Real-time accounting method and system for carbon footprint of ice and snow tourism

Abstract

The application is suitable for the technical field of carbon emission monitoring, and provides a real-time accounting method and system for carbon footprint of ice and snow tourism. The method collects snow surface reflection intensity value, skier GPS trajectory data and real-time energy consumption data of each device. According to the reflection intensity, the instantaneous reflection energy power value is determined and accumulated to the global reflection energy carbon integration pool. According to the GPS trajectory, the sliding density thermal map is generated, the frozen risk prediction value is calculated combined with the reflection intensity, and the dynamic frozen risk map is generated. According to the risk map, the minimum carbon operation path is determined, the path optimization emission reduction amount is determined according to the difference between the actual energy consumption and the expected energy consumption of the virtual reference path, and is added to the integration pool. According to the power of the carrying device and the number of passengers, the direct carbon footprint of tourists is determined and the integration is distributed, and the integration amount is adjusted according to the location reward coefficient. The location reward coefficient is converted into the expected additional carbon offset rate of each ski trail direction and sent to the tourist terminal. After the operation cycle ends, the mapping relationship is adjusted according to the comparison result of the actual total energy consumption and the theoretical total energy consumption.

Description

Technical Field

[0001] This invention belongs to the field of carbon emission monitoring technology, and in particular relates to a method and system for real-time calculation of carbon footprint of ice and snow tourism. Background Technology

[0002] As the scale of the ice and snow tourism industry continues to expand, carbon emissions from ski resort operations are receiving increasing attention. Ski resorts involve multiple energy-intensive processes, including snowmaking, snow grooming, cable car transportation, magic carpet upgrades, and slope maintenance, with carbon emission sources being dispersed and dynamically changing. Currently, carbon footprint accounting in the ice and snow tourism sector mainly relies on total data such as monthly or quarterly electricity bills and fuel purchase orders, dividing total energy consumption by total visitor numbers to obtain an estimated per capita carbon footprint. Some ski resorts have begun to try installing independent meters for key energy-consuming equipment such as cable cars and snowmaking machines for itemized metering; however, the energy consumption data for each device is stored independently and not interconnected, so carbon footprint accounting remains at the level of total statistics and rough allocation. Real-time data acquisition technology has mature applications in the industrial sector, but it has not yet been introduced into a carbon footprint accounting system in the ice and snow tourism scenario.

[0003] Current carbon footprint accounting relies entirely on post-event statistics of total energy consumption and total visitor numbers, failing to reflect the dynamic characteristics of carbon emissions fluctuating in real time with operational status. Energy consumption data for each piece of equipment is independent, making it impossible to differentiate carbon footprints at the individual visitor level. Furthermore, there is no correlation between actual visitor behavior and carbon footprint accounting results, rendering the results ineffective in supporting operational optimization and visitor behavior guidance. Summary of the Invention

[0004] The purpose of this invention is to provide a real-time carbon footprint calculation method for ice and snow tourism, aiming to solve the technical problems existing in the prior art as identified in the background section.

[0005] This invention is implemented as follows: a method for real-time calculation of the carbon footprint of ice and snow tourism, the method comprising:

[0006] Real-time data collection includes snow surface reflectivity, skier GPS trajectory data, and real-time energy consumption data for each device.

[0007] The instantaneous reflective energy power value is determined based on the snow surface reflective intensity data, and the instantaneous reflective energy power value is accumulated into the global reflective energy carbon integration pool;

[0008] A skiing density heatmap is generated based on the skier's GPS trajectory data.

[0009] The predicted caking risk value of the corresponding grid is calculated based on the sliding density heat map and the reflectivity value, and a dynamic caking risk map is generated.

[0010] The minimum carbon operation path is determined based on the dynamic caking risk map. The path optimization emission reduction is determined based on the difference between the actual energy consumption of the operation vehicle and the expected energy consumption of the virtual baseline path and is added to the global reflective energy carbon integral pool.

[0011] Based on real-time energy consumption data, the instantaneous power of the transport equipment is extracted, and the direct carbon footprint of each tourist is determined in combination with the number of passengers. Points are allocated from the global reflective energy carbon points pool according to the carbon footprint of each tourist. The point amount of the tourist is adjusted according to the reflective intensity value of the current location, the predicted value of caking risk and the recent working time of the operating vehicle.

[0012] The expected additional carbon credit rate for each ski run direction is determined based on the location reward coefficient and sent to the tourist terminal. At the same time, new GPS track data is received and incorporated into the ski density heatmap generation process.

[0013] At the end of each operating cycle, the mapping relationship between the reflective intensity value and the caking rate coefficient is adjusted based on the comparison between the actual total energy consumption of the vehicles operating that day and the theoretical total energy consumption based on the reflective intensity value and the preset value.

[0014] As a further embodiment of the present invention, the step of determining the instantaneous reflective energy power value based on the snow surface reflective intensity data and accumulating the instantaneous reflective energy power value into the global reflective energy carbon integration cell specifically includes:

[0015] The average value of the snow surface reflectance intensity obtained from each sampling point of each ski run is taken, and the average value is normalized and corrected by combining it with the pre-stored time series data of the solar altitude angle of the ski area. The corrected result is used as the instantaneous reflectance power value of the ski run.

[0016] The instantaneous reflective energy power values ​​of each ski slope are accumulated into a global variable in the order of timestamps. The global variable is the global reflective energy carbon integration pool.

[0017] As a further aspect of the present invention, generating a skiing density heatmap based on the skier's GPS trajectory data specifically includes:

[0018] The geographical area of ​​the ski resort is divided into rectangular grids of fixed size, and each grid is assigned a unique geographic identifier;

[0019] Map each coordinate point in the skier's GPS track data to the corresponding grid, count the number of coordinate points falling into each grid within a five-minute time window, and use the number of coordinate points as the skiing frequency of that grid.

[0020] Arrange the sliding frequencies of all grids into a frequency matrix according to their spatial location, and normalize the frequency matrix. Use the normalized frequency matrix as a sliding density heatmap.

[0021] As a further embodiment of the present invention, the step of generating a dynamic slab consolidation risk map specifically includes:

[0022] Read the sliding frequency of each grid in the sliding density heatmap, and at the same time read the reflective intensity value corresponding to the same grid position;

[0023] The reflective intensity value is used as an index to query the pre-configured caking rate coefficient mapping table to obtain the caking rate coefficient of the grid. The reflective intensity value and the caking rate coefficient in the caking rate coefficient mapping table are negatively correlated.

[0024] Multiply the sliding frequency by the slagging rate coefficient, and use the resulting product as the predicted slagging risk value for the grid.

[0025] Arrange the predicted values ​​of all grid slab caking risk into a prediction matrix according to their spatial location, and use the prediction matrix as a dynamic slab caking risk map.

