quantitative evaluation method for the occlusion impact of the meteorological station observation environment based on three-dimensional point clouds.

[0005] This invention provides a method for quantitatively evaluating the occlusion impact of meteorological station observation environment based on three-dimensional point clouds, including:

[0006] Acquire three-dimensional point cloud data of the observation environment surrounding the target meteorological station and the spatial position of the sensing elements of the observation instruments in the target meteorological station; Based on the three-dimensional point cloud data, extract the point cloud data of obstructions within the preset evaluation range of the target meteorological station; Based on the spatial position of the sensing element of the observation instrument, the point cloud data of the obstruction is converted to a local spatial coordinate system with the sensing element of the observation instrument as the origin, and the azimuth, elevation, horizontal distance and effective obstruction height corresponding to each point cloud point are determined. The sky hemisphere region corresponding to the sensing element of the observation instrument is divided into grids according to the azimuth and elevation angles to obtain multiple sky grids; For each of the sky grids, the effective occlusion points corresponding to the sky grid are determined based on the spatial occlusion relationship between the point cloud points located within the sky grid and the sensing element of the observation instrument. Based on the horizontal distance and effective occlusion height of each effective occlusion point, the distance-to-height ratio corresponding to each effective occlusion point is determined, and the occlusion influence degree corresponding to each effective occlusion point is determined based on the distance-to-height ratio and the attenuation coefficient. Wherein, the occlusion impact degree corresponding to the effective occlusion point is the occlusion impact degree of the sky grid corresponding to the effective occlusion point; if there is no corresponding effective occlusion point in the sky grid, the occlusion impact degree corresponding to the sky grid is determined as the preset optimal impact degree value. The preset optimal impact degree value (i.e. the optimal score) is preferably 0, and corresponds to the optimal evaluation value of the unoccluded state in the final percentage evaluation result. Based on the occlusion impact degree corresponding to each of the sky grids, the comprehensive occlusion impact degree of the observation environment surrounding the target meteorological station is determined; Based on the comprehensive shading impact, a quantitative evaluation result of the shading impact on the observation environment of the target meteorological station is generated.

[0007] In some embodiments, acquiring three-dimensional point cloud data of the observation environment surrounding the target meteorological station includes: Raw spatial data of the observation environment surrounding the target meteorological station are obtained through UAV oblique photography, airborne lidar, ground laser scanning, or 3D reconstruction equipment. The original spatial data is subjected to point cloud reconstruction, coordinate registration, noise point filtering, and coordinate unification to obtain the three-dimensional point cloud data. The three-dimensional point cloud data includes at least the three-dimensional coordinate information of the point cloud points.

[0008] In some embodiments, the step of extracting point cloud data of obstructions within a preset evaluation range of the target weather station based on the three-dimensional point cloud data includes: Candidate point cloud data are extracted according to a preset evaluation radius, centered on the spatial position of the sensing element of the observation instrument or the station position of the target meteorological station. The candidate point cloud data is subjected to ground point filtering and target classification processing to remove point cloud points that do not participate in the occlusion evaluation; Point cloud points whose height is higher than the sensing element of the observation instrument and which belong to buildings, structures, walls, towers or other fixed obstructions are identified as obstruction point cloud data.

[0009] In some embodiments, the step of converting the point cloud data of the obstruction to a local spatial coordinate system with the sensing element of the observation instrument as the origin, and determining the azimuth, elevation, horizontal distance, and effective obstruction height corresponding to each point cloud point, includes: A local spatial coordinate system is established by taking the center point of the sensing element of the observation instrument as the origin of the coordinate system, taking the horizontal plane as the first coordinate plane, and taking the vertical direction as the height direction. For any point cloud point, based on the coordinates of the point cloud point in the local spatial coordinate system, determine the horizontal distance, effective obstruction height, azimuth angle, and elevation angle of the point cloud point relative to the sensing element of the observation instrument; The effective occlusion height is the height difference between the point cloud point height and the height of the sensing element of the observation instrument. When the effective occlusion height is less than or equal to zero, the point cloud point is determined as an unoccluded contribution point.

[0010] In some embodiments, the step of dividing the sky hemisphere region corresponding to the sensing element of the observation instrument into multiple sky grids according to the azimuth and elevation angles includes: The sky hemisphere region above the sensing element of the observation instrument is divided into multiple angular grids; Each of the aforementioned sky grids corresponds to an azimuth range and an elevation range; The angular resolution of the sky grid is determined based on the point cloud density of the three-dimensional point cloud data, the type of weather station, or the required observation accuracy.

[0011] In some embodiments, determining the effective occlusion point corresponding to each sky grid based on the spatial occlusion relationship between point cloud points located within the sky grid and the sensing element of the observation instrument includes: Identify multiple point cloud points that fall within the same sky grid; According to the preset occlusion sorting rules, one point cloud point is selected from the plurality of point cloud points as the effective occlusion point corresponding to the sky grid. The preset occlusion sorting rule includes: selecting the point cloud point with the smallest horizontal distance from the sensing element of the observation instrument as the effective occlusion point.

