A tourist function evaluation and triggered early warning system based on mobile terminal sensing and terrain climate coupling

Abstract

The application discloses a kind of tourist function evaluation and trigger type early warning system based on mobile end sensing and topography climate coupling, system includes side multidimensional perception and benchmark filing module, cloud three-dimensional terrain and function attenuation calculation module, cloud microclimate space-time integration module and path dynamic compensation and optimization module.Method through end side sensor establishes individual physical fitness benchmark model in flat road section, real-time positioning coordinates are aligned with digital elevation model to calculate function attenuation coefficient, combined with three-dimensional sunshine model and meteorological parameter along historical trajectory integration obtains heat exposure dose, when attenuation coefficient or heat exposure dose breaks through threshold, trigger point of interest attribute matching and path re-planning are carried out, and high-risk road section is executed impedance weight heightening and risk-avoiding rendering.The application realizes the unobtrusive monitoring and trigger type early warning of tourist physiological state without wearing equipment, and is suitable for the field of scenic spot intelligent management and tourist safety protection.

Description

Technical Field

[0001] This invention belongs to the field of intelligent transportation and smart tourism technology, and in particular relates to a tourist function assessment and trigger-based early warning system based on mobile terminal sensing and terrain and climate coupling. Background Technology

[0002] As the tourism industry develops towards intelligence and precision, the issues of visitor flow management and safety assurance in large scenic areas, mountainous natural scenic spots, and outdoor education bases are becoming increasingly prominent. During their visits, especially under conditions of high temperature, high humidity, and complex three-dimensional terrain, tourists experience non-linearly accelerated physical exertion. Without timely intervention, this can easily lead to heatstroke, exhaustion, or even more serious physiological safety incidents. How to achieve real-time, seamless monitoring of the physiological state of individual tourists without relying on additional hardware, and accurately trigger early warnings and interventions when risks accumulate to a critical level, has become a pressing technical problem in this field.

[0003] In existing technologies, monitoring tourists' physiological state mainly relies on two types of methods. The first is based on wearable external devices, such as heart rate bracelets and smart wristbands, to collect vital signs like heart rate and skin temperature, thereby assessing fatigue levels. The second is based on assessment methods using mobile terminal GPS positioning and planar step counting data. This utilizes the mobile terminal's built-in GPS module to obtain the tourist's real-time location and displacement speed, indirectly inferring physical condition through speed changes. Regarding path planning, existing technologies mostly employ classic shortest path algorithms such as Dijkstra's or A*, using "shortest distance" or "shortest time" as the single optimization objective, and performing global path replanning when congestion is detected.

[0004] However, the aforementioned existing technologies have significant shortcomings. Wearable device solutions face compliance risks such as high equipment procurement costs, complex operation and maintenance management, cumbersome distribution and recycling processes, and infringement of personal privacy, making them difficult to promote in large-scale individual tourist scenarios. Single GPS and planar pedometer solutions ignore the unique three-dimensional terrain features of scenic areas, such as long-distance steps and steep slopes, as well as the non-linear impact of external weather conditions such as high temperatures and sun exposure on human physical function. Two-dimensional planar parameters alone cannot accurately reflect the true fatigue state of tourists, resulting in significant assessment errors. Regarding route planning, route optimization based on global replanning often adopts a one-size-fits-all global reset approach when facing sudden congestion, easily leading to secondary congestion on alternative routes. Furthermore, it fails to consider the tourist's current physical condition for personalized compensation and lacks targeted fatigue relief measures. Therefore, a tourist function assessment and trigger-based early warning system based on mobile terminal sensing coupled with terrain and climate needs to be designed to solve these problems. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a tourist function assessment and trigger-based early warning system based on mobile terminal sensing and terrain and climate coupling. It aims to overcome the problems of excessive reliance on external wearable devices, single fatigue judgment dimension and lack of physical adaptability in the existing technology. It has the characteristics of realizing non-sensory physiological monitoring based on mobile terminal built-in sensor and multi-source environmental data fusion, and accurately triggering attribute compensation matching and local impedance path optimization through multi-dimensional assessment model coupled with terrain and microclimate.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A tourist function assessment and trigger-based early warning system based on mobile terminal sensing and terrain-climate coupling includes: The edge-side multidimensional perception and benchmark establishment module is deployed on a mobile terminal. It is used to screen flat road sections with an elevation change rate close to zero within the initial time window, collect time series provided by the mobile terminal's built-in GPS module and triaxial accelerometer, extract instantaneous pace and vertical acceleration peak values, calculate the individual's flat ground benchmark average pace and benchmark vertical acceleration peak value, and establish a physical fitness benchmark model. The cloud-based 3D terrain and functional attenuation calculation module communicates with the edge-side multi-dimensional perception and benchmark archiving module. It is used to receive real-time latitude and longitude coordinates and real-time sensor data uploaded by the terminal, spatially match the real-time latitude and longitude coordinates with the digital elevation model to obtain the current slope, calculate the expected pace under the current terrain conditions by combining the physical fitness benchmark model, and calculate the comprehensive functional attenuation coefficient based on the deviation between the expected pace and the actual pace and the deviation between the benchmark vertical acceleration peak and the actual vertical acceleration peak. The cloud-based microclimate spatiotemporal integration module communicates with the cloud-based 3D terrain and functional attenuation calculation module. It is used to calculate the real-time perceived heat index of an individual based on the 3D spatial solar radiation model and real-time meteorological parameters, and to integrate the heat exposure considering the physical shading rate along the individual's historical movement trajectory to obtain the cumulative heat exposure dose. The path dynamic compensation and optimization module communicates with the cloud-based microclimate spatiotemporal integration module and the cloud-based 3D terrain and functional attenuation calculation module. It is used to search for candidate points of interest within a preset radius when the comprehensive functional attenuation coefficient or cumulative heat exposure dose exceeds the preset safety threshold, calculate the comprehensive matching degree of each candidate point of interest, and select the node with the highest matching degree as the compensation destination. At the same time, it obtains the set of main road segments ahead, increases the impedance weight of road segments with slope exceeding the upper limit of slope or passenger flow density exceeding the upper limit of density, and performs risk avoidance path rendering.