[0026] As a further embodiment of the present invention, the step of determining the path optimization emission reduction based on the difference between the actual energy consumption of the operating vehicle and the expected energy consumption of the virtual baseline path, and adding it to the global reflective energy carbon integral pool, specifically includes:

[0027] In the dynamic slab slab risk map, grids with slab slab risk prediction values ​​exceeding a preset high-risk threshold are marked as priority work areas, and grids with slab slab risk prediction values ​​below a preset low-risk threshold are marked as skip areas.

[0028] A path search algorithm is used to generate a continuous path on the snow track network map that passes through all priority work areas and avoids skipped areas. This path is defined as the lowest carbon work path and sent to the work vehicle control terminal. At the same time, a fixed inspection path covering the entire snow track grid is generated and defined as a virtual reference path.

[0029] The actual energy consumption value of the operating vehicle is read from the real-time energy consumption data. The difference between the actual energy consumption value and the expected energy consumption value at the corresponding position on the virtual reference path is calculated. The sum of the corresponding differences at all positions is used as the path optimization emission reduction. The path optimization emission reduction is added to the global reflective energy carbon integral pool.

[0030] As a further aspect of the present invention, adjusting the tourist's points balance specifically includes:

[0031] The instantaneous power value of the transportation equipment is extracted from the real-time energy consumption data of each device, and the ratio of the instantaneous power value of the transportation equipment to the current number of passengers on the corresponding transportation equipment is determined by combining the sampling time interval.

[0032] The total carbon emissions per second of the entire ski resort are determined based on the sum of the instantaneous direct carbon footprints of all visitors. An amount of points equal to the total carbon emissions per second of the entire ski resort is extracted from the global reflective energy carbon integral pool. The points allocated to each visitor's account are determined based on the ratio of each visitor's instantaneous direct carbon footprint to the total carbon emissions per second of the entire ski resort.

[0033] The location reward coefficient is determined by a weighted sum of the reflectivity value of the tourist's current location, the predicted caking risk value of the corresponding grid in the dynamic caking risk map, and the timestamp of the most recent operation vehicle passing through the location.

[0034] The adjusted point amount for the tourist is determined by multiplying the points allocated to the tourist by the location reward coefficient.

[0035] As a further aspect of the present invention, determining the expected additional carbon credit rate for each ski run direction specifically includes:

[0036] The expected additional carbon credit for each ski run direction is determined based on the average of the location bonus coefficients of all grids upstream of each ski run direction and sent to the visitor terminal.

[0037] The system continuously receives new GPS trajectory data transmitted from tourist terminals, merges the new GPS trajectory data with the original skier GPS trajectory data to obtain merged trajectory data, and regenerates a ski density heatmap based on the merged trajectory data.

[0038] As a further aspect of the present invention, adjusting the mapping relationship between the reflectivity value and the caking rate coefficient specifically includes:

[0039] At the end of each operating cycle, the actual energy consumption values ​​of all vehicles operating that day are read and summed to determine the total actual energy consumption of the vehicles operating that day.

[0040] Based on the reflectivity values ​​at different times of the day, the theoretical total energy consumption is determined by accumulating the values ​​segment by segment using the preset formula for calculating the theoretical total energy consumption. The relative deviation is then determined by combining this with the actual total energy consumption of the vehicles operating on that day.

[0041] When the relative deviation exceeds a preset deviation threshold, the mapping relationship between the reflective intensity value and the caking rate coefficient is adjusted according to the proportion of the relative deviation, and the adjusted mapping relationship is written into the system's non-volatile memory for use in the next operating cycle. Specifically:

[0042] If the actual total energy consumption is higher than the theoretical total energy consumption, the caking rate coefficient corresponding to each reflective intensity value will be uniformly reduced according to the proportion of the relative deviation; conversely, the caking rate coefficient corresponding to each reflective intensity value will be uniformly increased according to the proportion of the relative deviation.

[0043] Another objective of this invention is to provide a real-time carbon footprint calculation system for ice and snow tourism, the system comprising:

[0044] The data acquisition module is used to collect real-time snow surface reflectivity, skier GPS trajectory data, and real-time energy consumption data of each device.

[0045] The carbon integral calculation module is used to determine the instantaneous reflective energy power value based on the snow surface reflective intensity data, and to accumulate the instantaneous reflective energy power value into the global reflective energy carbon integral pool;

[0046] A heatmap generation module is used to generate a skiing density heatmap based on the skier's GPS trajectory data.

[0047] The risk prediction module is used to calculate the predicted caking risk value of the corresponding grid based on the sliding density heat map and the reflectivity value, and generate a dynamic caking risk map.

[0048] The path optimization module is used to determine the lowest carbon operation path based on the dynamic slagging risk map, and to determine the path optimization emission reduction based on the difference between the actual energy consumption of the operation vehicle and the expected energy consumption of the virtual baseline path, and add it to the global reflective energy carbon integral pool.

[0049] The carbon footprint allocation module is used to extract the instantaneous power of the transportation equipment based on real-time energy consumption data, determine the direct carbon footprint of each tourist in combination with the number of passengers, allocate points from the global reflective energy carbon points pool according to the carbon footprint ratio of each tourist, and adjust the amount of points obtained by the tourist based on the reflective intensity value of the current location, the predicted value of caking risk and the recent working time of the operating vehicle.

[0050] The reward coefficient adjustment module is used to determine the expected additional carbon offset rate for each ski run direction based on the location reward coefficient and send it to the tourist terminal. At the same time, it receives new GPS trajectory data and incorporates it into the ski density heat map generation process.

[0051] The mapping relationship adjustment module is used to adjust the mapping relationship between the reflective intensity value and the caking rate coefficient based on the comparison results between the actual total energy consumption of the vehicles operating that day and the theoretical total energy consumption based on the reflective intensity value and the preset value after each operating cycle.

[0052] The beneficial effects of this invention are:

[0053] This invention achieves spatiotemporal unification of environmental physical conditions, tourist behavior patterns, and equipment energy consumption levels by real-time collection of snow surface reflectivity, skier GPS trajectories, and energy consumption data of various devices within a gridded spatial unit. This transforms carbon emissions from a total statistical measure into a dynamic resource that each tourist can view in real time, actively influence, and benefit from. A reflectivity energy integration pool converts natural light and heat resources into carbon credits, allowing the total carbon credit value to dynamically change with environmental conditions rather than relying on fixed quotas. A GPS-track-driven dynamic compaction risk map is linked to the snow groomer's lowest carbon path, shifting operational paths from fixed inspections to on-demand intervention. Energy savings from path optimization are linked to tourist benefits through the integration pool, ensuring real-time linkage between operational emission reduction actions and tourist carbon gains. A location reward coefficient guides tourist skiing choices through three dimensions, and tourist behavior adjustments are immediately fed back to heatmap updates and path replanning, resulting in a continuously converging low-energy operation. Energy data-driven adaptive calibration of the mapping relationship allows the system to continuously approximate the actual compaction response characteristics without requiring complex snow quality models, maintaining long-term predictive accuracy. The entire solution constructs a real-time carbon footprint accounting system for ice and snow tourism, which has a complete closed loop of perception, decision-making, execution, feedback, and calibration. Attached Figure Description

