Real-time weather service early warning method and system for ski field

Abstract

The invention relates to the technical field of meteorological early warning, in particular to a skiing field real-time meteorological service early warning method and system, and the method comprises the steps: constructing a skiing field real-time information set; performing terrain deformation compensation based on the real-time information set of the ski field and the spatial topological relation of the functional areas of the ski track, and constructing a dynamic coupling field; defining a ski field risk rule base, inputting the dynamic coupling field into the ski field risk rule base, and executing weight distribution; performing tensor analysis on the risk rule output after weight distribution, detecting a three-field coupling abrupt change interface, and generating a grading early warning instruction; and extracting an equipment control parameter set mapped with the space coordinates of the execution terminal from a ski field risk rule base according to the geo-fence identifier in the graded early warning instruction. According to the invention, systematic misjudgment caused by meteorological risk judgment of a ski field and topographic spatial feature decoupling is effectively solved, and accurate meteorological disaster early warning of a key area is realized through dynamic coupling field construction and geofence mapping of meteorological parameters.

Description

Technical Field

[0001] The present invention relates to the field of meteorological early warning technology, and in particular to a real-time meteorological service early warning method and system for a ski resort. Background Art

[0002] Current ski resort safety and control systems generally adopt an early warning method based on environmental parameter threshold judgment. Environmental data is collected through meteorological sensors and snow mechanical sensors respectively. When a single parameter exceeds the preset threshold, a full-field broadcast alarm or equipment control command is triggered.

[0003] However, this method has the fundamental flaw of dynamically decoupling environmental data from the spatial structure of the terrain: since a real-time mapping relationship between meteorological fields, snow fields and snow track topological features has not been established, the risk assessment results cannot be associated with the spatial attributes of the specific snow track location. For example, the same wind speed threshold is mechanically applied to straight snow tracks and the leeward slopes of curves, while the actual wind vortex risk is only related to specific terrain features; the high-risk coordinates output by the avalanche model are not bound to the slope curvature characteristics, resulting in the snow grooming equipment scheduling instructions deviating from the actual high-risk sections. This spatial decoupling state causes the existing methods to produce systematic misjudgments and execution dislocations in complex mountain environments. Summary of the Invention

[0004] The present invention provides a ski resort real-time weather service warning method and system, thereby effectively solving the problems pointed out in the background technology.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is: A ski resort real-time weather service warning method, comprising: Obtain real-time meteorological monitoring data, snow status data, and personnel positioning topology data within the ski resort area to build a real-time information set for the ski resort; Based on the real-time information set of the ski resort and the spatial topological relationship between the functional areas of the ski slopes, terrain deformation compensation is performed to construct a dynamic coupling field; Defining a ski resort risk rule base, inputting the dynamic coupling field into the ski resort risk rule base, and performing weight assignment, wherein the ski resort risk rule base includes a relationship mapping table between warning level, geo-fence, and device control parameter; Perform tensor analysis on the risk rule output after weight allocation, detect the coupled mutation interface of weather, snow accumulation and personnel, and generate graded warning instructions including geo-fence identification; According to the geo-fence identifier in the graded warning instruction, a device control parameter set for executing terminal space coordinate mapping is extracted from the ski resort risk rule library.

[0006] Furthermore, terrain deformation compensation is performed to construct a dynamic coupling field, including: Calculate the slope angle, aspect angle and altitude gradient modulus of the target area based on the DEM to obtain a set of terrain parameters. Acquire a spatial topological relationship between the ski resort real-time information set and the ski slope functional area, and define a set of ski slope functional area types; Based on the terrain parameter set and the snow track functional area type set, performing three-dimensional spatial correction on the meteorological monitoring data; The three-dimensional space correction result is input into the terrain deformation coupling engine to obtain the dynamic coupling field.

[0007] Furthermore, the three-dimensional space correction result is input into the terrain deformation coupling engine to obtain the dynamic coupling field, including: Calculate the terrain dynamic parameters of the geo-fence boundaries of each ski functional area based on the digital elevation model; Based on the terrain dynamic parameters and the three-dimensional correction results, a three-dimensional field reconstruction operation is performed to output a geographical environment dynamic parameter data set; The geographic environment dynamic parameter data set is input into the terrain deformation coupling engine to generate the dynamic coupling field.

[0008] Furthermore, a ski resort risk rule base is defined, and the dynamic coupling field is input into the ski resort risk rule base to perform weight allocation, including: Based on the functional zoning results of the snow track and historical accident data, a multi-level risk threshold matrix was constructed; mapping the dynamic coupling field to the multi-level risk threshold matrix; Calculating terrain deformation correlation factors, temperature variation factors, and visibility interference factors based on the meteorological monitoring data and snow cover status data; The three factors are input into the decision tree model to complete the dynamic weight allocation of the risk threshold matrix.

[0009] Furthermore, based on the snow track functional zoning results and historical accident data, a multi-level risk threshold matrix was constructed, including: Divide the functional areas of ski slopes in the ski resort into dimensions according to safety criticality; Based on historical accident data, a four-element threshold group is configured for each functional area dimension; The threshold groups are organized in ascending order of risk level to form a three-dimensional matrix structure of dimension-level-threshold.

