Forest fire smoke three-dimensional reproduction and kinetic analysis method and system based on multi-elevation WER slice and storage medium

Abstract

The invention discloses a forest fire smoke three-dimensional reproduction and kinetic analysis method and system based on a multi-elevation WER slice and a storage medium, the method does not depend on vertical profile data, and the method comprises the following steps: obtaining multi-elevation reflectivity slice data collected by a multi-band weather radar, performing clutter suppression, biological echo suppression and X-band attenuation elimination processing, and obtaining a multi-elevation WER slice; cross-band intensity normalization is completed; performing back projection on the preprocessed multi-elevation angle reflectivity slice data to a unified three-dimensional voxel grid according to a radar observation geometrical relationship, and performing weighted fusion on back projection data based on a preset wave band weight and an elevation angle weight to generate a smoke three-dimensional reflectivity field; based on a preset reflectivity threshold value, a smoke contour surface is extracted from the three-dimensional reflectivity field, and the smoke column top height, the maximum reflectivity height and the maximum sectional area of smoke are calculated; and outputting a smoke three-dimensional structure reproduction result and a kinetic parameter time sequence containing the smoke column top height, the maximum reflectivity height and the maximum sectional area.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of meteorological radar remote sensing and forest fire monitoring, and particularly relates to a forest fire smoke three-dimensional reconstruction and dynamics analysis method and system based on multi-elevation WER slices and a storage medium. BACKGROUND

[0002] As a global ecological and safety disaster, forest fires produce smoke aerosols that not only significantly reduce regional visibility, interfere with air and land traffic safety, but also cause flashover failures by adhering to the surface of power transmission line insulators, posing a direct threat to energy infrastructure safety. In addition, harmful components in smoke can also affect air quality and human health in surrounding areas through atmospheric diffusion, so real-time spatial structure characterization and dynamic evolution monitoring of fire smoke are the core technical requirements for fire emergency disposal (such as fire judgment and evacuation path planning) and secondary disaster prevention (such as power grid operation and maintenance warning).

[0003] In the current mainstream forest fire monitoring technology system, satellite remote sensing, with its wide coverage and ability to monitor large-scale fire points and smoke macro-distribution, has become an important means of regional-scale fire survey. Among them, although polar orbit satellites (such as MODIS and VIIRS series) have relatively high spatial resolution, their revisit period is usually 1-2 days, which makes it difficult to capture the rapid diffusion process of fire smoke at the hour level or even minute level. Although geostationary satellites (such as FY-4 and GOES series) can achieve short-period observation (usually tens of minutes to an hour), their spatial resolution is relatively low (roughly 1 kilometer or more), which can only identify large-scale smoke plumes and cannot accurately restore the microstructure parameters such as the vertical height and volume of the smoke column. More importantly, satellite remote sensing is significantly limited by atmospheric conditions. Thin clouds or cirrus clouds can cause smoke signals to be obscured, and thick clouds directly block the observation path, especially in forest fire-prone areas with high rainfall and fog (such as hilly areas), where the effectiveness of observation is often severely restricted.

[0004] Ground-based optical video and infrared monitoring systems are widely used for fire point positioning and real-time observation of fire spread, but they also have technical limitations that are difficult to overcome. In terms of spatial coverage, such devices are significantly affected by terrain obstruction, and in complex terrain areas such as valleys and steep slopes, there are often large proportions of monitoring blind spots, which cannot form a continuous smoke monitoring network. In terms of environmental adaptability, smoke itself can reduce atmospheric transparency, causing the effective observation distance of optical devices to be shortened from several kilometers in normal environments to within 1 kilometer, while night-time infrared monitoring can penetrate some smoke, but it is easily disturbed by the temperature differences between the background of the ground vegetation and water, making it difficult to accurately distinguish the boundaries between smoke and the surrounding environment. In addition, ground-based devices rely on power and network transmission, which have high installation costs and maintenance difficulties in remote forest areas, further limiting their large-scale application.

[0005] Doppler weather radar, as an active remote sensing device with all-weather and high spatiotemporal resolution, has been increasingly applied to the field of fire smoke monitoring in recent years. By emitting electromagnetic waves and receiving the scattered echoes of smoke particles, it can obtain parameters such as echo intensity (Z, reflecting particle concentration) and velocity spectrum width (W, reflecting the degree of particle motion turbulence), providing a new technical approach for smoke detection. However, traditional radar smoke monitoring often employs two-dimensional threshold identification methods: either based on planar position indicator (PPI) data at a single elevation angle, extracting the horizontal distribution range of smoke by setting an echo intensity threshold, but failing to obtain key parameters such as the vertical height and volume of the smoke column; or obtaining a local vertical profile through range-height indicator (RHI) data, but RHI observation requires manual adjustment of the radar scanning angle, making it impossible to achieve large-scale continuous coverage, and a single observation can only reflect the smoke structure on one profile, making it difficult to reconstruct the complex three-dimensional spatial distribution of smoke.