[0054] Figure 1 A flowchart illustrating the real-time carbon footprint calculation method for ice and snow tourism provided in this embodiment of the invention;

[0055] Figure 2 A flowchart for determining the instantaneous reflective energy power value and accumulating it to the global reflective energy carbon integration cell, provided in an embodiment of the present invention;

[0056] Figure 3 This is a flowchart for generating a sliding density heatmap provided in an embodiment of the present invention;

[0057] Figure 4 A flowchart for generating a dynamic slab consolidation risk diagram provided in an embodiment of the present invention;

[0058] Figure 5 A flowchart for determining the path optimization emission reduction and adding it to the global reflective energy carbon integration pool provided in this embodiment of the invention;

[0059] Figure 6 A flowchart for adjusting the points earned by tourists, provided in an embodiment of the present invention;

[0060] Figure 7 A flowchart for determining the expected additional carbon credit rate for each ski run direction, provided for embodiments of the present invention;

[0061] Figure 8 A flowchart illustrating the mapping relationship between the reflectivity value and the caking rate coefficient, provided for an embodiment of the present invention;

[0062] Figure 9 This is a structural block diagram of the real-time carbon footprint calculation system for ice and snow tourism provided in an embodiment of the present invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0064] Figure 1 A flowchart of the real-time carbon footprint calculation method for ice and snow tourism provided in this embodiment of the invention is shown below. Figure 1 As shown, the method includes:

[0065] The S100 collects real-time snow reflectivity, skier GPS trajectory data, and real-time energy consumption data of each device.

[0066] Snow surface reflectivity data comes from wireless reflectivity sensors deployed at sampling points along the ski slopes. This data reflects the reflectivity of snow in different areas to solar radiation, directly determining the initial amount of the subsequent reflectivity carbon integral pool and providing environmental background parameters for predicting the compaction rate. Skier GPS track data is obtained through the positioning chip on the skier's ski card or the GPS module of an authorized mobile phone, reflecting the real-time spatial distribution and skiing density of skiers within the ski resort. This data is the core basis for generating skiing density heatmaps. Real-time energy consumption data for each device is directly read from the controllers of operating vehicles and transport equipment via fieldbus interfaces. The energy consumption of operating vehicles reflects the fuel or electricity consumption for snow grooming operations, while the energy consumption of transport equipment reflects the electricity consumption of cable cars and magic carpet lifts for skiers. These two types of energy consumption serve the calculation of emission reductions through path optimization and the calculation of direct carbon footprints for skiers, respectively.

[0067] The three data streams are synchronized in time and aligned spatially using the ski resort grid as a unified reference. Reflectivity data and GPS track data are both tagged with location information, while energy consumption data includes equipment identification and timestamps. The central accounting engine links these three data streams to the same spatial unit according to a unified time window and grid division rules. Through grid alignment, reflectivity within the same grid can be used to calculate the grid's compaction rate coefficient; the frequency of skiing within the grid can be multiplied by this coefficient to obtain a predicted compaction risk value; and the energy consumption of transportation equipment within the grid can be divided by the number of visitors passing through the grid to obtain the per capita carbon footprint.

[0068] S200, determine the instantaneous reflective energy power value based on the snow surface reflective intensity data, and accumulate the instantaneous reflective energy power value into the global reflective energy carbon integration pool;

[0069] The reflectivity of snow surface reflects the snow layer's ability to reflect shortwave solar radiation. Higher reflectivity indicates that the snow surface is closer to a fresh snow condition, the snow crystal structure is more complete, there is less heat absorption and melting, the snow quality ages slowly, and the tendency to harden is low. Lower reflectivity indicates that the snow surface is old, contains more impurities, absorbs more heat and melts more, the snow quality degrades quickly, and the tendency to harden is high. The system averages the reflectivity values ​​from multiple sampling points on each ski run and performs normalization correction by combining them with solar altitude angle time-series data to eliminate the interference of solar altitude angle changes at different times of the year on the reflectivity measurement values, thus obtaining a standardized instantaneous reflectivity power value.

[0070] The instantaneous reflective energy power values ​​of each ski slope are accumulated to the global reflective energy carbon credit pool according to the timestamp. This credit pool serves as the source of carbon credits allocated to visitor accounts in subsequent steps, with higher reflective intensity periods or areas contributing more credits. In traditional carbon accounting, natural lighting conditions only affect photovoltaic power generation output. The innovation of this scheme lies in using the physical characteristics of snow surface reflectivity directly as the basis for measuring carbon emission reduction resources, transforming the natural endowment of the ski resort into quantifiable, allocable, and incentivized carbon assets. This allows the carbon credit pool to no longer rely on historical emission quotas or fixed emission reduction targets, but rather to dynamically change with real-time environmental conditions.

[0071] S300, generate a skiing density heat map based on the skier's GPS trajectory data;

[0072] The geographical area of ​​the ski resort is divided into rectangular grids of fixed size. Each coordinate point falls into a unique corresponding grid. The number of coordinate points in each grid is counted in a five-minute time window as the skiing frequency. After forming a frequency matrix, it is normalized to obtain a skiing density heatmap.

[0073] Continuous and discrete point trajectories cannot be directly used for calculating snow compaction risk. Meshization discretizes the continuous space into computationally achievable basic units, and time window statistics transform the dynamic movement of tourists into static skiing frequencies within each unit. Skiing frequency directly reflects the skiing pressure in each area; areas with higher skiing frequencies experience more frequent compression, friction, and impact on the snow surface, corresponding to a higher snow compaction rate. Normalization eliminates the incomparability of frequencies caused by differences in grid area, allowing the thermal layer to be superimposed and calculated with subsequent reflectivity and snow compaction risk layers on the same dimension.

[0074] S400, calculate the predicted value of the caking risk of the corresponding grid based on the sliding density heat map and the reflectivity value, and generate a dynamic caking risk map;

[0075] The sliding frequency of each grid in the sliding density heatmap is read, and the reflectance intensity value at the same grid location is also read. The reflectance intensity value is used as an index to look up a pre-configured compaction rate coefficient mapping table to obtain the compaction rate coefficient for that grid. The reflectance intensity value and the compaction rate coefficient are negatively correlated; higher reflectance intensity corresponds to a lower compaction rate, and lower reflectance intensity corresponds to a higher compaction rate. The sliding frequency of the grid is multiplied by the compaction rate coefficient; the product is the predicted compaction risk value for that grid. The predicted compaction risk values ​​of all grids are arranged spatially into a prediction matrix, i.e., a dynamic compaction risk map.

