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

Abstract

The invention belongs to the technical field of laser measurement equipment, and discloses a multi-point laser scanning snow layer thickness measurement method and system. Comprising a meteorological data acquisition module used for acquiring meteorological data of a detection area at unit time; the snow layer thickness prediction module is used for predicting the snow layer thickness in unit time in the future based on the meteorological data to obtain the predicted snow layer thickness; the thickness measuring module measures the thickness of the snow layer in real time to obtain the real-time thickness of the snow layer; the measurement correction module is used for checking the data of the laser measurement equipment in each unit time, and correcting the real-time snow layer thickness if the phenomenon of virtual increase or virtual decrease occurs to obtain the corrected snow layer thickness; the snow layer thickness in future unit time is predicted in advance, and the real-time snow layer thickness is verified according to the prediction result, so that dynamic real-time snow layer thickness correction is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of laser measurement equipment, and more particularly to a method and system for measuring snow layer thickness by multi-point laser scanning. Background Art

[0002] Real-time monitoring of dynamic changes within the snow layer has become the key to cracking the "black box" of the snow layer and improving disaster warning capabilities. In existing technologies, although lidar is widely used for remote sensing monitoring of snow thickness, its technical approach, which relies on simple ranging models, has limitations. Traditional solutions only calculate the surface elevation difference through the round-trip time of the laser, but cannot capture the dynamic evolution of optical properties caused by density changes within the snow layer. When the density gradient of snow changes due to compaction, recrystallization, or cavity formation, the penetration depth of lasers in different bands will show nonlinear differences. The position and intensity of the reflection peak will also shift due to the optical mutation of the interlayer interface, ultimately resulting in systematic errors of "false increase" or "false decrease" in the measurement results. This technical bottleneck makes it difficult for traditional lidar to accurately depict subtle changes in the internal structure of the snow layer, thereby affecting the timeliness and accuracy of disaster warnings.

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

[0004] In order to overcome the above-mentioned defects of the prior art and achieve the above-mentioned objectives, the present invention provides the following technical solution: a multi-point laser scanning snow thickness measurement method, comprising:

[0005] Collect meteorological data of the detection area at each unit time;

[0006] Predicting the thickness of snow layer in unit time in the future based on meteorological data to obtain the predicted snow layer thickness;

[0007] Real-time measurement of snow thickness to obtain real-time snow thickness;

[0008] The data of the laser measuring device is checked at each unit time. If there is any false increase or decrease, the real-time snow layer thickness is corrected to obtain the corrected snow layer thickness.

[0009] Furthermore, the real-time snow layer thickness is corrected to obtain the corrected snow layer thickness. The method includes:

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

[0011] S302: After performing virtual subtraction correction, if the virtual subtraction correction thickness is within the corresponding confidence interval, the correction is completed and the current process ends; if the virtual subtraction correction thickness is not within the corresponding confidence interval, execute S303;

[0012] If the virtual increase correction thickness is within the corresponding confidence interval after the virtual increase correction, the correction is completed and the current process ends; if the virtual increase correction thickness is not within the corresponding confidence interval, execute S303;

[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 instruction is sent.

[0014] Furthermore, the method for correction of virtual subtraction includes:

[0015] The pulse laser transmitter emits vertically to the area to be measured, and simultaneously emits standard wavelength laser pulses and penetrating wavelength laser pulses to ensure that the spot positions coincide. The receiving end synchronously records the corresponding echo signal time series;

[0016] Perform mean filtering on the echo signal time series to extract the peak time of the standard wavelength laser echo and the peak time of the penetrating wavelength laser echo;

[0017] Calculate the virtual subtraction compensation amount 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 virtual subtraction compensation to obtain the virtual subtraction correction thickness.

[0019] Furthermore, the method for false increase correction includes:

[0020] The laser pulse group is synchronously emitted by a multi-wavelength laser emitting device, and each beam is vertically incident on the snow layer 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, collects the time domain signals through an avalanche photodetector array and a high-speed analog-to-digital converter, and obtains the digital signal sequence of each wavelength channel;

[0022] Wavelet packet denoising is performed on the digital signal sequence of each wavelength channel to remove noise, obtain the smooth signal of each wavelength channel, and construct a set of smooth signals of each wavelength; 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, and mark it as a time range set;

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

[0024] Based on the smoothed signal set 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 the reflection peak time series of each wavelength channel and the inter-layer time interval corresponding to each reflection peak;

[0025] The snow layer density is inverted using the time series of reflection peaks of each wavelength channel to obtain the density set of each snow layer;

[0026] The average refractive index of the snow layer at each wavelength is calculated based on the density set of each snow layer and the inter-layer time interval corresponding to each reflection peak;

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

[0028] Furthermore, based on the smoothed signal set 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 the reflection peak time series of each wavelength channel and the inter-layer time interval 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 laser pulses of different wavelengths;

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

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

[0032] Setting fitting constraints, wherein the fitting constraints include a reflection peak time constraint, a peak height constraint, and a layer number constraint;

[0033] Reflection peak time constraint: forces the time of all fitted peaks to fall within the valid echo region time range and increase in sequence;

[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] The fitting parameters were optimized by the Levenberg-Marquardt algorithm to obtain the time series of the reflection peaks of each wavelength channel, and accordingly, the inter-layer time interval corresponding to each reflection peak was obtained.

