A Method and Device for Evaluating the Effectiveness of Artificial Control of Avalanches on High Highway Slopes Based on Multi-Source Data

Abstract

This application discloses a method and apparatus for evaluating the effectiveness of artificial avalanche control on highway high slopes based on multi-source data. The method involves: determining the slope area to be intervened from a highway slope region; simulating the avalanche movement path in the slope area to be intervened to obtain the avalanche flow centerline and the avalanche deposition arrival line; selecting candidate intervention points along the avalanche flow centerline when there is spatial intersection between the avalanche deposition arrival line and the highway facility buffer zone; and filtering points within a preset range of the candidate intervention points based on the topographic curvature of the slope area in the descent direction to obtain preferred intervention points; filtering the preferred intervention points to obtain target intervention points; and after implementing artificial snow accumulation intervention on the slope area to be intervened based on the target intervention points, evaluating the artificial avalanche intervention on the slope area to be intervened based on the difference between the first and second snow accumulation data. This application improves the scientific rigor and reliability of avalanche disaster prevention and control.

Description

Technical Field

[0001] This application relates to the field of avalanche control, specifically to a method and apparatus for evaluating the effectiveness of artificial avalanche control on highway high slopes based on multi-source data. Background Technology

[0002] High slopes along mountain roads are highly susceptible to unstable snow accumulation due to the combined effects of complex weather conditions such as heavy snowfall and repeated freeze-thaw cycles, which can trigger avalanches. Such avalanches can directly lead to road burial, damage to retaining walls, slope instability, and prolonged traffic disruption. They can also trigger secondary geological disasters such as landslides and mudslides, seriously threatening road safety and the stability of infrastructure along the route.

[0003] Currently, human interventions for avalanche risk (such as targeted blasting and mechanical disturbance) largely rely on the experience and judgment of on-site technicians, lacking quantitative assessments and scientific decision-making support based on multi-source environmental data. Specifically, the determination of the timing, location, and intensity of interventions is often highly subjective and lacks standardization, making it difficult to accurately identify high-risk and vulnerable areas. Furthermore, the effectiveness of post-intervention management lacks objective and quantifiable verification methods, making it difficult to assess the effectiveness of artificial avalanche management on high highway slopes. Summary of the Invention

[0004] This application provides a method and apparatus for evaluating the effectiveness of artificial avalanche control on highway high slopes based on multi-source data. It can accurately identify key snow accumulation points in highway slope areas suitable for artificial intervention, and objectively evaluate the effectiveness of artificial avalanche control on high slopes, effectively preventing avalanche disasters and improving the scientificity and reliability of avalanche disaster prevention and control.

[0005] This application provides a method for evaluating the effectiveness of artificial avalanche control on high highway slopes based on multi-source data, including:

[0006] Acquire topographic data, meteorological data, and first snow accumulation data for the highway slope area, which is the slope area adjacent to the highway and located above the roadbed;

[0007] Based on topographic data, meteorological data, and first snow cover data, the slope areas to be intervened were identified from the highway slope area.

[0008] The avalanche movement path in the slope area to be intervened was simulated to obtain the avalanche flow centerline and the avalanche deposition arrival line.

[0009] When there is a spatial intersection between the avalanche accumulation arrival line and the road facility buffer zone, candidate intervention points are selected along the avalanche flow centerline. Based on the topographic curvature of the slope area to be intervened in the slope direction, points within a preset range of candidate intervention points are screened to obtain the preferred intervention points.

[0010] Based on the operational safety constraints of snow intervention equipment, the preferred intervention points are filtered to obtain the target intervention points;

[0011] After implementing artificial snow accumulation intervention in the slope area to be intervened based on the target intervention point, the second snow accumulation data of the slope area to be intervened is obtained. Based on the difference between the first and second snow accumulation data of the slope area to be intervened, the artificial avalanche intervention in the slope area to be intervened is evaluated, and the evaluation index is obtained. The evaluation index reflects the effect of artificial avalanche control in the slope area to be intervened.

[0012] This application also provides an embodiment of a device for evaluating the effectiveness of artificial avalanche control on high highway slopes based on multi-source data, including:

[0013] The data acquisition unit is used to acquire topographic data, meteorological data and first snow accumulation data of the highway slope area, which is the slope area adjacent to the highway and located above the roadbed.

[0014] The area determination unit is used to determine the slope area to be intervened from the highway slope area based on topographic data, meteorological data and first snow cover data;

[0015] The path simulation unit is used to simulate the avalanche movement path in the slope area to be intervened, and to obtain the avalanche flow centerline and the avalanche deposition arrival line.

[0016] The screening unit is used to select candidate intervention points along the avalanche flow centerline when there is a spatial intersection between the avalanche accumulation arrival line and the road facility buffer zone, and to screen points within a preset range of candidate intervention points based on the topographic curvature of the slope area to be intervened in the slope direction to obtain the preferred intervention points.

[0017] The target determination unit is used to filter the preferred intervention points based on the operational safety constraints of the snow intervention equipment to obtain the target intervention points;

[0018] The evaluation unit is used to obtain the second snow accumulation data of the slope area to be intervened after implementing artificial snow accumulation intervention based on the target intervention point, and to evaluate the artificial avalanche intervention in the slope area to be intervened based on the difference between the first and second snow accumulation data of the slope area to be intervened, thereby obtaining evaluation indicators. The evaluation indicators reflect the effect of artificial avalanche control in the slope area to be intervened.

[0019] This application also provides an electronic device, including a processor and a memory, the memory storing multiple instructions; the processor loads instructions from the memory to execute the steps in any of the methods for evaluating the effectiveness of artificial control of avalanches on highway high slopes based on multi-source data provided in this application.

[0020] This application also provides a computer-readable storage medium storing multiple instructions adapted for loading by a processor to execute steps in any of the methods for evaluating the effectiveness of artificial avalanche control on high highway slopes based on multi-source data provided in this application.

[0021] This application also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps in any of the methods for evaluating the effectiveness of artificial control of avalanches on highway high slopes based on multi-source data provided in this application.