[0076] Skiing density heatmaps only reflect the spatial distribution intensity of visitor behavior; using them alone, they cannot determine the actual impact of this distribution on snow quality. Reflectivity values ​​reflect the physical state of the snow surface itself and its inherent resistance to caking; using them alone, they cannot reflect the additional pressure caused by human skiing. The integration of these two metrics achieves a coupled assessment of environmental baseline conditions and human activity pressure. Under the same skiing frequency, ski runs with high reflectivity have a lower risk of caking, while those with low reflectivity have a higher risk. The system accurately distinguishes the differentiated caking consequences of the same skiing intensity under different snow quality conditions through multiplicative coupling. The dynamic caking risk map incorporates visitor behavior, environmental conditions, and snow quality response into a unified assessment framework, providing spatially clear risk inputs for subsequent operational path optimization.

[0077] Ski resort operators can use this map to identify areas with a higher risk of snow compaction in advance, and take preventative measures before the risk turns into actual snow quality deterioration, transforming passive remediation into proactive prevention and reducing overall operational energy consumption.

[0078] S500 determines the minimum carbon operation path based on the dynamic caking risk map, and determines the path optimization emission reduction based on the difference between the actual energy consumption of the operation vehicle and the expected energy consumption of the virtual baseline path, and adds it to the global reflective energy carbon integral pool.

[0079] Grids with predicted caking risk exceeding the high-risk threshold are marked as priority work areas, while those below the low-risk threshold are marked as skipped areas. A path search algorithm is used to generate a continuous path (the lowest-carbon work path) on the ski run network map that passes through all priority work areas and avoids skipped areas. Simultaneously, a fixed inspection path (the virtual baseline path) covering all ski run grids is generated. The actual energy consumption of the work vehicles is read, and the difference between this actual energy consumption and the expected energy consumption at the corresponding location on the virtual baseline path is calculated. All location differences are summed to obtain the path-optimized emission reduction, which is then added to the global reflective energy carbon integral pool.

[0080] The dynamic snow compaction risk map reflects the distribution of snow compaction risk in various areas within the next one to two hours, but it is not an operational instruction itself. It transforms the risk matrix into executable, continuous operational paths, achieving a shift from perception to decision-making. Priority work areas are those requiring timely intervention to prevent snow quality deterioration, while skipped areas are safe areas that do not require treatment. The path search algorithm finds the shortest or lowest-energy continuous path while meeting coverage requirements, avoiding redundant coverage and ineffective travel. The virtual baseline path represents the energy consumption level of the traditional fixed inspection mode. By comparing the actual path with the baseline path point by point, the energy-saving effect of dynamic path planning is quantified, avoiding the impact of weather and visitor volume fluctuations on the absolute value of energy consumption at different times. Emission reduction is the difference between the optimized path and the fixed path within the same operating cycle, rather than the absolute energy consumption value, ensuring the consistency of the accounting baseline. After being added to the global reflective energy carbon credit pool, the low-carbon behavior of snow grooming operations is indirectly allocated to visitor accounts through the credit pool, linking the carbon reduction actions on the operational side with the carbon rights on the visitor side.

[0081] S600 extracts the instantaneous power of the transport equipment based on real-time energy consumption data, determines the direct carbon footprint of each tourist by combining the number of passengers, allocates points from the global reflective energy carbon points pool according to the carbon footprint ratio of each tourist, and adjusts the points amount of the tourist based on the reflective intensity value of the current location, the predicted value of caking risk and the recent working time of the operating vehicle by determining the location reward coefficient.

[0082] The instantaneous power value of the transportation equipment is extracted from the real-time energy consumption data of each device. This power value is divided by the current number of passengers on the corresponding device to obtain the instantaneous direct carbon footprint of each tourist. The tourist's direct carbon footprint accurately reflects the real-time carbon emissions generated by riding the cable car or magic carpet, rather than the coarse-grained allocation of total energy consumption to total number of trips in traditional accounting. The sum of the direct carbon footprints of all tourists in the entire ski resort constitutes the total carbon emissions per second. The system extracts an equivalent amount of points from the global reflective energy carbon credit pool, and then distributes them to each account according to the proportion of each tourist's carbon footprint to the total carbon emissions. The points in the credit pool come from the accumulation of reflective energy power and the additional emission reduction from path optimization. After allocation, they are converted into carbon rights that tourists can obtain.

[0083] The location reward coefficient further adjusts the points earned by tourists. The coefficient is determined by three factors related to the tourist's current location: reflectivity, predicted tack risk, and the recent operation time of the grooming vehicle. These three factors represent the snow's natural resistance to tack, the tack pressure caused by the current skiing behavior, and the snow surface maintenance status, respectively. The location reward coefficient is obtained by weighted summation and nonlinear function transformation of these three factors. The higher the reflectivity of the tourist's skiing location, the lower the tack risk of the grid, and the shorter the time since the last grooming operation, the larger the location reward coefficient. The final points earned are equal to the base allocation points multiplied by this coefficient. Traditional carbon footprint accounting only displays the total carbon emissions of tourists but fails to incentivize them to change their behavior. The location reward coefficient directly links the tourist's real-time skiing location choice to carbon credit gains, guiding tourists to actively avoid high-tack-risk areas and move to areas with good snow quality. The behavioral adjustments made by tourists to obtain higher carbon credits effectively reduce local skiing density on the slopes, reducing the carbon emission demand from grooming operations at the source. This mechanism transforms tourists from passive bearers of carbon footprints to active contributors to low-carbon behavior.

[0084] S700 determines the expected additional carbon credit rate for each ski run direction based on the location reward coefficient and sends it to the tourist terminal. At the same time, it receives new GPS trajectory data and incorporates it into the ski density heatmap generation process.

[0085] For each ski run direction, the average positional bonus coefficient of all grids upstream in that direction is calculated. This average is converted into a percentage as the expected additional carbon credit for that ski run direction and sent to the visitor's terminal via wireless network, presented in a graphical interface. Visitors can see the differences in credit rates for different ski run directions while skiing and adjust their route accordingly, choosing the direction with the higher credit rate.

[0086] The system continuously receives new GPS trajectory data from tourist terminals, merges the new trajectory data with the existing trajectory data, and re-executes the ski density heatmap generation step based on the merged data. This merging triggers a new round of dynamic compaction risk map updates and snow groomer path planning adjustments, forming a complete closed-loop feedback chain. The location reward coefficient reflects the evaluation of the skiing position by the current environmental and operational conditions. After being converted into deduction rate information that tourists can understand, the system status is transmitted to tourist behavior. After tourists adjust their skiing positions based on this information, the new trajectory data is collected and integrated into the heatmap, allowing the system to perceive the behavioral changes and reassess the status. Through this cycle of information transmission, behavior adjustment, and status update, tourists obtain higher carbon credit benefits while objectively achieving spatial dispersion of skiing behavior, reducing compaction pressure in high-density areas, and reducing the intensity of subsequent snow grooming operations and corresponding carbon emissions.

[0087] S800, at the end of each operating cycle, the mapping relationship between the reflective intensity value and the caking rate coefficient is adjusted based on the comparison results between the actual total energy consumption of the vehicles operating that day and the theoretical total energy consumption based on the reflective intensity value and the preset value.

