Slope accumulated snow removing system and method

Abstract

The invention discloses a side slope accumulated snow removing system and method, and relates to the technical field of sound wave data processing. The method comprises the steps of obtaining multi-source remote sensing data of a first area of a slope; the multi-source remote sensing data comprises snow depth image data generated based on LiDAR data; using the multi-source remote sensing data and an avalanche risk assessment model to assess whether the first area has an accumulated snow risk; when the accumulated snow risk exists in the first area, first sound wave data of each accumulated snow vibration removal unit are obtained; based on the first sound wave data of each accumulated snow vibration removal unit, determining snow layer distribution characteristics of accumulated snow of each accumulated snow vibration removal unit; and based on the snow layer distribution characteristics, determining a target accumulated snow vibration removal unit for executing snow removal operation from all accumulated snow vibration removal units. According to the technical scheme provided by the invention, the accuracy and reliability of a slope monitoring result are improved, so that snow removal operation can be accurately and timely carried out, and accumulated snow can be efficiently and safely cleaned.

Description

Technical Field

[0001] The present application relates to the technical field of acoustic wave data processing, and in particular to a system and method for removing snow from a slope. Background Art

[0002] In transportation infrastructure such as highways, railways, and airports, snow accumulation is one of the key factors affecting traffic safety, especially on slopes such as slopes. When snow slides, it is very easy to cause natural disasters such as avalanches, thereby affecting road traffic and even damaging vehicles on the road, causing casualties. Therefore, snow accumulation on slopes poses a great threat to traffic safety. In the prior art, for areas with thick snow, manual on-site snow removal is often required. Manual snow removal is not only inefficient, but also very likely to trigger avalanches during snow removal, and it is easy for people to slip, which poses a great safety hazard. On the other hand, in the prior art, the monitoring of snow accumulation on slopes mainly relies on ground surveys, meteorological data, and historical records. However, with the trend of irregular climate change and more abnormal climate in recent years, it is difficult to accurately predict slope avalanches by relying on historical meteorological data. At the same time, the traditional slope snow monitoring method also has problems such as limited coverage, poor real-time performance, and difficulty in comprehensive monitoring, resulting in low accuracy of avalanche prediction results, which leads to the inability to timely and effectively remove snow on slopes, causing great safety hazards to the safe operation of transportation facilities.

[0003] Therefore, how to overcome the above-mentioned technical problems and defects becomes a key issue that needs to be solved. Summary of the invention

[0004] The purpose of the present application is to provide a slope snow removal system and method to solve the problems raised in the above-mentioned background technology, improve the accuracy and reliability of slope monitoring results, thereby realizing accurate and timely snow removal operations, and being able to clear snow efficiently and safely.

[0005] The technical solution of the embodiment of the present application is implemented as follows: The embodiment of the present application provides a slope snow removal system, which includes a plurality of snow shocking units, a communication unit and a processing unit, wherein the plurality of snow shocking units are continuously laid in a first area of ​​a highway slope; the snow shocking unit includes a shocking panel and an acoustic wave sensor, wherein the acoustic wave sensor is arranged on the surface of the shocking panel; The acoustic wave sensor is used to emit acoustic waves to the snow on the snow removal panel, receive echo signals returned by different snow layers in the snow, generate first acoustic wave data and send it to the processing unit; The vibration-removal panel is used to vibrate and remove snow on the surface upon receiving a control signal sent by the processing unit; The communication unit is used to obtain multi-source remote sensing data of the first area; the multi-source remote sensing data includes snow depth image data generated based on LiDAR data; The processing unit is used to use the multi-source remote sensing data as input data, and use an avalanche risk assessment model to evaluate whether there is a snow accumulation risk in the first area; when there is a snow accumulation risk in the first area, based on the first sound wave data sent by each snow removal unit in the first area, determine the structural differences of each snow layer in the snow of each snow removal unit along the vertical direction, and obtain the snow layer distribution characteristics of each snow removal unit; based on the snow layer distribution characteristics of each snow removal unit, determine the target snow removal unit that performs snow removal operations from all snow removal units, and send a control signal to the target snow removal unit.

[0006] In the above solution, the system further comprises a plurality of LiDAR detection units; the plurality of LiDAR detection units are evenly arranged in the first area; the LiDAR detection unit comprises a mounting rod and a phase laser rangefinder, the mounting rod is arranged on the slope surface, and the phase laser rangefinder is arranged on the upper end of the mounting rod; The phase laser rangefinder is used to emit laser to the snow, receive the reflected signals returned by the snow surface and the vibration removal panel respectively, and obtain LiDAR data; and determine the thickness of snow based on the time difference of the reflected signals of the snow surface and the vibration removal panel in the LiDAR data to obtain snow depth image data.

[0007] The present application also provides a method for removing snow from a slope, the method comprising: Acquire multi-source remote sensing data of a first area of ​​the slope; the multi-source remote sensing data includes snow depth image data generated based on LiDAR data; Using the multi-source remote sensing data as input data, and using an avalanche risk assessment model to assess whether there is a snow accumulation risk in the first area; When there is a risk of snow accumulation in the first area, obtaining first acoustic wave data of each snow-shock removal unit in the plurality of snow-shock removal units of the slope snow removal system; the first acoustic wave data includes at least one echo signal received after sending an ultrasonic signal to the snow of the snow-shock removal unit; Based on the first acoustic wave data of each snow removal unit, the snow layer distribution characteristics of the snow in each snow removal unit are determined; the snow layer distribution characteristics represent the structural differences of each snow layer in the snow along the vertical direction; Based on the snow layer distribution characteristics of each snow removal unit, a target snow removal unit that performs snow removal operations is determined from all snow removal units, and a control signal is sent to the target snow removal unit; the control signal is used to control the target snow removal unit to vibrate and remove surface snow.

