Tourism bearing capacity assessment method and system

Abstract

The invention provides a tourism bearing capacity evaluation method and system, and relates to the technical field of tourism bearing capacity evaluation.A target tourism area is divided into an ecological sensitive grid, a tourist dense grid and a transition buffer grid through a partition strategy of a digital elevation model and function planning; refined management is realized by fully combining terrain gradient and functional area attributes, real-time, short-term and long-term indexes of ecology, facilities and tourists are respectively generated by a multi-dimensional bearing capacity calculation model, and ecological degradation, facility aging and tourist flow distribution long-term evolution rules are accurately described. Performing weighted fusion on the real-time index, the short-term index and the long-term index according to the time decay weight to obtain an independent comprehensive bearing capacity index of each grid, and eliminating the evaluation influence of the time span on the bearing capacity; according to the grid adaptive weighting strategy, index weights are configured according to regional type differentiation, and the influence of ecological protection and tourism experience on bearing capacity evaluation is balanced, so that an accurate global bearing capacity evaluation result is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of tourism carrying capacity assessment, and in particular to a tourism carrying capacity assessment method and system. Background Art

[0002] With the rapid development of the tourism industry, the problems of overdevelopment and visitor overload in scenic areas have become increasingly prominent. Traditional tourism carrying capacity assessment methods, due to technical limitations, are unable to meet the demands of refined and dynamic management in modern scenic areas. Existing technologies often employ static threshold models, estimating carrying capacity based on historical statistical data or single environmental indicators. These methods fail to reflect dynamic changes in the ecology, facilities, and visitor behavior in real time, and lack flexibility and foresight, particularly when responding to sudden weather events, geological disasters, or peak holiday visitor flows. Furthermore, traditional methods often overlook the impact of terrain complexity on the spatial heterogeneity of ecologically sensitive areas and adopt a homogenized regional division strategy, leading to monitoring blind spots and assessment biases. For example, high-risk areas such as steep slopes and rock faces lack timely warnings of ecological risks such as landslides and soil erosion due to insufficient data collection density. At the data integration level, existing technologies often rely on a single data source, failing to effectively integrate multimodal real-time data. Furthermore, their ability to collaboratively analyze historical and real-time data is limited, making it difficult to accurately quantify the correlation between short-term visitor flow fluctuations and long-term ecological degradation.

[0003] Therefore, it is necessary to provide a tourism carrying capacity assessment method and system to solve the above technical problems. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a tourism carrying capacity assessment method and system, which can achieve the beneficial effect of real-time, effective and accurate assessment of tourism carrying capacity.

[0005] The present invention provides a tourism carrying capacity assessment method, comprising:

[0006] S1: Based on the digital elevation model and functional planning zoning map of the target tourist area, the target tourist area is divided into ecologically sensitive grids, tourist-intensive grids, and transitional buffer grids;

[0007] S2: Real-time acquisition of ecological monitoring data, facility load data, and visitor distribution data for each grid in the target tourist area, and simultaneous access to historical data in the historical database;

[0008] S3: Based on the real-time ecological monitoring data, facility load data, tourist distribution data and historical data of each grid, the real-time carrying capacity index, short-term carrying capacity index and long-term carrying capacity index of the ecology, facilities and tourists of each grid are calculated respectively;

[0009] S4: Based on the preset time decay weight rule, the real-time carrying capacity index, short-term carrying capacity index and long-term carrying capacity index of the ecology, facilities and tourists of each grid are weighted and integrated to obtain the ecological carrying capacity index, facility carrying capacity index and tourist carrying capacity index of each grid;

[0010] S5: Based on the preset weight allocation rules of the grid type, the ecological carrying capacity index, facility carrying capacity index and tourist carrying capacity index of each grid are weighted and integrated to obtain the comprehensive carrying capacity index of each grid;

[0011] S6: Based on the area proportion of each grid, the comprehensive carrying capacity index of each grid is weighted and integrated to obtain the global carrying capacity index. Based on the comprehensive carrying capacity index of each grid and the global carrying capacity index, the carrying capacity assessment result of the target tourist area is generated.

[0012] Preferably, in step S1, the grid division rule of the target tourist area is:

[0013] The areas in the digital elevation model with a slope greater than a preset first slope threshold and located within the ecological protection range in the functional planning zoning map are divided into ecologically sensitive grids;

[0014] The areas in the digital elevation model with a slope less than the preset second slope threshold and within the service range of artificial facilities in the functional planning zoning map are divided into tourist-dense grids, and the remaining areas are divided into transition buffer grids.

