A sand-dust warning method, program product, device, and medium

Abstract

This invention discloses a dust storm early warning method, program product, device, and medium, comprising: collecting dust storm data of the area to be analyzed, and obtaining standardized dust storm data for each grid point based on the dust storm data; obtaining a surface erosibility assessment result of the area to be analyzed based on an ideal critical friction wind speed function, a pre-set correction function, and the standardized dust storm data for each grid point; determining the type of dust rising at each grid point based on the standardized dust storm data and the surface erosibility assessment result, and determining the dust rising intensity of the area to be analyzed based on the type of dust rising at each grid point; identifying dust sources in the area to be analyzed based on the surface erosibility assessment result and the dust rising intensity, and obtaining dust source distribution information; performing transport diffusion simulation and sedimentation simulation based on a pre-determined trajectory model, according to the dust source distribution information and the dust rising intensity, to obtain the dust diffusion range; and generating and displaying dust storm early warning information for the area to be analyzed based on the dust rising intensity and the dust diffusion range.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of environmental engineering technology, and in particular to a method, program product, device and medium for sandstorm early warning. Background Technology

[0002] Dust storms are a common hazardous weather event in arid, semi-arid, and desertified regions. Released from the Earth's surface into the atmosphere by near-surface winds, dust can be lifted by boundary layer turbulence and transported, diffused, and deposited at regional and even trans-regional scales, significantly impacting the ecological environment, transportation, agricultural and livestock production, air quality, and human health. Therefore, high-precision and timely dust storm warnings are of great importance for disaster prevention and mitigation, ecological protection, and regional environmental management.

[0003] Most existing dust storm analysis and early warning methods utilize atmospheric transport and diffusion models or regional chemical transport models, combined with fixed source areas and meteorological forecast fields, to characterize the spatial movement direction and regional impact range of dust plumes. This approach is suitable for cross-regional transport and deposition zone distribution studies. However, this method typically uses pre-set fixed emission factors, historical statistical averages, or empirical source strength distribution maps as the basis for data analysis. In reality, dust storm environments are often more complex and change in real time. This method simulates initial conditions that deviate from actual conditions, limiting the stability and accuracy of its dust storm early warning results. Summary of the Invention

[0004] This invention provides a method, program product, device, and medium for sandstorm early warning, which can improve the stability and accuracy of sandstorm early warning results.

[0005] In a first aspect, embodiments of the present invention provide a method for sandstorm early warning, comprising: Collect dust data of the area to be analyzed, and obtain standardized dust data for each grid point based on the dust data; The surface erodibility assessment results of the area to be analyzed are obtained based on a predetermined ideal critical friction wind speed function, a pre-set correction function, and standardized dust data of each grid point; the correction function includes a soil moisture correction function, a surface roughness correction function, and a salinity correction function. The type of sand initiation at each grid point is determined based on the standardized dust data and the surface erodibility assessment results, and the sand initiation intensity of the area to be analyzed is determined based on the type of sand initiation at each grid point. Based on the surface erosibility assessment results and the sand-raising intensity, the sand source is identified in the area to be analyzed, and the sand source distribution information is obtained. By using a predetermined trajectory model, and based on the sand source distribution information and the sand initiation intensity, the transmission and diffusion simulation and sedimentation simulation are performed to obtain the sand and dust diffusion range; Based on the sandstorm intensity and the sandstorm diffusion range, sandstorm early warning information for the area to be analyzed is generated and displayed.

[0006] Secondly, embodiments of the present invention also provide an electronic device, the electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the sandstorm warning method as described in any of the embodiments of the present invention.

[0007] Thirdly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the sandstorm warning method as described in any of the embodiments of the present invention.

[0008] Fourthly, embodiments of the present invention provide a computer program product, including a computer program that, when executed by a processor, implements the sandstorm early warning method as described in any of the embodiments of the present invention.

[0009] In this embodiment of the invention, by comprehensively considering the influence of factors such as soil moisture, surface roughness, and salinity on the actual critical frictional wind speed, the dust-raising threshold (actual critical frictional wind speed) can be corrected according to different surface conditions, thereby improving the identification accuracy of highly sensitive wind erosion source areas. By calculating the erosibility assessment results, dust-raising intensity, and sand source distribution information, the intensity of dust release can be accurately analyzed. By analyzing the erosibility assessment results, dust-raising intensity, and sand source distribution information through a trajectory model, dynamic coupling of dust release and transmission is achieved, constructing a complete chain from data acquisition to early warning output. This allows for the simultaneous determination of key data such as "where dust rises, when dust rises, the intensity of dust rise, where it is transported, when it arrives, the duration of its impact, and where it settles," further improving the completeness, stability, and accuracy of early warning information. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A flowchart of a sandstorm early warning method provided in an embodiment of the present invention; Figure 2 A schematic diagram of an optional standard wind erosion observation field provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure for generating sandstorm early warning information provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0012] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the invention and not all structures. The acquisition, storage, use, and processing of data in the technical solutions of this application comply with relevant national laws and regulations. It should be noted that, in the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solutions of this application, and do not imply that the applicant has already used or necessarily used the relevant content of such solutions.

