Intelligent rural tourism safety monitoring and emergency response method and system

Abstract

The invention is suitable for the field of rural tourism safety, and provides an intelligent rural tourism safety monitoring and emergency response method and system, and the system comprises a tourist monitoring and environment data collection module, a high-risk region recognition module, a potential occurrence point recognition module, a risk assessment and influence quantification module, and an early warning system and emergency response module. According to the intelligent village tourism safety monitoring and emergency response method, through systematic data monitoring and risk management, the safety and experience of tourists are improved, the sustainability of the environment is promoted, the emergency response capability is enhanced, and comprehensive support is provided for the development of village tourism. According to the comprehensive management mode, a safer and more sustainable development environment is created for rural areas, and vigorous development of rural tourism is promoted.

Description

Technical Field

[0001] The present invention belongs to the field of rural tourism safety, and particularly relates to an intelligent rural tourism safety monitoring and emergency response method and system. Background Art

[0002] The field of rural tourism safety is a complex system involving multiple aspects. With the increasing interest of people in rural travel, safety issues have become prominent in rural tourism. Debris flow is an important geological disaster, especially in mountainous and hilly areas, posing a significant threat to the safety of tourists and the smooth progress of tourism activities. The occurrence of debris flow is mainly affected by geological conditions, rainfall, and human activities. Steep mountain slopes, loose soil, and heavy rainfall are the key factors inducing debris flow. Especially when precipitation is concentrated and there is heavy rainfall in a short period, the saturation of soil moisture will lead to the formation of debris flow.

[0003] Currently, during the process of rural tourism, there is a lack of a comprehensive assessment of the impact of the increase in tourist flow on the environment and water flow, making it difficult to timely identify problems such as soil compaction and vegetation damage. At the same time, the monitoring of water flow changes is not timely enough to effectively identify the increase in non-natural water flow. The analysis of the relationship between farmland drainage and tourist activities is insufficient, making the risk assessment of debris flow inaccurate. Especially under rainfall conditions, there is a lack of identification of potential debris flow occurrence points, and the risks of natural disasters such as debris flow and landslide cannot be quantitatively evaluated. There is also a lack of an effective early warning mechanism to quickly generate early warning information and plan tourist evacuation routes, thus being unable to timely respond to emergencies and prevent the occurrence of tourist fatalities and other safety accidents. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent rural tourism safety monitoring and emergency response method, aiming to solve the technical problems existing in the prior art determined in the background art.

[0005] The present invention is implemented as follows. An intelligent rural tourism safety monitoring and emergency response method, the method includes:

[0006] Real-time monitor tourist image data, count the number of tourists, analyze the tourist density in each area, identify tourist peak periods and crowded areas, and at the same time collect geological environment data, collect historical debris flow disaster records and farmland drainage paths, and monitor water flow changes;

[0007] Utilize tourist flow monitoring data, combined with historical tourism data, to analyze the impact of the increase in the number of tourists on the environment and water flow, and identify high-risk areas;

[0008] Combined with farmland drainage conditions and historical disaster records, analyze the contribution of tourist activities in crowded areas to the debris flow risk under rainfall conditions, and identify potential debris flow occurrence points;

[0009] Build a comprehensive risk assessment model. Based on tourist flow, environmental changes, and water flow monitoring data, assess the risks of natural disasters such as debris flows and landslides, and quantify the possible impacts.

[0010] According to the quantification results, set thresholds for tourist density, rainfall, and water flow intensity. When it is identified that the actually collected data is not within the threshold range, generate warning information and evacuation routes, and push them to the tourist side. At the same time, allocate emergency resources to the occurrence point.

[0011] As a further solution of the present invention, analyze the tourist density in each area, identify the peak tourist periods and crowded areas, and at the same time collect geological environment data, specifically including:

[0012] Capture tourist image data, identify and count the number of tourists in the images.

[0013] Merge the captured tourist data with the tourist data captured in other time periods to generate real-time tourist number data.

[0014] Divide the monitored area into several sub-areas, calculate the tourist density according to the number of tourists in each sub-area and its area, and generate a tourist density heat map.

[0015] Real-time collect geological environment data, store the geological environment data and tourist data uniformly, and update them regularly.

[0016] Obtain the historical debris flow disaster records of the current area, including the occurrence time, occurrence point, affected area, and disaster level.

[0017] Monitor the drainage systems in agricultural areas, including drainage pipes, ditches, and their flow directions, and integrate the drainage path data with the tourist density data.

[0018] As a further solution of the present invention, analyze the impacts of the increase in tourist numbers on the environment and water flow, and identify high-risk areas, specifically including:

[0019] Use tourist flow data and environmental data to analyze the impact of tourist activities on soil compaction, calculate the soil compaction degree under different tourist densities, and judge the soil stability in high tourist flow areas.

[0020] Analyze the impact of tourist activities on the surrounding vegetation, and combine historical vegetation data to identify the destructiveness caused by peak tourist periods to vegetation.

[0021] Establish a scoring model to comprehensively score all aspects of the impact of tourist activities, and quantify the negative impacts of tourist flow on the environment.

