An ice and snow research and learning activity whole-process management method and system based on a wisdom cloud platform

Abstract

The present application relates to activity monitoring management technical field, especially in kind, it is a kind of ice and snow research activity whole process management method and system based on wisdom cloud platform.The method includes the following steps: pre-import multiple structure maps of ice and snow field, construct digital twin base;Based on the smart terminal worn by research student, multiple original position signals and body state monitoring parameters are collected;The multiple original position signals are mapped to digital twin base, and a synchronous linkage ski field twin model is constructed;The body state monitoring parameters are time series calculated and motion risk comprehensive evaluated, real-time state detection is carried out based on the synchronous linkage ski field twin model, and the complete activity track is uploaded to the wisdom cloud platform for storage management.The present application realizes high-precision real-time perception, risk quantization evaluation and visual early warning and track traceable management in whole process, significantly enhances the safety control efficiency of ice and snow research activity, operation management standardization level.

Description

Technical Field

[0001] This invention relates to the field of activity monitoring and management technology, and in particular to a method and system for managing the entire process of ice and snow study tours based on a smart cloud platform. Background Technology

[0002] With the increasing scale of ice and snow study tours, the growing number of participants and the increasing complexity of activity scenarios have exposed the significant shortcomings of traditional management methods that rely primarily on manual patrols and experience-based judgment. Ice and snow venues typically include multiple functional areas, such as ski slopes, teaching areas, cable car access areas, and emergency shelters. The complex spatial structure and dynamic changes between these areas make it difficult to achieve continuous and accurate unified management of personnel locations and activity trajectories. Students encounter challenges such as rapid speed changes, numerous obstructed areas, and signal attenuation during skiing, making traditional single-positioning technologies insufficient for high-precision continuous positioning. Students' physiological states are significantly affected by ambient temperature, exercise intensity, and individual differences during exercise, with significant fluctuations in indicators such as heart rate, respiratory rate, and body temperature. However, existing technologies often lack dynamic assessment mechanisms that integrate with individual benchmarks, making accurate risk identification difficult.

[0003] Furthermore, in the management of large-scale study tours, safety status information is often fragmented, lacking unified quantitative indicators for comprehensive expression, resulting in lags in risk classification and emergency response. Existing systems have limited capabilities in visualization, making it difficult to intuitively present the differences in status among multiple personnel in complex spatial environments, thus hindering managers from timely identifying potential risk areas. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a method and system for managing the entire process of ice and snow study tours based on a smart cloud platform, thereby resolving at least one of the aforementioned technical issues.

[0005] To achieve the above objectives, this invention provides a method for managing the entire process of ice and snow study tours based on a smart cloud platform, comprising the following steps: Step S1: Import multiple structural diagrams of the ice and snow venue in advance to construct a digital twin base; Step S2: Collect multiple raw location signals and body status monitoring parameters based on the smart terminals worn by the students during the study tour; The multiple original location signals are mapped to a digital twin base to construct a synchronized and interconnected ski resort twin model; Step S3: Perform time-series calculations and comprehensive risk assessments on the body status monitoring parameters to generate a safety status score; Step S4: Classify the status based on the safety status score and perform differentiated rendering on the synchronous linked ski resort twin model; Step S5: Based on the synchronized ski resort twin model, perform real-time status detection and generate traceable trajectory playback to construct a complete activity trajectory; upload the complete activity trajectory to the smart cloud platform for storage and management.

[0006] In this invention, step S1 specifically involves the following steps: Import the snow track structure map, teaching zone map, cable car operation route map, equipment operation area distribution map and emergency evacuation route map of the ice and snow venue in advance to establish the basic layer architecture of the venue; The three-dimensional spatial coordinates of BeiDou differential positioning base stations, environmental sensing nodes and intelligent camera terminals within the site are collected to form a spatial anchor point coordinate set; A unified coordinate set is obtained by performing a unified coordinate transformation on the spatial anchor point coordinate set; Identify the actual boundaries of the site's basic layer architecture, divide it into spatial units, and construct a three-dimensional spatial grid index. A digital twin foundation is constructed by dynamically mapping a three-dimensional spatial grid index based on a unified coordinate set.