[0008] In the above solution, before acquiring the multi-source remote sensing data of the first area of ​​the slope, the method further includes: Acquire synthetic aperture radar (SAR) images of the slopes; Extracting features from the SAR image to generate feature information of the slope; the feature information represents the difference in echo intensity of different pixels in the SAR image; the feature information includes statistical features, texture features and polarization features; Based on the feature information, the SAR image is segmented to obtain a plurality of second regions; different second regions have different region types; the region types include snow-covered regions and non-snow-covered regions; The second area whose area type is a snow area is used as the first area to be detected.

[0009] In the above scheme, the method of determining the snow layer distribution characteristics of the snow accumulated in each snow removal unit based on the first sound wave data of each snow removal unit includes: For each snow removal unit, based on the first acoustic wave data, determine the structural information of snow in the corresponding area; the structural information includes the structural attributes of each snow layer in at least one snow layer of the snow; Based on the structural information, the structural differences of the snow from top to bottom are determined, and the snow layer distribution characteristics of the snow in the corresponding snow removal unit are obtained.

[0010] In the above solution, determining the structural information of snow in the corresponding area based on the first sound wave data includes: Based on the acoustic response characteristics of each echo signal in the multiple echo signals of the first acoustic wave data, determine the snow layer type and structural parameters corresponding to each echo signal; the acoustic response characteristics include multiple characteristic factors, and the characteristic factors are echo intensity, scattering characteristics or phase characteristics; Based on the snow layer types and structural parameters of all echo signals, the structural information of the snow in the corresponding area is generated.

[0011] In the above solution, the step of determining the snow layer type corresponding to each echo signal based on the acoustic response characteristics of each echo signal among the multiple echo signals of the first acoustic wave data includes: For each echo signal, each characteristic factor in the acoustic response feature is compared with a corresponding preset threshold value to obtain a comparison result of each characteristic factor; Based on the comparison result of each characteristic factor, determining the type of the snow layer as dry snow, wet snow or compacted snow, and obtaining at least one initial determination result; Based on the proportion of each snow layer type in all initial judgment results, the snow layer type of the corresponding snow layer is determined.

[0012] In the above solution, determining the structural parameter corresponding to each echo signal based on the acoustic response characteristics of each echo signal in the plurality of echo signals based on the first acoustic wave data includes: determining a first thickness of each snow layer based on a snow layer type of each snow layer and an initial sound wave propagation velocity of each snow layer; the initial sound wave propagation velocity being preconfigured according to the snow layer type; The snow thickness on the surface of the snow removal unit is taken as an iterative target, and the initial sound wave propagation velocity of each snow layer is iteratively optimized using a gradient descent method to obtain an optimized sound wave propagation velocity of each snow layer; determining a second thickness of each snow layer based on the optimized sound wave propagation speed; Based on the first acoustic wave data of each snow removal unit and the second thickness of each snow layer, the structural parameters of the snow in the corresponding area are determined; wherein, In the iterative process, the error function defined is used to evaluate the difference between the snow thickness predicted by the model and the actual measured snow thickness, and the sound wave propagation speed is adjusted using the gradient descent valve to minimize the error function; the error function is expressed as: ; in, represents the error function, represents the thickness of the i-th snow layer, and D represents the actual measured snow thickness; The gradient descent law is expressed as: ; in, represents the sound wave propagation speed of snow layer i after the k+1th iteration, represents the speed of sound wave propagation after the kth iteration, represents the learning rate; The acoustic wave propagation velocity of snow layer i after the k+1th iteration is used to calculate the thickness of the snow layer after the corresponding iteration to determine the difference between the predicted snow thickness and the actual measured snow thickness; the thickness of the snow layer after each iteration optimization is expressed as: ; in, represents the thickness of snow layer i after the k+1th iteration, Indicates the echo duration of snow layer i.

[0013] In the above scheme, the snow layer distribution characteristics also include the porosity and effective density of each snow layer; the method also includes: Using the optimized sound wave propagation speed for each snow layer, calculate the corresponding effective density: Using the effective density of each snow layer, the porosity of the corresponding snow layer is calculated; where, The calculation formula of effective density and porosity is expressed as: ; ; in, represents the effective density of snow layer i, represents the effective bulk modulus of the snow layer, represents the optimized sound wave propagation speed of the snow layer, represents the porosity of the snow layer, Represents and density.

[0014] In the above scheme, the step of determining a target snow-vibrating unit for performing snow removal operations from all snow-vibrating units based on the snow layer distribution characteristics of each snow-vibrating unit includes: Divide the first area into grids to obtain a plurality of first grids; the first grids correspond to the snow removal units one by one; Based on the snow layer distribution characteristics corresponding to each first grid, determining whether the first grid has avalanche risk; When there is an avalanche risk in the first grid, using the first grid and grids around the first grid as second grids; The snow removal units corresponding to the second grid and the radiation grids of the second grid are used as target snow removal units; the radiation grids include a first grid located downstream of the second grid.

