Night residual layer height range detection method and system thereof

Abstract

The application discloses a night residual layer height range detection method and system, and relates to the technical field of meteorological data analysis.The method comprises the following steps: acquiring a vertical profile of a particle extinction coefficient, wherein the vertical profile is profile data of the distribution of the atmospheric particle extinction coefficient with height; preprocessing the vertical profile to suppress noise and retain gradient change characteristics; performing continuous wavelet covariance transformation on the preprocessed vertical profile according to a wavelet function, generating a two-dimensional wavelet coefficient matrix, and calculating a wavelet energy spectrum diagram; extracting an energy spectrum ridge line representing the boundary of the night residual layer according to the wavelet energy spectrum diagram, and inversely calculating a lower boundary height and an upper boundary height according to the energy spectrum ridge line; and confirming the height range of the night residual layer according to the lower boundary height and the upper boundary height, and outputting a visual detection result according to the height range. The application can improve the accuracy and reliability of the night residual layer height range detection process.

Description

Technical Field

[0001] This application relates to the technical field of meteorological data analysis, and in particular to a method and system for detecting the range of nighttime residual layer height. Background Technology

[0002] The nighttime residual layer refers to the atmospheric stratification located above the stable nighttime boundary layer, evolving from the previous day's daytime convective boundary layer and retaining its largely homogeneous mixing characteristics. This layer stores most of the atmospheric pollutants that were thoroughly mixed in the convective boundary layer during the previous day, forming a vertical "pollution reservoir." As the convective boundary layer redevelops the following day, the residual layer gradually disintegrates, and the stored pollutants are remixed, affecting surface air quality. Therefore, accurately determining the height range of the nighttime residual layer is a crucial prerequisite for quantifying its pollutant storage capacity and assessing the evolution of air quality the following day. However, accurately determining the height range of the nighttime residual layer still faces challenges: because the residual layer itself retains homogeneous mixing characteristics, the vertical variation in particulate matter concentration within it is gradual, resulting in a transition between it and the upper free atmosphere and the lower stable boundary layer being a gradual process with weak gradient changes and inflection point characteristics. This presents a fundamental difficulty for boundary identification.

[0003] In related technologies, gradient methods or threshold methods are mainly used to automatically identify residual layer boundaries. However, traditional gradient methods rely on finding obvious gradient extrema, which are difficult to effectively capture gentle gradient changes at residual layer boundaries, often leading to an underestimation of residual layer thickness and pollutant storage. Both gradient thresholds and absolute thresholds for extinction coefficients are highly dependent on human presets. These parameters lack physical universality and have poor adaptability to different regions, seasons, or pollution conditions, resulting in low comparability of judgment results. Furthermore, in the presence of low-altitude thin clouds, data noise, or complex multi-layered structures, gradient methods or threshold methods based on fixed rules are prone to misjudgment or omission, resulting in insufficient algorithm stability.

[0004] Therefore, there is an urgent need to develop a nighttime residual layer height range detection method that can adaptively extract weak gradient abrupt change features in vertical profiles, has strong anti-interference capabilities, and provides objective and stable results. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for detecting the height range of residual layers at night, which can improve the accuracy and reliability of the nighttime residual layer height range detection process.

[0006] To achieve the above objectives, this application provides the following solution:

[0007] In a first aspect, this application provides a method for detecting the height range of the nighttime residual layer. The method includes: acquiring a vertical profile of the extinction coefficient of particles, wherein the vertical profile is profile data of the distribution of the extinction coefficient of atmospheric particulate matter with height; preprocessing the vertical profile to suppress noise and retain gradient change characteristics; selecting a wavelet function, performing a continuous wavelet covariance transform on the preprocessed vertical profile according to the wavelet function to generate a two-dimensional wavelet coefficient matrix, and calculating a wavelet energy spectrum map through the two-dimensional wavelet coefficient matrix; extracting energy spectrum ridges characterizing the boundary of the nighttime residual layer according to the wavelet energy spectrum map, and inverting the lower boundary height and upper boundary height according to the energy spectrum ridges; confirming the height range of the nighttime residual layer according to the lower boundary height and the upper boundary height, and outputting a visualized detection result according to the height range.