[0010] Furthermore, the risk rule output after weight assignment is subjected to tensor analysis to detect the coupled mutation interface of weather, snow accumulation, and personnel, and generate graded warning instructions with geo-fence identification, including: Expanding the dynamic weight vector into a 4D convolution kernel whose size matches the ski resort geofence grid resolution; Based on the terrain deformation correlation factor, temperature variation factor, visibility interference factor and human behavior, the shear stress gradient tensor, latent heat flux tensor, fog concentration tensor and human behavior tensor are constructed respectively. The three-field coupling mutation detection is performed through the four-dimensional convolution kernel, and the graded warning instruction is generated when any channel value exceeds the risk threshold.

[0011] Furthermore, the calculation formulas for the shear stress gradient tensor, latent heat flux tensor, fog concentration tensor and personnel behavior tensor are as follows: ; ; ; ; in, is the shear stress gradient tensor, is the gradient operator, is the snow density, is the snow layer thickness, is the slope angle of the ski slope, is the latent heat flux tensor, is the thermal conductivity coefficient, For snow temperature, is the fog concentration tensor, is the fog probability, is the personnel behavior tensor, is the population density, For the safety area, is the probability of moving in the same direction.

[0012] Furthermore, the three-field coupled mutation detection is implemented through a three-level edge computing architecture, and the three-layer edge computing mechanism includes a terminal layer, an edge gateway layer and a cloud decision layer.

[0013] A real-time weather service warning system for ski resorts, comprising: The information set construction module obtains meteorological monitoring data, snow status data and personnel positioning topology data in the ski resort area in real time to build a real-time information set of the ski resort; A coupling field construction module, which performs terrain deformation compensation and constructs a dynamic coupling field based on the real-time information set of the ski resort and the spatial topological relationship of the ski trail functional areas; a weight allocation module, defining a ski resort risk rule base, inputting the dynamic coupling field into the ski resort risk rule base, and performing weight allocation, wherein the ski resort risk rule base includes a relationship mapping table between warning level, geo-fence, and device control parameter; The instruction generation module performs tensor analysis on the risk rule output after weight allocation, detects the coupled mutation interface of the weather, snow accumulation, and personnel, and generates graded warning instructions including geo-fence identification; The parameter extraction module extracts the device control parameter set mapped to the terminal space coordinates in the ski resort risk rule library according to the geographical fence identifier in the graded warning instruction.

[0014] Furthermore, the instruction generation module includes: A vector expansion unit that expands the dynamic weight vector into a four-dimensional convolution kernel whose size matches the ski resort geofence grid resolution; The tensor construction unit constructs the shear stress gradient tensor, latent heat flux tensor, fog concentration tensor and personnel behavior tensor based on terrain deformation correlation factors, temperature variation factors, visibility interference factors and personnel behavior respectively; The mutation detection unit performs three-field coupled mutation detection through the four-dimensional convolution kernel and generates the graded warning instruction when any channel value exceeds the risk threshold.

[0015] The technical solution of the present invention can achieve the following technical effects: It effectively solves the systematic misjudgment caused by the decoupling of meteorological risk assessment and terrain spatial characteristics in ski resorts, and realizes accurate meteorological disaster warning in key areas through the construction of dynamic coupling fields and geographic fence mapping of meteorological parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 The figure is a flowchart of a method for real-time weather service warning at a ski resort; Figure 2 Schematic diagram of the process for compensating for terrain deformation and constructing a dynamic coupling field; Figure 3 A schematic diagram of the process of inputting the 3D spatial correction results into the terrain deformation coupling engine; Figure 4 A flowchart for performing weight assignment; Figure 5 A schematic diagram of the process for constructing a multi-level risk threshold matrix; Figure 6 A flowchart for generating graded warning instructions that include geo-fence identifiers. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items. Example

[0020] like Figure 1 As shown, the present invention provides a real-time weather service warning method for a ski resort, the method comprising: S1: Real-time acquisition of meteorological monitoring data, snow status data, and personnel positioning topology data within the ski resort area to build a real-time information set for the ski resort; Specifically, by acquiring real-time meteorological monitoring data, snow cover status data, and personnel location topology data within the ski resort area, a comprehensive real-time information set is constructed. This data includes meteorological conditions (such as temperature, wind speed, humidity, and snowfall), snow cover status (such as snow thickness, density, and snow quality), and personnel distribution (such as the location and density of skiers). By integrating this data, environmental changes and personnel dynamics within the ski resort can be reflected in real time, providing a basis for subsequent risk assessment and early warning. Real-time meteorological and snow cover data help determine weather changes and snow conditions at the ski resort, while personnel location data helps assess the distribution of skiers within the ski resort and their impact on potentially dangerous areas, providing a basis for generating accurate early warning instructions and implementing effective safety and control measures.

[0021] S2: Based on the real-time information set of the ski resort and the spatial topological relationship of the ski slope functional areas, terrain deformation compensation is performed to construct a dynamic coupling field; Specifically, by combining the real-time information set of the ski resort and the spatial topological relationship of the functional areas of the ski slopes, terrain deformation compensation is performed, and then a dynamic coupling field is constructed. The core purpose of this process is to eliminate the impact of terrain changes, thereby ensuring that the meteorological data and snow conditions in the ski resort can more accurately reflect the actual terrain conditions. Through terrain deformation compensation, the meteorological and snow data can be corrected according to the slope, direction and altitude differences of the ski slope, so that these data are more closely integrated with the spatial relationship of the terrain, thereby providing a more accurate environmental status assessment. The construction of the dynamic coupling field is to organically combine terrain, meteorological and snow data to form a scene that can dynamically reflect the changes in the ski resort environment, providing a more reliable basis for subsequent risk assessment and early warning.