[0015] The slope snow removal system and method provided in the embodiment of the present application uses multi-source remote sensing data to determine the surface snow coverage and snow thickness of each area of ​​the slope, thereby determining areas with thicker snow, achieving comprehensive and efficient determination of risk areas, improving monitoring efficiency, and reducing consumption of manpower and material resources; At the same time, by laying snow-vibrating units on the slopes, the snow on the surface of the thick snow area can be removed in advance, so as to trigger small avalanches in advance, thereby preventing the occurrence of large-scale avalanches and ensuring the safety of transportation facilities. Furthermore, by arranging an acoustic sensor on the snow-vibrating unit, the acoustic sensor of each snow-vibrating unit is used to analyze the snow layer structure in the snow by the response of the transmitted signal, so as to determine the specific avalanche risk point in the snow-covered area, and then the snow at the avalanche risk point and its radiation area can be vibrated away. Since the concept that the weak interface between different snow layers is likely to cause snow layer sliding is introduced, the avalanche risk can be evaluated more accurately, thereby improving the accuracy of the avalanche monitoring results, and then ensuring the accuracy and reliability of the snow-vibrating effect; Furthermore, by remotely controlling the snow removal unit to vibrate and remove snow, remote snow removal on the slope is achieved. Since there is no need for manual on-site snow removal, the problems of low efficiency and low safety caused by manual snow removal can be avoided, thereby improving the timeliness and safety of snow removal. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of the structure of a slope snow removal system provided in an embodiment of the present application; Figure 2 This is a schematic diagram of the three-dimensional structure of a snow-vibrating unit in the slope snow removal system of the embodiment of the present application; Figure 3 A schematic diagram of the side structure of a snow-vibrating unit in a slope snow-clearing system according to an embodiment of the application; Figure 4 This is a schematic diagram of the structure of the LiDAR detection unit in the embodiment of the present application; Figure 5 A schematic flow chart of a method for clearing snow from a slope provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] The stability of slope snow and potential avalanche risks have always been important concerns for public safety and infrastructure protection, but traditional slope snow monitoring methods have the problem of low accuracy of monitoring results; at the same time, for monitored avalanche risk areas, manual snow removal is often required, which has low execution efficiency and safety; external large-scale equipment (snow shovels, etc.) can also be used for snow removal operations, but in this process, on the one hand, the installation of the equipment has high requirements on the terrain of the installation location, which makes it impossible to install snow removal equipment in many areas; on the other hand, the process from discovering avalanche risks to installing snow removal equipment takes a lot of time, resulting in a large lag in snow removal operations and low efficiency.

[0019] Based on this, in various embodiments of the present application, the slope snow removal method provided in the embodiments of the present application, by utilizing multi-source remote sensing data, determines the surface snow coverage and snow thickness of each area of ​​the slope, thereby determining the area with thicker snow, and comprehensively and efficiently determining the risk area, improving monitoring efficiency, and reducing manpower and material consumption; at the same time, by laying snow shock removal units on the slope, the surface snow in the area with thicker snow can be shock removed in advance, and small avalanches can be triggered in advance, thereby preventing the occurrence of large-scale avalanches and ensuring the safety of transportation facilities; further, by arranging acoustic wave sensors on the snow shock removal units, the acoustic wave sensors of each snow shock removal unit are used to emit signals The snow layer structure in the snow is analyzed by the response, so as to determine the specific avalanche risk point in the snow area, and then the snow at the avalanche risk point and its radiation area can be vibrated away. Since the concept of weak interface between different snow layers is likely to cause snow layer sliding is introduced, the avalanche risk can be assessed more accurately, thereby improving the accuracy of avalanche monitoring results, and thus ensuring the accuracy and reliability of the snow vibration effect; further, the snow is remotely cleared by remotely controlling the snow vibration unit to vibrate and remove snow, thereby realizing remote snow removal on the slope. Since there is no need for manual on-site snow removal, the low efficiency and low safety problems caused by manual snow removal can be avoided, thereby improving the timeliness and safety of snow removal.

[0020] The present application embodiment provides a slope snow removal system, such as Figure 1 As shown, the system may include a plurality of snow shaking units 101, a communication unit 102 and a processing unit 103, wherein the plurality of snow shaking units 101 are continuously laid in a first area of ​​a road slope.

[0021] like Figure 2 and Figure 3 As shown, the snow shocking unit 101 includes a shocking panel 201 and an acoustic wave sensor 202, and the acoustic wave sensor 202 is arranged on the surface of the shocking panel 201; the snow shocking unit 101 may also include a support foot 203, and the shocking panel 201 is installed on the slope through the support foot 203 and is parallel to the slope surface; a power module 204 is arranged under the vibration panel 201, and the power module 204 includes a vibration motor, which is used to provide vibration power to the vibration panel 201; in order to reduce the vibration effect of the vibration motor on the slope surface, a shock absorbing module 205 may be arranged between the support foot 203 and the vibration panel 201; the snow shocking unit 101 may also include a communication module 206, and the communication module 206 may be arranged on the support foot 203, and the communication module 206 may be a 5G communication module, which is used to send the detection signal of the acoustic wave sensor 202 to the processing unit 103, and receive the control signal sent by the processing unit 103 and send the control signal to the power module 204.

[0022] In actual application, the communication module 206 of the snow removal unit 101 may also include a power source for supplying power to the power module 204 .