[0007] In this invention, step S2 specifically involves the following steps: The system collects multiple raw location signals and body status monitoring parameters based on the smart terminals worn by students during their study tours. Coordinate correction and trajectory continuity compensation are performed on multiple original position signals to generate a dynamic position sequence for each student; The dynamic location sequence is mapped to the digital twin base, and corresponding dynamic virtual personnel identifiers are generated and personnel location changes are updated to construct a synchronized ski resort twin model.

[0008] In this invention, the specific steps for performing coordinate correction and trajectory continuity compensation on multiple original position signals to generate a dynamic position sequence for each student are as follows: The intelligent terminal integrates a Beidou positioning module, an UWB ultra-wideband ranging unit, and a six-axis inertial navigation sensor to collect multiple raw position signals at a fixed sampling period. The original position signal includes BeiDou positioning signal, UWB short-range high-precision ranging signal and inertial navigation data; Using the BeiDou positioning signal as the coordinate reference; and based on the UWB short-range high-precision ranging signal, the coordinate reference is locally corrected to obtain the corrected position information; Identify the location sequence of the signal blockage period of the corrected location information; Based on the inertial navigation data, trajectory continuity compensation is performed on the position sequence during the signal obstruction period to output the dynamic position sequence of each student.

[0009] In this invention, step S3 specifically involves the following steps: The body condition monitoring parameters are subjected to time-series calculations to extract a time-series state feature set; the time-series state feature set includes heart rate, respiratory rate, blood pressure, and body temperature; Retrieve pre-stored student age, past athletic ability level, and historical physiological baseline data from the cloud platform to construct a personal profile baseline; Based on the personal profile benchmark, the deviation values ​​of heart rate change amplitude, respiratory rate fluctuation rate, blood pressure trend slope and body temperature change value are calculated frame by frame for the time-series state feature set to generate four deviation indicators. A comprehensive assessment of physical load and exercise risk is conducted based on four deviation indicators to generate a safety status score.

[0010] In this invention, step S4 specifically involves the following steps: The state is classified based on the safety status score to obtain the state classification results; The state classification results include normal state, fatigue state, dangerous state, and emergency state; the state classification results are then subjected to differentiated color rendering to obtain differentiated rendering nodes. The differentiated rendering nodes are as follows: normal state is rendered as a green node, fatigue state is rendered as a yellow node, dangerous state is rendered as an orange node, and emergency state is rendered as a flashing red node. The differentiated rendering nodes are mapped to the synchronized ski resort twin model in real time.

[0011] In this invention, the real-time status detection specifically refers to: Real-time status detection is performed based on a synchronized ski resort twin model. When a red flashing node or an orange node is detected, the corresponding instructor is identified, and the student's spatial coordinates and the instructor's location information are extracted. Calculate the real-time distance and direction of movement of the student's spatial coordinates and the instructor's location information; Based on the real-time distance and direction of movement, a path is planned to generate the arrival path. The arrival path and student spatial coordinates are sent to the instructor's smart terminal, and a safety warning signal is generated.

[0012] In this invention, the specific steps for generating the traceable trajectory playback and constructing a complete activity trajectory, and uploading the complete activity trajectory to the smart cloud platform for storage management, are as follows: Based on the synchronous and linked ski resort twin model, the movement coordinates, stopping areas, movement speed and physical status scores of students during the study tour are collected to generate a full-cycle movement trajectory chain and physical status change curve. The entire lifecycle movement trajectory chain and body state change curve are traceable trajectory playback generated to construct a complete activity trajectory; The complete activity trajectory is uploaded to the smart cloud platform for storage and management.

[0013] This specification provides a full-process management system for ice and snow study tours based on a smart cloud platform, used to execute the full-process management method for ice and snow study tours based on a smart cloud platform as described above, including: Pre-built modules are used to import multiple structural diagrams of ice and snow venues in advance to build a digital twin base; The data acquisition module is used to collect multiple raw location signals and body status monitoring parameters based on the smart terminals worn by students during the study tour; the multiple raw location signals are mapped to the digital twin base to build a synchronized and linked ski resort twin model; The risk assessment module is used to perform time-series calculations and comprehensive assessments of exercise risks on the body status monitoring parameters, and generate a safety status score. The rendering module is used to classify the status based on the safety status score and to perform differentiated rendering of the synchronously linked ski resort twin model. The storage management module is used to perform real-time status detection based on the synchronous linked ski resort twin model, generate traceable trajectory playback, and construct a complete activity trajectory; the complete activity trajectory is then uploaded to the smart cloud platform for storage management.