[0007] Preferably, a method for assessing tourist functionality and triggering early warning based on mobile terminal sensing coupled with terrain and climate includes the following steps: S1, Personalized benchmark physical fitness profile based on edge sensing: Within the initial time window, the system filters road segment data where the elevation change rate corresponding to the mobile terminal positioning parameters is close to zero, extracts the instantaneous speed and acceleration of the terminal, and calculates the benchmark pace and benchmark vertical acceleration peak of the terminal user. S2, 3D terrain alignment and dynamic estimation of functional attenuation coefficient: Align real-time latitude and longitude coordinates with digital elevation model, obtain current slope, and calculate expected pace and comprehensive functional attenuation coefficient; S3, Dynamic micro-meteorological superposition and thermal exposure dose integration: Based on the three-dimensional spatial model, the temporal occlusion rate of the current coordinates is extracted, and the cumulative thermal exposure dose is calculated in combination with meteorological parameters; S4, Status assessment and compensation node selection: When the comprehensive functional attenuation coefficient is greater than the functional attenuation threshold or the cumulative heat exposure dose is greater than the heat exposure threshold, the candidate interest point set is retrieved within the preset radius, the comprehensive matching degree is calculated and the optimal node is selected; S5, Asymmetric Impedance Layer Generation and Auxiliary Rendering: The node with the highest overall matching degree is used as the compensation destination to generate a sub-path. At the same time, the set of main road segments ahead is obtained. The impedance weight is increased for road segments with a slope exceeding the upper limit or a passenger flow density exceeding the upper limit, and the risk avoidance path rendering is performed.

[0008] Preferably, in step S1, the reference pace Peak vertical acceleration compared to the reference value The calculation formula is: ; ; in, Representing time slices The instantaneous pace, Representing time slices The peak vertical acceleration along the Z-axis is below. and It originates from the GPS module and three-axis accelerometer built into the mobile terminal. This represents the total number of valid sampling points.

[0009] Preferably, in step S2, the expected pace The calculation formula is: ; in, The real-time slope is derived from the spatial matching of a pre-loaded digital elevation model in the cloud and the terminal's GPS coordinates. This represents the road surface resistance constant.

[0010] Preferably, in step S2, the comprehensive functional attenuation coefficient The calculation formula is: ; in, This indicates the current actual pace. This represents the current peak value of actual vertical acceleration, derived from the real-time sensor data stream of the mobile terminal. This represents the weighting factor for pace decay. This represents the gait attenuation weighting coefficient.

[0011] Preferably, in step S3, the real-time perceived heat index is... and cumulative heat exposure dose The calculation formula is: ; ; in, Indicates temperature, The relative humidity is derived from the array of micro weather stations deployed in the scenic area or from a third-party real-time weather API. Indicates time step Physical shadow occlusion rate under the following conditions Indicates the humidity regulation coefficient. Indicates the sampling step size. This indicates the total number of time steps.

[0012] Preferably, in step S4, the comprehensive matching degree The calculation formula is: ; in, Indicates to candidate points of interest The path distance is calculated using the road network topology map. Indicate candidate points of interest The attribute compensation score, This represents the distance weighting coefficient. This represents the attribute compensation weighting coefficient; when the cumulative heat exposure dose... Exceeding the heat exposure threshold At the same time, increase the attribute compensation score for interest points with indoor or cold drink tags. When the overall functional attenuation coefficient Exceeding the functional decline threshold At the same time, increase the attribute compensation score of interest points with seat or flat ground tags. .

[0013] Preferably, the pace attenuation weighting coefficient and gait attenuation weighting coefficient Online updates will be performed according to the following adaptive rules: In length of Calculate the pace decay rate within the sliding time window. and gait decay rate ; when Persistently greater than When, increase and reduce ;when Persistently greater than When, increase and reduce ; The weight update formula is: ; ; Normalization will be performed after the update: ; in, and They represent the first Second and third The updated pace decay weighting factor and This represents the corresponding gait attenuation weighting coefficient. Indicates the learning rate. This indicates the upper limit of a single weight adjustment. Represents a symbolic function. Represents a sliding time window Rate of change of pace within, Represents a sliding time window Peak rate of change of vertical acceleration within the range The value is set to the preset duration.

[0014] Preferably, the functional degradation threshold is set with three threshold levels. , and The heat exposure threshold is set with three levels. , and ,satisfy and ; when or When the first level of alert is triggered, the high-risk road section is silently marked on the front-end interface without pushing notifications; when or When this occurs, a second-level alert is triggered, a mild suggestion is pushed, and candidate points of interest are searched within a preset radius, but the route is not automatically replanned; when or When the third-level intervention warning is triggered, the complete interest point matching process in step S4 and the path replanning and risk avoidance path rendering process in step S5 are executed. The three-level functional attenuation threshold is dynamically calibrated using an individual baseline model: ; in, Indicates the first Level functional degradation reference threshold Indicates the average pace based on flat terrain. Indicates the preset reference pace; The three-level heat exposure threshold , and Adopted and The same dynamic calibration method.

[0015] Preferably, the physical shadow occlusion rate in step S3 The calculation method is as follows: The direction vector of sunlight is determined based on the current solar azimuth and solar altitude angles. A digital surface model is constructed by overlaying building height layers and vegetation height layers on top of the digital elevation model, with the tourist's GPS coordinates as the center. A sampling ray is emitted in the opposite direction from the tourist's coordinate position along the direction of sunlight. It is then detected whether the ray intersects with any grid in the digital surface model before reaching the height corresponding to the tourist's coordinates. If it intersects, the occlusion of the sampling ray is determined to be true; if it does not intersect, the occlusion is determined to be false. Centered on the tourist's location, with a radius of [missing information] Launch within a circular area A uniformly distributed sampling ray is used to count the number of rays that are determined to be true due to occlusion. Calculate the physical shadow occlusion rate: ; in, Take 3 to 5 times the resolution of the digital elevation model raster.

