Grid density determination method and device for meteorological monitoring, computer equipment and storage medium
By using a dynamic grid density adjustment method based on terrain data and micrometeorological parameters, the problems of low monitoring accuracy and resource waste caused by fixed grid density in traditional micrometeorological monitoring are solved, and sub-kilometer-level meteorological field reconstruction and refined monitoring are realized.
Patent Information
- Application Number
- CN202511614622.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-06
- Publication Date
- 2026-02-17
AI Technical Summary
In traditional micro-meteorological monitoring, the uniform grid layout with fixed density cannot dynamically adjust the grid density according to changes in terrain features and meteorological elements, resulting in low monitoring accuracy, serious waste of resources, and difficulty in meeting the needs of sub-kilometer-level fine monitoring.
Based on the topographic data and micro-meteorological parameters of the target monitoring area, the grid density is dynamically adjusted. The target grid density is determined by the topographic complexity and variation parameters, and the grid density is dynamically adjusted by combining multi-source data and historical spatiotemporal trends.
It improved the matching degree and accuracy of grid density with monitoring area, realized the reconstruction of meteorological field at the sub-kilometer level, optimized resource allocation, and improved the accuracy and efficiency of micro-meteorological monitoring in harsh environments.
Smart Images
Figure CN121542556A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of meteorological monitoring technology, and in particular to a method, apparatus, computer equipment, and storage medium for determining grid density in meteorological monitoring. Background Technology
[0002] In order to gain a deeper understanding of the microscopic processes of energy and matter exchange between the Earth's surface and the atmosphere, and to meet the urgent needs of fields such as precision agriculture, ecological research, renewable energy, aviation safety, and urban environmental management for meteorological data at the "centimeter to kilometer" scale, it is necessary to develop specialized technologies that can conduct high-frequency, in-situ, and refined observations. Micrometeorological monitoring technology has emerged as a result.
[0003] Traditional techniques employ a uniform grid layout with a fixed density for micrometeorological monitoring, which suffers from low monitoring accuracy. Summary of the Invention
[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, and storage medium for determining grid density in meteorological monitoring that can improve the accuracy of micrometeorological monitoring, addressing the aforementioned technical problems.
[0005] In a first aspect, this application provides a method for determining grid density in meteorological monitoring, the method comprising:
[0006] Based on the terrain data of the target monitoring area, the terrain complexity of the target monitoring area is determined;
[0007] Based on the micrometeorological parameters of the target monitoring area, variation parameters are determined; the variation parameters are used to characterize the spatiotemporal variation of the micrometeorological parameters.
[0008] The target grid density is determined based on the variation parameters and the terrain complexity.
[0009] In one embodiment, adjusting the initial grid density based on the variation parameter and the terrain complexity to determine the target grid density includes:
[0010] Determine whether the variation parameters and the terrain complexity meet the parameter threshold requirements;
[0011] If satisfied, the target mesh density is determined according to the preset side length correspondence.
[0012] If the conditions are not met, the target grid density is determined based on the preset side length correspondence and interpolation algorithm.
[0013] In one embodiment, determining the terrain complexity of the target monitoring area based on terrain data of the target monitoring area includes:
[0014] Acquire terrain data of the target monitoring area; the terrain data includes basic terrain parameters and terrain occlusion.
[0015] Determine the terrain fragmentation based on the aforementioned basic terrain parameters;
[0016] The terrain complexity is determined based on the basic terrain parameters, the terrain fragmentation, the terrain occlusion, and the preset weighting coefficient.
[0017] In one embodiment, determining the variation parameters based on the micrometeorological parameters of the target monitoring area includes:
[0018] Based on the standard deviation and mean of each micrometeorological parameter within a preset time period, the time dimension variation parameter of each micrometeorological parameter is determined;
[0019] Based on the spatial standard deviation and spatial mean of each micrometeorological parameter across all monitoring nodes, determine the spatial dimension variation parameter of each micrometeorological parameter;
[0020] The variation parameters are determined based on the temporal and spatial variation parameters of each micrometeorological parameter.
[0021] In one embodiment, determining the variation parameters based on the temporal and spatial variation parameters of each of the micrometeorological parameters includes:
[0022] Based on the monitoring target, determine the time weight coefficient and spatial weight coefficient of the target monitoring area;
[0023] The variation parameters are determined based on the time dimension variation parameters, the spatial dimension variation parameters, the time weighting coefficient, and the spatial weighting coefficient.
[0024] In one embodiment, the method further includes:
[0025] Compare the trends of the micrometeorological parameters with historical data;
[0026] If the micrometeorological parameters do not match the historical data trend, the target grid density is adjusted by densifying the grid.
[0027] Secondly, this application also provides a grid density determination device for meteorological monitoring, comprising:
[0028] The first determining module is used to determine the terrain complexity of the target monitoring area based on the terrain data of the target monitoring area;
[0029] The second determining module is used to determine the variation parameters based on the micro-meteorological parameters of the target monitoring area; the variation parameters are used to characterize the spatiotemporal variation of the micro-meteorological parameters.