[0038] Furthermore, the snow layer density is inverted using the reflection peak time series of each wavelength channel to obtain the density set of each snow layer. The method includes:

[0039] S401: record the number of snow layers as SL, where SL is equal to the number of snow layers plus 1; let the initial value of i be 1, and the value range of i be 1 to SL;

[0040] S402: Construct the equation for the relationship between the refractive index of the i-th layer and the snow density of the i-th layer according to the Gladstone-Dale law;

[0041] Construct the equation of the relationship between the thickness of the snow layer at layer i and the refractive index at layer i;

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

[0043] S403: Calculate the refractive index of the i-th layer according to the relationship equation between the refractive index of the i-th layer and the density of the snow layer of the i-th layer;

[0044] S404: Calculate the thickness of the i-th snow layer according to the relationship equation between the thickness of the i-th snow layer and the refractive index of the i-th layer;

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

[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 thickness set of each snow layer and execute S407.

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

[0048] An iterative algorithm is used to solve the density of each snow layer for the least squares optimization objective function. The density of each snow layer is minimized by changing the value of the density of each snow layer. The snow layer density corresponding to the minimum value of the least squares optimization objective function is constructed into a set of snow layer densities, and the current process ends.

[0049] Furthermore, the method for predicting the snow thickness within a unit time in the future based on meteorological data includes:

[0050] Divide the detection area into q×p detection sub-areas, where q is the number of rows in the detection sub-area and p is the number of columns in the detection sub-area;

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

[0052] According to meteorological data, a corresponding dynamic equation of snow thickness is established for each detection sub-area;

[0053] The predicted snow thickness and the corresponding confidence interval of each detection sub-area in the next 1, 2, ..., Time time steps are calculated according to the dynamic equation of snow thickness, where Time is the total number of time steps.

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

[0055] S101: Preset the initial value of t to 1, the value range of t is 1 to T, and the time interval between each two time points is Δt; pre-collect the snow layer thickness corresponding to the first time point;

[0056] S102: constructing a snow layer thickness dynamic equation based on the meteorological data and snow layer thickness at the t-th time point to obtain the snow layer thickness at the t+1-th time point;

[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 thickness at the Tth time point and end the current process.

[0058] The snow layer thickness dynamic equation is coupled through multi-physical processes to achieve dynamic prediction of snow layer thickness; the multi-physical processes include the snowfall accumulation term at the t-th time point, the thickness melting term at the t-th time point, the thickness sublimation term at the t-th time point, and the wind transport term at the t-th time point;

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

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

[0061] S501: presetting relative position information between each detection sub-region and its adjacent detection sub-region, recorded as adjacent sub-region relative position information;

[0062] S502: Let the initial value of P be 1, the initial value of Q be 1, the value range of P be 1 to p, and the value range of Q be 1 to q;

[0063] S503: Determine an upwind detection sub-region of the detection sub-region in the P-th row and Q-th column according to the upwind direction relationship, and record it as an upwind detection sub-region of the detection sub-region in the P-th row and Q-th column;

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

[0065] If Q is greater than or equal to q, and P is less than p, set 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 thickness measurement system implements the multi-point laser scanning snow thickness measurement method, comprising:

[0068] Meteorological data collection module, used to collect meteorological data of the detection area at a specific time;

[0069] The snow thickness prediction module predicts the snow thickness in the future unit time based on meteorological data to obtain the predicted snow thickness;

[0070] Thickness measurement module, used to measure the thickness of the snow layer in real time and obtain the real-time snow layer thickness;