[0016] Preferably, the process of screening road segment data with an elevation change rate approaching zero in step S1 is performed according to the following steps: With length as The sliding window scans the terminal's GPS positioning sequence, and calculates the elevation difference between the first and last points in each window in chronological order. With horizontal displacement Calculate the rate of change of elevation within the window. ;when Less than the preset flatness threshold When the time comes, all sampling points within the window are marked as candidate points for flat road sections; consecutively marked candidate points are merged into flat road section intervals, and intervals with a length greater than the minimum effective length are selected. The interval is designated as a valid flat road segment; within the initial time window, after excluding stationary points where the terminal instantaneous speed is zero, the remaining sampling points are used to calculate the baseline pace. Peak vertical acceleration compared to the reference value .

[0017] Preferably, the heat exposure threshold Determined dynamically according to the following rules: ; in, Indicates the reference ambient temperature The baseline heat exposure threshold, Indicates the temperature sensitivity coefficient. Indicates the current temperature; when hour, ;when hour, The temperature increases linearly, making the criteria for heat exposure more stringent as the environment deteriorates.

[0018] Preferably, a computer device includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the aforementioned method for tourist function assessment and trigger-based early warning based on mobile terminal sensing and terrain and climate coupling.

[0019] Preferably, a computer-readable storage medium stores computer instructions that cause a computer to execute the aforementioned method for assessing tourist functionality and triggering early warning based on mobile terminal sensing coupled with terrain and climate.

[0020] The beneficial effects of this invention are as follows: 1. This invention reuses the built-in sensors of smart mobile terminals carried by tourists, utilizing GPS modules and triaxial accelerometers to collect time-series data. It establishes an individual's physical fitness baseline model on flat terrain and uses this model to assess functional decline in real time, achieving large-scale, unobtrusive monitoring without the need for any dedicated physiological monitoring equipment. This invention achieves large-scale, unobtrusive monitoring by distributing sensor data acquisition and processing across the device and cloud. The device is only responsible for raw data acquisition and baseline archiving, while the cloud handles 3D terrain alignment and functional decline calculations. This effectively reduces front-end hardware costs and overall system maintenance complexity, solving the problem of limited adoption caused by excessive reliance on external wearable devices in existing technologies.

[0021] 2. This invention obtains real-time slope by spatially matching the positioning coordinates of a mobile terminal with a digital elevation model, calculates the expected pace and comprehensive functional attenuation coefficient by combining an individual physical fitness benchmark model, and calculates the cumulative heat exposure dose by integrating a three-dimensional spatial solar radiation model with real-time meteorological parameters along the tourist's historical movement trajectory. This achieves deep spatiotemporal coupling of three dimensions: terrain slope, microclimate heat exposure, and individual gait characteristics. This invention overcomes the limitations of existing technologies that rely solely on planar displacement and velocity for fatigue assessment, constructing a more objective multi-dimensional functional attenuation evaluation model, significantly improving the accuracy and scientific rigor of assessing tourists' physiological state under complex terrain and variable weather conditions.

[0022] 3. This invention employs a threshold-triggered local compensation mechanism instead of a global path reset strategy. Intervention is only triggered when the comprehensive functional attenuation coefficient or cumulative heat exposure dose exceeds a preset safety threshold. Interest points with corresponding compensation attributes are matched according to different exceedance types. Simultaneously, impedance weighting and hazard avoidance rendering are applied to road sections with high slopes or high passenger density in the terminal view. This invention achieves a three-level early warning and tiered response, matching the intervention intensity with the risk level. While ensuring the physiological safety of visitors, it effectively avoids secondary congestion on alternative paths caused by global path rerouting, achieving micro-level flexible management of road network pressure and improving the visitor experience. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the system structure in an embodiment of the present invention; Figure 2 This is a schematic diagram of the method flow in an embodiment of the present invention; Figure 3 This is a flowchart illustrating the logic for terrain alignment and performance degradation calculations in an embodiment of the present invention. Figure 4 This is a graph showing the evolution of the functional attenuation coefficient and the thermal exposure dose over time in an embodiment of the present invention; Figure 5 This is a comparison chart of adaptive weight evolution and functional attenuation coefficients in different terrain regions in an embodiment of the present invention; Figure 6 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0024] Example 1: like Figure 1 As shown, a tourist function assessment and trigger-based early warning system based on mobile terminal sensing and terrain and climate coupling includes: The edge-side multidimensional perception and benchmark establishment module is deployed on a mobile terminal. It is used to screen flat road sections with an elevation change rate close to zero within the initial time window, collect time series provided by the mobile terminal's built-in GPS module and triaxial accelerometer, extract instantaneous pace and vertical acceleration peak values, calculate the individual's flat ground benchmark average pace and benchmark vertical acceleration peak value, and establish a physical fitness benchmark model. The cloud-based 3D terrain and functional attenuation calculation module communicates with the edge-side multi-dimensional perception and benchmark archiving module. It is used to receive real-time latitude and longitude coordinates and real-time sensor data uploaded by the terminal, spatially match the real-time latitude and longitude coordinates with the digital elevation model to obtain the current slope, calculate the expected pace under the current terrain conditions by combining the physical fitness benchmark model, and calculate the comprehensive functional attenuation coefficient based on the deviation between the expected pace and the actual pace and the deviation between the benchmark vertical acceleration peak and the actual vertical acceleration peak. The cloud-based microclimate spatiotemporal integration module communicates with the cloud-based 3D terrain and functional attenuation calculation module. It is used to calculate the real-time perceived heat index of an individual based on the 3D spatial solar radiation model and real-time meteorological parameters, and to integrate the heat exposure considering the physical shading rate along the individual's historical movement trajectory to obtain the cumulative heat exposure dose. The path dynamic compensation and optimization module communicates with the cloud-based microclimate spatiotemporal integration module and the cloud-based 3D terrain and functional attenuation calculation module. It is used to search for candidate points of interest within a preset radius when the comprehensive functional attenuation coefficient or cumulative heat exposure dose exceeds the preset safety threshold, calculate the comprehensive matching degree of each candidate point of interest, and select the node with the highest matching degree as the compensation destination. At the same time, it obtains the set of main road segments ahead, increases the impedance weight of road segments with slope exceeding the upper limit of slope or passenger flow density exceeding the upper limit of density, and performs risk avoidance path rendering.