[0013] Figure 1 This is a flowchart illustrating a sandstorm early warning method provided by an embodiment of the present invention. The method of this embodiment can improve the stability and accuracy of sandstorm early warning results. (Reference) Figure 1 The method may specifically include the following steps:

[0014] Step 101: Collect dust data of the area to be analyzed, and obtain standardized dust data for each grid point based on the dust data.

[0015] The area to be analyzed is the region requiring dust storm warnings; in this scheme, this area can be a dried-up lake basin. Dust storm data includes remote sensing data, meteorological data, surface attribute data, station observation data, and vertical observation data. Remote sensing data includes vegetation indices, surface water changes, soil moisture content, and snow cover. Meteorological data includes 10-meter wind speed, global three-dimensional meteorological fields, and numerical weather prediction fields. Surface attribute data includes soil particle size distribution, soil salinity, crust condition, and lake basin boundary evolution information. Station observation data includes inhalable particulate matter data, fine particulate matter data, and visibility. Vertical observation data includes lidar extinction coefficient, depolarization ratio, cloud height, and dust layer height.

[0016] Specifically, after obtaining the dust data for the area to be analyzed, the dust data can be preprocessed. Preprocessing includes temporal unification, spatial resampling, coordinate projection transformation, outlier removal, missing value interpolation, noise reduction, and data standardization. For example, data from different time systems can be unified to the same time zone; data from different time formats can be unified to a standard format; and data from different acquisition frequencies can be aligned to a unified time step. Data with different spatial resolutions can be unified to a suitable resolution; for example, high-resolution remote sensing data can be reduced in resolution through aggregation, while low-resolution meteorological data can be increased in resolution through interpolation. Data exceeding physically reasonable ranges can be removed based on preset reasonable thresholds. Temporal interpolation can be used to supplement missing values ​​from station data. Coordinate projection transformation unifies the dust data to the same grid point coordinate system, ensuring that each type of data corresponds to a regular grid cell with the same spatial resolution, ultimately forming a standardized dust dataset for each grid point.

[0017] This solution addresses the problem of sparse conventional observation stations and insufficient data in the dried-up lake basin and surrounding areas by designing a deployable standardized observation field. The observation field includes a meteorological station, a drone, a soil moisture sensor, a soil temperature sensor, and optional dust deposition / collection auxiliary components. The meteorological station continuously monitors wind speed, wind direction, temperature, humidity, and air pressure. The drone is used for topographic mapping, surface image acquisition, bare area boundary identification, and salt crust distribution surveys. The soil moisture sensor monitors soil moisture. The soil temperature sensor monitors soil temperature status. For example, Figure 2 This is a schematic diagram of an optional standard wind erosion observation field provided in an embodiment of the present invention, such as... Figure 2 As shown, the observation field is divided into 9 sub-areas by short dashed lines. T represents the observation meteorological station, R represents the UAV, S is the optional surface dust deposition / collection auxiliary component, and long dashed lines represent the deployment of soil moisture sensor and soil temperature sensor.

[0018] Step 102: Based on the predetermined ideal critical friction wind speed function, the pre-set correction function, and the standardized dust data of each grid point, the surface erodibility assessment results of the area to be analyzed are obtained.

[0019] Critical friction wind speed quantifies surface erodibility, and the assessment result of surface erodibility can be determined based on the actual critical friction wind speed. The ideal critical friction wind speed function is used to calculate the ideal critical friction wind speed, which is the friction wind speed under ideal conditions (dry, flat, unvegetated, loose sand without crust). The ideal critical friction wind speed is only related to soil particle size. However, in actual arid lake basin environments, soil moisture, surface roughness, and soil salinity all affect the critical friction wind speed. Correction functions are used to correct the ideal critical friction wind speed to the actual critical friction wind speed. These correction functions include soil moisture correction functions, surface roughness correction functions, and salinity correction functions.