[0012] In some embodiments, for the i-th effective occlusion point, the distance-to-height ratio is determined according to the following formula: In some embodiments, determining the distance-to-height ratio corresponding to each effective occlusion point based on the horizontal distance and effective occlusion height of each effective occlusion point, and determining the occlusion impact degree corresponding to each effective occlusion point based on the distance-to-height ratio and the attenuation coefficient, includes: For the i-th effective occlusion point, the distance-to-height ratio is determined according to the following formula:

[0013] in, This represents the distance-to-height ratio corresponding to the i-th effective occlusion point. This represents the horizontal distance between the i-th effective obstruction point and the sensing element of the observation instrument. This represents the effective obstruction height of the i-th effective obstruction point relative to the sensing element of the observation instrument; when When the value is greater than 0, the occlusion influence degree corresponding to the i-th effective occlusion point is determined according to the following formula:

[0014] in, This represents the occlusion impact degree corresponding to the i-th effective occlusion point. This represents the attenuation coefficient.

[0015] In some embodiments, the attenuation coefficient is determined based on the station type of the target weather station; When the target weather station is a first type of weather station, the attenuation coefficient is determined based on a first no-significant-impact distance-to-height ratio threshold. When the target weather station is a second type of weather station, the attenuation coefficient is determined based on the second no significant influence distance-to-height ratio threshold. When the target weather station is a third type of weather station, the attenuation coefficient is determined based on the third no significant influence distance-to-height ratio threshold. The attenuation coefficient satisfies:

[0016] in, This indicates the target evaluation value that is not significantly affected by occlusion. This represents the distance-to-height ratio threshold that has no significant impact on the station type of the target meteorological station.

[0017] In some embodiments, determining the comprehensive occlusion impact of the observation environment surrounding the target weather station based on the occlusion impact corresponding to each of the sky grids includes: The occlusion impact degree corresponding to each of the aforementioned sky grids is sorted to obtain an occlusion impact degree sequence; Remove the preset low-end and preset high-end proportion data from the occlusion impact sequence, and calculate the mean of the remaining occlusion impact to obtain the truncated mean. The occlusion influence degree corresponding to the preset high quantile position is determined from the occlusion influence degree sequence to obtain the high quantile; The overall occlusion impact is determined based on the truncated mean and the high quantile. Wherein, the comprehensive occlusion impact degree satisfies:

[0018] in, Indicates the overall impact of occlusion. This represents the truncated mean. Indicates the higher quantile. and Let represent the weighting coefficients, and .

[0019] This invention also provides a device for quantitatively evaluating the impact of occlusion on meteorological station observation environments based on three-dimensional point clouds, comprising: The data acquisition module is used to acquire three-dimensional point cloud data of the observation environment around the target meteorological station and the spatial position of the sensing element of the observation instrument in the target meteorological station; The point cloud extraction module is used to extract point cloud data of obstructions within a preset evaluation range of the target meteorological station based on the three-dimensional point cloud data. The coordinate transformation module is used to transform the point cloud data of the obstruction object to a local spatial coordinate system with the sensor element of the observation instrument as the origin, based on the spatial position of the sensor element of the observation instrument, and to determine the azimuth, elevation, horizontal distance and effective obstruction height of each point cloud point. The grid division module is used to divide the sky hemisphere region corresponding to the sensing element of the observation instrument into multiple sky grids according to the azimuth and elevation angles. The effective occlusion point determination module is used to determine the effective occlusion point corresponding to each sky grid based on the spatial occlusion relationship between the point cloud points located in the sky grid and the sensing element of the observation instrument. The occlusion impact determination module is used to determine the distance-to-height ratio corresponding to each effective occlusion point based on the horizontal distance and effective occlusion height of each effective occlusion point, and to determine the occlusion impact corresponding to each effective occlusion point based on the distance-to-height ratio and the attenuation coefficient; wherein, the occlusion impact corresponding to an effective occlusion point is the occlusion impact of the sky grid corresponding to that effective occlusion point; if there is no corresponding effective occlusion point in the sky grid, the occlusion impact corresponding to the sky grid is a preset optimal impact value; the preset optimal impact value (i.e., optimal score) is preferably 0; The comprehensive impact determination module is used to determine the comprehensive occlusion impact of the observation environment around the target meteorological station based on the occlusion impact corresponding to each of the sky grids. The evaluation result generation module is used to generate a quantitative evaluation result of the shading impact on the observation environment of the target meteorological station based on the comprehensive shading impact degree.

[0020] The method for quantitatively evaluating the occlusion impact of meteorological station observation environment based on three-dimensional point clouds provided by this invention has at least the following beneficial effects: First, by acquiring three-dimensional point cloud data of the observation environment around the target meteorological station and establishing a local spatial coordinate system based on the sensing elements of the observation instrument, the three-dimensional spatial relationship between the obstruction and the observation instrument can be accurately expressed, thereby improving the spatial accuracy of the observation environment obstruction assessment.

[0021] Second, by dividing the sky hemisphere region into grids according to azimuth and elevation angles, and determining effective occlusion points for each sky grid, we can avoid redundant point clouds and back clouds in the same direction from participating in the calculation repeatedly, thus reducing the interference of front and back occlusion superposition on the evaluation results.

[0022] Third, by determining the occlusion impact degree corresponding to the sky grid without effective occlusion points as the preset optimal impact degree value, the unobstructed sky area is included in the comprehensive evaluation, avoiding the exaggeration of the overall occlusion impact by only statistically analyzing the grid with occlusion points.