[0025] like Figure 2 As shown, a method for assessing tourist functionality and triggering early warning based on mobile terminal sensing coupled with terrain and climate includes the following steps: S1, Personalized benchmark physical fitness profile based on edge sensing: Within the initial time window, the system filters road segment data where the elevation change rate corresponding to the mobile terminal positioning parameters is close to zero, extracts the instantaneous speed and acceleration of the terminal, and calculates the benchmark pace and benchmark vertical acceleration peak of the terminal user. S2, 3D terrain alignment and dynamic estimation of functional attenuation coefficient: Align real-time latitude and longitude coordinates with digital elevation model, obtain current slope, and calculate expected pace and comprehensive functional attenuation coefficient; S3, Dynamic micro-meteorological superposition and thermal exposure dose integration: Based on the three-dimensional spatial model, the temporal occlusion rate of the current coordinates is extracted, and the cumulative thermal exposure dose is calculated in combination with meteorological parameters; S4, Status assessment and compensation node selection: When the comprehensive functional attenuation coefficient is greater than the functional attenuation threshold or the cumulative heat exposure dose is greater than the heat exposure threshold, the candidate interest point set is retrieved within the preset radius, the comprehensive matching degree is calculated and the optimal node is selected; S5, Asymmetric Impedance Layer Generation and Auxiliary Rendering: The node with the highest overall matching degree is used as the compensation destination to generate a sub-path. At the same time, the set of main road segments ahead is obtained. The impedance weight is increased for road segments with a slope exceeding the upper limit or a passenger flow density exceeding the upper limit, and the risk avoidance path rendering is performed.

[0026] Preferably, in step S1, the reference pace Peak vertical acceleration compared to the reference value The calculation formula is: ; ; in, Representing time slices The instantaneous pace, Representing time slices The peak vertical acceleration along the Z-axis is below. and It originates from the GPS module and three-axis accelerometer built into the mobile terminal. This represents the total number of valid sampling points.

[0027] Furthermore, the road segments with an elevation change rate close to zero selected within the initial time window refer to road segments with an elevation change rate of less than 0.5% corresponding to the mobile terminal positioning parameters.

[0028] Preferably, in step S2, the expected pace The calculation formula is: ; in, The real-time slope is derived from the spatial matching of a pre-loaded digital elevation model in the cloud and the terminal's GPS coordinates. This represents the road surface resistance constant.

[0029] like Figure 3 As shown, in step S2, the comprehensive functional attenuation coefficient The calculation formula is: ; in, This indicates the current actual pace. This represents the current peak value of actual vertical acceleration, derived from the real-time sensor data stream of the mobile terminal. This represents the weighting factor for pace decay. This represents the gait attenuation weighting coefficient.

[0030] Preferably, in step S3, the real-time perceived heat index is... and cumulative heat exposure dose The calculation formula is: ; ; in, Indicates temperature, The relative humidity is represented by the array of micro weather stations deployed in the scenic area or by a third-party real-time weather API. Indicates time step Physical shadow occlusion rate under the following conditions Indicates the humidity regulation coefficient. Indicates the sampling step size. This indicates the total number of time steps.

[0031] Preferably, in step S4, the comprehensive matching degree The calculation formula is: ; in, Indicates to candidate points of interest The path distance is calculated using the road network topology map. Indicate candidate points of interest The attribute compensation score, This represents the distance weighting coefficient. This represents the attribute compensation weighting coefficient; when the cumulative heat exposure dose... Exceeding the heat exposure threshold At the same time, increase the attribute compensation score for interest points with indoor or cold drink tags. When the overall functional attenuation coefficient Exceeding the functional decline threshold At the same time, increase the attribute compensation score of interest points with seat or flat ground tags. .

[0032] Furthermore, in step S5, a set of main road segments ahead is obtained. If the road section Meets the slope requirements greater than the upper limit of slope Or passenger flow density Greater than the upper limit of density If so, the impedance weight of that road segment in the specific terminal view is increased, and the avoidance path rendering is performed.

[0033] Preferably, the pace attenuation weighting coefficient and gait attenuation weighting coefficient Online updates will be performed according to the following adaptive rules: In length Calculate the pace decay rate within the sliding time window. and gait decay rate ; when Persistently greater than When, increase and reduce ;when Persistently greater than When, increase and reduce ; The weight update formula is: ; ; Normalization will be performed after the update: ; in, and They represent the first Second and third The updated pace decay weighting factor and This represents the corresponding gait attenuation weighting coefficient. Indicates the learning rate. This indicates the upper limit of a single weight adjustment. Represents a symbolic function. Represents a sliding time window Rate of change of pace within, Represents a sliding time window Peak rate of change of vertical acceleration within the range The value is set to the preset duration.

[0034] Preferably, the functional degradation threshold is set with three threshold levels. , and The heat exposure threshold is set with three levels. , and ,satisfy and ; when or When the first level of alert is triggered, the high-risk road section is silently marked on the front-end interface without pushing notifications; when or When this occurs, a second-level alert is triggered, providing mild suggestions and searching for candidate points of interest within a preset radius, but without automatically replanning the route; when or When the third-level intervention warning is triggered, the complete interest point matching process in step S4 and the path replanning and risk avoidance path rendering process in step S5 are executed. The three-level functional attenuation threshold is dynamically calibrated using an individual baseline model: ; in, Indicates the first Level functional degradation reference threshold This represents the average pace based on flat terrain. Indicates the preset reference pace; The three-level heat exposure threshold , and Adopted and The same dynamic calibration method.