[0020] In this scheme, optionally, the surface erosibility assessment results of the area to be analyzed are obtained based on the ideal critical friction wind speed function, correction function, and standardized dust data of each grid point. This includes: for each grid point, obtaining soil moisture data, surface roughness data, and salinity data of the current grid point based on the standardized dust data of the current grid point; obtaining a moisture influence factor based on the soil moisture data and soil moisture correction function; obtaining a roughness influence factor based on the surface roughness data and surface roughness correction function; obtaining a salinity influence factor based on the salinity correction function and salinity data; determining the actual critical friction wind speed of the current grid point based on the ideal critical friction wind speed function, moisture influence factor, roughness influence factor, and salinity influence factor; and determining the surface erosibility assessment results of the current grid point based on the actual critical friction wind speed.

[0021] Specifically, soil moisture data is substituted into a soil moisture correction function to obtain a moisture influence factor; surface roughness data is substituted into a surface roughness correction function to obtain a roughness influence factor; and salinity data is substituted into a salinity correction function to obtain a salinity influence factor. The ideal critical friction wind speed is then corrected using the moisture influence factor, roughness influence factor, and salinity influence factor to obtain the actual critical friction wind speed. For example, the formula for calculating the actual critical friction wind speed is: ;in, This represents the actual critical frictional wind speed. The ideal critical frictional wind speed. For soil moisture correction function, This is a surface roughness correction function. This is the salt correction function. For soil moisture, For surface roughness, Soil salinity, d This represents the soil particle size. The ideal critical frictional wind speed function is: ;in and These are pre-set coefficients. Indicates soil particle density, Let be the air density, g be the acceleration due to gravity, and d be the soil particle size. Surface roughness correction function. , where m r It is an adjustment parameter with a value less than 1, representing the non-uniformity of surface stress distribution; σ r It is the ratio of the base area to the leading edge area; β r It is the ratio of the piezoresistive coefficient to the frictional resistance coefficient. Soil moisture correction function. , where θ r Let A represent the moisture content of the air-dried soil, and let A and b be dimensionless parameters. , where S is the soil salinity and α is the fitting coefficient.

[0022] Step 103: Determine the type of sand rising at each grid point based on the standardized dust data and surface erodibility assessment results, and determine the sand rising intensity of the area to be analyzed based on the sand rising type at each grid point.

[0023] The dust-raising type is categorized as either strong wind or weak wind. A strong wind indicates strong near-surface winds at the grid point, causing surface sand particles to undergo abrupt shifts. These shifting sand particles bombard the surface, releasing even finer particles, while agglomerates disintegrate under mechanical action. In this case, the dust-raising process is dominated by sand particle shifts, resulting in a large release flux. A weak wind indicates insufficient wind force at the grid point to drive sand particle shifts, but near-surface turbulent fluctuations may still directly lift extremely fine dust particles from the surface into the atmosphere. In this case, the dust-raising process is dominated by direct turbulent dust generation, resulting in a relatively small release flux. In this scheme, the type of sand initiation at each grid point is determined based on the standardized dust data and the surface erosibility assessment results. This includes: determining the surface friction wind speed at the current grid point based on the standardized dust data; determining the sand initiation type as strong wind when the surface friction wind speed is greater than or equal to the actual critical friction wind speed; and determining the sand initiation type as weak wind when the surface friction wind speed is less than the actual critical friction wind speed.

[0024] Surface friction wind speed is the characteristic velocity corresponding to the shear stress generated by the near-surface atmospheric wind field acting on the Earth's surface. Surface friction wind speed reflects the intensity of momentum loss due to friction near the Earth's surface during wind-surface interaction. When the surface friction wind speed is greater than or equal to the actual critical friction wind speed, the sand-raising type at the current grid point is determined to be a strong wind type; when the surface friction wind speed is less than the actual critical friction wind speed, the sand-raising type at the current grid point is determined to be a weak wind type.

[0025] The dust-raising intensity of the area to be analyzed is determined based on the dust-raising type of each grid point, including: if the dust-raising type of the current grid point is strong wind type, the dust-raising intensity of the current grid point is determined based on the soil particle size distribution, sand particle transition flux, salt crust fragmentation state, and a pre-determined formula for calculating strong wind dust release in the standardized dust data of the current grid point; if the dust-raising type of the current grid point is weak wind type, the dust-raising intensity of the current grid point is determined based on the viscous layer thickness, turbulence intensity, particle cohesion, and a pre-determined formula for calculating weak wind dust release in the standardized dust data of the current grid point; the dust-raising intensity of the area to be analyzed is determined based on the dust-raising intensity of all grid points.