[0022] S3: Define a ski resort risk rule base, input the dynamic coupling field into the ski resort risk rule base, and perform weight assignment. The ski resort risk rule base contains a relationship mapping table between warning level, geo-fence, and equipment control parameters. Specifically, by defining a risk rule base for the ski resort and inputting the dynamic coupling field into it, risk weight allocation is performed to enable effective management and early warning according to different risk levels. The risk rule base contains a relationship mapping table between warning levels, geographic fences, and equipment control parameters, which is used to associate different risk levels with specific ski areas (such as ski slopes, specific areas) and corresponding equipment control measures (such as automated equipment adjustment, alarm triggering, etc.). In this process, the dynamic coupling field provides a comprehensive risk assessment basis based on terrain, weather, and snow conditions. Through weight allocation, the risks of different areas can be accurately divided, allowing the ski resort to take appropriate safety measures in a timely manner according to real-time environmental changes to ensure the safety of skiers.

[0023] S4: Perform tensor analysis on the risk rule output after weight allocation, detect the coupled mutation interface of weather, snow accumulation and personnel, and generate graded warning instructions including geo-fence identification; Specifically, by performing tensor analysis on the output of the risk rules after weight assignment, the coupled mutation interface of the three factors of weather, snow accumulation, and personnel is detected, thereby generating a graded warning instruction containing geo-fence identification. This process converts the risk rules after weight assignment into tensors for analysis, which can identify abnormal changes in weather, snow accumulation, and personnel distribution. In particular, when these changes exceed the preset safety threshold, potential high-risk areas are identified. On this basis, the generated warning instructions will include specific geo-fence identification, indicating the areas or paths that require special attention, helping to quickly respond and take targeted safety measures. This process can effectively identify risk mutation points within the ski resort and provide protection for skiers' safety through accurate warning information.

[0024] S5: According to the geo-fence identifier in the graded warning instruction, the device control parameter set for executing the terminal spatial coordinate mapping is extracted from the ski resort risk rule library.

[0025] Specifically, when generating graded warning instructions, each instruction includes one or more geofence identifiers, identifying ski areas requiring special attention. These geofences are dynamically determined based on factors such as real-time weather conditions, snow cover, and the ski resort's terrain. For example, if the wind speed on a ski run exceeds a safety threshold or the snow cover is thick, the relevant area will be designated as a high-risk zone and marked with geofences to facilitate the implementation of appropriate control measures. Then, based on these geofence identifiers, the corresponding equipment control parameters are extracted. Equipment control parameters include control information for various safety facilities within the ski resort, such as the scheduling of ski run machinery, the triggering of alarms, and the adjustment of lighting facilities. For example, if the risk level of a certain area is "red," indicating a high safety risk, the ski run machinery in that area can adjust its operating intensity as needed, or trigger alarms to issue warnings. When extracting equipment control parameters, spatial coordinates are mapped between the geofence identifiers and the specific geographic locations within the ski resort to ensure that the correct equipment parameters are assigned to the corresponding areas. The extracted control parameter set may include equipment start and stop, operating mode, and workload intensity. These control parameters are implemented through the execution terminal. For example, when the risk level in a certain area reaches a high level, lighting facilities can be increased in that area, and alarms can be activated to warn skiers to stay away. The advantage of this implementation is that it can precisely adjust safety equipment within the ski resort based on real-time changes in weather, snow accumulation, and occupant distribution data, allowing timely implementation of necessary measures to mitigate safety hazards. This dynamic adjustment ensures that all equipment at the ski resort is always in optimal working condition, effectively responding to changing environmental conditions and potential safety risks. For example, in actual application, if a ski slope at a ski resort reaches a high risk level due to excessive snow accumulation due to windy and snowy weather, a geo-fence will be automatically generated, marking the area as a high-risk zone. At this time, snow-clearing equipment will accelerate operation, increasing the frequency of snow clearing, and alarms will be used to notify staff and skiers of potential hazards in the area. This precise equipment control and early warning system can maximize skier safety.

[0026] The present invention effectively solves the systematic misjudgment caused by the decoupling of meteorological risk assessment and terrain spatial characteristics in ski resorts, and realizes accurate meteorological disaster warning in key areas through the construction of dynamic coupling fields and geographic fence mapping of meteorological parameters.

[0027] As a preferred embodiment of the above, Figure 2As shown, terrain deformation compensation is performed and a dynamic coupling field is constructed, including: A10: Calculate the slope angle, aspect angle, and altitude gradient modulus of the target area based on the DEM to obtain a set of terrain parameters. A20: Obtain the spatial topological relationship between the real-time information set of the ski resort and the ski slope functional area, and define the ski slope functional area type set; A30: Perform three-dimensional spatial correction on meteorological monitoring data based on a set of terrain parameters and a set of snow trail functional zone types; A40: Input the three-dimensional spatial correction results into the terrain deformation coupling engine to obtain the dynamic coupling field.