[0014] The specific benefits of this invention are as follows: By constructing a unified digital twin base, a standardized expression of the spatial structure of ice and snow venues is achieved, providing a high-precision spatial benchmark for multi-source data fusion and effectively improving the consistency and analytical capabilities of spatial modeling. Based on the synchronous acquisition of student location signals and physical states by intelligent terminals and mapping them to the twin model, real-time linkage between personnel movement and physiological states is achieved, thereby enhancing dynamic perception capabilities in complex snowfield environments. Through time-series analysis and individualized deviation calculation of multi-dimensional physiological parameters such as heart rate, respiration, blood pressure, and body temperature, complex physiological information is compressed into a unified safety status score, improving the quantitative ability and consistency of risk assessment. On this basis, using hierarchical rules and color rendering mechanisms, different risk levels are visualized, enabling abnormal states to have intuitive identification characteristics and significantly improving the efficiency of situational awareness in large-scale personnel management. Through real-time status detection and trajectory playback reconstruction, traceable recording and data storage of the entire study tour process are achieved, supporting not only post-event analysis and behavior review but also enhancing the overall system's safety management capabilities and decision support level. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the steps of a method for managing the entire process of ice and snow study tours based on a smart cloud platform, as described in this invention. Figure 2 This is a detailed flowchart illustrating the implementation steps of step S1. Figure 3 This is a detailed flowchart illustrating the implementation steps of step S2; Figure 4 A schematic diagram of the digital twin base; Figure 5 A schematic diagram of a synchronized twin model of the ski resort; Figure 6 This is a schematic diagram of the basic layer structure of the site. Detailed Implementation

[0016] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0017] This application provides a method and system for managing the entire process of ice and snow study tours based on a smart cloud platform. The implementing entities of the method and system for managing the entire process of ice and snow study tours based on a smart cloud platform include, but are not limited to, mechanical equipment, data processing platforms, cloud server nodes, and network upload devices that can be considered as general computing nodes in this application. The data processing platform includes, but is not limited to, at least one of an audio-visual management system, an information management system, and a cloud-based data management system.

[0018] Please see Figures 1 to 6 This invention provides a method for managing the entire process of ice and snow study tours based on a smart cloud platform, including the following steps: Step S1: Import multiple structural diagrams of the ice and snow venue in advance to construct a digital twin base; Step S2: Collect multiple raw location signals and body status monitoring parameters based on the smart terminals worn by the students during the study tour; The multiple original location signals are mapped to a digital twin base to construct a synchronized and interconnected ski resort twin model; Step S3: Perform time-series calculations and comprehensive risk assessments on the body status monitoring parameters to generate a safety status score; Step S4: Classify the status based on the safety status score and perform differentiated rendering on the synchronous linked ski resort twin model; Step S5: Based on the synchronized ski resort twin model, perform real-time status detection and generate traceable trajectory playback to construct a complete activity trajectory; upload the complete activity trajectory to the smart cloud platform for storage and management.

[0019] In one specific embodiment, a computational model for the entire process of ice and snow study tours was constructed, taking 300 students as the subjects. The basic site structure was standardized as follows: 8 beginner ski runs, 12 intermediate and advanced ski runs, 3 children's teaching areas, 4 cable car lines, and 6 emergency shelter areas. A total of 246 spatial anchor points were deployed within the site, including 6 BeiDou differential base stations, 48 ​​UWB ranging base stations, 120 environmental sensor nodes, and 72 intelligent camera terminals. A unified coordinate system mapping was achieved through a seven-parameter spatial transformation model, and translation parameters were... Scale factor This ensures that the overall spatial error is controlled within 0.05m. At the same time, a 5m×5m×2m three-dimensional grid is used to form a digital twin base, with a total of approximately 100,000 grids.

[0020] In the dynamic positioning calculation for students, 300 students' smart terminals synchronously collect BeiDou, UWB, and inertial data at a 200ms cycle, and a fusion model is used:

[0021] To achieve high-precision positioning fusion, for example, if a student's BeiDou coordinates are (100, 200), with UWB correction of +0.8m and IMU correction of +0.3m, the fused result would be (100.33, 200.25). The trajectory velocity is then calculated according to... Calculations show that the velocity is 3.0 m / s when the displacement is 1.5 m and the time is 0.5 s. The total trajectory points generated by all 300 students within 10 minutes are approximately... One, to realize the construction of continuous trajectory chain.