[0026] In this scheme, the intensity of sandstorms is quantified using dust release. The total dust release from the ground surface under wind erosion can be expressed as: F = F a + F b + F c ;in F a This is the dust release flux caused by dust generation directly from near-surface turbulence. F b This refers to the amount of dust released due to the impact of sand grains during their transition. F c It is the dust release caused by the disintegration of aggregates. F b and F c All are generated through sand grain transition, with wind speed only affecting surface friction. u * Exceeding the critical frictional wind speed u *t This occurs over time. Therefore, the dust release amount can be expressed as:

[0027] when u * ≥ u *t At that time, it indicates that the sandstorm type is a strong wind type. F b and F c The dust release process was dominated by sand particle migration, and the formula for calculating the dust release amount in strong winds is as follows: ; F ( d i , d s ) indicates soil particle size of d sThe particle size generated by the sand grain transition motion is d i The dust flux. Among them, c y It is a pre-set proportional coefficient. Soil particle size is d i The mass fraction of dust particles in the soil. γ These are pre-defined weighting factors related to particle size distribution. σ m It is the effective bombardment rate (which can be determined based on the salt crust fragmentation state). g It is gravitational acceleration. This indicates the ratio of free dust to aggregated dust. This refers to the flux of sand grains during their transition. Where c0 is the Kawamura coefficient, Represents the roughness fraction. represents air density, and g represents gravitational acceleration.

[0028] when u * < u *t When the wind type of dust in a weak wind is indicated, surface dust will be lifted into the atmosphere by turbulence. Considering the dust particle motion equation, the thickness of the sticky layer, and the cohesive force between free dust particles, the formula for calculating the dust release in a weak wind is: ;in, η mi Particle size d i ± δd i / 2 The area fraction occupied by particles, It is a pre-set adjustment coefficient. T pi It is the particle response time. h It is the thickness of the adhesive layer. f li It is the force that lifts air (which can be determined based on the intensity of turbulence). f ci It is the resistance to particle lifting (the sum of gravity and particle cohesion). p ( f li ) yes f li The probability density function, p ( τ )yes f ci The probability density function. It is the differential unit corresponding to cohesion. This is a differential element corresponding to shear stress. After obtaining the sand-lifting strength of the grid points as described above, the sand-lifting strength of the region to be analyzed is determined based on the sand-lifting strength of all grid points.

[0029] Based on a comparison of actual frictional wind speeds with critical frictional wind speeds, two mechanisms are distinguished: one dominated by rapid shifts in strong winds and the other by turbulence in weak winds. Different mathematical models are used to calculate these mechanisms respectively. Rapid shift bombardment and agglomerate disintegration are considered in strong winds, while turbulent fluctuations and viscous layer uplift are considered in weak winds. This makes the calculation results more consistent with actual conditions and improves the accuracy of emission flux calculations.

[0030] Step 104: Based on the surface erodibility assessment results and sand-raising intensity, identify the sand sources in the area to be analyzed and obtain sand source distribution information.

[0031] The information on sand source distribution includes sand source attribute information for each grid point; the sand source attribute information includes source area type, salt crust sub-type, and basic sand-raising sensitivity parameters. Source area types include exposed lake basin land, salt crust land, sandy bare land, degraded grassland, and seasonal wetland retreat areas, etc. Salt crust sub-types include hard, intact salt crust, broken salt crust, and disintegrating salt crust, etc. (for salt crust land). Basic sand-raising sensitivity parameters include the critical frictional wind speed benchmark value, transition flux coefficient, aggregate disintegration rate, and dust release efficiency corresponding to each type of source area, etc.

[0032] Specifically, the surface erosibility assessment results include the actual critical frictional wind speed, and the sand-lifting intensity includes the emission flux field. After obtaining the surface erosibility assessment results and sand-lifting intensity, high emission intensity areas in the emission flux field are identified. Based on pre-acquired historical water body boundary change data, it is determined whether the medium-to-high emission intensity areas are located within the historical water area of ​​the lake. If so, the area is confirmed as a lakebed exposure area, belonging to a typical potential sand source. Based on pre-acquired seasonal exposure area range data, the medium-to-high emission intensity areas are distinguished as perennial exposure areas and seasonal wet-dry alternation areas. Seasonal areas are exposed only during specific periods, and their sand source attributes change with the seasons. By comparing with remote sensing inversion results (including surface water content, surface temperature anomalies, and salinization characteristics, etc.), it is verified whether the medium-to-high emission intensity areas are currently actually exposed and whether they have wind erosion conditions. Through the above three-layer comparison, the true exposed areas are screened from the medium-to-high emission intensity areas, and false high emission areas caused by parameter errors are eliminated.