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

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

[0073] The multi-point laser scanning snow thickness measurement system proposed in the present invention addresses the problem that traditional lidar relies on a simple ranging model and cannot capture the dynamic evolution of optical properties and systematic measurement errors caused by density changes inside the snow layer. Through a dynamic snow thickness prediction model that couples meteorological data acquisition with multi-physical processes, a prediction confidence interval is constructed to verify the measurement data in real time. Combined with the calculation of the subtraction compensation amount of the dual-wavelength laser and the false increase refractive index correction technology of the multi-wavelength signal, it effectively solves the influence of density gradient changes caused by snow compaction, recrystallization or cavity formation on the measurement accuracy, and realizes the accurate characterization of subtle changes in the internal structure of the snow layer; through the dynamic comparison of real-time measurement values ​​and prediction results and the iterative correction mechanism, the "false increase" and "false decrease" errors caused by the reflection peak offset caused by the optical mutation of the interlayer interface in traditional technology are eliminated, which significantly improves the accuracy and reliability of snow thickness measurement, provides more timely and accurate core data support for ice and snow disaster warning, and breaks through the bottleneck that traditional technology is difficult to reveal the "black box" dynamics of the snow layer. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0076] Figure 3 Schematic diagram of real-time thickness measurement;

[0077] Figure 4 Schematic diagram of the distribution of detection areas. DETAILED DESCRIPTION

[0078] The technical solutions in the embodiments of the present invention will be described in detail, clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. It should be noted that the specific embodiments described below are only used to better illustrate and describe 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 essence of the present invention, those skilled in the art may modify, adjust or make equivalent replacements based on the contents disclosed in the present invention, and these should all be regarded as the scope of protection of the present invention.

[0079] Example 1

[0080] See also Figure 1 As shown, this embodiment discloses a multi-point laser scanning snow thickness measurement system, which is applied to laser measurement equipment, including a meteorological data acquisition module, a snow thickness prediction module, a thickness measurement module and a measurement correction module. Each module realizes data transmission through wired and / or wireless connections.

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

[0082] The meteorological data is obtained through a meteorological station in the detection area.

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

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

[0085] See Figure 4 As shown, the detection area is divided into q×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;

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

[0087] According to meteorological data, a corresponding dynamic equation of snow thickness is established for each detection sub-area;

[0088] The predicted snow thickness and the corresponding confidence interval of each detection sub-area in the next 1, 2, ..., Time time steps are calculated according to the dynamic equation of snow thickness.

[0089] It should be noted that the confidence interval can be set based on the complexity and time scale of the snow thickness dynamic equation. In this embodiment, due to the high accuracy of meteorological data and the short time scale of the prediction, 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 snow layer thickness dynamic equation includes:

[0091] S101: Preset the initial value of t to 1, the value range of t is 1 to T, and the time interval between each two time points is Δt; pre-collect the snow layer thickness corresponding to the first time point;

[0092] S102: constructing a snow layer thickness dynamic equation based on the meteorological data and snow layer thickness at the t-th time point to obtain the snow layer thickness at the t+1-th time point;

[0093] 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.

[0094] It should be noted that the snow thickness corresponding to the first time point is obtained by measuring with a snow ruler to ensure the accuracy of the initial data of the snow thickness dynamic equation, thereby improving the accuracy of the predicted snow thickness at subsequent times.

[0095] The dynamic equation of 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] Where S(t+1) is the predicted snow thickness at the t+1th time point, S(t) is the snow thickness at the tth time point, and ΔS(t) 降雪 is the cumulative snowfall at time point t, ΔS(t) 融化 is the thickness melting term at time point t, ΔS(t) 升华 is the thickness sublimation term at the tth time point, ΔS(t) 风力 is the wind transport term at the tth time point.

[0098] Where, ΔS(t) 降雪 The thickness of the snow layer in the detection area during normal snowfall; ΔS(t) 融化 and ΔS(t) 升华 It is the thermodynamic process of snow layer change, indicating the thickness of the snow layer in the detection area is reduced; ΔS(t) 风力 This represents the dynamic process of snow layer changes, indicating the dynamic changes in snow thickness in the detection area. The dynamic snow thickness equation achieves dynamic prediction of snow thickness by coupling multiple physical processes.

[0099] The snowfall accumulation item 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 specific example of this embodiment is as follows:

[0100]

[0101] Among them, ρ snow is the snow density. The new snow density is usually 100-300. In this embodiment, it is 200. snow (t) is the snowfall at the t-th time point.

[0102] The thickness melting term at the t-th time point is obtained by comprehensively characterizing the temperature and melting coefficient at the t-th time point. The specific examples of this embodiment are as follows:

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

[0104] Among them, Temp(t) is the temperature at the tth time point, Temp melting is the melting threshold temperature, the default is 0, k m is the melting coefficient, which reflects the melting speed of snow at different temperatures, k m The calculation method is:

[0105]

[0106] Where Q(t) is the solar radiation at time point t; α s is the albedo of the snow surface, which can be set according to the type of snow. In this embodiment, it is set to 0.5; L f is the latent heat of melting of water, which is the basic parameter for measuring the energy consumption of the melting process and determines the conversion relationship between heat and melting amount. In this embodiment, the value can be 334; water is the density of water, which can be set to 1000 kg / m 3 ;Q conductionThe heat is conducted to the underlying surface, which is determined by the underlying surface type. The underlying surface type is obtained by technicians in advance through field surveys when installing the equipment. The underlying surface under the snow will transfer energy to the snow through heat conduction, promoting melting, and is an important component of heat input.

