Laser radar melt layer height and thickness detection method based on extensible two-dimensional matching

Abstract

The application discloses a laser radar melting layer height and thickness detection method based on extensible two-dimensional matching, which comprises the following steps: acquiring a multi-channel original observation signal of a laser radar, and cutting each channel signal into a preset physical reasonable value interval to obtain a preprocessed signal; constructing data normalization and a two-dimensional matrix; constructing a physical model driven multi-thickness template library; calculating the similarity value of the two-dimensional data matrix and each thickness template at different height positions by using weighted normalized cross-correlation; at the same time, based on a preset physical standard threshold, each candidate position is jointly verified by multiple channels to screen out positions meeting the physical characteristics of the melting layer; and optimal parameter determination is performed; and the application can accurately determine the height and thickness parameters of the melting layer at the same time.

Description

Technical Field

[0001] This invention relates to the field of atmospheric detection technology, and more specifically to a lidar method for detecting the height and thickness of the melting layer based on scalable two-dimensional matching. Background Technology

[0002] Currently, there is a significant imbalance in the development of melting layer detection technologies across different radar systems: in the field of microwave weather radar, the identification technology for melting layers (bright bands) has matured and been operationalized. Microwave radar utilizes the dramatic changes in the dielectric constant of ice-water mixtures to easily observe the significant enhancement of reflectivity factors (i.e., the "bright band" phenomenon), and related algorithms have become standard features in standard meteorological products.

[0003] However, in the field of lidar detection, specialized detection technologies for melt layers are almost nonexistent. Although lidar has superior temporal resolution (second-level) and spatial resolution (meter-level), theoretically capable of providing more detailed information on the microphysical structure of melt layers, its applications have long focused on the detection of aerosols, cloud height, and boundary layer height. Due to the fundamental differences in the scattering mechanisms between optical and microwave bands (microwaves primarily undergo Rayleigh / Mille scattering and are sensitive to dielectric constants; while lasers are primarily sensitive to particle shape, orientation, and surface roughness), mature "bright band" identification algorithms from microwave lidar cannot be directly applied to lidar signal processing.

[0004] Currently, limited exploratory research on lidar melting layer detection still faces the following key technical bottlenecks when dealing with complex meteorological conditions: 1. Limitations of the "one-dimensional" threshold method: Existing attempts mostly adopt a simple "single-parameter threshold" approach (such as only judging whether the depolarization ratio is greater than a certain value). This hard segmentation method based on one-dimensional signals completely ignores the overall morphological characteristics of the signal evolution with altitude within the melting layer. Under conditions of low signal-to-noise ratio or the presence of multi-layer clouds or aerosol interference, the false judgment rate is extremely high, failing to meet operational requirements. 2. "Isolated" processing of multi-channel information: For example, polarization lidar can acquire orthogonal information in multiple dimensions such as horizontal, vertical, and depolarization ratio, but existing technologies lack effective fusion mechanisms, often analyzing each channel in isolation. This strategy severs the intrinsic correlation between the signals of each channel during the physical phase transition process, resulting in low detection accuracy. 3. Lack of direct inversion capability for the "thickness" parameter: The "bright band thickness" parameter, which is of great interest in microwave lidar, is rarely involved in existing lidar algorithms. Most existing methods can only provide a rough height of the top or bottom of the layer, and cannot directly model and invert the "thickness" as an independent variable, resulting in the inability to quantitatively assess the intensity of the phase transition process. 4. Lack of a scalable and general framework: Most existing algorithms are "customized" for radars with specific wavelengths or configurations, and lack a general detection mode based on a physical model that can adapt to different numbers of channels. Summary of the Invention

[0005] Purpose of the invention: The purpose of this invention is to provide a lidar melting layer height and thickness detection method based on scalable two-dimensional matching, which can simultaneously and accurately measure the height and thickness parameters of the melting layer, solving the problems of strong single-parameter dependence, weak anti-interference ability, poor method compatibility, and lack of thickness parameters in the prior art.