[0023] The acoustic wave sensor 202 is used to emit acoustic waves to the snow on the desiccation panel 201, receive echo signals returned by different snow layers in the snow, generate first acoustic wave data and send it to the processing unit 103; The vibration-removal panel 201 is used to vibrate and remove snow on the surface upon receiving a control signal sent by the processing unit 103; The communication unit 102 is used to obtain multi-source remote sensing data of the first area; the multi-source remote sensing data includes snow depth image data generated based on LiDAR data; The processing unit 103 is used to use the multi-source remote sensing data as input data, and use the avalanche risk assessment model to evaluate whether there is a snow accumulation risk in the first area; when there is a snow accumulation risk in the first area, based on the first sound wave data sent by each snow removal unit in the first area, determine the structural differences of each snow layer in the snow of each snow removal unit along the vertical direction, and obtain the snow layer distribution characteristics of each snow removal unit; based on the snow layer distribution characteristics of each snow removal unit, determine the target snow removal unit that performs snow removal operations from all snow removal units, and send a control signal to the target snow removal unit.

[0024] In practical applications, the slope can be divided into regions according to the SAR data, and the area with a higher snow accumulation risk can be selected from the divided areas as the first area.

[0025] In one embodiment, the system further comprises a plurality of LiDAR detection units, and the plurality of LiDAR detection units are evenly arranged in the first area; In actual application, the layout density of the LiDAR detection unit can be determined according to the detection range of the LiDAR detection unit to ensure that all snow removal units in the first area can be detected by the LiDAR detection unit.

[0026] In one embodiment, if Figure 4 As shown, the LiDAR detection unit includes a mounting rod 401 and a phase laser rangefinder 402, wherein the mounting rod 401 is arranged on the slope surface, and the phase laser rangefinder 402 is arranged on the upper end of the mounting rod; The phase laser rangefinder is used to use the phase laser rangefinder 402 to emit laser to the snow, receive the echo signals returned by the snow surface and the vibration removal panel respectively, and obtain LiDAR data; and determine the thickness of snow based on the time difference between the echo signals of the snow surface and the vibration removal panel 201 in the LiDAR data to obtain snow depth image data.

[0027] Here, the time difference of the echo signals between the snow surface and the shock removal panel 201 can be understood as the time difference between the echo signal returned from the snow surface and the echo signal returned from the shock removal panel 201; after determining the snow thickness of each snow shock removal unit, an image of the snow surface in the first area is generated, that is, the snow depth image data.

[0028] Based on the architecture of the above-mentioned slope snow removal system, the embodiment of the present application also provides a slope snow removal method, such as Figure 5 As shown, the method includes S501 to S505; S501 to S505 are described in detail below in conjunction with a specific embodiment.

[0029] S501: Acquire multi-source remote sensing data of a first area of ​​a slope; the multi-source remote sensing data includes snow depth image data generated based on LiDAR data.

[0030] In practical applications, the LiDAR detection unit can be used to generate snow depth image data.

[0031] Based on this, in one embodiment, the method may further include: The phase laser rangefinder 402 of the LiDAR detection unit is used to emit laser to the snow, and the echo signals returned by the snow surface and the vibrating panel are received respectively to obtain LiDAR data; and based on the time difference between the echo signals of the snow surface and the vibrating panel in the LiDAR data, the thickness of the snow is determined to obtain snow depth image data.

[0032] In practical application, the first area can be understood as an area on the slope where the snow is thick and there is a risk of snow accumulation.

[0033] In actual application, before executing S501, the slope area may be divided into regions to determine the areas in the slope where the snow is thick and there are risks.

[0034] Based on this, in one embodiment, before acquiring the multi-source remote sensing data of the first area of ​​the slope, the method may further include: Acquire SAR images of the slope; Extracting features from the SAR image to generate feature information of the slope; the feature information represents the difference in echo intensity of different pixels in the SAR image; the feature information includes statistical features, texture features and polarization features; Based on the feature information, the SAR image is segmented to obtain a plurality of second regions; different second regions have different region types; the region types include snow-covered regions and non-snow-covered regions; The second area whose area type is a snow area is used as the first area to be detected.

[0035] In practical applications, statistical features can be statistics such as the mean and variance of each pixel or region. Texture features can be extracted using methods such as gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP). Polarization features can be determined by analyzing the echo intensity ratio under different polarization combinations.

[0036] After obtaining the slope feature information, a classification algorithm is used to classify each pixel area in the SAR image, thereby performing image segmentation on the SAR image; here, the classification algorithm can use supervised classification algorithms such as support vector machines and random forests, or unsupervised classification algorithms such as clustering algorithms, or deep learning algorithms such as convolutional neural networks (CNNs), which are not limited in the embodiments of the present application.

[0037] S502: Using the multi-source remote sensing data as input data, and using an avalanche risk assessment model to assess whether there is a risk of snow accumulation in the first area.

[0038] In actual application, the snow depth image data can be input into the avalanche risk assessment model. The avalanche risk assessment model calculates the snow accumulation risk index based on the input data. When the snow accumulation risk index is higher than the risk threshold, there is a snow accumulation risk in the first area; correspondingly, when the snow accumulation risk index is not higher than the risk threshold, there is no snow accumulation risk in the first area.

[0039] In practical application, the risk index can be expressed as: ; in, represents the risk factor of region j, represents the snow thickness in region j, represents the slope of region j, represents the temperature of region j, represents the wind speed in area j, to Represents weight.

[0040] Here, risk thresholds can be pre-configured based on historical data and expert experience.