[0030] The third determining module is used to determine the target grid density based on the variation parameters and the terrain complexity.
[0031] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:
[0032] Based on the terrain data of the target monitoring area, the terrain complexity of the target monitoring area is determined;
[0033] Based on the micrometeorological parameters of the target monitoring area, variation parameters are determined; the variation parameters are used to characterize the spatiotemporal variation of the micrometeorological parameters.
[0034] The target grid density is determined based on the variation parameters and the terrain complexity.
[0035] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:
[0036] Based on the terrain data of the target monitoring area, the terrain complexity of the target monitoring area is determined;
[0037] Based on the micrometeorological parameters of the target monitoring area, variation parameters are determined; the variation parameters are used to characterize the spatiotemporal variation of the micrometeorological parameters.
[0038] The target grid density is determined based on the variation parameters and the terrain complexity.
[0039] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:
[0040] Based on the terrain data of the target monitoring area, the terrain complexity of the target monitoring area is determined;
[0041] Based on the micrometeorological parameters of the target monitoring area, variation parameters are determined; the variation parameters are used to characterize the spatiotemporal variation of the micrometeorological parameters.
[0042] The target grid density is determined based on the variation parameters and the terrain complexity.
[0043] The aforementioned method, apparatus, computer equipment, and storage medium for determining grid density in meteorological monitoring determine the topographic complexity of the target monitoring area based on topographic data; determine the variation parameters based on micrometeorological parameters of the target monitoring area; the variation parameters are used to characterize the spatiotemporal variation of the micrometeorological parameters; and the target grid density is determined based on the variation parameters and topographic complexity. By dynamically determining the target grid density of the target monitoring area based on topographic complexity and micrometeorological parameters, the matching degree and accuracy between the target grid density and the target monitoring area are improved. Furthermore, determining the target grid density based on both variation parameters and topographic complexity makes the determination process more comprehensive and reliable. Attached Figure Description
[0044] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0045] Figure 1 This is an application environment diagram of a grid density determination method for meteorological monitoring in one embodiment;
[0046] Figure 2 This is a flowchart illustrating a method for determining grid density in meteorological monitoring in one embodiment;
[0047] Figure 3 This is a flowchart illustrating a method for determining grid density for meteorological monitoring in another embodiment;
[0048] Figure 4 This is a flowchart illustrating a method for determining grid density for meteorological monitoring in another embodiment;
[0049] Figure 5 This is a flowchart illustrating a method for determining grid density for meteorological monitoring in another embodiment;
[0050] Figure 6 This is a flowchart illustrating a method for determining grid density for meteorological monitoring in another embodiment;
[0051] Figure 7 This is a flowchart illustrating a method for determining grid density for meteorological monitoring in another embodiment;
[0052] Figure 8 This is a flowchart illustrating a method for determining grid density for meteorological monitoring in another embodiment;
[0053] Figure 9This is a structural block diagram of a grid density determination device for meteorological monitoring in one embodiment;
[0054] Figure 10 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation
[0055] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0056] As meteorological monitoring demands increasingly higher spatial resolution and environmental adaptability, traditional micro-meteorological monitoring technologies are gradually revealing significant limitations. Regarding grid division, existing technologies mostly employ a fixed-density, uniform grid layout. This fails to dynamically adjust the grid density based on the terrain features and the intensity of meteorological changes in the monitoring area. For example, in areas with complex terrain such as mountains and valleys, or in areas with drastic changes in meteorological parameters such as severe convective weather, the fixed grid's insufficient density leads to inadequate spatial sampling of monitoring data, making it difficult to capture details of local micro-meteorological changes. Conversely, in areas with flat terrain and stable meteorological conditions, the fixed grid's excessive density wastes monitoring resources, increases equipment deployment and data transmission costs, resulting in insufficient monitoring point redundancy in complex terrain areas and resource waste in flat areas, making it difficult to achieve high-resolution coverage.
[0057] In the data processing and meteorological field reconstruction stages, traditional technologies often rely on monitoring data from a single source (such as data from fixed ground stations alone), lacking effective integration of multi-source data (such as data from ground sensors, UAV remote sensing, and satellite remote sensing). Furthermore, they fail to fully utilize the trend characteristics of historical spatiotemporal meteorological data, resulting in high data redundancy and weak outlier identification capabilities. Under harsh environments such as high humidity and strong electromagnetic interference, monitoring data is easily affected by noise, leading to biases. Existing algorithms struggle to accurately identify and correct outliers by incorporating historical spatiotemporal trends. Ultimately, this results in meteorological field reconstruction resolutions mostly remaining at the kilometer level or higher, failing to meet the needs for sub-kilometer level fine-grained monitoring. This severely restricts the application effectiveness in scenarios such as micro-meteorological disaster early warning and refined ecological environment management under harsh environments.