[0008] For example, when preprocessing the vertical profile, a moving average filtering algorithm is used to calculate the neighborhood average of the data points of the vertical profile with a specified window width. While suppressing random noise and impulse interference, the gradient change characteristics of the vertical profile at the boundary of the nighttime residual layer are preserved; wherein the gradient change characteristics are physical signals of the change of particle extinction coefficient with height.

[0009] For example, the calculation method for the specified window width in the moving average filtering algorithm is as follows:

[0010]

[0011] In the formula, The physical smoothness scale ranges from 30 meters to 150 meters. This indicates the vertical resolution of the vertical profile. This specifies the width of the window.

[0012] For example, the selected wavelet function is the Mexican hat wavelet function, and its expression is as follows:

[0013]

[0014] In the formula, The independent variable is the position relative to the center within the wavelet function window. The wavelet function is sensitive to the inflection points of the input signal and is used to capture the gradient abrupt change features that characterize the layering changes in the vertical profile.

[0015] For example, the step of performing continuous wavelet covariance transform to generate a two-dimensional wavelet coefficient matrix and calculate the wavelet energy spectrum specifically includes: based on the typical physical thickness of the nighttime residual layer, a preset range of values ​​for the scale parameter is established, and a scale sequence containing multiple discrete scale values ​​is generated within the range of values; based on each scale parameter in the scale sequence and each translation parameter covered by the vertical profile, wavelet coefficients are calculated, and the calculation method is as follows:

[0016]

[0017] in, The scale parameter characterizes the thickness of the analyzed feature, with values ​​ranging from 300 meters to 1500 meters. The translation parameter represents the height position of the window center in the wavelet function. This represents the preprocessed vertical profile. Let be the integral variable, representing height. Represents the wavelet function; and The minimum and maximum effective height values ​​of the profile data are represented; all scale parameters and translation parameters are traversed to calculate the corresponding wavelet coefficients and arrange them to obtain a two-dimensional wavelet coefficient matrix; the wavelet energy spectrum is obtained by squaring each element in the two-dimensional wavelet coefficient matrix.

[0018] For example, based on the wavelet energy spectrum, extracting the energy spectrum ridges that characterize the boundary of the nighttime residual layer specifically includes: for each scale parameter, finding the local maxima of the wavelet energy spectrum along the height direction as candidate boundary points at the corresponding scale; connecting the candidate boundary points between different scales according to the principle of spatial proximity to form a curve that changes continuously in the scale-height space as candidate energy spectrum ridges; and selecting the two most continuous energy spectrum ridges extending along a specific height from all candidate energy spectrum ridges, which are respectively identified as feature ridges characterizing the gradient abrupt change at the lower and upper boundaries of the nighttime residual layer.

[0019] For example, the lower boundary height and upper boundary height are obtained by inverting the energy spectrum ridge line, specifically including: traversing each point on the energy spectrum ridge line, performing a scale-weighted average based on the position on the height coordinate of the energy spectrum ridge line, and calculating the weighted average height of the energy spectrum ridge line; taking the weighted average height of the two energy spectrum ridge lines as the lower boundary height and upper boundary height of the nighttime residual layer, respectively, and the height range of the nighttime residual layer is the difference between the upper boundary height and the lower boundary height.

[0020] For example, the visualized detection results include a wavelet energy spectrum and a composite distribution map labeled with the lower and upper boundary heights.

[0021] For example, the vertical profile of the particle extinction coefficient is obtained by means of: real-time observation by lidar, querying from an atmospheric environment monitoring database, or downloading from a public scientific research data platform; the time resolution of the vertical profile of the particle extinction coefficient is no higher than 15 minutes, the vertical resolution is no higher than 30 meters, and the effective detection height is no less than 3 kilometers.