[0022] Analyze the contribution of tourist activities to water flow changes in combination with the monitored water flow change data, especially in areas with high tourist density, and evaluate the impact of tourist activities on water flow intensity and velocity;

[0023] By comparing historical water flow data, identify unnatural water flow phenomena caused by tourist activities, analyze their frequency and intensity, and judge the potential impact on disasters such as debris flows and landslides;

[0024] Combine tourist flow data, environmental impact analysis results, and water flow monitoring data for cross-analysis, identify high-risk areas, and mark the high-risk areas in the tourist density heat map.

[0025] As a further aspect of the present invention, comprehensively score various aspects of judging the soil stability in high tourist flow areas, identifying the damage caused to vegetation during tourist peak periods, evaluating the impact of tourist activities on water flow intensity and velocity, and judging the potential impact on disasters such as debris flows and landslides, specifically:

[0026]

[0027] Among them, P is the soil compaction degree, N represents the pressure on the soil in the area, D is the number of tourists, is the average pressure exerted by each tourist, and A is the effective area of the area;

[0028]

[0029] Among them, V is the vegetation damage index, reflecting the degree of damage to the vegetation, C is the damaged vegetation coverage area, that is, the area of vegetation damaged during the tourist peak period, and T represents the total vegetation coverage area, that is, the original vegetation coverage area in the area;

[0030]

[0031] Among them, RI is the comprehensive environmental impact score, I i is the score for each environmental impact, and the score value is determined according to the degree of impact on the environment, that is, obtained from the soil compaction degree P and the vegetation damage index V, W i is the weight for each environmental impact, reflecting the importance of this impact in the overall score;

[0032] R = W current -W historical ;

[0033] Among them, R is the amount of unnatural water flow change, representing the difference between the current water flow and the historical water flow, W current is the current water flow, that is, the real-time monitoring data during the tourist peak period, W historical is the historical water flow, that is, the historical average water flow in the same time period;

[0034]

[0035] Among them, A is the abnormal percentage of water flow, which indicates the abnormal degree of current water flow relative to historical water flow, R is the change of unnatural water flow, and W is the abnormal percentage of water flow. historical is the historical water flow;

[0036] As a further solution of the present invention, the high-risk areas are identified and marked in the tourist density heat map, specifically:

[0037] Calculate the high risk score for each area:

[0038] H=W density ×F+RI×E+A×W;

[0039] Among them, H is the high risk score, which indicates the comprehensive risk level of the area, and W density represents the density of tourists, RI is the comprehensive impact score, A is the percentage of abnormal water flow, and F, E, and W are weight coefficients;

[0040] Identify high-risk areas with the visitor density heat map:

[0041]

[0042] Among them, K is the optimal number of clusters, which is used to determine the number of high-risk areas, and C j is the jth cluster, indicating the areas classified into the same category, μ j It represents the mean of the jth cluster, reflecting the average risk score of this type of area, and x is the high risk score of each area.

[0043] As a further solution of the present invention, the analysis of the contribution of tourist activities in crowded areas to the risk of debris flow under rainfall conditions and the identification of potential debris flow occurrence points specifically include:

[0044] Assess the correlation between rainfall and tourist flow and analyze the risk level of high tourist flow areas under rainfall conditions;

[0045] Combined with historical farmland drainage path data, possible water flow directions and waterlogging areas after rainfall are identified;

[0046] Assess the drainage capacity of each drainage path, consider the impact of tourist activities on the drainage system, and analyze the possible drainage problems caused by tourist activities;

[0047] Using historical debris flow disaster records and geological environment data, a debris flow risk model was constructed to calculate the contribution of tourist activities to debris flow risk under different rainfall conditions and quantify the relationship between tourist activities and the probability of debris flow occurrence;

[0048] Based on the debris flow risk model and the contribution of tourist activities to the risk, potential debris flow occurrence points are identified, and the high-risk areas of the tourist density heat map are updated accordingly for the second time.

[0049] As a further solution of the present invention, analyzing the risk level of high tourist flow areas under rainfall conditions, analyzing the poor drainage conditions that may be caused by tourist activities, and calculating the contribution of tourist activities to debris flow risk under different rainfall conditions specifically include:

[0050] R mud = k × Q n ;

[0051] wherein, R mud is the debris flow risk score, reflecting the impact of rainfall on potential debris flow, Q is the rainfall, n is an exponent reflecting the non-linear relationship between rainfall and debris flow risk, and k is a constant representing the sensitivity of rainfall to debris flow risk;

[0052]

[0053] wherein, C mud is the contribution score of tourist activities to debris flow risk, D is the number of tourists, O is the activity intensity of tourists in this area, and N is the carrying capacity of the area, that is, the maximum number of tourists that the area can bear under rainfall conditions;

[0054]

[0055] wherein, R drain is the drainage capacity score, indicating the effectiveness of the drainage system under rainfall conditions, Q is the rainfall, and C is the drainage capacity of the farmland drainage system, that is, the amount of water that can be effectively drained per unit time;

[0056] H total = R mud × A + C mud × B + R drain × C;

[0057] wherein, H total is the comprehensive debris flow score, that is, the final risk assessment obtained by combining rainfall, tourist activities and drainage capacity, and A, B, and C are weight coefficients.