[0058] Furthermore, existing technologies lack sufficient research on the correlation mechanism between grid density and meteorological field reconstruction accuracy, and have failed to establish a dynamic grid density adjustment model based on the spatiotemporal variation characteristics of micrometeorological parameters, further exacerbating the contradiction between monitoring accuracy and resource costs. Therefore, there is an urgent need for a micrometeorological monitoring technology that can achieve sub-kilometer-level meteorological field reconstruction by dynamically adjusting grid density, fusing multi-source data, and combining historical spatiotemporal trends, in order to overcome the application bottleneck of existing technologies in harsh environments.
[0059] The grid density determination method for meteorological monitoring provided in this application can be applied to, for example... Figure 1 In the application environment shown, the data acquisition device 102 communicates with the server 104 via a network. A data storage system can store the data that the server 104 needs to process. The data storage system can be integrated onto the server 104, or it can be located in the cloud or on another network server. The server 104 acquires terrain data of the target monitoring area from the data acquisition device 102, and determines the target grid density based on the terrain data and micro-meteorological data, thereby performing meteorological monitoring based on the target grid density. The data acquisition device 102 can be, but is not limited to, a GIS system, a drone, or a satellite. The server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.
[0060] In one embodiment, such as Figure 2 As shown, a method for determining grid density in meteorological monitoring is provided, which can be applied to... Figure 1 Taking the server in the example of this, the explanation includes:
[0061] S201, Based on the terrain data of the target monitoring area, determine the terrain complexity of the target monitoring area.
[0062] The terrain data may include the altitude, slope, aspect, and surface roughness of the target monitoring area.
[0063] In this embodiment, the server receives terrain data collected by the acquisition device, and then evaluates the target monitoring area based on the terrain data to obtain the terrain complexity of the target area. Optionally, the terrain complexity of the target monitoring area can be determined based on the correlation between the terrain complexity and all terrain data.
[0064] Optionally, terrain data of the target monitoring area can be obtained through a GIS system or terrain data of the target monitoring area; and / or, terrain data of the target monitoring area can be obtained through drone aerial photography; and / or, terrain data of the target monitoring area can be obtained through satellite remote sensing.
[0065] S202, Based on the micro-meteorological parameters of the target monitoring area, determine the variation parameters; the variation parameters are used to characterize the spatiotemporal variation of the micro-meteorological parameters.
[0066] Among them, micrometeorological parameters include temperature, humidity, air pressure, wind speed, etc. of the target area.
[0067] In the embodiments of the present application, a calculation model for the coefficient of variation of micro meteorological parameters can be pre - constructed. The calculation model for the coefficient of variation of micro meteorological parameters is used to quantify the severity of the spatio - temporal changes of each micro meteorological parameter in the monitoring area, providing a meteorological - dimension basis for grid density adjustment. Thus, according to the micro meteorological parameters of the target monitoring area and the calculation model for the coefficient of variation of micro meteorological parameters, the variation parameters are determined. Optionally, the calculation model for the coefficient of variation of micro meteorological parameters can determine the variation parameters corresponding to the target monitoring area according to the preset correlation between the micro meteorological parameters and the variation parameters.
[0068] Optionally, the calculation model for the coefficient of variation of micro meteorological parameters is based on historical monitoring data and real - time monitoring data. By calculating the coefficient of variation of each parameter, it reflects the degree of dispersion of the parameter in the spatial and temporal dimensions. The larger the coefficient of variation, the more剧烈 the change of the micro meteorological parameters in this area, and a higher - density grid is required to capture the change details. Among them, the historical monitoring data includes hourly data for at least one complete year.
[0069] S203. Determine the target grid density according to the variation parameters and the terrain complexity.
[0070] As an optional implementation manner, a first correspondence between the terrain complexity and the grid density can be established in advance. Thus, according to the terrain complexity of the target monitoring area and the first correspondence, the initial grid density is determined, and then the initial grid density is adjusted according to the variation parameters to determine the target grid density.
[0071] Exemplarily, the extremely high, high, low, and extremely low complexity thresholds are set as C0, C1, C2, and C3 respectively, and the terrain complexity of the target area is C. For example, C0 = 0.9 and C3 = 0.1. When C≥C0 (such as steep canyons and multi - peak mountains), the grid side length is set as L0. For example, L0 = 300m, to achieve ultra - high - density monitoring. When C3 < C≤C2 (such as gentle hills), the grid side length is set as L4. For example, L0 = 1000m, to further optimize resource allocation. At the same time, the thresholds can be dynamically adjusted according to the climate type of the monitoring area. For example, in rainy coastal mountainous areas, the high - humidity environment is likely to exacerbate the influence of terrain on micro meteorology, and C1 can be appropriately reduced to 0.6 to encrypt the grid in advance to improve the monitoring accuracy.
[0072] As another optional implementation manner, a second correspondence between the terrain complexity, the variation parameters, and the grid density can be established in advance. Thus, according to the variation parameters, the terrain complexity of the target area, and the second correspondence, the target grid density can be directly determined.