[0006] The limitations of traditional radar monitoring are particularly pronounced in forest fires in hilly areas. Smoke columns in these regions are typically characterized by being "low-lying" (mostly below 2km in height) and "irregular" (their shape is easily deformed by valley airflow) due to topographical thermal differences and local circulation. Furthermore, smoke column echo signals are often superimposed with ground clutter (such as fixed echoes from mountains and buildings) and biological echoes (such as flocks of birds and insects flying low). On one hand, single-layer scans of two-dimensional data cannot distinguish between smoke and clutter. Using high-intensity thresholds to suppress clutter can easily lead to weak echo signals from low-lying smoke columns being misjudged as interference and rejected. On the other hand, traditional methods lack the ability to collaboratively utilize radar data from different elevation angles and bands, making it difficult to integrate the detailed advantages of high-resolution data (such as the X-band) with the anti-interference advantages of high-stability data (such as the S / C band). Ultimately, this fails to accurately reconstruct the spatial structure and diffusion dynamics of smoke, and also fails to meet the actual needs of fire emergency response for "minute-level dynamic monitoring." Summary of the Invention

[0007] To address the shortcomings and deficiencies of existing technologies, this invention provides a method, system, and non-transitory computer-readable storage medium for three-dimensional reconstruction and dynamic analysis of forest fire smoke based on multi-band radar. It aims to solve the technical problems of satellite remote sensing's revisit cycle and cloud cover limitations preventing minute-level dynamic monitoring; ground-based optical / infrared monitoring being susceptible to terrain obstruction and aerosol interference; and traditional Doppler weather radar's two-dimensional threshold identification method failing to characterize the spatial volume, height variation, and diffusion dynamics of smoke (especially the difficulty in accurately reconstructing the structure of low smoke columns in hilly areas).

[0008] This method does not rely on vertical profile data. The core steps include: first, acquiring multi-elevation reflectivity slice data from multi-band (X-band, C-band, S-band) weather radar, and simultaneously acquiring the corresponding dual polarization parameters for each band; performing clutter suppression, bio-echo suppression, and X-band attenuation elimination processing on the acquired data, and performing cross-band intensity normalization based on the more stable bands to ensure data quality; then, back-projecting the preprocessed multi-elevation reflectivity slice data onto a unified three-dimensional voxel grid according to radar observation geometry, and combining preset band weights (higher weights for high spatial resolution bands) and elevation weights (higher weights for low elevation data, suitable for monitoring low smoke columns <2km in hilly areas)... Weighted fusion is performed to generate a three-dimensional reflectivity field for smoke and dust. Based on a preset reflectivity threshold (which can be a fixed range of 10-15 dBZ or an adaptive adjustment strategy), isosurfaces of smoke and dust are extracted from the three-dimensional reflectivity field. Core dynamic parameters such as the top height of the smoke column, the maximum reflectivity height, and the maximum cross-sectional area are calculated. The total volume of smoke and dust can also be statistically analyzed, and the advection velocity can be calculated based on the horizontal displacement and time interval within a continuous observation period. Finally, the three-dimensional structure reconstruction results of the smoke and dust are output (which can be superimposed with a digital elevation model of the terrain) and a time series containing dynamic parameters are output. Standardized formats such as raster and vector are supported for output, and even four-dimensional evolution animation products can be generated to achieve minute-level dynamic monitoring (where the X-band volume scan period is usually shorter than that of the S-band and C-band).

[0009] The corresponding system includes a data input module, a quality control module, a voxel reconstruction and weight fusion module, a dynamic analysis module, and a result output module. Each module's function corresponds one-to-one with the steps described above, ensuring the solution's feasibility for implementation. A non-transitory computer-readable storage medium stores a computer program that, when executed by a processor, implements the aforementioned methods. Furthermore, this invention can expand the spatial coverage of the three-dimensional reflectivity field through multi-radar collaborative acquisition and shared data from overlapping observation areas. It is applicable to scenarios such as forest fire emergency response, power transmission line safety early warning, and scientific research analysis, effectively improving the monitoring accuracy and dynamic response capability of forest fire smoke in complex terrain.

[0010] The present invention specifically adopts the following technical solution: A method for three-dimensional reconstruction and dynamic analysis of forest fire smoke based on multi-elevation WER slices, without relying on vertical profile data, includes: The system acquires multi-elevation angle reflectivity slice data from multi-band weather radar, performs clutter suppression, bio-echo suppression, and X-band attenuation elimination, and completes cross-band intensity normalization. The preprocessed multi-elevation angle reflectivity slice data is back-projected onto a unified three-dimensional voxel grid according to the radar observation geometry. Then, the back-projected data is weighted and fused based on the preset band weight and elevation angle weight to generate a three-dimensional reflectivity field of smoke and dust. Based on a preset reflectivity threshold, the smoke and dust isosurface is extracted from the three-dimensional reflectivity field, and the smoke column height, maximum reflectivity height, and maximum cross-sectional area are calculated. Output the three-dimensional structure reconstruction results of the smoke and dust, as well as the time series of dynamic parameters including the top height of the smoke column, the maximum reflectivity height, and the maximum cross-sectional area.

[0011] Furthermore, the multi-band weather radar includes X-band, C-band, and S-band. The multi-elevation reflectivity slice data collected are WER slice data generated by the volume scan of each band radar, and the corresponding dual polarization parameters of each band are acquired simultaneously. The volume scan period of each band is adapted to the data resolution requirements. By establishing a unified time stack, the time synchronization processing of the multi-band data is performed by time interpolation or advection extrapolation to achieve dynamic monitoring.