[0022] In the physical condition assessment section, deviations are calculated for heart rate, respiratory rate, blood pressure, and body temperature:

[0023] Taking a baseline heart rate of 80 bpm as an example, if a student's HR = 140, then... Respiratory fluctuation 0.32, blood pressure slope 0.8, body temperature deviation 0.6, and these values ​​were substituted into the safety scoring model:

[0024] Calculated This corresponds to an emergency situation. The overall distribution of the 300 people is as follows: 180 are in normal condition, 70 are fatigued, 40 are in danger, and 10 are in an emergency.

[0025] The status grading rules are as follows: S≥90 - green; 70~89 - yellow; 50~69 - orange; <50 - flashing red. Ten red nodes trigger the emergency response mechanism. The distance between teachers and students is calculated using a Euclidean formula.

[0026] For example, the distance between teacher (110, 210) and student (100, 200) is 14.14m. Path planning uses the A* model:

[0027] With a distance of 120m, a slope cost of 0.3, and a congestion cost of 0.2, the total path cost is 143, and the optimal grid path sequence is generated.

[0028] Finally, the trajectories of all 300 students were replayed and reconstructed, with approximately 600 trajectory points per person, totaling 180,000 points. Each point is 50 bytes, and the total storage is approximately 9 MB, forming a traceable and complete activity trajectory chain and uploading it to the cloud, realizing full-process management from spatial positioning, status assessment, risk classification, path response to trajectory storage.

[0029] In this embodiment, see Figure 2 The diagram below illustrates the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Import the snow track structure map, teaching zone map, cable car operation route map, equipment operation area distribution map and emergency evacuation route map of the ice and snow venue in advance to establish the basic layer architecture of the venue; The three-dimensional spatial coordinates of BeiDou differential positioning base stations, environmental sensing nodes and intelligent camera terminals within the site are collected to form a spatial anchor point coordinate set; A unified coordinate set is obtained by performing a unified coordinate transformation on the spatial anchor point coordinate set; Identify the actual boundaries of the site's basic layer architecture, divide it into spatial units, and construct a three-dimensional spatial grid index. A digital twin foundation is constructed by dynamically mapping a three-dimensional spatial grid index based on a unified coordinate set.

[0030] In this embodiment, a snow and ice research and study base in Baishan is used as the application scenario, with a total area of ​​approximately 2.8 km². 2 The area includes 8 beginner ski runs, 12 intermediate and advanced ski runs, 3 children's teaching areas, 4 cable car lines, and 6 emergency shelter areas. First, the CAD drawings of the ski run structures, teaching area plans, cable car operation diagrams, snow groomer operation diagrams, and emergency evacuation diagrams were imported into the data layer of the smart cloud platform. The ski run structure diagram records the length, slope, width, and level information of the ski runs. For example, ski run S-07 is 1260m long, has an average slope of 18°, a width of 22m, and a maximum capacity of 420 people. The teaching area map marks the boundaries of different learning areas, with the children's teaching area A1 covering an area of ​​6200m². 2 It can accommodate 120 students, and the intermediate training area B2 has an area of ​​11,500 square meters. 2It can accommodate 260 trainees. The cable car route map records the coordinates of the cable car stations, the direction of travel, and the speed of travel. The No. 1 cable car line is 1480m long and the speed of travel is 4.5m / s.

[0031] The imported layers are organized into four layers: basic terrain layer, teaching and operational layer, equipment operation layer, and safety evacuation layer. The basic terrain layer is defined as a data layer reflecting the mountain slope and ski slope elevation; the teaching and operational layer is defined as a data layer recording the boundaries of the study and teaching area; the equipment operation layer is defined as a data layer recording the operating range of cable cars, snow groomers, and snowmaking machines; and the safety evacuation layer is defined as a data layer describing emergency evacuation routes and safe areas. Layer data is managed using a unified GIS spatial format. The layer resolution parameter is set to 0.1m / pixel to ensure the accuracy of ski slope boundary display; the layer transparency parameter is set to 35% to improve the spatial visualization effect after multiple layers are overlaid; and the topological integrity parameter is used to detect whether the layer boundaries form a closed structure. During boundary detection, when the ski slope boundary intersects with the emergency evacuation area, the boundary node position is automatically corrected, controlling the boundary offset error within 0.2m, thus forming a complete basic site layer architecture.

