Method and system for assessing meteorological risks over cities

Abstract

The present application relates to a kind of urban overhead weather risk assessment method and system, wherein the urban overhead weather risk assessment method includes the following steps: determining the spatial location information corresponding to target evaluation object;Obtain the near-surface wind speed forecast data of target evaluation object in preset prediction period;Based on the wind speed mapping relationship library constructed in advance, the near-surface wind speed forecast data is converted into the high-altitude wind speed data of the spatial location where target evaluation object is located, the height layer where it is located;According to high-altitude wind speed data, in combination with preset risk level threshold, the high-altitude wind risk assessment result of target evaluation object is output;The present application realizes the quick conversion of near-surface wind speed data to high-altitude wind speed data by constructing wind speed mapping relationship library in advance, without re-running complex three-dimensional wind field model at each evaluation time, significantly improve the calculation efficiency of high-altitude wind risk assessment.

Description

Technical Field

[0001] This invention relates to the field of meteorological risk assessment technology, and specifically to a method and system for assessing meteorological risks over urban areas. Background Technology

[0002] In recent years, extreme weather events have become more frequent and intense, and meteorological disasters have shown new characteristics of being long-term, sudden, catastrophic, and complex, constantly threatening the safe operation of cities. Due to the superposition of uncontrollable factors such as the height, density, and natural aging of the exterior, high-rise buildings are increasingly at risk of safety accidents caused by extreme weather such as strong winds and heavy rainfall, such as falling objects from heights, falling exterior walls, and instability of high-altitude work platforms.

[0003] In existing technologies, high-altitude wind risk assessment typically employs the following approach: after acquiring near-surface wind speed forecast data, a complex three-dimensional wind field numerical model is run to calculate and deduce wind speed data at various altitude levels, which is then combined with risk thresholds for risk assessment. However, this approach has significant technical drawbacks: the calculation process of the three-dimensional wind field numerical model is complex and time-consuming, requiring the model to be run again for each assessment, resulting in low assessment efficiency and failing to meet the timeliness requirements for rapid response in high-altitude operation scenarios. In particular, when multiple target objects and multiple time windows need to be assessed in batches, the computational time consumption problem becomes more prominent, severely restricting the practicality and scalability of high-altitude wind risk assessment. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for assessing meteorological risks above cities. This solves the problem that the existing high-altitude wind risk assessment methods require repeated running of complex three-dimensional wind field numerical models for calculation, resulting in low assessment efficiency and failing to meet the timeliness requirements for rapid response in high-altitude operation scenarios.

[0005] To achieve the above objectives, the present invention provides a method and system for assessing meteorological risks overhead in cities, comprising the following steps: Determine the spatial location information corresponding to the target assessment object; Obtain near-surface wind speed forecast data for the target assessment object within a preset forecast period; Based on a pre-built wind speed mapping database, near-surface wind speed forecast data is converted into upper-air wind speed data indicating the spatial location and altitude of the target assessment object. Based on upper-level wind speed data and a preset risk level threshold, the upper-level wind risk assessment results for the target assessment object are output.

[0006] By adopting this technical solution, a rapid conversion from near-surface wind speed data to high-altitude wind speed data is achieved through the pre-construction of a wind speed mapping database. This eliminates the need to re-run complex three-dimensional wind field models for each assessment, significantly improving the computational efficiency of high-altitude wind risk assessment. At the same time, by combining spatial location information and risk level thresholds, precise risk assessments are achieved for specific assessment objects, providing technical support for safety decisions in high-altitude operations.

[0007] Furthermore, the wind speed mapping database is a three-dimensional grid wind speed standardized retrieval dictionary, which establishes a one-to-one mapping relationship between near-surface wind speed and high-altitude wind speed at different spatial locations and altitudes.

[0008] By adopting this technical solution, the wind speed mapping relationship database is specifically defined as a three-dimensional grid wind speed standardized retrieval dictionary, clarifying the implementation form of the mapping relationship. This transforms the acquisition of upper-level wind speed data from real-time calculation to pre-storage + fast query, which is the key to improving assessment efficiency.