[0058] Another object of the present invention is to provide an intelligent rural tourism safety monitoring and emergency response system, and the system includes:

[0059] The tourist monitoring and environmental data collection module is used to monitor tourist image data in real time, count the number of tourists, analyze the tourist density in each area, identify peak tourist periods and crowded areas, and at the same time collect geological environment data, collect historical debris flow disaster records and farmland drainage paths, and monitor water flow changes;

[0060] The high-risk area identification module is used to analyze the impact of the increase in the number of tourists on the environment and water flow by using tourist flow monitoring data and combining historical tourism data, and identify high-risk areas;

[0061] The potential occurrence point identification module is used to analyze the contribution of tourist activities in crowded areas to debris flow risk under rainfall conditions by combining farmland drainage conditions and historical disaster records, and identify potential debris flow occurrence points;

[0062] The risk assessment and impact quantification module is used to establish a comprehensive risk assessment model, and based on tourist flow, environmental changes and water flow monitoring data, assess the risks of natural disasters such as debris flows and landslides, and quantify the possible impacts;

[0063] The early warning system and emergency response module is used to set tourist density thresholds, rainfall thresholds and water flow intensity thresholds according to the quantification results. When it is identified that the actually collected data is not within the threshold range, generate early warning information and evacuation routes, and push them to the tourist side, and at the same time allocate emergency resources to the occurrence points.

[0064] The beneficial effects of the present invention are:

[0065] By monitoring the number and density of tourists and environmental changes in real time, this method can effectively identify crowded areas and peak periods, and thus issue early warnings in a timely manner. This real-time feedback mechanism not only improves the safety of tourists, avoids accidents caused by overcrowding, but also enhances the sense of security and satisfaction of tourists, enabling them to enjoy a better experience during the tour. At the same time, analyzing the impact of tourist flow on the environment and water flow helps managers identify high-risk areas and take corresponding ecological protection measures, which is helpful to maintain the health of the rural ecosystem, avoid soil compaction and vegetation damage caused by over-tourism, and achieve the harmonious coexistence of tourism activities and the natural environment;

[0066] Combined with historical disaster records and real-time data, this method can accurately identify potential debris flow and landslide risks, especially in areas with high tourist activities under rainfall conditions. The comprehensive risk assessment model provides managers with quantitative risk analysis, enabling rapid response in case of emergencies, timely pushing of early warning information and evacuation routes, ensuring that tourists can evacuate from dangerous areas quickly and safely, and greatly reducing the likelihood of accidents. The method integrates multiple data sources and provides comprehensive decision-making support through analysis, enhancing the scientificity and rationality of decision-making, providing feasible suggestions for the long-term development of rural tourism, and helping managers find the best balance among resource allocation, ecological protection, and tourism development;

[0067] This intelligent rural tourism safety monitoring and emergency response method not only improves the safety and experience of tourists, promotes environmental sustainability, but also enhances the emergency response ability through systematic data monitoring and risk management, providing comprehensive support for the development of rural tourism. This comprehensive management method will create a safer and more sustainable development environment for rural areas and promote the booming development of rural tourism. Brief Description of the Drawings

[0068] Figure 1 It is a flowchart of an intelligent rural tourism safety monitoring and emergency response method provided by an embodiment of the present invention;

[0069] Figure 2 It is a flowchart of analyzing the tourist density in each area, identifying tourist peak periods and crowded areas, and simultaneously collecting geological environment data provided by an embodiment of the present invention;

[0070] Figure 3 It is a flowchart of analyzing the impact of the increase in the number of tourists on the environment and water flow and identifying high-risk areas provided by an embodiment of the present invention;

[0071] Figure 4 It is a flowchart of analyzing the contribution of tourist activities in crowded areas to debris flow risk under rainfall conditions and identifying potential debris flow occurrence points provided by an embodiment of the present invention;

[0072] Figure 5 It is a structural block diagram of an intelligent rural tourism safety monitoring and emergency response system provided by an embodiment of the present invention. Detailed Embodiments

[0073] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present 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 only used to explain the present invention and are not used to limit the present invention.

[0074] It will be understood that the terms "first", "second", etc. used in the present application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of the present application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0075] Figure 1 FIG. is a flowchart of an intelligent rural tourism safety monitoring and emergency response method provided by an embodiment of the present invention. As Figure 1 shown, the method includes:

[0076] S100, real-time monitoring of tourist image data, counting the number of tourists, analyzing the tourist density in each area, identifying the peak tourist period and crowded areas, while collecting geological environment data, collecting historical debris flow disaster records and farmland drainage paths, and monitoring water flow changes;

[0077] In this step, by using high-resolution cameras and computer vision technology, tourist image data in the scenic area is captured in real time, and image processing algorithms (such as face recognition and object detection) are applied to identify the number of tourists in the images to ensure accurate counting of the number of tourists at different times. Subsequently, the real-time captured tourist data is compared with historical data, and data fusion technology is used to merge the number of tourists at different times to generate comprehensive real-time tourist number data. At the same time, a dynamic update mechanism is adopted to ensure the timeliness and accuracy of the data.

[0078] In addition, the monitoring area is divided into several sub-areas, the tourist density is calculated by counting the number of tourists and the area of each sub-area, and the tourist density information is displayed in real time by using heat map visualization technology, which is convenient for management personnel to quickly identify crowded areas and peak tourist periods.