[0012] Furthermore, the clutter suppression is achieved through ground object echo identification, multi-time series data difference analysis, and spectral width feature filtering; the X-band attenuation elimination combines near-range reference data with path integral correction, and avoids overcorrection through the stability constraint of dual polarization parameters; the cross-band intensity normalization uses the more stable band data as a benchmark and adjusts the intensity bias of other band data to eliminate inter-band systematic errors.

[0013] Furthermore, the band weights are allocated according to data resolution and reliability, with higher spatial resolution band data having higher weights; the elevation angle weights are adapted to the observation scenario, assigning higher weights to reflectivity slice data in the low elevation angle range to improve the accuracy of three-dimensional structure reconstruction of low-altitude targets.

[0014] Furthermore, the resolution of the unified three-dimensional voxel grid is dynamically adjusted according to the fused data type to adapt to the accuracy requirements of the corresponding band data.

[0015] Furthermore, the preset reflectivity threshold is dynamically adjusted according to the observation environment, or a fixed threshold range adapted to the monitoring scenario is adopted; the calculated smoke and dust parameters also include the total volume of smoke and dust and the advection velocity, wherein the advection velocity is calculated based on the horizontal displacement of smoke and dust within a continuous observation period and the observation time interval, and the total volume of smoke and dust is statistically based on the voxel space range surrounded by the smoke and dust isosurface.

[0016] Furthermore, the dynamic parameter time series is formed by extracting parameters from continuous observation periods, and the different stages of target evolution are divided by combining the changing trends and peak correlations of each parameter; the output three-dimensional structure reconstruction results of smoke and dust can be superimposed with terrain digital elevation model data, and the output format includes raster format, vector format, or dynamic evolution products formed by time series correlation.

[0017] Furthermore, by employing multi-radar collaborative data acquisition and sharing reflectivity slice data from overlapping areas observed by each radar, the spatial coverage of the three-dimensional reflectivity field is expanded.

[0018] And, a three-dimensional reconstruction and dynamic analysis system for forest fire smoke based on multi-elevation angle WER slices, comprising: The data input module is configured to acquire multi-elevation angle reflectivity slice data collected by multi-band weather radar; The quality control module is configured to perform clutter suppression, bio-echo suppression, and X-band attenuation elimination processing on the multi-elevation angle reflectivity slice data, and to complete cross-band intensity normalization. The voxel reconstruction and weighted fusion module is configured to back-project the preprocessed multi-elevation angle reflectivity slice data onto a unified three-dimensional voxel grid according to the radar observation geometry, and then perform weighted fusion of the back-projected data based on preset band weights and elevation angle weights to generate a three-dimensional reflectivity field of smoke and dust. The dynamic analysis module is configured to extract the smoke and dust isosurface from the three-dimensional reflectivity field based on a preset reflectivity threshold, and to calculate the smoke column top height, maximum reflectivity height, and maximum cross-sectional area. The results output module is configured to output the three-dimensional structure reproduction results of the smoke and dust and the time series of dynamic parameters including the top height of the smoke column, the maximum reflectivity height, and the maximum cross-sectional area.

[0019] And an 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 steps of the method described above.

[0020] A non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0021] Compared with the prior art, the present invention and its preferred embodiments have at least the following beneficial effects: First, it effectively overcomes the limitations of existing monitoring technologies in terms of scenarios and accuracy. It does not rely on vertical profile data, thus avoiding the observation delay caused by traditional radar acquisition of vertical profile data and simplifying the monitoring process. It successfully achieves minute-level dynamic monitoring of forest fire smoke and dust, which can meet the core real-time requirements in fire emergency response. At the same time, in response to the problem of the difficulty in accurately reconstructing low smoke columns in complex terrains such as hilly areas, a fusion strategy that assigns higher weight to low elevation angle data, combined with multi-band collaborative acquisition, significantly improves the accuracy of reconstructing the spatial structure of smoke and dust in these special scenarios, making up for the shortcomings of traditional two-dimensional threshold recognition methods that cannot characterize the volume and height changes of smoke and dust.

[0022] Secondly, the reliability of data processing and fusion is significantly improved. Through targeted preprocessing such as clutter suppression, biological echo suppression, and X-band attenuation elimination, combined with cross-band intensity normalization based on stable bands, interference from ground objects, organisms, and band attenuation is effectively filtered out, ensuring the consistency of the original data. Furthermore, by combining the two-dimensional fusion design of band weight (adapting to the advantages of high-resolution data details) and elevation angle weight, the constructed three-dimensional reflectivity field is more consistent with the real spatial distribution of smoke and dust, providing a high-quality foundation for subsequent dynamic parameter calculations and avoiding the one-sidedness of single-band or single-layer scanning data.