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

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

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

[0110] Among them, the saturated water vapor pressure of the snow surface is e s (t) and the actual water vapor pressure e in the air a The difference in (t) reflects the water vapor pressure gradient between the snow surface and the air. The larger the difference, the higher the sublimation rate. The greater the wind speed V(t), the faster the water vapor transport, maintaining the water vapor pressure gradient, and thus promoting sublimation. The sublimation coefficient k e Used to calibrate the difference between calculated results and actual process.

[0111] The calculation method of the snow surface saturated water vapor pressure is:

[0112]

[0113] Where 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 have shown that the saturated water vapor pressure on the snow surface increases rapidly with increasing temperature. This formula captures this nonlinear change relationship in an exponential form by fitting a large amount of observational data. The parameters in the exponent are determined by analyzing the relationship between water vapor pressure and temperature, and can accurately reflect the changing characteristics of the saturated water vapor pressure on the snow surface with temperature Temp(t). For example, when the temperature rises, the exponential part increases, and the saturated water vapor pressure on the snow surface e s(t) increases significantly, which is consistent with the actual physical laws.

[0115] The calculation method of the actual water vapor pressure in the air is:

[0116] e a (t)=RH(t)×e s (t) / 100;

[0117] Among them, RH(t) is the humidity at the tth time point, indicating that the higher the humidity, the higher the actual water vapor pressure in the air.

[0118] The wind transport term at the t-th time point is obtained by subtracting the difference between the wind input snow mass per unit area and the wind output snow mass per unit area, divided by the snow density:

[0119]

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

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

[0122]

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

[0124] This formula quantifies the snow mass M output per unit area by wind power. out , which provides a specific calculation method for analyzing the effect of wind on snow transportation. Through this formula, the wind output snow mass under different wind speed and snow density conditions can be accurately simulated.

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

[0126] The method for determining the upwind detection sub-region of the current detection sub-region includes:

[0127] S501: presetting relative position information between each detection sub-region and its adjacent detection sub-region, recorded as adjacent sub-region relative position information;

[0128] S502: Let the initial value of P be 1, the initial value of Q be 1, the value range of P be 1 to p, and the value range of Q be 1 to q;

[0129] S503: Determine an upwind detection sub-region of the detection sub-region in the P-th row and Q-th column according to the upwind direction relationship, and record it as an upwind detection sub-region of the detection sub-region in the P-th row and Q-th column;

[0130] The upwind direction relationship is:

[0131] θ(t)-45° <Dir P,Q <θ(t)+45°;

[0132] Among them, θ(t) is the wind direction at the tth time point, Dir P,Q The relative position information of the adjacent sub-regions of the detection sub-region in the P-th row and the Q-th column is the sub-region position information pre-stored in the system;

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

[0134] If Q is greater than or equal to q, and P is less than p, set 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 thickness of the snow layer 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 thickness includes:

[0138] A pulsed laser transmitter emits laser pulses of a preset wavelength vertically toward the area to be measured; the laser pulses sequentially penetrate the atmospheric environment and generate echo signals on the snow surface and the ground surface respectively;

[0139] The echo signal is received by a receiver coaxially arranged 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 500 MHz. The wavelength of the laser pulse can be set according to its ability to penetrate the snow layer. For example, it can be set to 850 nm.

[0141] Preprocess the digital signal sequence and remove high-frequency noise by setting 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 here.

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

[0144] Filter out the smoothed signal sequence whose amplitude is greater than the amplitude threshold T h and constructing a first signal set;

[0145] Filtering digital signals with a time interval greater than Δn from the first signal set and constructing them into a second signal set;

[0146] A digital signal with the longest interval time is selected from the second signal set and marked as the longest digital signal, and a starting point reflection peak value and an ending point reflection peak value of the longest digital signal are obtained.

[0147] The first time difference is calculated based on the sampling frequency, the sampling point reference value corresponding to the laser pulse emission time, and the time point corresponding to the starting point reflection peak; the second time difference is calculated based on the sampling frequency, the sampling point reference value corresponding to the laser pulse emission time, and the time point corresponding to the end point reflection peak; 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] Among them, Δt1 is the first time difference, n1 is the time point corresponding to the starting point reflection peak, n0 is the sampling point reference value corresponding to the laser pulse emission moment, and f is the sampling frequency.

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

[0152] Δt2=(n2-n0) / f;

[0153] Wherein, Δt2 is the second time difference, and n2 is the time point corresponding to the end point reflection peak.