[0079] Meanwhile, sensors and monitoring devices are deployed to collect geological environment data in real time, including soil humidity, slope, soil type, etc. The geological environment data is stored together with the tourist number and density data to establish a comprehensive database, and it is updated regularly to keep the data in the latest state.

[0080] In terms of obtaining historical debris flow disaster records, historical debris flow disaster records of the current area will be obtained from local governments and meteorological departments, including information such as occurrence time, origin point, affected area, and disaster level. These historical disaster records will be correlated and analyzed with real-time monitoring data to provide data support for subsequent risk assessment. At the same time, the drainage systems in agricultural areas will be monitored, including drainage pipes, ditches, and their flow directions. The drainage path map will be drawn using GIS technology, and the drainage path data will be integrated with tourist density data to analyze the potential impact of tourist activities on the drainage system, especially the risks that may be exacerbated during rainfall.

[0081] This step ensures the immediate tracking of tourist numbers and environmental data through real-time monitoring technology, enabling the timely detection and response to potential safety hazards, thereby reducing the probability of accidents. In addition, the comprehensive risk identification ability can comprehensively identify and predict possible natural disaster risks through the comprehensive analysis of tourist density, environmental changes, and historical disaster records, especially debris flow and landslide risks in areas with concentrated tourist activities. The advantages of data fusion and visualization lie in the integration of multiple data sources (image data, environmental monitoring, historical records, etc.) to provide more comprehensive decision-making support. At the same time, the visualization effect of the heat map facilitates managers to quickly understand the current tourist distribution and related risks.

[0082] As Figure 2 shown, analyze the tourist density in each area, identify peak tourist periods and crowded areas, and at the same time collect geological environment data, specifically including:

[0083] S110, capture tourist image data, identify and count the number of tourists in the images;

[0084] S120, merge the captured tourist data with the tourist data captured in other time periods to generate real-time tourist number data;

[0085] S130, divide the monitored area into several sub-areas, calculate the tourist density based on the number of tourists and the area of each sub-area, and generate a tourist density heat map;

[0086] S140, collect geological environment data in real time, store the geological environment data and tourist data uniformly, and update them regularly;

[0087] S150, obtain historical debris flow disaster records of the current area, including occurrence time, emission point, affected area, and disaster level;

[0088] S160, monitor the drainage systems in agricultural areas, including drainage pipes, ditches, and their flow directions, and integrate the drainage path data with tourist density data.

[0089] S200. Utilize the tourist flow monitoring data, combine with historical tourism data, analyze the impact of the increase in the number of tourists on the environment and water flow, and identify high-risk areas;

[0090] In this step, the impact of tourist activities on soil compaction will be analyzed to calculate the degree of soil compaction under different tourist densities and judge the soil stability in high-tourist-flow areas. By setting soil compaction parameters, the potential threats of tourist activities to the soil can be accurately evaluated, thus providing a reference for scenic area management. Secondly, it is necessary to analyze the impact of tourist activities on the surrounding vegetation, combine with historical vegetation data, identify the destructiveness caused to the vegetation during the peak tourist season, establish relevant scoring models, comprehensively score all aspects of the impact of tourist activities, quantify the negative impact of tourist flow on the environment, and provide a basis for subsequent protection measures.

[0091] In terms of water flow monitoring, combine the monitored water flow change data, analyze the contribution of tourist activities to water flow changes, especially in areas with high tourist densities, and evaluate the impact of tourist activities on water flow intensity and velocity. By comparing historical water flow data, unnatural water flow phenomena caused by tourist activities can be identified, and their frequency and intensity can be analyzed to judge the potential impact on disasters such as debris flows and landslides. In addition, cross-analysis of tourist flow data, environmental impact analysis results and water flow monitoring data can identify high-risk areas and mark these high-risk areas in the tourist density heat map.

[0092] Through multi-dimensional data analysis, the impact of tourist activities on the environment can be more comprehensively evaluated, providing a scientific basis to help managers formulate reasonable tourism management strategies. At the same time, this module can dynamically monitor environmental changes and provide strong data support for timely response to potential risks. Through quantitative evaluation, managers can clearly understand the specific impact of tourist activities on the environment, so as to implement targeted protection measures to ensure the sustainable development of the ecological environment. In addition, using the visualization method of the tourist density heat map can intuitively display high-risk areas, improve management efficiency, optimize the tourist experience, and promote the dual guarantee of safety and the environment.

[0093] Such as Figure 3 As shown, the analysis of the impact of the increase in the number of tourists on the environment and water flow and the identification of high-risk areas specifically include:

[0094] S210. Utilize tourist flow data and environmental data to analyze the impact of tourist activities on soil compaction, calculate the degree of soil compaction under different tourist densities, and judge the soil stability in high-tourist-flow areas;

[0095] S220. Analyze the impact of tourist activities on the surrounding vegetation, combine with historical vegetation data, and identify the destructiveness caused to the vegetation during the peak tourist season;

[0096] S230. Establish a scoring model to comprehensively score all aspects of the impact of tourist activities and quantify the negative impact of tourist flow on the environment.

[0097] S240. Combine the monitored water flow change data to analyze the contribution of tourist activities to water flow changes, especially in areas with high tourist density, and evaluate the impact of tourist activities on water flow intensity and velocity.