[0023] Furthermore, the monitoring results are more practical and adaptable. The dynamic analysis system built around core parameters such as smoke column height, maximum reflectivity height, and maximum cross-sectional area can quantify the evolution characteristics of smoke and even support the stage division of the smoke diffusion process, providing accurate quantitative basis for fire intensity assessment and early warning of secondary disasters (such as power transmission line safety risks). The output results not only include three-dimensional structural reproduction content, but also support standardized data formats and visualization products (such as three-dimensional maps with overlaid terrain information and four-dimensional evolution animations), adapting to various application scenarios such as emergency response and scientific research analysis. The modular design of the corresponding system ensures the engineering feasibility of the method, and the supporting non-transitory storage media facilitates the promotion and application of the technology. The optimal scheme of multi-radar collaboration can further expand the spatial coverage of monitoring and improve the overall monitoring capability of smoke and dust from large-scale forest fires. Attached Figure Description

[0024] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: Figure 1 This is a schematic diagram of the three-dimensional reproduction effect of smoke and dust based on multi-elevation angle WER slices in an embodiment of the present invention; Figure 2 This is a radar echo structure diagram of a local case according to an embodiment of the present invention (the left side of the diagram is PPI, and the right side is RHI corresponding to the PPI region). Figure 3 This is a diagram of the smoke and dust body structure formed by stacking multiple elevation angle slices according to an embodiment of the present invention (the left side of the diagram is PPI, and the right side is the WER corresponding to the PPI region). Figure 4 This is a graph showing the variation of the maximum reflectivity height H_Zmax over time in an embodiment of the present invention. Figure 5 This is a graph showing the variation of the maximum cross-sectional area Amax of the volume in an embodiment of the present invention over time. Figure 6 This is a graph showing the change in smoke column height H_top over time according to an embodiment of the present invention. Figure 7 This is a flowchart illustrating the overall process of the method according to an embodiment of the present invention. Figure 8 This is a module framework diagram of the systematic implementation of an embodiment of the present invention. Detailed Implementation

[0025] In the following, specific embodiments of this application will be described in detail with reference to the accompanying drawings. Based on these detailed descriptions, those skilled in the art will be able to clearly understand and implement this application. Without departing from the principles of this application, features from various embodiments can be combined to obtain new implementations, or certain features from some embodiments can be substituted to obtain other preferred implementations.

[0026] To make the features and advantages of the present invention more apparent and understandable, specific embodiments are described below in conjunction with the accompanying drawings: The purpose of this invention is to provide a method for three-dimensional reconstruction and dynamic analysis of forest fire smoke based on multi-elevation-angle WER (Weather Elevation Reflectivity) slices. Specifically, it is a method that reconstructs the three-dimensional structure and dynamic characteristics of smoke solely based on multi-elevation-angle WER slices, i.e., by superimposing echo slices at different elevation angles. This method achieves minute-level three-dimensional reconstruction and evolution analysis of fire smoke without the need for a vertical cross-section (VCS). Through voxelization, weighted fusion, and three-dimensional isosurface extraction techniques, it achieves high-precision inversion of key parameters such as smoke height, volume, and diffusion velocity, meeting the requirements for minute-level dynamic monitoring.

[0027] It acquires WER data from multiple elevation angles in the X, C, and S bands (preferably S / C 250 m resolution, 6 min period; X 75 m resolution, 3 min period); performs clutter and bio-echo suppression and attenuation correction; obtains the three-dimensional reflectivity field Z* through back projection and weighted fusion; extracts and calculates dynamic parameters such as smoke top height H_top, maximum reflectivity height H_Zmax, volume V_f, maximum cross-sectional area Amax, and advection velocity U based on isosurfaces; and outputs the three-dimensional structure and time evolution curve of the smoke. This invention does not require vertical profiles and can achieve minute-level three-dimensional reconstruction and quantitative dynamic analysis of smoke solely based on multi-elevation angle WER, making it suitable for forest fire and power transmission line safety monitoring.

[0028] The implementation process of this solution can be referenced as follows: (1) Collect WER slices and dual polarization parameters at multiple elevation angles in the X, C, and S bands; (2) Perform clutter, bioecho and weak precipitation suppression, perform near-range reference window and path integral attenuation correction on X-band echo, and use ZDR / CC drift as constraint; (3) Using the S or C band as the intensity reference, complete the cross-band intensity normalization; (4) Back-project onto a unified voxel grid and fuse it according to the band and elevation angle weights to generate a three-dimensional reflectivity field Z*(x,y,z); (5) Extract the dust isosurface based on the threshold Zo and segment the connected components, and calculate H_top, H_Zmax, V_f, Amax and advection velocity U; (6) Output the three-dimensional reproduction results and dynamic curves of smoke and dust.

[0029] The default preferred configuration is an S / C band resolution of 250m and a volume scan period of 6min, and an X band resolution of 75m and a volume scan period of 3min.

[0030] The band weights satisfy w_X≥0.5, w_S≈w_C≈(1-w_X) / 2, and lower elevation angles are assigned higher weights.

[0031] The horizontal resolution of the three-dimensional voxel grid is 250m, and the vertical resolution is 100–200m. When the X-band is dominant, the horizontal resolution is 75–125m.

[0032] The threshold Zo is set to 10–15 dBZ or an adaptive strategy is adopted.

[0033] The implementation of the present invention is further demonstrated and described below through an embodiment: A. Data Acquisition and Default Configuration A1. Collect volume scan data from X, C, and S band Doppler weather radar, including multiple elevation WER slices and dual polarization parameters (Z, ZDR, CC, KDP, W).

[0034] A2. Default Configuration: The equivalent spatial resolution of S-band and C-band is 250m, and the volume scan period is 6min. The equivalent spatial resolution of the X-band is 75m, and the volume scan period is 3min.

[0035] A3. Establish a unified time stack {t k} (Window width W) t =±1.5min), time interpolation or semi-Lagrange advection extrapolation is performed on volume scans of different bands to form quasi-synchronous slices.

