Multi-point laser scanning method and system for measuring snow thickness

Abstract

This invention belongs to the field of laser measurement equipment technology. It discloses a multi-point laser scanning snow layer thickness measurement method and system, comprising: a meteorological data acquisition module for acquiring meteorological data of the detection area at a given time; a snow layer thickness prediction module for predicting the snow layer thickness within a given time period based on the meteorological data; a thickness measurement module for measuring the snow layer thickness in real time; and a measurement correction module for verifying the data from the laser measurement equipment at each time unit. If any overestimation or underestimation is found, the real-time snow layer thickness is corrected to obtain the corrected snow layer thickness. By predicting the snow layer thickness within a given time period in advance and verifying the real-time snow layer thickness based on the prediction results, dynamic real-time snow layer thickness correction is achieved.

Description

Technical Field

[0001] This invention relates to the field of laser measurement equipment technology, and more specifically, to a method and system for measuring snow layer thickness using multi-point laser scanning. Background Technology

[0002] Real-time monitoring of dynamic changes within the snow layer is crucial for deciphering the "black box" of snow and improving disaster early warning capabilities. While lidar is widely used for remote sensing monitoring of snow thickness, its reliance on simple ranging models has limitations. Traditional methods calculate surface elevation differences solely based on laser round-trip time, failing to capture the dynamic evolution of optical properties caused by density changes within the snow layer. When snow compaction, recrystallization, or cavity formation alters the density gradient, the penetration depth of lasers at different wavelengths exhibits nonlinear differences. The position and intensity of reflection peaks also shift due to optical abrupt changes at interlayer interfaces, ultimately leading to systematic errors in measurement results, such as "false overstatement" or "false understatement." This technical bottleneck makes it difficult for traditional lidar to accurately characterize subtle changes in the internal structure of the snow layer, thus affecting the timeliness and accuracy of disaster early warning.

[0003] In view of this, the present invention proposes a multi-point laser scanning snow layer thickness measurement method and system to solve the above problems. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art and achieve the above objectives, the present invention provides the following technical solution: a multi-point laser scanning snow layer thickness measurement method, comprising:

[0005] Collect meteorological data per unit time in the detection area;

[0006] The predicted snow thickness is obtained by predicting the snow layer thickness per unit time in the future based on meteorological data.

[0007] The snow layer thickness is measured in real time to obtain the real-time snow layer thickness;

[0008] The data from the laser measuring device is checked at each unit of time. If any false increases or decreases are found, the real-time snow layer thickness is corrected to obtain the corrected snow layer thickness.

[0009] Furthermore, methods for correcting the real-time snow layer thickness to obtain the corrected snow layer thickness include:

[0010] S301. Compare the real-time snow layer thickness with the confidence interval of the predicted snow layer thickness. If the real-time snow layer thickness is within the confidence interval of the predicted snow layer thickness, no correction is performed. If the real-time snow layer thickness is less than the minimum value of the confidence interval of the predicted snow layer thickness, a false subtraction correction is performed. If the real-time snow layer thickness is greater than the maximum value of the confidence interval of the predicted snow layer thickness, a false increase correction is performed.

[0011] S302. If, after the false reduction correction, the false reduction correction thickness is within the corresponding confidence interval, then the correction is complete and the current process ends; if the false reduction correction thickness is not within the corresponding confidence interval, then execute S303.

[0012] If, after the spurious correction is performed, the spurious correction thickness falls within the corresponding confidence interval, then the correction is complete and the current process ends; otherwise, if the spurious correction thickness is not within the corresponding confidence interval, then S303 is executed.

[0013] S303: Repeat S301 and S302; if the number of repetitions exceeds the predetermined number, it indicates that there is an abnormality in the device, and a device abnormality command is sent.

[0014] Furthermore, the method for correcting the false subtraction includes:

[0015] A pulsed laser emitter vertically projects a laser beam onto the area to be tested, simultaneously emitting standard wavelength laser pulses and penetrating wavelength laser pulses to ensure that the laser spot positions coincide. The receiver then synchronously records the corresponding echo signal time series.

[0016] The time series of echo signals is subjected to mean filtering to extract the peak time of standard wavelength laser echo and the peak time of penetrating wavelength laser echo.

[0017] The false reduction compensation amount is calculated based on the peak time of the standard wavelength laser echo and the peak time of the penetrating wavelength laser echo.

[0018] The real-time snow layer thickness is added to the false reduction compensation amount to obtain the false reduction correction thickness.

[0019] Furthermore, the method for correcting spurious increases includes:

[0020] A group of laser pulses is emitted synchronously by a multi-wavelength laser emitting device, and each beam is vertically incident on the snow layer after passing through an aberration-correcting collimating device.

[0021] The receiver, equipped with a spectral splitting unit, separates the echo signals of each wavelength channel of the laser pulse group, and acquires the time-domain signals through an avalanche photodetector array and a high-speed analog-to-digital converter to obtain the digital signal sequence of each wavelength channel;

[0022] Wavelet packet denoising is performed on the digital signal sequences of each wavelength channel to remove noise and obtain smoothed signals for each wavelength channel. A set of smoothed signals for each wavelength is constructed. Adaptive threshold segmentation is used to extract the effective echo region time range of each smoothed signal in the set of smoothed signals for each wavelength, and the region is marked as a time range set.

[0023] A multi-layer interface reflection model is constructed using the effective echo region time range;

[0024] Based on the set of smoothed signals of each wavelength, the set of time ranges, and the parameters of the multi-layer interface reflection model, multi-peak fitting and reflection peak identification are performed to obtain the time series of reflection peaks of each wavelength channel and the interlayer time intervals corresponding to each reflection peak.

[0025] Snow layer density inversion was performed using the time series of reflection peaks of each wavelength channel to obtain the set of snow layer densities for each layer;

[0026] The average refractive index of the snow layer at each wavelength is calculated based on the set of snow layer densities and the interlayer time intervals corresponding to each reflection peak.

[0027] The real-time snow layer thickness is corrected based on the average refractive index of the snow layer to obtain the artificially increased corrected thickness.

[0028] Furthermore, based on the sets of smoothed signals at each wavelength, the set of time ranges, and the parameters of the multi-layer interface reflection model, a method is used to perform multi-peak fitting and reflection peak identification to obtain the time series of reflection peaks for each wavelength channel and the interlayer time intervals corresponding to each reflection peak:

[0029] The interface reflectivity of each snow layer is calculated based on the refractive index of each snow layer to different wavelength laser pulses.