[0041] In actual application, historical LiDAR and SAR data from areas where avalanches have occurred can be collected to build a training sample set. The historical data includes snow thickness, slope, temperature, wind speed, etc. in the avalanche area, and then the training sample set can be used to build an avalanche risk assessment model.

[0042] S503: When there is a risk of snow accumulation in the first area, obtain first acoustic wave data of each snow removal unit in the multiple snow removal units of the slope snow removal system; the first acoustic wave data includes at least one echo signal received after sending an ultrasonic signal to the snow in the snow removal unit.

[0043] In actual application, each second area is provided with a snow shocking unit, and the snow removal system may include the snow shocking units of all the second areas; here, when there is a risk of snow accumulation in the first area, obtaining the first sound wave data of each snow shocking unit among the multiple snow shocking units of the slope snow removal system can be understood as obtaining the first sound wave data of each snow shocking unit among the multiple snow shocking units corresponding to the first area in the snow removal system, that is, obtaining the first sound wave data of each snow shocking unit among the multiple snow shocking units located in the first area in the snow removal system.

[0044] In actual application, the snow risk index of the first area can be used for preliminary screening of avalanche risk areas, and the snow layer structure of each snow removal unit can be used to judge the stability of the snow layer, so as to further refine the screening of avalanche risk areas, thereby improving the accuracy of the screening results and snow removal results, and ensuring the effectiveness and reliability of avalanche prevention; at the same time, due to the large number of snow removal units, the ultrasonic data of all snow removal units are processed, and the data processing volume is large, which affects the reliability and efficiency of the calculation results. By performing ultrasonic detection operations on the first area determined after the initial screening, the ultrasonic data processing volume can be reduced, and the reliability and efficiency of the calculation results can be improved.

[0045] In actual application, after the acoustic wave sensor installed on the snow removal unit receives the control command sent by the processing unit, it emits ultrasonic waves into the snow. Since snow layers with different densities will form reflection interfaces, echo signals returned from different reflection interfaces will be received. Therefore, the structure of the snow layer in the snow can be judged according to the number and intensity of the received echo signals.

[0046] S504: Based on the first sound wave data of each snow removal unit, determine the snow layer distribution characteristics of the snow in each snow removal unit; the snow layer distribution characteristics represent the structural differences of each snow layer in the snow along the vertical direction.

[0047] In one embodiment, determining the snow layer distribution characteristics of snow accumulated in each snow removal unit based on the first sound wave data of each snow removal unit includes: For each snow removal unit, based on the first acoustic wave data, determine the structural information of snow in the corresponding area; the structural information includes the structural attributes of each snow layer in at least one snow layer of the snow; Based on the structural information, the structural differences of the snow from top to bottom are determined, and the snow layer distribution characteristics of the snow in the corresponding snow removal unit are obtained.

[0048] In one embodiment, determining the structural information of snow in the corresponding area based on the first sound wave data includes: Based on the acoustic response characteristics of each echo signal in the multiple echo signals of the first acoustic wave data, determine the snow layer type and structural parameters corresponding to each echo signal; the acoustic response characteristics include multiple characteristic factors, and the characteristic factors are echo intensity, scattering characteristics or phase characteristics; Based on the snow layer types and structural parameters of all echo signals, the structural information of the snow in the corresponding area is generated.

[0049] In one embodiment, the determining the snow layer type corresponding to each echo signal based on the acoustic response characteristics of each echo signal among the multiple echo signals of the first acoustic wave data includes: For each echo signal, each characteristic factor in the acoustic response feature is compared with a corresponding preset threshold value to obtain a comparison result of each characteristic factor; Based on the comparison result of each characteristic factor, determining the snow layer type of the snow layer as dry snow, wet snow or compacted snow, and obtaining at least one initial determination result; Based on the proportion of each snow layer type in all initial judgment results of the same snow layer, the snow layer type of the corresponding snow layer is determined.

[0050] In practical applications, the characteristics of different types of snow layers for characteristic factors are shown in Table 1: Table 1:

[0051] In actual application, for the echo intensity, the echo intensity range can be configured in advance for each snow layer type, and the snow layer type can be determined based on the comparison result between the echo signal intensity and the intensity range of each snow layer; According to the scattering characteristics, the coherence coefficient range can be configured in advance for each snow layer type, and the snow layer type can be determined based on the comparison result between the coherence coefficient of the echo signal and the coherence coefficient range of each snow layer; According to the phase characteristics, the phase standard deviation range can be configured in advance for each snow layer type, and the snow layer type can be determined based on the comparison result between the phase standard deviation of the echo signal and the phase standard deviation range of each snow layer.

[0052] In actual application, a snow layer type judgment result will be obtained by using each characteristic factor. According to the ratio of the judgment results, the judgment result with the highest ratio can be used as the final snow layer type judgment result; for example, for snow layer S1, if two judgment results are dry snow and one judgment result is wet snow, and the dry snow ratio is greater than the wet snow ratio, then the snow layer type of this snow layer is dry snow.