[0023] Fourth, by calculating the degree of shading impact based on the distance-to-height ratio and the attenuation coefficient, we can reflect the law that the shading impact decreases as the horizontal distance increases and the effective shading height decreases, making the quantitative evaluation results more consistent with the actual shading characteristics of the meteorological station's observation environment.

[0024] Fifth, by combining the truncated mean with the high quantile to determine the overall occlusion impact, we can reflect the overall occlusion level while also taking into account local high-risk occlusion areas, reducing excessive interference from outliers on the evaluation results, and improving the stability and comparability of the evaluation results. Attached Figure Description

[0025] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0026] Figure 1 This is a flowchart illustrating the quantitative evaluation method for the environmental occlusion impact of meteorological stations based on three-dimensional point clouds, provided in an embodiment of the present invention. Figure 2 This is a schematic diagram illustrating the spatial relationship between the sensing element of a meteorological station observation instrument and surrounding obstructions, provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of the grid division of the sky hemisphere region provided in an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the effective occlusion point determination process provided in an embodiment of the present invention; Figure 5This is a schematic diagram of the structure of a device for quantitatively evaluating the occlusion impact of a meteorological station observation environment based on three-dimensional point clouds, provided in an embodiment of the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Example 1 This embodiment provides a method for quantitatively evaluating the impact of occlusion on meteorological station observation environments based on 3D point clouds. This method can be executed by a server, computer equipment, edge computing devices, meteorological station operation and maintenance terminals, or electronic devices deployed with point cloud processing programs.

[0029] like Figure 1 As shown, the method includes the following steps.

[0030] Step S101: Obtain three-dimensional point cloud data of the observation environment around the target meteorological station and the spatial position of the sensing element of the observation instrument in the target meteorological station.

[0031] The target weather station can be a type I weather station, a type II weather station, or a type III weather station; Among them, the first type of meteorological station is a national-level reference station or a national-level basic station; The second type of meteorological station is the national general meteorological station; The third type of weather station is the regional automatic weather station.

[0032] A national reference climatological station is a ground-based meteorological observation station established in accordance with national climate zoning and the requirements of the global climate observation system to obtain sufficiently representative long-term and continuous data.

[0033] National basic stations, also known as national basic synoptic stations, are ground-based meteorological observation stations established to meet the needs of national climate analysis and weather forecasting.

[0034] National general meteorological stations refer to ground meteorological observation stations set up according to the administrative divisions of provinces (autonomous regions and municipalities).

[0035] Regional automatic weather stations, also known as regional meteorological observation stations, are ground meteorological observation stations built on the basis of the national-level ground meteorological observation station layout to meet the needs of early warning of small and medium-scale severe weather, meteorological forecasting services for large and medium-sized cities, special areas and exclusive economic zones, and local economic and social development.

[0036] The observation instruments can be wind speed and direction sensors, temperature and humidity sensors, radiation sensors, precipitation sensors, or other meteorological observation instruments. The sensing element of the observation instrument refers to the sensitive component that actually receives external meteorological elements, such as the wind cup or ultrasonic probe of an anemometer, the sensing end of a temperature and humidity sensor, and the receiving end of a radiation sensor.

[0037] 3D point cloud data can be obtained through UAV oblique photography, airborne lidar, ground-based laser scanning, or 3D reconstruction equipment. After point cloud reconstruction, coordinate registration, noise point filtering, and coordinate unification processing, the raw spatial data yields 3D point cloud data of the observation environment surrounding the target meteorological station. 3D point cloud data includes at least the 3D coordinate information of multiple point clouds, and may further include color information, reflection intensity information, semantic category information, or acquisition time information.

[0038] In one implementation, the evaluation range of the observation environment surrounding the target weather station can be a preset radius range centered on the location of the target weather station or the location of the sensing element of the observation instrument, such as 50 meters, 100 meters, 300 meters, or 500 meters. The preset radius can be determined according to the type of weather station, the observation items, and the evaluation accuracy requirements.

[0039] Step S102: Extract point cloud data of obstructions within the preset evaluation range of the target meteorological station based on three-dimensional point cloud data.

[0040] After obtaining the 3D point cloud data, candidate point cloud data can be extracted according to a preset evaluation radius, centered on the spatial location of the observation instrument's sensing element or the location of the target meteorological station. Subsequently, the candidate point cloud data undergoes ground point filtering and target classification processing to eliminate point cloud points that do not participate in the occlusion evaluation.

[0041] Point cloud points not included in the occlusion evaluation can include ground point clouds, noise point clouds, low-lying grass, or other point cloud points that do not create fixed occlusion effects. For temporary objects such as vehicles, temporary facilities, and construction equipment, exclusion can be selected based on the evaluation purpose. For vegetation objects such as trees, in some embodiments they can be evaluated as non-building fixed occlusions, while in other embodiments they can be classified and evaluated separately as vegetation occlusions.

[0042] The point cloud data for obstructions may include point cloud points corresponding to buildings, structures, walls, towers, or other fixed obstructions. Preferably, only point cloud points whose height is higher than the sensing element of the observation instrument and which have the potential to contribute to obstruction are included as point cloud data for obstructions.

[0043] Step S103: Based on the spatial position of the sensor element of the observation instrument, the point cloud data of the obstruction is converted to a local spatial coordinate system with the sensor element of the observation instrument as the origin, and the azimuth, elevation, horizontal distance and effective obstruction height corresponding to each point cloud point are determined.

