CN-121523803-B - Cloud computing method for earth surface sand risk based on earth surface ecology and meteorological conditions

CN121523803BCN 121523803 BCN121523803 BCN 121523803BCN-121523803-B

Abstract

The application provides a cloud computing method of earth surface sand risk based on earth surface ecology and meteorological states, which comprises the steps of constructing an earth surface sand risk index model of multi-factor coupling in advance, mirroring an earth surface sand risk index model container of the multi-factor coupling and deploying the earth surface sand risk index model container on a cloud computing platform, responding to an earth surface sand risk prediction request, collecting container index data and earth surface sand risk prediction request feature data, computing a matching value of the container and the earth surface sand risk prediction request according to the container index data and the earth surface sand risk prediction request feature data, selecting a container with the largest matching value with the earth surface sand risk prediction request at a cloud, and processing the earth surface sand risk prediction request. According to the cloud computing method for the surface sand risk based on the surface ecology and the meteorological state, the sand risk prediction is realized at the cloud, and the sand risk of a large-scale and high-concurrency area is rapidly evaluated.

Inventors

LIU HAIJIANG
LI MINGSHENG
DONG GUIHUA
JI HONGCHAO
WANG GANG

Assignees

中国环境监测总站

Dates

Publication Date: 20260512
Application Date: 20251119

Claims (8)

1. A cloud computing method for surface sand risk based on surface ecology and meteorological conditions, which is characterized by comprising the following steps: pre-constructing a multi-factor coupled earth surface sand risk index model; mirroring the multi-factor coupled earth surface sand risk index model container and deploying the container on a cloud computing platform; responding to a local surface sand risk prediction request, and collecting container index data and local surface sand risk prediction request characteristic data; Calculating a matching value of the container and the local surface sand risk prediction request according to the container index data and the local surface sand risk prediction request characteristic data; selecting a container with the largest matching value with the local surface sand risk prediction request at the cloud, and processing the local surface sand risk prediction request according to a multi-factor coupled surface sand risk index model; the method for processing the local surface sand risk prediction request comprises the following steps: Loading a remote sensing earth surface ecological parameter set, analyzing a meteorological parameter set and terrain and land utilization data on a cloud computing platform side through a space-time alignment interface; Calling a selected container, inputting the loaded remote sensing earth surface ecological parameter set, the re-analysis meteorological parameter set and the terrain and land utilization data into a multi-factor coupled earth surface sand-forming risk index model for prediction, and obtaining an initial risk value; performing a wind erosion impact coefficient calculation process in the vessel; inputting the wind erosion impact coefficient into a trained multi-factor coupled earth surface sand risk index model, and carrying out error correction on an initial risk value to generate a final sand risk index; the method for executing the wind erosion impact coefficient calculation process in the container comprises the following steps: calculating the instantaneous sand start wind speed according to the surface roughness length and the aerodynamic impedance; calculating effective sand-lifting kinetic energy according to the instantaneous sand-starting wind speed and the ecological inhibition factor; Calculating the instantaneous wind-driven soil loss according to the effective sand-lifting kinetic energy; Calculating a wind erosion impact coefficient according to the instantaneous wind-driven soil loss; The calculation method of the matching value of the container and the local surface sand risk prediction request comprises the following steps: ; Wherein, the Representing a container and local surface sand risk prediction request matching value; an impact weight representing the container load on the matching value; Representing the total category number of the container index data; Represent the first A weight factor for seed container index data; Represent the first A value of seed container index data; Representing the influence weight of the data localization factor on the matching value; Representing a data localization factor; an impact weight representing the effective computing power of the container on the matching value; representing the effective computing power of the container; representing the minimum computational power required for the request; the influence weight of the model version compatibility factor on the matching value is represented; Wherein the container has an efficient computing power HCPU represents the CPU core number, HGPU represents the GPU core number; wherein, all the required data in the node cache area where the container is located, then If the container is located in the node cache part of the data If the node where the container is located has no cache, then ; Representing model version compatibility factors, if the model version fully supports the regional parameters If the model version needs to be slightly adapted, then If the model version is not supported, then 。
2. The cloud computing method of surface sanding risk based on surface ecology and meteorological conditions of claim 1, wherein the final sanding risk index is written into a cloud primary spatiotemporal database for real-time invocation by a business system.
3. The cloud computing method of surface sanding risk based on surface ecology and meteorological conditions according to claim 1, wherein the final sanding risk index is displayed on a visual interface of a Web end and a mobile end.
4. The cloud computing method of the earth's surface risk of sand generation based on the earth's surface ecology and the meteorological state according to claim 1, wherein the calculation formula of the instantaneous sand start wind speed is: ; Wherein, the Representing an instantaneous sand start wind speed; A critical friction speed representing an ideal smooth surface; representing a rough length correction coefficient; representing aerodynamic impedance correction coefficients; represents the surface roughness length; Representing aerodynamic impedance.
5. The cloud computing method for surface sand risk based on surface ecology and meteorological conditions according to claim 1, wherein, The calculation formula of the effective sand-forming kinetic energy is as follows: ; Wherein, the Representing the effective sand-lifting kinetic energy; Representing the wind erosion force coefficient; representing the actual wind speed at a height of 2 meters; representing an instantaneous sand start wind speed; representing vegetation coverage; Represents the water content of the soil per unit volume; representing the earth coverage type inhibitor.
6. The cloud computing method of surface sand risk based on surface ecology and meteorological conditions of claim 1, wherein mirroring the multifactor coupled surface sand risk index model container and deploying it on the cloud computing platform comprises: Constructing a mirror image of the multi-factor coupled earth surface sand risk index model according to the multi-factor coupled earth surface sand risk index model; A plurality of containers is created based on mirroring the multifactor coupled surface sanding risk index model.
7. The cloud computing method for surface sand risk based on surface ecology and meteorological conditions according to claim 1, wherein, The container index data comprises CPU utilization rate, memory occupancy rate, request processing delay, current request queue length and regional evaluation task number to be processed; the regional surface sand risk prediction request characteristic data comprises request regional geographic data and regional priority.
8. The cloud computing method for surface sand risk based on surface ecology and meteorological conditions according to claim 1, wherein, The remote sensing earth surface ecological parameter set comprises vegetation coverage, normalized vegetation index, land coverage, soil texture type, net ecosystem carbon exchange quantity, soil microorganism respiration quantity, earth surface energy flux and water content; The re-analysis meteorological parameter set comprises wind speed, wind direction, air temperature, soil temperature, humidity, precipitation and snow depth; terrain and land use data, grade, slope direction and land cover type.