[0032] Six BeiDou RTK differential positioning base stations are deployed on site, with an average spacing of 420m between them. Each base station has an effective coverage radius of approximately 650m, providing centimeter-level spatial positioning correction data. Positioning accuracy parameters are set to ±2.5cm for horizontal error and ±4cm for elevation error. A total of 98 environmental sensing nodes are deployed, including 36 temperature and humidity sensors, 18 wind speed and direction sensors, 22 snow thickness sensors, and 22 air quality monitoring nodes. Environmental node E-17 in children's teaching area A1 is installed at a height of 2.5m, with a temperature sampling frequency of 1Hz, a wind speed sampling range of 0m / s to 35m / s, and a snow thickness detection range of 0cm to 180cm.

[0033] A total of 72 intelligent camera terminals were installed, including panoramic cameras, AI behavior recognition cameras, and cable car monitoring cameras. The panoramic cameras have a 360° horizontal field of view, a video resolution of 3840×2160, and a target recognition frame rate of 30fps. The camera terminals can identify crowd gatherings, falls, wrong-way walking, and boundary crossings. During the spatial anchor point coordinate acquisition process, BeiDou RTK technology was used to obtain the three-dimensional spatial position data of all devices. The X-axis coordinate is defined as the east-west position value, the Y-axis coordinate as the north-south position value, and the Z-axis coordinate as the device's altitude value. For example, the spatial coordinates of camera terminal C-25 are X=438.225m, Y=1268.442m, and Z=148.337m.

[0034] Spatial anchor points are defined as physical nodes with fixed spatial locations in the digital twin space; the installation height parameter is defined as the vertical distance of the device from the ground reference plane; the spatial coverage radius parameter is defined as the area that a single device can effectively monitor; and the time synchronization error parameter is defined as the deviation value between the timestamps of different devices. All device coordinates are associated with device number, device type, installation height, and sampling frequency, with the time synchronization error controlled within 20ms, thus forming a complete set of spatial anchor point coordinates.

[0035] The BeiDou positioning equipment in the site uses the CGCS2000 national coordinate system, some imported camera terminals use the WGS84 coordinate system, and some snowmaking equipment uses the Local-ENU local engineering coordinate system. Spatial discrepancies exist between these different coordinate systems. First, the original coordinate system type of all equipment was identified, and a coordinate mapping table was established. Then, a seven-parameter spatial transformation model was used for unified coordinate transformation.

[0036] The seven parameters include three translation parameters (Tx, Ty, Tz), three rotation parameters (Rx, Ry, Rz), and one scale factor parameter (S). The translation parameters are defined as the spatial offset between the origins of different coordinate systems; the rotation parameters are defined as the angular deviation between the coordinate axes; and the scale factor parameter is defined as the proportional relationship between length units in different coordinate systems. For example, in the conversion from WGS84 to CGCS2000, Tx = -0.423m, Ty = +1.127m, Tz = +0.682m, and the scale factor S = 0.9999987.

[0037] The least squares fitting method is used for error correction during coordinate transformation. The horizontal error parameter is defined as the positional deviation in the XY plane; the elevation error parameter is defined as the height deviation along the Z-axis; and the coordinate drift parameter is defined as the spatial fluctuation value of the same device over continuous time. After transformation, the horizontal error is controlled within ±4cm, the elevation error within ±6cm, and the coordinate drift error within 0.03m / min. For example, if the original camera coordinates are X=438.884m, Y=1267.912m, Z=148.102m, after unified transformation, the coordinates become X=438.227m, Y=1268.441m, Z=148.336m. All devices are ultimately mapped to the CGCS2000 spatial reference frame, forming a unified coordinate set.