[0073] The aforementioned method for determining grid density in meteorological monitoring involves: determining the topographic complexity of the target monitoring area based on topographic data; determining the variation parameters based on micrometeorological parameters of the target monitoring area; and determining the target grid density based on the variation parameters and topographic complexity. This dynamic determination of the target grid density based on topographic complexity and micrometeorological parameters improves the matching degree and accuracy between the target grid density and the target monitoring area. Furthermore, determining the target grid density based on both variation parameters and topographic complexity makes the process more comprehensive and reliable.
[0074] In one embodiment, one implementation of the above-described S203 is provided, such as... Figure 3 As shown, the above-mentioned "adjusting the initial grid density based on variation parameters and terrain complexity to determine the target grid density" includes:
[0075] S301, determine whether the variation parameters and terrain complexity meet the parameter threshold requirements.
[0076] S302, if satisfied, then the target mesh density is determined according to the preset side length correspondence.
[0077] S303, if not satisfied, then the target grid density is determined according to the preset side length correspondence and interpolation algorithm.
[0078] In this embodiment of the application, based on the coefficient of variation of micrometeorological parameters A two-dimensional grid density adjustment matrix is constructed by combining it with the terrain complexity index C. A threshold for the coefficient of variation is set. and And terrain complexity thresholds C1 and C2, where C1 is greater than C2, and when C≥C1 and ≥ When the target area is characterized by high terrain complexity and high parameter variation, the grid side length takes the minimum value L0 (e.g., 300m); when C ≤ C2 and ≤ When the target region is characterized by low terrain complexity and low parameter variation, the grid edge length is taken as the maximum value L3 (e.g., 1200m). In cases other than high terrain complexity and high parameter variation, and low terrain complexity and low parameter variation, the grid edge length is determined by an interpolation algorithm, for example, C=0.5 and... =0.13, meaning that when the terrain complexity is medium and the parameter variation is medium, the grid side length can be set to 700m, so as to achieve precise grid adjustment driven by both terrain and meteorological factors, and further improve the spatial resolution and data effectiveness of micro-meteorological monitoring in harsh environments.
[0079] In this embodiment, with the goal of sub-kilometer-level meteorological field reconstruction accuracy, a correlation function is set between grid density and terrain complexity and the coefficient of variation of micro-meteorological parameters. When the terrain complexity is high (e.g., slope > 25°, elevation undulation > 300m) and the coefficient of variation of micro-meteorological parameters is large (e.g., coefficient of variation > 0.3), the grid density is automatically increased, and the grid side length is adjusted to 300-500m to ensure that the monitoring nodes in this area can fully capture local micro-meteorological changes. When the terrain complexity is low (e.g., slope < 5°, elevation undulation < 50m) and the coefficient of variation of micro-meteorological parameters is small (e.g., coefficient of variation < 0.1), the grid density is automatically reduced, and the grid side length is adjusted to 800-1000m to reduce resource waste while ensuring monitoring accuracy. For transitional areas between the two, the grid side length is adjusted to 500-800m to achieve a balance between accuracy and cost.
[0080] Optional, such as Figure 4 As shown, the above-mentioned method for determining grid density for meteorological monitoring also includes:
[0081] S401, compare micrometeorological parameters with historical data trends.
[0082] S402, if the micro-meteorological parameters do not match the historical data trend, the target grid density will be adjusted by densifying the grid.
[0083] In this embodiment of the application, during the dynamic adjustment process, the micro-meteorological data of the current monitoring period is received in real time and compared with the historical data trend. If it is found that the trend of meteorological parameters in a certain area deviates from the historical pattern, for example, a sudden heavy rainfall causes a sudden increase in humidity, an emergency adjustment mechanism for grid density is triggered to temporarily encrypt the grid nodes in the target monitoring area to ensure accurate capture of abnormal micro-meteorological events. When the meteorological conditions return to stability, the grid density automatically returns to the normal level, realizing the dynamic optimization and allocation of monitoring resources.
[0084] In the above-mentioned embodiments, the target grid density can be directly determined in the two cases of high terrain complexity and high parameter variation, and low terrain complexity and low parameter variation, which improves the efficiency of determining the target grid density. In other cases, the target grid density is determined according to the interpolation algorithm, which improves the accuracy of determining the target grid density.
[0085] In one embodiment, one implementation of S201 above is provided, such as... Figure 5 As shown, the above-mentioned "determining the terrain complexity of the target monitoring area based on the terrain data of the target monitoring area" includes:
[0086] S501, acquire terrain data of the target monitoring area; terrain data includes basic terrain parameters and terrain occlusion.
[0087] In this embodiment, the basic terrain parameters may include the area, boundary length, altitude, slope, aspect, and surface roughness of the target monitoring area. Terrain shading is determined by simulating the solar radiation incident angle using a digital elevation model (DEM) to identify the percentage of the target area that is blocked from direct solar radiation, reflecting the indirect impact of terrain on the micro-meteorological environment.
[0088] S502, Determine the terrain fragmentation based on the basic terrain parameters.