[0006] Technical solution: The method for detecting the height and thickness of the melt layer based on scalable two-dimensional matching using lidar according to the present invention includes the following steps:

[0007] Step S1: Multi-channel data preprocessing: Acquire the original observation signals of the multi-channel lidar and trim the signals of each channel to a preset physical reasonable value range to obtain the preprocessed signal;

[0008] Step S2: Constructing data normalization and a two-dimensional matrix: Perform percentile normalization on the preprocessed channel signals, and combine the normalized channel signals to construct a two-dimensional data matrix;

[0009] Step S3: Construct a physical model-driven multi-thickness template library: Based on the microphysical characteristics of the melting layer and the scattering mechanism of lidar, establish a unified parameterized physical model for multiple channels. Generate two-dimensional templates for multiple thickness values ​​within a preset thickness range according to the model, thus forming a multi-thickness template library under physical constraints. The templates can reflect the differences in the morphology, indentation degree and thickness dependence of different channel signals within the melting layer.

[0010] Step S4: Verify multi-channel weighted fusion and physical standards: Define the weight of each channel, and use weighted normalized cross-correlation to calculate the similarity value between the two-dimensional data matrix and each thickness template at different height positions; at the same time, based on the preset physical standard threshold, perform multi-channel joint verification on each candidate position to select the position that meets the physical characteristics of the melting layer.

[0011] Step S5: Determine the optimal parameters: Find the maximum value and its corresponding thickness and height from the similarity values ​​verified by physical standards, and use them as the detected melting layer thickness and height; and determine whether the detection is successful based on the preset confidence threshold.

[0012] Furthermore, in step S1, the multi-channel raw observation signal includes at least two of the following: horizontal channel signal intensity, vertical channel signal intensity, depolarization ratio, spectral width, signal skewness, or vertical velocity of the lidar.

[0013] Furthermore, in step S2, percentile normalization is performed by normalizing the signal using the 1% and 99% percentiles of the signal sequence.

[0014] Furthermore, in step S3, when constructing the multi-thickness template library, a two-dimensional template is generated for each thickness, with each row corresponding to height coordinates and each column corresponding to different signal channels; the signal value of each channel in the template is generated by a unified parameterized mathematical model, which includes parameters controlling the shape, width, center minimum value and edge maximum value of the signal indentation, and the center minimum value and edge maximum value change linearly with the thickness.

[0015] Furthermore, in step S4, the channel weights are set according to the principle that the horizontal channel signal is dominant in the melting layer detection, the vertical channel signal is auxiliary, and the debias ratio signal is a reference, and the horizontal channel is given the highest weight.

[0016] Furthermore, in step S4, the ratio of the average signal strength of the data in the melted layer region and the non-melted layer region defined by the template is calculated and compared with the preset physical threshold of each channel. All verified channels are required to simultaneously satisfy the ratio being less than the threshold of the corresponding channel, and the location is deemed to have passed the verification.

[0017] Furthermore, in step S5, the confidence threshold can be set between 0.3 and 0.8, depending on the data type and the number of channels; when the maximum similarity value is greater than or equal to the threshold, the detection is considered successful.

[0018] The present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the methods described herein.

[0019] An electronic device according to the present invention includes a memory and a processor, wherein the memory stores a computer program, and when the program is executed by the processor, it implements any of the methods described herein.

[0020] Beneficial Effects: Compared with existing technologies, this invention has the following significant advantages: 1. This invention breaks through the accuracy bottleneck of traditional single-parameter inversion and significantly reduces the false alarm rate. Existing lidar algorithms mostly rely on hard thresholding of a single channel (such as depolarization ratio), which is easily affected by aerosol layers or multi-layer clouds. This invention innovatively proposes a "multi-channel two-dimensional collaborative" detection mechanism, using the physical orthogonal information of horizontal, vertical, and depolarization ratio channels to construct a two-dimensional feature matrix. Through the dual constraints of physical standard verification (screening) and weighted normalized cross-correlation (decision), the false alarm problem under low signal-to-noise ratio and complex weather conditions is effectively solved. 2. Filling the technical gap in direct inversion of "melting layer thickness" in lidar. Addressing the deficiency of existing methods that can only identify the height of the top / bottom of the layer but cannot accurately quantify the thickness, this invention introduces "thickness" as an independent invertible physical variable into the model. By constructing a physical template library containing fine thickness gradients, synchronous, automated, and accurate measurement of melting layer height and thickness is achieved, providing a new quantitative dimension for the intensity assessment of cloud precipitation microphysical processes. 3. A physical-driven, general-purpose extended framework with strong hardware compatibility was constructed. This invention abandons empirical formulas for specific radar models and establishes a parameterized physical model based on scattering theory. This model has strong scalability and can adaptively generate matching templates according to the wavelength, number of channels, and configuration differences of the lidar. This makes this method applicable not only to classic three-channel polarization lidars but also seamlessly adaptable to future new multi-wavelength or multi-channel lidar systems, possessing broad application value. 4. Robust detection in low signal-to-noise ratio environments was achieved. Utilizing the mathematical characteristics of the Normalized Cross-Correlation (NCC) algorithm—sensitive to overall waveform features but insensitive to local noise—this invention can maintain the stability of detection results even when facing data with weak signals or background noise interference. Combined with a confidence discrimination mechanism, it effectively overcomes the problem of traditional threshold methods failing in weak echo regions, significantly improving the reliability of all-weather operational use. Attached Figure Description