[0038] The solution of the seven-parameter spatial transformation model is based on the Bursa-Wolf seven-parameter similarity transformation model. In actual calculations, multiple sets of observation equations for corresponding control points in the source and target coordinate systems are established, and parameter inversion is performed using least squares adjustment. Assuming there are n sets of control points, each set containing three-dimensional coordinates (X, Y, Z) in different coordinate systems, a nonlinear observation model based on translation parameters Tx, Ty, Tz, rotation parameters Rx, Ry, Rz, and scale factor S can be constructed and linearized using Taylor expansion. During the parameter solution process, defined engineering constraints are introduced: the time synchronization error is controlled within 20ms to ensure the consistency of data acquisition timing from multiple sources, thereby avoiding the superposition of spatial coordinate drift errors caused by time deviations. Simultaneously, least squares fitting is used to constrain and optimize the residuals, controlling the horizontal error within ±4cm, the elevation error within ±6cm, and constraining the coordinate drift error to no more than 0.03m / min. For the rotation parameters Rx, Ry, and Rz, a small-angle approximation model is used for linearization (in radians), and an iterative weighted least squares (IRLS) method is combined to remove outlier control points to improve parameter stability. The scale factor S participates in the overall solution as a global scaling constraint. Its initial approximation value of 0.9999987 is used as the initial value for iteration to accelerate convergence during the WGS84 to CGCS2000 conversion. The optimal solution for the seven parameters is obtained through joint adjustment of multiple control points. Combined with the established coordinate mapping table, unified conversion and fusion between multiple coordinate systems such as CGCS2000, WGS84, and Local-ENU are achieved, forming a high-precision spatial coordinate set that satisfies the unified spatial anchor point constraint condition.

[0039] Three-dimensional terrain data of the site was acquired using UAV oblique photogrammetry and LiDAR scanning. The number of UAVs was set to 8, the flight altitude to 120m, the aerial image overlap rate to 80%, and the LiDAR point cloud density to 320 points / m². 2 Edge recognition algorithms are used to extract ski slope boundaries, building boundaries, and hazardous area boundaries. For example, a dangerous steep slope area of ​​approximately 12m wide was identified on the east side of the advanced ski slope G-03 and marked as a high-risk area.

[0040] After boundary identification, an octree spatial partitioning algorithm was used to divide the space into three-dimensional units. The grid size parameters for the ordinary ski slope area were set to 5m×5m×2m; the grid size parameters for the densely populated teaching area were set to 2m×2m×1m; and the grid size parameters for the cable car operation area were set to 10m×10m×5m. The children's teaching area A1 was divided into 1520 spatial grids, each with a unique index code, and the number of people, ambient temperature, equipment operating status, and risk level were recorded.

[0041] The spatial hierarchy parameter is defined as the mesh depth level; the space occupancy parameter is defined as the proportion of entities within the mesh; and the spatial complexity parameter is defined as the number of objects and their frequency of change within a unit of space. For areas with high population density, the mesh hierarchy is increased to the sixth level to improve spatial positioning accuracy; for edge areas, the mesh hierarchy is reduced to the third level to reduce computational resource consumption, thereby completing the construction of the 3D spatial mesh index.

[0042] All spatial anchor points in the unified coordinate set are mapped to the corresponding three-dimensional spatial grid according to their spatial location, and a device-space-business relationship is established. The devices include camera terminals, environmental sensor nodes, and Beidou positioning terminals; the space includes the ski slope grid, the teaching area grid, and the cable car operation grid; the businesses include teaching activity management, equipment scheduling management, safety inspection management, and emergency evacuation management.

[0043] The data refresh cycle parameter is set to 2 seconds, the camera behavior recognition refresh cycle parameter is set to 1 second, and the personnel positioning update frequency parameter is set to 5 Hz. For example, camera terminal C-25 is mapped to grid number G-A1-245 to record the number of people, average dwell time, and number of behavioral events in that area in real time. When the personnel density in the teaching area exceeds 4 people / 10m... 2 In such cases, the system automatically generates early warning information about crowd gatherings and re-plans the flow routes of people.

[0044] The dynamic mapping parameter is defined as the real-time synchronization relationship between spatial objects and digital models; the state synchronization rate parameter is defined as the degree of data consistency between digital space and real space; the object activity parameter is defined as the frequency of position changes of the target per unit time; and the grid load parameter is defined as the data processing pressure value within a single grid. Emergency evacuation routes are dynamically planned using the A* path search algorithm, taking into account factors such as personnel density, ski slope congestion, wind speed, and passable width during route calculation. For example, when a blizzard occurs on the advanced ski slope G-03, the nearest evacuation assembly point is automatically matched as the E-02 evacuation zone, with an optimal evacuation route length of 286m and an estimated evacuation time of 4.5 minutes, thus completing the construction of a digital twin foundation for the entire ice and snow study tour activity.