[0009] Furthermore, the specific method for constructing a standardized retrieval dictionary for three-dimensional grid wind speed is as follows: Based on a high spatiotemporal resolution three-dimensional wind field model, the range and interval of near-surface wind speed and the range and interval of vertical height are set. By substituting the near-surface wind speed values ​​in each spatial grid cell into the high spatiotemporal resolution three-dimensional wind field model, the high-altitude wind speed values ​​corresponding to each vertical height layer in the grid cell are calculated. By integrating the calculation results, a standardized retrieval dictionary is constructed, using spatial grid index, altitude index, and near-surface wind speed as keys and upper-air wind speed as values.

[0010] By adopting this technical solution, the mapping results corresponding to all possible combinations are pre-calculated by systematically traversing the range of near-surface wind speed and vertical height values, forming a complete retrieval dictionary. This ensures that the corresponding mapping results can be found in the dictionary during actual assessment, reducing the computational delay in the assessment process.

[0011] Furthermore, the steps for outputting high-altitude wind risk assessment results based on high-altitude wind speed data include a probability quantification sub-step: The corresponding wind force level is determined based on the near-surface wind speed forecast data, and the corresponding wind speed range is determined based on the wind force level. Discretize the samples within the wind speed range according to the preset sampling interval to obtain a set of wind speed samples; Convert each wind speed sample into its corresponding upper-level wind speed and wind force level; The frequency of occurrence of each wind force level is statistically analyzed to determine the probability of that wind force level occurring. The results of the upper-level wind risk assessment include the probability of occurrence for each wind force level.

[0012] By adopting this technical solution, continuous wind speed ranges are transformed into discrete wind speed sample sets through discretization sampling. These samples are then converted into corresponding upper-level wind force levels through a mapping database. Finally, the probability of occurrence of each wind force level is quantified through frequency statistics. This fully considers the randomness and volatility characteristics of wind fields, providing a more scientific and comprehensive risk assessment basis for decision-making in high-risk operations.

[0013] Furthermore, the preset sampling interval is set according to the time resolution and accuracy requirements of the risk assessment, and the value range of the preset sampling interval is 0.1m / s to 1.0m / s.

[0014] By adopting this technical solution, the range of 0.1m / s to 1.0m / s is an empirical range that strikes a balance between evaluation accuracy and computational efficiency, ensuring both the accuracy of probability quantification and maintaining high computational efficiency.

[0015] Furthermore, the step of determining the spatial location information corresponding to the target assessment object includes a spatial matching sub-step: The spatial coordinates of the target assessment object are registered with the three-dimensional wind field grid on which the three-dimensional grid wind speed standardization retrieval dictionary is based, thereby determining the spatial grid index and height index corresponding to the target assessment object.

[0016] By adopting this technical solution, the spatial coordinates of the target evaluation object are registered with the three-dimensional wind field grid on which the three-dimensional grid wind speed standardization retrieval dictionary is based through a spatial matching sub-step, thereby determining its corresponding grid index and height index, thus ensuring the accuracy of subsequent data conversion.

[0017] Furthermore, the spatial matching sub-step includes: Unify the coordinates of the target evaluation object and the coordinates of the three-dimensional wind field grid to the same horizontal coordinate system; Calculate the horizontal grid index of the target evaluation object in the three-dimensional wind field grid based on its horizontal coordinates. Based on the vertical height of the target evaluation object, determine its corresponding vertical height layer in the three-dimensional wind field grid.

[0018] By adopting this technical solution, the benchmark deviation of different data sources is eliminated through the coordinate system, the horizontal grid index is determined by horizontal coordinate calculation, and the vertical height layer is determined by vertical height matching, forming a complete three-dimensional spatial matching technical solution; this ensures the accurate alignment of multi-source data in spatial dimensions, which is a prerequisite for ensuring the accuracy of the evaluation results.

[0019] Furthermore, the target evaluation object can be a point-like object or a surface-like object: When the target evaluation object is a point object, the spatial location information is the three-dimensional coordinates of that point object; When the target evaluation object is a planar object, the spatial location information is the set of all grid cells covered by the planar object.

[0020] By adopting this technical solution, the applicable scenarios of the present invention are expanded, and risk assessment of both point objects (such as a single building or aerial work platform) and area objects (such as administrative districts or streets) can be supported simultaneously.

[0021] Furthermore, the risk level thresholds include low-risk thresholds, medium-risk thresholds, and high-risk thresholds, each corresponding to different upper limits or probability thresholds for wind force levels.