[0053] In one embodiment, the determining the structural parameter corresponding to each echo signal based on the acoustic response characteristics of each echo signal in the plurality of echo signals of the first acoustic wave data may include: determining a first thickness of each snow layer based on a snow layer type of each snow layer and an initial sound wave propagation velocity of each snow layer; the initial sound wave propagation velocity being preconfigured according to the snow layer type; The snow thickness on the surface of the snow removal unit is taken as an iterative target, and the initial sound wave propagation velocity of each snow layer is iteratively optimized using a gradient descent method to obtain an optimized sound wave propagation velocity of each snow layer; determining a second thickness of each snow layer based on the optimized sound wave propagation speed; Based on the first acoustic wave data of each snow removal unit and the second thickness of each snow layer, the structural parameters of the snow in the corresponding area are determined; wherein, In the iterative process, the error function defined is used to evaluate the difference between the snow thickness predicted by the model and the actual measured snow thickness, and the sound wave propagation speed is adjusted using the gradient descent valve to minimize the error function; the error function is expressed as: ; in, represents the error function, represents the thickness of the i-th snow layer, and D represents the actual measured snow thickness; The gradient descent law is expressed as: ; in, represents the sound wave propagation speed of snow layer i after the k+1th iteration, represents the speed of sound wave propagation after the kth iteration, represents the learning rate; The sound wave propagation velocity of snow layer i after the k+1th iteration is used to calculate the thickness of the snow layer after the corresponding iteration, that is, , to determine the difference between the predicted snow thickness and the actual measured snow thickness; The thickness of the snow layer after each iteration optimization, that is, the thickness of the snow layer determined according to the sound wave propagation velocity of the snow layer after each iteration, is expressed as: ; in, represents the thickness of snow layer i after the k+1th iteration, Indicates the echo duration of snow layer i.

[0054] In one embodiment, the snow layer distribution characteristics further include the porosity and effective density of each snow layer; the method further includes: Using the optimized sound wave propagation speed for each snow layer, calculate the corresponding effective density: Using the effective density of each snow layer, the porosity of the corresponding snow layer is calculated; where, The calculation formula of effective density and porosity is expressed as: ; ; in, represents the effective density of snow layer i, represents the effective bulk modulus of the snow layer, It can be obtained through experiments or literature data. represents the optimized sound wave propagation speed of snow layer i, represents the porosity of snow layer i, Represents the density of ice.

[0055] Here, porosity directly reflects the proportion of air in the snow layer. High porosity means that the snow layer is loose and prone to sliding. By measuring the porosity, the stability of the snow layer can be judged more accurately. The effective density reflects the mass per unit volume of the snow layer. High density usually means that the snow layer is denser and more stable. By measuring the effective density, the carrying capacity and stability of the snow layer can be more accurately evaluated. Therefore, by introducing porosity and effective density, more complex multi-factor comprehensive evaluation models can be constructed, such as linear weighted models or nonlinear models (Logistic regression, machine learning, etc.). These models can more comprehensively consider the physical properties of the snow layer and improve the accuracy of the prediction.

[0056] S505: Based on the snow layer distribution characteristics of each snow removal unit, determine the target snow removal unit that performs snow removal operation from all snow removal units, and send a control signal to the target snow removal unit; the control signal is used to control the target snow removal unit to vibrate and remove surface snow.

[0057] In one embodiment, determining a target snow shocking unit for performing snow removal operation from all snow shocking units based on the snow layer distribution characteristics of each snow shocking unit may include: Divide the first area into grids to obtain a plurality of first grids; the first grids correspond to the snow removal units one by one; Based on the snow layer distribution characteristics corresponding to each first grid, determining whether the first grid has avalanche risk; When there is an avalanche risk in the first grid, using the first grid and grids around the first grid as second grids; The snow removal units corresponding to the second grid and the radiation grids of the second grid are used as target snow removal units; the radiation grids include a first grid located downstream of the second grid.

[0058] In actual application, the snow layer distribution characteristics of each first grid can be input into the comprehensive assessment model, that is, the snow layer distribution characteristics of each snow removal unit can be input into the comprehensive assessment model as input data, and the comprehensive assessment model can be used to assess whether there is an avalanche risk in the first grid and output the assessment results; the comprehensive assessment model can adopt a linear weighted model or a nonlinear model, such as Logistic regression, machine learning, etc.

[0059] In practical applications, porosity refers to the proportion of air in the snow layer, reflecting the looseness of the snow layer; high porosity means that the snow layer is loose and prone to sliding; low porosity means that the snow layer is dense and has better stability; for dry snow, when the porosity is high, the snow layer is loose and prone to avalanches; for wet snow, although the water in the wet snow increases adhesion, if the porosity is still high, there is still a high risk of avalanches; Effective density refers to the mass per unit volume of the snow layer, which reflects the compactness of the snow layer. High density usually means that the snow layer is denser and more stable, but too high density may lead to local stress concentration and increase the risk of avalanches. For dry snow, when the effective density is low, the snow layer is loose and prone to sliding. For wet snow, when the effective density is high, the water in the wet snow increases the density, but it may also form larger snow blocks, increasing the possibility of avalanches. For compacted snow, the structure is relatively stable, but when the effective density is high, it may also trigger larger-scale avalanches.

[0060] Based on this, it can be judged that the first grid has avalanche risk when one of the following conditions is met: The snow accumulation corresponding to the first grid contains a wet snow layer and a dry snow layer, and the dry snow layer is located above the wet snow layer; When there is a wet snow layer in the first grid, and the porosity of the wet snow layer is higher than the preset wet snow porosity threshold, or the effective density of the wet snow layer is higher than the preset wet snow density threshold; for example, when the porosity of the wet snow layer is greater than 0.5, or the effective density is greater than 500kg / m 3 There is a risk of avalanches when There is only dry snow layer in the first grid, the porosity of the dry snow layer is higher than the preset dry snow porosity threshold, or the effective density of the dry snow layer is lower than the preset dry snow density threshold; for example, when the porosity of the dry snow layer is greater than 0.7 or the effective density is less than 200kg / m 3 There is a risk of avalanches when When there is compacted snow in the first grid and the compacted snow effective density is higher than the preset compacted snow density threshold; for example, when the compacted snow effective density is greater than 800kg / m 3 There is a risk of avalanches.