[0021] Figure 1 This is a flowchart of the present invention;

[0022] Figure 2 This is the original data processing diagram of the present invention;

[0023] Figure 3 This is an example cross-sectional view of the physical template of the present invention;

[0024] Figure 4 This is an example diagram of a two-dimensional template heat map of the present invention;

[0025] Figure 5 This is a comparison chart of the thickness fusion fraction curves for all components of this invention;

[0026] Figure 6This is the NCC fraction curve of the melt layer height and thickness measured by this invention;

[0027] Figure 7 This is a physical verification diagram of the melt layer obtained by this invention, showing that it meets the required standards.

[0028] Figure 8 This is a comparison diagram of the original data profile and the melted layer of the present invention;

[0029] Figure 9 This is a physical verification illustration of the method of the present invention when there is no melted layer;

[0030] Figure 10 This is a graph showing the change in the height of the melt layer over a continuous time period as measured by the present invention. Detailed Implementation

[0031] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0032] like Figure 1 As shown, this embodiment of the invention provides a method for detecting the height and thickness of a lidar melting layer based on scalable two-dimensional matching, including the following steps:

[0033] Step S1: Multi-channel data preprocessing. For example... Figure 2 As shown, it includes the following steps:

[0034] Step S11, assuming the original radar observation signal is ,in Different observation variables can correspond to the signal types that various lidar systems can acquire, such as signal intensity, depolarization ratio, spectral width, signal skewness, and vertical velocity in the horizontal and vertical channels of a lidar system. Let... These are the lower and upper threshold values ​​for each signal, which can be set according to the specific lidar type and observation conditions.

[0035] Let the original signal of the lidar be Set the horizontal channel signal strength to Vertical channel signal strength set to The height coordinates are set to .

[0036] Deviation ratio δ The calculation uses the following formula:

[0037]

[0038] in It is a very small positive number to prevent division by zero errors.

[0039] Step S12: The signal is cropped to limit it to a physically reasonable numerical range.

[0040]

[0041] This processing will convert the raw radar observation signal The intensity constraint is within a preset effective range, for example, ensuring that parameters such as the depolarization ratio are within a physically meaningful range.

[0042]

[0043]

[0044] δ′(z) min(max(δ(z),0),δmax)

[0045] in These represent the processed horizontal channel signal strength, vertical channel signal strength, and depolarization ratio, respectively. `Min` and `max` represent the minimum and maximum values, respectively. This processing limits the signal strength values ​​of the horizontal and vertical channels to a reasonable range of 0 to 5, while simultaneously limiting the depolarization ratio to a physically meaningful range of 0 to 2. This is the preset maximum debias ratio threshold.

[0046] Example image of the processed data Figure 2 The results showed that the data retained complete and valid meteorological information, and was intuitive and concise, significantly improving the robustness and reliability of subsequent calculations using the normalized cross-correlation fusion method based on the physical model, thus laying a solid foundation for accurate melting layer detection.

[0047] This processing is based on the following technical considerations: First, most of the erroneous values ​​that occur in actual measurements are caused by background noise or instrument errors, and these meaningless interferences can be eliminated by lower limit pruning; second, setting an appropriate upper limit can effectively suppress extreme outliers that may occur during signal acquisition and prevent them from having an adverse effect on the subsequent template matching algorithm.

[0048] This signal cropping process significantly improves the robustness and reliability of subsequent calculations using the normalized cross-correlation fusion method based on physical models, while preserving all effective meteorological information, thus laying a solid foundation for accurate melting layer detection.