[0089] In this embodiment of the application, the dispersion of terrain units is quantified and the terrain fragmentation is determined by monitoring the area proportion and boundary density of different terrain units (such as mountains, valleys, and plains) within the region, as shown in Equation 1:
[0090] (Equation 1)
[0091] Where F represents the terrain fragmentation. Let A be the boundary length of the i-th terrain unit (in meters), and let A be the total area of the monitoring area (in square meters). 2 );
[0092] S503 determines the terrain complexity based on basic terrain parameters, terrain fragmentation, terrain occlusion, and preset weighting coefficients.
[0093] In this embodiment of the application, the terrain complexity is as shown in Equation 2:
[0094] (Equation 2)
[0095] Where S is the slope (unit: °), R is the surface roughness (dimensionless), H is the elevation relief (unit: m), and F is the terrain fragmentation (unit: m). -1 Sh represents the terrain shading degree (dimensionless, value range 0-1). , , , , For the weighting coefficients, satisfying .
[0096] Optionally, in mountainous monitoring scenarios, slope and altitude undulation have a greater impact on microclimate distribution, and settings can be configured accordingly. =0.3、 =0.25, while the weights of terrain fragmentation and terrain shading in complex landform areas can be set to 0.25 respectively. =0.15、 =0.1, surface roughness weight =0.2, to ensure that the index can more comprehensively reflect the impact of topography on micrometeorological monitoring.
[0097] In the above-mentioned embodiments, terrain fragmentation and terrain shading are introduced to make the determined terrain complexity more accurate. Furthermore, based on the influence of each terrain parameter on micro-meteorological monitoring, the weight coefficients of each terrain parameter are pre-set, which further improves the accuracy of terrain complexity.
[0098] In one embodiment, one implementation of S202 above is provided, such as... Figure 6 As shown, the above-mentioned "determining variation parameters based on micrometeorological parameters of the target monitoring area" includes:
[0099] S601, based on the standard deviation and mean of each micrometeorological parameter within a preset time period, determine the time dimension variation parameter of each micrometeorological parameter.
[0100] In this embodiment of the application, for a single monitoring node, the time dimension variation parameter CVt of a certain micrometeorological parameter within a set time period (such as 1 day or 1 week) is determined as shown in Equation 3:
[0101] (Equation 3)
[0102] in, This is the standard deviation of the parameter over the time period. The mean is used as an example. For instance, to determine the daily temperature variation coefficient at a given node, it is necessary to first obtain the hourly temperature data for that node over 24 hours, determine the standard deviation and mean, and then obtain the mean. ,like A value greater than 0.2 indicates that the daily temperature variation at this node is drastic.
[0103] S602, based on the spatial standard deviation and spatial mean of each micrometeorological parameter across all monitoring nodes, determine the spatial dimension variation parameter of each micrometeorological parameter.
[0104] In this embodiment of the application, the spatial variation coefficient of a certain micrometeorological parameter across all nodes within the monitoring area is determined for the same timestamp. As shown in Equation 4:
[0105] (Equation 4)
[0106] in, The spatial standard deviation of this parameter across all nodes. This represents the spatial mean. For example, at 2 PM daily (the peak temperature period), the spatial standard deviation and mean of temperature for all nodes are determined, yielding... ,like A value > 0.15 indicates that the spatial distribution of temperature varies significantly within the region during this period.
[0107] S603, determine the variation parameters based on the temporal and spatial variation parameters of each micrometeorological parameter.
[0108] In this embodiment, for each micrometeorological parameter, the variation parameter is determined by combining the temporal and spatial variation parameters of that micrometeorological parameter. Optionally, the sum of the temporal and spatial variation parameters can be used as the variation parameter.
[0109] In the above-mentioned application embodiments, the time dimension variation parameters and spatial dimension variation parameters of micrometeorological parameters are first determined separately, and then the time dimension variation parameters and spatial dimension variation parameters are combined to determine the variation parameters, thereby improving the comprehensiveness and accuracy of the variation parameters.
[0110] In one embodiment, one implementation of S603 described above is provided, such as... Figure 7 As shown, the above-mentioned "determining the variation parameters based on the temporal and spatial variation parameters of each micrometeorological parameter" includes:
[0111] S701, based on the monitoring target, determine the time weight coefficient and spatial weight coefficient of the target monitoring area.
[0112] The monitoring targets include severe convective weather, regional climate assessment, and urban heat island effect.
[0113] In this embodiment, the monitoring targets of the target monitoring area are obtained. Optionally, the monitoring targets can be determined based on information input to the server by the monitoring personnel; or, the monitoring targets corresponding to the target monitoring area can be extracted from a preset target library. Further, the time weight coefficient and spatial weight coefficient of the target monitoring area are determined from a weight coefficient library based on the monitoring targets.
[0114] For example, This is a time-weighted coefficient; if the focus is on capturing short-term weather changes (such as severe convective weather), it can be set to... =0.6; if the focus is on analyzing regional differences in meteorological distribution, it can be set to 0.6. =0.4.