[0022] This application embodiment can acquire topographic data, meteorological data, and first snow accumulation data of a highway slope area, which is a slope area adjacent to the highway and located above the roadbed. Based on the topographic data, meteorological data, and first snow accumulation data, the slope area to be intervened is determined from the highway slope area. The avalanche movement path of the slope area to be intervened is simulated to obtain the avalanche flow centerline and the avalanche deposition arrival line. When there is a spatial intersection between the avalanche deposition arrival line and the highway facility buffer zone, candidate intervention points are selected along the avalanche flow centerline, and the slope of the slope area to be intervened is considered. The terrain curvature in the direction is used to screen points within a preset range of candidate intervention points to obtain preferred intervention points; based on the operational safety constraints of snow intervention equipment, the preferred intervention points are filtered to obtain target intervention points; after implementing artificial snow intervention on the slope area to be intervened based on the target intervention points, the second snow data of the slope area to be intervened is obtained, and the difference between the first and second snow data of the slope area to be intervened is used to evaluate the artificial avalanche intervention in the slope area to be intervened, and the evaluation index is obtained. The evaluation index reflects the effect of artificial avalanche control in the slope area to be intervened.

[0023] In this application, topographic data, meteorological data, and initial snow accumulation data of a highway slope area adjacent to a highway and located above the roadbed can be obtained. Using these data, it can be determined whether the highway slope area requires snow accumulation treatment to prevent avalanches. Then, the avalanche movement path of the slope area to be intervened is simulated to obtain the avalanche flow centerline and avalanche deposition arrival line. When there is a spatial intersection between the avalanche deposition arrival line and the highway facility buffer zone, candidate intervention points are selected along the avalanche flow centerline. Finally, based on the topographic curvature of the slope area in the descent direction, further targeted interventions are performed at the candidate intervention points. Points within a preset range are screened to obtain preferred intervention points. Then, considering the operational safety constraints of snow intervention equipment, target intervention points are selected from these preferred intervention points. This allows for the accurate identification of key snow accumulation points suitable for artificial intervention in highway slope areas. After implementing artificial snow intervention on the slope area to be intervened based on the target intervention points, an assessment of avalanche artificial intervention is conducted based on the difference between the first and second snow accumulation data before and after the implementation of artificial snow intervention. This achieves an objective evaluation of the effectiveness of artificial avalanche control on high slopes, effectively preventing avalanche disasters and improving the scientific rigor and reliability of avalanche disaster prevention and control. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the first process of the method for evaluating the effect of artificial control of avalanches on highway high slopes based on multi-source data provided in the embodiments of this application;

[0026] Figure 2 This is a schematic diagram of the second process of the method for evaluating the effect of artificial control of avalanches on highway high slopes based on multi-source data provided in the embodiments of this application;

[0027] Figure 3 This is a schematic diagram of the structure of the highway high slope avalanche artificial control effect evaluation device based on multi-source data provided in the embodiments of this application; Detailed Implementation

[0028] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] This application provides a method and apparatus for evaluating the effectiveness of artificial avalanche control on high highway slopes based on multi-source data.

[0030] The method for evaluating the effectiveness of artificial avalanche control on highway high slopes using multi-source data can be implemented in a device that integrates this device into an electronic device, such as a terminal or server. The terminal can be a mobile phone, tablet, smart Bluetooth device, laptop, or personal computer (PC); the server can be a single server or a server cluster consisting of multiple servers.

[0031] In some embodiments, the multi-source data highway high slope avalanche artificial control effect evaluation device can also be integrated into multiple electronic devices. For example, the multi-source data highway high slope avalanche artificial control effect evaluation device can be integrated into multiple servers, and multiple servers can implement the multi-source data highway high slope avalanche artificial control effect evaluation method of this application.

[0032] In some embodiments, the server may also be implemented as a terminal.

[0033] The following sections provide detailed descriptions of each example. It should be noted that the sequence numbers of the following embodiments are not intended to limit the preferred order of the embodiments.

[0034] In this embodiment, a method for evaluating the effectiveness of artificial avalanche control on high highway slopes based on multi-source data is provided, such as... Figure 1 As shown, the specific process of this method for evaluating the effectiveness of artificial avalanche control on high highway slopes based on multi-source data can be as follows:

[0035] 101. Obtain topographic data, meteorological data, and first snow accumulation data for the highway slope area. The highway slope area refers to the slope area adjacent to the highway and located above the roadbed.

[0036] Among them, the highway slope area refers to the slope area adjacent to the highway and located above the roadbed, specifically the sloping area adjacent to both sides of the highway and located above the roadbed.

[0037] Topographic data can reflect the topography of the highway slope area. For example, topographic data can include slope, profile curvature, surface roughness, aspect, elevation, underlying surface, etc.

[0038] In some embodiments, topographic data for the highway slope area can be obtained by acquiring a digital elevation model (DEM) covering the study area, preferably ALOS PALSAR 12.5-meter resolution DEM data. Based on this DEM, the following topographic factors are calculated and derived using geographic information processing software such as ArcGIS or GDAL:

[0039] Slope (S) is the first derivative of DEM, representing the degree of inclination of the ground surface at a point or within a certain area.

[0040] Section curvature ( The slope index (SCI) is the curvature along the direction of maximum gradient, reflecting the acceleration or deceleration trend of a fluid or substance in the slope direction. A positive value indicates a convex slope, which is favorable terrain for snow fracturing; a negative value indicates a concave slope, which is favorable terrain for avalanche accumulation. Concave slopes are considered when constructing the susceptibility index; convex slopes are considered when selecting blasting sites.

[0041] Surface roughness (TR) is a key factor in snow accumulation. Higher surface roughness leads to greater snow accumulation. During an avalanche, roughness significantly increases resistance to movement, reducing the speed, distance traveled, and thickness of the snow mass. The formula for calculating TR is:

[0042] TR= ;in, For surface roughness, Indicates slope, This represents the ratio between the horizontal projected area and the actual surface area of ​​the earth. The larger the value, the steeper and more rugged the terrain. This formula is often used to calculate terrain relief from digital elevation models.

[0043] The slope direction (A) is divided into eight directions: north, northeast, east, southeast, south, southwest, west, and northwest.

[0044] Altitude (E) is classified according to elevation values.

[0045] The underlying surface (GC), based on the classification results of remote sensing images, is divided into types such as snow and ice, bare land, and vegetation.

[0046] Meteorological data can reflect the weather conditions in the area along highway slopes. For example, meteorological data can include wind speed, temperature, precipitation, etc.

[0047] The primary snow cover data reflects the snow cover conditions in the roadside slope area. For example, the primary snow cover data may include data such as snow moisture (which can be divided into dry snow and wet snow based on snow moisture), snow cover area (SCA), snow water equivalent, snow water equivalent increment in the past 24 to 72 hours, snow thickness, snow density, and average snow thickness.

[0048] In some embodiments, the first snow cover data may be obtained by analyzing and calculating the acquired optical remote sensing data, radar remote sensing data, and hyperspectral data.