Description

Cloud computing method for earth surface sand risk based on earth surface ecology and meteorological conditions Technical Field The application relates to the technical field of data processing, in particular to a cloud computing method for ground surface sand risk based on ground surface ecology and meteorological states. Background Sand weather is an important natural disaster affecting the quality of the atmospheric environment, traffic safety and human health. The traditional sand forming model (RWEQ, WEPS, DPM) usually runs off-line, and needs manual downloading of TB-level remote sensing/meteorological data, local interpolation, format conversion, long period and poor timeliness. The existing sand prediction method is mostly based on single meteorological factors or remote sensing inversion data, lacks support of cloud computing architecture, and is difficult to realize rapid assessment of sand risk in large-scale and high-concurrency areas. The recognition capability of the precursor state of the sand dust is insufficient, and the potential sand forming area is difficult to recognize in advance. The existing cloud scheme only carries partial data or result visualization to the cloud, the core wind erosion equation is still calculated locally, and the problems of elasticity calculation force, parallelization and data homology cannot be solved. Therefore, the technical problem to be solved at present is how to provide a cloud computing method for the surface sand risk based on the surface ecology and meteorological states, so that the sand risk prediction is realized at the cloud, the rapid evaluation of the sand risk in a large-scale and high-concurrency area is realized, and the problems of elasticity computing power, parallelization and data homology are solved. Disclosure of Invention The application aims to provide a cloud computing method for the surface sand risk based on the surface ecology and meteorological states, which is used for realizing sand risk prediction at a cloud end, realizing rapid assessment of the sand risk of a large-scale and high-concurrency area and solving the problems of elasticity computing power, parallelization and data homology. The application provides a cloud computing method of earth surface sand risk based on earth surface ecology and meteorological states, which comprises the steps of constructing an earth surface sand risk index model with multiple factors in advance, mirroring and deploying containers of the earth surface sand risk index model with the multiple factors in a cloud computing platform, responding to an earth surface sand risk prediction request, collecting container index data and earth surface sand risk prediction request characteristic data, computing a matching value of the container and the earth surface sand risk prediction request according to the container index data and the earth surface sand risk prediction request characteristic data, selecting a container with the largest matching value with the earth surface sand risk prediction request at a cloud, and processing the earth surface sand risk prediction request according to the earth surface sand risk index model with the multiple factors. The cloud computing method for the earth surface sand risk based on the earth surface ecology and the meteorological state comprises the steps of loading a remote sensing earth surface ecology parameter set, analyzing the meteorological parameter set and the terrain and land utilization data through a space-time alignment interface on a cloud computing platform side, calling a selected container, inputting the loaded remote sensing earth surface ecology parameter set, the analysis meteorological parameter set and the terrain and land utilization data into a multi-factor coupled earth surface sand risk index model to conduct prediction to obtain an initial risk value, executing a wind erosion impact coefficient computing process in the container, inputting the wind erosion impact coefficient into the trained multi-factor coupled earth surface sand risk index model, conducting error correction on the initial risk value, and generating a final sand risk index. The cloud computing method for the earth surface sand-forming risk based on the earth surface ecology and the meteorological state comprises the steps of computing instantaneous sand-forming starting wind speed according to the earth surface roughness length and aerodynamic impedance, computing effective sand-forming kinetic energy according to the instantaneous sand-forming starting wind speed and ecological inhibition factors, computing instantaneous wind-driven soil loss according to the effective sand-forming kinetic energy, and computing the wind erosion impact coefficient according to the instantaneous wind-driven soil loss. According to the cloud computing method for the earth surface sand risk based on the earth surface ecology and the meteorological state, the final sand risk index is writt