[0088] The actual total energy consumption is calculated by reading the real-time energy consumption data of all operating vehicles for the day and summing them up. Simultaneously, the theoretical total energy consumption is calculated by summing the reflectivity values ​​for each time period of the day using the theoretical total energy consumption calculation formula. The ratio of the difference between the two values ​​to the theoretical total energy consumption is used as the relative deviation. When the relative deviation exceeds a preset threshold, an adjustment is triggered: if the actual total energy consumption is higher than the theoretical total energy consumption, the caking rate coefficient corresponding to each reflectivity value is uniformly reduced according to the relative deviation ratio; conversely, it is uniformly increased. The adjusted mapping relationship is written to non-volatile memory for use in the next operating cycle.

[0089] The compaction rate coefficient mapping table is a key parameter for compaction risk prediction in S400. The correspondence between reflectivity and compaction rate determines different predicted compaction risk values ​​for the same skiing frequency, thus affecting the minimum carbon operating route planning and emission reduction calculation of S500. This mapping relationship is initially configured empirically based on typical snow conditions. However, snow crystal morphology in different seasons, snowmaking frequency on different slopes, and temperature fluctuations at different times can all cause deviations in the actual correspondence between reflectivity and compaction rate. Continuous use of a fixed mapping table will lead to accumulated prediction biases. By comparing the deviation between theoretical energy consumption calculated based on the current mapping table and actual energy consumption, the system can determine whether the mapping relationship deviates from the actual snow quality response characteristics. If actual energy consumption is higher than theoretical energy consumption, it means the mapping table underestimates the compaction rate, resulting in insufficient coverage of the route planning and actual operating energy consumption exceeding expectations. In this case, the compaction rate coefficient corresponding to each reflectivity intensity in the mapping table needs to be reduced to make the prediction more conservative. Conversely, if the coefficient is lower than theoretical energy consumption, it means the mapping table overestimates the compaction rate and the route planning over-covers the area, requiring an increase in the coefficient.

[0090] The idea of ​​calibrating prediction parameters using real energy consumption data forms a closed-loop learning mechanism. It does not require the establishment of a complex snow quality evolution model. It can continuously approximate the real snow quality response law through daily energy consumption feedback, thus maintaining the long-term accuracy of compaction prediction and path optimization.

[0091] like Figure 2 As shown, the step of determining the instantaneous reflective energy power value based on the snow surface reflectivity data and accumulating the instantaneous reflective energy power value into the global reflective energy carbon integration pool specifically includes:

[0092] S210, take the average value of the snow surface reflectivity obtained from each sampling point of each ski run, and normalize the average value by combining it with the pre-stored time series data of the ski resort solar altitude angle, and use the corrected result as the instantaneous reflectivity power value of the ski run.

[0093] S220, the instantaneous reflective energy power values ​​of each ski slope are accumulated into a global variable in the order of timestamps, and the global variable is the global reflective energy carbon integration pool.

[0094] like Figure 3 As shown, a skiing density heatmap is generated based on the skier's GPS trajectory data, specifically including:

[0095] S310 divides the geographical area of ​​the ski resort into rectangular grids of fixed size and assigns a unique geographic identifier to each grid.

[0096] S320 maps each coordinate point in the skier's GPS track data to the corresponding grid, counts the number of coordinate points falling into each grid within a five-minute time window, and uses the number of coordinate points as the skiing frequency of that grid.

[0097] S330, arrange the sliding frequencies of all grids into a frequency matrix according to their spatial positions, and normalize the frequency matrix. Use the normalized frequency matrix as a sliding density heatmap.

[0098] like Figure 4 As shown, the steps for generating the dynamic slab consolidation risk map specifically include:

[0099] S410, read the sliding frequency of each grid in the sliding density heat map, and at the same time read the reflective intensity value corresponding to the same grid position;

[0100] S420, using the reflected light intensity value as an index to query the pre-configured caking rate coefficient mapping table to obtain the caking rate coefficient of the grid. The reflected light intensity value and the caking rate coefficient in the caking rate coefficient mapping table are negatively correlated.

[0101] The compaction rate coefficient mapping table is the core intermediate carrier connecting snow surface environmental perception data and compaction risk quantification calculation. It establishes a negative correlation between snow surface reflectivity and compaction rate, converting the real-time collectable physical quantity of reflectivity into a compaction rate parameter that can be used for calculation. Higher reflectivity corresponds to a more complete snow crystal structure and a snow quality closer to that of fresh snow, resulting in a lower compaction rate; conversely, lower reflectivity corresponds to an aged snow surface with more impurities, resulting in a higher compaction rate. The corresponding coefficient can be directly retrieved through the mapping table without the need for complex real-time snow quality calculations. Simultaneously, this mapping table serves as the calculation basis for a dynamic compaction risk map. Combined with skiing frequency data, matrix operations can quickly generate a global compaction risk distribution, achieving a coupled assessment of environmental factors and tourist skiing behavior. Furthermore, the parameters of the mapping table can be uniformly adjusted after each operating cycle based on the deviation between actual and theoretical energy consumption, enabling continuous iteration of the compaction prediction model to adapt to snow quality changes under different seasons and snow conditions, thus improving the accuracy of risk prediction.

[0102] S430, Multiply the sliding frequency by the slagging rate coefficient, and use the resulting product as the predicted slagging risk value for the grid;

[0103] S440, arrange the predicted values ​​of all grid slab caking risk into a prediction matrix according to their spatial location, and use the prediction matrix as a dynamic slab caking risk map.

[0104] The predicted value of caking risk (in matrix form) is expressed as follows:

[0105] R = F⊙K;

[0106] in:

[0107] R is the compaction risk prediction matrix, which is the matrix form of the dynamic compaction risk map. Each element in the matrix corresponds to the compaction risk prediction value of one grid in the ski resort.

[0108] F is the skiing frequency matrix, which is the matrix form of the skiing density heatmap. Each element in the matrix corresponds to the number of skiing coordinate points of a grid in the ski resort within a unit time window.

[0109] K is the compaction rate coefficient matrix. Each element in the matrix corresponds to the compaction rate coefficient of a grid in the ski area, which is obtained by looking up the snow surface reflectivity value of that grid in the mapping table.

[0110] ⊙ represents the Hadamard product operation, which involves multiplying corresponding elements of two matrices of the same dimension one by one, and outputting a result matrix of the same dimension as the input.

[0111] like Figure 5 As shown, the step of determining the path optimization emission reduction based on the difference between the actual energy consumption of the operating vehicle and the expected energy consumption of the virtual baseline path, and adding it to the global reflective energy carbon integral pool, specifically includes:

[0112] S510, grids in the dynamic slab slab risk map whose predicted slab slab risk value exceeds a preset high-risk threshold are marked as priority work areas, and grids whose predicted slab slab risk value is lower than a preset low-risk threshold are marked as skip areas;

[0113] S520 uses a path search algorithm to generate a continuous path on the ski run network map that passes through all priority work areas and avoids skipped areas, and defines it as the lowest carbon work path and sends it to the work vehicle control terminal. At the same time, it generates a fixed inspection path that covers the entire ski run grid and defines it as a virtual reference path.