[0033] Within the identified actual bare areas, the areas are further refined based on vegetation cover, bare land ratio, salt crust characteristics, surface moisture, and surface disturbance characteristics. The source areas are divided into various types, such as hard, intact salt crust land, broken salt crust land, loose silty sand bare land, degraded grassland, seasonal wetland retreat areas, or anthropogenically disturbed bare land. Salt crust characteristics include thickness, hardness, integrity, and mineral type. Surface disturbance characteristics include natural disturbances such as animal footprints and plant root damage, as well as anthropogenic disturbances such as vehicle compaction and engineering excavation.

[0034] Furthermore, salt crusts are further subdivided based on mineral composition, formation age, and recent state changes, classifying them into hard and intact types, fractured types, partially fragmented types, or completely disintegrated types. Basic dust-moving sensitive parameters are assigned to each type of source region, including: critical frictional wind speed baseline, transition flux coefficient, agglomerate disintegration rate, and dust release efficiency. These basic dust-moving sensitive parameters are determined through historical wind erosion event inversion, wind tunnel calibration, or field observation calibration.

[0035] Furthermore, for each grid point belonging to a source region type, corresponding parameters are determined based on its source region type, and dynamic sand source distribution information, such as a sand source distribution map, is generated based on the formation parameters. The sand source distribution map in this scheme is not static and can be updated at preset time intervals (such as weekly or monthly). When special events are detected, the sand source distribution information can also be updated. Special events include rainfall events, snowmelt events, lake basin water receding events (newly exposed lakebeds due to water level drop are newly formed potential sand sources that have not yet formed a stable crust), strong wind crust breaking events (continuous strong winds directly damage the integrity of the salt crust, turning the originally protected surface into an active sand source), human-induced surface disturbance events (engineering construction, agricultural activities, traffic crushing, etc., instantly damage the stable surface layer), and vegetation degradation events, etc.

[0036] Step 105: Using a pre-determined trajectory model, conduct transport and diffusion simulations and sedimentation simulations based on sand source distribution information and sand initiation intensity to obtain the sand and dust diffusion range.

[0037] The trajectory model in this scheme is the Hybrid Single-Particle Lagrange Comprehensive Trajectory Model (HYSPLIT). The HYSPLIT model combines the Lagrange and Eulerian methods. The Lagrange method uses a moving reference frame to track the trajectory of a single particle, capable of describing the advection transport of pollutants; the Eulerian method uses a fixed three-dimensional grid to calculate the pollutant concentration distribution, capable of describing the regional diffusion pattern. Using the pre-determined trajectory model, transport and diffusion simulations and sedimentation simulations are performed based on the sand source distribution information and sand initiation intensity to obtain the dust diffusion range. This includes: initializing each particle based on the sand initiation intensity and sand source distribution information using the trajectory model, and calculating the vector velocity at each particle's location; obtaining the stable concentration value at each grid point using the trajectory model; and obtaining the dust diffusion range based on the stable concentration value and vector velocity.

[0038] The dust dispersion range includes the set of particle trajectories, the regional concentration field, and the distribution of deposition flux. Specifically, the trajectory model acquires three-dimensional gridded meteorological data from GDAS (Global Data Assimilation System) and performs temporal and spatial interpolation. Temporal interpolation interpolates discrete meteorological moments to the continuous time steps required by the model. Spatial interpolation interpolates the coarse-resolution gridded meteorological field to the precise location of the particles. At the grid points specified at the location of the active dust source (which can be determined based on the dust source distribution information), the trajectory model releases a large number of virtual particles according to the mass determined by the dust release intensity. Each particle represents a certain mass of dust particles, carrying attribute information such as particle size, density, and release time. The initial height of the particles can be set in the near-surface layer. For each particle, the position of the particle at the next moment is calculated according to the following formula:

[0039] 𝑷(𝑡+∆𝑡)=𝑷(𝑡)+0.5[𝑾(𝑷,𝑡)+𝑾({𝑷(𝑡)+𝑾(𝑷,𝑡)∆𝑡},𝑡+∆𝑡)]∆𝑡; where 𝑷(𝑡) is the position vector of the particle at time t, ∆𝑡 is the time step, 𝑷(𝑡+∆𝑡) is the position of the particle at the next time step, and 𝑾(𝑷,𝑡) is the three-dimensional wind speed vector at time t and position P.

[0040] Simultaneously, the trajectory model calculates the impact of turbulent diffusion on particle motion. An autoregressive method is used to generate a turbulent velocity term to assess the contribution of the turbulent velocity component.

[0041] + 2 ) 0.5 Where 𝑊′(𝑡) is the turbulent velocity component at time t, and 𝑊′(𝑡+∆𝑡) is the turbulent velocity component at the next time step. RIt is a pre-set velocity autocorrelation coefficient, and 𝑊′′ is an automatically generated random component.