[0035] Preferably, the physical shadow occlusion rate in step S3 The calculation method is as follows: The direction vector of sunlight is determined based on the current solar azimuth and solar altitude angles. A digital surface model is constructed by overlaying building height layers and vegetation height layers on top of the digital elevation model, with the tourist's GPS coordinates as the center. A sampling ray is emitted in the opposite direction from the tourist's coordinates along the direction of sunlight. The ray is then tested to see if it intersects with any grid in the digital surface model before reaching the height corresponding to the tourist's coordinates. If it intersects, the occlusion of the sampling ray is determined to be true; otherwise, the occlusion is determined to be false. Centered on the tourist's location, with a radius of [missing information] Launch within a circular area A uniformly distributed sampling ray is used to count the number of rays that are determined to be true due to occlusion. Calculate the physical shadow occlusion rate: ; in, Take 3 to 5 times the resolution of the digital elevation model raster.

[0036] Furthermore, the three-dimensional spatial solar radiation model described in step S3 is constructed in the following manner: A three-dimensional digital surface model is constructed by overlaying a building vector boundary layer and a vegetation canopy height distribution layer on top of the scenic area's digital elevation model as the base data layer. The digital surface model is then voxelized according to the raster resolution of the digital elevation model. Pre-loaded solar ephemeris data is imported, and the solar azimuth and solar altitude angles are calculated based on the current date and time to generate a parallel light projection with the sun's position as the light source. For each raster cell of the digital surface model, it is calculated whether the cell is occluded by the projection of building or vegetation voxels along the direction of sunlight, generating a binary shadow layer. The physical shadow occlusion rate is... This refers to the occlusion status value of the raster corresponding to the current tourist's coordinates in the binary shadow layer.

[0037] Preferably, the process of screening road segment data with an elevation change rate approaching zero in step S1 is performed according to the following steps: With length as The sliding window scans the terminal's GPS positioning sequence, and calculates the elevation difference between the first and last points in each window in chronological order. With horizontal displacement Calculate the rate of change of elevation within the window. ;when Less than the preset flatness threshold When the time comes, all sampling points within the window are marked as candidate points for flat road sections; consecutively marked candidate points are merged into flat road section intervals, and intervals with a length greater than the minimum effective length are selected. The interval is designated as a valid flat road segment; within the initial time window, after excluding stationary points where the terminal instantaneous speed is zero, the remaining sampling points are used to calculate the baseline pace. Peak vertical acceleration compared to the reference value .

[0038] Preferably, the heat exposure threshold Determined dynamically according to the following rules: ; in, Indicates the reference ambient temperature The baseline heat exposure threshold, Indicates the temperature sensitivity coefficient. Indicates the current temperature; when hour, ;when hour, The temperature increases linearly, making the criteria for heat exposure more stringent as the environment deteriorates.

[0039] Furthermore, the learning rate The value range is from 0.01 to 0.05, and the upper limit of the single weight adjustment is... The value range is from 0.05 to 0.15, and the sliding time window... The value range is from 30 seconds to 120 seconds; The principle for determining the value is: when an individual shows a large difference between pace and gait decay rate in historical records, take a larger value; when the difference is small, take a smaller value.

[0040] Furthermore, the first Level Functional Decay Reference Threshold By statistically analyzing historical functional decline data of tourist groups in similar scenic areas, the percentile of the functional decline coefficient distribution of this group was taken as the benchmark threshold. Specifically: Take the 60th percentile. Take the 80th percentile. Take the 95th percentile; the preset reference pace Take the statistical median of the baseline pace for this group.

[0041] Furthermore, the humidity adjustment coefficient The rule for determining the value is: when the relative humidity When it is below 40%, ;when Between 40% and 70% ;when When it exceeds 70%, .

[0042] Preferably, a computer device includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the aforementioned method for tourist function assessment and trigger-based early warning based on mobile terminal sensing and terrain and climate coupling.

[0043] Preferably, a computer-readable storage medium stores computer instructions that cause a computer to execute the aforementioned method for assessing tourist functionality and triggering early warning based on mobile terminal sensing coupled with terrain and climate.

[0044] Example 2: This embodiment uses the dam scenic area of ​​a large-scale water conservancy project as the application scenario. The dam crest is approximately 900m long and 180m high. The scenic area open to tourists includes the entrance plaza in front of the dam, the sightseeing walkway on the dam crest, the downstream viewing platform behind the dam, the road leading up to the dam, and the right bank viewing platform. The elevation difference between the highest and lowest points is approximately 210m. The experimental platform consists of a smartphone on the user side and a cloud server. The smartphones on the user side are all flagship models from a mainstream brand, with a built-in GPS module sampling frequency of 1Hz and a three-axis accelerometer sampling frequency of 50Hz, running a self-developed data acquisition application. The cloud-based digital elevation model has a raster resolution of 5m. The three-dimensional digital surface model includes data on the main structure of the dam, the power plant, the floodgate opening and closing machine room, and the distribution of structures and vegetation such as streetlights and roadside trees along the road leading up to the dam. Meteorological data is collected in real time through three micro-meteorological stations deployed in the dam area, with an update cycle of 5 minutes. The experiment selected 20 healthy adult tourists as test subjects, aged 22 to 58, with a male-to-female ratio of 1:1. Each person independently completed the same dam area tour route, which was about 3.8 km long, and took about 110 minutes.

[0045] Key parameter settings are as follows: road surface resistance constant Set the humidity adjustment coefficient to 0.3. The initial value of the adaptive weight is set to 1.0 in the medium humidity range. Learning rate Set to 0.03, the upper limit for a single weight adjustment. Set to 0.1, and slide the window. Take 60 seconds as the integral step size for heat exposure. Take 60 seconds, sample the number of rays Take 64 lines, shadow detection radius Take 20m. Level 3 functional attenuation threshold. Take 0.35, Take 0.55, Take 0.75, the third-level heat exposure threshold. Take 600, Take 1000, Take 1400, distance weighting coefficient Set to 0.6, the attribute compensation weighting coefficient. Take 0.4.

[0046] Existing methods for comparative testing include two types. One existing method is the fixed-weight function assessment method, which maintains... Unchanged, using only a single threshold Fatigue assessment is performed, ignoring cumulative heat exposure and graded warnings. Existing Method Two is a fatigue monitoring method based on wearable heart rate straps. Tourists need to wear an additional chest strap heart rate sensor, using heart rate recovery rate as the basis for fatigue assessment, with the threshold calculated using a general age formula. Setup. Data was collected simultaneously under the same weather conditions and along the same tour route for both comparison methods.