[0115] S702, determine the variation parameters based on the time dimension variation parameters, the spatial dimension variation parameters, the time weight coefficient, and the spatial weight coefficient.
[0116] In this embodiment, the variation parameter is shown in Equation 5:
[0117] (Equation 5)
[0118] in, These are the variation parameters.
[0119] In the above-mentioned application embodiments, by integrating the variation information of the two dimensions of time and space with the weight coefficients set based on the monitoring target, a comprehensive quantitative index is generated, which effectively overcomes the limitations of traditional monitoring that only focuses on time trends or spatial distribution, and realizes a unified and quantitative description of the spatiotemporal evolution law of meteorological phenomena.
[0120] In one embodiment, a complete method for determining grid density for meteorological monitoring is provided, such as... Figure 8 As shown, the above method includes:
[0121] S1, acquire terrain data of the target monitoring area; terrain data includes basic terrain parameters and terrain occlusion.
[0122] S2, determine the terrain fragmentation based on the basic terrain parameters.
[0123] S3 determines the terrain complexity based on basic terrain parameters, terrain fragmentation, terrain occlusion, and preset weighting coefficients.
[0124] S4. Based on the standard deviation and mean of each micrometeorological parameter within a preset time period, determine the time dimension variation parameter of each micrometeorological parameter.
[0125] S5. Based on the spatial standard deviation and spatial mean of each micrometeorological parameter across all monitoring nodes, determine the spatial dimension variation parameter of each micrometeorological parameter.
[0126] S6. Based on the monitoring target, determine the time weight coefficient and spatial weight coefficient of the target monitoring area.
[0127] S7. Determine the variation parameters based on the time dimension variation parameters, the spatial dimension variation parameters, the time weight coefficient, and the spatial weight coefficient.
[0128] S8, determine whether the variation parameters and terrain complexity meet the parameter threshold requirements.
[0129] S9, if satisfied, the target grid density is determined according to the preset side length correspondence; if not satisfied, the target grid density is determined according to the preset side length correspondence and the interpolation algorithm.
[0130] S10, comparing micrometeorological parameters with historical data trends.
[0131] S11. If the micro-meteorological parameters do not match the historical data trend, the target grid density will be adjusted by densifying the grid.
[0132] The aforementioned method for determining grid density in meteorological monitoring involves: determining the topographic complexity of the target monitoring area based on topographic data; determining the variation parameters based on micrometeorological parameters of the target monitoring area; and determining the target grid density based on the variation parameters and topographic complexity. This dynamic determination of the target grid density based on topographic complexity and micrometeorological parameters improves the matching degree and accuracy between the target grid density and the target monitoring area. Furthermore, determining the target grid density based on both variation parameters and topographic complexity makes the process more comprehensive and reliable.
[0133] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.
[0134] Based on the same inventive concept, this application also provides a grid density determination device for meteorological monitoring to implement the above-described method for determining grid density for meteorological monitoring. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the grid density determination device for meteorological monitoring provided below can be found in the limitations of the method for determining grid density for meteorological monitoring described above, and will not be repeated here.
[0135] In one embodiment, such as Figure 9 As shown, a grid density determination device for meteorological monitoring is provided, comprising: a first determination module 10, a second determination module 11, and a third determination module 12, wherein:
[0136] The first determining module 10 is used to determine the terrain complexity of the target monitoring area based on the terrain data of the target monitoring area.
[0137] The second determining module 11 is used to determine the variation parameters based on the micro-meteorological parameters of the target monitoring area; the variation parameters are used to characterize the spatiotemporal variation of the micro-meteorological parameters.
[0138] The third determination module 12 is used to determine the target grid density based on the variation parameters and terrain complexity.
[0139] In one embodiment, the third determining module 12 includes: a first determining unit, a second determining unit, and a third determining unit, wherein:
[0140] The first determining unit is used to determine whether the variation parameters and terrain complexity meet the parameter threshold requirements.
[0141] The second determining unit is used to determine the target mesh density according to a preset side length correspondence, provided that certain conditions are met.
[0142] The third determining unit is used to determine the target grid density based on the preset side length correspondence and interpolation algorithm when the conditions are not met.
[0143] In one embodiment, the first determining module includes: an acquisition unit, a fourth determining unit, and a fifth determining unit, wherein:
[0144] The acquisition unit is used to acquire terrain data of the target monitoring area; the terrain data includes basic terrain parameters and terrain occlusion.
[0145] The fourth determining unit is used to determine the degree of terrain fragmentation based on the basic terrain parameters.
[0146] The fifth determining unit is used to determine the terrain complexity based on the basic terrain parameters, terrain fragmentation, terrain occlusion and preset weight coefficients.
[0147] In one embodiment, the second determining module includes: a sixth determining unit, a seventh determining unit, and an eighth determining unit, wherein:
[0148] The sixth determining unit is used to determine the time dimension variation parameters of each micrometeorological parameter based on the standard deviation and mean of each micrometeorological parameter within a preset time period.