[0036] This step achieves time synchronization and spatial unification of multi-band data, providing a spatiotemporal consistency foundation for 3D reconstruction.

[0037] B. Data Preprocessing and Quality Control B1. Clutter suppression: Static ground cover maps, multi-time difference and spectral width filtering are used to remove fixed echoes.

[0038] B2. Identification of biological and weak precipitation: Non-fire targets are filtered out by CC, ZDR and time consistency.

[0039] B3.X band attenuation correction: Reflectivity is recovered using a near-range reference window and path integral method, and overcompensation is suppressed by ZDR / CC drift constraint; wet radome gain is corrected by inter-volume scan background consistency.

[0040] B4. Cross-band intensity normalization: Using the S or C band as the intensity reference, adjust the offset of the X band.

[0041] This step improves the consistency and reliability of data from different bands, ensuring that subsequent fusion is not affected by attenuation errors.

[0042] C. Voxelization and fusion of WER slices at multiple elevation angles The embodiments of the present invention achieve integrated three-dimensional reconstruction of multi-band and multi-elevation radar data through unified voxel mesh construction, multi-source data back projection, and weighted fusion based on consistency probability, providing core support for accurate inversion of smoke and dust spatial structure.

[0043] C1. Construct a unified 3D voxel mesh To accommodate the differences in spatial resolution and detection capabilities of radars across different bands, and to adapt to the polar coordinate observation characteristics of multi-elevation angle scanning, this invention first establishes a unified three-dimensional voxel grid based on the geometric relationship of radar station locations: The horizontal resolution is set to Δx=Δy=250m by default, and the vertical resolution is Δz=100~200m; When using high-resolution X-band data, the horizontal resolution is adjusted to a fine grid projection with Δx=Δy=75m to avoid blurring of the smoke and dust structure caused by spatial scale mismatch.

[0044] This grid provides a unified spatial reference for radar data from different sources and with different scanning strategies, ensuring spatiotemporal consistency in the subsequent reconstruction process.

[0045] C2. Multi-elevation WER slice backprojection voxelization Combining radar emission direction, elevation angle, scanning radius, and terrain height, this invention accurately backprojects WER reflectivity slice data from various bands and multiple elevation angles onto the aforementioned unified voxel grid. The backprojection process focuses on considering the following factors: 1. The "fan-shaped observation volume" corresponding to the radar beam width and elevation angle ensures the spatial positioning accuracy of reflectivity data; 2. Earth curvature and refraction correction to avoid spatial bias during long-distance observation; 3. For areas with localized occlusion caused by complex terrain, the occluded voxels are marked as invalid to eliminate interference from invalid data.

[0046] This invention maps reflectance data Zb,θ(x,y,z) (where b∈{X,C,S} and θ represents all scanning elevation angles) to a voxel grid by elevation angle and waveband, forming a reflectance sample set {Zb,θ(x,y,z)} from different observations on each voxel. This differs from the traditional isolated processing method of single elevation angle slices and provides a comprehensive input basis for subsequent fusion algorithms.

[0047] C3. Multi-source weighted fusion based on consistency probability Pobs To address the "voxel inconsistency" problem caused by system bias, attenuation differences, or ground object pseudo-echoes among observations at different bands and elevation angles, this invention proposes observation consistency probabilities (Pobs) and integrates them into the core framework of 3D fusion, as specifically implemented below: (1) Construction of observation consistency probability Pobs This invention constructs the consistency probability using the statistical dissimilarity of all observed samples within a voxel. The core expression includes two implementable methods: Method 1 (based on variance): Pobs(x,y,z)=exp(-Var(Zb,θ(x,y,z)) / σo 2 ) Where Var() represents the variance of reflectivity data at different elevation angles and different bands at this voxel; σo is the normalization coefficient, typically ranging from 3 to 5 dBZ.

[0048] Method 2 (Robust optimization, based on median absolute deviation): Pobs = 1 / (1 + MAD / μ) Where MAD is the median absolute deviation of the voxel reflectance samples, and μ is the sample mean.

[0049] The core logic of the two methods is the same: the more consistent the multi-source observation data, the smaller the sample variance (or MAD), the closer the Pobs value is to 1, and the higher the reliability of the voxel data. If there are clutter, abnormal slices, or severe attenuation, the sample variability increases, and the Pobs value automatically tends to decrease, realizing adaptive identification of unreliable data. It is not only a reliability measurement factor, but also an important mechanism for suppressing pseudo voxels.

[0050] (2) Implementable rules for setting band weight wb To accommodate the different characteristics of the X, C, and S bands in detecting smoke particles, attenuation stability, and spatial resolution, this invention employs a multi-factor weighting method to construct band weights. The specific formula is as follows: wb=α·(1 / Δr_b)+β·Pb+γ·Sb in: Δr_b is the spatial resolution of the corresponding band; Pb represents the sensitivity of the band to dust scattering, satisfying the relationship X>C≈S; Sb represents the attenuation stability of the band, satisfying the relationship S>C>X. α, β, and γ are normalization parameters, with typical value combinations of (0.4, 0.4, 0.2).

[0051] Based on the above rules, the typical band weight range can be obtained: X band wX≈0.50–0.55 (highlighting the advantages of high resolution), C band wC≈0.25–0.30 (balancing stability and resolution), and S band wS≈0.20–0.25 (enhancing penetration capability).