[0042] When an air mass represented by a single particle expands during transport and its volume exceeds the size of a meteorological grid cell, the model breaks it down into several sub-air masses to maintain spatial resolution. At each fixed grid point, the trajectory model can statistically analyze the contribution of all air masses passing through that point, calculate the concentration by dividing the air mass mass by its volume, and average the contributions of multiple overlapping air masses to obtain the stable concentration value for that grid point. The calculation formula is: ∆₶ = ∫(∆₁∆₂∆₃∆₄) -1 Where ∆₶ is the air concentration, q is the particle mass, and ∆x∆y∆z is the 3D air mass volume. Further, the trajectory model outputs a set of particle trajectories, a regional concentration field, and a deposition flux distribution based on the stable concentration values ​​of all grid points and the vector velocities at each particle location. The set of particle trajectories records the three-dimensional location time series of each releasing particle, used to map the dust transport path. The regional concentration field represents the dust mass concentration at each grid point and at each time point, used to assess the dust impact range and intensity level. The deposition flux distribution identifies the main deposition areas and cumulative deposition, used to determine the final destination of the dust.

[0043] Step 106: Generate and display dust warning information for the area to be analyzed based on the intensity of sandstorm and the range of sandstorm diffusion.

[0044] Dust storm warning information includes the dust storm risk level; dust transport path, main direction of impact, and estimated arrival time of dust in downwind areas; duration of dust storm impact; peak dust concentration and deposition range. Specifically, based on the emission flux corresponding to the dust storm intensity, source area area, and surface erodibility level, the dust storm risk in the area to be analyzed can be divided into different levels, with different risk levels corresponding to different active dust release and intensities. Based on the numerous particle trajectories determined by the trajectory model, the main transport channels, main movement direction of the dust plume, bifurcation trends, and potentially affected geographical areas can be determined. Based on the time series of particle trajectories, the arrival time of the dust front in each affected area can be calculated, obtaining the estimated arrival time of dust in downwind areas, thus providing advance warning for downstream areas. Based on the time window of particle release and transport velocity, the duration of the dust storm from its onset to its end in each affected area can be estimated. The maximum concentration value and occurrence time in each affected area can be extracted from the regional concentration field; based on the deposition flux distribution, the main deposition areas and cumulative deposition volume can be identified.

[0045] Furthermore, by integrating the above five dimensions with factors such as the number of affected people, the distribution of sensitive areas, and the intensity of economic activity, an overall risk level can be determined, resulting in a comprehensive dust storm risk level for the area under analysis. Once the comprehensive dust storm risk level is obtained, it can be displayed using a spatial distribution map, showing the risk level, concentration distribution, and deposition range in a grid or contour line format, facilitating intuitive identification of high-risk areas and key protection zones. Alternatively, a time series curve can be used to display the concentration change trend, arrival time, peak time, and dissipation process for the area under analysis over future periods, facilitating precise emergency response planning. Finally, a warning text report can be used, describing the event overview, source area information, transport path, affected area, timeliness analysis, and defense recommendations in structured text.

[0046] For example, Figure 3 This is a schematic diagram of the structure for generating sandstorm early warning information provided in an embodiment of the present invention. Figure 3 As shown, after a dust storm event, dust release and transport analyses are performed. In the dust release calculation section, the dust release intensity is output based on wind erosion simulation data, meteorological reanalysis data, normalized difference vegetation index (NDVI), and soil sand and clay content. In the dust transport section, the dust diffusion range is output through a trajectory model based on the dust release data, the duration of the dust impact, and active dust sources. Finally, wind erosion early warning information is obtained based on the dust release intensity and the dust diffusion range.

[0047] The input data for the trajectory model in this scheme are not static empirical values, but rather dynamically adjusted data items based on changes in factors such as surface exposure, humidity variations, salt crust fragmentation, vegetation degradation, and strong wind disturbance. This reduces systematic errors caused by fixed source items and improves the simulation accuracy of dust concentration, transport range, impact period, and settlement area.