[0114] The virtual baseline path is a digital simulation of the traditional fixed inspection operation mode of the entire ski slope in a ski resort. Traditional snow grooming operations typically adopt a fixed inspection mode covering the entire ski slope. The expected energy consumption corresponding to the virtual baseline path is the baseline energy consumption of the traditional operation mode, providing a unified benchmark for measuring the emission reduction effect of low-carbon operations. By comparing the actual energy consumption of the lowest-carbon operation path with the expected energy consumption of the virtual baseline path, the energy consumption difference brought about by dynamic path planning can be accurately calculated, i.e., the emission reduction of path optimization. This difference can be directly added to the global reflective energy carbon integral pool to realize the quantification of the carbon value of low-carbon operations. The fixed baseline path rules eliminate the baseline fluctuation under different time periods, different weather conditions, and different visitor volumes, ensuring that the emission reduction calculation caliber is consistent across different operating cycles, avoiding distortion of the carbon integral accounting system. At the same time, by continuously comparing the difference between the actual operation energy consumption and the baseline energy consumption, the carbon reduction effect of operation path planning driven by dynamic caking risk can be quantitatively evaluated, providing a quantitative basis for algorithm iteration and optimization.

[0115] S530: Read the actual energy consumption value of the working vehicle from the real-time energy consumption data, calculate the difference between the actual energy consumption value and the expected energy consumption value at the corresponding position on the virtual reference path, and use the sum of the corresponding differences at all positions as the path optimization emission reduction amount, and add the path optimization emission reduction amount to the global reflective energy carbon integral pool.

[0116] like Figure 6 As shown, adjusting the tourist's points balance specifically includes:

[0117] S610 extracts the ratio of the instantaneous power value of the transportation equipment to the current number of passengers on the corresponding transportation equipment from the real-time energy consumption data of each device, and determines the instantaneous direct carbon footprint of each tourist by combining the sampling time interval.

[0118] S620: Determine the total carbon emissions per second of the entire ski resort based on the sum of the instantaneous direct carbon footprints of all tourists in the entire ski resort; extract an amount of points equal to the total carbon emissions per second of the entire ski resort from the global reflective energy carbon integral pool; and determine the points allocated to each tourist's account based on the ratio of each tourist's instantaneous direct carbon footprint to the total carbon emissions per second of the entire ski resort.

[0119] S630 performs a function transformation based on the weighted sum of the reflectivity value of the tourist's current location, the predicted value of the caking risk of the corresponding grid in the dynamic caking risk map, and the timestamp of the most recent operation vehicle passing through the location, and determines the location reward coefficient based on the transformation result.

[0120] Calculate the weighted composite score after normalization in the same direction:

[0121]

[0122] The location reward coefficient is obtained by performing a nonlinear transformation using the Sigmoid function.

[0123]

[0124] in:

[0125] λ is the reward coefficient for the tourist's current location, which is used to multiply by the base allocation points to obtain the final points amount;

[0126] S is the weighted composite score of the three influencing factors, with a value range of [0, 1].

[0127] w1, w2, and w3 are the weight coefficients of the three influencing factors, satisfying w1 + w2 + w3 = 1, and all of them are positive values;

[0128] I represents the real-time collected value of the snow surface reflectivity of the grid where the tourist is currently located;

[0129] I max This is the preset maximum value for the reflectivity of the snow surface in the ski resort, used to normalize the reflectivity.

[0130] r is the predicted risk value of slab compaction in the current grid of the tourist, taken from the dynamic slab compaction risk map;

[0131] r max The preset maximum value for the predicted risk of caking is used to normalize the risk of caking.

[0132] Δt is the time interval between the current moment and the last time the working vehicle passed through this grid, i.e., Δt = t now -t last ;

[0133] Δt max This is a preset value for the maximum inspection cycle of the work vehicle, used to normalize the time since the last operation.

[0134] k is the slope adjustment parameter of the Sigmoid function, used to control the sensitivity of the reward coefficient to changes.

[0135] The core objective of the location incentive coefficient is to guide tourists to choose low-carbon skiing areas through differentiated carbon credit incentives, thereby reducing the overall carbon emissions of the ski resort while also considering the skiing experience. Reflectivity intensity represents the natural anti-caking baseline of the snow surface, caking risk represents the skiing pressure in the current area, and recent operation time represents the maintenance status of the snow surface. These three factors influence the carbon cost of skiing from three dimensions: environmental baseline, behavioral pressure, and maintenance status. Using a weighted summation method integrates these multi-dimensional factors into a unified comprehensive indicator, avoiding incentive bias caused by a single factor.

[0136] The three factors have completely different physical dimensions, and the risk of caking, the time since the last operation, and the reward intensity are negatively correlated, while the reflectivity intensity is positively correlated with the reward intensity. By normalizing in the same direction before weighting, the difference in dimensions can be eliminated, the direction of influence can be unified, and the rationality of the comprehensive score can be guaranteed. Function transformation can map the weighted score to a reasonable coefficient range, ensuring that the adjustment range of the integral is controllable and avoiding extreme values ​​from impacting the integral system. At the same time, the nonlinearity can achieve a gradient effect with a smooth reward in the middle range and a sensitive reward at both ends, improving the rationality of the guidance strategy.

[0137] The three weights correspond to the respective proportions of influence of three factors in the reward calculation. The larger the weight, the stronger the regulatory effect of the corresponding factor on the tourist points reward. The weight corresponding to reflectivity intensity represents the degree of influence of the natural properties of the snow surface on the low-carbon reward. Increasing this weight can strengthen the guidance of skiing in areas with high reflectivity and good snow quality, which is suitable for use in scenarios where snow melts and snow quality degrades rapidly. The weight corresponding to the predicted value of snow compaction risk represents the degree of influence of the current snow compaction pressure on the low-carbon reward. Increasing this weight can strengthen the diversion of tourist flow in areas with low snow compaction risk and quickly alleviate the rate of snow compaction deterioration in high-density skiing areas, which is suitable for use during holidays and peak tourist seasons. The weight corresponding to the most recent operation time represents the degree of influence of the snow surface maintenance status on the low-carbon reward. Increasing this weight can guide tourists to prioritize skiing on slopes that have just been groomed, which can improve the tourist skiing experience, extend the interval between operations, reduce the frequency of operations, and achieve a balance between experience and carbon reduction.

[0138] S640, determine the adjusted points amount for the tourist based on the product of the points allocated to the tourist and the location reward coefficient.