[0047] The complete process of the embodiment is as follows: Twenty tourists simultaneously departed from the dam entrance plaza, each carrying a smartphone with a data collection program installed. Ten tourists also wore heart rate monitors for comparison testing with the existing Method 2. Following a predetermined route, the tourists traversed approximately 400m of flat land at the dam entrance plaza, an 800m ascent along the dam access road, a 900m dam-top sightseeing trail, a 600m descent down the downstream viewing stairs, a 500m ascent to and from the right bank viewing platform, and a 600m return journey along the dam-top road. The on-device application recorded GPS latitude and longitude and instantaneous pace at 1Hz, and triaxial acceleration data at 50Hz, uploading sensor data to the cloud every 10 seconds. The cloud server synchronously received the sensor data and performed real-time terrain alignment, performance degradation calculations, microclimate heat exposure integration, and a three-level early warning system. The dam-top sightseeing trail was an unshaded area completely exposed to sunlight, and the upstream face of the dam was a concrete surface without vegetation cover. The dam area had a high ambient temperature and strong surface reflection. The entire process took approximately 110 minutes. The cloud recorded each tourist's functional attenuation coefficient, heat exposure dose, current warning level, and adaptive weight value every minute; the data are shown in Tables 1 and 2 below.

[0048] Table 1: Operational data of this method in different terrain regions;

[0049] Table 2: Comparison data of this method and existing methods on key indicators;

[0050] Tables 1 and 2 above demonstrate that this method is superior to existing technologies in several aspects in the specific scenario of a dam scenic area. Regarding the accuracy of terrain fatigue assessment, the dam access road is a continuous ascent with a slope between 12° and 14°, where tourists' physical exertion gradually accumulates. The fixed-weight method maintains equal weighting for pace and gait attenuation during the continuous ascent, resulting in a less accurate assessment of gait dimension changes. The contribution was not fully reflected in the observation deck's ascent section. The mean value is only 0.621, far lower than the 0.720 of this method. This method tracks the rate of change of pace in real time through a sliding window. and vertical acceleration rate of change When the speed reduction rate increases significantly in the middle section of the Shangba Highway, the system will automatically adjust the speed control settings. The weighting was increased to 0.59, giving higher weight to the pace-dominant dimension. However, in the scenario of long-distance flat walking on the dam crest trail under high temperatures and direct sunlight, gait stability gradually decreases, and the weight matrix begins to adjust accordingly. It more evenly reflects the two types of attenuation. In the comparative data, the detection rate of terrain fatigue exceeding the standard increased from 68.5% with the fixed weight method to 86.7%, the accuracy rate of triggering the level 3 warning increased from 60.2% to 84.9%, and the false alarm rate of level 1 decreased from 21.6% to 7.1%, which fully verified the assessment accuracy and warning reliability of the adaptive weight mechanism in the scenario of continuous climbing and high temperature flat walking.

[0051] The unique environment of the dam scenic area lies in the lack of natural shelter on the dam crest sightseeing trail. The concrete surface and upstream face of the dam form a large area of ​​heat radiation under intense summer sunlight, posing a heat environment threat even greater than in typical mountain scenic areas. This method uses a microclimate spatiotemporal integration module to accumulate heat exposure data along the historical trajectory of tourists along the sun-exposed dam crest trail, focusing on the middle section of the dam crest trail. It has reached 924, breaking through. The level 2 alert has been triggered, and the viewing platform has been returned to the highway section. Reaching 1753, breaking through This triggers a Level 3 intervention warning. Wearable heart rate monitoring only reflects the current cardiac workload and cannot provide an independent, cumulative assessment of heat exposure. Although the heart rate may have decreased after the initial exposure to the sun on the dam top, the cumulative damage from heat exposure to the tourists has already occurred and is irreversible. This method's independent heat exposure warning is a core capability that heart rate monitoring completely lacks in this scenario.

[0052] Regarding path compensation optimization, the tourist route network in the dam area is fixed, and the available alternative routes are limited. When tourists are descending the stairs behind the dam... Exceed Then, the system automatically searches and recommends the nearest rest areas with "seat" and "flat ground" tags, such as the viewing platform or exhibition hall behind the dam; when the dam crest walkway... Exceed Subsequently, the system recommends nodes with indoor and cold drink tags, such as the reception room of the dam top control building. The satisfaction rate for matching rest nodes reached 87.5%. At the same time, sections of road ahead with a slope exceeding the upper limit or excessive passenger flow density are rendered as high-impedance warning areas, realizing a complete closed loop from individual tourist status assessment and risk classification early warning to precise compensation intervention and evacuation route assistance.

[0053] Figure 4 The upper-middle subplot illustrates the overall performance decay coefficient under both fixed-weight and adaptive-weight modes. The following subplot illustrates the evolution over a 120-minute simulation period, showing the cumulative heat exposure dose. The integral growth curve is shown. The subplot above indicates the three warning threshold levels. , and The corresponding horizontal reference line is marked on the lower sub-graph. , and .from Figure 4 It is clear that in the steep section of the third segment, the journey takes 40 to 60 minutes. First breakthrough The first level of alert was triggered; this occurred between 60 and 75 minutes into the fourth steep slope section. Exceed And it continues to rise, triggering the second-level alert; after entering the sixth level of unshaded sun exposure for 90 to 105 minutes, It reflects gait decay more sensitively due to adaptive weight adjustment and approaches the target gait earlier. , Then, under conditions of high temperature and low shading, the temperature rises sharply to over [a certain level]. Comparing fixed weights and adaptive weights The curve, with its adaptive weights, exhibits a more sensitive response on high-slope road sections, indicating that it can automatically enhance the contribution of the dominant attenuation dimension based on the actual distribution of individual attenuation rates. It more accurately reflects an individual's physiological state.