[0015] Preferably, step S1 further comprises segmenting the ecologically sensitive grid, including:

[0016] Real-time calculation of slope mutation rate between adjacent ecologically sensitive grids based on digital elevation model;

[0017] Based on the slope mutation rate and the preset subdivision mutation rate threshold, adjacent ecologically sensitive grids whose slope mutation rate exceeds the preset subdivision mutation rate threshold are divided into multiple ecologically sensitive grids.

[0018] Preferably, in step S2, the acquisition of ecological monitoring data further includes a step of real-time outlier removal and calibration through edge computing nodes, specifically including:

[0019] The three-sigma principle was used to eliminate data that deviated from the mean by plus or minus three times the standard deviation. At the same time, the soil moisture and precipitation data in the ecological monitoring data were synchronized and aligned;

[0020] Subsequently, the theoretical humidity change is calculated based on the preset permeability coefficient of the soil type and compared with the actual change in the actual monitored value of soil humidity; if the deviation between the actual change and the theoretical humidity change exceeds the preset percentage threshold, the manual calibration process is automatically triggered.

[0021] Preferably, in step S2, the visitor distribution data is obtained by weighted fusion of base station positioning signaling data and wireless network probe data.

[0022] Preferably, in step S3, the calculation time window of the short-term carrying capacity index is adjusted according to the preset tourist off-season and tourist peak season of the target tourist area.

[0023] Preferably, in step S3, the calculation of the long-term bearing capacity index also includes calculating the mean of historical data within a preset sliding time window, and replacing the data within the preset sliding time window that exceeds a preset tolerance range with the mean, wherein the preset tolerance range is set based on the three standard deviation principle of the historical data within the sliding time window.

[0024] Preferably, in step S6, the calculation of the global bearing capacity index further includes introducing a dynamic protection level coefficient based on the grid type.

[0025] Preferably, the dynamic protection level coefficient is dynamically adjusted according to real-time weather and emergencies.

[0026] The present invention also provides a tourism carrying capacity evaluation system, which is applied to a tourism carrying capacity evaluation method, comprising:

[0027] A grid zoning module is used to divide the target tourist area into ecologically sensitive grids, tourist-intensive grids, and transition buffer grids based on the digital elevation model and functional planning zoning map of the target tourist area;

[0028] Multi-source data acquisition module, used to obtain real-time ecological monitoring data, facility load data, and visitor distribution data for each grid in the target tourist area, and simultaneously access historical data in the historical database;

[0029] The multi-dimensional carrying capacity calculation module is used to calculate the real-time, short-term, and long-term carrying capacity indexes of the ecology, facilities, and tourists of each grid based on real-time ecological monitoring data, facility load data, tourist distribution data, and historical data of each grid;

[0030] The time decay weight fusion module is used to weight the real-time carrying capacity index, short-term carrying capacity index, and long-term carrying capacity index of the ecology, facilities, and tourists of each grid based on the preset time decay weight rule to obtain the ecological carrying capacity index, facility carrying capacity index, and tourist carrying capacity index of each grid;

[0031] The grid adaptive weighting module is used to weight and integrate the ecological carrying capacity index, facility carrying capacity index, and tourist carrying capacity index of each grid based on the preset weight distribution rules of the grid type to obtain the comprehensive carrying capacity index of each grid;

[0032] The global assessment decision module is used to obtain the global carrying capacity index by weighted fusion of the comprehensive carrying capacity index of each grid based on the area proportion of each grid, and generate the carrying capacity assessment result of the target tourist area based on the comprehensive carrying capacity index of each grid and the global carrying capacity index.

[0033] Compared with related technologies, the tourism carrying capacity assessment method and system provided by the present invention have the following beneficial effects:

[0034] This invention uses a zoning strategy based on digital elevation models and functional planning to divide target tourist areas into ecologically sensitive grids, tourist-intensive grids, and transition buffer grids. This strategy fully integrates terrain slope with functional zone attributes to achieve refined management. Furthermore, to address the monitoring challenges posed by complex terrain, the system calculates the slope mutation rate between adjacent ecologically sensitive grids in real time. When the mutation rate exceeds a safety threshold, it automatically subdivides high-risk grids into multiple ecologically sensitive grids. This significantly increases the data collection density and monitoring frequency for terrains like steep cliffs and canyons, resolving the monitoring blind spot problem under traditional homogenized grid division. At the data collection and processing layer, edge computing nodes efficiently clean ecological monitoring data, using the three-sigma principle to eliminate sensor noise and synchronously calibrate the temporal consistency of soil moisture and precipitation data. Manual intervention is triggered when the deviation between the theoretical penetration model calculation value and the actual monitoring value exceeds the limit, ensuring data reliability. Tourist distribution data integrates the wide-area coverage advantage of base station signaling with the high-precision positioning capabilities of wireless probes to generate real-time passenger flow heat maps and identify risky behaviors such as aggregation and detention, providing data support for dynamic flow control and route guidance. The multidimensional carrying capacity calculation model generates real-time, short-term, and long-term indices for ecology, facilities, and tourists, accurately depicting the long-term evolution of ecological degradation, facility aging, and tourist flow distribution. The real-time, short-term, and long-term indices are then weighted and integrated based on time-decay weights to obtain a comprehensive carrying capacity index for each grid, providing a refined and independent carrying capacity assessment for each grid. A grid-adaptive weighting strategy allocates indicator weights based on regional type, with the ecological index dominating ecologically sensitive areas, the facility load index dominating tourist-intensive areas, and a balanced consideration of multiple factors in transitional areas, thereby precisely balancing ecological protection and tourist experience. Finally, a dynamic protection coefficient is introduced to calculate a global carrying capacity index. This, combined with the comprehensive carrying capacity index of each grid, forms the carrying capacity assessment for the target tourist area, achieving a comprehensive and refined carrying capacity assessment of the target tourist area. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a flow chart of a tourism carrying capacity evaluation method of the present invention;