[0028] Specifically, a digital elevation model (DEM) is constructed by measuring the terrain height of the ski resort area to create a three-dimensional topographic map. Based on this, the slope angle, aspect angle, and altitude gradient modulus of the area can be obtained by calculating the gradient tensor of the ski trail surface. These are key parameters for evaluating the terrain characteristics of the ski trail. The slope angle reflects the steepness of the trail, the aspect angle indicates the direction of the trail, and the altitude gradient modulus helps to understand the vertical changes in the area. This collection of terrain parameters provides an accurate topographic foundation, providing support for subsequent meteorological data correction. Next, the ski trails within the ski resort are divided into multiple functional areas, each with different usage characteristics and safety requirements. For example, some areas may be gentle slopes for beginners, while others are challenging slopes for advanced skiers. Based on real-time meteorological data, snow accumulation status, and personnel location data, the different functional areas within the ski resort area are spatially topologically divided. The goal of this step is to clarify the characteristics and requirements of each area and provide a framework for subsequent three-dimensional spatial correction and terrain deformation compensation. Then, based on the terrain parameters calculated in step A10 and the ski slope functional zone type defined in step A20, these parameters are used to perform a three-dimensional spatial correction on the real-time meteorological monitoring data (such as temperature, humidity, and wind speed). This correction enables the meteorological data to more accurately reflect the impact of different terrain conditions on meteorological conditions. For example, steeper ski slopes may result in higher wind speeds or looser snow due to terrain changes. This correction results in meteorological data that is more consistent with actual conditions, thereby improving the accuracy of early warnings. Finally, the improved meteorological data obtained through the three-dimensional spatial correction in step A30 is input into the terrain deformation coupling engine. This engine combines terrain parameters, meteorological data, and snow cover data to generate a dynamic coupled field. This coupled field can reflect the actual environmental changes of the ski slopes and other areas of the ski resort in real time, including meteorological changes, snow cover characteristics, and human activities. The dynamic coupled field provides a comprehensive environmental model, allowing risk assessments within the ski resort to be updated based on actual conditions, providing an accurate basis for further operation of the early warning system.

[0029] As a preferred embodiment of the above, Figure 3 As shown, in step A30, the three-dimensional space correction result is input into the terrain deformation coupling engine to obtain a dynamic coupling field, including: A31: Determine the terrain dynamic parameters of the geo-fence boundaries of each ski area based on the digital elevation model; A32: Based on the terrain dynamic parameters and 3D correction results, perform 3D field reconstruction operations and output a dataset of geographic environment dynamic parameters; A33: Input the geographic environment dynamic parameter dataset into the terrain deformation coupling engine to generate a dynamic coupling field.

[0030] Specifically, first, by using digital elevation model (DEM) data, the terrain of each functional area in the ski resort can be analyzed and calculated in detail. At the boundary of each ski slope or ski area, the terrain characteristics of each area can be obtained by solving terrain dynamic parameters such as slope, aspect and altitude change. These terrain dynamic parameters help to understand the impact of terrain changes on meteorological factors (such as wind speed, temperature, humidity, etc.) and the stability of the snow layer. For example, in some steeper areas or areas facing the windward side, stronger winds or loose snow layers may be generated, and these factors have a greater impact on the safety of skiers. Therefore, the calculation of terrain dynamic parameters can provide an accurate spatial data foundation for subsequent risk analysis. Next, the terrain dynamic parameters calculated from A31 and the three-dimensional spatial correction results performed in A30 are combined to perform a three-dimensional field reconstruction operation. This process integrates terrain changes with actual meteorological monitoring data to generate a comprehensive geographic environment dynamic parameter dataset. This dataset contains real-time geographic information and meteorological data for different areas of the ski resort, such as wind speed, snow thickness, and temperature on different slopes. It can reflect the environmental changes in each area. This data provides a comprehensive perspective for more accurately predicting and assessing the safety risks of the ski resort. Finally, the geographic environment dynamic parameter dataset obtained through three-dimensional field reconstruction is input into the terrain deformation coupling engine to generate the final dynamic coupling field. The dynamic coupling field is a multi-dimensional data field that can integrate meteorological, snow accumulation, and terrain information from different areas of the ski resort in real time, thereby providing a comprehensive environmental model. This model can not only reflect the changing meteorological conditions on the slopes but also identify potential safety threats posed by factors such as snow accumulation and occupant distribution. Through this process, the dynamic changes in the environment within the ski resort can be accurately captured, especially with the real-time changes in meteorological conditions, snow conditions, terrain changes, and personnel distribution. The generated dynamic coupling field can provide strong support for risk assessment and early warning. For example, when extreme weather events such as strong winds or snowfall occur in a certain area of ​​the ski resort, the dynamic coupling field can quickly identify the high-risk status of the area and generate corresponding early warning instructions in the risk rule library.

[0031] As a preferred embodiment of the above, Figure 4 As shown in the figure, a ski resort risk rule base is defined, and the dynamic coupling field is input into the ski resort risk rule base to perform weight allocation, including: B10: Construct a multi-level risk threshold matrix based on the snow track functional zoning results and historical accident data; B20: Mapping the dynamic coupling field to a multi-level risk threshold matrix; B30: Calculate terrain deformation correlation factors, temperature variation factors, and visibility interference factors based on meteorological monitoring data and snow cover status data; B40: Input the three factors into the decision tree model to complete the dynamic weight allocation of the risk threshold matrix.