[0049] For example, operating land imagers such as Landsat 8 / 9 OLI or multispectral imagers such as Sentinel-2 (MSI data) and synthetic aperture radar, Sentinel-1 (SAR data), after atmospheric correction of the images, the following indices are calculated:

[0050] 1) The formula for calculating the Normalized Difference Snow Index (NDSI) is as follows:

[0051] ;

[0052] in, For green light reflectance, Shortwave infrared reflectance. It measures the relative difference in reflectance of the same pixel in visible green light and shortwave infrared light, and is used to extract snow cover area (SCA). It is a standard method for automatically drawing snow maps from satellite imagery.

[0053] 2) The formula for calculating the Normalized Difference Vegetation Index (NDVI) is as follows:

[0054] ;

[0055] in, Near-infrared reflectance, Red reflectance. It measures the relative difference in reflectance of a pixel in red light and near-infrared light, and can help distinguish between snow and vegetation, aiding in snow mapping.

[0056] 3) Changes in backscattering coefficient and interference coherence They are used to identify wet snow.

[0057] Set a remote sensing discrimination threshold, with a range of NDSI > 0.40~0.45 and NDVI < 0.4~0.5. >0.1~0.15, VIS is the visible light band, NIR is the near-infrared band, and the SAR wet snow criterion is set to ≤−3dB ≤0.35.

[0058] Acquire hyperspectral images (such as ZY-1 02D), and use a dedicated algorithm to invert snow particle size and albedo. The surface water content (W) and surface moisture content (W) were used to cross-validate the SAR wet snow discrimination results.

[0059] We acquired high-resolution meteorological reanalysis datasets such as ERA5-Land and extracted snow water equivalent (SWE) and its 24- to 72-hour increments. Snow depth (SD), air temperature (T), wind slope consistency (WSA).

[0060] Wind slope consistency (WSA) is an indicator of the degree of snow accumulation on the leeward side, and the formula is:

[0061] ;

[0062] in, The azimuth of the prevailing wind. The azimuth of the slope. This indicates the similarity between wind direction and slope aspect. When WSA ∈ [0.85, 1.00], it indicates strong leeward wind; when WSA ∈ [0.65, 0.85), it indicates consistent leeward wind; when WSA ∈ [0.25, 0.65), it indicates crosswind; when WSA ∈ [−0.25, 0.25), it indicates near crosswind; and when WSA ∈ [−1.00, −0.25), it indicates windward wind. In avalanche hazard assessment, it is used to evaluate the degree of influence of wind direction factor on avalanche risk.

[0063] 102. Based on topographic data, meteorological data, and first snow cover data, determine the slope areas to be intervened in the highway slope area.

[0064] Among them, the slope areas to be intervened refer to highway slope areas that are prone to avalanches.

[0065] In some embodiments, in order to accurately determine whether snow accumulation in a highway slope area requires human intervention, the area of ​​slope to be intervened is determined from the highway slope area based on topographic data, meteorological data, and first snow accumulation data, including:

[0066] Based on topographic data, meteorological data, and first snow accumulation data, an avalanche risk index is generated for the highway slope area.

[0067] Based on the avalanche hazard index, the slope areas to be intervened were identified from the highway slope area.

[0068] The avalanche hazard index is used to measure the likelihood of an avalanche occurring in the area along a highway slope and the degree of its potential harm.

[0069] In some embodiments, in order to objectively analyze whether an avalanche has occurred in a highway slope area, an avalanche hazard index corresponding to the highway slope area is generated based on topographic data, meteorological data, and first snow accumulation data, including:

[0070] Based on topographic data and first snow cover data, an avalanche susceptibility index is calculated. The avalanche susceptibility index characterizes the ease with which an avalanche will occur in a highway slope area under the conditions of topography and snow cover.

[0071] Based on meteorological data and first snow accumulation data, the avalanche triggering index is calculated. The avalanche triggering index represents the ease with which an avalanche is triggered in the roadside slope area under the current meteorological and snow physical conditions.

[0072] The avalanche hazard index is obtained by weighted fusion of the avalanche susceptibility index and the avalanche triggering index.

[0073] Understandably, this involves constructing an avalanche risk assessment model and identifying high and extremely high hazard zones. It also involves defining an avalanche susceptibility index. Avalanche trigger index Avalanche risk index This indicates where snow is naturally prone to accumulate, and the avalanche trigger index. This indicates where snow is likely to accumulate under meteorological conditions.

[0074] In order to accurately assess avalanche risk, it is necessary to classify the various influencing factors into different levels and use a judgment matrix to evaluate the relative importance of the various factors affecting avalanche occurrence in the hierarchical model. The values ​​and meanings are shown in Table 1.

[0075]

[0076] Table 1 Importance values ​​and their meanings

[0077] Based on the actual conditions of the study area, the degree of influence of each evaluation factor on avalanches, and the experience of relevant literature, a graded value system for avalanche hazard evaluation factors was constructed, as detailed in Table 2.

[0078]

[0079] Table 2. Avalanche Hazard Assessment Factor Classification and Value Assignment Table

[0080] For constructing an avalanche susceptibility index Select slope (S), profile curvature ( Surface roughness (TR), slope aspect (A), elevation (E), and underlying surface (GC) were used as static susceptibility evaluation factors. Each factor was normalized. An analytic hierarchy process (AHP) was used to construct a judgment matrix, and the weights of each factor were determined using a 1-9 scale, passing a consistency test (CR < 0.10).

[0081] Avalanche risk index The calculation formula is:

[0082] ;

[0083] in, It is the first The weight coefficients of each factor, It is the first after standardization The value of a primitive factor in a certain pixel This means summing up all the weighted factor values. It indicates the ease with which an avalanche will occur under the inherent, static conditions of a location; The higher the score, the more likely the location is to experience an avalanche due to its inherent terrain conditions.

[0084] Avalanche Trigger Index The calculation formula is:

[0085] ;

[0086] in, It is the first The weight coefficients of each factor, It is the first after standardization The value of a primitive factor in a certain pixel This means summing up all the weighted factor values. It indicates the ease with which an avalanche will occur under dynamic conditions at a given location; A higher score indicates that the current snow accumulation and weather conditions are more likely to trigger an avalanche. This is relevant for constructing the trigger index. Select Dynamic meteorological factors such as temperature (T) are used as triggering factors. The AHP method is also used for weighting, and after consistency testing, a weighted linear combination is obtained to obtain the triggering index. .