[0022] By adopting this technical solution, the risk assessment results are combined with the risk level thresholds to output a visualized risk level result.

[0023] This invention also provides a meteorological risk assessment system for urban areas, comprising: The spatial positioning module is used to determine the spatial location information corresponding to the target evaluation object; The data acquisition module is used to acquire near-surface wind speed forecast data for the target assessment object within a preset forecast period. A mapping database is used to store pre-built mapping relationships between near-surface wind speeds and upper-air wind speeds at different spatial locations and altitudes. The conversion module is used to convert near-surface wind speed forecast data into upper-air wind speed data based on the spatial location and altitude layer of the target assessment object, using a mapping relationship library. The assessment output module is used to output the high-altitude wind risk assessment results of the target assessment object based on high-altitude wind speed data and preset risk level thresholds.

[0024] Compared with the prior art, the present invention has the following advantages: 1. By pre-constructing a wind speed mapping database, the complex wind field calculation process is completed in advance. In actual assessment, high-altitude wind speed data at any spatial location and any height layer can be quickly obtained by simply looking up a table, which significantly improves the calculation efficiency of high-altitude wind risk assessment.

[0025] 2. To address the uncertainty in wind speed forecasting, a method is adopted to infer wind speed ranges from wind force levels and to perform discretization sampling and frequency probability statistics. This upgrades the traditional single-point fixed-value forecast to a probabilistic risk assessment, quantifies the probability of occurrence of wind events of different levels, and improves the scientificity and accuracy of the risk assessment.

[0026] 3. Through spatial matching technology, precise coupling between point objects and surface objects and three-dimensional wind field mesh is achieved, which can adapt to the risk assessment needs of various high-altitude operation scenarios and has wide applicability. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating the meteorological risk assessment method for urban areas in this invention. Detailed Implementation

[0028] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. Example

[0029] This embodiment uses platform operation as a typical application scenario, and the target evaluation object is a single typical building. Specifically, it takes a certain central building as an example to illustrate the implementation process of the method of the present invention.

[0030] Step 1: Determine the spatial location information corresponding to the target evaluation object; The central building is a point-like object, and its spatial location information includes longitude, latitude, and vertical height, with the vertical height being 632m.

[0031] First, the discrete point coordinates of the central building and the coordinates of the three-dimensional wind field grid are unified to the same horizontal coordinate system (such as the WGS84 coordinate system) to eliminate the horizontal coordinate reference deviation; at the same time, the vertical height of the central building, 632m, is aligned with the vertical reference standard of the three-dimensional wind field grid (such as mean sea level).

[0032] Secondly, based on the completion of horizontal coordinate unification, and relying on the known parameters of the three-dimensional wind field grid (latitude and longitude resolution) DeltaLon , DeltaLat Starting latitude and longitude Lon min , Lat min The horizontal grid index corresponding to the central building is calculated using the following formula ( i , j ): I =( Lon - Lon min ) / DeltaLon J =( Lat - Lat min ) / DeltaLat Finally, based on the vertical height of the central building, 632m, its corresponding three-dimensional wind field vertical layer was matched; the vertical resolution of the three-dimensional wind field model includes 40m, 60m, 80m, 100m...600m; the height of the central building, 632m, is closest to the 600m height layer, and according to the nearest neighbor matching principle, the 600m height layer was selected as its corresponding vertical height layer.

[0033] By following the steps above, the spatial grid index and height index corresponding to the central building are determined, thus completing the determination of spatial location information.

[0034] Step 2: Obtain near-surface wind speed forecast data for the target assessment object within the preset forecast period; The near-surface wind speed forecast data comes from the intelligent grid forecast 10-meter wind forecast product; the time resolution of this product is 1 hourly for 0-48 hours and 3 hoursly for 48-72 hours, the spatial resolution is 1km, and the update frequency is 08:00 and 20:00 every day.

[0035] In this embodiment, the preset prediction period is set to the next 0-72 hours. Near-surface wind speed forecast data of the 2*2 horizontal grid cells (i.e., the horizontal grid index and its adjacent grids determined in step 1) around the location of the central building are obtained in each time window. The near-surface wind speed forecast data is provided in the form of wind speed intervals, such as "the near-surface wind speed in the next 3 hours is [5m / s, 5.6m / s, 4.8m / s]".