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

[0155] SSXS=0.5×c×(Δt1-Δt2);

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

[0157] The measurement correction module is used to correct the data of the laser measurement device in each unit time.

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

[0159] S301. Compare the real-time snow thickness with the confidence interval of the predicted snow thickness. If the real-time snow thickness is within the confidence interval of the predicted snow thickness, no correction is required. If the real-time snow thickness is less than the minimum value of the confidence interval of the predicted snow thickness, it indicates that the measurement is falsely reduced, and a false reduction correction is performed. If the real-time snow thickness is greater than the maximum value of the confidence interval of the predicted snow thickness, it indicates that the measurement is falsely increased, and a false increase correction is performed.

[0160] The reason for the "false reduction" phenomenon is that when the snow layer is compacted or frozen, its density increases, reducing the laser's penetration depth. The reflection peak moves from the deep layer to the snow surface or the denser middle layer. The device mistakenly interprets the closer reflecting surface as the ground, resulting in a shorter distance measurement and a "false reduction."

[0161] The method for correction of virtual subtraction comprises:

[0162] The pulse laser transmitter emits vertically to the area to be measured, and simultaneously emits standard wavelength laser pulses and penetrating wavelength laser pulses to ensure that the spot positions coincide. The receiving end synchronously records the corresponding echo signal time series.

[0163] It should be noted that this embodiment is described by taking the wavelength of the standard wavelength laser pulse as 850 nm and the wavelength of the penetrating wavelength laser pulse as 1550 nm as an example.

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

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

[0166] Calculate the virtual subtraction compensation amount 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 virtual subtraction compensation to obtain the virtual subtraction correction thickness.

[0168] The method for calculating the subtraction compensation amount based on the standard wavelength laser echo peak time and the penetrating wavelength laser echo peak time is:

[0169]

[0170] Among them, Δh is the virtual reduction compensation amount, t p1 is the peak time of the standard wavelength laser echo, t p2 is the peak time of the penetrating wavelength laser echo, and n is the average refractive index of the snow layer, which can be taken as 1.34.

[0171] The reason for the false increase phenomenon is that the snow layer contains loose snow layers. The laser pulses injected into the snow layer may be reflected at the layer interface, causing the final ranging results to include the depth of the interlayer, misjudging the ground position as deeper, resulting in "false increase".

[0172] The method for false increase correction includes:

[0173] The laser pulse group is synchronously emitted by a multi-wavelength laser emitting device, and each beam is vertically incident on the snow layer 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, collects the time domain signals through an avalanche photodetector array and a high-speed analog-to-digital converter, and obtains the digital signal sequence of each wavelength channel;

[0175] Wavelet packet denoising is performed on the digital signal sequence of each wavelength channel to remove high-frequency noise, thereby obtaining a smooth signal of each wavelength channel and constructing a set of smooth signals of each wavelength; 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, forming a set, which is marked as a time range set;

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

[0177] Based on the smoothed signal set 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 the reflection peak time series of each wavelength channel and the inter-layer time interval corresponding to each reflection peak;

[0178] The snow layer density is inverted using the time series of reflection peaks of each wavelength channel to obtain the density set of each snow layer;

[0179] The average refractive index of the snow layer at each wavelength is calculated based on the density set of each snow layer and the inter-layer time interval corresponding to each reflection peak;

[0180] The real-time snow thickness is corrected based on the average refractive index of the snow layer to obtain the inflated 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 penetrabilities such as 850nm, 1064nm, and 1550nm can be selected. The multi-wavelength laser emitting device of this embodiment synchronously emits a laser pulse group using laser pulses of at least three different wavelengths. The echo signal data of laser pulses of multiple wavelengths can better reflect the internal conditions of the snow layer.

[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 here.

[0183] The smoothed signal set of each wavelength is expressed as follows:

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

[0185] Wherein, N is the number of lasers with different wavelengths in the laser pulse group.

[0186] The method of extracting the effective echo region time range of each smooth signal in each wavelength smooth signal set by using adaptive threshold segmentation includes:

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

[0188]

[0189] Where μ is the mean signal amplitude, S′ k (t) is the smoothed signal of the kth wavelength laser pulse in the set of smoothed signals of each wavelength.

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

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

[0192] Initialize the number of snow layers m,

[0193] Where Δt min is the two-way time corresponding to the minimum resolvable thickness of the snow layer, determined according to 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] Construct a multi-layer interface reflection model based on the number of snow layers and the theoretical reflection peak time:

[0197]

[0198] Among them, S model (t) is the multi-layer interface reflection model, A k,i is the reflection signal amplitude of the i-th snow layer at the k-th wavelength laser pulse, δ(·) is the Dirac function, which is a theoretical simplification. In the subsequent steps, the actual broadened reflection peak will be fitted by the Gaussian function. The role of the Dirac function is to determine the ideal time position of the reflection peak as the initial constraint of the Gaussian fitting. k,0 =t start , t k,m+1 =t end , t k,1 ~t k,m Uniformly distributed in [t start ,t end ] as the initial guess value for multi-peak fitting.