[0032] Specifically, first, the ski slopes of the ski resort are divided into functional areas. Different functional areas are classified according to their usage nature, terrain characteristics and potential risks. For example, beginner areas may have lower risks, while advanced ski areas face higher risks. Combined with the historical accident data of the ski resort, different risk thresholds can be configured for each functional area. Historical accident data reflects the nature and frequency of safety accidents that have occurred in different areas. Through this data, the potential risks of each functional area can be identified, so that appropriate risk thresholds can be set for each area. The multi-level risk threshold matrix is ​​a structured table that contains different risk levels, area types and their corresponding safety thresholds. This matrix provides the basis for subsequent risk assessment and weight allocation; then, by matching the geographic information, meteorological data and snow cover status in the dynamic coupling field with the risk thresholds in the multi-level risk threshold matrix, the various areas within the ski resort can be divided into different risk levels. For example, some areas with higher wind speeds and thicker snow may be classified as "high-risk" areas, while other areas may be classified as "low-risk" areas. The dynamic coupled field provides real-time data input for the risk matrix, ensuring that the risk assessment reflects the actual conditions of the current environment. Then, by analyzing meteorological monitoring data (such as temperature, humidity, and wind speed) and snow cover data (such as snow thickness and density), three key factors are calculated: the terrain deformation correlation factor, the temperature variation factor, and the visibility interference factor. The terrain deformation correlation factor reflects the impact of terrain changes on meteorology and snow distribution. For example, steep areas may lead to uneven distribution of wind speed and snow thickness, thus affecting risk assessment. The temperature variation factor is related to temperature changes in different areas of the ski resort. Temperature fluctuations may lead to unstable snow layers, thereby increasing the risk of avalanches. The visibility interference factor is related to changes in visibility within the ski resort. Low visibility may increase the probability of skiing accidents, especially in windy and snowy weather. By calculating these three factors, the impact of terrain, weather and snow conditions on ski resort safety can be quantified, providing detailed parameter support for subsequent risk assessment; finally, the terrain deformation association factor, temperature variation factor and visibility interference factor are used as input, and the decision tree model is applied to dynamically allocate weights. The decision tree model can automatically calculate the risk weights of different areas based on the values ​​of the input factors, and adjust the risk level of the area according to the multi-level thresholds in the risk matrix. For example, when the temperature variation factor is large and the visibility is low, the risk level of the ski slope may increase. The decision tree model will adjust the risk weight according to the changes in these factors to ensure that the risk assessment of the ski resort is always based on real-time environmental data.

[0033] As a preferred embodiment of the above, Figure 5 As shown, step B20, based on the snow track functional area division results and historical accident data, construct a multi-level risk threshold matrix, including: B21: Divide the ski slope functional areas into dimensions according to safety criticality; B22: Based on historical accident data, configure a four-element threshold group for each functional area dimension; B23: Organize the threshold groups in ascending order of risk level to form a three-dimensional matrix structure of dimension-level-threshold.

[0034] Specifically, first, the ski resort's ski slopes are divided into functional areas according to safety criticality. In this step, the ski slopes of the ski resort are divided according to safety criticality. Different ski slope functional areas have different safety risks. For example, the ski slopes used by beginners usually have gentler slopes and lower risks, while the ski slopes used by advanced skiers have larger slopes, higher difficulty and greater risks. Therefore, it is necessary to first divide the ski slopes of the ski resort into functional areas, including beginner areas, intermediate areas and advanced areas. Each functional area represents different safety needs. According to the characteristics and potential risks of each functional area, these ski slopes are assigned different safety criticalities. For example, the beginner area is classified as a low safety risk area, while the advanced ski area is classified as a high safety risk area; next, by analyzing the historical accident data of the ski resort, the frequency and type of accidents in different functional areas can be discovered. The historical accident data can provide safety risk information for each functional area, for example, which areas have more falls, collisions or other types of accidents. These data help determine the risk threshold group within each functional area. The four-element threshold group includes four different thresholds (for example: low risk, medium risk, high risk and extremely high risk), each of which represents the risk level of the functional area under different safety conditions. These thresholds are based not only on historical data but also on factors such as terrain and meteorological conditions, establishing appropriate risk assessment criteria for each functional area. This data is then organized into a three-dimensional matrix structure in ascending order of risk level, based on the safety criticality of the functional area and the configured four-element threshold group. Each dimension of the three-dimensional matrix corresponds to a functional area, a risk level, and a threshold. In this way, the risk level and corresponding risk threshold of each functional area can be clearly mapped into the matrix. Each entry in the matrix represents the risk threshold of a functional area under a specific risk level, for example, the safety threshold of a beginner area under low-risk conditions, or the safety threshold of an advanced ski area under extremely high-risk conditions. The advantage of this three-dimensional matrix structure is that it can intuitively display the assessment results of each functional area within a ski resort under different risk conditions, helping managers adjust ski slope restrictions or safety measures based on real-time environmental changes. By using information from different dimensions in the matrix, an appropriate safety level can be dynamically assigned to each functional area, and possible safety hazards can be responded to in a timely manner. For example, suppose the advanced ski area of ​​a ski resort has loose snow and avalanches may occur due to heavy snowfall and high wind speeds. At this time, through the three-dimensional matrix generated based on historical accident data and functional area divisions, it can be determined that the risk level of the area has been upgraded to "extremely high risk" and the corresponding emergency plan can be activated, such as suspending skiing activities in the area or increasing monitoring efforts.