[0061] In practical application, the grids around the first grid can be grids adjacent to the first grid, or the first grid and the eight first grids around the first grid can be used as the second grid with the first grid as the center and a 3x3 window size.

[0062] In practical application, the snow-vibrating unit corresponding to the radiation grid may be the snow-vibrating unit of the second grid below the mapping point of the slope.

[0063] In summary, the slope snow removal method provided in the embodiment of the present application determines the surface snow coverage and snow thickness of each area of ​​the slope by utilizing multi-source remote sensing data, thereby determining the area with thicker snow, and achieving comprehensive and efficient determination of risk areas, improving monitoring efficiency, and reducing manpower and material consumption; at the same time, by laying snow vibration and removal units on the slope, the surface snow in the area with thicker snow can be vibrated and removed in advance, thereby triggering small avalanches in advance, thereby preventing the occurrence of large-scale avalanches and ensuring the safety of transportation facilities; further, by arranging acoustic wave sensors on the snow vibration and removal units, the acoustic wave sensors of each snow vibration and removal unit are used to analyze the accumulated snow through the response of the transmitted signal The snow layer structure in the snow can be determined, so as to determine the specific avalanche risk point in the snow accumulation area, and then the snow at the avalanche risk point and its radiation area can be vibrated away. Since the concept of weak interface between different snow layers is easily caused by snow layer sliding is introduced, the avalanche risk can be assessed more accurately, thereby improving the accuracy of avalanche monitoring results, and then ensuring the accuracy and reliability of the snow vibration effect; further, the snow is remotely controlled by the snow vibration unit to vibrate and remove snow, thereby realizing remote snow removal on the slope. Since there is no need for manual on-site snow removal, the low efficiency and low safety problems caused by manual snow removal can be avoided, thereby improving the timeliness and safety of snow removal.

[0064] It should be noted that: the slope snow removal system provided in the above embodiment is only illustrated by the division of the above program modules when performing slope snow removal. In actual application, the above processing can be assigned to different program modules as needed, that is, the internal structure of the system is divided into different program modules to complete all or part of the above-described processing. In addition, the slope snow removal method provided in the above embodiment and the slope snow removal system embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0065] It should be noted that: "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0066] In addition, the technical solutions described in the embodiments of the present application can be combined arbitrarily without conflict.

[0067] The above description is only a preferred embodiment of the present application and is not intended to limit the protection scope of the present application.

Claims

1. A slope snow removal system, characterized in that: The system includes a plurality of snow-shock units, a communication unit and a processing unit, wherein the plurality of snow-shock units are continuously laid in a first area of ​​a road slope; the snow-shock units include a snow-shock panel and an acoustic wave sensor, wherein the acoustic wave sensor is arranged on the surface of the snow-shock panel; The acoustic wave sensor is used to emit acoustic waves to the snow on the snow removal panel, receive echo signals returned by different snow layers in the snow, generate first acoustic wave data and send it to the processing unit; The vibration-removal panel is used to vibrate and remove snow on the surface upon receiving a control signal sent by the processing unit; The communication unit is used to obtain multi-source remote sensing data of the first area; the multi-source remote sensing data includes snow depth image data generated based on LiDAR data; The processing unit is used to use the multi-source remote sensing data as input data, and use an avalanche risk assessment model to evaluate whether there is a snow accumulation risk in the first area; when there is a snow accumulation risk in the first area, based on the first sound wave data sent by each snow removal unit in the first area, determine the structural differences of each snow layer in the snow of each snow removal unit along the vertical direction, and obtain the snow layer distribution characteristics of each snow removal unit; based on the snow layer distribution characteristics of each snow removal unit, determine the target snow removal unit that performs snow removal operations from all snow removal units, and send a control signal to the target snow removal unit.

2. The system according to claim 1, characterized in that The system further comprises a plurality of LiDAR detection units; the plurality of LiDAR detection units are evenly arranged in the first area; the LiDAR detection unit comprises a mounting rod and a phase laser rangefinder, the mounting rod is arranged on the slope surface, and the phase laser rangefinder is arranged on the upper end of the mounting rod; The phase laser rangefinder is used to emit laser to the snow, receive the reflected signals returned by the snow surface and the vibration removal panel respectively, and obtain LiDAR data; and determine the thickness of snow based on the time difference of the reflected signals of the snow surface and the vibration removal panel in the LiDAR data to obtain snow depth image data.

3. A method for removing snow from a slope, characterized in that: The method comprises: Acquire multi-source remote sensing data of a first area of ​​the slope; the multi-source remote sensing data includes snow depth image data generated based on LiDAR data; Using the multi-source remote sensing data as input data, and using an avalanche risk assessment model to assess whether there is a snow accumulation risk in the first area; When there is a risk of snow accumulation in the first area, obtaining first acoustic wave data of each snow-shock removal unit in the plurality of snow-shock removal units of the slope snow removal system; the first acoustic wave data includes at least one echo signal received after sending an ultrasonic signal to the snow of the snow-shock removal unit; Based on the first acoustic wave data of each snow removal unit, the snow layer distribution characteristics of the snow in each snow removal unit are determined; the snow layer distribution characteristics represent the structural differences of each snow layer in the snow along the vertical direction; Based on the snow layer distribution characteristics of each snow removal unit, a target snow removal unit that performs snow removal operations is determined from all snow removal units, and a control signal is sent to the target snow removal unit; the control signal is used to control the target snow removal unit to vibrate and remove surface snow.