[0044] In this embodiment, warehouse station Figure 2 By taking the center point of the instrument's sensor as the origin of the local spatial coordinate system, the horizontal plane as the first coordinate plane, and the vertical direction as the height direction, point cloud points in the global coordinate system can be converted into local coordinates relative to the instrument's sensor through coordinate transformation.

[0045] For any point cloud Its coordinates in the local spatial coordinate system can be expressed as:

[0046] in, and This represents the coordinate components of the point cloud point relative to the sensing element of the observation instrument in the horizontal plane. This represents the height component of the point cloud relative to the sensing element of the observation instrument in the vertical direction.

[0047] The horizontal distance between the point cloud point and the sensing element of the observation instrument can be expressed as:

[0048] in, This represents the horizontal distance between the i-th point cloud point and the sensing element of the observation instrument.

[0049] In a local spatial coordinate system with the sensor element of the observation instrument as the origin, the effective occlusion height of this point cloud can be expressed as:

[0050] In another coordinate representation, if the point cloud point height is... The height of the sensor element of the observation instrument is The effective shading height can also be expressed as:

[0051] in, This represents the effective occlusion height of the i-th point cloud point relative to the sensing element of the observation instrument. When When the value is ≤0, it means that the height of the point cloud is not higher than the height of the sensing element of the observation instrument, and will not make an effective occlusion contribution to the observation instrument. The point cloud can be identified as an unoccupied contribution point and will not participate in the calculation of effective occlusion points.

[0052] The azimuth angle of this point cloud point relative to the sensing element of the observation instrument can be expressed as:

[0053] in, This represents the azimuth angle corresponding to the i-th point cloud point.

[0054] The elevation angle of this point cloud point relative to the sensing element of the observation instrument can be expressed as:

[0055] in, This represents the elevation angle corresponding to the i-th point cloud point.

[0056] Through the above processing, the point cloud data of occlusion objects can be converted from ordinary three-dimensional spatial points into angular domain data with the observation instrument's sensing element as the observation center, thus providing a basis for subsequent sky hemisphere grid division and effective occlusion point determination.

[0057] Step S104: Divide the sky hemisphere region corresponding to the sensing element of the observation instrument into grids according to the azimuth and elevation angles to obtain multiple sky grids.

[0058] like Figure 3 As shown, the hemispherical region of the sky above the sensor of the observation instrument can be divided into multiple angular grids. Each sky grid corresponds to an azimuth range and an elevation range.

[0059] For example, the azimuth range can be divided into 0° to 360°, and the elevation range into 0° to 90°. The angular resolution of the sky grid can be 1°×1°, 0.5°×0.5°, or 2°×2°, or it can be adaptively determined based on the point cloud density of the 3D point cloud data, the type of weather station, or the required observation accuracy. It should be noted that... Figure 3 This is merely an illustration and not a limitation on the angular resolution of the sky grid.

[0060] By dividing the sky into hemispherical grids, the complex occlusion relationships of 3D point clouds can be transformed into the occlusion states of cells on grids at multiple angles, which makes it easier to identify the actual occlusion contribution in each direction.

[0061] Step S105: For each sky grid, determine the effective occlusion point corresponding to the sky grid based on the spatial occlusion relationship between the point cloud points located in the sky grid and the sensing element of the observation instrument.

[0062] Reference Figure 4In this embodiment, for each sky grid, multiple point cloud points falling within that sky grid are first identified. Point cloud points falling within the same grid are viewed by the sensing element of the observation instrument as being within a similar azimuth and elevation range, and may have a superimposed relationship.

[0063] To avoid repeated participation of point cloud points in the occlusion evaluation, this embodiment selects one point cloud point from multiple point cloud points within the same sky grid as the effective occlusion point corresponding to that sky grid, according to a preset occlusion sorting rule. The preset occlusion sorting rule includes: selecting the point cloud point with the smallest horizontal distance to the sensing element of the observation instrument as the effective occlusion point.

[0064] In other implementations, a composite sorting rule can be constructed by combining elevation angle, spatial distance, or the type of obstruction to which the point cloud belongs. For example, when the difference in elevation angle among multiple point cloud points within the same grid is less than a preset angle threshold, the point cloud point with the smallest horizontal distance can be selected as the effective obstruction point; when the difference in elevation angle is large, the point cloud point with the larger elevation angle can be selected as the candidate obstruction point. All of the above variations can be used to determine the point cloud points within the sky grid that have an actual obstruction contribution to the sensing elements of the observation instrument.

[0065] If there are no occlusion point cloud points within a certain sky grid, or if all point cloud points within that sky grid are occlusion-free contribution points, then that sky grid is an occlusion-free grid and no valid occlusion points are generated.

[0066] Step S106: Based on the horizontal distance and effective shading height of each effective shading point, determine the distance-to-height ratio corresponding to each effective shading point, and determine the shading influence degree corresponding to each effective shading point based on the distance-to-height ratio and the attenuation coefficient.

[0067] For the i-th effective occlusion point, the distance-to-height ratio is determined according to the following formula:

[0068] in, This represents the distance-to-height ratio corresponding to the i-th effective occlusion point. This represents the horizontal distance between the i-th effective obstruction point and the sensing element of the observation instrument. This represents the effective occlusion height of the i-th effective occlusion point relative to the sensing element of the observation instrument.