[0098] S250. By comparing historical water flow data, identify unnatural water flow phenomena caused by tourist activities, analyze their frequency and intensity, and judge the potential impact on disasters such as debris flows and landslides.

[0099] S260. Combine tourist flow data, environmental impact analysis results, and water flow monitoring data for cross-analysis to identify high-risk areas and mark them on the tourist density heat map.

[0100] Among them, judging the soil stability in areas with high tourist flow, identifying the damage caused to vegetation during peak tourist seasons, comprehensively scoring all aspects of the impact of tourist activities, evaluating the impact of tourist activities on water flow intensity and velocity, and judging the potential impact on disasters such as debris flows and landslides are specifically as follows:

[0101]

[0102] Among them, P is the soil compaction degree, N represents the pressure on the soil in the area, D is the number of tourists, is the average pressure exerted by each tourist, and A is the effective area of the area.

[0103]

[0104] Among them, V is the vegetation damage index, reflecting the degree of damage to vegetation, C is the damaged vegetation coverage area, that is, the area of vegetation damaged during peak tourist seasons, and T represents the total vegetation coverage area, that is, the original vegetation coverage area in the area.

[0105]

[0106] Among them, RI is the comprehensive environmental impact score, I i is the score for each environmental impact, and the score is determined according to the degree of impact on the environment, that is, obtained through the soil compaction degree P and the vegetation damage index V, W i is the weight for each environmental impact, reflecting the importance of this impact in the overall score.

[0107] R = W current -W historical ;

[0108] Among them, R is the non-natural water flow change, representing the difference between the current water flow and the historical water flow, and W current is the current water flow, that is, the real-time monitoring data during the peak tourist season, and W historical is the historical water flow, that is, the historical average water flow in the same time period;

[0109]

[0110] Among them, A is the percentage of abnormal water flow, representing the degree of abnormality of the current water flow relative to the historical water flow, R is the non-natural water flow change, and W historical is the historical water flow;

[0111] In this step, the high-risk areas are identified and marked in the tourist density heat map, specifically:

[0112] Calculate the high-risk scores of each area:

[0113] H = W density ×F + RI×E + A×W;

[0114] Among them, H is the high-risk score, representing the comprehensive risk degree of this area, and W density represents the tourist density, RI is the comprehensive impact score, A is the percentage of abnormal water flow, and F, E, and W are weight coefficients;

[0115] Identify the high-risk areas in the tourist density heat map:

[0116]

[0117] Among them, K is the optimal number of clusters, used to determine the number of high-risk areas, and C j is the jth cluster, representing the areas divided into the same category, and μ j represents the mean of the jth cluster, reflecting the average risk score of this type of area, and x is the high-risk score of each area.

[0118] S300. Combine the farmland drainage situation and historical disaster records to analyze the contribution of tourist activities in crowded areas to the debris flow risk under rainfall conditions and identify potential debris flow occurrence points;

[0119] This step determines the risk level of high tourist flow areas during rainfall by evaluating the correlation between rainfall and tourist flow. This process involves cross-analysis of historical rainfall data and tourist flow data to identify the impact of tourist activities on debris flow risk under different meteorological conditions. Next, combine the historical farmland drainage path data to identify the possible water flow directions and water accumulation areas after rainfall. This analysis can help managers anticipate in advance the risk points that may be caused by water flow, so as to take corresponding preventive measures.

[0120] In evaluating the drainage capacity, the impact of tourist activities on the drainage system is considered, and the situations of poor drainage that may be caused by tourist activities are analyzed. By monitoring the actual performance of the drainage paths, it is possible to evaluate whether the drainage system can effectively drain the accumulated water caused by rainfall under high tourist flow conditions and reduce the risk of debris flow. Using historical debris flow disaster records and geological environment data, a debris flow risk model is constructed to calculate the contribution of tourist activities to the debris flow risk under different rainfall conditions and quantify the relationship between tourist activities and the probability of debris flow occurrence. This process will help identify potential debris flow occurrence points and update the high-risk areas on the tourist density heat map accordingly.

[0121] Through multi-factor analysis, it is possible to effectively identify and quantify the specific contribution of tourist activities to the debris flow risk, thus providing a scientific basis for emergency management. In addition, this module integrates data such as rainfall, tourist flow, and drainage capacity to ensure a comprehensive assessment of potential risks. By establishing a debris flow risk model, it is possible to predict high-risk areas in advance under heavy rain conditions and provide timely warning information to tourists and management parties, enhancing the effectiveness of emergency response. This forward-looking risk identification and management mechanism can not only ensure the safety of tourists but also effectively protect the local ecological environment and promote the sustainable development of tourism.

[0122] As Figure 4 shown, the analysis of the contribution of tourist activities in crowded areas to the debris flow risk under rainfall conditions to identify potential debris flow occurrence points specifically includes:

[0123] S310, evaluate the correlation between rainfall and tourist flow, and analyze the risk level in areas with high tourist flow under rainfall conditions;

[0124] S320, combine historical farmland drainage path data to identify possible water flow directions and water accumulation areas after rainfall;

[0125] S330, evaluate the drainage capacity of each drainage path, consider the impact of tourist activities on the drainage system, and analyze the situations of poor drainage that may be caused by tourist activities;

[0126] S340, use historical debris flow disaster records and geological environment data to construct a debris flow risk model, calculate the contribution of tourist activities to the debris flow risk under different rainfall conditions, and quantify the relationship between tourist activities and the probability of debris flow occurrence;

[0127] S350, based on the debris flow risk model and the contribution of tourist activities to the risk, identify potential debris flow occurrence points and update the high-risk areas on the tourist density heat map accordingly.