[0049] Step S2: Data normalization and two-dimensional matrix construction. The steps are as follows:

[0050] Step S2.1, Percentile Normalization. For each channel signal... (in Represent Perform percentile normalization: Calculate of and Quantiles:

[0051] , ,

[0052] The normalized signal is then:

[0053]

[0054] Step S2.2: Construct a two-dimensional data matrix :

[0055]

[0056] Step S3: Construction of a multi-thickness template library driven by a physical model. Based on the microphysical characteristics of the melting layer, this invention constructs a multi-thickness template library under physical constraints. The establishment of this template library strictly follows the physical laws governing lidar detection of the melting layer and is expressed through a unified mathematical model, ensuring the physical necessity and correctness of the template design. The steps are as follows:

[0057] Step S3.1: Construct an echo signal curve template based on scattering theory and lidar equations.

[0058] When lidar detects the melt layer, the signal changes in the horizontal channel, vertical channel, and depolarization ratio channel exhibit systematic differences. These differences stem from the physical nature of the changes in particle shape, orientation, and phase state during the melting process: the horizontal channel is most sensitive to the melt layer, showing the most obvious signal dip, reflecting that the backscattering ability of liquid spherical particles to parallel polarized light is the weakest; the vertical channel shows a moderate signal dip, reflecting that some non-spherical or incompletely melted particles still have a depolarization effect; the depolarization ratio channel shows a gentler signal dip, reflecting that the degree of non-sphericality of the particles decreases and tends to be spherical.

[0059] Ignoring these physical constraints would prevent the template from accurately representing the signal response of the actual melt layer, thus reducing the accuracy and reliability of the detection. Therefore, this invention transforms the aforementioned physical laws into strict mathematical constraints and embeds them into the template design.

[0060] Step S3.2: Construction of a highly adaptable unified physical template model. This invention provides a multi-channel unified mathematical model based on physical meaning, adaptable to different radars and different numbers of channels.

[0061] For a given melt layer of each thickness ϵ( (Unit: meters), template is a two-dimensional matrix ,in This represents the number of points along the template's height. Template number... List( ... (corresponding to different channels) within the area to be tested (assuming... These are the start and end points of the pixel index, and the pixel point. The value of ) is generated by the following unified formula:

[0062]

[0063] in: For normalized distance, The center of the melt layer The value of half the width of the melting layer is set so that the full width at half the height of the template recess is equal to the thickness T. For channel The normalization factor controls the width of the indentation; This is the concavity function, which describes the shape of the signal concavity. and Channels The minimum value at the center and the maximum value at the edge of the melt layer both increase with thickness. Linear variation, reflecting thickness dependence.

[0064] Here =20m, =600m, the formula parameters for the three channels are as follows:

[0065] Horizontal channel ( =0): Normalization factor =1.5 (the depressions are most concentrated); Depression function Simulates sharp central depressions and gentle edges; minimum value Maximum value .

[0066] Vertical channel Normalization factor =1.3 (medium depression width); Depression function Minimum value Maximum value .

[0067] deflection ratio channel Normalization factor (Widest width of the depression); Depression function Minimum value Maximum value .

[0068] After the template is created, the one-dimensional profile and two-dimensional heatmap effects are as follows: Figure 3 and Figure 4 As shown.

[0069] Step S4: Multi-channel weighted fusion and physical standard verification.

[0070] This invention measures the similarity between templates and data using weighted normalized cross-correlation (NCC) matching and verifies the matching results based on strict physical standards. The steps are as follows:

[0071] Step S4.1: Define channel weights. Within the melt layer, liquid spherical particles have the weakest backscattering ability for parallel polarized light, resulting in the most significant dip in the horizontal channel signal. This is the most reliable feature for identifying the melt layer, therefore the horizontal channel signal is dominant. The vertical channel signal also shows a dip, but due to the presence of some non-spherical or incompletely melted particles, its dip is less significant than that of the horizontal channel, thus the vertical channel signal is auxiliary. The depolarization ratio reflects the degree of particle non-sphericity, tending to decrease within the melt layer, but the change is relatively gradual and easily affected by noise; therefore, it is assigned the lowest weight, thus determining the reference value of the depolarization ratio. Let the channel weight vector be... ,satisfy ,and (For example ).

[0072] Step S4.2: Define the physical standard threshold.