[0022] Secondly, this application provides a system for detecting the height range of a nighttime residual layer, comprising: a data acquisition module for acquiring a vertical profile of particle extinction coefficients; a preprocessing module for preprocessing the vertical profile to suppress noise and retain gradient change characteristics; a wavelet transform module for performing continuous wavelet covariance transform on the preprocessed vertical profile according to a selected wavelet function to generate a two-dimensional wavelet coefficient matrix, and calculating a wavelet energy spectrum using the two-dimensional wavelet coefficient matrix; a ridge extraction and inversion module for extracting energy spectrum ridges characterizing the boundary of the nighttime residual layer according to the wavelet energy spectrum, and inverting the lower boundary height and upper boundary height according to the energy spectrum ridges; and a result calculation and output module for confirming the height range of the nighttime residual layer according to the lower boundary height and the upper boundary height, and outputting a visualized detection result according to the height range.

[0023] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0024] This application provides a method and system for detecting the height range of the nighttime residual layer. Through data preprocessing, observation noise and instantaneous spike interference are effectively suppressed. Wavelet covariance transform is introduced, and through multi-scale analysis, weak and gentle gradient changes in the one-dimensional vertical profile are transformed and enhanced into obvious extreme value features (ridges) in the two-dimensional energy spectrum space. This overcomes the deficiency of insensitivity to weak signals at the residual layer boundary, improving the accuracy and reliability of boundary identification. Core boundary determination relies on the global and continuous characteristics of the energy spectrum ridges in the wavelet energy spectrum map, rather than simple comparison of local thresholds, reducing reliance on manually preset parameters and significantly enhancing the objectivity, stability, and repeatability of the results. It can adaptively capture the dynamic changes of the residual layer under different meteorological conditions, exhibiting better spatiotemporal universality. The final output, a composite map labeled with upper and lower boundary heights, can intuitively display the location and range of the nighttime residual layer, facilitating automated algorithm processing and providing clear and intuitive evidence for manual verification and in-depth analysis. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart of a nighttime residual layer height range detection method in an embodiment of this application.

[0027] Figure 2 This is a schematic diagram of the vertical profile of the particle extinction coefficient based on lidar observation in an embodiment of this application.

[0028] Figure 3 This is a wavelet energy spectrum diagram from an embodiment of this application.

[0029] Figure 4 This is the final output distribution composite map in the embodiments of this application. Detailed Implementation

[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0031] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0032] like Figure 1 As shown in the figure, this application provides a method for detecting the height range of residual layers at night, including the following steps:

[0033] S110. Obtain the vertical profile of the extinction coefficient of particles, where the vertical profile is the profile data of the distribution of the extinction coefficient of atmospheric particulate matter with height.

[0034] S120. Preprocess the vertical profile to suppress noise and preserve gradient change characteristics.

[0035] S130. Select a wavelet function, perform continuous wavelet covariance transformation on the preprocessed vertical profile according to the wavelet function, generate a two-dimensional wavelet coefficient matrix, and calculate the wavelet energy spectrum through the two-dimensional wavelet coefficient matrix.

[0036] S140. Based on the wavelet energy spectrum, extract the energy spectrum ridge line that represents the boundary of the nighttime residual layer, and obtain the lower boundary height and upper boundary height based on the energy spectrum ridge line inversion.

[0037] S150. Determine the height range of the residual layer at night based on the lower and upper boundary heights, and output the visual detection results based on the height range.

[0038] The nighttime residual layer height range detection method provided in this application effectively suppresses observation noise and instantaneous spike interference through data preprocessing steps. It introduces wavelet covariance transform and, through multi-scale analysis, converts and enhances the weak and gentle gradient change information in the one-dimensional vertical profile into obvious extreme value features (ridges) in the two-dimensional energy spectrum space. This overcomes the deficiency of insensitivity to weak signals at the residual layer boundary, improving the accuracy and reliability of boundary identification. Core boundary determination relies on the global and continuous characteristics of the energy spectrum ridges in the wavelet energy spectrum map, rather than a simple comparison of local thresholds, reducing reliance on manually preset parameters and significantly enhancing the objectivity, stability, and repeatability of the results. It can adaptively capture the dynamic change characteristics of the residual layer under different meteorological conditions, exhibiting better spatiotemporal universality. The final output composite map, labeled with upper and lower boundary heights, intuitively displays the location and range of the nighttime residual layer, facilitating automated algorithm processing and providing clear and intuitive evidence for manual verification and in-depth analysis.