[0048] The technical solution of this embodiment involves collecting dust data of the area to be analyzed and obtaining standardized dust data for each grid point based on the dust data; obtaining the surface erosibility assessment result of the area to be analyzed based on a pre-determined ideal critical friction wind speed function, a pre-set correction function, and the standardized dust data of each grid point; the correction function includes a soil moisture correction function, a surface roughness correction function, and a salinity correction function; determining the dust-raising type of each grid point based on the standardized dust data and the surface erosibility assessment result, and determining the dust-raising intensity of the area to be analyzed based on the dust-raising type of each grid point; identifying the dust source in the area to be analyzed based on the surface erosibility assessment result and the dust-raising intensity to obtain dust source distribution information; using a pre-determined trajectory model, performing transport diffusion simulation and sedimentation simulation based on the dust source distribution information and dust-raising intensity to obtain the dust diffusion range; and generating and displaying dust warning information for the area to be analyzed based on the dust-raising intensity and the dust diffusion range. The technical solution of this embodiment comprehensively considers the influence of factors such as soil moisture, surface roughness, and salinity on the actual critical frictional wind speed. It can correct the sand-raising threshold (actual critical frictional wind speed) according to different surface conditions, thereby improving the identification accuracy of highly sensitive wind erosion source areas in dried-up lake basins. By calculating the erosibility assessment results, sand-raising intensity, and sand source distribution information, the intensity of sand release can be accurately analyzed. By analyzing the erosibility assessment results, sand-raising intensity, and sand source distribution information through a trajectory model, dynamic coupling of sand release and transmission is achieved, constructing a complete chain from data acquisition to early warning output. It can simultaneously determine key data such as "where sand rises, when sand rises, the intensity of sand rises, where it is transported, when it arrives, the duration of the impact, and where it settles," further improving the completeness, stability, and accuracy of early warning information.

[0049] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, with reference to... Figure 4 , Figure 4 The electronic device 12 shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this application. Figure 4 As shown, the electronic device 12 is represented in the form of a general-purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0050] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0051] Electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0052] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (… Figure 4 Not shown; usually referred to as a "hard drive"). Although Figure 4 As not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0053] A program / utility 40 having a set (at least one) of program modules 46 may be stored, for example, in system memory 28. Such program modules 46 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 46 typically perform the functions and / or methods described in the embodiments of this application.

[0054] Electronic device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with electronic device 12, and / or with any device that enables electronic device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, electronic device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of electronic device 12 via bus 18. It should be understood that, although... Figure 4 As not shown, other hardware and / or software modules may be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0055] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28. For example, it implements a dust storm early warning method provided in this embodiment of the invention: collecting dust storm data of the area to be analyzed, and obtaining standardized dust storm data for each grid point based on the dust storm data; obtaining the surface erosibility assessment result of the area to be analyzed based on a predetermined ideal critical friction wind speed function, a predetermined correction function, and the standardized dust storm data for each grid point; the correction function includes a soil moisture correction function, a surface roughness correction function, and a salinity correction function; the surface erosibility assessment result includes... The actual critical frictional wind speed is determined; the type of sand-raising at each grid point is determined based on the standardized dust data and the surface erodibility assessment results, and the sand-raising intensity of the area to be analyzed is determined based on the sand-raising type of each grid point; the sand source is identified in the area to be analyzed based on the surface erodibility assessment results and the sand-raising intensity to obtain sand source distribution information; the transmission and diffusion simulation and sedimentation simulation are performed using a pre-determined trajectory model based on the sand source distribution information and the sand-raising intensity to obtain the dust diffusion range; and dust warning information for the area to be analyzed is generated and displayed based on the sand-raising intensity and the dust diffusion range.

[0056] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a dust storm early warning method as provided in all embodiments of this invention: collecting dust storm data of the area to be analyzed, and obtaining standardized dust storm data for each grid point based on the dust storm data; obtaining a surface erosibility assessment result of the area to be analyzed based on a predetermined ideal critical friction wind speed function, a predetermined correction function, and the standardized dust storm data for each grid point; the correction function includes a soil moisture correction function, a surface roughness correction function, and a salinity correction function; the surface erosibility assessment result includes... The process includes: determining the actual critical frictional wind speed; identifying the type of dust initiation at each grid point based on standardized dust data and surface erosibility assessment results; determining the dust initiation intensity in the area to be analyzed based on the dust initiation type; identifying dust sources in the area to be analyzed based on the surface erosibility assessment results and dust initiation intensity to obtain dust source distribution information; performing transport and diffusion simulations and sedimentation simulations based on the dust source distribution information and dust initiation intensity using a pre-determined trajectory model to obtain the dust diffusion range; and generating and displaying dust warning information for the area to be analyzed based on the dust initiation intensity and dust diffusion range. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor electronic devices, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: electrical connections having one or more wires; portable computer disks; hard disks; random access memory (RAM); read-only memory (ROM); erasable programmable read-only memory (EPROM or flash memory); optical fiber; portable compact disk read-only memory (CD-ROM); optical storage devices; magnetic storage devices; or any suitable combination of the foregoing. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an electronic device, apparatus, or device that executes instructions.