[0036] Figure 2 This is a module structure diagram of a tourism carrying capacity assessment system of the present invention. DETAILED DESCRIPTION

[0037] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all of the structures. Furthermore, the embodiments of the present invention and the features of the embodiments may be combined with one another unless there is a conflict.

[0038] It should also be noted that, for ease of description, only the part relevant to the present invention, rather than all of the content, is shown in the accompanying drawings. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processing or methods depicted as flow charts. Although the flow charts describe each operation (or step) as being processed sequentially, many operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of each operation can be rearranged. When its operation is completed, the processing can be terminated, but can also have additional steps not included in the accompanying drawings. The processing can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0039] Example 1

[0040] A tourism carrying capacity assessment method, in the specific implementation process, such as Figure 1 , which shows a flow chart of a tourism carrying capacity assessment method, including:

[0041] Step S1: Based on the digital elevation model and functional planning zoning map of the target tourist area, the target tourist area is divided into ecologically sensitive grids, tourist-intensive grids, and transition buffer grids.

[0042] Specifically, in step S1, the grid division rule of the target tourist area is:

[0043] The areas in the digital elevation model with a slope greater than a preset first slope threshold and located within the ecological protection range in the functional planning zoning map are divided into ecologically sensitive grids;

[0044] The areas in the digital elevation model with a slope less than the preset second slope threshold and within the service range of artificial facilities in the functional planning zoning map are divided into tourist-dense grids, and the remaining areas are divided into transition buffer grids.

[0045] Specifically, step S1 also includes further segmentation of the ecologically sensitive grid, including:

[0046] Real-time calculation of slope mutation rate between adjacent ecologically sensitive grids based on digital elevation model;

[0047] Based on the slope mutation rate and the preset subdivision mutation rate threshold, adjacent ecologically sensitive grids whose slope mutation rate exceeds the preset subdivision mutation rate threshold are divided into multiple ecologically sensitive grids.

[0048] During the specific implementation process, first, for example, a digital elevation model of the target tourist area is obtained through technologies including, but not limited to, high-precision satellite remote sensing and drone aerial surveys. This is then processed using geographic information system software to generate a gridded slope distribution map, where the slope value is calculated using the inverse tangent function of the elevation difference between adjacent grid points and the horizontal distance. Simultaneously, a functional planning zoning map is provided by the scenic area management, clearly marking the vector boundaries of the ecological protection core area, buffer zone, and artificial facility service area. During the gridding phase, the system spatially overlays the digital elevation model with the functional planning zoning map. Areas with slopes greater than a first preset threshold and located within the ecological protection area are identified as ecologically sensitive grids. These areas typically correspond to steep slopes, cliffs, and high-risk areas for soil erosion, requiring focused monitoring of vegetation cover, soil moisture, and geological disaster risks. Areas with slopes less than a second preset threshold and located within the artificial facility service area are classified as tourist-dense grids. These areas include basic service facilities such as visitor centers, parking lots, and restrooms, requiring real-time monitoring of visitor density and facility load. Other areas with slopes between the two thresholds or not clearly covered by the functional zoning are classified as transitional buffer grids, serving as flexible links between ecological protection and tourist activities. To further improve the monitoring accuracy of ecologically sensitive areas, the system calculates the slope mutation rate between adjacent ecologically sensitive grids in real time, that is, the absolute value of the slope change within a unit distance. When the mutation rate exceeds the subdivision threshold, it indicates that there are steep slopes, faults or potential landslide risks in the area, and the dynamic encryption mechanism needs to be triggered. For example, the original ecologically sensitive grid is equally divided into four sub-grids, that is, four smaller ecologically sensitive grids, and the area of ​​each sub-grid is one-fourth of the original ecologically sensitive grid. These divided ecologically sensitive grids are marked, and the sampling frequency of ecological monitoring data of these marked ecologically sensitive grids is increased to ensure high-frequency monitoring of areas with sudden terrain changes. On the basis of taking into account the complexity of the terrain and management needs, the precise division and dynamic optimization of the spatial grid of the tourist area are achieved, laying the foundation for subsequent multi-source data collection and carrying capacity assessment.