[0087] avalanche risk index The calculation formula is:

[0088] ;

[0089] in, for Weighting coefficients for The weighting coefficients are used to set the risk level according to Table 3. It is a comprehensive avalanche risk score for a location, an index that assesses the degree of avalanche risk in a region, and is a weighted sum of two factors: inherent susceptibility and current triggering conditions.

[0090]

[0091] Table 3 Avalanche Hazard Classification Table

[0092] It can be understood that within the 1.5km buffer zone of the mountains on both sides of the highway, the avalanche hazard index is used to indicate a risk level of "high risk" or "extremely high risk", forming a candidate list, which is a list of slope areas to be intervened.

[0093] 103. Simulate the avalanche movement path in the slope area to be intervened to obtain the avalanche flow centerline and avalanche deposition arrival line.

[0094] The avalanche flow centerline is a virtual line along the path of the snow mass flow, representing the trajectory of the main direction of the avalanche flow.

[0095] The avalanche deposition line refers to the boundary where avalanche material eventually stops moving, that is, the farthest edge of the avalanche deposition zone.

[0096] In some embodiments, in order to simulate the movement path of an avalanche in the slope area to be intervened, further determine whether intervention is necessary, and provide a reference for intervention, the first snow data includes snow thickness and snow density. The avalanche movement path in the slope area to be intervened is simulated to obtain the avalanche flow centerline and the avalanche deposition arrival line, including:

[0097] Based on the topographic data of the slope area to be intervened, obtain the friction coefficient and resistance coefficient;

[0098] Using an avalanche dynamics model, the avalanche movement path of the slope area to be intervened is simulated based on snow thickness, snow density, friction coefficient, and drag coefficient, resulting in the avalanche flow centerline and avalanche deposition arrival line.

[0099] Understandably, when the avalanche risk index... When the snow depth exceeds a preset threshold, it indicates an increased avalanche risk, signaling the start of a window for human intervention. During this window, the RAMMS model input parameters are updated using avalanche dynamics models, such as the latest meteorological data. The latest snow depth is then used as the basis for further analysis. Snow density coefficient of friction With drag coefficient Run RAMMS fast forward simulation; if the avalanche accumulation reaches the line If there is an intersection with the buffer zone of highway facilities, it is determined that human intervention is necessary.

[0100] Latest snow depth The calculation formula is:

[0101] ;

[0102] Where, in the formula The cohesive force between the snow cover and the slope (g / cm²) ), Snow density (g / cm³) ), The slope angle of the hillside. The internal friction angle between the snow and the slope surface. The coefficient of internal friction, To promote lubrication, This is the positive pressure component. This represents the net sliding stress. This formula is used to calculate the critical thickness of a snowpack at which it is just about to experience sliding instability. When the snowpack thickness... < At that time, the snow body is in a stable state. When the snow depth... At that time, an avalanche will occur.

[0103] Snow density Based on snow condition settings, the density of freshly fallen snow in calm conditions is taken as 50–70 kg / m³, the density of fresh wet snow as 100–200 kg / m³, the density of accumulated snow as 200–300 kg / m³, and the density of wind-pressed snow as 350–400 kg / m³. The Coulomb friction coefficient is selected according to the topography and altitude of the study area, referring to the table below. and turbulent drag parameters .

[0104]

[0105] Table 4. Coulomb friction coefficient and turbulent friction coefficient under different terrains and altitudes.

[0106] Input the above parameters and run the RAMMS model to output the avalanche accumulation range. If the simulated avalanche accumulation line intersects with the road infrastructure buffer zone (such as a 100-meter buffer zone at the outer edge of the roadbed), then it is determined that human intervention is necessary.

[0107] 104. When there is a spatial intersection between the avalanche accumulation arrival line and the road facility buffer zone, candidate intervention points are selected along the avalanche flow centerline. Based on the topographic curvature of the slope area to be intervened in the slope direction, points within the preset range of the candidate intervention points are screened to obtain the preferred intervention points.

[0108] A highway infrastructure buffer zone is a specific area designated around a highway and its related infrastructure to protect these facilities from potential damage or impact from external factors. In the context of avalanche risk assessment and management, this buffer zone primarily defines the area where avalanches may pose a threat to highway infrastructure.

[0109] The topographic curvature of the slope area to be intervened in the descent direction specifically refers to the slope curvature of the slope area to be intervened in the descent direction.

[0110] In some embodiments, the first snow data includes average snow thickness, and candidate intervention points are selected along the avalanche flow centerline, including:

[0111] The intervention spacing is determined based on the average snow depth, and the intervention spacing is positively correlated with the average snow depth.

[0112] Candidate intervention points are set up along the avalanche flow centerline according to the intervention interval, wherein the distance between two adjacent candidate intervention points is equal to the intervention interval.

[0113] In some embodiments, points within a preset range of candidate intervention points are selected based on the topographic curvature of the slope area to be intervened in the slope direction to obtain preferred intervention points, including:

[0114] Multiple candidate location points are obtained with the candidate intervention point as the center and a preset size as the radius;

[0115] Based on the topographic curvature of the slope area to be intervened in the slope direction, the curvature of each candidate location point along the slope direction is obtained;

[0116] For each candidate location point, the neighboring candidate location points corresponding to the candidate location point are determined from multiple candidate location points according to the preset neighborhood range;

[0117] If the curvature of a candidate location point is not less than the curvature of a neighboring candidate location point, the candidate location point is determined as the preferred intervention point.

[0118] Understandably, the RAMMS simulation avalanche flow centerline along high-risk and extremely high-risk zones is based on the average snow thickness of the slope area to be intervened. Initial site selection is conducted, with the spacing between the sites being the intervention interval. Set as ,in, =1~1.5. To better implement blasting and achieve the best blasting effect, the intervention spacing needs to take into account the snow accumulation during on-site blasting. Using each candidate intervention point as the center, within a preset search radius (e.g., 20 meters), find the profile curvature. The local maxima locations are identified, and these candidate locations are selected as preferred intervention points. These preferred intervention points are then filtered for safety and operability to form the final set of blasting locations.

[0119] 105. Based on the operational safety constraints of snow intervention equipment, the preferred intervention points are filtered to obtain the target intervention points.

[0120] In some embodiments, based on the operational safety constraints of snow intervention equipment, preferred intervention points are filtered to obtain target intervention points, including:

[0121] Obtain the operational environment parameters corresponding to the preferred intervention point. The operational environment parameters include slope, real-time wind speed, horizontal distance from critical infrastructure, no-fly zone status, and communication information strength.

[0122] When the operating environment parameters meet the operational safety constraints of the snow intervention equipment, the preferred intervention point will be selected as the target intervention point.