[0030] Within the effective echo region, Gaussian multi-peak fitting is performed on the smoothed signal set of each wavelength to obtain fitting parameters, which include reflection peak time, peak width and peak height.

[0031] Set the reflection peak time to the initial guess value of Gaussian multi-peak fitting; set the peak width to twice the sampling interval; set the peak height to the product of interface reflectivity and incident light intensity.

[0032] Set fitting constraints, including reflection peak time constraints, peak height constraints, and layer number constraints;

[0033] Reflection peak time constraint: Force the time of all fitted peaks to fall within the effective echo region time range, and increase sequentially;

[0034] Peak height constraint: The peak height of the current snow layer is greater than or equal to the product of the peak height of the next snow layer and the interface reflectivity;

[0035] Layer number constraint: The number of fitted peaks is less than or equal to the sum of the number of snow layers and 2;

[0036] Construct the least squares optimization objective function;

[0037] By optimizing the fitting parameters using the Levenberg-Marquardt algorithm, the time series of reflection peaks for each wavelength channel were obtained, and correspondingly, the interlayer time intervals for each reflection peak were obtained.

[0038] Furthermore, methods for inverting snow layer density using the time series of reflection peaks in each wavelength channel to obtain the set of snow layer densities include:

[0039] S401: Let the number of snow layers be SL, where SL equals the number of snow layers plus 1; let the initial value of i be 1, and the value of i ranges from 1 to SL;

[0040] S402: Construct an equation relating the refractive index of the i-th layer to the snow density of the i-th layer based on the Gladstone-Dale law;

[0041] Construct an equation relating the thickness of the i-th snow layer to the refractive index of the i-th layer;

[0042] Set an initial value for the density of each snow layer.

[0043] S403: The refractive index of the i-th layer is calculated based on the equation relating the refractive index of the i-th layer to the snow density of the i-th layer.

[0044] S404: The thickness of the i-th snow layer is calculated based on the equation relating the thickness of the i-th snow layer to the refractive index of the i-th layer.

[0045] S405: Add the thickness of the i-th snow layer to the set of snow layer thicknesses;

[0046] S406: Let i = i + 1. If i is less than or equal to SL, continue to execute S403 to S404; if i is greater than SL, obtain the set of snow layer thicknesses and execute S407.

[0047] S407: Construct a least-squares optimization objective function based on the interlayer time intervals and snow layer thicknesses corresponding to each reflection peak;

[0048] The snow density of each layer is solved by using an iterative algorithm to optimize the least squares objective function. By changing the value of the snow density of each layer, the least squares objective function is minimized. The snow density corresponding to the minimized least squares objective function is then used to construct a set of snow density of each layer, thus ending the current process.

[0049] Furthermore, methods for predicting snow thickness per unit time in the future based on meteorological data include:

[0050] The detection area is divided into q×p detection sub-regions, where q is the number of rows in the detection sub-region and p is the number of columns in the detection sub-region;

[0051] Divide the unit of time into T time points;

[0052] A dynamic equation for snow layer thickness is established for each detection sub-region based on meteorological data.

[0053] The predicted snow thickness and corresponding confidence interval for each detection sub-region are calculated based on the dynamic equation of snow thickness, with Time being the total number of time steps in the next 1, 2, ..., Time time steps.

[0054] Furthermore, the method for constructing the dynamic equation for snow layer thickness includes:

[0055] S101: The initial value of t is preset to 1, and the value of t ranges from 1 to T. The time interval between two time points is Δt. The snow layer thickness corresponding to the first time point is collected in advance.

[0056] S102: Based on the meteorological data and snow thickness at time point t, construct a dynamic equation for snow thickness to obtain the snow thickness at time point (t+1).

[0057] S103: Let t = t + 1. If t is less than or equal to T, continue to execute S102; if t is greater than T, obtain the predicted snow layer thickness at the Tth time point and end the current process.

[0058] The dynamic equation for snow thickness is coupled through multiple physical processes to achieve dynamic prediction of snow thickness; the multiple physical processes include the cumulative snowfall term at time t, the thickness melting term at time t, the thickness sublimation term at time t, and the wind transport term at time t.

[0059] The cumulative snowfall at time point t is obtained by multiplying the snowfall at time point t by the unit time interval and dividing by the snow density; the thickness melting at time point t is obtained by comprehensively characterizing the temperature and melting coefficient at time point t; the thickness sublimation at time point t is calculated by the sublimation coefficient, the saturated water vapor pressure on the snow surface, the actual water vapor pressure in the air, and the wind speed at time point t; the wind transport at time point t is obtained by dividing the difference between the wind-driven snow mass per unit area and the wind-driven snow mass per unit area by the snow density; the wind-driven snow mass per unit area uses the wind-driven snow mass per unit area of ​​the upwind detection sub-region of the current detection sub-region.

[0060] Furthermore, the method for determining the upwind detection sub-region of the current detection sub-region includes:

[0061] S501: Pre-set the relative orientation information of each detection sub-region and its adjacent detection sub-regions, and record it as the relative orientation information of adjacent sub-regions;

[0062] S502: Let P be initially 1, Q be initially 1, P range from 1 to p, and Q range from 1 to q.

[0063] S503: Determine the upwind detection sub-region of the detection sub-region in row P and column Q based on the upwind direction relationship, and denot it as the upwind detection sub-region of the detection sub-region in row P and column Q.

[0064] S504: If Q is less than q, then let Q+1 and execute S503;

[0065] If Q is greater than or equal to q and P is less than p, then let P+1, Q=1, and execute S503;

[0066] If Q is greater than or equal to q and P is greater than or equal to p, then the process ends.

[0067] A multi-point laser scanning snow layer thickness measurement system, implementing the aforementioned multi-point laser scanning snow layer thickness measurement method, includes:

[0068] The meteorological data acquisition module is used to collect meteorological data per unit time in the detection area;

[0069] The snow thickness prediction module predicts the snow thickness per unit time in the future based on meteorological data, and obtains the predicted snow thickness.

[0070] The thickness measurement module is used to measure the snow layer thickness in real time and obtain the real-time snow layer thickness.

[0071] The measurement and correction module is used to check the data of the laser measurement device at each unit of time. If there is a false increase or decrease, the real-time snow layer thickness is corrected to obtain the corrected snow layer thickness.