[0069] The smaller the distance-to-height ratio, the closer the obstruction is to the sensing element of the observation instrument, or the greater the effective obstruction height, and the more significant its obstruction effect. The larger the distance-to-height ratio, the farther the obstruction is, or the smaller the effective obstruction height, and the weaker its obstruction effect.

[0070] when When the value is greater than 0, the occlusion impact degree corresponding to the i-th effective occlusion point is determined according to the following formula:

[0071] in, This represents the occlusion impact degree corresponding to the i-th effective occlusion point; This represents the attenuation coefficient.

[0072] According to the above formula, when the distance to height is small, A larger value indicates a stronger shading effect; when the distance-to-height ratio is large, it indicates a stronger shading effect. A smaller value indicates a weaker shading effect. Therefore, this formula reflects the law that the shading effect decreases as the distance-to-height ratio increases.

[0073] The occlusion influence of an effective occlusion point is the occlusion influence of the corresponding sky grid. For the j-th sky grid, its grid occlusion influence can be expressed as:

[0074] in, This represents the occlusion impact degree corresponding to the j-th sky grid. This represents the preset optimal impact value, i.e., the optimal score. Preferably: =0 In other words, for a sky grid without any effective occlusion points, its occlusion impact is 0, corresponding to an unoccluded state. In the quantitative evaluation results of this application, this unoccluded state corresponds to the optimal grid occlusion impact.

[0075] By including all sky grids in the comprehensive evaluation, we can avoid overestimating the overall occlusion impact by only counting grids with occlusion points, and make the evaluation results closer to the overall occlusion status of the sky view.

[0076] In some embodiments, to avoid calculation anomalies caused by extreme values ​​and to better reflect the actual observation scenarios of weather stations, the following hard boundary rules are added to ensure the rationality and accuracy of the scoring results: Step S107: Determine the comprehensive occlusion impact of the observation environment surrounding the target weather station based on the occlusion impact of each sky grid.

[0077] After obtaining the occlusion impact degree corresponding to each sky grid, the occlusion impact degree corresponding to each sky grid is sorted to obtain the occlusion impact degree sequence.

[0078] Subsequently, the preset low-end and preset high-end proportions of data in the occlusion impact sequence are removed, and the mean of the remaining occlusion impact is calculated to obtain the truncated mean. The preset low-end and preset high-end proportions can both be 5%, or they can be adjusted according to the effective point cloud quantity, point cloud noise level, or weather station type.

[0079] The occlusion impact degree corresponding to the preset high quantile position is determined from the occlusion impact degree sequence, and the high quantile is obtained. The preset high quantile position can be the 95th quantile position, or the 90th quantile position, the 97.5th quantile position, or other quantile positions.

[0080] The overall occlusion impact is determined based on the truncated mean and high quantiles. The overall occlusion impact satisfies:

[0081] in, Indicates the overall impact of occlusion. This represents the truncated mean. Indicates the higher quantile. and Let represent the weighting coefficients, and:

[0082] In one specific implementation =0.6, =0.4, and the high quantile is the 95th quantile, then the overall occlusion impact can be expressed as:

[0083] in, This represents the 95th percentile of the occlusion impact sequence.

[0084] By combining truncated mean and high quantile, the overall occlusion level of the observation environment can be reflected by the truncated mean, while the high quantile can take into account local high-risk occlusion areas. This avoids individual outliers from overly dominating the evaluation results and also avoids completely ignoring local severe occlusion.

[0085] Step S108: Based on the comprehensive shading impact degree, generate a quantitative evaluation result of the shading impact of the target meteorological station's observation environment.

[0086] After determining the overall occlusion impact, the overall occlusion impact can be converted into a percentage-based quantitative evaluation result. The quantitative evaluation result should satisfy the following:

[0087] in, This represents a quantitative evaluation value indicating the impact of environmental obstruction on the observation of the target meteorological station. This indicates the overall impact of occlusion.

[0088] The greater the overall occlusion impact, the stronger the occlusion impact and the lower the quantitative evaluation value; conversely, the smaller the overall occlusion impact, the weaker the occlusion impact and the higher the quantitative evaluation value.

[0089] It should be noted that, in some cases, to avoid calculation anomalies caused by extreme values ​​and to better reflect the actual observation scenarios of weather stations, the following hard boundary rules are added to ensure the rationality and accuracy of the scoring results: 1. When H ≤ 0 (the height of all nearby buildings is less than or equal to the height of the instrument, and there is no possibility of obstruction), a score of 100 is directly assigned. 2. If the R of all obstruction points reaches the calibration threshold of no significant impact for the corresponding meteorological station (e.g., R ≥ 8 for general national meteorological stations, R ≥ 10 for national benchmark stations), a score of 100 is directly assigned without the need for additional calculation in the formula; 3. When R < 0.1 (the buildings are very close together and the obstruction height is extremely high, almost completely blocking the observation instrument), the score S = 0 is directly assigned.

[0090] In some embodiments, an evaluation level can be output based on a quantitative evaluation value.

[0091] For example, a quantitative evaluation value of 90 to 100 is considered excellent; 75 to 89 is considered good; 60 to 74 is considered acceptable; 40 to 59 is considered poor; and less than 40 is considered very poor. These grading standards are merely examples and can be adjusted according to meteorological observation specifications, station types, or operational needs.