[0123] Understandably, taking drones as an example, the selection criteria are as follows: Verify that the drone's maximum takeoff weight meets the requirements for carrying delivery devices (including explosives). Operational safety constraints include: ensuring sufficient flight time to complete the operation and return safely; assessing that the local slope at the deployment site is less than 45° to ensure delivery safety; ensuring a real-time wind speed of less than 12 m / s at the deployment site; ensuring a horizontal distance of more than 50 meters between the deployment site and power lines, and a horizontal distance of more than 100 meters between the deployment site and the edge of the roadbed; excluding deployment sites within no-fly zones; and ensuring good communication signal strength at the deployment site.

[0124] 106. After implementing artificial snow cover intervention in the slope area to be intervened based on the target intervention point, obtain the second snow cover data of the slope area to be intervened, and evaluate the avalanche artificial intervention in the slope area to be intervened based on the difference between the first snow cover data and the second snow cover data of the slope area to be intervened, and obtain the evaluation index. The evaluation index reflects the avalanche artificial control effect of the slope area to be intervened.

[0125] In some embodiments, in order to accurately assess avalanche intervention, the first snow data includes a first snow-covered area and a first surface snow backscattering intensity, and the second snow data includes a second snow-covered area and a second surface snow backscattering intensity.

[0126] Based on the difference between the first and second snow accumulation data of the slope area to be intervened, an assessment of artificial avalanche intervention in the slope area to be intervened is conducted, resulting in assessment indicators, including:

[0127] The actual snow removal area is determined based on the difference between the first and second snow-covered areas of the slope to be intervened.

[0128] The area of ​​scattering intensity variation is determined based on the difference between the backscattering intensity of the first and second surface snow cover in the slope area to be intervened.

[0129] By identifying the regions with varying scattering intensity from the regions with varying scattering intensity, the predicted snow removal area can be obtained.

[0130] Based on the actual snow removal area and the predicted snow removal area, an assessment of artificial avalanche intervention was conducted on the slope area to be intervened, and assessment indicators were obtained.

[0131] In some embodiments, determining a region with interconnected scattering intensity variation from the scattering intensity variation region to obtain a snow cover prediction clearing region includes:

[0132] Morphological closing operations and connected component analysis are performed on the regions where scattering intensity changes. Isolated patches with an area smaller than the threshold T are removed, and the remaining connected regions are used as the snow cover prediction and removal areas.

[0133] In some embodiments, in order to accurately assess avalanche intervention, an assessment of avalanche intervention is conducted on the slope area to be intervened, based on the actual snow removal area and the predicted snow removal area, to obtain assessment indicators, including:

[0134] Determine the area intersection-union ratio between the actual snow removal area and the predicted snow removal area;

[0135] The recall rate is obtained by determining the overlap area between the actual snow removal area and the predicted snow removal area, which is the proportion of the actual snow removal area.

[0136] Determine the overlap area between the actual snow removal area and the predicted snow removal area, and its proportion to the predicted snow removal area to obtain the accuracy rate;

[0137] Based on regional crossover ratio, recall, and precision, an evaluation index was obtained for artificial avalanche intervention in the slope area to be intervened.

[0138] Understandably, by comparing the changes in Snow Cover Area (SCA) before and after the intervention, a snow cover change map is generated (-1 indicates clearing, +1 indicates new addition, and 0 indicates no change), representing the actual cleared snow area. Before and after the intervention, a high-precision Digital Surface Model (DSM) is generated using a UAV equipped with LiDAR or oblique photogrammetry to calculate the differential DSM, which is used to accurately verify the thickness and volume of snow cleared by the intervention. Using the ΔDSM or Snow Depth (SD) before and after the intervention, the volume of cleared snow is accurately estimated through volume calculation. Using the change area extracted from the high-precision DSM change (ΔDSM) as the ground truth, the predicted snow clearing area is obtained. The accuracy of the actual snow clearing area extracted from optical (e.g., NDSI) and SAR imagery is verified by calculating the following indicators:

[0139] Iou (Intersection over Union) directly measures the degree of overlap between the predicted snow removal area and the actual snow removal area. It is commonly used in change detection and can intuitively reflect the spatial accuracy of the detection results. The formula for calculating Iou is:

[0140] ;

[0141] Among them, TP indicates that a real change was successfully detected, FP indicates that noise or unchanged areas were mistakenly identified as changes, and FN indicates that a real change in the snow body was missed.

[0142] Recall represents the proportion of areas where snow was actually cleared—that is, areas where snow was actually removed—that the model successfully detected. It measures the effectiveness of the method and helps avoid false negatives. The formula for calculating recall is:

[0143] ;

[0144] in, The value represents recall rate, TP indicates successful detection of real changes, and FN indicates that real snow body changes were missed.

[0145] Precision represents the proportion of areas identified by the model as having cleared snow—that is, areas predicted to have cleared snow—that have actually been cleared. It measures the accuracy of the method and helps avoid false positives. The formula for calculating precision is:

[0146] ;

[0147] in, TP indicates the accuracy rate, TP indicates the successful detection of real changes, and FP indicates the incorrect identification of noise or unchanged areas as changes.

[0148] The F1 score, the harmonic mean of precision and recall, is a comprehensive metric for evaluating model performance, avoiding the limitations of relying on a single indicator. The formula for calculating this evaluation metric is:

[0149] ;

[0150] in, Indicates accuracy, This indicates the recall rate.

[0151] The specific implementation method of this scheme, which is a multi-source data-based evaluation scheme for the artificial control of avalanches on highway high slopes, is as follows: Figure 2 As shown. Data acquisition refers to acquiring topographic data, meteorological data, optical remote sensing data, radar remote sensing data, hyperspectral data, etc. Data preprocessing refers to analyzing and calculating the acquired optical remote sensing data, radar remote sensing data, and hyperspectral data to obtain the first snow cover data. The first snow cover data can be used to delineate the snow zone boundary of the highway slope area. An avalanche susceptibility index and an avalanche triggering index are constructed, and then an avalanche hazard index is further determined. Then, it is determined whether the avalanche hazard index exceeds a predetermined threshold. If it exceeds the predetermined threshold, the highway slope area is identified as a high-risk area, i.e., a slope area requiring intervention. Avalanches were simulated using the RAMMS model, and a buffer zone for highway facilities was constructed. When there was a spatial intersection between the avalanche accumulation arrival line and the buffer zone, candidate intervention points were selected along the avalanche flow centerline. Points within a preset range of the candidate intervention points were screened, and the point with the maximum curvature of the profile was selected as the preferred intervention point. Then, using UAV parameters, a target intervention point, which is the blasting point, was selected from these preferred intervention points. After implementing artificial snow accumulation intervention on the slope area to be intervened based on the target intervention point, the snow accumulation change in the slope area to be intervened was detected, and then an evaluation of the artificial avalanche intervention was conducted on the slope area to be intervened.