[0072] Compared with existing technologies, the technical effects and advantages of the multi-point laser scanning snow layer thickness measurement method and system of the present invention are as follows:

[0073] This invention proposes a multi-point laser scanning snow layer thickness measurement system that addresses the problems of traditional lidar relying on simple ranging models, failing to capture the dynamic evolution of optical properties caused by changes in snow layer density, and systematic measurement errors. It utilizes a dynamic snow layer thickness prediction model coupled with meteorological data acquisition and multiple physical processes to construct a prediction confidence interval for real-time verification of measurement data. By combining the calculation of dummy reduction compensation with dual-wavelength lasers and the correction of dummy refractive index with multi-wavelength signals, it effectively solves the impact of density gradient changes caused by snow compaction, recrystallization, or cavity formation on measurement accuracy, achieving precise characterization of subtle changes in the internal structure of the snow layer. Through dynamic comparison and iterative correction mechanisms between real-time measurements and prediction results, it eliminates the "dummy increase" and "dummy decrease" errors caused by reflection peak shifts due to optical abrupt changes at interlayer interfaces, significantly improving the accuracy and reliability of snow layer thickness measurement. This provides more timely and accurate core data support for snow and ice disaster early warning, breaking through the bottleneck of traditional technologies' inability to reveal the dynamics of the snow layer "black box." Attached Figure Description

[0074] Figure 1 This is a schematic diagram of a multi-point laser scanning snow layer thickness measurement system according to an embodiment of the present invention;

[0075] Figure 2 This is a flowchart of the snow layer thickness correction method according to an embodiment of the present invention;

[0076] Figure 3 This is a schematic diagram of real-time thickness measurement;

[0077] Figure 4 This is a schematic diagram of the distribution of the detection area. Detailed Implementation

[0078] The technical solutions of the embodiments of the present invention will be described in detail, clearly, and completely below with reference to the accompanying drawings. It should be particularly noted that the specific embodiments described below are only for better illustrating and explaining the technical solutions of the present invention, and are intended to enable those skilled in the art to better understand and implement the present invention, and should not be construed as limiting the scope of protection of the present invention. Without departing from the spirit and substance of the present invention, those skilled in the art can modify, adjust, or make equivalent substitutions based on the content disclosed in the present invention, and these should all be considered within the scope of protection of the present invention.

[0079] Example 1

[0080] Please see Figure 1 As shown, this embodiment discloses a multi-point laser scanning snow layer thickness measurement system, which is applied in laser measurement equipment. It includes a meteorological data acquisition module, a snow layer thickness prediction module, a thickness measurement module, and a measurement correction module. Each module is connected by wires and / or wirelessly to realize data transmission.

[0081] The meteorological data acquisition module is used to collect meteorological data of the detection area at a given time. The meteorological data includes air pressure, temperature, humidity, wind speed, wind direction, snowfall, and solar radiation.

[0082] The meteorological data was obtained from meteorological stations in the detection area.

[0083] The snow thickness prediction module predicts the snow thickness per unit time in the future based on meteorological data, denoted as the predicted snow thickness.

[0084] Methods for predicting snow thickness per unit time based on meteorological data include:

[0085] See Figure 4 As shown, the detection area is divided into q×p detection sub-regions, where q is the number of rows in the detection sub-region and p is the number of columns in the detection sub-region;

[0086] Divide the unit of time into T time points;

[0087] A dynamic equation for snow layer thickness is established for each detection sub-region based on meteorological data.

[0088] The predicted snow thickness and corresponding confidence interval for each detection sub-region are calculated based on the dynamic equation of snow thickness for the next 1, 2, ..., Time time steps.

[0089] It should be noted that the confidence interval can be set according to the complexity and time scale of the snow thickness dynamic equation. In this embodiment, because the meteorological data is highly accurate and the prediction is made on a relatively short time scale, the confidence interval can be set narrower. For example, the confidence interval can be set to ±5% of the predicted snow thickness.

[0090] The method for constructing the dynamic equation for snow layer thickness includes:

[0091] S101: The initial value of t is preset to 1, and the value of t ranges from 1 to T. The time interval between two time points is Δt. The snow layer thickness corresponding to the first time point is collected in advance.

[0092] S102: Based on the meteorological data and snow thickness at time point t, construct a dynamic equation for snow thickness to obtain the snow thickness at time point (t+1).

[0093] S103: Let t = t + 1. If t is less than or equal to T, then continue to execute S102; if t is greater than T, then obtain the predicted snow layer thickness at the Tth time point and end the current process.

[0094] It should be noted that the snow thickness at the first time point was obtained by snow gauge measurement to ensure the accuracy of the initial data of the dynamic equation for snow thickness, thereby improving the accuracy of snow thickness prediction at subsequent time points.

[0095] The dynamic equation for snow layer thickness is as follows:

[0096] S(t+1)=S(t)+ΔS(t) 降雪 +ΔS(t) 融化 +ΔS(t) 升华 +ΔS(t) 风力 ;

[0097] In the formula, S(t+1) is the predicted snow layer thickness at time point t+1, S(t) is the snow layer thickness at time point t, and ΔS(t) is the snow layer thickness at time point t. 降雪 Let ΔS(t) be the cumulative snowfall amount at time point t. 融化 Let ΔS(t) be the thickness melting term at time point t. 升华 Let ΔS(t) be the thickness sublimation term at time point t. 风力 Let t be the wind transport term at time t.

[0098] Where, ΔS(t) 降雪 ΔS(t) represents the increase in snow thickness in the monitored area during normal snowfall. 融化 and ΔS(t) 升华 This represents the thermodynamic process of snow layer change, indicating the reduction in snow layer thickness in the detection area; ΔS(t) 风力 This represents the dynamic process of snow layer changes, indicating the dynamic changes in snow layer thickness in the detection area. The dynamic equation for snow layer thickness, through the coupling of multiple physical processes, enables dynamic prediction of snow layer thickness.

[0099] The cumulative snowfall at time point t is obtained by multiplying the snowfall at time point t by the unit time interval and dividing by the snow density. A specific example is shown below:

[0100]

[0101] Where, ρ snow R represents snow density. Fresh snow density is typically taken as 100-300; in this embodiment, it is taken as 200. snow (t) represents the snowfall at time point t.