[0049] Step S2: Acquire ecological monitoring data, facility load data, and visitor distribution data of each grid in the target tourist area in real time, and simultaneously access historical data in the historical database.

[0050] Specifically, in step S2, the acquisition of ecological monitoring data also includes real-time outlier removal and calibration steps through edge computing nodes, specifically including:

[0051] The three-sigma principle was used to eliminate data that deviated from the mean by plus or minus three times the standard deviation. At the same time, the soil moisture and precipitation data in the ecological monitoring data were synchronized and aligned;

[0052] Subsequently, the theoretical humidity change is calculated based on the preset permeability coefficient of the soil type and compared with the actual change in the actual monitored value of soil humidity; if the deviation between the actual change and the theoretical humidity change exceeds the preset percentage threshold, the manual calibration process is automatically triggered.

[0053] Specifically, in step S2, the visitor distribution data is obtained by weighted fusion of base station positioning signaling data and wireless network probe data.

[0054] During the specific implementation process, the three-standard deviation principle is first used to eliminate outliers in the data. For example, the mean and standard deviation of the same type of sensor data in the current grid are calculated, and the abnormal data that exceeds the range of plus or minus three times the standard deviation of the mean is marked as invalid and temporarily replaced with the mean of the adjacent grid or historical data of the same period to avoid noise pollution caused by sensor failure or environmental interference; then the soil moisture and precipitation data are aligned in time and space. Based on the minute-level precipitation data provided by the meteorological station and the location coordinates of the soil moisture sensor, the spatial distribution weight of the precipitation event in each grid is calculated, and the precipitation data is distributed to the corresponding grid according to the weight to ensure the temporal and spatial consistency of soil moisture changes and precipitation input. After alignment, the system calculates theoretical moisture changes based on the preset permeability coefficients for the soil types within the grid. For example, if the permeability coefficient of sandy soil is 0.8 mm per hour, then if a grid experiences 5 mm of cumulative precipitation within an hour, the theoretical moisture increment should be 4 mm. This is then compared with the actual moisture changes measured by the sensors. If the deviation between the actual and theoretical values ​​exceeds a preset threshold, a manual calibration process is automatically triggered. Technicians remotely control the sensors for zero-point calibration or on-site maintenance, and the theoretical model parameters are updated. Regarding visitor distribution data collection, the system uses base station positioning signaling data to obtain large-scale visitor movement trajectories. This is supplemented by high-precision positioning of wireless network probes in key areas. For example, the two data sources are fused with a six-to-four weighting: base station signaling data, which covers the entire area but has lower accuracy, is used to calculate grid-level visitor flow. Wireless probes, based on MAC address recognition and signal strength triangulation, have an accuracy of up to 5 meters and are used to identify micro-behaviors such as gathering and lingering. The fused visitor distribution data is visualized as a heat map, highlighting areas with excessive density or abnormal behavior, providing real-time input for subsequent carrying capacity calculations.

[0055] Step S3: Based on the real-time ecological monitoring data, facility load data, visitor distribution data and historical data of each grid, the real-time carrying capacity index, short-term carrying capacity index and long-term carrying capacity index of the ecology, facilities and tourists of each grid are calculated respectively.

[0056] Specifically, in step S3, the calculation time window of the short-term carrying capacity index is adjusted according to the preset tourist off-season and tourist peak season of the target tourist area.

[0057] Specifically, in step S3, the calculation of the long-term bearing capacity index also includes calculating the mean of the historical data within a preset sliding time window, and replacing the data within the preset sliding time window that exceeds a preset tolerance range with the mean, and the preset tolerance range is set based on the three standard deviation principle of the historical data within the sliding time window.