[0092] In some embodiments, occlusion heatmaps, occlusion azimuth distribution maps, or ranking results of the contribution of major occlusion objects can be generated based on the occlusion impact degree corresponding to each sky grid, so that meteorological station operation and maintenance personnel can determine the main occlusion direction and the main occlusion source.

[0093] In some embodiments, the attenuation coefficient is determined as follows.

[0094] The attenuation coefficient k is used to control the rate at which the occlusion effect decreases as the distance-to-height ratio increases. Different types of weather stations have different requirements for observation accuracy and environmental conditions; therefore, different distance-to-height ratio thresholds with no significant impact can be set according to the type of weather station.

[0095] For example, for the first type of weather station, a larger threshold for the distance-to-height ratio with no significant impact can be set; for example, the threshold for the distance-to-height ratio with no significant impact can be greater than or equal to 10; specifically, the threshold for the distance-to-height ratio with no significant impact can be 10. For the second type of weather station, a moderate distance-to-height ratio threshold with no significant impact can be set; for example, the distance-to-height ratio threshold with no significant impact can be greater than or equal to 8 and less than 10; specifically, the distance-to-height ratio threshold with no significant impact can be 8. For the third type of weather station, a smaller threshold for the distance-to-height ratio with no significant impact can be set. For example, the threshold for the distance-to-height ratio with no significant impact can be greater than or equal to 4 and less than 8; specifically, the threshold for the distance-to-height ratio with no significant impact can be 4. Furthermore, in some embodiments, the first type of weather station, the second type of weather station, and the third type of weather station can be classified based on the accuracy requirements. It should be noted that in the solution provided in this application, the accuracy requirement of the first type of weather station is greater than that of the second type of weather station, which in turn is greater than that of the third type of weather station.

[0096] Specifically, the accuracy requirement for the first type of weather station is 0.05m. 0.20m, preferably 0.10m; the accuracy requirement range for the second type of weather station is 0.20m. 0.50m, preferably 0.30m; specifically, the accuracy requirement range for the third type of weather station is 0.50m. 1.00m, preferably 0.50m or 0.80m.

[0097] Furthermore, the attenuation coefficient satisfies:

[0098] in, This indicates the target evaluation value that is not significantly affected by occlusion. This represents the distance-to-height ratio threshold that has no significant impact on the target meteorological station's station type.

[0099] If the target evaluation value corresponding to no significant occlusion effect is set to 99, then the attenuation coefficient can be simplified to:

[0100] The above method allows for the dynamic determination of attenuation coefficients based on different station types, rather than fixing the attenuation coefficient to a certain empirical value, thus improving the adaptability of the evaluation method to different meteorological station types.

[0101] like Figure 5 As shown, this embodiment provides a device for quantitative evaluation of the occlusion impact of meteorological station observation environment based on three-dimensional point clouds, including a data acquisition module 510, a point cloud extraction module 520, a coordinate transformation module 530, a grid division module 540, an effective occlusion point determination module 550, an occlusion impact degree determination module 560, a comprehensive impact degree determination module 570, and an evaluation result generation module 580.

[0102] The data acquisition module 510 is used to acquire three-dimensional point cloud data of the observation environment around the target meteorological station and the spatial position of the sensing elements of the observation instruments in the target meteorological station.

[0103] The point cloud extraction module 520 is used to extract point cloud data of obstructions within a preset evaluation range of the target meteorological station based on three-dimensional point cloud data.

[0104] The coordinate transformation module 530 is used to transform the point cloud data of the obstruction object to a local spatial coordinate system with the sensor of the observation instrument as the origin, based on the spatial position of the sensor of the observation instrument, and to determine the azimuth, elevation, horizontal distance and effective obstruction height of each point cloud point.

[0105] The grid division module 540 is used to divide the sky hemisphere region corresponding to the sensing element of the observation instrument into grids according to the azimuth and elevation angles, resulting in multiple sky grids.

[0106] The effective occlusion point determination module 550 is used to determine the effective occlusion point corresponding to each sky grid based on the spatial occlusion relationship between the point cloud points located in the sky grid and the sensing element of the observation instrument.

[0107] The occlusion impact determination module 560 is used to determine the distance-to-height ratio corresponding to each effective occlusion point based on the horizontal distance and effective occlusion height of each effective occlusion point, and to determine the occlusion impact corresponding to each effective occlusion point based on the distance-to-height ratio and the attenuation coefficient. Specifically, the occlusion impact corresponding to an effective occlusion point is the occlusion impact of the corresponding sky grid; if there is no corresponding effective occlusion point in the sky grid, the occlusion impact corresponding to the sky grid is a preset optimal impact value.

[0108] The comprehensive impact determination module 570 is used to determine the comprehensive occlusion impact of the observation environment around the target meteorological station based on the occlusion impact corresponding to each sky grid.

[0109] The evaluation result generation module 580 is used to generate a quantitative evaluation result of the shading impact on the observation environment of the target meteorological station based on the comprehensive shading impact degree.

[0110] The apparatus in this embodiment can be used to perform the steps in the above method embodiments. Its implementation principle and technical effects are the same as those in the above method embodiments, and will not be repeated here.