[0102] The thickness melting term at time point t is obtained by comprehensively characterizing the temperature and melting coefficient at time point t. A specific example is shown below in this embodiment:

[0103] ΔS(t) 融化 =-k m ×max(0,Temp(t)-Temp melting )×Δt;

[0104] Where Temp(t) is the temperature at time point t, Temp melting This is the melting threshold temperature, which defaults to 0, k. m k is the melting coefficient, which reflects the rate at which snow melts at different temperatures. m The calculation method is as follows:

[0105]

[0106] Where Q(t) is the solar radiation at time t; α s This represents the snow surface albedo, which can be set according to the type of snow; in this embodiment, it is set to 0.5; L f The latent heat of fusion of water is a fundamental parameter for measuring the energy consumption during the melting process. It determines the conversion relationship between heat and the amount of water melted. In this embodiment, a value of 334 can be used; ρ water The density of water, in this embodiment, can be set to 1000 kg / m³. 3 Q conductionThe heat transfer from the underlying surface is determined by the type of underlying surface, which is determined by on-site surveys conducted by technicians during equipment installation. The underlying surface beneath the snow transfers energy to the snow through heat conduction, promoting melting and is an important component of heat input.

[0107] The thickness sublimation term at time point t is calculated using the sublimation coefficient, the saturated water vapor pressure on the snow surface, the actual water vapor pressure in the air, and the wind speed at time point t. The calculation formula is as follows:

[0108] ΔS(t) 升华 =-k e ×(e s (t)-e a (t))×V(t)×Δt;

[0109] In the formula, k e The sublimation coefficient is related to the roughness of the snow surface and reflects the influence of different snow qualities on the sublimation process. In this embodiment, a value of 0.05 can be used; e s (t) represents the saturated vapor pressure on the snow surface at time t, calculated based on the temperature at time t; e a V(t) represents the actual water vapor pressure in the air at time t, which can be calculated from the humidity, temperature, and snow surface saturation water vapor pressure at time t; V(t) represents the wind speed at time t.

[0110] Among them, the saturated water vapor pressure on the snow surface e s (t) and the actual water vapor pressure in the air e a The difference between V(t) and the air vapor pressure gradient reflects the sublimation rate; the larger the difference, the higher the sublimation rate. A higher wind speed V(t) accelerates water vapor transport, maintains the vapor pressure gradient, and thus promotes sublimation. The sublimation coefficient k... e Used to calibrate the difference between the calculation results and the actual process.

[0111] The method for calculating the saturated water vapor pressure on the snow surface is as follows:

[0112]

[0113] In the formula, exp(·) is the natural exponential function;

[0114] This calculation method is an empirical formula for calculating the saturated water vapor pressure on the snow surface. Experiments and theory show that the saturated water vapor pressure on the snow surface increases rapidly with increasing temperature. This formula, through fitting a large amount of observational data, uses an exponential form to capture this nonlinear relationship. The parameters in the exponent are determined through analysis of the relationship between water vapor pressure and temperature, accurately reflecting the variation characteristics of the saturated water vapor pressure on the snow surface with temperature Temp(t). For example, as the temperature increases, the exponential part increases, and the saturated water vapor pressure e on the snow surface increases. s(t) increases significantly, which is consistent with actual physical laws.

[0115] The method for calculating the actual water vapor pressure in the air is as follows:

[0116] e a (t)=RH(t)×e s (t) / 100;

[0117] Where RH(t) represents the humidity at time t, indicating that the higher the humidity, the higher the actual water vapor pressure in the air.

[0118] The wind transport term at time point t is obtained by dividing the difference between the snow mass input per unit area and the snow mass output per unit area by the snow density.

[0119]

[0120] Among them, M in M represents the mass of snow input per unit area by wind. out This formula quantifies the effect of wind on the transport of snow by wind per unit area and is used to simulate dynamic changes in snow layers.

[0121] The wind power output snow mass per unit area M out The calculation method is as follows:

[0122]

[0123] In the formula, k ω The wind erosion coefficient is an empirical value that takes into account factors such as wind characteristics and snow surface friction, which are not clearly quantified. In this embodiment, it can be set to 2.25; ω This is an empirical index that reflects the nonlinear relationship between snow density and output snow quality; in this embodiment, it can be set to 2.2.

[0124] This formula quantifies the wind-driven snow mass M per unit area. out This provides a specific calculation method for analyzing the effect of wind on the transport of snow layers. Through this formula, the wind output of snow mass under different wind speeds and snow densities can be accurately simulated.

[0125] The wind force input snow mass per unit area uses the wind force output snow mass per unit area of ​​the upwind detection sub-region;

[0126] Methods for determining the upwind detection sub-region of the current detection sub-region include:

[0127] S501: Pre-set the relative orientation information of each detection sub-region and its adjacent detection sub-regions, and record it as the relative orientation information of adjacent sub-regions;

[0128] S502: Let P be initially 1, Q be initially 1, P range from 1 to p, and Q range from 1 to q.

[0129] S503: Determine the upwind detection sub-region of the detection sub-region in row P and column Q based on the upwind direction relationship, and denot it as the upwind detection sub-region of the detection sub-region in row P and column Q.

[0130] The upwind direction relationship is as follows:

[0131] θ(t)-45° <Dir P,Q <θ(t)+45°;

[0132] Where θ(t) is the wind direction at time t, Dir P,Q The relative orientation information of adjacent sub-regions of the detected sub-region in row P and column Q is the sub-region orientation information pre-stored in the system;

[0133] S504: If Q is less than q, then let Q+1 and execute S503;

[0134] If Q is greater than or equal to q and P is less than p, then let P+1, Q=1, and execute S503;

[0135] If Q is greater than or equal to q and P is greater than or equal to p, then the process ends.

[0136] The thickness measurement module is used to measure the snow layer thickness in real time, which is recorded as the real-time snow layer thickness.

[0137] See Figure 3 As shown, the method for real-time measurement of snow layer thickness includes:

[0138] A laser pulse of a preset wavelength is vertically emitted into the area to be tested by a pulsed laser emitter; the laser pulse penetrates the atmospheric environment in sequence and generates echo signals on the snow surface and the ground surface respectively.

[0139] The echo signal is received by a receiver coaxially positioned with the laser transmitter, and the echo signal is sampled by a high-speed analog-to-digital converter at a preset sampling frequency to obtain a digital signal sequence containing time series information;

[0140] It should be noted that the sampling frequency can be set according to the required measurement accuracy. For example, it can be set to 500MHz. The wavelength of the laser pulse can be set according to the snow penetration capability. For example, it can be set to 850nm.

[0141] The digital signal sequence is preprocessed by removing high-frequency noise using a sliding window filtering algorithm to obtain a smooth signal sequence;

[0142] It should be noted that the sliding window filtering algorithm is a commonly used denoising method in the field of signal processing, and the specific process will not be described in detail here.