[0128] In this step, the risk level of high tourist flow areas under rainfall conditions is analyzed, the poor drainage conditions that may be caused by tourist activities are analyzed, and the contribution of tourist activities to debris flow risks under different rainfall conditions is calculated, specifically:

[0129] R mudd = k × Q n ;

[0130] Among them, R mud is the debris flow risk score, which reflects the impact of rainfall on potential debris flow, Q is the rainfall, n is an index reflecting the nonlinear relationship between rainfall and debris flow risk, and k is a constant, which indicates the sensitivity of rainfall to debris flow risk;

[0131]

[0132] Among them, C mud is the contribution score of tourist activities to the risk of debris flow, D is the number of tourists, O is the intensity of tourists' activities in the area, and N is the carrying capacity of the area, that is, the maximum number of tourists that the area can withstand under rainfall conditions;

[0133]

[0134] Among them, R drain It is the drainage capacity score, which indicates the effectiveness of the drainage system under rainfall conditions. Q is the rainfall, and C is the drainage capacity of the farmland drainage system, that is, the amount of water that can be effectively drained per unit time.

[0135] H total =R mudd ×A+C mud ×B+R ddrain ×C;

[0136] Among them, H total It is the comprehensive debris flow score, which is the final risk assessment based on rainfall, tourist activities and drainage capacity, with A, B and C being weight coefficients.

[0137] S400, establish a comprehensive risk assessment model to assess the risk of natural disasters such as debris flow and landslides based on tourist flow, environmental changes and water flow monitoring data, and quantify the possible impacts;

[0138] This step integrates the data from S100, S200, and S300, including real-time tourist flow, environmental changes (such as soil compaction degree, vegetation damage situation), water flow monitoring data (such as water flow intensity and velocity), and rainfall data. Next, through risk factor identification, the key factors affecting debris flow and landslide risks are determined, such as the relationship between tourist flow and soil stability, the relationship between rainfall and water flow intensity, and geological environmental factors (slope, soil type, etc.). In the model construction stage, statistical and machine learning techniques are used to construct a comprehensive risk assessment model. The model can adopt algorithms such as multiple regression analysis, decision tree, or random forest to evaluate the contribution of each risk factor to the probability of disaster occurrence. In the risk assessment process, through model calculation, the occurrence probabilities of debris flow and landslide under different tourist flows and rainfall conditions are evaluated, and the possible impacts are quantified, so as to generate risk levels in different scenarios, such as low-risk, medium-risk, and high-risk areas. Finally, the assessment results are presented in the form of visual charts for easy understanding and decision-making by managers. At the same time, a detailed risk assessment report is generated, providing response suggestions for different scenarios.

[0139] By integrating multiple data sources, various potential risks can be comprehensively evaluated to ensure a multi-dimensional understanding of natural disasters. Using real-time data and dynamic models, managers can quickly respond to environmental changes, timely adjust emergency plans, and reduce the likelihood of accidents. In addition, by quantifying risks and potential impacts, managers can make more scientific and reasonable decisions, thus effectively protecting the safety of tourists and the natural environment. The visualization of risk assessment results can intuitively display high-risk areas, helping managers quickly identify and take corresponding actions. Through the effective assessment and management of natural disasters such as debris flow and landslide, this module also promotes the sustainable development of rural tourism, protects the ecological environment, and enhances the tourist experience.

[0140] S500, based on the quantification results, sets the thresholds for tourist density, rainfall, and water flow intensity. When it is identified that the actually collected data is not within the threshold range, warning information and evacuation routes are generated and pushed to the tourist end, and at the same time, emergency resources are allocated to the occurrence point.

[0141] This step sets specific thresholds for tourist density, rainfall, and water flow intensity according to the quantification results of the comprehensive risk assessment model. The setting of these thresholds is based on a comprehensive analysis of historical data, real-time monitoring, and expert opinions to ensure their scientificity and rationality. When the actually collected data of tourist flow, rainfall, and water flow intensity are not within the set safe range, the warning mechanism will be automatically triggered. The warning information will not only be pushed to the tourist end to remind tourists to pay attention to safety and evacuate in time, but also send an alarm to the tourism management department and the emergency response team to ensure that relevant personnel can respond quickly.

[0142] After the warning information is generated, the system will automatically generate recommended evacuation routes based on the current risk assessment results. These evacuation routes will take into account the distribution of tourists, environmental changes, and terrain data to ensure that tourists can evacuate safely and orderly in case of an emergency. At the same time, the system will also allocate emergency resources such as rescue personnel, medical support, and transportation to ensure a rapid response after an emergency occurs and reduce potential harm.

[0143] Through real-time monitoring and dynamic adjustment in this step, managers can quickly identify potential risks and take prompt measures to reduce the probability of accidents. The warning information received by tourists in a timely manner can enhance their self-protection awareness and improve the effectiveness of escape. In addition, the evacuation routes and emergency resource allocation plans generated by the system can ensure the efficiency of the process in case of an emergency and reduce chaos and harm. Generally speaking, the S5 module not only improves the safety of rural tourism, safeguards the life and property safety of tourists, but also enhances the scientific and intelligent level of tourism management, contributing to the construction of a safer and more sustainable tourism environment.