[0035] As a preferred embodiment of the above, Figure 6As shown in the figure, the risk rule output after weight allocation is subjected to tensor analysis to detect the coupled mutation interface of weather, snow accumulation and personnel, and generate graded warning instructions containing geo-fence identification, including: S41: Expand the dynamic weight vector into a 4D convolution kernel whose size matches the ski resort geofence grid resolution; S42: Based on the terrain deformation correlation factor, temperature variation factor, visibility interference factor and personnel behavior, the shear stress gradient tensor, latent heat flux tensor, fog concentration tensor and personnel behavior tensor are constructed respectively; S43: Perform three-field coupling mutation detection through a four-dimensional convolution kernel, and generate a graded warning instruction when any channel value exceeds the risk threshold.

[0036] Specifically, first, the dynamic weight vector assigned based on the risk rule is expanded into a four-dimensional convolution kernel. The size of the convolution kernel needs to match the grid resolution of the ski resort's geographic fence, so as to ensure that the convolution kernel accurately processes and responds to environmental changes in each geographic area. Each grid represents a specific area of ​​the ski resort, and the convolution kernel gradually analyzes the environmental data of different areas through a sliding window. In this way, the ski resort can perform efficient calculations on each area to ensure that the risk assessment can cover all key areas of the entire ski resort; then, based on the previously calculated terrain deformation correlation factor, temperature variation factor, visibility interference factor and skier behavior data, corresponding tensors are constructed respectively. Tensors are mathematical objects used to represent multidimensional data and can accurately capture the complex spatial and temporal changes within the ski resort; then, the four-dimensional convolution kernel will perform convolution operations on the constructed shear stress gradient tensor, latent heat flux tensor, fog concentration tensor and personnel behavior tensor. Convolution operations can efficiently process data in different dimensions (space, time, etc.) and detect coupled mutations between three fields (weather, snow cover, and personnel). When the value of any tensor channel exceeds the set risk threshold, a graded warning instruction containing a geo-fence identifier will be generated. These warning instructions will accurately identify areas that require special attention based on the actual geographical location, risk level, and mutation of the ski resort, and remind skiers and managers to take appropriate safety measures in real time. In this way, ski resorts can quickly identify potential high-risk areas and take targeted safety measures. For example, when the wind speed and snow cover on a certain ski slope vary greatly, and skiers gather in this area, the convolution kernel operation will combine these factors. Once the combined change of these factors exceeds the set risk threshold, an early warning will be issued, and the alarm system will remind staff and skiers to avoid the area.

[0037] As a preferred embodiment of the above embodiment, the calculation formulas for the shear stress gradient tensor, latent heat flux tensor, fog concentration tensor and personnel behavior tensor are: ; ; ; ; in, is the shear stress gradient tensor, is the gradient operator, is the snow density, is the snow layer thickness, is the slope angle of the ski slope, is the latent heat flux tensor, is the thermal conductivity coefficient, For snow temperature, is the fog concentration tensor, is the fog probability, is the personnel behavior tensor, is the population density, For the safety area, is the probability of moving in the same direction.

[0038] Specifically, the shear stress gradient tensor is used to describe the stress changes on snow on different terrains, especially the stress on snow on steeper ski slopes; the latent heat flux tensor reflects the heat exchange of the snow layer under changing temperature conditions, especially in areas with large temperature variations, the stability of the snow layer will be affected; the fog concentration tensor describes the changes in visibility in the ski resort. Low visibility may make it difficult for skiers to judge the safe area, so it needs to be analyzed; the personnel behavior tensor is used to analyze the behavioral characteristics of skiers, such as skiing speed, residence time and density, to help determine whether people gather in high-risk areas or whether there are potential dangerous behaviors; the construction of these tensors enables each factor (topography, weather, personnel behavior) to be accurately represented as high-dimensional data, and can be further processed and analyzed by mathematical methods. Assume that the snow layer in a certain area of ​​the ski resort is thick and the slope is steep. At the same time, there are many skiers in this area, and some skiers ski at a fast speed. Through the above calculations, the shear stress gradient tensor will indicate that there is a greater risk of snow stability in this area. The latent heat flux tensor will reveal that temperature changes in this area may cause snow instability. The fog concentration tensor indicates that the visibility in this area is low, increasing the collision risk of skiers. The personnel behavior tensor shows that the personnel density in this area is high and the probability of same-direction movement is high, suggesting that this area has a higher potential risk of accidents.

[0039] As a preferred embodiment of the above embodiment, three-field coupling mutation detection is implemented through a three-level edge computing architecture, and the three-layer edge computing mechanism includes a terminal layer, an edge gateway layer and a cloud decision layer.

[0040] Specifically, the terminal layer is the layer closest to environmental perception within a ski resort, primarily responsible for collecting real-time data from the resort. This data includes meteorological sensors (temperature, humidity, wind speed, snowfall, etc.), snow accumulation sensors (snow thickness, density, snow quality, etc.), location data (GPS, sensors, etc.), and skier behavior data (speed, movement direction, etc.). The terminal layer primarily relies on various smart devices and sensors within the ski resort to collect data and immediately performs preliminary processing and screening of the data. This layer's mission is to ensure the real-time and accuracy of the data and promptly detect any abnormal changes in the ski resort environment. For example, if the temperature in a certain area suddenly drops sharply or the snow suddenly thickens, the terminal layer will immediately collect this data and begin preliminary analysis to determine whether there is any anomaly.