[0058] During the specific implementation process, the real-time ecological carrying capacity index of each grid is generated by weighted calculation based on the ecological monitoring data in the grid at the current moment, which includes but is not limited to rainfall, vegetation cover index and soil moisture saturation; the short-term ecological carrying capacity index is generated by, for example, a calculation time window based on the ecological monitoring data of the past 24 hours is used, and the calculation time window is shortened to six hours in the peak tourist season to capture the trend of damage to soil structure caused by instantaneous heavy rain or tourist trampling, and extended to forty-eight hours in the off-season to analyze slowly changing ecological pressure, and the sensor noise interference is eliminated by the exponential smoothing method; the long-term ecological carrying capacity index is generated by, for example, the vegetation index, soil erosion amount and the average annual number of tourist trampling incidents recorded monthly in a certain ecologically sensitive grid over five years. For each indicator, the system calculates the mean and standard deviation of the data within the window. For example, the mean of the vegetation index within five years is 0.75 and the standard deviation is 0.05, then the three standard deviation tolerance range is 0.75±0.15, that is, 0.60 to 0.90; when the vegetation index drops sharply to 0.55 due to a wildfire in a certain month, the data is judged to be out of tolerance, and the system automatically replaces it with the mean of the data of the previous and next two months. For example, it is replaced with the mean of 0.73 of the previous month and 0.70 of the next month, which is 0.715, thereby eliminating the impact of outliers on the long-term trend and maintaining the stability of the long-term trend. For the evaluation of the real-time carrying capacity index of the facility, for example, the real-time carrying capacity index of the facility is generated by normalizing the extreme values ​​of toilet utilization rate, parking lot saturation and facility maintenance response time; for the short-term carrying capacity index of the facility, for example, the load fluctuation rate of the facility in the past twelve hours is counted, and the calculation time window is shortened to three hours during the peak passenger flow period on holidays to monitor the instantaneous overload risk. For example, the parking lot saturation is updated every hour during the Golden Week, and a short-term index warning is triggered when the capacity is exceeded; for the long-term carrying capacity index of the facility, the corrosion rate of the facility material and the annual maintenance frequency are analyzed, and the remaining service life is predicted in combination with the random forest model. For example, if the concrete toilet facilities are repaired more than three times a year, the long-term index decreases year by year according to the linear regression model. The real-time tourist carrying capacity index is calculated by fusing base station signaling data and wireless network probe data to generate a passenger flow density heat map. This heat map is then combined with the maximum density threshold to generate a real-time tourist carrying capacity index. The short-term tourist carrying capacity index is calculated by using a three-hour calculation window during the peak tourist season and a twelve-hour window during the off-season. The long-term index is calculated by calculating the peak density and maximum density threshold using five years of historical passenger flow data. This provides data input for the subsequent calculation of the independent carrying capacity index for each grid.

[0059] Step S4: Based on the preset time decay weight rule, the real-time carrying capacity index, short-term carrying capacity index and long-term carrying capacity index of the ecology, facilities and tourists of each grid are weighted and integrated to obtain the ecological carrying capacity index, facility carrying capacity index and tourist carrying capacity index of each grid.

[0060] During the specific implementation process, the preset time decay weight rule sets the initial weights of the real-time carrying capacity index, short-term carrying capacity index and long-term carrying capacity index for the ecological, facility and tourist dimensions of each grid, respectively. For example, the initial weights of the ecological dimension are 50% of the real-time carrying capacity index, 30% of the short-term carrying capacity index and 20% of the long-term carrying capacity index; the initial weights of the facility dimension are 60% of the real-time carrying capacity index, 25% of the short-term carrying capacity index and 15% of the long-term carrying capacity index; and the initial weights of the tourist dimension are 70% of the real-time carrying capacity index, 20% of the short-term carrying capacity index and 10% of the long-term carrying capacity index. When real-time monitoring data deviates from a preset safety threshold by more than a certain amount, the system triggers a dynamic weighting adjustment mechanism. For example, for the ecological dimension, if real-time rainfall exceeds the soil infiltration threshold, causing a sudden increase in ecological pressure, the weight of the real-time carrying capacity index for the ecological dimension is increased to 80%, the short-term carrying capacity index is compressed to 15%, and the long-term carrying capacity index is reduced to 5%. If real-time data is stable and the volatility of the short-term index is below the historical average, the weight of the real-time carrying capacity index is reduced to 40%, the short-term carrying capacity index is increased to 40%, and the long-term carrying capacity index remains at 20%. After the weighting adjustment, it is normalized to ensure that the sum of the three is 100%. This provides the data foundation for the subsequent calculation of the independent comprehensive carrying capacity index for each grid. Through this mechanism, the assessment results are both real-time sensitive and scientifically sound.

[0061] Step S5: Based on the weight distribution rule preset for each grid type, the ecological carrying capacity index, facility carrying capacity index, and tourist carrying capacity index of each grid are weighted and integrated to obtain the comprehensive carrying capacity index of each grid.