[0152] Therefore, this scheme can obtain topographic data, meteorological data, and initial snow accumulation data for the highway slope area adjacent to the highway and located above the roadbed. Using these data, it can determine whether the highway slope area requires snow accumulation treatment to prevent avalanches. Then, the avalanche movement path of the affected slope area is simulated to obtain the avalanche flow centerline and avalanche deposition arrival line. When there is a spatial intersection between the avalanche deposition arrival line and the highway facility buffer zone, candidate intervention points are selected along the avalanche flow centerline. Finally, based on the topographic curvature of the affected slope area in the descent direction, targeted interventions are then implemented at the candidate intervention points. Points within a preset range are screened to obtain preferred intervention points. Then, considering the operational safety constraints of snow intervention equipment, target intervention points are selected from these preferred intervention points. This allows for the accurate identification of key snow accumulation points suitable for artificial intervention in highway slope areas. After implementing artificial snow intervention on the slope area to be intervened based on the target intervention points, an assessment of avalanche artificial intervention is conducted based on the difference between the first and second snow accumulation data before and after the implementation of artificial snow intervention. This achieves an objective evaluation of the effectiveness of artificial avalanche control on high slopes, effectively preventing avalanche disasters and improving the scientific rigor and reliability of avalanche disaster prevention and control.

[0153] To better implement the above methods, this application also provides a device for evaluating the effectiveness of artificial avalanche control on high highway slopes based on multi-source data. This device can be integrated into an electronic device, such as a terminal or server. The terminal can be a mobile phone, tablet, smart Bluetooth device, laptop, or personal computer; the server can be a single server or a server cluster consisting of multiple servers.

[0154] For example, in this embodiment, the method of this application embodiment will be described in detail by taking the evaluation device for artificial control of avalanches on highway high slopes based on multi-source data as specifically integrated into an electronic device.

[0155] For example, such as Figure 3 As shown, the highway high slope avalanche artificial control effect evaluation device based on multi-source data may include a data acquisition unit 301, a region determination unit 302, a path simulation unit 303, a screening unit 304, a target determination unit 305, and an evaluation unit 306, as follows:

[0156] (a) Data acquisition unit 301.

[0157] The data acquisition unit 301 is used to acquire topographic data, meteorological data and first snow accumulation data of the highway slope area, which is the slope area adjacent to the highway and located above the roadbed.

[0158] (ii) Region determination unit 302.

[0159] The region determination unit 302 is used to determine the slope area to be intervened from the highway slope area based on topographic data, meteorological data and first snow cover data.

[0160] In some embodiments, based on topographic data, meteorological data, and first snow cover data, the area of ​​slope to be intervened is determined from the highway slope area, including:

[0161] Based on topographic data, meteorological data, and first snow accumulation data, an avalanche risk index is generated for the highway slope area.

[0162] Based on the avalanche hazard index, the slope areas to be intervened were identified from the highway slope area.

[0163] In some embodiments, an avalanche hazard index corresponding to a highway slope area is generated based on terrain data, meteorological data, and first snow accumulation data, including:

[0164] Based on topographic data and first snow cover data, an avalanche susceptibility index is calculated. The avalanche susceptibility index characterizes the ease with which an avalanche will occur in a highway slope area under the conditions of topography and snow cover.

[0165] Based on meteorological data and first snow accumulation data, the avalanche triggering index is calculated. The avalanche triggering index represents the ease with which an avalanche is triggered in the roadside slope area under the current meteorological and snow physical conditions.

[0166] The avalanche hazard index is obtained by weighted fusion of the avalanche susceptibility index and the avalanche triggering index.

[0167] (III) Path simulation unit 303.

[0168] The path simulation unit 303 is used to simulate the avalanche movement path in the slope area to be intervened, and obtain the avalanche flow centerline and the avalanche deposition arrival line.

[0169] In some embodiments, the first snow data includes snow thickness and snow density. The avalanche movement path of the slope area to be intervened is simulated to obtain the avalanche flow centerline and the avalanche deposition arrival line, including:

[0170] Based on the topographic data of the slope area to be intervened, obtain the friction coefficient and resistance coefficient;

[0171] Using an avalanche dynamics model, the avalanche movement path of the slope area to be intervened is simulated based on snow thickness, snow density, friction coefficient, and drag coefficient, resulting in the avalanche flow centerline and avalanche deposition arrival line.

[0172] (iv) Screening unit 304.

[0173] The screening unit 304 is used to select candidate intervention points along the avalanche flow centerline when there is a spatial intersection between the avalanche accumulation arrival line and the road facility buffer zone, and to screen points within a preset range of candidate intervention points based on the topographic curvature of the slope area to be intervened in the slope direction to obtain the preferred intervention points.

[0174] In some embodiments, the first snow data includes average snow thickness, and candidate intervention points are selected along the avalanche flow centerline, including:

[0175] The intervention spacing is determined based on the average snow depth, and the intervention spacing is positively correlated with the average snow depth.

[0176] Candidate intervention points are set up along the avalanche flow centerline according to the intervention interval, wherein the distance between two adjacent candidate intervention points is equal to the intervention interval.

[0177] In some embodiments, points within a preset range of candidate intervention points are selected based on the topographic curvature of the slope area to be intervened in the slope direction to obtain preferred intervention points, including:

[0178] Multiple candidate location points are obtained with the candidate intervention point as the center and a preset size as the radius;

[0179] Based on the topographic curvature of the slope area to be intervened in the slope direction, the curvature of each candidate location point along the slope direction is obtained;

[0180] For each candidate location point, the neighboring candidate location points corresponding to the candidate location point are determined from multiple candidate location points according to the preset neighborhood range;

[0181] If the curvature of a candidate location point is not less than the curvature of a neighboring candidate location point, the candidate location point is determined as the preferred intervention point.

[0182] (V) Target Determination Unit 305.

[0183] The target determination unit 305 is used to filter the preferred intervention points based on the operational safety constraints of the snow intervention equipment to obtain the target intervention points.

[0184] In some embodiments, based on the operational safety constraints of snow intervention equipment, preferred intervention points are filtered to obtain target intervention points, including:

[0185] Obtain the operational environment parameters corresponding to the preferred intervention point. The operational environment parameters include slope, real-time wind speed, horizontal distance from critical infrastructure, no-fly zone status, and communication information strength.