4. The method according to claim 3, characterized in that Before acquiring the multi-source remote sensing data of the first area of ​​the slope, the method further includes: Acquire SAR images of the slope; Extracting features from the SAR image to generate feature information of the slope; the feature information represents the difference in echo intensity of different pixels in the SAR image; the feature information includes statistical features, texture features and polarization features; Based on the feature information, the SAR image is segmented to obtain a plurality of second regions; different second regions have different region types; the region types include snow-covered regions and non-snow-covered regions; The second area whose area type is a snow area is used as the first area to be detected.

5. The method according to claim 3, characterized in that: The step of determining the snow layer distribution characteristics of the snow accumulated in each snow removal unit based on the first sound wave data of each snow removal unit includes: For each snow removal unit, based on the first acoustic wave data, determine the structural information of snow in the corresponding area; the structural information includes the structural attributes of each snow layer in at least one snow layer of the snow; Based on the structural information, the structural differences of the snow from top to bottom are determined, and the snow layer distribution characteristics of the snow in the corresponding snow removal unit are obtained.

6. The method according to claim 5, characterized in that The determining, based on the first sound wave data, structural information of snow in a corresponding area includes: Based on the acoustic response characteristics of each echo signal in the multiple echo signals of the first acoustic wave data, determine the snow layer type and structural parameters corresponding to each echo signal; the acoustic response characteristics include multiple characteristic factors, and the characteristic factors are echo intensity, scattering characteristics or phase characteristics; Based on the snow layer types and structural parameters of all echo signals, the structural information of the snow in the corresponding area is generated.

7. The method according to claim 6, characterized in that The step of determining the snow layer type corresponding to each echo signal based on the acoustic response characteristics of each echo signal among the plurality of echo signals of the first acoustic wave data comprises: For each echo signal, each characteristic factor in the acoustic response feature is compared with a corresponding preset threshold value to obtain a comparison result of each characteristic factor; Based on the comparison result of each characteristic factor, determining the type of the snow layer as dry snow, wet snow or compacted snow, and obtaining at least one initial determination result; Based on the proportion of each snow layer type in all initial judgment results, the snow layer type of the corresponding snow layer is determined.

8. The method according to claim 6, characterized in that The determining, based on the acoustic response characteristics of each echo signal among the plurality of echo signals based on the first acoustic wave data, a structural parameter corresponding to each echo signal comprises: determining a first thickness of each snow layer based on a snow layer type of each snow layer and an initial sound wave propagation velocity of each snow layer; the initial sound wave propagation velocity being preconfigured according to the snow layer type; The snow thickness on the surface of the snow removal unit is taken as an iterative target, and the initial sound wave propagation velocity of each snow layer is iteratively optimized using a gradient descent method to obtain an optimized sound wave propagation velocity of each snow layer; determining a second thickness of each snow layer based on the optimized sound wave propagation speed; Based on the first acoustic wave data of each snow removal unit and the second thickness of each snow layer, the structural parameters of the snow in the corresponding area are determined; wherein, In the iterative process, the error function defined is used to evaluate the difference between the snow thickness predicted by the model and the actual measured snow thickness, and the sound wave propagation speed is adjusted using the gradient descent valve to minimize the error function; the error function is expressed as: ； in, represents the error function, represents the thickness of the i-th snow layer, and D represents the actual measured snow thickness; The gradient descent law is expressed as: ； in, represents the sound wave propagation speed of snow layer i after the k+1th iteration, represents the speed of sound wave propagation after the kth iteration, represents the learning rate; The acoustic wave propagation velocity of snow layer i after the k+1th iteration is used to calculate the thickness of the snow layer after the corresponding iteration to determine the difference between the predicted snow thickness and the actual measured snow thickness; the thickness of the snow layer after each iteration optimization is expressed as: ； in, represents the thickness of snow layer i after the k+1th iteration, Indicates the echo duration of snow layer i.

9. The method according to claim 8, characterized in that The snow layer distribution characteristics also include the porosity and effective density of each snow layer; the method also includes: Using the optimized sound wave propagation speed for each snow layer, calculate the corresponding effective density: Using the effective density of each snow layer, the porosity of the corresponding snow layer is calculated; where, The calculation formula of effective density and porosity is expressed as: ； ； in, represents the effective density of snow layer i, represents the effective bulk modulus of the snow layer, represents the optimized sound wave propagation speed of the snow layer, represents the porosity of the snow layer, Represents and density.

10. The method according to claim 3, characterized in that: The step of determining a target snow-vibrating removal unit for performing snow removal operations from all snow-vibrating removal units based on the snow layer distribution characteristics of each snow-vibrating removal unit includes: Divide the first area into grids to obtain a plurality of first grids; the first grids correspond to the snow removal units one by one; Based on the snow layer distribution characteristics corresponding to each first grid, determining whether the first grid has avalanche risk; When there is an avalanche risk in the first grid, using the first grid and grids around the first grid as second grids; The snow removal units corresponding to the second grid and the radiation grids of the second grid are used as target snow removal units; the radiation grids include a first grid located downstream of the second grid.

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