[0062] During implementation, for ecologically sensitive grids, the system pre-sets a weight of 70% for the ecological carrying capacity index, with the facility and visitor carrying capacity indices each accounting for 15%, prioritizing the ecological stability of steep slopes and areas with fragile vegetation. For visitor-dense grids, the system pre-sets a weight of 60% for the facility carrying capacity index, with the ecological and visitor carrying capacity indices each accounting for 20%, prioritizing the carrying capacity of service facilities such as restrooms and parking lots, as well as visitor safety. Transitional buffer grids adopt a balanced weighting strategy, with the ecological, facility, and visitor carrying capacity indices each accounting for one-third, serving as a flexible transition zone between ecological protection and tourist activity. A weighted fusion is performed using weight allocation rules to determine the comprehensive carrying capacity index for each grid. These weight allocation rules include, but are not limited to, those based on historical data analysis and expert experience.

[0063] Step S6: Based on the area proportion of each grid, the comprehensive carrying capacity index of each grid is weighted and integrated to obtain the global carrying capacity index, and the carrying capacity assessment result of the target tourist area is generated based on the comprehensive carrying capacity index of each grid and the global carrying capacity index.

[0064] Specifically, in step S6, the calculation of the global bearing capacity index further includes introducing a dynamic protection level coefficient based on the grid type.

[0065] Specifically, the dynamic protection level coefficient is dynamically adjusted according to real-time weather and emergencies.

[0066] In practice, the global carrying capacity index is calculated by first weighting the combined carrying capacity index based on the area percentage of each grid. For example, a scenic area consists of three grids: an ecologically sensitive grid (30%), a tourist-dense grid (50%), and a transition buffer grid (20%). Their combined indices are 0.7, 0.9, and 0.5, respectively. The initial global index value is 0.7 × 0.3 + 0.9 × 0.5 + 0.5 × 0.2 = 0.76. This is based on a dynamic protection level coefficient, with preset initial weights for different grid types: 3.0 for ecologically sensitive grids, 0.8 for tourist-dense grids, and 1.0 for transition buffer grids. These coefficients are dynamically adjusted based on real-time weather and emergencies. For example, during a red alert for heavy rain, the coefficient for ecologically sensitive grids is increased to 5.0. During an orange alert for high temperatures, the coefficient for tourist-dense grids is reduced to 0.5. During geological disaster emergency response, the coefficient for transition buffer grids is temporarily set to 2.0. The calculation formula for the adjusted global index is the sum of the products of the comprehensive index, area share and dynamic coefficient of each grid divided by the sum of the products of the dynamic coefficient and area share. For example, during a rainstorm, the comprehensive index of the ecologically sensitive grid is 0.7, the area is 30%, and the coefficient is 5.0; the index of the tourist-dense grid is 0.9, the area is 50%, and the coefficient is 0.8; the index of the transition buffer grid is 0.5, the area is 20%, and the coefficient is 1.0. The global index is (0.7×0.3×5.0+0.9×0.5×0.8+0.5×0.2×1.0) / (0.3×5.0+0.5×0.8+0.2×1.0)=(1.05+0.36+0.1) / (1.5+0.4+0.2)=1.51 / 2.1≈0.72. Through this mechanism, heavy rains caused the weight of ecologically sensitive areas to increase, and the global index dropped from 0.76 to 0.72, triggering a level-three emergency response. The system automatically closed the entrance to the high-risk ecological area and initiated diversion collaboration with adjacent scenic spots. The final generated global carrying capacity index and the comprehensive carrying capacity index of each grid were output in the form of a visual heat map superimposed with dynamic coefficient adjustment records, becoming the global and local carrying capacity assessment results covering the target tourist area, providing scenic area managers with full-cycle decision support from real-time monitoring to long-term planning.

[0067] The working principle of the tourism carrying capacity assessment method provided by the present invention is as follows:

[0068] First, based on a digital elevation model and a functional planning zoning map, the target area was divided into ecologically sensitive grids, tourist-intensive grids, and transition buffer grids. The ecologically sensitive grids were dynamically subdivided into subgrids based on the real-time slope mutation rate to improve the monitoring accuracy of complex terrain. Real-time ecological monitoring data, facility load data, and tourist distribution data were collected from each grid. Outlier cleaning and spatiotemporal alignment of multi-source data were performed through edge computing nodes. Real-time, short-term, and long-term carrying capacity indices were then calculated. The real-time carrying capacity index captured instantaneous risks, the short-term carrying capacity index adjusted the calculation time window according to the peak and off-season to analyze trend fluctuations, and the long-term carrying capacity index eliminated interference from extreme events using a five-year sliding window and a three-standard deviation tolerance mechanism. The indices for each time period were dynamically fused using time-attenuated weights, and a comprehensive carrying capacity index for each grid was generated based on preset weights for each grid type. Finally, a dynamic protection level coefficient was introduced, combined with the grid area ratio to weight the global carrying capacity index. Combined with the comprehensive carrying capacity index of each grid, hierarchical control instructions were driven to complete the assessment of tourism carrying capacity.