[0139] like Figure 7 As shown, determining the expected additional carbon credit rate for each ski run direction specifically includes:

[0140] S710, based on the average of the position reward coefficients of all grids upstream of each ski run direction, determines the expected additional carbon credit rate for each ski run direction and sends it to the visitor terminal.

[0141] S720 continuously receives new GPS trajectory data transmitted back from the tourist terminal, merges the new GPS trajectory data with the original skier GPS trajectory data to obtain merged trajectory data, and regenerates the ski density heat map based on the merged trajectory data.

[0142] like Figure 8 As shown, adjusting the mapping relationship between the reflectivity value and the caking rate coefficient specifically includes:

[0143] S810 reads and sums the actual energy consumption values ​​of all vehicles operating that day after the end of each operating cycle to determine the total actual energy consumption of the vehicles operating that day.

[0144] S820 determines the theoretical total energy consumption by accumulating the reflectivity values ​​at different times of the day through the preset formula for calculating the total energy consumption of the theoretical total energy consumption segment by segment, and determines the relative deviation by combining the actual total energy consumption of the vehicles operating on the day.

[0145]

[0146] in:

[0147] E theory This represents the theoretical total energy consumption of the operating vehicles within a single operating cycle.

[0148] N is the total number of time periods divided into a single operating cycle;

[0149] μ is the energy consumption conversion coefficient for operation, which is a preset constant and represents the energy consumption per unit time for operation corresponding to the unit caking rate coefficient.

[0150] k(I i ) is the caking rate coefficient under the reflective intensity corresponding to the i-th time period, which is obtained by looking up the reflective intensity-caking rate coefficient mapping table;

[0151] I i The average reflectance of the snow surface in the entire ski area during the i-th time period;

[0152] Δt i Let be the length of the i-th time period.

[0153] S830, when the relative deviation exceeds a preset deviation threshold, the mapping relationship between the reflective intensity value and the caking rate coefficient is adjusted according to the proportion of the relative deviation, and the adjusted mapping relationship is written into the system's non-volatile memory for use in the next operating cycle. Specifically:

[0154] If the actual total energy consumption is higher than the theoretical total energy consumption, the caking rate coefficient corresponding to each reflective intensity value will be uniformly reduced according to the proportion of the relative deviation; conversely, the caking rate coefficient corresponding to each reflective intensity value will be uniformly increased according to the proportion of the relative deviation.

[0155] k′(I)=k(I)·(1-δ);

[0156] in:

[0157] k′(I) is the caking rate coefficient corresponding to a certain reflectivity after adjustment;

[0158] k(I) is the original caking rate coefficient corresponding to the same reflective intensity before adjustment;

[0159] δ represents the relative deviation between the actual total energy consumption and the theoretical total energy consumption of the operating vehicle.

[0160] Figure 9 This is a structural block diagram of the real-time carbon footprint calculation system for ice and snow tourism provided in an embodiment of the present invention, as shown below. Figure 9 As shown, the system includes:

[0161] The data acquisition module 100 is used to collect real-time snow surface reflectivity, skier GPS trajectory data, and real-time energy consumption data of each device.

[0162] The carbon integral calculation module 200 is used to determine the instantaneous reflective energy power value based on the snow surface reflective intensity data, and to accumulate the instantaneous reflective energy power value into the global reflective energy carbon integral pool;

[0163] The heat map generation module 300 is used to generate a skiing density heat map based on the skier's GPS trajectory data.

[0164] The risk prediction module 400 is used to calculate the predicted value of the caking risk of the corresponding grid based on the sliding density heat map and the reflectivity value, and to generate a dynamic caking risk map.

[0165] The path optimization module 500 is used to determine the lowest carbon operation path based on the dynamic slagging risk map, and to determine the path optimization emission reduction based on the difference between the actual energy consumption of the operation vehicle and the expected energy consumption of the virtual baseline path and add it to the global reflective energy carbon integral pool.

[0166] The carbon footprint allocation module 600 is used to extract the instantaneous power of the transportation equipment based on real-time energy consumption data, determine the direct carbon footprint of each tourist in combination with the number of passengers, allocate points from the global reflective energy carbon points pool according to the carbon footprint ratio of each tourist, and adjust the amount of points obtained by the tourist based on the reflective intensity value of the current location, the predicted value of caking risk and the recent working time of the operating vehicle.

[0167] The reward coefficient adjustment module 700 is used to determine the expected additional carbon offset rate for each ski run direction based on the location reward coefficient and send it to the tourist terminal. At the same time, it receives new GPS trajectory data and incorporates it into the ski density heat map generation process.

[0168] The mapping relationship adjustment module 800 is used to adjust the mapping relationship between the reflective intensity value and the caking rate coefficient based on the comparison results of the actual total energy consumption of the vehicles working on that day and the theoretical total energy consumption based on the reflective intensity value and the preset value after each operating cycle.

[0169] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0170] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0171] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for real-time carbon footprint calculation in ice and snow tourism, characterized in that, The method includes: Real-time data collection includes snow surface reflectivity, skier GPS trajectory data, and real-time energy consumption data for each device. The instantaneous reflective energy power value is determined based on the snow surface reflective intensity data, and the instantaneous reflective energy power value is accumulated into the global reflective energy carbon integration pool; A skiing density heatmap is generated based on the skier's GPS trajectory data. The predicted caking risk value of the corresponding grid is calculated based on the sliding density heat map and the reflectivity value, and a dynamic caking risk map is generated. The minimum carbon operation path is determined based on the dynamic caking risk map. The path optimization emission reduction is determined based on the difference between the actual energy consumption of the operation vehicle and the expected energy consumption of the virtual baseline path and is added to the global reflective energy carbon integral pool. Based on real-time energy consumption data, the instantaneous power of the transport equipment is extracted, and the direct carbon footprint of each tourist is determined in combination with the number of passengers. Points are allocated from the global reflective energy carbon points pool according to the carbon footprint of each tourist. The point amount of the tourist is adjusted according to the reflective intensity value of the current location, the predicted value of caking risk and the recent working time of the operating vehicle. The expected additional carbon credit rate for each ski run direction is determined based on the location reward coefficient and sent to the tourist terminal. At the same time, new GPS track data is received and incorporated into the ski density heatmap generation process. At the end of each operating cycle, the mapping relationship between the reflective intensity value and the caking rate coefficient is adjusted based on the comparison between the actual total energy consumption of the vehicles operating that day and the theoretical total energy consumption based on the reflective intensity value and the preset value.

2. The method according to claim 1, characterized in that, The step of determining the instantaneous reflective energy power value based on the snow surface reflectivity data and accumulating the instantaneous reflective energy power value into the global reflective energy carbon integration pool specifically includes: The average value of the snow surface reflectance intensity obtained from each sampling point of each ski run is taken, and the average value is normalized and corrected by combining it with the pre-stored time series data of the solar altitude angle of the ski area. The corrected result is used as the instantaneous reflectance power value of the ski run. The instantaneous reflective energy power values ​​of each ski slope are accumulated into a global variable in the order of timestamps. The global variable is the global reflective energy carbon integration pool.