[0041] Edge Gateway Layer: Located between the terminal layer and the cloud decision layer, the edge gateway layer is responsible for data aggregation and preliminary analysis. The edge gateway layer performs deeper processing and analysis of data from the terminal layer, using edge computing for preliminary data calculations and risk assessments. The advantage of this layer lies in its low-latency processing and reduced reliance on the cloud, thereby accelerating the decision-making process. At this layer, the edge gateway combines geofencing information from the ski resort to calculate shear stress gradient tensors, latent heat flux tensors, fog concentration tensors, and occupant behavior tensors, ensuring rapid local data analysis and risk assessment. When the edge gateway layer detects sudden coupled changes in weather, snow cover, and occupant behavior, it forwards the relevant data and preliminary analysis results to the cloud decision layer for further decision-making and early warning. For example, if the edge gateway detects a sharp temperature change in a certain area, loose snow cover, and a high occupant density, it will determine the area as "high risk" based on pre-defined rules and generate a preliminary risk assessment report.

[0042] Cloud Decision Layer: The cloud decision layer is the core layer of the three-tier architecture, primarily responsible for aggregating data from the edge gateway layer, conducting in-depth analysis, and making final decisions. The cloud decision layer combines historical data, complex algorithms, and machine learning models to perform global risk assessments, detect sudden changes, and generate warning instructions. The advantage of cloud computing lies in its powerful computing power, which can process large amounts of data and perform complex multi-dimensional analysis, ensuring more accurate safety decisions for ski resorts. At this layer, the cloud decision layer conducts in-depth sudden change analysis based on multi-dimensional data from different regions. For example, if the cloud decision layer detects a significant correlation between the shear stress gradient tensor and temperature changes in a certain area and a high occupancy density, it will combine historical accident data to conduct a final risk assessment and generate graded warning instructions. These instructions may include safety measures such as ski area closures, alerts, and evacuation. These instructions are then transmitted to the ski resort's emergency response team or equipment control system. For example, at the cloud decision layer, the system uses models to analyze the coupled changes in meteorological, snow accumulation, and occupancy data to ultimately determine that avalanche risk is elevated in a particular ski area. A corresponding warning signal is generated in the cloud and sent to terminals and edge gateways, activating ski slope machinery and alarm systems.

[0043] Example 2 Based on the same inventive concept as the ski resort real-time weather service warning method in the aforementioned embodiment, the present invention further provides a ski resort real-time weather service warning system, the system comprising: The information set construction module obtains meteorological monitoring data, snow status data and personnel positioning topology data in the ski resort area in real time to build a real-time information set of the ski resort; The coupling field construction module compensates for terrain deformation and constructs a dynamic coupling field based on the real-time information set of the ski resort and the spatial topological relationship of the ski slope functional areas; The weight allocation module defines the ski resort risk rule base, inputs the dynamic coupling field into the ski resort risk rule base, and performs weight allocation. The ski resort risk rule base contains a relationship mapping table between warning level, geo-fence, and equipment control parameters; The instruction generation module performs tensor analysis on the risk rule output after weight allocation, detects the coupled mutation interface of the weather, snow accumulation, and personnel, and generates graded warning instructions including geo-fence identification; The parameter extraction module extracts the device control parameter set that executes the terminal spatial coordinate mapping from the ski resort risk rule library according to the geo-fence identifier in the graded warning instruction.

[0044] The above-mentioned early warning system in the present invention can effectively implement the real-time weather service early warning method for ski resorts, and the technical effects that can be achieved are as described in the above-mentioned embodiments and will not be repeated here.

[0045] As a preferred embodiment of the above, the instruction generation module includes: A vector expansion unit that expands the dynamic weight vector into a four-dimensional convolution kernel whose size matches the resolution of the ski resort geofence grid; The tensor construction unit constructs the shear stress gradient tensor, latent heat flux tensor, fog concentration tensor and personnel behavior tensor based on terrain deformation correlation factors, temperature variation factors, visibility interference factors and personnel behavior respectively; The mutation detection unit performs three-field coupled mutation detection through a four-dimensional convolution kernel and generates a graded warning instruction when any channel value exceeds the risk threshold.

[0046] Similarly, the above-mentioned optimization schemes for the system can also respectively achieve the corresponding optimization effects of the method in Example 1, which will not be repeated here.

[0047] Although the present application has been described with reference to specific features and embodiments thereof, it is apparent that various modifications and combinations thereof may be made without departing from the spirit and scope of the present application. Accordingly, this specification and the drawings are merely illustrative of the present application as defined herein, and are intended to cover any and all modifications, variations, combinations or equivalents within the scope of the present application.

[0048] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A ski resort real-time weather service warning method, characterized in that: include: Obtain real-time meteorological monitoring data, snow status data, and personnel positioning topology data within the ski resort area to build a real-time information set for the ski resort; Based on the real-time information set of the ski resort and the spatial topological relationship between the functional areas of the ski slopes, terrain deformation compensation is performed to construct a dynamic coupling field; Defining a ski resort risk rule base, inputting the dynamic coupling field into the ski resort risk rule base, and performing weight assignment, wherein the ski resort risk rule base includes a relationship mapping table between warning level, geo-fence, and device control parameter; Perform tensor analysis on the risk rule output after weight allocation, detect the coupled mutation interface of weather, snow accumulation and personnel, and generate graded warning instructions including geo-fence identification; According to the geo-fence identifier in the graded warning instruction, a device control parameter set for executing terminal space coordinate mapping is extracted from the ski resort risk rule library.