[0186] When the operating environment parameters meet the operational safety constraints of the snow intervention equipment, the preferred intervention point will be selected as the target intervention point.

[0187] (vi) Evaluation Unit 306.

[0188] The evaluation unit 306 is used to obtain the second snow data of the slope area to be intervened after implementing artificial snow control based on the target intervention point, and to evaluate the artificial avalanche control of the slope area to be intervened based on the difference between the first snow data and the second snow data of the slope area to be intervened, thereby obtaining evaluation indicators. The evaluation indicators reflect the effect of artificial avalanche control in the slope area to be intervened.

[0189] In some embodiments, the first snow data includes a first snow-covered area and a first surface snow backscattering intensity, and the second snow data includes a second snow-covered area and a second surface snow backscattering intensity.

[0190] Based on the difference between the first and second snow accumulation data of the slope area to be intervened, an assessment of artificial avalanche intervention in the slope area to be intervened is conducted, resulting in assessment indicators, including:

[0191] The actual snow removal area is determined based on the difference between the first and second snow-covered areas of the slope to be intervened.

[0192] The area of ​​scattering intensity variation is determined based on the difference between the backscattering intensity of the first and second surface snow cover in the slope area to be intervened.

[0193] By identifying the regions with varying scattering intensity from the regions with varying scattering intensity, the predicted snow removal area can be obtained.

[0194] Based on the actual snow removal area and the predicted snow removal area, an assessment of artificial avalanche intervention was conducted on the slope area to be intervened, and assessment indicators were obtained.

[0195] In some embodiments, an assessment of avalanche intervention in the slope area to be intervened is conducted based on the actual snow removal area and the predicted snow removal area, yielding assessment indicators, including:

[0196] Determine the area intersection-union ratio between the actual snow removal area and the predicted snow removal area;

[0197] The recall rate is obtained by determining the overlap area between the actual snow removal area and the predicted snow removal area, which is the proportion of the actual snow removal area.

[0198] Determine the overlap area between the actual snow removal area and the predicted snow removal area, and its proportion to the predicted snow removal area to obtain the accuracy rate;

[0199] Based on regional crossover ratio, recall, and precision, an evaluation index was obtained for artificial avalanche intervention in the slope area to be intervened.

[0200] In practice, each of the above units can be implemented as an independent entity or can be arbitrarily combined to be implemented as the same or several entities. For the specific implementation of each of the above units, please refer to the previous method embodiments, which will not be repeated here.

[0201] This application also provides an electronic device, which can be a terminal, a server, or other similar device. The terminal can be a mobile phone, tablet computer, smart Bluetooth device, laptop computer, personal computer, etc.; the server can be a single server or a server cluster composed of multiple servers, etc.

[0202] In some embodiments, the device for evaluating the effect of artificial avalanche control on highway high slopes based on multi-source data can also be integrated into multiple electronic devices. For example, the device for evaluating the effect of artificial avalanche control on highway high slopes based on multi-source data can be integrated into multiple servers, and multiple servers can implement the method for evaluating the effect of artificial avalanche control on highway high slopes based on multi-source data of this application.

[0203] Therefore, embodiments of this application provide a computer-readable storage medium storing a plurality of instructions that can be loaded by a processor to execute steps in any of the methods for evaluating the effectiveness of artificial control of avalanches on highway high slopes based on multi-source data provided in embodiments of this application.

[0204] The storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0205] According to one aspect of this application, a computer program product or computer program is provided, comprising a computer program / instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer program / instructions from the computer-readable storage medium and executes the computer program / instructions, causing the electronic device to perform the methods provided in various optional implementations of the above-described embodiments regarding the evaluation of the effectiveness of artificial avalanche control on high highway slopes based on multi-source data.

[0206] Since the instructions stored in the storage medium can execute the steps in any of the methods for evaluating the effect of artificial control of avalanches on highway high slopes based on multi-source data provided in the embodiments of this application, the beneficial effects that any of the methods for evaluating the effect of artificial control of avalanches on highway high slopes based on multi-source data provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0207] The above provides a detailed description of a method and apparatus for evaluating the effect of artificial control of avalanches on highway high slopes based on multi-source data, as provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and its core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for evaluating the effectiveness of artificial avalanche control on high highway slopes based on multi-source data, characterized in that, include: Acquire topographic data, meteorological data, and first snow accumulation data for the highway slope area, wherein the highway slope area is the slope area adjacent to the highway and located above the roadbed; Based on the terrain data, the meteorological data, and the first snow accumulation data, the area of ​​slope to be intervened is determined from the highway slope area, including: Based on the terrain data and the first snow cover data, an avalanche susceptibility index is calculated, which characterizes the ease with which an avalanche will occur in the roadside slope area under the conditions of terrain and snow cover. Based on the meteorological data and the first snow accumulation data, an avalanche triggering index is calculated. The avalanche triggering index characterizes the ease with which an avalanche is triggered in the roadside slope area under the current meteorological and snow accumulation physical conditions. The avalanche susceptibility index and the avalanche triggering index are weighted and fused to obtain the avalanche hazard index. Based on the avalanche hazard index, the slope areas to be intervened in the highway slope area are determined; The avalanche movement path of the slope area to be intervened is simulated to obtain the avalanche flow centerline and avalanche deposition arrival line. The first snow accumulation data includes snow thickness and snow density, including: Based on the topographic data of the slope area to be intervened, the friction coefficient and resistance coefficient are obtained; Using an avalanche dynamics model, based on the snow thickness, snow density, friction coefficient, and drag coefficient, the avalanche movement path of the slope area to be intervened is simulated to obtain the avalanche flow centerline and avalanche deposition arrival line. When there is a spatial intersection between the avalanche accumulation arrival line and the highway facility buffer zone, candidate intervention points are selected along the avalanche flow centerline, and points within a preset range of the candidate intervention points are screened according to the topographic curvature of the slope area to be intervened in the slope direction to obtain the preferred intervention points. Based on the operational safety constraints of snow intervention equipment, the preferred intervention points are filtered to obtain the target intervention points; After implementing artificial snow cover intervention on the slope area to be intervened based on the target intervention point, the second snow cover data of the slope area to be intervened is obtained. Based on the difference between the first snow cover data and the second snow cover data of the slope area to be intervened, the artificial snow avalanche intervention of the slope area to be intervened is evaluated to obtain evaluation indicators. The evaluation indicators reflect the artificial snow avalanche control effect of the slope area to be intervened. The first snow cover data includes the first snow cover area and the first surface snow backscattering intensity. The second snow cover data includes the second snow cover area and the second surface snow backscattering intensity. The assessment of avalanche intervention in the slope area to be intervened is based on the difference between the first and second snow accumulation data, yielding assessment indicators, including: The actual snow removal area is determined based on the difference between the first and second snow-covered areas of the slope to be intervened in. The area of ​​scattering intensity variation is determined based on the difference between the backscattering intensity of the first and second surface snow accumulations in the slope area to be intervened. From the regions of varying scattering intensity, a region of scattering intensity variation with regional connectivity is determined to obtain the snow accumulation prediction and removal region; Based on the actual snow removal area and the predicted snow removal area, an assessment of artificial avalanche intervention is conducted on the slope area to be intervened, and assessment indicators are obtained.