[0052] (3) Implementable setting rules for elevation angle weight wθ To address the monitoring needs of low-lying smoke columns (<2km) in hilly areas, this invention employs an exponential decay model to establish elevation angle weights, automatically enhancing the contribution of low-altitude observation data. The formula is as follows: wθ=exp(-k·θ) Where θ is the scanning elevation angle (in degrees) and k is the attenuation coefficient, typically ranging from 0.05 to 0.15.

[0053] Example: When θ=0.5°, wθ≈1; when θ=2°, wθ≈0.8; when θ=6°, wθ≈0.5, effectively improving the structural restoration accuracy of low-altitude smoke columns and suppressing structural deformation in high-angle slices.

[0054] (4) Multi-source fusion formula Taking into account the consistency probability Pobs, band weight wb, and elevation angle weight wθ, the three-dimensional reflectivity field fusion formula of this embodiment of the invention is finally expressed as follows: Z*(x,y,z)=Pobs(x,y,z)·Σ_bΣ_θ[wb·wθ·Zb,θ(x,y,z)] in: Pobs(x,y,z) is responsible for filtering inconsistent voxels. When Pobs < 0.3, the voxel is determined to be untrustworthy and set to zero, further reducing noise interference. wb ensures that the three-band data are weighted reasonably according to the detection capability, balancing resolution and stability; wθ focuses on enhancing its contribution to low-altitude observation and adapting to the monitoring needs of low-lying smoke columns.

[0055] This fusion strategy ultimately generates a continuous and reliable three-dimensional reflectivity field Z*(x,y,z), laying a high-quality data foundation for subsequent extraction of dust isosurfaces and calculation of dynamic parameters.

[0056] D. Three-dimensional reconstruction and dynamic analysis of smoke and dust D1. Isosurface extraction: Extract the dust mass from Z* based on the threshold Zo (10–15 dBZ).

[0057] D2. Calculation of key parameters: The top height of the smoke column, H_top, is the highest height of Z*≥Zo. Maximum reflectivity height H_Zmax: The height corresponding to the maximum value of Z; Smoke volume V_f: Total volume of Z*≥Zo; Layer area A(z): Projected area of ​​each layer at each height; Advection velocity .

[0058] D3. Construction of Dynamic Curves: Using time-series volume scan data, extract the three curves H_top(t), Zmax(t), and Amax(t). Based on the peak value variation relationship, divide the data into three stages: "rapid rise phase - diffusion phase - decay phase" (e.g., ...). Figures 4-6 (As shown).

[0059] This step obtains quantitative parameters such as smoke volume, top height, and diffusion velocity, which can reflect the fire intensity and evolution trend in real time.

[0060] E. Visualization and Data Product Output E1. Overlay the Z* isosurface with the terrain DEM to generate a 3D reconstruction map of the smoke and dust, such as... Figure 1 As shown.

[0061] E2. Output product formats: voxel raster (NetCDF / HDF5) and feature vector (GeoJSON / CSV).

[0062] E3. The results can be connected by time series to form a four-dimensional (3D+T) evolutionary animation product.

[0063] This step provides standardized data output and 3D visualization, facilitating applications in emergency response, scientific research, and power grid safety assessment.

[0064] For the step-by-step implementation of the above embodiments, please refer to [link / reference]. Figure 7 ,include: S1: Data Acquisition: Data containing multiple elevation WER slices and dual polarization parameters is collected using X, C, and S band Doppler weather radar.

[0065] S2: Clutter Suppression: Use static ground maps, multi-time difference and spectral width filtering and other methods to remove fixed echo interference.

[0066] S3: Attenuation correction: The X-band reflectivity is recovered using a near-range reference window and path integral method, and overcorrection suppression is performed using ZDR / CC drift as a constraint condition.

[0067] S4: WER slice voxelization: Construct a unified three-dimensional voxel mesh and back-project the multi-elevation WER slices onto the mesh according to the observation geometry to form a voxel set.

[0068] S5: Weighted Fusion: Calculate the observation consistency probability Pobs for each voxel based on the band weight wb and elevation angle weight wθ, and perform weighted fusion to generate a three-dimensional reflectivity field Z.

[0069] S6: 3D structure extraction: Extract the smoke and dust isosurface from Z based on the set threshold Zo, and segment the connected components.

[0070] S7: Dynamics calculation: Calculate key parameters such as smoke column top height H_top, maximum reflectivity height H_Zmax, smoke volume V_f, and advection velocity U.

[0071] S8: Visualization and Output: Overlay the Z-isosurface with the terrain DEM to generate a 3D reconstruction map and output it in NetCDF, GeoJSON, or graphic product formats.

[0072] Its corresponding systematic implementation reference consists of six modules, including: Data input module: responsible for collecting data from volume scans of radar in different frequency bands.

[0073] Quality control module: Performs clutter suppression and attenuation correction to ensure data quality.

[0074] Voxel Reconstruction Module: Processes WER slices with multiple elevation angles and reconstructs 3D spatial information on a unified voxel mesh through back projection and interpolation.

[0075] Fusion Analysis Module: Applying a fusion strategy of band weighting and elevation angle weighting, a continuous three-dimensional reflectivity field Z is generated by combining the observation results from various angles.