[0199] Based on the smoothed signal set 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 the reflection peak time series of each wavelength channel and the inter-layer time interval corresponding to each reflection peak:

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

[0201]

[0202] in, is the interface reflectivity of the i-th snow layer to the k-th wavelength laser pulse, n i,k is the refractive index of the i-th snow layer to the k-th wavelength laser pulse;

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

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

[0205]

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

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

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

[0209] Setting fitting constraints, wherein the fitting constraints include a reflection peak time constraint, a peak height constraint, and a layer number constraint;

[0210] Reflection peak time constraint: force the time t of all fitted peaks to be k,i ∈[t start ,t end ], and satisfy t k,1 <t k,2 <…… <t k,i ;

[0211] Peak height constraints:

[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 fitting parameters are optimized by the Levenberg-Marquardt algorithm to obtain the reflection peak time series of each wavelength channel in, is the reflection peak time of the Nth wavelength laser pulse. Accordingly, the interlayer time interval corresponding to each reflection peak is obtained

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

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

[0218] S401: record the number of snow layers as SL, where SL is equal to the number of snow layers plus 1; let the initial value of i be 1, and the value range of i be 1 to SL;

[0219] S402: Construct the equation for the relationship between the refractive index of the i-th layer and the snow density of the i-th layer according to the Gladstone-Dale law;

[0220] Construct the equation of the relationship between the thickness of the snow layer at layer i and the refractive index at layer i;

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

[0222] S403: Calculate the refractive index of the i-th layer according to the relationship equation between the refractive index of the i-th layer and the density of the snow layer of the i-th layer;

[0223] S404: Calculate the thickness of the i-th snow layer according to the relationship equation between the thickness of the i-th snow layer and the refractive index of the i-th layer;

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

[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 thickness set of each snow layer and execute S407.

[0226] The relationship equation between the refractive index of the i-th layer and the snow layer density of the i-th layer is:

[0227] n i,k =1+K k ρ i ;

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

[0229] The relationship equation between the thickness of the snow layer at layer i and the refractive index at layer i is:

[0230]

[0231] Among them, d i is the thickness of the i-th snow layer, λ k is the wavelength of the corresponding laser pulse.

[0232] S407: Construct the least squares optimization objective function J based on the inter-layer time intervals corresponding to each reflection peak and the thickness of each snow layer:

[0233]

[0234] An iterative algorithm is used to solve the least squares optimization objective function for the density of each snow layer. J is minimized by changing the value of the density of each snow layer. The snow layer density corresponding to the minimized J is constructed into a set of snow layer densities, and the current process ends.

[0235] It should be noted that the iterative algorithm can also be solved using the Levenberg-Marquardt algorithm or a similar algorithm, and the specific solution process is not described here in detail.

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

[0237]

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

[0239] The light speed is corrected based on the average refractive index of the snow layer, and the real-time snow layer thickness is corrected. The calculation method for the inflated correction thickness is:

[0240]

[0241] Among them, h 2,d To inflate the correction thickness, correct the thickness calculation deviation caused by the refractive index difference to ensure the accuracy of the thickness measurement; 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 is the refractive index of air at each wavelength, calculated as:

[0242]

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

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

[0245] S302: After performing virtual subtraction correction, if the virtual subtraction correction thickness is within the corresponding confidence interval, the correction is completed and the current process ends; if the virtual subtraction correction thickness is not within the corresponding confidence interval, execute S303;

[0246] If the virtual increase correction thickness is within the corresponding confidence interval after the virtual increase correction, the correction is completed and the current process ends; if the virtual increase correction thickness is not within the corresponding confidence interval, execute S303;

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

[0248] Example 2

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

[0250] Collect meteorological data of the detection area at each unit time;

[0251] Predicting the thickness of snow layer in unit time in the future based on meteorological data to obtain the predicted snow layer thickness;

[0252] Real-time measurement of snow thickness to obtain real-time snow thickness;

[0253] The data of the laser measuring device is checked at each unit time. If there is any false increase or decrease, 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 modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

[0255] Finally: 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 in the scope of protection of the present invention.