[0036] Step 3: Based on the pre-built wind speed mapping relationship library, convert the near-surface wind speed forecast data into high-altitude wind speed data of the spatial location and altitude layer of the target assessment object; (1) Construction of wind speed mapping database In this embodiment, the wind speed mapping relationship database is a three-dimensional grid wind speed standardized retrieval dictionary.

[0037] The construction of this three-dimensional grid wind speed standardization retrieval dictionary is based on a high spatiotemporal resolution three-dimensional wind field model. The temporal resolution of this model is 0-48 hours incremented by 1 hour and 48-72 hours incremented by 3 hours, the spatial resolution is 100m, and the vertical resolution includes 40m, 60m, 80m, 100m...600m. The update frequency is 08:00 and 20:00 every day.

[0038] The range for near-surface wind speed is set to 0.0 m / s - 60.0 m / s, with intervals of [missing value]. delta v (In this embodiment) delta v (Take 0.5 m / s); set the vertical height range to 10 m - 650 m, with intervals of [missing value]. delta h (In this embodiment) delta h (Using a 20m depth, the final output will use the existing vertical resolution layer of the 3D wind field model).

[0039] Traverse all grid cells within the spatial range of the target area (e.g., the city where the central building is located). For each grid cell, substitute the corresponding near-surface wind speed values ​​(0.0, 0.5, 1.0, ..., 60.0 m / s) into the high spatiotemporal resolution three-dimensional wind field model one by one to calculate the high-altitude wind speed values ​​corresponding to each vertical height layer (40m, 60m, 80m, 100m...600m) within the grid cell.

[0040] The calculation results of all grid cells are integrated to construct a standardized retrieval dictionary, namely the wind speed lookup table, with spatial grid index, height index, and near-surface wind speed as keys and upper-air wind speed as values. (It should be noted that the construction of this standardized retrieval dictionary is completed once before the risk assessment and is not related to the specific assessment object.)

[0041] (2) Data transformation and probability quantization First, based on the near-surface wind speed forecast data of the 2*2 horizontal grid cells around the central building obtained in step 2 within each time window, the near-surface wind speed forecast data value corresponding to the location of the central building is calculated using the bilinear interpolation method. The bilinear interpolation formula is as follows:

[0042] in, Q 11 , Q 12 , Q 21 , Q 22 Four grid points for a 2x2 horizontal grid cell. u ( Q () represents the near-surface wind speed forecast data for each grid point. P The location of the central building, u ( P (The last part is a data point representing the near-surface wind speed forecast for the central building, obtained through interpolation.) V .

[0043] Secondly, based on the altitude index (600m) determined in step 1 and the near-surface wind speed forecast data obtained through interpolation above... V The system searches for the corresponding grid cell and the corresponding high-altitude wind speed at the corresponding height level (600m) in the pre-built three-dimensional grid wind speed standardization retrieval dictionary. V high,i .

[0044] Then, based on the correspondence between wind force level and wind speed in the standard GB / T28591-2012 "Wind Force Classification", the following is determined: V high,iReverse conversion to wind force level L i This generates a list of all possible wind force values ​​within the corresponding time window. L 0, L 1, L 2,... L n-1 ].

[0045] Finally, the probability of occurrence of wind events of different levels is calculated using the frequency probability method. The core formula is as follows: P ( L k )= n k / n In the formula, P ( L k (For wind force to reach) k The probability of occurrence of level 1; n k The wind force level is listed in the possible wind force values ​​list. k The number of values ​​in each level; n This represents the total number of values ​​in the list of possible wind force values.

[0046] Step 4: Based on the high-altitude wind speed data and the preset risk level threshold, output the high-altitude wind risk assessment results for the target assessment object; The preset risk level threshold table in this embodiment is as follows: The probability of occurrence of each wind level calculated in step 3. P ( L k The wind force levels and their corresponding probabilities are compared and judged one by one with the risk level threshold table above to select the wind force levels that exceed (or reach) the risk threshold.

[0047] The final output risk products include: wind force level, risk level, and risk probability for the forecast object at the roof height of a typical building; time resolution of 0-24 hours (3-hour interval), 24-48 hours (6-hour interval), and 48-72 hours (12-hour interval); update frequency of 08:00 and 20:00 daily; and risk levels divided into three levels: low risk (blue), medium risk (yellow), and high risk (red).