[0057] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in conjunction with an electronic device, apparatus, or device that executes instructions.

[0058] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0059] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0060] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for early warning of sandstorms, characterized in that, The method includes: Collect dust data of the area to be analyzed, and obtain standardized dust data for each grid point based on the dust data; The surface erodibility assessment results of the area to be analyzed are obtained based on a predetermined ideal critical friction wind speed function, a pre-set correction function, and standardized dust data of each grid point; the correction function includes a soil moisture correction function, a surface roughness correction function, and a salinity correction function; the surface erodibility assessment results include the actual critical friction wind speed. The type of sand initiation at each grid point is determined based on the standardized dust data and the surface erodibility assessment results, and the sand initiation intensity of the area to be analyzed is determined based on the type of sand initiation at each grid point. Based on the surface erosibility assessment results and the sand-raising intensity, the sand source is identified in the area to be analyzed, and the sand source distribution information is obtained. By using a predetermined trajectory model, and based on the sand source distribution information and the sand initiation intensity, the transmission and diffusion simulation and sedimentation simulation are performed to obtain the sand and dust diffusion range; Based on the sandstorm intensity and the sandstorm diffusion range, sandstorm early warning information for the area to be analyzed is generated and displayed.

2. The method according to claim 1, characterized in that, Based on a pre-determined ideal critical friction wind speed function, a pre-set correction function, and standardized dust data for each grid point, the surface erosibility assessment results for the area to be analyzed are obtained, including: For each grid point, soil moisture data, surface roughness data, and salinity data are obtained based on the standardized dust data of the current grid point. A humidity influence factor is obtained based on the soil moisture data and the soil moisture correction function; a roughness influence factor is obtained based on the surface roughness data and the surface roughness correction function; a salinity influence factor is obtained based on the salinity correction function and the salinity data. The actual critical friction wind speed of the current grid point is determined based on the ideal critical friction wind speed function, the humidity influence factor, the roughness influence factor, and the salinity influence factor, and the surface erodibility assessment result of the current grid point is determined based on the actual critical friction wind speed.

3. The method according to claim 2, characterized in that, Based on the standardized dust data of each grid point and the surface erosibility assessment results, the dust generation type of each grid point is determined, including: The surface friction wind speed of the current grid point is determined based on the standardized dust data of the current grid point. When the surface friction wind speed is greater than or equal to the actual critical friction wind speed, the sand-raising type of the current grid point is determined to be a strong wind type. When the surface friction wind speed is less than the actual critical friction wind speed, the sand-raising type of the current grid point is determined to be a weak wind type.

4. The method according to claim 3, characterized in that, The sand-raising intensity of the area to be analyzed is determined based on the sand-raising type of each grid point, including: If the sand-raising type of the current grid point is the strong wind type, the sand-raising intensity of the current grid point is determined according to the soil particle size distribution, sand particle migration flux, salt crust fragmentation state and the predetermined strong wind dust release calculation formula in the standardized dust data of the current grid point. If the current grid point's sand-raising type is the weak wind type, the sand-raising intensity of the current grid point is determined based on the viscosity layer thickness, turbulence intensity, particle cohesion, and a pre-determined formula for calculating weak wind dust release in the standardized dust data of the current grid point. The sand-raising intensity of the area to be analyzed is determined based on the sand-raising intensity of all grid points.

5. The method according to claim 1, characterized in that, The sand source distribution information includes sand source attribute information for each grid point; the sand source attribute information includes source area type, salt crust subdivision type, and basic sand-raising sensitive parameters.

6. The method according to claim 1, characterized in that, Using a pre-determined trajectory model, based on the sand source distribution information and the sand initiation intensity, transport and diffusion simulations and sedimentation simulations are performed to obtain the sand and dust diffusion range, including: Using the trajectory model, each mass point is initialized based on the sand initiation intensity and the sand source distribution information, and the vector velocity of each mass point's position is calculated; The stable concentration values ​​of each grid point were obtained through trajectory modeling. The dust diffusion range is obtained based on the stable concentration value and the vector velocity; the dust diffusion range includes the set of particle trajectories, the regional concentration field, and the sedimentation flux distribution.

7. The method according to claim 1, characterized in that, The dust data includes remote sensing data, meteorological data, surface attribute data, station observation data, and vertical observation data; the dust warning information includes the dust rise risk level, dust transport path, main direction of impact, expected arrival time of dust in downwind areas, duration of dust impact, peak dust concentration, and deposition range.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the sandstorm early warning method as described in any one of claims 1-7.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the sandstorm early warning method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the sandstorm early warning method as described in any one of claims 1-7.

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