[0076] Kinetics Analysis Module: Calculates key kinetic parameters reflecting the diffusion of smoke and dust based on Z*.

[0077] Product output module: Generates standardized data products for further analysis and application.

[0078] Compared to existing technologies, the above solutions in the embodiments of the present invention are as follows: (1) Only WER multi-elevation angle data is used to realize the three-dimensional reconstruction of smoke and dust, without the need for vertical profile (VCS). (2) The system solves the problems of X-band attenuation and multi-band matching, resulting in a more realistic three-dimensional structure; (3) A dust dynamics parameter system with H_top, H_Zmax, Amax, V_f, and U as the core was proposed for the first time; (4) Generate visualized 3D products, supporting minute-level dynamic display and risk assessment.

[0079] The following test examples further demonstrate and illustrate the implementation of the above solutions in the embodiments of the present invention: I. Example 1 (Fine X-band Reproduction) Using an X-band radar with a resolution of 75m, a volume scan period of 3 minutes, and an elevation sequence of 0.5°–3°, the system was processed according to steps A–E to extract the Zo = 12dBZ isosurface. H_top≈1.8km and V_f≈1.6×10⁻⁶ km were calculated. 7 m³, the smoke and dust center moved eastward by the wind for 1.2 km, with an advection velocity of approximately 6.7 m / s. The treatment results are as follows: Figure 2 As shown.

[0080] II. Example 2 (S / C / X three-band coordination) Using the default configuration (S / C 250m / 6min, X 75m / 3min), band weights w_X=0.5, w_S=w_C=0.25. Output results show that the S / C stabilization constraint improves long-range consistency, with H_top error less than 5% and V_f deviation less than 10%. The processing results are as follows: Figure 3 As shown.

[0081] III. Example 3 (Multi-station collaboration and dynamic analysis) The two stations share overlapping data, and after P_obs fusion, H_top(t), Amax(t), and Zmax(t) are dynamically tracked, such as... Figures 4-6 The diagram shows a three-stage dynamic curve.

[0082] Figure 4 In the fire echo, Zmax represents the strongest scattering intensity of the fire echo, which is a comprehensive reflection of combustion heat and turbulent particle density. Initially, Zmax = 26 dBZ (14:21:05), and as combustion intensifies, it reaches a peak of 27.5 dBZ at 14:40:46. It then gradually decreases to 24.05 dBZ (14:46:24), 20.5 dBZ (15:06:05), and finally drops to 11.5 dBZ (15:48:15).

[0083] The peak value of the Zmax curve occurs significantly earlier than the area peak value by approximately 25 minutes, indicating that the energy release at the fire site exhibits a "precursor of intensity." The strong scattering signal at this stage originates from the concentration zone of high-temperature smoke entrainment and turbulent particles, suggesting that the fire rapidly forms a high-temperature, strong echo core. The intensity decays over time, indicating that the heat release process is inhibited by fuel consumption and gas layer stability.

[0084] Figure 5 In the figure, A_max represents the maximum horizontal coverage of fire smoke within each sweep cycle. As shown in the figure above, A_max is only 0.28 km in the initial stage (14:21:05).2 It then continued to increase, reaching 1.43 km at 14:40:46. 2 It reached 2.10 km at 14:46:24. 2 It reached its peak speed of 4.18 km at 15:06:05. 2 It then gradually decreased to 0.72 km. 2 (15:48:15).

[0085] The A_max curve exhibits a typical "single-peak structure," with the rising segment coinciding with the intensification of the fire, and the peak corresponding to the most significant stage of horizontal smoke expansion. The rapid increase in area indicates that the fire enters a stable combustion and strong entrainment phase in the middle stage, with smoke plume diffusion dominating the near-surface fire area. The subsequent decrease in area after the peak indicates a weakening of the fire source energy, reduced thermal convection, and the entry of smoke into a settling and dilution process.

[0086] Figure 6 In the figure, H_top represents the highest detection height of the echo signal (≥5 dBZ) during volume scan, which is an important indicator reflecting the vertical transport capacity of the smoke column. Initially, H_top ≈ 1.1 km, rapidly rising to 1.6 km (14:40:46) as the fire intensified, reaching a peak of 2.00 km at 14:46:24, then slowly decreasing to 1.70 km (15:06:05), and falling back to 1.2 km at 15:48:15.

[0087] The variation in H_top indicates that the fire smoke column undergoes a significant vertical upward surge during the intense combustion phase. The peak height occurs between the intensity peak and the area peak, reflecting the lag in heat convection and the timeliness of vertical transport. The peak value of 2 km corresponds to the vicinity of the upper boundary layer, indicating that the rising heat flow in the fire can briefly break through the mixing layer, but its duration is limited by cold advection and turbulent dissipation.

[0088] Based on the same inventive concept, this invention also provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the program includes program instructions, and the processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, used to implement one or more instructions, specifically for loading and executing one or more instructions stored in a computer storage medium to implement the above-described method.

[0089] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, performs the above-described method. This storage medium can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, 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 device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0090] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0091] The foregoing has shown and described the basic principles, main features, and advantages of this disclosure. Those skilled in the art should understand that this disclosure is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this disclosure. Various changes and modifications can be made to this disclosure without departing from its spirit and scope, and all such changes and modifications fall within the scope of this disclosure as claimed.