[0048] An example output is: "The probability of a level 5 wind (medium risk) on the roof of the central building in the next 3 hours is 20%, and the probability of a level 6 wind or above (high risk) is 35%. It is recommended to be cautious when working at heights and to prepare for and respond to sudden meteorological disasters." Example

[0049] This embodiment uses spider-man operations as a typical application scenario, and the target evaluation object is an administrative region. Specifically, it takes an administrative region as an example to further illustrate the applicability of the method described in this invention.

[0050] Step 1: Determine the spatial location information corresponding to the target evaluation object; As an area object, the spatial location information of an administrative region includes the vector boundary coordinates of the administrative region and the vertical height range of the spider operation. The vertical height range of the spider operation corresponds to the vertical height layers of the three-dimensional wind field, including 40m, 60m, 80m, 100m...600m.

[0051] First, the administrative division vector coordinates and the three-dimensional wind field grid coordinates are unified to the same horizontal coordinate system (such as the WGS84 coordinate system) to eliminate the horizontal coordinate reference deviation.

[0052] Secondly, using the geometric boundaries of administrative divisions as constraints, and through point-to-surface spatial inclusion relationship determination, all horizontal grid cells falling within the administrative division area are selected; the specific steps are as follows: (1) Extract the preprocessed administrative division vector data and extract the geometric boundaries of the administrative region. Geo district ; (2) Analyze the longitude of the three-dimensional wind field grid Lon ,latitude Lat Dimensional information is used to construct a latitude and longitude grid with the same dimensions as the 3D wind field grid. Lons , Lats ; (3) Latitude and longitude grid Lons , Lats Each grid point in ( Lon i , Lat j Determine whether it falls within the geometric boundary of the administrative region. Geo district Within, a Boolean mask matrix with the same dimensions as the 3D wind field mesh is generated. Mask ,satisfy:

[0053] in, Mask i,j The middle part is the mask matrix. i Line 1 j Boolean value of column True This indicates that the grid cell belongs to the target administrative region. False This is indicated as an external invalid unit.

[0054] (4) Mask matrix Mask Applying to 3D wind field raster data, only retaining Maski,j = True The corresponding grid cells shield invalid external data, enabling accurate extraction of horizontal grids within the administrative region.

[0055] Finally, based on the defined administrative region horizontal grid constraint range, the vertical height dimension information of the high spatiotemporal resolution 3D wind field dataset is directly read, and all vertical height layers K = {k1,k2,...,k} in the 3D wind field are defined. n This directly serves as the potential height range for Spider-Man operations.

[0056] By following the steps above, the spatial grid index set and vertical height layer set corresponding to the administrative region are determined, thus completing the determination of spatial location information.

[0057] Step 2: Obtain near-surface wind speed forecast data for the target assessment object within the preset forecast period; Similar to Example 1, the near-surface wind speed forecast data comes from the intelligent grid forecast 10-meter wind forecast product. The time resolution of this product is 1 hourly for 0-48 hours and 3 hours for 48-72 hours, the spatial resolution is 1 km, and the update frequency is 08:00 and 20:00 every day.

[0058] In this embodiment, the preset prediction period is set to the next 0-72 hours. Near-surface wind speed forecast data is obtained from all grid cells (i.e., the spatial grid index set determined in step 1) within each time window of the administrative region. The near-surface wind speed forecast data is a wind speed interval […]. V min , V max ].

[0059] Step 3: Based on the pre-built wind speed mapping relationship library, convert the near-surface wind speed forecast data into high-altitude wind speed data of the spatial location and altitude layer of the target assessment object; (1) Construction of wind speed mapping database Similar to Example 1, a pre-built three-dimensional grid wind speed standardization retrieval dictionary is used as the wind speed mapping relationship library. The construction method is the same as in Example 1, and will not be repeated here.

[0060] (2) Data transformation and probability quantization First, based on the set of spatial grid indices and vertical height layers of administrative regions determined in step 1, and the wind speed ranges within the administrative region obtained in step 2 for each time window, […]. V min , V maxBased on the correspondence between wind force level and wind speed in the national standard GB / T28591-2012 "Wind Force Classification", the corresponding wind force level range is determined; and based on the wind force level range, the corresponding new wind speed range is determined. V min , V max ].