[0144] Figure 5 The following is a structural block diagram of an intelligent rural tourism safety monitoring and emergency response system provided by an embodiment of the present invention, as Figure 5 shown, the system includes:

[0145] A tourist monitoring and environmental data collection module 100, which is used to monitor tourist image data in real time, count the number of tourists, analyze the tourist density in each area, identify tourist peak periods and crowded areas, and at the same time collect geological environment data, collect historical debris flow disaster records and farmland drainage paths, and monitor water flow changes;

[0146] A high-risk area identification module 200, which is used to analyze the impact of the increase in the number of tourists on the environment and water flow by using tourist flow monitoring data in combination with historical tourism data, and identify high-risk areas;

[0147] A potential occurrence point identification module 300, which is used to analyze the contribution of tourist activities in crowded areas to the debris flow risk under rainfall conditions by combining farmland drainage conditions and historical disaster records, and identify potential debris flow occurrence points;

[0148] A risk assessment and impact quantification module 400, which is used to establish a comprehensive risk assessment model, evaluate the risks of natural disasters such as debris flows and landslides based on tourist flow, environmental changes, and water flow monitoring data, and quantify the possible impacts;

[0149] The early warning system and the emergency response module 500 are used to set the tourist density threshold, rainfall threshold, and water flow intensity threshold according to the quantification results. When it is identified that the actually collected data is not within the threshold range, warning information and evacuation routes are generated and pushed to the tourist terminal, and at the same time, emergency resources are allocated to the occurrence point.

[0150] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same moment, but can be executed at different moments, and the execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0151] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0152] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered to be within the scope described in this specification.

[0153] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the appended claims.

[0154] The above is only the preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent rural tourism safety monitoring and emergency response method, characterized in that: The method comprises: Real-time monitoring of tourist image data, counting of tourist numbers, and analysis of tourist density in each area, identifying tourist peak hours and crowded areas, while collecting geological environment data, historical records of debris flow disasters and farmland drainage paths, and monitoring of water flow changes; Using tourist flow monitoring data, combined with historical tourism data, to analyze the impact of increased tourist numbers on the environment and water flows, and identify high-risk areas; Combined with farmland drainage conditions and historical disaster records, the contribution of tourist activities in crowded areas to debris flow risk under rainfall conditions was analyzed to identify potential debris flow occurrence sites; Establish a comprehensive risk assessment model to assess the risk of natural disasters such as debris flows and landslides based on tourist flow, environmental changes and water flow monitoring data, and quantify the possible impacts; Based on the quantitative results, the tourist density threshold, rainfall threshold and water flow intensity threshold are set. When it is identified that the actual collected data is not within the threshold range, early warning information and evacuation routes are generated and pushed to the tourist end, and emergency resources are deployed to the occurrence point at the same time.

2. The method according to claim 1, characterized in that The analysis of tourist density in each area, identification of tourist peak periods and crowded areas, and collection of geological environment data specifically include: Capture tourist image data, identify and count the number of tourists in the image; Combine the captured tourist data with the trees captured in other time periods to generate real-time tourist number data; The monitoring area is divided into several sub-areas, and the tourist density is calculated according to the number of tourists in each sub-area and its area, and a tourist density heat map is generated; Collect geological environment data in real time, store geological environment data and tourist data in a unified manner, and update them regularly; Obtain historical debris flow disaster records in the current area, including occurrence time, launch point, impact range and disaster level; Monitor drainage systems in agricultural areas, including drainage pipes, ditches and their flow directions, and integrate drainage path data with visitor density data.

3. The method according to claim 2, characterized in that The analysis of the impact of the increase in the number of tourists on the environment and water flows will identify high-risk areas, including: Using tourist flow data and environmental data, we analyzed the impact of tourist activities on soil compaction, calculated the degree of soil compaction under different tourist densities, and determined the soil stability in areas with high tourist flow; Analyze the impact of tourist activities on surrounding vegetation and, combined with historical vegetation data, identify the damage caused to vegetation during peak tourist seasons; Establish a scoring model to comprehensively score various aspects of tourist activities and quantify the negative impact of tourist flow on the environment; Combined with the monitored water flow change data, analyze the contribution of tourist activities to water flow changes, especially in areas with high tourist density, and evaluate the impact of tourist activities on water flow intensity and flow rate; By comparing historical water flow data, we can identify unnatural water flow phenomena caused by tourist activities, analyze their frequency and intensity, and determine the potential impact on disasters such as debris flows and landslides; By combining tourist flow data, environmental impact analysis results and water flow monitoring data, a cross-analysis is conducted to identify high-risk areas, which are then marked in the tourist density heat map.