[0111] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for quantitatively evaluating the impact of environmental occlusion on meteorological station observations based on three-dimensional point clouds, characterized in that, include: Acquire three-dimensional point cloud data of the observation environment surrounding the target meteorological station and the spatial position of the sensing elements of the observation instruments in the target meteorological station; Based on the three-dimensional point cloud data, extract the point cloud data of obstructions within the preset evaluation range of the target meteorological station; Based on the spatial position of the sensing element of the observation instrument, the point cloud data of the obstruction is converted to a local spatial coordinate system with the sensing element of the observation instrument as the origin, and the azimuth, elevation, horizontal distance and effective obstruction height corresponding to each point cloud point are determined. The step of converting the obstruction point cloud data to a local spatial coordinate system with the observation instrument's sensing element as the origin, and determining the azimuth, elevation, horizontal distance, and effective obstruction height corresponding to each point cloud point, includes: establishing a local spatial coordinate system with the center point of the observation instrument's sensing element as the coordinate origin, the horizontal plane as the first coordinate plane, and the vertical direction as the height direction; for any point cloud point, determining the horizontal distance, effective obstruction height, azimuth, and elevation angle of the point cloud point relative to the observation instrument's sensing element based on the point cloud point's coordinates in the local spatial coordinate system; wherein, the effective obstruction height is the height difference between the height of the point cloud point and the height of the observation instrument's sensing element, and when the effective obstruction height is less than or equal to zero, the point cloud point is determined as an unobstructed contribution point; The sky hemisphere region corresponding to the sensing element of the observation instrument is divided into grids according to the azimuth and elevation angles to obtain multiple sky grids; For each of the sky grids, the effective occlusion points corresponding to the sky grid are determined based on the spatial occlusion relationship between the point cloud points located within the sky grid and the sensing element of the observation instrument. Based on the horizontal distance and effective occlusion height of each effective occlusion point, the distance-to-height ratio corresponding to each effective occlusion point is determined, and the occlusion influence degree corresponding to each effective occlusion point is determined based on the distance-to-height ratio and the attenuation coefficient. The occlusion impact of an effective occlusion point is the occlusion impact of the sky grid corresponding to that effective occlusion point; if there is no corresponding effective occlusion point in the sky grid, the occlusion impact of the sky grid is the preset optimal score. Based on the occlusion impact degree corresponding to each of the sky grids, the comprehensive occlusion impact degree of the observation environment surrounding the target meteorological station is determined; Based on the comprehensive shading impact, a quantitative evaluation result of the shading impact on the observation environment of the target meteorological station is generated; For each of the sky grids, the effective occlusion point corresponding to the sky grid is determined based on the spatial occlusion relationship between the point cloud points located within the sky grid and the sensing element of the observation instrument. This includes: determining multiple point cloud points falling within the same sky grid; and selecting one point cloud point from the multiple point cloud points as the effective occlusion point corresponding to the sky grid according to a preset occlusion sorting rule. The preset occlusion sorting rule includes: selecting the point cloud point with the smallest horizontal distance from the sensing element of the observation instrument as the effective occlusion point.

2. The method for quantitative evaluation of the occlusion impact of meteorological station observation environment based on three-dimensional point clouds according to claim 1, characterized in that, The acquisition of three-dimensional point cloud data of the observation environment surrounding the target meteorological station includes: Raw spatial data of the observation environment surrounding the target meteorological station are obtained through UAV oblique photography, airborne lidar, ground laser scanning, or 3D reconstruction equipment. The original spatial data is subjected to point cloud reconstruction, coordinate registration, noise point filtering, and coordinate unification to obtain the three-dimensional point cloud data. The three-dimensional point cloud data includes at least the three-dimensional coordinate information of the point cloud points.

3. The method for quantitative evaluation of the occlusion impact of meteorological station observation environment based on three-dimensional point clouds according to claim 1, characterized in that, The step of extracting point cloud data of obstructions within the preset evaluation range of the target meteorological station based on the three-dimensional point cloud data includes: Candidate point cloud data are extracted according to a preset evaluation radius, centered on the spatial position of the sensing element of the observation instrument or the station position of the target meteorological station. The candidate point cloud data is subjected to ground point filtering and target classification processing to remove point cloud points that do not participate in the occlusion evaluation; Point cloud points whose height is higher than the sensing element of the observation instrument and which belong to buildings, structures, walls, towers or other fixed obstructions are identified as obstruction point cloud data.

4. The method for quantitative evaluation of the occlusion impact of meteorological station observation environment based on three-dimensional point clouds according to claim 1, characterized in that, The observation instrument's sensing element is divided into grids according to azimuth and elevation angles, resulting in multiple sky grids, including: The sky hemisphere region above the sensing element of the observation instrument is divided into multiple angular grids; Each of the aforementioned sky grids corresponds to an azimuth range and an elevation range; The angular resolution of the sky grid is determined based on the point cloud density of the three-dimensional point cloud data, the type of weather station, or the required observation accuracy.

5. The method for quantitative evaluation of the occlusion impact of meteorological station observation environment based on three-dimensional point clouds according to claim 1, characterized in that, The process of determining the distance-to-height ratio for each effective occlusion point based on its horizontal distance and effective occlusion height, and determining the occlusion impact degree for each effective occlusion point based on the distance-to-height ratio and the attenuation coefficient, includes: For the i-th effective occlusion point, the distance-to-height ratio is determined according to the following formula: in, This represents the distance-to-height ratio corresponding to the i-th effective occlusion point. This represents the horizontal distance between the i-th effective obstruction point and the sensing element of the observation instrument. This represents the effective obstruction height of the i-th effective obstruction point relative to the sensing element of the observation instrument; when When the value is greater than 0, the occlusion influence degree corresponding to the i-th effective occlusion point is determined according to the following formula: in, This represents the occlusion impact degree corresponding to the i-th effective occlusion point. This represents the attenuation coefficient.