[0061] Secondly, in the new wind speed range [ V min , V max Within the preset sampling interval delta v Perform uniform value selection (in this embodiment) delta v Taking 0.5 m / s, a set of wind speed samples was obtained.

[0062] Then, each wind speed sample is substituted into the three-dimensional grid wind speed standardization retrieval dictionary to find the corresponding high-altitude wind speed range for each vertical height level within the administrative region. V high,min, V high,max Then, it is converted into the corresponding wind force level according to the standard GB / T28591-2012 "Wind Force Level". L high,min, L high,max ].

[0063] Finally, the frequency of each wind force level at all vertical heights within the administrative region is statistically analyzed to determine the probability of that wind force level occurring at that height.

[0064] Step 4: Based on the high-altitude wind speed data and the preset risk level threshold, output the high-altitude wind risk assessment results for the target assessment object; The preset risk level threshold table in this embodiment is as follows: The probability of occurrence of each level of wind force calculated in step 3 is compared with the above-mentioned risk level thresholds for judgment.

[0065] The output risk products include: wind force level, risk level, and risk probability for spider operations in 16 administrative regions; spatial resolution for 16 administrative regions; temporal resolution for 0-24 hours (3-hour interval), 24-48 hours (6-hour interval), and 48-72 hours (12-hour interval); vertical resolution for 40m, 60m, 80m, 100m...600m; update frequency for 08:00 and 20:00 daily; and risk levels divided into three categories: low risk (blue), medium risk (orange), and high risk (red).

[0066] An example output is: "Within a certain administrative region, the probability of a level 4 wind (medium risk) at the 40m working height level for Spider-Man operations in the next 6 hours is 30%, and the probability of a level 5 wind or above (high risk) is 45%; it is recommended to suspend Spider-Man operations and prepare for the response and handling of sudden meteorological disasters." The present invention also provides a system for implementing the above-mentioned method for assessing meteorological risks overhead in cities, comprising: The spatial positioning module is used to determine the spatial location information of the target evaluation object. When the target evaluation object is a point object, the module obtains the three-dimensional coordinates of the point object and determines its corresponding spatial grid index and height index by registering with the three-dimensional wind field grid. When the target evaluation object is a surface object, the module obtains the vector boundary coordinates and vertical height range of the surface object and determines the set of all grid cells it covers by determining the spatial inclusion relationship.

[0067] The data acquisition module is used to acquire near-surface wind speed forecast data for the target assessment object within a preset forecast period. This module interfaces with the intelligent grid forecast 10-meter wind forecast product to acquire near-surface wind speed forecast data within 0-72 hours.

[0068] The mapping relationship library is used to store the pre-built mapping relationship between near-surface wind speed and upper-air wind speed at different spatial locations and altitudes. Specifically, the mapping relationship library is a three-dimensional grid wind speed standardized retrieval dictionary, which uses spatial grid index, altitude index, and near-surface wind speed as keys and upper-air wind speed as values ​​to realize fast lookup from near-surface wind speed to upper-air wind speed.

[0069] The conversion module is used to convert the near-surface wind speed forecast data into upper-level wind speed data based on the mapping relationship library, indicating the spatial location and altitude layer of the target assessment object. The module also includes a probability quantization subunit, which is used to determine the corresponding wind force level based on the near-surface wind speed forecast data, and determine the corresponding wind speed range based on the wind force level. Within the wind speed range, operations such as discretization sampling, wind speed sample conversion, and wind force level statistics are performed to generate the probability of occurrence of each wind force level.

[0070] The assessment output module is used to output the high-altitude wind risk assessment result of the target assessment object based on the high-altitude wind speed data and a preset risk level threshold. The output risk assessment result includes the probability of occurrence of each wind force level within different time windows, as well as the corresponding risk level prompts and suggestions. The present invention has been described in detail above with reference to the accompanying drawings and embodiments. Those skilled in the art can make various modifications to the present invention based on the above description. Therefore, certain details in the embodiments should not be construed as limiting the present invention, and the scope of protection of the present invention shall be defined by the appended claims.

Claims

1. A method for assessing meteorological risks over urban areas, characterized in that, Includes the following steps: Determine the spatial location information corresponding to the target assessment object; Obtain near-surface wind speed forecast data for the target assessment object within a preset forecast period; Based on a pre-built wind speed mapping database, near-surface wind speed forecast data is converted into upper-air wind speed data indicating the spatial location and altitude of the target assessment object. Based on upper-level wind speed data and a preset risk level threshold, the upper-level wind risk assessment results for the target assessment object are output.