[0073] Verification basis: Melting layer regions defined by the template (denoted as a set) ) and non-melting layer regions (denoted as set) ), calculate the ratio of the average signal strength of the two regions in the data window:

[0074] Where Mean[·] represents the arithmetic mean of the data in the set, and M is the template K. T In the middle of μ T and σ T The set of row indices corresponding to the defined melting layer height range, where N is the set of row indices for the remaining height ranges other than M within the current detection window.

[0075] The physical threshold setting is used to reflect the degree of signal attenuation. The verification logic is that only when all n channels simultaneously satisfy r... k < The location was then considered to conform to the physical characteristics of the melt layer. This n-fold verification mechanism ensures that the detection results have a solid physical basis, greatly reducing false alarms caused by noise or interference. Specifically:

[0076] Physical threshold setting, horizontal channel set to Strong signal attenuation, vertical channel set to The response exhibits moderate signal attenuation, with the debiasing channel set to... The response shows slight signal attenuation. The verification logic is that all three channels must simultaneously satisfy the condition. < The location was then deemed to conform to the physical characteristics of the melt layer. This triple verification mechanism ensures that the detection results have a solid physical basis and greatly reduces false alarms caused by noise or interference.

[0077] Step S4.3: Relationship between NCC calculation and physical standard verification. In this invention, the complete NCC value is calculated regardless of whether the physical standard verification passes or fails. That is, for each height position... and thickness template The weighted NCC value is calculated using the following formula:

[0078] For channel k (k=0,1,2...n):

[0079]

[0080] For each thickness Calculate the height sequence Weighted normalized cross-correlation values ​​at each point :

[0081]

[0082] The results of physical standard verification (denoted as Boolean variables) These are recorded separately for subsequent filtering. Only when determining the optimal parameters are considered. The setting =True indicates that the maximum value is found only among the NCC values ​​that conform to physical standards. All thickness NCC score curves are shown below. Figure 5 As shown, the best matching result is also marked here.

[0083] Step S5: Determine the optimal parameters. The steps are as follows:

[0084] Step S5.1: Determine the optimal thickness and height.

[0085] For each thickness Find the maximum value and its corresponding height from the valid NCC values:

[0086]

[0087]

[0088] Compare all thicknesses and select the thickness and height corresponding to the maximum value.

[0089] Step S5.2: Successful detection judgment. Here, θ is set to θ=0.6. This threshold of 0.6 is a balance point obtained through extensive experimental verification. Setting it too low can easily lead to false alarms, mistaking noise for signals; setting it too high can easily lead to missed alarms, missing the true melting layer. 0.6 can most reliably identify the true features.

[0090] Here, if C ≥ 0.6, the result is considered successful; otherwise, it fails. The confidence level mechanism ensures the reliability of the result and reduces false positives and false negatives.

[0091] like Figure 6 The curve showing the highest NCC score is clearly 0.7 > 0.6, which determines that the height of the melt layer is 0.9 km and the thickness is 160 m. Figure 7 Demonstrates physical verification Figure 6 The correctness of the results and the compliance of the test results with physical constraints. Figure 8 The data demonstrates a high degree of agreement between the melt layer and the initial data, further validating the correctness of this method. To verify the feasibility and correctness of this method in reverse, a timeframe with absolutely no melt layer was taken, and physical verification showed... Figure 9 This correctly ruled out the possibility of a melt layer appearing at this moment. Figure 10 This is a graph showing the change in the height of the melt layer over continuous time, measured using this method.

[0092] Figure 6 The figure shows the curve of the highest NCC score as a function of altitude. The curve plots the normalized cross-correlation (NCC) score as a function of altitude, with the peak value corresponding to the center height of the melt zone. A significant peak appears at an altitude of approximately 0.9 km (C = 0.7 > 0.6), indicating the presence of a melt zone signal at this location. Based on the full width at half maximum (FWHM) of the curve, the melt zone thickness is estimated to be approximately 160 m. This visually demonstrates the accurate ability of the NCC method to capture the location of the melt zone, and the threshold determination is clear and effective.

[0093] Figure 7 Physical verification results: The weighted NCC score changes with height within the range of physical characteristic constraints, verifying... Figure 6 The detection of the melt layer height was examined to determine if it fell within the ice-water mixed phase range. Data showed it to be around 0.9 km, within the range consistent with physical characteristics. This physically confirms the validity of the detection results, demonstrating that the method relies not only on statistical characteristics but also on physical characteristics.