[0143] Set amplitude threshold T h T h ≥0.3×SM max SM max The maximum amplitude of the signal; a time interval constraint Δn is set, Δn ≥ n min n min The number of sampling points corresponding to the minimum snow layer thickness can be calculated by comprehensively analyzing the historical data, geographical environment, and climate conditions of the detection area to determine the minimum possible snow layer thickness, and then calculating the corresponding number of sampling points based on the sampling frequency. In this embodiment, it can be set to 2.

[0144] Filter out signals from a smooth signal sequence whose amplitude is greater than the amplitude threshold T. h The digital signals are constructed into a first signal set;

[0145] Digital signals with time intervals greater than Δn are selected from the first signal set and used to construct the second signal set;

[0146] Select the digital signal with the longest interval from the second signal set and mark it as the longest digital signal. Then obtain the starting reflection peak and the ending reflection peak of the longest digital signal.

[0147] The first time difference is calculated based on the sampling frequency, the reference value of the sampling point corresponding to the laser pulse emission time, and the time point corresponding to the peak reflection at the starting point; the second time difference is calculated based on the sampling frequency, the reference value of the sampling point corresponding to the laser pulse emission time, and the time point corresponding to the peak reflection at the ending point; the real-time snow layer thickness is calculated based on the first time difference, the second time difference, and the speed of light.

[0148] The first time difference is calculated as follows:

[0149] Δt1=(n1-n0) / f;

[0150] Where Δt1 is the first time difference, n1 is the time point corresponding to the peak reflection at the starting point, n0 is the reference value of the sampling point corresponding to the laser pulse emission time, and f is the sampling frequency.

[0151] The second time difference is calculated as follows:

[0152] Δt2=(n2-n0) / f;

[0153] Where Δt2 is the second time difference, and n2 is the time point corresponding to the peak reflection at the endpoint.

[0154] The method for calculating real-time snow layer thickness based on the first time difference, the second time difference, and the speed of light is as follows:

[0155] SSXS=0.5×c×(Δt1-Δt2);

[0156] Where SSXS is the real-time snow thickness and c is the speed of light.

[0157] The measurement and calibration module is used to calibrate the data of the laser measurement equipment at each unit of time.

[0158] See Figure 2 As shown, the correction method includes:

[0159] S301. Compare the real-time snow layer thickness with the confidence interval of the predicted snow layer thickness. If the real-time snow layer thickness is within the confidence interval of the predicted snow layer thickness, no correction is required. If the real-time snow layer thickness is less than the minimum value of the confidence interval of the predicted snow layer thickness, it indicates that the measurement has been understated, so understating correction is performed. If the real-time snow layer thickness is greater than the maximum value of the confidence interval of the predicted snow layer thickness, it indicates that the measurement has been overstated, so overstating correction is performed.

[0160] The reason for the false distance reduction phenomenon is that when the snow layer is compacted or frozen, the density increases, the laser penetration depth decreases, and the reflection peak shifts from the deep layer to the snow surface or the middle dense layer. The equipment mistakenly regards the closer reflecting surface as the ground, resulting in an underestimation of the distance and causing the false distance reduction.

[0161] The method for correcting the false subtraction includes:

[0162] A pulsed laser emitter vertically projects laser pulses onto the area to be tested, simultaneously emitting standard wavelength laser pulses and penetrating wavelength laser pulses to ensure that the laser spot positions coincide. The receiver synchronously records the corresponding echo signal time series.

[0163] It should be noted that this embodiment uses a standard wavelength laser pulse with a wavelength of 850nm and a penetrating wavelength laser pulse with a wavelength of 1550nm as examples for illustration.

[0164] The time series of echo signals is subjected to mean filtering to extract the peak time of standard wavelength laser echo and the peak time of penetrating wavelength laser echo.

[0165] It should be noted that mean filtering is a commonly used noise reduction method in the field of signal processing, and the specific process will not be described in detail here.

[0166] The false reduction compensation amount is calculated based on the peak time of the standard wavelength laser echo and the peak time of the penetrating wavelength laser echo.

[0167] The real-time snow layer thickness is added to the false reduction compensation amount to obtain the false reduction correction thickness.

[0168] The method for calculating the underestimation compensation based on the peak time of the standard wavelength laser echo and the peak time of the penetrating wavelength laser echo is as follows:

[0169]

[0170] Where Δh is the false reduction compensation amount, t p1 The peak time of the standard wavelength laser echo, t p2 The peak time of the laser echo at the penetrating wavelength is denoted as n, which is the average refractive index of the snow layer and can be 1.34.

[0171] The reason for the false increase is that the snow layer contains loose snow layers. The laser pulse that enters the snow layer may be reflected at the layer interface, causing the final distance measurement result to include the depth of the interlayer, misjudging the ground position as deeper, thus causing the "false increase".

[0172] The method for correcting artificial increases includes:

[0173] A group of laser pulses is emitted synchronously by a multi-wavelength laser emitting device, and each beam is vertically incident on the snow layer after passing through an aberration-correcting collimating device.

[0174] The receiver, equipped with a spectral splitting unit, separates the echo signals of each wavelength channel of the laser pulse group, and acquires the time-domain signals through an avalanche photodetector array and a high-speed analog-to-digital converter to obtain the digital signal sequence of each wavelength channel;

[0175] Wavelet packet denoising is performed on the digital signal sequences of each wavelength channel to remove high-frequency noise, resulting in smoothed signals for each wavelength channel, and a set of smoothed signals for each wavelength is constructed. Adaptive threshold segmentation is used to extract the effective echo region time range of each smoothed signal in the set of smoothed signals for each wavelength, forming a set, which is labeled as the time range set.

[0176] A multi-layer interface reflection model is constructed using the effective echo region time range;

[0177] Based on the set of smoothed signals of each wavelength, the set of time ranges, and the parameters of the multi-layer interface reflection model, multi-peak fitting and reflection peak identification are performed to obtain the time series of reflection peaks of each wavelength channel and the interlayer time intervals corresponding to each reflection peak.

[0178] Snow layer density inversion was performed using the time series of reflection peaks of each wavelength channel to obtain the set of snow layer densities for each layer;

[0179] The average refractive index of the snow layer at each wavelength is calculated based on the set of snow layer densities and the interlayer time intervals corresponding to each reflection peak.

[0180] The real-time snow layer thickness is corrected based on the average refractive index of the snow layer to obtain the artificially increased corrected thickness.

[0181] It should be noted that the wavelength of the laser pulse group is set by the parameters of the emission module. For example, wavelengths with different penetration, such as 850nm, 1064nm, and 1550nm, can be selected. The multi-wavelength laser emission device in this embodiment uses at least three different wavelengths of laser pulses to synchronously emit laser pulse groups. By using the echo signal data of multiple wavelength laser pulses, the internal condition of the snow layer can be better reflected.