2. The ski resort real-time weather service warning method according to claim 1, characterized in that: Compensate for terrain deformation and construct a dynamic coupling field, including: Calculate the slope angle, aspect angle and altitude gradient modulus of the target area based on the DEM to obtain a set of terrain parameters. Acquire a spatial topological relationship between the ski resort real-time information set and the ski slope functional area, and define a set of ski slope functional area types; Based on the terrain parameter set and the snow track functional area type set, performing three-dimensional spatial correction on the meteorological monitoring data; The three-dimensional space correction result is input into the terrain deformation coupling engine to obtain the dynamic coupling field.

3. The ski resort real-time weather service warning method according to claim 2, characterized in that: The three-dimensional space correction result is input into the terrain deformation coupling engine to obtain the dynamic coupling field, including: Calculate the terrain dynamic parameters of the geo-fence boundaries of each ski functional area based on the digital elevation model; Based on the terrain dynamic parameters and the three-dimensional correction results, a three-dimensional field reconstruction operation is performed to output a geographical environment dynamic parameter data set; The geographic environment dynamic parameter data set is input into the terrain deformation coupling engine to generate the dynamic coupling field.

4. The ski resort real-time weather service warning method according to claim 1, characterized in that: A ski resort risk rule base is defined, and the dynamic coupling field is input into the ski resort risk rule base, and weight allocation is performed, including: Based on the functional zoning results of the snow track and historical accident data, a multi-level risk threshold matrix was constructed; mapping the dynamic coupling field to the multi-level risk threshold matrix; Calculating terrain deformation correlation factors, temperature variation factors, and visibility interference factors based on the meteorological monitoring data and snow cover status data; The three factors are input into the decision tree model to complete the dynamic weight allocation of the risk threshold matrix.

5. The ski resort real-time weather service warning method according to claim 4, characterized in that: Based on the functional zoning results of the snow track and historical accident data, a multi-level risk threshold matrix was constructed, including: Divide the functional areas of ski slopes in the ski resort into dimensions according to safety criticality; Based on historical accident data, a four-element threshold group is configured for each functional area dimension; The threshold groups are organized in ascending order of risk level to form a three-dimensional matrix structure of dimension-level-threshold.

6. The ski resort real-time weather service warning method according to claim 1, characterized in that: Perform tensor analysis on the risk rule output after weight allocation, detect the coupled mutation interface of weather, snow accumulation, and personnel, and generate graded warning instructions with geo-fence identification, including: Expanding the dynamic weight vector into a 4D convolution kernel whose size matches the ski resort geofence grid resolution; Based on the terrain deformation correlation factor, temperature variation factor, visibility interference factor and human behavior, the shear stress gradient tensor, latent heat flux tensor, fog concentration tensor and human behavior tensor are constructed respectively. The three-field coupling mutation detection is performed through the four-dimensional convolution kernel, and the graded warning instruction is generated when any channel value exceeds the risk threshold.

7. The ski resort real-time weather service warning method according to claim 6, characterized in that: The calculation formulas for the shear stress gradient tensor, latent heat flux tensor, fog concentration tensor and personnel behavior tensor are: ; ; ; ; in, is the shear stress gradient tensor, is the gradient operator, is the snow density, is the snow layer thickness, is the slope angle of the ski slope, is the latent heat flux tensor, is the thermal conductivity coefficient, For snow temperature, is the fog concentration tensor, is the fog probability, is the personnel behavior tensor, is the population density, For the safety area, is the probability of moving in the same direction.

8. The ski resort real-time weather service warning method according to claim 6, characterized in that: The three-field coupling mutation detection is achieved through a three-level edge computing architecture, and the three-layer edge computing mechanism includes a terminal layer, an edge gateway layer and a cloud decision layer.

9. A real-time weather service warning system for ski resorts, characterized by: include: The information set construction module obtains meteorological monitoring data, snow status data and personnel positioning topology data in the ski resort area in real time to build a real-time information set of the ski resort; A coupling field construction module, which performs terrain deformation compensation and constructs a dynamic coupling field based on the real-time information set of the ski resort and the spatial topological relationship of the ski trail functional areas; a weight allocation module, defining a ski resort risk rule base, inputting the dynamic coupling field into the ski resort risk rule base, and performing weight allocation, wherein the ski resort risk rule base includes a relationship mapping table between warning level, geo-fence, and device control parameter; The instruction generation module performs tensor analysis on the risk rule output after weight allocation, detects the coupled mutation interface of the weather, snow accumulation, and personnel, and generates graded warning instructions including geo-fence identification; The parameter extraction module extracts the device control parameter set mapped to the terminal space coordinates in the ski resort risk rule library according to the geographical fence identifier in the graded warning instruction.

10. The ski resort real-time weather service warning system according to claim 9, characterized in that: The instruction generation module includes: A vector expansion unit that expands the dynamic weight vector into a four-dimensional convolution kernel whose size matches the ski resort geofence grid resolution; The tensor construction unit constructs the shear stress gradient tensor, latent heat flux tensor, fog concentration tensor and personnel behavior tensor based on terrain deformation correlation factors, temperature variation factors, visibility interference factors and personnel behavior respectively; The mutation detection unit performs three-field coupled mutation detection through the four-dimensional convolution kernel and generates the graded warning instruction when any channel value exceeds the risk threshold.

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