[0069] Example 2

[0070] A tourism carrying capacity assessment system is applied to a tourism carrying capacity assessment method. In the specific implementation process, Figure 2 As shown, it shows a module structure diagram of a tourism carrying capacity evaluation system, including:

[0071] The grid partitioning module 100 is used to divide the target tourist area into an ecologically sensitive grid, a tourist-intensive grid, and a transition buffer grid based on a digital elevation model and a functional planning zoning map of the target tourist area;

[0072] Multi-source data acquisition module 200, used to obtain ecological monitoring data, facility load data and visitor distribution data of each grid in the target tourist area in real time, and synchronously access historical data in the historical database;

[0073] The multi-dimensional carrying capacity calculation module 300 is used to calculate the real-time carrying capacity index, short-term carrying capacity index, and long-term carrying capacity index of the ecology, facilities, and tourists of each grid based on the real-time ecological monitoring data, facility load data, tourist distribution data, and historical data of each grid;

[0074] The time decay weight fusion module 400 is used to weight and fuse the real-time carrying capacity index, short-term carrying capacity index, and long-term carrying capacity index of the ecology, facilities, and tourists of each grid based on a preset time decay weight rule to obtain the ecological carrying capacity index, facility carrying capacity index, and tourist carrying capacity index of each grid;

[0075] The grid adaptive weighting module 500 is used to weight and integrate the ecological carrying capacity index, facility carrying capacity index, and tourist carrying capacity index of each grid based on the preset weight distribution rules of the grid type to obtain the comprehensive carrying capacity index of each grid;

[0076] The global assessment decision module 600 is used to weight the comprehensive carrying capacity index of each grid based on the area proportion of each grid to obtain the global carrying capacity index, and generate the carrying capacity assessment result of the target tourist area based on the comprehensive carrying capacity index of each grid and the global carrying capacity index.

[0077] The working principle of the tourism carrying capacity evaluation system provided by the present invention is as follows:

[0078] The present invention divides the scenic area into ecologically sensitive, tourist-intensive and transition buffer grids through a grid partitioning module 100 based on a digital elevation model and a functional planning map. The ecologically sensitive grid is dynamically subdivided into high-density monitoring subgrids through real-time slope mutation rate detection to cover complex terrains such as steep slopes and canyons; the multi-source data acquisition module 200 obtains ecological monitoring data, facility load data and tourist distribution data of each grid in real time through Internet of Things sensors, and uses edge computing nodes to clean outliers and align multi-source data; the multi-dimensional carrying capacity calculation module 300 constructs real-time, short-term and long-term index models respectively. The real-time index captures instantaneous risks, and the short-term index captures the risk of accidents. The time window is dynamically adjusted according to the peak and off-season, and the long-term index eliminates interference from extreme events by replacing the five-year sliding window mean; the time decay weight fusion module 400 dynamically adjusts the weight of each time period according to the risk level, and the grid adaptive weighting module 500 presets differentiated weights according to the regional type. Finally, the global assessment decision module 600 introduces a dynamic protection coefficient and generates a global index based on the area ratio. Together with the comprehensive carrying capacity index of each grid, it drives the hierarchical response instructions to realize the carrying capacity assessment of the tourist area. The present invention combines the accuracy of ecological protection, the controllability of facility load and the real-time safety management of tourists, providing full-cycle decision support for the sustainable development of scenic spots.

[0079] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0080] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0081] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

Claims

1. A tourism carrying capacity assessment method, characterized in that: The bearing capacity assessment method comprises the following steps: S1: Based on the digital elevation model and functional planning zoning map of the target tourist area, the target tourist area is divided into ecologically sensitive grids, tourist-intensive grids, and transitional buffer grids; S2: Real-time acquisition of ecological monitoring data, facility load data, and visitor distribution data for each grid in the target tourist area, and simultaneous access to historical data in the historical database; S3: Based on the real-time ecological monitoring data, facility load data, tourist distribution data and historical data of each grid, the real-time carrying capacity index, short-term carrying capacity index and long-term carrying capacity index of the ecology, facilities and tourists of each grid are calculated respectively; S4: Based on the preset time decay weight rule, the real-time carrying capacity index, short-term carrying capacity index and long-term carrying capacity index of the ecology, facilities and tourists of each grid are weighted and integrated to obtain the ecological carrying capacity index, facility carrying capacity index and tourist carrying capacity index of each grid; S5: Based on the preset weight allocation rules of the grid type, the ecological carrying capacity index, facility carrying capacity index and tourist carrying capacity index of each grid are weighted and integrated to obtain the comprehensive carrying capacity index of each grid; S6: Based on the area proportion of each grid, the comprehensive carrying capacity index of each grid is weighted and integrated to obtain the global carrying capacity index. Based on the comprehensive carrying capacity index of each grid and the global carrying capacity index, the carrying capacity assessment result of the target tourist area is generated.