[0092] This invention is not limited to the preferred embodiment described above. Anyone inspired by this invention can derive other various forms of methods, systems, and storage media for three-dimensional reconstruction and dynamic analysis of forest fire smoke based on multi-elevation WER slices. All equivalent variations and modifications made within the scope of the claims of this invention shall fall within the scope of this invention.

Claims

1. A forest fire smoke three-dimensional reproduction and dynamics analysis method based on multi-elevation WER slice, characterized in that, Without relying on vertical profile data, comprising: Obtaining multi-elevation reflectivity slice data collected by a multi-band weather radar, performing clutter suppression, biological echo suppression, X-band attenuation elimination processing, and completing cross-band intensity normalization; Projecting the pre-processed multi-elevation reflectivity slice data to a unified three-dimensional voxel grid according to the radar observation geometric relationship, and then weighting and fusing the projected data based on preset band weight and elevation weight to generate a smoke three-dimensional reflectivity field; Based on a preset reflectivity threshold, extracting a smoke isosurface from the three-dimensional reflectivity field, and calculating the smoke plume top height, maximum reflectivity height, and maximum cross-sectional area of the smoke; Outputting the smoke three-dimensional structure reproduction result and the kinetic parameter time sequence including the smoke plume top height, the maximum reflectivity height, and the maximum cross-sectional area.

2. The method according to claim 1, wherein the method is characterized by: The multi-band weather radar includes X-band, C-band, and S-band, and the multi-elevation reflectivity slice data collected is WER slice data generated by each band radar volume scan, and the corresponding dual-polarization parameters of each band are synchronously obtained; each band volume scan period is adapted to the data resolution requirement, time synchronization processing of multi-band data is performed through establishing a unified time stack, time interpolation or advection extrapolation, and dynamic monitoring is realized. 3.The forest fire smoke three-dimensional reconstruction and dynamics analysis method based on multi-elevation WER slice according to claim 1, characterized in that: The clutter suppression is realized through ground object echo recognition, multi-time sequence data difference analysis, and spectral width feature filtering; the X-band attenuation elimination is combined with short-range reference data and path integral correction, and the stability of the dual-polarization parameters is used to avoid over-correction; The cross-band intensity normalization takes the band data with higher stability as a reference to adjust the intensity bias of other band data, so as to eliminate the system error between bands.

4. The method according to claim 1, wherein the method is characterized by: The band weight is allocated according to data resolution and reliability, and the weight of high spatial resolution band data is higher; the elevation weight is adapted to the observation scene, and higher weight is given to the reflectivity slice data in the low-elevation range, so as to improve the three-dimensional structure restoration precision of low-altitude targets.

5. The method of claim 1, wherein the method is characterized by: The resolution of the unified three-dimensional voxel grid is dynamically adjusted according to the type of fused data, so as to adapt to the precision requirement of corresponding band data.

6. The method of claim 1, wherein the method is characterized by: The preset reflectivity threshold is dynamically adjusted according to the observation environment, or a fixed threshold range adapted to the monitoring scene is used; the calculated smoke parameters further include smoke total volume and advection velocity, wherein the advection velocity is calculated based on the horizontal displacement of the smoke in a continuous observation period and the observation time interval, and the smoke total volume is calculated based on the voxel space range surrounded by the smoke isosurface.

7. The method of claim 1, wherein the method is characterized by: The kinetic parameter time sequence is formed by parameter extraction in a continuous observation period, the different stages of target evolution are divided by combining the change trend and peak value correlation of each parameter; the output smoke three-dimensional structure reproduction result can be superimposed with terrain digital elevation model data, and the output format includes raster format, vector format, or dynamic evolution product formed by time sequence correlation.

8. The method of claim 1, wherein the method is a multi-elevation WER slice-based forest fire smoke plume 3D reconstruction and dynamics analysis method. Multi-radar cooperative data collection is adopted, the reflectivity slice data of the overlapping area of each radar observation is shared, and the spatial coverage range of the three-dimensional reflectivity field is expanded.

9. A multi-elevation WER slice based forest fire smoke plume 3D reconstruction and dynamics analysis system, characterized in that, Comprising: A data input module configured to obtain multi-elevation reflectivity slice data collected by a multi-band weather radar; a quality control module configured to perform clutter suppression, biological echo suppression, X-band attenuation cancellation processing on the multi-elevation reflectivity slice data, and complete cross-band intensity normalization; a voxel reconstruction and weight fusion module configured to project the preprocessed multi-elevation reflectivity slice data to a unified three-dimensional voxel grid according to radar observation geometry, and then perform weighted fusion on the projected data based on preset band weights and elevation weights to generate a smoke three-dimensional reflectivity field; a dynamics analysis module configured to extract a smoke isosurface from the three-dimensional reflectivity field based on a preset reflectivity threshold, and calculate a smoke plume top height, a maximum reflectivity height, and a maximum cross-sectional area of the smoke; a result output module configured to output a smoke three-dimensional structure reproduction result and a dynamics parameter time sequence including the smoke plume top height, the maximum reflectivity height, and the maximum cross-sectional area.

10. A non-transitory computer-readable storage medium, comprising: The storage medium has a computer program stored thereon, and the computer program is executed by the processor to implement the method in any of claims 1-8.

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