[0149] The seventh determination unit is used to determine the spatial dimension variation parameter of each micrometeorological parameter based on the spatial standard deviation and spatial mean of each micrometeorological parameter across all monitoring nodes.
[0150] The eighth determining unit is used to determine the variation parameters based on the temporal and spatial variation parameters of each micrometeorological parameter.
[0151] In one embodiment, the eighth determining unit is specifically used to determine the time weight coefficient and spatial weight coefficient of the target monitoring area based on the monitoring target; and to determine the variation parameters according to the time dimension variation parameter, the spatial dimension variation parameter, the time weight coefficient, and the spatial weight coefficient.
[0152] In one embodiment, the above-mentioned grid density determination device for meteorological monitoring further includes: a comparison module and an adjustment module, wherein:
[0153] The comparison module is used to compare micrometeorological parameters with historical data trends.
[0154] The adjustment module is used to refine the target grid density when the micro-meteorological parameters do not match the historical data trend.
[0155] The various modules in the aforementioned grid density determination device for meteorological monitoring can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.
[0156] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 10 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores grid density determination data for meteorological monitoring. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for determining grid density for meteorological monitoring.
[0157] Those skilled in the art will understand that Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0158] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:
[0159] Based on the terrain data of the target monitoring area, determine the terrain complexity of the target monitoring area;
[0160] Based on the micrometeorological parameters of the target monitoring area, the variation parameters are determined; the variation parameters are used to characterize the spatiotemporal variation of the micrometeorological parameters.
[0161] The target grid density is determined based on the variation parameters and terrain complexity.
[0162] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0163] Determine whether the variation parameters and terrain complexity meet the parameter threshold requirements;
[0164] If satisfied, the target mesh density is determined based on the preset side length correspondence.
[0165] If the conditions are not met, the target grid density is determined based on the preset side length correspondence and interpolation algorithm.
[0166] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0167] Acquire terrain data for the target monitoring area; terrain data includes basic terrain parameters and terrain occlusion.
[0168] Determine the degree of terrain fragmentation based on basic terrain parameters;
[0169] The terrain complexity is determined based on basic terrain parameters, terrain fragmentation, terrain occlusion, and preset weighting coefficients.
[0170] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0171] Based on the standard deviation and mean of each micrometeorological parameter within a preset time period, the time dimension variation parameters of each micrometeorological parameter are determined.
[0172] Based on the spatial standard deviation and spatial mean of each micrometeorological parameter across all monitoring nodes, the spatial dimension variation parameter of each micrometeorological parameter is determined;
[0173] The variation parameters are determined based on the temporal and spatial variation parameters of each micrometeorological parameter.
[0174] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0175] Based on the monitoring target, determine the time weight coefficient and spatial weight coefficient of the target monitoring area;
[0176] The variation parameters are determined based on the time dimension variation parameters, the spatial dimension variation parameters, the time weighting coefficient, and the spatial weighting coefficient.
[0177] In one embodiment, the processor, when executing a computer program, also performs the following steps:
[0178] Compare the trends of micrometeorological parameters and historical data;
[0179] If the micro-meteorological parameters do not match the historical data trend, the target grid density will be adjusted by densifying the grid.
[0180] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:
[0181] Based on the terrain data of the target monitoring area, determine the terrain complexity of the target monitoring area;
[0182] Based on the micrometeorological parameters of the target monitoring area, the variation parameters are determined; the variation parameters are used to characterize the spatiotemporal variation of the micrometeorological parameters.
[0183] The target grid density is determined based on the variation parameters and terrain complexity.
[0184] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0185] Determine whether the variation parameters and terrain complexity meet the parameter threshold requirements;
[0186] If satisfied, the target mesh density is determined based on the preset side length correspondence.
[0187] If the conditions are not met, the target grid density is determined based on the preset side length correspondence and interpolation algorithm.
[0188] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0189] Acquire terrain data for the target monitoring area; terrain data includes basic terrain parameters and terrain occlusion.
[0190] Determine the degree of terrain fragmentation based on basic terrain parameters;
[0191] The terrain complexity is determined based on basic terrain parameters, terrain fragmentation, terrain occlusion, and preset weighting coefficients.
[0192] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0193] Based on the standard deviation and mean of each micrometeorological parameter within a preset time period, the time dimension variation parameters of each micrometeorological parameter are determined.
[0194] Based on the spatial standard deviation and spatial mean of each micrometeorological parameter across all monitoring nodes, the spatial dimension variation parameter of each micrometeorological parameter is determined;
[0195] The variation parameters are determined based on the temporal and spatial variation parameters of each micrometeorological parameter.
[0196] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0197] Based on the monitoring target, determine the time weight coefficient and spatial weight coefficient of the target monitoring area;
[0198] The variation parameters are determined based on the time dimension variation parameters, the spatial dimension variation parameters, the time weighting coefficient, and the spatial weighting coefficient.