2. The method for evaluating the effectiveness of artificial avalanche control on high highway slopes based on multi-source data as described in claim 1, characterized in that, The first snow accumulation data includes the average snow thickness, and the selection of candidate intervention points along the avalanche flow centerline includes: The intervention spacing is determined based on the average snow thickness, and the intervention spacing is positively correlated with the average snow thickness; Candidate intervention points are deployed along the avalanche flow centerline according to the intervention interval, wherein the distance between two adjacent candidate intervention points is equal to the intervention interval.

3. The method for evaluating the effectiveness of artificial avalanche control on high highway slopes based on multi-source data as described in claim 1, characterized in that, The step of selecting preferred intervention points based on the topographic curvature of the slope area to be intervened in the slope direction, within a preset range of the candidate intervention points, includes: Multiple candidate location points are obtained with the candidate intervention point as the center and a preset size as the radius; Based on the topographic curvature of the slope area to be intervened in the slope direction, the curvature of each candidate location point along the slope direction is obtained. For each candidate location point, a neighboring candidate location point is determined from the plurality of candidate location points according to a preset neighborhood range; If the curvature corresponding to the candidate location point is not less than the curvature corresponding to the neighboring candidate location points, the candidate location point is determined as the preferred intervention point.

4. The method for evaluating the effectiveness of artificial avalanche control on high highway slopes based on multi-source data as described in claim 1, characterized in that, The operational safety constraints based on snow intervention equipment are used to filter the preferred intervention points to obtain target intervention points, including: Obtain the operational environment parameters corresponding to the preferred intervention point, including slope, real-time wind speed, horizontal distance from critical infrastructure, no-fly zone status, and communication information strength. When the operating environment parameters meet the operational safety constraints of the snow intervention equipment, the preferred intervention point is taken as the target intervention point.

5. The method for evaluating the effectiveness of artificial avalanche control on high highway slopes based on multi-source data as described in claim 1, characterized in that, The assessment of avalanche intervention in the slope area to be intervened, based on the actual snow removal area and the predicted snow removal area, yields assessment indicators, including: Determine the region intersection-union ratio between the actual snow removal area and the predicted snow removal area; The recall rate is obtained by determining the proportion of the overlapping area between the actual snow removal area and the predicted snow removal area to the actual snow removal area. The accuracy rate is obtained by determining the proportion of the overlapping area between the actual snow removal area and the predicted snow removal area to the predicted snow removal area. Based on the intersection-union ratio, recall, and precision of the region, an evaluation index is obtained for the artificial intervention of avalanches in the slope area to be intervened.

6. A device for evaluating the effectiveness of artificial avalanche control on high highway slopes based on multi-source data, characterized in that, include: The data acquisition unit is used to acquire topographic data, meteorological data and first snow accumulation data of the highway slope area, wherein the highway slope area is the slope area adjacent to the highway and located above the roadbed; The region determination unit is used to determine the slope area to be intervened from the highway slope area based on the terrain data, the meteorological data, and the first snow accumulation data, including: Based on the terrain data and the first snow cover data, an avalanche susceptibility index is calculated, which characterizes the ease with which an avalanche will occur in the roadside slope area under the conditions of terrain and snow cover. Based on the meteorological data and the first snow accumulation data, an avalanche triggering index is calculated. The avalanche triggering index characterizes the ease with which an avalanche is triggered in the roadside slope area under the current meteorological and snow accumulation physical conditions. The avalanche susceptibility index and the avalanche triggering index are weighted and fused to obtain the avalanche hazard index. Based on the avalanche hazard index, the slope areas to be intervened in the highway slope area are determined; The path simulation unit is used to simulate the avalanche movement path in the slope area to be intervened, and obtain the avalanche flow centerline and the avalanche accumulation arrival line. The first snow accumulation data includes snow thickness and snow density, including: Based on the topographic data of the slope area to be intervened, the friction coefficient and resistance coefficient are obtained; Using an avalanche dynamics model, based on the snow thickness, snow density, friction coefficient, and drag coefficient, the avalanche movement path of the slope area to be intervened is simulated to obtain the avalanche flow centerline and avalanche deposition arrival line. The screening unit is used to select candidate intervention points along the avalanche flow centerline when there is a spatial intersection between the avalanche accumulation arrival line and the road facility buffer zone, and to screen points within a preset range of the candidate intervention points according to the topographic curvature of the slope area to be intervened in the slope direction to obtain preferred intervention points. The target determination unit is used to filter the preferred intervention points based on the operational safety constraints of the snow intervention equipment to obtain the target intervention points; An evaluation unit is used to obtain second snow data of the slope area to be intervened after implementing artificial snow control based on the target intervention point, and to evaluate the artificial snow control of the slope area to be intervened based on the difference between the first snow data and the second snow data, thereby obtaining evaluation indicators. The evaluation indicators reflect the artificial snow control effect of the slope area to be intervened. The first snow data includes the first snow cover area and the first surface snow backscattering intensity, and the second snow data includes the second snow cover area and the second surface snow backscattering intensity. The assessment of avalanche intervention in the slope area to be intervened is based on the difference between the first and second snow accumulation data, yielding assessment indicators, including: The actual snow removal area is determined based on the difference between the first and second snow-covered areas of the slope to be intervened in. The area of ​​scattering intensity variation is determined based on the difference between the backscattering intensity of the first and second surface snow accumulations in the slope area to be intervened. From the regions of varying scattering intensity, a region of scattering intensity variation with regional connectivity is determined to obtain the snow accumulation prediction and removal region; Based on the actual snow removal area and the predicted snow removal area, an assessment of artificial avalanche intervention is conducted on the slope area to be intervened, and assessment indicators are obtained.

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