4. The method according to claim 3, characterized in that The above measures include determining soil stability in areas with high tourist flow, identifying the damage caused to vegetation during peak tourist seasons, comprehensively scoring various aspects of the impact of tourist activities, assessing the impact of tourist activities on water flow intensity and velocity, and determining the potential impact on disasters such as debris flows and landslides. Specifically: Among them, P is the degree of soil compaction, N represents the pressure of the soil on the area of ​​the region, D is the number of tourists, is the average pressure applied by each tourist, and A is the effective area of ​​the region; Among them, V is the vegetation destruction index, which reflects the degree of damage to vegetation, C is the damaged vegetation coverage area, that is, the vegetation area destroyed during the peak tourist season, and T represents the total vegetation coverage area, that is, the original vegetation coverage area in the area; Among them, RI is the comprehensive environmental impact score, I i The score for each environmental impact is determined by the degree of impact on the environment, that is, the soil compaction degree P and the vegetation damage index V, W i The weight of each environmental impact reflects the importance of the impact in the overall score; R=W current -W historical ; Among them, R is the change of unnatural water flow, which means the difference between current water flow and historical water flow, and W current is the current water flow, i.e., the real-time monitoring data during the peak tourist season, W historical is the historical water flow, i.e., the historical average water flow in the same period of time; Among them, A is the abnormal percentage of water flow, which indicates the abnormal degree of current water flow relative to historical water flow, R is the change of unnatural water flow, and W is the abnormal percentage of water flow. historical The historical water flow.

5. The method according to claim 3, characterized in that: The high-risk areas are identified and marked in the tourist density heat map, specifically: Calculate the high risk score for each area: H=W density ×F+RI×E+A×W; Among them, H is the high risk score, which indicates the comprehensive risk level of the area, and W density represents the density of tourists, RI is the comprehensive impact score, A is the percentage of abnormal water flow, and F, E, and W are weight coefficients; Identify high-risk areas with the visitor density heat map: Among them, K is the optimal number of clusters, which is used to determine the number of high-risk areas, and C j is the jth cluster, indicating the areas classified into the same category, μ j It represents the mean of the jth cluster, reflecting the average risk score of this type of area, and x is the high risk score of each area.

6. The method according to claim 4, characterized in that The analysis of the contribution of tourist activities in crowded areas to debris flow risk under rainfall conditions and the identification of potential debris flow occurrence points include: Assess the correlation between rainfall and tourist flow and analyze the risk level of high tourist flow areas under rainfall conditions; Combined with historical farmland drainage path data, possible water flow directions and waterlogging areas after rainfall are identified; Assess the drainage capacity of each drainage path, consider the impact of tourist activities on the drainage system, and analyze the possible drainage problems caused by tourist activities; Using historical debris flow disaster records and geological environment data, a debris flow risk model was constructed to calculate the contribution of tourist activities to debris flow risk under different rainfall conditions and quantify the relationship between tourist activities and the probability of debris flow occurrence; Based on the debris flow risk model and the contribution of tourist activities to the risk, potential debris flow occurrence points are identified, and the tourist density heat map is used to update the high-risk areas for the second time.

7. The method according to claim 6, characterized in that The analysis of the risk level of high tourist flow areas under rainfall conditions, the analysis of the poor drainage that may be caused by tourist activities, and the calculation of the contribution of tourist activities to the risk of debris flow under different rainfall conditions are specifically as follows: R mud =k×Q n ; Among them, R mud is the debris flow risk score, which reflects the impact of rainfall on potential debris flow, Q is the rainfall, n is an index reflecting the nonlinear relationship between rainfall and debris flow risk, and k is a constant, which indicates the sensitivity of rainfall to debris flow risk; Among them, C mud is the contribution score of tourist activities to the risk of debris flow, D is the number of tourists, O is the intensity of tourists' activities in the area, and N is the carrying capacity of the area, that is, the maximum number of tourists that the area can withstand under rainfall conditions; Among them, R drain It is the drainage capacity score, which indicates the effectiveness of the drainage system under rainfall conditions. Q is the rainfall, and C is the drainage capacity of the farmland drainage system, that is, the amount of water that can be effectively drained per unit time. H total =R mud ×A+C mud ×B+R drain ×C; Among them, H total It is the comprehensive debris flow score, which is the final risk assessment based on rainfall, tourist activities and drainage capacity, with A, B and C being weight coefficients.

8. An intelligent rural tourism safety monitoring and emergency response system, characterized in that: The system comprises: The tourist monitoring and environmental data collection module is used to monitor tourist image data in real time, count the number of tourists, analyze the tourist density in each area, identify tourist peak hours and crowded areas, collect geological environmental data, collect historical debris flow disaster records and farmland drainage paths, and monitor water flow changes; High-risk area identification module, which uses tourist flow monitoring data and historical tourism data to analyze the impact of increased tourist numbers on the environment and water flow and identify high-risk areas; Potential occurrence point identification module, which is used to analyze the contribution of tourist activities in crowded areas to debris flow risk under rainfall conditions and identify potential debris flow occurrence points by combining farmland drainage conditions and historical disaster records; The risk assessment and impact quantification module is used to establish a comprehensive risk assessment model to assess the risks of natural disasters such as debris flows and landslides, and quantify the possible impacts based on tourist flow, environmental changes and water flow monitoring data; The early warning system and emergency response module are used to set the tourist density threshold, rainfall threshold and water flow intensity threshold based on the quantitative results. When it is identified that the actual collected data is not within the threshold range, early warning information and evacuation routes are generated and pushed to the tourist end, and emergency resources are deployed to the occurrence point at the same time.

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