[0094] Figure 8 Comparison of the melt layer with the original radar data. The detected melt layer location (height and thickness) is superimposed on the original radar data profile image. It can be seen that the melt layer area highly matches the depression in the original radar data profile. This further verifies the high consistency between the proposed method and the actual observation data, enhancing the reliability of the results.

[0095] Figure 9Reverse verification in the case of no melting layer. A meteorological process with absolutely no melting layer was selected, and this method was applied for detection. The NCC score was below 0.6 throughout the process, with no obvious peak, and the system correctly identified it as "no melting layer". This reverse verification demonstrated the specificity and anti-interference ability of this method, avoiding erroneous results in inapplicable scenarios.

[0096] Figure 10 This is a time-series graph showing the continuous changes in the height of the thaw layer over a period of time. The thaw layer height gradually changes with atmospheric thermodynamic conditions, consistent with the development patterns of weather processes. This demonstrates the stability and practicality of this method in time-series monitoring, and it can be used for dynamic analysis of the thaw layer and support for weather forecasting.

Claims

1. A method for detecting the height and thickness of a melt layer using lidar based on scalable two-dimensional matching, characterized in that, Includes the following steps: Step S1: Multi-channel data preprocessing: Acquire the original observation signals of the multi-channel lidar and trim the signals of each channel to a preset physical reasonable value range to obtain the preprocessed signal; Step S2: Constructing data normalization and a two-dimensional matrix: Perform percentile normalization on the preprocessed channel signals, and combine the normalized channel signals to construct a two-dimensional data matrix; Step S3: Construct a physical model-driven multi-thickness template library: Based on the microphysical characteristics of the melting layer and the scattering mechanism of lidar, a unified parameterized physical model for multiple channels is established. Two-dimensional templates are generated for multiple thickness values ​​within a preset thickness range according to the model, forming a multi-thickness template library under physical constraints. The templates can reflect the differences in the morphology, indentation degree, and thickness dependence of different channel signals within the melting layer. When constructing the multi-thickness template library, for each thickness, the generated two-dimensional template has each row corresponding to height coordinates and each column corresponding to different signal channels. The signal value of each channel in the template is generated by a unified parameterized mathematical model. The model includes parameters that control the indentation morphology, width, center minimum value, and edge maximum value of the signal, and the center minimum value and edge maximum value change linearly with the thickness. The ratio of the average signal strength in the melted layer region to the non-melted layer region defined by the template is calculated and compared with the preset physical threshold of each channel. All verified channels are required to simultaneously satisfy the ratio being less than the threshold of the corresponding channel to determine that the location has passed the verification. Step S4: Verify multi-channel weighted fusion and physical standards: Define the weights of each channel, and use weighted normalized cross-correlation to calculate the similarity values ​​between the two-dimensional data matrix and each thickness template at different height positions; at the same time, based on the preset physical standard threshold, perform multi-channel joint verification on each candidate position to screen out positions that meet the physical characteristics of the melting layer; among them, the channel weights are set according to the principle that the horizontal channel signal is dominant in melting layer detection, the vertical channel signal is auxiliary, and the debias ratio signal is a reference, and the horizontal channel is given the highest weight; Step S5: Determine the optimal parameters: Find the maximum value and its corresponding thickness and height from the similarity values ​​verified by physical standards, and use them as the detected melting layer thickness and height; and determine whether the detection is successful based on the preset confidence threshold.

2. The method for detecting the height and thickness of a lidar melting layer based on scalable two-dimensional matching according to claim 1, characterized in that, In step S1, the multi-channel raw observation signal includes at least two of the following: horizontal channel signal intensity, vertical channel signal intensity, depolarization ratio, spectral width, signal skewness, or vertical velocity of the lidar.

3. The method for detecting the height and thickness of a lidar melting layer based on scalable two-dimensional matching according to claim 1, characterized in that, In step S2, percentile normalization is performed by normalizing the signal using the 1% and 99% percentiles of the signal sequence.

4. The method for detecting the height and thickness of a lidar melting layer based on scalable two-dimensional matching according to claim 1, characterized in that, In step S5, the confidence threshold is set between 0.3 and 0.8, depending on the data type and the number of channels; when the maximum similarity value is greater than or equal to the threshold, the detection is considered successful.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-4.

6. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the method as described in any one of claims 1-4.

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