[0199] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0200] Compare the trends of micrometeorological parameters and historical data;
[0201] If the micro-meteorological parameters do not match the historical data trend, the target grid density will be adjusted by densifying the grid.
[0202] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:
[0203] Based on the terrain data of the target monitoring area, determine the terrain complexity of the target monitoring area;
[0204] Based on the micrometeorological parameters of the target monitoring area, the variation parameters are determined; the variation parameters are used to characterize the spatiotemporal variation of the micrometeorological parameters.
[0205] The target grid density is determined based on the variation parameters and terrain complexity.
[0206] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0207] Determine whether the variation parameters and terrain complexity meet the parameter threshold requirements;
[0208] If satisfied, the target mesh density is determined based on the preset side length correspondence.
[0209] If the conditions are not met, the target grid density is determined based on the preset side length correspondence and interpolation algorithm.
[0210] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0211] Acquire terrain data for the target monitoring area; terrain data includes basic terrain parameters and terrain occlusion.
[0212] Determine the degree of terrain fragmentation based on basic terrain parameters;
[0213] The terrain complexity is determined based on basic terrain parameters, terrain fragmentation, terrain occlusion, and preset weighting coefficients.
[0214] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0215] Based on the standard deviation and mean of each micrometeorological parameter within a preset time period, the time dimension variation parameters of each micrometeorological parameter are determined.
[0216] Based on the spatial standard deviation and spatial mean of each micrometeorological parameter across all monitoring nodes, the spatial dimension variation parameter of each micrometeorological parameter is determined;
[0217] The variation parameters are determined based on the temporal and spatial variation parameters of each micrometeorological parameter.
[0218] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0219] Based on the monitoring target, determine the time weight coefficient and spatial weight coefficient of the target monitoring area;
[0220] The variation parameters are determined based on the time dimension variation parameters, the spatial dimension variation parameters, the time weighting coefficient, and the spatial weighting coefficient.
[0221] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:
[0222] Compare the trends of micrometeorological parameters and historical data;
[0223] If the micro-meteorological parameters do not match the historical data trend, the target grid density will be adjusted by densifying the grid.
[0224] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.
[0225] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.
[0226] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.
Claims
1. A method for determining grid density in meteorological monitoring, characterized in that, The method includes: Based on the terrain data of the target monitoring area, the terrain complexity of the target monitoring area is determined; Based on the micrometeorological parameters of the target monitoring area, variation parameters are determined; the variation parameters are used to characterize the spatiotemporal variation of the micrometeorological parameters. The target grid density is determined based on the variation parameters and the terrain complexity.
2. The method according to claim 1, characterized in that, The step of adjusting the initial grid density based on the variation parameters and the terrain complexity to determine the target grid density includes: Determine whether the variation parameters and the terrain complexity meet the parameter threshold requirements; If satisfied, the target mesh density is determined according to the preset side length correspondence. If the conditions are not met, the target grid density is determined based on the preset side length correspondence and interpolation algorithm.
3. The method according to claim 1, characterized in that, The determination of terrain complexity of the target monitoring area based on terrain data of the target monitoring area includes: Acquire terrain data of the target monitoring area; the terrain data includes basic terrain parameters and terrain occlusion. Determine the terrain fragmentation based on the aforementioned basic terrain parameters; The terrain complexity is determined based on the basic terrain parameters, the terrain fragmentation, the terrain occlusion, and the preset weighting coefficient.
4. The method according to claim 1, characterized in that, The determination of variation parameters based on the micrometeorological parameters of the target monitoring area includes: Based on the standard deviation and mean of each micrometeorological parameter within a preset time period, the time dimension variation parameter of each micrometeorological parameter is determined; Based on the spatial standard deviation and spatial mean of each micrometeorological parameter across all monitoring nodes, determine the spatial dimension variation parameter of each micrometeorological parameter; The variation parameters are determined based on the temporal and spatial variation parameters of each micrometeorological parameter.
5. The method according to claim 4, characterized in that, The step of determining the variation parameters based on the time-dimensional and spatial-dimensional variation parameters of each of the micrometeorological parameters includes: Based on the monitoring target, determine the time weight coefficient and spatial weight coefficient of the target monitoring area; The variation parameters are determined based on the time dimension variation parameters, the spatial dimension variation parameters, the time weighting coefficient, and the spatial weighting coefficient.
6. The method according to any one of claims 1-5, characterized in that, The method further includes: Compare the trends of the micrometeorological parameters with historical data; If the micrometeorological parameters do not match the historical data trend, the target grid density is adjusted by densifying the grid.
7. A grid density determination device for meteorological monitoring, characterized in that, The device includes: The first determining module is used to determine the terrain complexity of the target monitoring area based on the terrain data of the target monitoring area; The second determining module is used to determine the variation parameters based on the micro-meteorological parameters of the target monitoring area; the variation parameters are used to characterize the spatiotemporal variation of the micro-meteorological parameters. The third determining module is used to determine the target grid density based on the variation parameters and the terrain complexity.
8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.