6. The method for quantitative evaluation of the occlusion impact of meteorological station observation environment based on three-dimensional point clouds according to claim 5, characterized in that, The attenuation coefficient is determined based on the station type of the target meteorological station; When the target weather station is a first type of weather station, the attenuation coefficient is determined based on a first no-significant-impact distance-to-height ratio threshold. When the target weather station is a second type of weather station, the attenuation coefficient is determined based on the second no significant influence distance-to-height ratio threshold. When the target weather station is a third type of weather station, the attenuation coefficient is determined based on the third no significant influence distance-to-height ratio threshold. The attenuation coefficient satisfies: in, This indicates the target evaluation value that is not significantly affected by occlusion. This represents the distance-to-height ratio threshold that has no significant impact on the station type of the target meteorological station.

7. The method for quantitative evaluation of the occlusion impact of meteorological station observation environment based on three-dimensional point clouds according to claim 1, characterized in that, The determination of the comprehensive occlusion impact of the observation environment surrounding the target meteorological station based on the occlusion impact of each of the sky grids includes: The occlusion impact degree corresponding to each of the aforementioned sky grids is sorted to obtain an occlusion impact degree sequence; Remove the preset low-end and preset high-end proportion data from the occlusion impact sequence, and calculate the mean of the remaining occlusion impact to obtain the truncated mean. The occlusion influence degree corresponding to the preset high quantile position is determined from the occlusion influence degree sequence to obtain the high quantile; The overall occlusion impact is determined based on the truncated mean and the high quantile. Wherein, the comprehensive occlusion impact degree satisfies: in, Indicates the overall impact of occlusion. This represents the truncated mean. Indicates the higher quantile. and Let represent the weighting coefficients, and .

8. A device for quantitatively evaluating the impact of environmental occlusion on meteorological station observations based on three-dimensional point clouds, characterized in that, include: The data acquisition module is used to acquire three-dimensional point cloud data of the observation environment around the target meteorological station and the spatial position of the sensing element of the observation instrument in the target meteorological station; The point cloud extraction module is used to extract point cloud data of obstructions within a preset evaluation range of the target meteorological station based on the three-dimensional point cloud data. The coordinate transformation module is used to transform the point cloud data of the obstruction object to a local spatial coordinate system with the sensor element of the observation instrument as the origin, based on the spatial position of the sensor element of the observation instrument, and to determine the azimuth, elevation, horizontal distance and effective obstruction height of each point cloud point. The grid division module is used to divide the sky hemisphere region corresponding to the sensing element of the observation instrument into multiple sky grids according to the azimuth and elevation angles. The effective occlusion point determination module is used to determine the effective occlusion point corresponding to each sky grid based on the spatial occlusion relationship between the point cloud points located in the sky grid and the sensing element of the observation instrument. The occlusion impact determination module is used to determine the distance-to-height ratio corresponding to each effective occlusion point based on the horizontal distance and effective occlusion height of each effective occlusion point, and to determine the occlusion impact degree corresponding to each effective occlusion point based on the distance-to-height ratio and the attenuation coefficient. Among them, the occlusion impact degree corresponding to the effective occlusion point is the occlusion impact degree of the sky grid corresponding to the effective occlusion point; if there is no corresponding effective occlusion point in the sky grid, the occlusion impact degree corresponding to the sky grid is the preset optimal score. The comprehensive impact determination module is used to determine the comprehensive occlusion impact of the observation environment around the target meteorological station based on the occlusion impact corresponding to each of the sky grids. The evaluation result generation module is used to generate a quantitative evaluation result of the shading impact on the observation environment of the target meteorological station based on the comprehensive shading impact degree. The step of converting the obstruction point cloud data to a local spatial coordinate system with the observation instrument's sensing element as the origin, and determining the azimuth, elevation, horizontal distance, and effective obstruction height corresponding to each point cloud point, includes: establishing a local spatial coordinate system with the center point of the observation instrument's sensing element as the coordinate origin, the horizontal plane as the first coordinate plane, and the vertical direction as the height direction; for any point cloud point, determining the horizontal distance, effective obstruction height, azimuth, and elevation angle of the point cloud point relative to the observation instrument's sensing element based on the point cloud point's coordinates in the local spatial coordinate system; wherein, the effective obstruction height is the height difference between the height of the point cloud point and the height of the observation instrument's sensing element, and when the effective obstruction height is less than or equal to zero, the point cloud point is determined as an unobstructed contribution point; For each of the sky grids, the effective occlusion point corresponding to the sky grid is determined based on the spatial occlusion relationship between the point cloud points located within the sky grid and the sensing element of the observation instrument. This includes: determining multiple point cloud points falling within the same sky grid; and selecting one point cloud point from the multiple point cloud points as the effective occlusion point corresponding to the sky grid according to a preset occlusion sorting rule. The preset occlusion sorting rule includes: selecting the point cloud point with the smallest horizontal distance from the sensing element of the observation instrument as the effective occlusion point.

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