[0039] For example, the vertical profile represents the cross-sectional data of the atmospheric particulate extinction coefficient distribution with height. The vertical profile of the particulate extinction coefficient is obtained through the following methods: real-time observation using lidar, querying from an atmospheric environmental monitoring database, or downloading from a publicly available scientific research data platform. The temporal resolution of the vertical profile of the particulate extinction coefficient is no higher than 15 minutes, the vertical resolution is no higher than 30 meters, and the effective detection height is no less than 3 kilometers. Figure 2 The figure shown is a schematic diagram of the vertical profile of particle extinction coefficient based on lidar observations. The horizontal axis represents time, and the vertical axis represents altitude. The horizontal axis in the figure is local time (e.g., UTC+8 time zone), and the vertical axis is altitude (unit: meters). Figure 2 As can be seen, the effective detection altitude ranges from the ground to approximately 3 kilometers. From night to dawn, there exists a stratification with a relatively uniform extinction coefficient and a weak vertical gradient, which is the candidate region for the nighttime residual layer to be detected, demonstrating the spatiotemporal distribution characteristics of the original data.

[0040] To ensure the effectiveness and stability of wavelet covariance transform analysis during the preprocessing of the vertical profile, this embodiment employs a moving average filtering algorithm: neighborhood averaging is performed on the data points of the vertical profile with a specified window width. This suppresses random noise and impulse interference while preserving the gradient change characteristics of the vertical profile at the boundary of the nighttime residual layer. The gradient change characteristics are the physical signal of how the particle extinction coefficient changes with the rate of change of height. The calculation method for the specified window width in the moving average filtering algorithm is as follows:

[0041]

[0042] In the formula, The physical smoothness scale ranges from 30 meters to 150 meters. This indicates the vertical resolution of the vertical profile. This indicates the specified window width. In some embodiments, the vertical resolution of the vertical outline is 7.5 meters. The physical smoothness value is 30 meters, corresponding to a specified window width. There are 4 data points.

[0043] In this embodiment, the wavelet function selected is the Mexican hat wavelet function, whose expression is as follows:

[0044]

[0045] in, The independent variable represents the position relative to the center within the wavelet function window. In this embodiment, the wavelet function, in its second derivative form, is sensitive to the inflection point of the input signal and is used to capture the gradient abrupt change characteristics that characterize the layering changes in the vertical profile.

[0046] After preprocessing the vertical profile, a continuous wavelet covariance transform is performed using a wavelet function to generate a two-dimensional wavelet coefficient matrix and calculate the wavelet energy spectrum. The specific process is as follows:

[0047] Based on the typical physical thickness of the nighttime residual layer, a preset range of scale parameters is established, and a scale sequence containing multiple discrete scale values ​​is generated within this range. Wavelet coefficients are calculated based on each scale parameter in the scale sequence and each translation parameter covered by the vertical profile, as follows:

[0048]

[0049] in, The scale parameter characterizes the thickness of the analyzed feature, with values ​​ranging from 300 meters to 1500 meters. The translation parameter represents the height position of the window center in the wavelet function. This represents the preprocessed vertical profile. Let be the integral variable, representing height. Represents the wavelet function; and This represents the minimum and maximum effective height values ​​of the profile data.