[0182] It should be noted that wavelet packet denoising is a commonly used denoising method in the field of signal processing, and the specific process will not be described in detail here.

[0183] The sets of smoothed signals for each wavelength are expressed as follows:

[0184] {S′1(t),S′2(t),…,S′ k (t)}(k=1,2,……,N);

[0185] Where N is the number of laser pulses of different wavelengths in the laser pulse group.

[0186] Methods for extracting the effective echo region time range of each smoothed signal in each wavelength smoothed signal set using adaptive threshold segmentation include:

[0187] Calculate the average signal amplitude based on each smoothed signal in the set of smoothed signals for each wavelength:

[0188]

[0189] Where μ is the average signal amplitude, S′ k (t) represents the smoothed signal of the k-th wavelength laser pulse in the set of smoothed signals for each wavelength.

[0190] Set the adaptive threshold to βμ, where β is a constant, for example, β is 1.5. Extract the time region set corresponding to the signal region whose amplitude exceeds the adaptive threshold from each wavelength smoothed signal set. Select the widest region from the time region set as the effective echo region time range [t]. start ,t end ]; where t start t represents the time of the snow surface reflection peak. end This represents the time of the surface reflection peak.

[0191] Methods for constructing multi-layer interface reflection models using the effective echo region time range include:

[0192] Initialize the number of snow layers m.

[0193] Where, Δt min The two-way travel time corresponding to the minimum resolvable thickness of the snow layer is determined based on the sampling interval;

[0194] Initialize theoretical reflection peak time t k,i , t start ≤t k,i ≤t end ;

[0195] It should be noted that the sampling interval is uniformly determined by the frequency of the analog-to-digital converter.

[0196] A multi-layer interface reflection model is constructed based on the number of snow layers and the theoretical reflection peak time.

[0197]

[0198] Among them, S model (t) represents the multi-layer interface reflection model, A k,i Let t be the amplitude of the reflected signal of the i-th snow layer at the k-th wavelength laser pulse, and δ(·) be the Dirac function, a theoretical simplification. Subsequent steps will fit the actual broadened reflection peak to a Gaussian function. The Dirac function determines the ideal time position of the reflection peak, serving as the initial constraint for the Gaussian fitting. k,0 =t start , t k,m+1 =t end , t k,1 ~t k,m Evenly distributed in [t start ,t end Within ], as the initial guess value for multi-peak fitting.

[0199] The method for obtaining the time series of reflection peaks for each wavelength channel and the interlayer time intervals corresponding to each reflection peak is based on the set of smoothed signals of each wavelength, the set of time ranges, and the parameters of the multilayer interface reflection model.

[0200] The interface reflectivity of each snow layer is calculated based on the refractive index of each snow layer to different wavelength laser pulses. The calculation method is as follows:

[0201]

[0202] in, Let n be the interfacial reflectivity of the i-th snow layer to the k-th wavelength laser pulse. i,k Let be the refractive index of the i-th snow layer for the k-th wavelength laser pulse;

[0203] Within the effective echo region, Gaussian multi-peak fitting is performed on the smoothed signal set of each wavelength to obtain fitting parameters, which include reflection peak time, peak width and peak height.

[0204] Each reflection peak is represented by the following Gaussian function:

[0205]

[0206] Where, μ i For the reflection peak time, σ i For peak width, A i For peak height;

[0207] Set the reflection peak time to the initial guess value of Gaussian multi-peak fitting; set the peak width to twice the sampling interval; set the peak height to the product of interface reflectivity and incident light intensity.

[0208] It should be noted that the incident light intensity can be set to the maximum amplitude of the smoothed signal for each wavelength.

[0209] Set fitting constraints, including reflection peak time constraints, peak height constraints, and layer number constraints;

[0210] Time constraint on reflection peaks: Force the time t of all fitted peaks. k,i ∈[t start ,t end ], and satisfy t k,1 <t k,2 <…… <t k,i ;

[0211] Peak height constraint:

[0212] Layer number constraint: Number of fitted peaks N ≤ m + 2;

[0213] Construct the least squares optimization objective function J k (θ k ):

[0214]

[0215] The Levenberg-Marquardt algorithm was used to optimize the fitting parameters, resulting in the time series of reflection peaks for each wavelength channel. in, Let be the reflection peak time of the Nth wavelength laser pulse. Correspondingly, the interlayer time intervals corresponding to each reflection peak are obtained.

[0216] It should be noted that the Levenberg-Marquardt algorithm is a commonly used technique for multi-peak fitting in the field of signal processing, and its specific fitting process will not be elaborated here.

[0217] Methods for inverting snow layer density using time series of reflection peaks from different wavelength channels to obtain the set of snow layer densities for each layer include:

[0218] S401: Let the number of snow layers be SL, where SL equals the number of snow layers plus 1; let the initial value of i be 1, and the value of i ranges from 1 to SL;

[0219] S402: Construct an equation relating the refractive index of the i-th layer to the snow density of the i-th layer based on the Gladstone-Dale law;

[0220] Construct an equation relating the thickness of the i-th snow layer to the refractive index of the i-th layer;

[0221] Set an initial value for the density of each snow layer.

[0222] S403: The refractive index of the i-th layer is calculated based on the equation relating the refractive index of the i-th layer to the snow density of the i-th layer.

[0223] S404: The thickness of the i-th snow layer is calculated based on the equation relating the thickness of the i-th snow layer to the refractive index of the i-th layer.

[0224] S405: Add the thickness of the i-th snow layer to the set of snow layer thicknesses;

[0225] S406: Let i = i + 1. If i is less than or equal to SL, continue to execute S403 to S404; if i is greater than SL, obtain the set of snow layer thicknesses and execute S407.

[0226] The equation relating the refractive index of the i-th layer to the snow density of the i-th layer is as follows:

[0227] n i,k =1+K k ρ i ;

[0228] Where, n i,k Let K be the refractive index of the i-th layer. k K is a wavelength-dependent proportionality constant; for example, for a laser pulse with a wavelength of 1550 nm, K k =0.8, ρ i Let be the density of the i-th snow layer.

[0229] The equation relating the thickness of the i-th snow layer to its refractive index is as follows:

[0230]

[0231] Where, d i Let λ be the thickness of the i-th snow layer. k This corresponds to the wavelength of the laser pulse.

[0232] S407: Construct a least-squares optimization objective function J based on the interlayer time intervals corresponding to each reflection peak and the set of snow layer thicknesses for each layer.