2. The method for assessing meteorological risks over urban areas according to claim 1, characterized in that, The wind speed mapping database is a three-dimensional grid wind speed standardized retrieval dictionary. The three-dimensional grid wind speed standardized retrieval dictionary establishes a one-to-one mapping relationship between near-surface wind speed and upper-air wind speed at different spatial locations and altitudes.

3. The method for assessing meteorological risks over urban areas according to claim 2, characterized in that, The specific method for constructing a standardized retrieval dictionary for three-dimensional grid wind speed is as follows: Based on a high spatiotemporal resolution three-dimensional wind field model, the range and interval of near-surface wind speed and the range and interval of vertical height are set. By substituting the near-surface wind speed values ​​in each spatial grid cell into the high spatiotemporal resolution three-dimensional wind field model, the high-altitude wind speed values ​​corresponding to each vertical height layer in the grid cell are calculated. By integrating the calculation results, a standardized retrieval dictionary is constructed, using spatial grid index, altitude index, and near-surface wind speed as keys and upper-air wind speed as values.

4. The method for assessing meteorological risks over urban areas according to claim 1, characterized in that, The steps for outputting high-altitude wind risk assessment results based on high-altitude wind speed data include a probability quantification sub-step: The corresponding wind force level is determined based on the near-surface wind speed forecast data, and the corresponding wind speed range is determined based on the wind force level. Discretize the samples within the wind speed range according to the preset sampling interval to obtain a set of wind speed samples; Convert each wind speed sample into its corresponding upper-level wind speed and wind force level; The frequency of occurrence of each wind force level is statistically analyzed to determine the probability of that wind force level occurring. The results of the upper-level wind risk assessment include the probability of occurrence for each wind force level.

5. The method for assessing meteorological risks over urban areas according to claim 4, characterized in that, The preset sampling interval is set according to the time resolution and accuracy requirements of the risk assessment, and the value range of the preset sampling interval is 0.1m / s to 1.0m / s.

6. The method for assessing meteorological risks over urban areas according to claim 3, characterized in that, The steps for determining the spatial location information corresponding to the target evaluation object include the spatial matching sub-step: The spatial coordinates of the target assessment object are registered with the three-dimensional wind field grid on which the three-dimensional grid wind speed standardization retrieval dictionary is based, thereby determining the spatial grid index and height index corresponding to the target assessment object.

7. The method for assessing meteorological risks over urban areas according to claim 6, characterized in that, The spatial matching sub-steps include: Unify the coordinates of the target evaluation object and the coordinates of the three-dimensional wind field grid to the same horizontal coordinate system; Calculate the horizontal grid index of the target evaluation object in the three-dimensional wind field grid based on its horizontal coordinates. Based on the vertical height of the target evaluation object, determine its corresponding vertical height layer in the three-dimensional wind field grid.

8. The method for assessing meteorological risks over urban areas according to claim 1, characterized in that, The target evaluation object is either a point object or a surface object: When the target evaluation object is a point object, the spatial location information is the three-dimensional coordinates of that point object; When the target evaluation object is a planar object, the spatial location information is the set of all grid cells covered by the planar object.

9. The method for assessing meteorological risks over urban areas according to claim 1, characterized in that, Risk level thresholds include low risk threshold, medium risk threshold and high risk threshold, which correspond to different upper limits or probability thresholds of wind force level.

10. A system for implementing the urban meteorological risk assessment method as described in any one of claims 1-9, characterized in that, include: The spatial positioning module is used to determine the spatial location information corresponding to the target evaluation object; The data acquisition module is used to acquire near-surface wind speed forecast data for the target assessment object within a preset forecast period. A mapping database is used to store pre-built mapping relationships between near-surface wind speeds and upper-air wind speeds at different spatial locations and altitudes. The conversion module is used to convert near-surface wind speed forecast data into upper-air wind speed data based on the spatial location and altitude layer of the target assessment object, using a mapping relationship library. The assessment output module is used to output the high-altitude wind risk assessment results of the target assessment object based on high-altitude wind speed data and preset risk level thresholds.

Images