Claims

1. Multi-point laser scanning snow thickness measurement method, characterized in that: include: Collect meteorological data of the detection area at each unit time; Predicting the thickness of snow layer in unit time in the future based on meteorological data to obtain the predicted snow layer thickness; Real-time measurement of snow thickness to obtain real-time snow thickness; The data of the laser measuring device is checked at each unit time. If there is any false increase or decrease, the real-time snow layer thickness is corrected to obtain the corrected snow layer thickness.

2. The multi-point laser scanning snow thickness measurement method according to claim 1, characterized in that: Methods for correcting the real-time snow thickness to obtain the corrected snow thickness include: S301. Compare the real-time snow thickness with the confidence interval of the predicted snow thickness. If the real-time snow thickness is within the confidence interval of the predicted snow thickness, no correction is performed. If the real-time snow thickness is less than the minimum value of the confidence interval of the predicted snow thickness, a subtraction correction is performed. If the real-time snow thickness is greater than the maximum value of the confidence interval of the predicted snow thickness, an increase correction is performed. S302: After performing virtual subtraction correction, if the virtual subtraction correction thickness is within the corresponding confidence interval, the correction is completed and the current process ends; if the virtual subtraction correction thickness is not within the corresponding confidence interval, execute S303; If the virtual increase correction thickness is within the corresponding confidence interval after the virtual increase correction, the correction is completed and the current process ends; if the virtual increase correction thickness is not within the corresponding confidence interval, execute S303; 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 instruction is sent.

3. The multi-point laser scanning snow thickness measurement method according to claim 2, characterized in that: The method for correction of virtual subtraction comprises: The pulse laser transmitter emits vertically to the area to be measured, and simultaneously emits standard wavelength laser pulses and penetrating wavelength laser pulses to ensure that the spot positions coincide. The receiving end synchronously records the corresponding echo signal time series; Perform mean filtering on the echo signal time series to extract the peak time of the standard wavelength laser echo and the peak time of the penetrating wavelength laser echo; Calculate the virtual subtraction compensation amount based on the peak time of the standard wavelength laser echo and the peak time of the penetrating wavelength laser echo; The real-time snow layer thickness is added to the virtual subtraction compensation to obtain the virtual subtraction correction thickness.

4. The multi-point laser scanning snow thickness measurement method according to claim 2, characterized in that: The method for false increase correction includes: The laser pulse group is synchronously emitted by a multi-wavelength laser emitting device, and each beam is vertically incident on the snow layer through an aberration-correcting collimating device; The receiver equipped with a spectral splitting unit separates the echo signals of each wavelength channel of the laser pulse group, collects the time domain signals through an avalanche photodetector array and a high-speed analog-to-digital converter, and obtains the digital signal sequence of each wavelength channel; Wavelet packet denoising is performed on the digital signal sequence of each wavelength channel to remove noise, obtain the smooth signal of each wavelength channel, and construct a set of smooth signals of each wavelength; 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, and mark it as a time range set; A multi-layer interface reflection model is constructed using the effective echo area and time range; Based on the smoothed signal set 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 the reflection peak time series of each wavelength channel and the inter-layer time interval corresponding to each reflection peak; The snow layer density is inverted using the time series of reflection peaks of each wavelength channel to obtain the density set of each snow layer; The average refractive index of the snow layer at each wavelength is calculated based on the density set of each snow layer and the inter-layer time interval corresponding to each reflection peak; The real-time snow thickness is corrected based on the average refractive index of the snow layer to obtain the inflated corrected thickness.

5. The multi-point laser scanning snow thickness measurement method according to claim 4, characterized in that: Based on the smoothed signal set 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 the reflection peak time series of each wavelength channel and the inter-layer time interval corresponding to each reflection peak: The interface reflectivity of each snow layer is calculated based on the refractive index of each snow layer to laser pulses of different wavelengths; In the effective echo region, Gaussian multi-peak fitting is performed on the smoothed signal set of each wavelength to obtain fitting parameters, wherein the fitting parameters include reflection peak time, peak width and peak height; The reflection peak time is set as the initial guess value of Gaussian multi-peak fitting; the peak width is set to twice the sampling interval; the peak height is set to the product of the interface reflectivity and the incident light intensity; Setting fitting constraints, wherein the fitting constraints include a reflection peak time constraint, a peak height constraint, and a layer number constraint; Reflection peak time constraint: forces the time of all fitted peaks to fall within the valid echo region time range and increase in sequence; 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; Layer number constraint: the number of fitted peaks is less than or equal to the sum of the number of snow layers and 2; Construct the least squares optimization objective function; The fitting parameters were optimized by the Levenberg-Marquardt algorithm to obtain the time series of the reflection peaks of each wavelength channel and the inter-layer time interval corresponding to each reflection peak.