[0233]

[0234] The snow density of each layer is solved by using an iterative algorithm to optimize the objective function using least squares. By changing the value of each snow density, J is minimized. The snow density corresponding to the minimized J is then used to construct a set of snow densities for each layer, thus ending the current process.

[0235] It should be noted that the iterative algorithm can also be solved using the Levenberg-Marquardt algorithm or similar algorithms, and the specific solution process will not be elaborated here.

[0236] The method for calculating the average refractive index of snow layers at each wavelength based on the density set of each snow layer and the interlayer time interval corresponding to each reflection peak is as follows:

[0237]

[0238] Where, n avg,k is the average refractive index of the snow layer at each wavelength.

[0239] The method for calculating the artificially increased correction thickness is as follows: The light speed is corrected based on the average refractive index of the snow layer, and the real-time snow layer thickness is also corrected.

[0240]

[0241] Among them, h 2,d To artificially inflate the thickness, this corrects for thickness calculation errors caused by refractive index differences, ensuring the accuracy of thickness measurements; Δtime is the time difference between the surface reflection peak and the snow surface reflection peak of the longest wavelength laser pulse; n air,k The air refractive index for each wavelength is calculated as follows:

[0242]

[0243] Where P is air pressure and Temp is temperature;

[0244] It should be noted that the air pressure and temperature are collected meteorological data. The snow thickness measured by laser is corrected by considering the effects of air pressure and temperature on the air refractive index, as well as the average characteristics of multi-wavelength refractive indices.

[0245] S302. If, after the false reduction correction, the false reduction correction thickness is within the corresponding confidence interval, then the correction is complete and the current process ends; if the false reduction correction thickness is not within the corresponding confidence interval, then execute S303.

[0246] If, after the spurious correction is performed, the spurious correction thickness falls within the corresponding confidence interval, then the correction is complete and the current process ends; otherwise, if the spurious correction thickness is not within the corresponding confidence interval, then S303 is executed.

[0247] S303: Repeat S301 and S302; if the number of repetitions exceeds the predetermined number, such as 3 times, it indicates that there is an abnormality in the device, and a device abnormality command is sent.

[0248] Example 2

[0249] This embodiment provides a multi-point laser scanning method for measuring snow layer thickness, including:

[0250] Collect meteorological data per unit time in the detection area;

[0251] The predicted snow thickness is obtained by predicting the snow layer thickness per unit time in the future based on meteorological data.

[0252] The snow layer thickness is measured in real time to obtain the real-time snow layer thickness;

[0253] The data from the laser measuring device is checked at each unit of time. If any false increases or decreases are found, the real-time snow layer thickness is corrected to obtain the corrected snow layer thickness.

[0254] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0255] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. Method for measuring the thickness of a snow cover by means of multiple laser scanning, characterized in that, The method comprises the following steps: Collecting meteorological data of the detection area at a unit time; Based on the meteorological data, the snow thickness in a future unit time is predicted to obtain a predicted snow thickness, and the specific method comprises the following steps: dividing a detection area into p detection sub-areas, q is the number of rows in the detection sub-area, and p is the number of columns in the detection sub-area; dividing a unit time into T time points; establishing a corresponding snow thickness dynamic equation for each detection sub-area according to meteorological data; calculating the predicted snow thickness and the corresponding confidence interval of each detection sub-area at future 1, 2, …, Time time steps according to the snow thickness dynamic equation, where Time is the total number of time steps; and the method for establishing the snow thickness dynamic equation comprises the following steps: S101: preset the initial value of t as 1, the value range of t is 1 to T, and the time interval between every two time points is ; the snow thickness corresponding to the first time point is collected in advance; S102: constructing a snow layer thickness dynamic equation based on the meteorological data and the snow layer thickness at the tth time point, and obtaining the snow layer thickness at the t+1th time point; S103: setting t=t+1, if t is less than or equal to T, continue to execute S102; if t is greater than T, obtain the predicted snow layer thickness at the Tth time point, and end the current process; The snow layer thickness dynamic equation is coupled through multiple physical processes to realize dynamic prediction of the snow layer thickness; the multiple physical processes include a snowfall accumulation term at the tth time point, a thickness melting term at the tth time point, a thickness sublimation term at the tth time point, and a wind transport term at the tth time point; The method for determining the upwind detection sub-area of the current detection sub-area comprises the following steps: S501: presetting the relative orientation information of each detection sub-area and its adjacent detection sub-area, denoted as adjacent sub-area relative orientation information; S502: setting the initial value of P as 1 and the initial value of Q as 1, the value range of P being 1 to p, and the value range of Q being 1 to q; S503: judging the upwind detection sub-area of the detection sub-area in the Pth row and the Qth column according to the upwind direction relationship, denoted as the upwind detection sub-area of the detection sub-area in the Pth row and the Qth column; S504: if Q is less than q, setting Q+1 and executing S503; if Q is greater than or equal to q and P is less than p, setting P+1, Q=1, and executing S503; if Q is greater than or equal to q and P is greater than or equal to p, ending the process; Real-time measurement of the snow layer thickness to obtain the real-time snow layer thickness; In each unit time, the data of the laser measurement device is verified, if there is a phenomenon of virtual increase or virtual decrease, the real-time snow layer thickness is corrected to obtain the corrected snow layer thickness.

2. The method of claim 1, wherein, The method for correcting the real-time snow layer thickness to obtain the corrected snow layer thickness comprises the following steps: S301: comparing the real-time snow layer thickness with the confidence interval of the predicted snow layer thickness, if the real-time snow layer thickness is within the confidence interval of the predicted snow layer thickness, no correction is performed; if the real-time snow layer thickness is less than the minimum value of the confidence interval of the predicted snow layer thickness, virtual decrease correction is performed; if the real-time snow layer thickness is greater than the maximum value of the confidence interval of the predicted snow layer thickness, virtual increase correction is performed; S302: if the virtual decrease correction is performed, if the virtual decrease correction thickness is within the corresponding confidence interval, the correction is completed, and the current process is ended; if the virtual decrease correction thickness is not within the corresponding confidence interval, S303 is executed; if the virtual increase correction is performed, if the virtual increase correction thickness is within the corresponding confidence interval, the correction is completed, and the current process is ended; if the virtual increase correction thickness is not within the corresponding confidence interval, S303 is executed; S303: repeating S301 and S302, if the number of repetitions exceeds a predetermined number, it is indicated that the device is abnormal, and a device abnormality instruction is sent.