[0054] Figure 5 The left subplot shows the adaptive weighting coefficients. and The evolution trajectory over 120 minutes. Initially, both were 0.5; as tourists entered the medium and steep slope sections, the actual pace... relative to the expected pace The attenuation rate increased significantly, and the pace attenuation rate increased. Continuously greater than the gait decay rate ,drive Gradually rising The corresponding decrease occurs until the end of the fourth paragraph. Reaching approximately 0.62, The pace dropped to approximately 0.38. Upon entering the fifth rest area, the pace began to recover, and both weights shifted back towards the balanced value. In the sixth sun-exposed zone, high temperatures and heat exposure led to decreased gait stability. Relative increase It rebounded slightly again.

[0055] Figure 5 The right subplot uses a bar chart to compare the average weights under fixed and adaptive weights across seven different terrain regions. The difference. In steep slope regions, the average adaptive weights... The significantly higher weights compared to the fixed weights indicate that the adaptive mechanism reinforces the pace dimension weights, which decay faster, in this interval. It is more sensitive to the actual fatigue state; while in the sun-exposed area, due to the relatively increased gait decay rate, the adaptive weight is appropriately adjusted to make the evaluation more balanced and avoid the excessive dominance of a single dimension.

[0056] Example 3: like Figure 6 As shown, this embodiment of the invention also provides a computer device, which includes one or more processors 10, a memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory for displaying graphical information of a GUI on external input / output devices, such as display devices coupled to the interfaces. In some alternative embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations, for example, as a server array, a group of blade servers, or a multiprocessor system.

[0057] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0058] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0059] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0060] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0061] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0062] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

Claims

1. A tourist function assessment and trigger-based early warning system based on mobile terminal sensing and terrain-climate coupling, characterized in that, include: The edge-side multidimensional perception and benchmark establishment module is deployed on a mobile terminal. It is used to screen flat road sections with an elevation change rate close to zero within the initial time window, collect time series provided by the mobile terminal's built-in GPS module and triaxial accelerometer, extract instantaneous pace and vertical acceleration peak values, calculate the individual's flat ground benchmark average pace and benchmark vertical acceleration peak value, and establish a physical fitness benchmark model. The cloud-based 3D terrain and functional attenuation calculation module communicates with the edge-side multi-dimensional perception and benchmark archiving module. It is used to receive real-time latitude and longitude coordinates and real-time sensor data uploaded by the terminal, spatially match the real-time latitude and longitude coordinates with the digital elevation model to obtain the current slope, calculate the expected pace under the current terrain conditions by combining the physical fitness benchmark model, and calculate the comprehensive functional attenuation coefficient based on the deviation between the expected pace and the actual pace and the deviation between the benchmark vertical acceleration peak and the actual vertical acceleration peak. The cloud-based microclimate spatiotemporal integration module communicates with the cloud-based 3D terrain and functional attenuation calculation module. It is used to calculate the real-time perceived heat index of an individual based on the 3D spatial solar radiation model and real-time meteorological parameters, and to integrate the heat exposure considering the physical shading rate along the individual's historical movement trajectory to obtain the cumulative heat exposure dose. The path dynamic compensation and optimization module communicates with the cloud-based microclimate spatiotemporal integration module and the cloud-based 3D terrain and functional attenuation calculation module. It is used to search for candidate points of interest within a preset radius when the comprehensive functional attenuation coefficient or cumulative heat exposure dose exceeds the preset safety threshold, calculate the comprehensive matching degree of each candidate point of interest, and select the node with the highest matching degree as the compensation destination. At the same time, it obtains the set of main road segments ahead, increases the impedance weight of road segments with slope exceeding the upper limit of slope or passenger flow density exceeding the upper limit of density, and performs risk avoidance path rendering.

2. The method for tourist function assessment and trigger-based early warning based on mobile terminal sensing and terrain-climate coupling as described in claim 1, characterized in that, Includes the following steps: S1, Personalized benchmark physical fitness profile based on edge sensing: Within the initial time window, the system filters road segment data where the elevation change rate corresponding to the mobile terminal positioning parameters is close to zero, extracts the instantaneous speed and acceleration of the terminal, and calculates the benchmark pace and benchmark vertical acceleration peak of the terminal user. S2, 3D terrain alignment and dynamic estimation of functional attenuation coefficient: Align real-time latitude and longitude coordinates with digital elevation model, obtain current slope, and calculate expected pace and comprehensive functional attenuation coefficient; S3, Dynamic micro-meteorological superposition and thermal exposure dose integration: Based on the three-dimensional spatial model, the temporal occlusion rate of the current coordinates is extracted, and the cumulative thermal exposure dose is calculated in combination with meteorological parameters; S4, Status assessment and compensation node selection: When the comprehensive functional attenuation coefficient is greater than the functional attenuation threshold or the cumulative heat exposure dose is greater than the heat exposure threshold, the candidate interest point set is retrieved within the preset radius, the comprehensive matching degree is calculated and the optimal node is selected; S5, Asymmetric Impedance Layer Generation and Auxiliary Rendering: The node with the highest overall matching degree is used as the compensation destination to generate a sub-path. At the same time, the set of main road segments ahead is obtained. The impedance weight is increased for road segments with a slope exceeding the upper limit or a passenger flow density exceeding the upper limit, and the risk avoidance path rendering is performed.

3. The method for tourist function assessment and trigger-based early warning based on mobile terminal sensing and terrain-climate coupling as described in claim 2, characterized in that, In step S1, the reference pace Peak vertical acceleration compared to the reference value The calculation formula is: ； ； in, Representing time slices The instantaneous pace, Representing time slices The peak vertical acceleration along the Z-axis is below. and It originates from the GPS module and three-axis accelerometer built into the mobile terminal. This represents the total number of valid sampling points.

4. The method for tourist function assessment and trigger-based early warning based on mobile terminal sensing and terrain-climate coupling as described in claim 2, characterized in that, In step S2, the expected pace The calculation formula is: ； in, The real-time slope is derived from the spatial matching of a pre-loaded digital elevation model in the cloud and the terminal's GPS coordinates. This represents the road surface resistance constant.