3. The method according to claim 2, characterized in that, A skiing density heatmap is generated based on the skier's GPS track data, specifically including: The geographical area of ​​the ski resort is divided into rectangular grids of fixed size, and each grid is assigned a unique geographic identifier; Map each coordinate point in the skier's GPS track data to the corresponding grid, count the number of coordinate points falling into each grid within a five-minute time window, and use the number of coordinate points as the skiing frequency of that grid. Arrange the sliding frequencies of all grids into a frequency matrix according to their spatial location, and normalize the frequency matrix. Use the normalized frequency matrix as a sliding density heatmap.

4. The method according to claim 3, characterized in that, The steps for generating the dynamic slab congestion risk map specifically include: Read the sliding frequency of each grid in the sliding density heatmap, and at the same time read the reflective intensity value corresponding to the same grid position; The reflective intensity value is used as an index to query the pre-configured caking rate coefficient mapping table to obtain the caking rate coefficient of the grid. The reflective intensity value and the caking rate coefficient in the caking rate coefficient mapping table are negatively correlated. Multiply the sliding frequency by the slagging rate coefficient, and use the resulting product as the predicted slagging risk value for the grid. Arrange the predicted values ​​of all grid slab caking risk into a prediction matrix according to their spatial location, and use the prediction matrix as a dynamic slab caking risk map.

5. The method according to claim 4, characterized in that, The step of determining the path optimization emission reduction based on the difference between the actual energy consumption of the operating vehicle and the expected energy consumption of the virtual baseline path, and adding it to the global reflective energy carbon integral pool, specifically includes: In the dynamic slab slab risk map, grids with slab slab risk prediction values ​​exceeding a preset high-risk threshold are marked as priority work areas, and grids with slab slab risk prediction values ​​below a preset low-risk threshold are marked as skip areas. A path search algorithm is used to generate a continuous path on the snow track network map that passes through all priority work areas and avoids skipped areas. This path is defined as the lowest carbon work path and sent to the work vehicle control terminal. At the same time, a fixed inspection path covering the entire snow track grid is generated and defined as a virtual reference path. The actual energy consumption value of the operating vehicle is read from the real-time energy consumption data. The difference between the actual energy consumption value and the expected energy consumption value at the corresponding position on the virtual reference path is calculated. The sum of the corresponding differences at all positions is used as the path optimization emission reduction. The path optimization emission reduction is added to the global reflective energy carbon integral pool.

6. The method according to claim 5, characterized in that, The adjustment of the tourist's accumulated points specifically includes: The instantaneous power value of the transportation equipment is extracted from the real-time energy consumption data of each device, and the ratio of the instantaneous power value of the transportation equipment to the current number of passengers on the corresponding transportation equipment is determined by combining the sampling time interval. The total carbon emissions per second of the entire ski resort are determined based on the sum of the instantaneous direct carbon footprints of all visitors. An amount of points equal to the total carbon emissions per second of the entire ski resort is extracted from the global reflective energy carbon integral pool. The points allocated to each visitor's account are determined based on the ratio of each visitor's instantaneous direct carbon footprint to the total carbon emissions per second of the entire ski resort. The location reward coefficient is determined by a weighted sum of the reflectivity value of the tourist's current location, the predicted caking risk value of the corresponding grid in the dynamic caking risk map, and the timestamp of the most recent operation vehicle passing through the location. The adjusted point amount for the tourist is determined by multiplying the points allocated to the tourist by the location reward coefficient.

7. The method according to claim 6, characterized in that, The determination of the expected additional carbon credit rate for each ski run direction specifically includes: The expected additional carbon credit for each ski run direction is determined based on the average of the location bonus coefficients of all grids upstream of each ski run direction and sent to the visitor terminal. The system continuously receives new GPS trajectory data transmitted from tourist terminals, merges the new GPS trajectory data with the original skier GPS trajectory data to obtain merged trajectory data, and regenerates a ski density heatmap based on the merged trajectory data.

8. The method according to claim 7, characterized in that, The adjustment of the mapping relationship between the reflectivity value and the caking rate coefficient specifically includes: At the end of each operating cycle, the actual energy consumption values ​​of all vehicles operating that day are read and summed to determine the total actual energy consumption of the vehicles operating that day. Based on the reflectivity values ​​at different times of the day, the theoretical total energy consumption is determined by accumulating the values ​​segment by segment using the preset formula for calculating the theoretical total energy consumption. The relative deviation is then determined by combining this with the actual total energy consumption of the vehicles operating on that day. When the relative deviation exceeds a preset deviation threshold, the mapping relationship between the reflective intensity value and the caking rate coefficient is adjusted according to the proportion of the relative deviation, and the adjusted mapping relationship is written into the system's non-volatile memory for use in the next operating cycle. Specifically: If the actual total energy consumption is higher than the theoretical total energy consumption, the caking rate coefficient corresponding to each reflective intensity value will be uniformly reduced according to the proportion of the relative deviation; conversely, the caking rate coefficient corresponding to each reflective intensity value will be uniformly increased according to the proportion of the relative deviation.

9. A real-time carbon footprint accounting system for ice and snow tourism, characterized in that, The system includes: The data acquisition module is used to collect real-time snow surface reflectivity, skier GPS trajectory data, and real-time energy consumption data of each device. The carbon integral calculation module is used to determine the instantaneous reflective energy power value based on the snow surface reflective intensity data, and to accumulate the instantaneous reflective energy power value into the global reflective energy carbon integral pool; A heatmap generation module is used to generate a skiing density heatmap based on the skier's GPS trajectory data. The risk prediction module is used to calculate the predicted caking risk value of the corresponding grid based on the sliding density heat map and the reflectivity value, and generate a dynamic caking risk map. The path optimization module is used to determine the lowest carbon operation path based on the dynamic slagging risk map, and to determine the path optimization emission reduction based on the difference between the actual energy consumption of the operation vehicle and the expected energy consumption of the virtual baseline path, and add it to the global reflective energy carbon integral pool. The carbon footprint allocation module is used to extract the instantaneous power of the transportation equipment based on real-time energy consumption data, determine the direct carbon footprint of each tourist in combination with the number of passengers, allocate points from the global reflective energy carbon points pool according to the carbon footprint ratio of each tourist, and adjust the amount of points obtained by the tourist based on the reflective intensity value of the current location, the predicted value of caking risk and the recent working time of the operating vehicle. The reward coefficient adjustment module is used to determine the expected additional carbon offset rate for each ski run direction based on the location reward coefficient and send it to the tourist terminal. At the same time, it receives new GPS trajectory data and incorporates it into the ski density heat map generation process. The mapping relationship adjustment module is used to adjust the mapping relationship between the reflective intensity value and the caking rate coefficient based on the comparison results between the actual total energy consumption of the vehicles operating that day and the theoretical total energy consumption based on the reflective intensity value and the preset value after each operating cycle.

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