[0050] By iterating through all scale and translation parameters, the corresponding wavelet coefficients are calculated and arranged to obtain a two-dimensional wavelet coefficient matrix W(a, b). Squaring each element in the two-dimensional wavelet coefficient matrix yields the wavelet energy spectrum, i.e., E(a, b) = [W(a, b)]. 2 , where is the wavelet energy spectrum of E(a, b). Figure 3 The image shown is a wavelet energy spectrum diagram from an embodiment of this application. The horizontal axis represents height b (in meters), and the vertical axis represents scale (in meters). It can be seen from the image that near heights of approximately 400 meters and 1400 meters, two bright yellow bands with significantly enhanced energy and nearly vertical extension appear; these are the energy spectrum ridges. The scales (vertical axis) corresponding to these two energy spectrum ridges indicate that this gradient abrupt change feature has a multi-scale manifestation. The energy values ​​in the energy spectrum ridge region are significantly higher than the background noise, clearly separating and enhancing the layer boundary signal from the data.

[0051] After obtaining the wavelet energy spectrum, the energy ridge lines representing the boundary of the nighttime residual layer are extracted based on the wavelet energy spectrum. The specific process is as follows:

[0052] For each scale parameter, local maxima of the wavelet energy spectrum are searched along the height direction, serving as candidate boundary points for the corresponding scale. These candidate boundary points are connected according to spatial proximity principles across different scales, forming a curve that continuously varies in the scale-height space, serving as candidate energy spectrum ridges. From all candidate energy spectrum ridges, the two most continuous energy spectrum ridges extending along a specific height are selected and identified as characteristic ridges representing the abrupt changes in the lower and upper boundary gradients of the nighttime residual layer, respectively.

[0053] By using wavelet energy spectrum maps, local maxima are searched scale by scale, and the maxima points are tracked across scales to obtain candidate ridges. These candidate ridges are then filtered to obtain the strongest ridge. The strongest ridge represents the candidate ridge with the highest energy and best continuity. The positions of these two energy spectrum ridges on the height coordinate are inverted to the actual altitude using a scale-weighted average, and these are used as the lower boundary height (i.e., nighttime boundary layer height, NBLH) and upper boundary height (i.e., residual layer height, RLH) of the residual layer, respectively. The specific process is as follows:

[0054] For each point on the energy spectrum ridge, a scale-weighted average is calculated based on the point's position on the height coordinate within the ridge, thus determining the weighted average height of the energy spectrum ridge. For example, the height coordinate of each point is assigned the energy value E(a) of its corresponding point on the wavelet energy spectrum. i ,b i The weighted average is calculated as follows:

[0055]

[0056] in, The translation parameter represents the height position of the window center in the wavelet function, and i represents the number of points traversed along the energy spectrum ridge. and Let be the scale value and height value corresponding to the i-th point, respectively. The weighted average height is represented by the weighted average height of the two energy spectrum ridges. These heights are used as the lower and upper boundaries of the nighttime residual layer, respectively. The height range of the nighttime residual layer is the difference between the upper and lower boundary heights, i.e., RLH - NBLH.

[0057] After calculating the height range of the nighttime residual layer, a visual detection result is output based on this height range. The visual result includes a wavelet energy spectrum and a composite distribution map labeled with the lower and upper boundary heights. For example... Figure 4 The figure shown is the final output distribution composite map in this embodiment. It typically uses the vertical profile of the particle extinction coefficient as a background, and superimposed boundary lines at corresponding heights for visual visualization and manual verification. The horizontal and vertical axes represent time and altitude, respectively. The identified NBLH and RLH are 437±87m and 1418±210m, respectively. The distribution composite map integrates the original profile morphology and transform domain features, achieving visual verification of the detection process and results. Furthermore, the mixing layer height (MLH) is also marked in the figure. This height is obtained by analyzing the daytime observation data using the same wavelet covariance transform method. The method provided by this invention is not only applicable to the nighttime residual layer but can also be used to identify atmospheric stratification boundaries such as the daytime mixing layer top, demonstrating the universality of this method.

[0058] This application also provides a nighttime residual layer height range detection system, which includes: a data acquisition module, a preprocessing module, a wavelet transform module, a ridge extraction and inversion module, and a result calculation and output module.