3. The method of claim 2, wherein, The method for virtual decrease correction comprises the following steps: vertically emitting standard wavelength laser pulses and penetrating wavelength laser pulses through the pulsed laser emitter to the to-be-measured area, ensuring that the positions of the light spots coincide, and the receiving end synchronously records the corresponding echo signal time sequence; The echo signal time sequence is subjected to mean filtering to extract standard wavelength laser echo peak time and penetrating wavelength laser echo peak time; The virtual reduction compensation amount is calculated based on the standard wavelength laser echo peak time and the penetrating wavelength laser echo peak time; The real-time snow thickness is added to the virtual reduction compensation amount to obtain the virtual reduction correction thickness.

4. The method of Claim 2, wherein, The method for virtual increase correction comprises: A multi-wavelength laser emitting device synchronously emits a laser pulse group, and each light beam is vertically incident on the snow layer through an aberration-corrected collimating device; A receiver carrying a spectral light splitting unit separates echo signals of each wavelength channel of the laser pulse group, and an avalanche photodetector array and a high-speed analog-to-digital converter collect time-domain signals to obtain a digital signal sequence of each wavelength channel; The digital signal sequence of each wavelength channel is subjected to wavelet packet denoising to remove noise and obtain a smooth signal of each wavelength channel, and a set of smooth signals of each wavelength is constructed; an adaptive threshold segmentation is used to extract the effective echo area time range of each smooth signal in the set of smooth signals of each wavelength, which is marked as a time range set; A multi-layer interface reflection model is constructed using the effective echo area time range; Based on the set of smooth signals of each wavelength, the time range set and the multi-layer interface reflection model parameters, multi-peak fitting and reflection peak identification are performed to obtain a reflection peak time sequence of each wavelength channel and an interlayer time interval corresponding to each reflection peak; Snow layer density inversion is performed using the reflection peak time sequence of each wavelength channel to obtain a set of snow layer densities of each layer; Based on the set of snow layer densities of each layer and the interlayer time interval corresponding to each reflection peak, the average refractive index of the snow layer of each wavelength is calculated; Based on the average refractive index of the snow layer, the real-time snow thickness is corrected to obtain a virtual increase correction thickness.

5. The method of claim 4, wherein, The method for performing multi-peak fitting and reflection peak identification based on the set of smooth signals of each wavelength, the time range set and the multi-layer interface reflection model parameters to obtain a reflection peak time sequence of each wavelength channel and an interlayer time interval corresponding to each reflection peak comprises: The interface reflectivity of each snow layer is calculated based on the refractive index of the snow layer to different wavelength laser pulses; Within the effective echo area, Gaussian multi-peak fitting is performed on the set of smooth signals of each wavelength to obtain fitting parameters, which include reflection peak time, peak width and peak height; The reflection peak time is set as the initial guess value of the Gaussian multi-peak fitting; the peak width is set as 2 times the sampling interval; and the peak height is set as the product of the interface reflectivity and the incident light intensity; Fitting constraint conditions are set, which include reflection peak time constraint conditions, peak height constraint conditions and layer number constraint conditions; The reflection peak time constraint conditions: all fitting peaks are forced to belong to the effective echo area time range and to be sequentially increased; The peak height constraint conditions: the peak height of the current snow layer is greater than or equal to the product of the peak height of the next snow layer and the interface reflectivity; Layer number constraint: the number of fitted peaks is less than or equal to the sum of the number of snow layers and the addition; A least squares optimization objective function is constructed; The fitting parameters are optimized through the Levenberg-Marquardt algorithm to obtain the reflection peak time sequence of each wavelength channel and the interlayer time interval corresponding to each reflection peak.

6. The method of claim 4, wherein, The method for performing snow layer density inversion using the reflection peak time sequence of each wavelength channel to obtain a set of snow layer densities of each layer comprises: S401: record the number of snow layers as SL, SL is equal to the number of snow layer sandwich layers plus 1; let the initial value of i be 1, and the value range of i be 1 to SL; S402: construct the equation of the relationship between the refractive index of the i-th layer and the snow layer density of the i-th layer according to the Gladstone-Dale law; construct the equation of the relationship between the snow layer thickness of the i-th layer and the refractive index of the i-th layer; Setting an initial value for each snow layer density ; S403: calculate the refractive index of the i-th layer according to the equation of the relationship between the refractive index of the i-th layer and the snow layer density of the i-th layer; S404: calculate the snow layer thickness of the i-th layer according to the equation of the relationship between the snow layer thickness of the i-th layer and the refractive index of the i-th layer; S405: add the snow layer thickness of the i-th layer to the set of snow layer thicknesses of each layer; S406: let i = i + 1, if i is less than or equal to SL, continue to execute S403 to S404; if i is greater than SL, obtain the set of snow layer thicknesses of each layer, and execute S407; S407: construct a least squares optimization objective function based on the interlayer time interval corresponding to each reflection peak and the set of snow layer thicknesses of each layer; use an iterative algorithm to solve the least squares optimization objective function for the snow layer density of each layer, change the value of the snow layer density of each layer to minimize the least squares optimization objective function, and construct the set of snow layer densities of each layer when the least squares optimization objective function is minimized, end the current process.

7. The method of Claim 1, wherein, The snowfall accumulation term at the t-th time point is obtained by multiplying the snowfall at the t-th time point by the unit time interval and dividing by the snow density; the thickness melting term at the t-th time point is obtained by comprehensively representing the temperature at the t-th time point and the melting coefficient; the thickness sublimation term at the t-th time point is calculated by the sublimation coefficient, the snow surface saturation water vapor pressure, the actual water vapor pressure in the air, and the wind speed at the t-th time point; the wind force carrying term at the t-th time point is obtained by subtracting the wind force output snow mass per unit area from the wind force input snow mass per unit area and dividing by the snow density; the wind force input snow mass per unit area uses the wind force output snow mass per unit area of the upwind detection sub-region of the current detection sub-region.

8. A multi-point laser scanning snow depth measurement system, implementing the multi-point laser scanning snow depth measurement method according to any one of claims 1 to 7, characterized in that, It comprises: a meteorological data acquisition module for acquiring meteorological data of a detection area at a unit time; a snow layer thickness prediction module for predicting the snow layer thickness in a future unit time based on the meteorological data to obtain a predicted snow layer thickness; a thickness measurement module for measuring the snow layer thickness in real time to obtain a real-time snow layer thickness; a measurement correction module for verifying the data of the laser measurement device at each unit time, and correcting the real-time snow layer thickness if there is a virtual increase or virtual decrease phenomenon to obtain a corrected snow layer thickness.

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