2. A tourism carrying capacity evaluation method according to claim 1, characterized in that: In step S1, the grid division rule of the target tourist area is: The areas in the digital elevation model with a slope greater than a preset first slope threshold and located within the ecological protection range in the functional planning zoning map are divided into ecologically sensitive grids; The areas in the digital elevation model with a slope less than the preset second slope threshold and within the service range of artificial facilities in the functional planning zoning map are divided into tourist-dense grids, and the remaining areas are divided into transition buffer grids.

3. A tourism carrying capacity evaluation method according to claim 2, characterized in that: Step S1 also includes further segmentation of the ecologically sensitive grid, including: Real-time calculation of slope mutation rate between adjacent ecologically sensitive grids based on digital elevation model; Based on the slope mutation rate and the preset subdivision mutation rate threshold, adjacent ecologically sensitive grids whose slope mutation rate exceeds the preset subdivision mutation rate threshold are divided into multiple ecologically sensitive grids.

4. A tourism carrying capacity evaluation method according to claim 3, characterized in that: In step S2, the acquisition of ecological monitoring data also includes real-time outlier removal and calibration steps through edge computing nodes, specifically including: The three-sigma principle was used to eliminate data that deviated from the mean by plus or minus three times the standard deviation. At the same time, the soil moisture and precipitation data in the ecological monitoring data were synchronized and aligned; Subsequently, the theoretical humidity change is calculated based on the preset permeability coefficient of the soil type and compared with the actual change in the actual monitored value of soil humidity; if the deviation between the actual change and the theoretical humidity change exceeds the preset percentage threshold, the manual calibration process is automatically triggered.

5. A tourism carrying capacity evaluation method according to claim 4, characterized in that: In step S2, the visitor distribution data is obtained by weighted fusion of base station positioning signaling data and wireless network probe data.

6. A tourism carrying capacity evaluation method according to claim 5, characterized in that: In step S3, the calculation time window of the short-term carrying capacity index is adjusted according to the preset tourist off-season and tourist peak season of the target tourist area.

7. A tourism carrying capacity evaluation method according to claim 6, characterized in that: In step S3, the calculation of the long-term bearing capacity index also includes calculating the mean of the historical data within a preset sliding time window, and replacing the data within the preset sliding time window that exceeds a preset tolerance range with the mean, wherein the preset tolerance range is set based on the three standard deviation principle of the historical data within the sliding time window.

8. A tourism carrying capacity evaluation method according to claim 7, characterized in that: In step S6, the calculation of the global bearing capacity index also includes introducing a dynamic protection level coefficient based on the grid type.

9. A tourism carrying capacity evaluation method according to claim 8, characterized in that: The dynamic protection level coefficient is dynamically adjusted according to real-time weather and emergencies.

10. A tourism carrying capacity assessment system, characterized in that: Applied to a tourism carrying capacity assessment method according to any one of claims 1 to 9, the carrying capacity assessment system comprises: A grid zoning module is used to divide the target tourist area into ecologically sensitive grids, tourist-intensive grids, and transition buffer grids based on the digital elevation model and functional planning zoning map of the target tourist area; Multi-source data acquisition module, used to obtain real-time ecological monitoring data, facility load data, and visitor distribution data for each grid in the target tourist area, and simultaneously access historical data in the historical database; The multi-dimensional carrying capacity calculation module is used to calculate the real-time, short-term, and long-term carrying capacity indexes of the ecology, facilities, and tourists of each grid based on real-time ecological monitoring data, facility load data, tourist distribution data, and historical data of each grid; The time decay weight fusion module is used to weight the real-time carrying capacity index, short-term carrying capacity index, and long-term carrying capacity index of the ecology, facilities, and tourists of each grid based on the preset time decay weight rule to obtain the ecological carrying capacity index, facility carrying capacity index, and tourist carrying capacity index of each grid; The grid adaptive weighting module is used to weight and integrate the ecological carrying capacity index, facility carrying capacity index, and tourist carrying capacity index of each grid based on the preset weight distribution rules of the grid type to obtain the comprehensive carrying capacity index of each grid; The global assessment decision module is used to obtain the global carrying capacity index by weighted fusion of the comprehensive carrying capacity index of each grid based on the area proportion of each grid, and generate the carrying capacity assessment result of the target tourist area based on the comprehensive carrying capacity index of each grid and the global carrying capacity index.

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