[0059] The system comprises several modules: a data acquisition module to obtain the vertical profile of particle extinction coefficients; a preprocessing module to preprocess the vertical profiles to suppress noise and preserve gradient variation characteristics; a wavelet transform module to perform continuous wavelet covariance transform on the preprocessed vertical profiles using a selected wavelet function to generate a two-dimensional wavelet coefficient matrix, and then calculate the wavelet energy spectrum using this matrix; a ridge extraction and inversion module to extract the energy spectrum ridges representing the boundaries of the nighttime residual layer based on the wavelet energy spectrum, and invert the lower and upper boundary heights based on these ridges; and a results calculation and output module to determine the height range of the nighttime residual layer based on the lower and upper boundary heights, and output visualized detection results based on this height range.

[0060] The nighttime residual layer height range detection method and system provided in this application effectively suppress observation noise and instantaneous spike interference through data preprocessing steps. By introducing wavelet covariance transform and multi-scale analysis, it transforms and enhances the weak and gentle gradient change information in the one-dimensional vertical profile into obvious extreme value features (ridges) in the two-dimensional energy spectrum space, overcoming the deficiency of insensitivity to weak signals at residual layer boundaries and improving the accuracy and reliability of boundary identification. Core boundary determination relies on the global and continuous characteristics of the energy spectrum ridges in the wavelet energy spectrum map, rather than a simple comparison of local thresholds, reducing reliance on manually preset parameters and significantly enhancing the objectivity, stability, and repeatability of the results. It can adaptively capture the dynamic change characteristics of the residual layer under different meteorological conditions, exhibiting better spatiotemporal universality. The final output composite map, labeled with upper and lower boundary heights, can intuitively display the location and range of the nighttime residual layer, facilitating automated algorithm processing and providing clear and intuitive evidence for manual verification and in-depth analysis.

[0061] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0062] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0063] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0064] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0065] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0066] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0067] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0068] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method of detecting a range of night residual layer height, characterized by, The method comprises the following steps: obtaining a vertical profile of the extinction coefficient of particles, wherein the vertical profile is profile data of the distribution of the extinction coefficient of atmospheric particles with height; using a sliding average filtering algorithm to preprocess the vertical profile, and performing neighborhood average calculation on the data points of the vertical profile with a specified window width, so as to suppress random noise and impulse interference while retaining the gradient change characteristics of the vertical profile at the boundary of the residual layer at night; wherein the gradient change characteristics are physical signals of the change in the rate of change of the extinction coefficient of particles with height; selecting a wavelet function, and performing continuous wavelet covariance transformation on the preprocessed vertical profile according to the wavelet function to generate a two-dimensional wavelet coefficient matrix, and calculating a wavelet energy spectrum diagram from the two-dimensional wavelet coefficient matrix; extracting an energy spectrum ridge line representing the boundary of the residual layer at night from the wavelet energy spectrum diagram, specifically including: for each scale parameter, finding the local maximum points of the wavelet energy spectrum along the height direction as the candidate boundary points at the corresponding scale; connecting the candidate boundary points according to the spatial proximity principle between different scales to form a continuously changing curve in the scale-height space as a candidate energy spectrum ridge line; from all candidate energy spectrum ridge lines, selecting the two most continuous energy spectrum ridge lines extending along a specific height as the characteristic ridge lines representing the gradient mutation of the lower boundary and the upper boundary of the residual layer at night respectively; and inversely calculating the lower boundary height and the upper boundary height according to the energy spectrum ridge line; confirming the height range of the residual layer at night according to the lower boundary height and the upper boundary height, and outputting a visual detection result according to the height range.

2. The night residual layer height range detection method according to claim 1, characterized by, The calculation method of the specified window width in the sliding average filtering algorithm is as follows: wherein is a physical smoothing scale, taking values in the range 30 meters to 150 meters, denotes the vertical resolution of the vertical profile, denotes the specified window width.

3. The method of claim 1, wherein The selected wavelet function is a Mexican hat wavelet function, and the expression is as follows: wherein is an independent variable representing a position relative to the center of a wavelet function window having a sensitive response to a kink of an input signal for capturing a gradient jump feature characteristic of a layer junction change in the vertical profile.