5. The method for tourist function assessment and trigger-based early warning based on mobile terminal sensing and terrain-climate coupling as described in claim 4, characterized in that, In step S2, the comprehensive functional attenuation coefficient The calculation formula is: ； in, This indicates the current actual pace. This represents the current peak value of actual vertical acceleration, derived from the real-time sensor data stream of the mobile terminal. This represents the weighting factor for pace decay. This represents the gait attenuation weighting coefficient.

6. A method for tourist function assessment and trigger-based early warning based on mobile terminal sensing and terrain-climate coupling as described in claim 2, characterized in that, In step S3, the real-time perceived heat index and cumulative heat exposure dose The calculation formula is: ； ； in, Indicates temperature, The relative humidity is derived from the array of micro weather stations deployed in the scenic area or from a third-party real-time weather API. Indicates time step Physical shadow occlusion rate under the following conditions Indicates the humidity regulation coefficient. Indicates the sampling step size. This indicates the total number of time steps.

7. A method for tourist function assessment and trigger-based early warning based on mobile terminal sensing and terrain-climate coupling as described in claim 2, characterized in that, In step S4, the overall matching degree The calculation formula is: ； in, Indicates to candidate points of interest The path distance is calculated using the road network topology map. Indicate candidate points of interest The attribute compensation score, This represents the distance weighting coefficient. This represents the attribute compensation weighting coefficient; when the cumulative heat exposure dose... Exceeding the heat exposure threshold At the same time, increase the attribute compensation score for interest points with indoor or cold drink tags. When the overall functional attenuation coefficient Exceeding the functional decline threshold At the same time, increase the attribute compensation score of interest points with seat or flat ground tags. .

8. A method for tourist function assessment and trigger-based early warning based on mobile terminal sensing and terrain-climate coupling as described in claim 5, characterized in that, The pace attenuation weighting coefficient and gait attenuation weighting coefficient Online updates will be performed according to the following adaptive rules: In length Calculate the pace decay rate within the sliding time window. and gait decay rate ; when Persistently greater than When, increase and reduce ;when Persistently greater than At that time, increase and reduce ; The weight update formula is: ； ； Normalization will be performed after the update: ； in, and They represent the first Second and third The updated pace decay weighting factor and This represents the corresponding gait attenuation weighting coefficient. Indicates the learning rate. This indicates the upper limit of a single weight adjustment. Represents a symbolic function. Represents a sliding time window Rate of change of pace within, Represents a sliding time window Peak rate of change of vertical acceleration within the range The value is set to the preset duration.

9. A method for tourist function assessment and trigger-based early warning based on mobile terminal sensing and terrain-climate coupling as described in claim 7, characterized in that, The functional degradation threshold is set to a three-level threshold. , and The heat exposure threshold is set with three levels. , and ,satisfy and ; when or When the first level of alert is triggered, the high-risk road section is silently marked on the front-end interface without pushing notifications; when or When this occurs, a second-level alert is triggered, a mild suggestion is pushed, and candidate points of interest are searched within a preset radius, but the route is not automatically replanned; when or When the third-level intervention warning is triggered, the complete interest point matching process in step S4 and the path replanning and risk avoidance path rendering process in step S5 are executed. The three-level functional attenuation thresholds are dynamically calibrated using an individual baseline model: ； in, Indicates the first Level functional degradation reference threshold This represents the average pace based on flat terrain. Indicates the preset reference pace; The three-level heat exposure threshold , and Adopted and The same dynamic calibration method.

10. A method for tourist function assessment and trigger-based early warning based on mobile terminal sensing and terrain-climate coupling as described in claim 6, characterized in that, The physical shadow occlusion rate mentioned in step S3 The calculation method is as follows: The direction vector of sunlight is determined based on the current solar azimuth and solar altitude angles. A digital surface model is constructed by overlaying building height layers and vegetation height layers on top of the digital elevation model, with the tourist's GPS coordinates as the center. A sampling ray is emitted in the opposite direction from the tourist's coordinates along the direction of sunlight. The ray is then tested to see if it intersects with any grid in the digital surface model before reaching the height corresponding to the tourist's coordinates. If it intersects, the occlusion of the sampling ray is determined to be true; otherwise, the occlusion is determined to be false. Centered on the tourist's location, with a radius of [missing information] Launch within a circular area A uniformly distributed sampling ray is used to count the number of rays that are determined to be true due to occlusion. Calculate the physical shadow occlusion rate: ； in, Take 3 to 5 times the resolution of the digital elevation model raster.

11. A method for tourist function assessment and trigger-based early warning based on mobile terminal sensing and terrain-climate coupling as described in claim 2, characterized in that, The process of filtering road segment data with an elevation change rate close to zero, as described in step S1, is performed as follows: With length as The sliding window scans the terminal's GPS positioning sequence, and calculates the elevation difference between the first and last points in each window in chronological order. With horizontal displacement Calculate the rate of change of elevation within the window. ;when Less than the preset flatness threshold At that time, all sampling points within the window are marked as candidate points for flat road sections; Consecutive marked candidate points are merged into flat road segment intervals, and intervals with a length greater than the minimum effective length are selected. The interval is designated as a valid flat road segment; within the initial time window, after excluding stationary points where the terminal instantaneous speed is zero, the remaining sampling points are used to calculate the baseline pace. Peak vertical acceleration compared to the reference value .

12. A method for tourist function assessment and trigger-based early warning based on mobile terminal sensing and terrain-climate coupling as described in claim 7, characterized in that, The heat exposure threshold Determined dynamically according to the following rules: ； in, Indicates the reference ambient temperature The baseline heat exposure threshold, Indicates the temperature sensitivity coefficient. Indicates the current temperature; when hour, ;when hour, The temperature increases linearly, making the criteria for heat exposure more stringent as the environment deteriorates.

13. A computer device, characterized in that, It includes a memory and a processor, which are interconnected and communicate with each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the tourist function assessment and trigger-based early warning method based on mobile terminal sensing and terrain and climate coupling as described in any one of claims 2 to 12.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute any one of claims 2 to 12, a method for tourist function assessment and trigger-based early warning based on mobile terminal sensing and terrain and climate coupling.

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