6. The multi-point laser scanning snow thickness measurement method according to claim 4, characterized in that: Methods for inverting snow layer density using the time series of reflection peaks of each wavelength channel to obtain the density set of each snow layer include: S401: record the number of snow layers as SL, where SL is equal to the number of snow 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 for the relationship between the refractive index of the i-th layer and the snow density of the i-th layer according to the Gladstone-Dale law; Construct the equation of the relationship between the thickness of the snow layer at layer i and the refractive index at layer i; Set an initial value for the density of each snow layer S403: Calculate the refractive index of the i-th layer according to the relationship equation between the refractive index of the i-th layer and the density of the snow layer of the i-th layer; S404: Calculate the thickness of the i-th snow layer according to the relationship equation between the thickness of the i-th snow layer and the refractive index of the i-th layer; S405: adding the thickness of the i-th snow layer to the set of thicknesses of all snow layers; 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 thickness set of each snow layer and execute S407. S407: constructing a least squares optimization objective function based on the inter-layer time intervals corresponding to each reflection peak and the set of snow layer thicknesses; An iterative algorithm is used to solve the density of each snow layer for the least squares optimization objective function. The density of each snow layer is minimized by changing the value of the density of each snow layer. The snow layer density corresponding to the minimum value of the least squares optimization objective function is constructed into a set of snow layer densities, and the current process ends.

7. The multi-point laser scanning snow thickness measurement method according to claim 1, characterized in that: Methods for predicting snow thickness in future unit time based on meteorological data include: Divide the detection area into q×p detection sub-areas, where q is the number of rows in the detection sub-area and p is the number of columns in the detection sub-area; Divide the unit time into T time points; According to meteorological data, a corresponding dynamic equation of snow thickness is established for each detection sub-area; The predicted snow thickness and the corresponding confidence interval of each detection sub-area in the next 1, 2, ..., Time time steps are calculated according to the dynamic equation of snow thickness, where Time is the total number of time steps.

8. The multi-point laser scanning snow thickness measurement method according to claim 7, characterized in that: The method for constructing the snow layer thickness dynamic equation includes: S101: Preset the initial value of t to 1, the value range of t is 1 to T, and the time interval between each two time points is Δt; pre-collect the snow layer thickness corresponding to the first time point; S102: constructing a snow layer thickness dynamic equation based on the meteorological data and snow layer thickness at the t-th time point to obtain the snow layer thickness at the t+1-th time point; 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 thickness at the Tth time point and end the current process. The snow layer thickness dynamic equation is coupled through multi-physical processes to achieve dynamic prediction of snow layer thickness; the multi-physical processes include the snowfall accumulation term at the t-th time point, the thickness melting term at the t-th time point, the thickness sublimation term at the t-th time point, and the wind transport term at the t-th time point; The accumulated snowfall item 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 item at the t-th time point is obtained by comprehensively representing the temperature and melting coefficient at the t-th time point; the thickness sublimation item at the t-th time point is calculated by the sublimation coefficient, the saturated water vapor pressure of the snow surface, the actual water vapor pressure in the air, and the wind speed at the t-th time point; the wind transport item at the t-th time point is obtained by dividing the difference between the wind input snow mass per unit area and the wind output snow mass per unit area by the snow density; the wind input snow mass per unit area uses the wind output snow mass per unit area in the upwind detection sub-area of ​​the current detection sub-area.

9. The multi-point laser scanning snow thickness measurement method according to claim 8, characterized in that: The method for determining the upwind detection sub-region of the current detection sub-region includes: S501: presetting relative position information between each detection sub-region and its adjacent detection sub-region, recorded as adjacent sub-region relative position information; S502: Let the initial value of P be 1, the initial value of Q be 1, the value range of P be 1 to p, and the value range of Q be 1 to q; S503: Determine an upwind detection sub-region of the detection sub-region in the P-th row and Q-th column according to the upwind direction relationship, and record it as an upwind detection sub-region of the detection sub-region in the P-th row and Q-th column; S504: If Q is less than q, set Q+1 and execute S503; If Q is greater than or equal to q, and P is less than p, set P+1, Q=1, and execute S503; If Q is greater than or equal to q, and P is greater than or equal to p, then the process ends.

10. A multi-point laser scanning snow thickness measurement system, implementing the multi-point laser scanning snow thickness measurement method according to any one of claims 1 to 9, characterized in that: include: Meteorological data collection module, used to collect meteorological data of the detection area at a specific time; The snow thickness prediction module predicts the snow thickness in the future unit time based on meteorological data to obtain the predicted snow thickness; Thickness measurement module, used to measure the thickness of the snow layer in real time and obtain the real-time snow layer thickness; The measurement correction module is used to check the data of the laser measurement equipment at each unit time. If there is any false increase or decrease, the real-time snow layer thickness is corrected to obtain the corrected snow layer thickness.

Images