4. The method of claim 3, wherein The continuous wavelet covariance transformation, the generation of the two-dimensional wavelet coefficient matrix and the calculation of the wavelet energy spectrum diagram specifically include: presetting the value range of the scale parameter according to the typical physical thickness of the residual layer at night, and generating a scale sequence containing multiple discrete scale values within the value range; based on each scale parameter in the scale sequence and each translation parameter covered by the vertical profile, calculating the wavelet coefficient in the following manner: wherein, is a scale parameter, representing the thickness of the analyzed feature, and taking values in the range 300-1500 meters, is a translation parameter, representing the height position of the window center in the wavelet function, represents the vertical profile after pre-processing, is an integration variable, representing the height, represents the wavelet function; and represent the lowest and highest significant height values of the profile data; traversing all scale parameters and translation parameters to calculate the corresponding wavelet coefficient and arrange the wavelet coefficient to obtain a two-dimensional wavelet coefficient matrix; squaring each element in the two-dimensional wavelet coefficient matrix to obtain the wavelet energy spectrum diagram.

5. The method of claim 4, wherein The inverse calculation of the lower boundary height and the upper boundary height from the energy spectrum ridge line specifically includes: traversing each point on the energy spectrum ridge line, and performing scale weighted average according to the position of the height coordinate in the energy spectrum ridge line to calculate the weighted average height of the energy spectrum ridge line; taking the weighted average heights of the two energy spectrum ridge lines as the lower boundary height and the upper boundary height of the residual layer at night respectively, and the height range of the residual layer at night is the difference between the upper boundary height and the lower boundary height.

6. The night residual layer height range detection method according to any one of claims 1 to 5, characterized by, The visual detection result includes a wavelet energy spectrum diagram and a distribution synthesis diagram with the lower boundary height and the upper boundary height labeled.

7. The method of detecting the range of residual layer height at night according to any one of claims 1 to 5, characterized by, The vertical profile of the particle extinction coefficient is obtained by at least one of the following ways: real-time observation by a laser radar, query from an atmospheric environment monitoring database, and download from a public scientific research data platform; the time resolution of the vertical profile of the particle extinction coefficient is not higher than 15 minutes, the vertical resolution is not higher than 30 meters, and the effective detection height is not lower than 3 kilometers.

8. A system for detecting a range of heights of a night residue layer, characterized by The night residual layer height range detection system comprises: a data acquisition module configured to acquire a vertical profile of a particle extinction coefficient; a preprocessing module configured to preprocess the vertical profile by using a sliding average filtering algorithm, to perform neighborhood average calculation on data points of the vertical profile with a specified window width, to suppress random noise and impulse interference, and to retain gradient change characteristics of the vertical profile at a boundary of a night residual layer; wherein the gradient change characteristics are physical signals of a change in a rate of change of the particle extinction coefficient with height; a wavelet transform module configured to perform continuous wavelet covariance transformation on the preprocessed vertical profile according to a selected wavelet function, to generate a two-dimensional wavelet coefficient matrix, and to calculate a wavelet energy spectrum diagram from the two-dimensional wavelet coefficient matrix; a ridge line extraction and inversion module configured to extract an energy spectrum ridge line representing the boundary of the night residual layer according to the wavelet energy spectrum diagram, and specifically comprising: finding, for each scale parameter, a local maximum value point of the wavelet energy spectrum along the height direction as a candidate boundary point at the corresponding scale; connecting the candidate boundary points according to a spatial proximity principle between different scales to form a continuously changing curve in the scale-height space as a candidate energy spectrum ridge line; selecting two most continuous energy spectrum ridge lines extending along a specific height from all candidate energy spectrum ridge lines as characteristic ridge lines representing gradient mutations of lower and upper boundaries of the night residual layer, respectively; and inversing the lower boundary height and the upper boundary height according to the energy spectrum ridge lines; a result calculation and output module configured to confirm the height range of the night residual layer according to the lower boundary height and the upper boundary height, and to output a visual detection result according to the height range.

Images