[0045] In this embodiment, see Figure 3 The diagram below illustrates the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: The system collects multiple raw location signals and body status monitoring parameters based on the smart terminals worn by students during their study tours. Coordinate correction and trajectory continuity compensation are performed on multiple original position signals to generate a dynamic position sequence for each student; The dynamic location sequence is mapped to the digital twin base, and corresponding dynamic virtual personnel identifiers are generated and personnel location changes are updated to construct a synchronized ski resort twin model.

[0046] In this embodiment, approximately 300 students at a snow and ice research base in Baishan uniformly wore multimodal smart terminal devices. These terminals integrated a BeiDou GNSS positioning module, an IMU inertial measurement unit, a Bluetooth-assisted positioning module, and sensors for heart rate, blood oxygen, body surface temperature, and exercise intensity. This enabled simultaneous acquisition of spatial positioning and physiological status data. The GNSS position signal outputs three-dimensional coordinates (X, Y, Z) at a frequency of 1Hz, with horizontal positioning errors controlled within ±3cm and elevation errors within ±5cm. The IMU module outputs acceleration and angular velocity information at a sampling frequency of 50Hz for short-term trajectory calculation. Bluetooth-assisted positioning was used to detect snow track obstructions or cable car closures. The vehicle structure obstructs the area, with a coverage radius of approximately 25m, for local signal correction. Regarding physiological status, heart rate parameters are measured in bpm, with a sampling frequency of 0.2Hz and a normal range of 60–160 bpm. Blood oxygen saturation is expressed as a percentage, with a normal range of 90%–100%. Body surface temperature ranges from 32℃ to 37.5℃. The exercise intensity index is calculated using acceleration integral, with a value range of 0–10, reflecting the gliding or walking load level. All data are accompanied by 100ms-level timestamps and transmitted back via a 5G private network, forming a multi-source raw data stream containing spatial location, motion status, and physiological indicators, providing basic data input for subsequent trajectory reconstruction.

[0047] To address the positioning jump issues caused by GNSS obstruction, snow slope reflection, and cable car structure interference in icy and snowy environments, multi-source position signals from 300 students were fused, corrected, and their trajectories reconstructed. Kalman filtering was used for the fusion calculation of GNSS, IMU, and Bluetooth signals, with weighting coefficients set to 0.6 for GNSS, 0.3 for IMU, and 0.1 for Bluetooth, ensuring that the variance of the fused position was controlled within 0.02m. 2 Within 3 seconds, when the positioning signal interruption time is less than 3 seconds, IMU inertial integration is used for trajectory interpolation compensation. When the interruption time exceeds 3 seconds, historical trajectory pattern matching combined with snow track topology constraints is used for path reconstruction. Savitzky-Golay filtering (window length 7) is introduced to smooth the trajectory to eliminate jitter and abrupt changes, thereby generating a continuous dynamic position sequence. This sequence consists of three-dimensional coordinate points sorted by time, and each trajectory point is given a position confidence parameter (0~1) and a trajectory continuity parameter (0~100%). The trajectory continuity target is maintained above 98% to ensure that the movement trajectory of 300 students in the complex snow track environment has high consistency and traceability.

[0048] The dynamic location sequences of 300 students are uniformly mapped to a constructed 3D spatial grid index system, spatially binding them with the ski slopes, teaching areas, and equipment operation areas in the ski resort's digital twin base. A unique virtual personnel identifier (VP-ID) is generated for each student, such as VP-S102. This identifier is linked to the student's identity information, real-time coordinates, grid number, and health status parameters, thus forming a one-to-one correspondence between real personnel and virtual mappings. During the mapping process, the location refresh frequency is set to 2Hz, i.e., updating the virtual location status every 0.5 seconds, and the physiological status refresh frequency is set to 0.2Hz, i.e., updating health indicators every 5 seconds. The distribution of personnel in the area is reflected in real time through grid density calculation. When the personnel density in a certain grid exceeds 4 people / 10m... 2 The system triggers congestion warnings and adjusts teaching or travel routes accordingly. Virtual personnel are presented as three-dimensional dynamic icons in the twin model, and their color status is automatically adjusted according to the risk level (green for normal, yellow for dense, orange for warning). The position synchronization error is controlled within 0.05m, thereby achieving high-precision real-time linkage mapping between 300 students in the real ski resort environment and the digital twin space.

[0049] In this embodiment, the specific steps for performing coordinate correction and trajectory continuity compensation on multiple original position signals to generate a dynamic position sequence for each student are as follows: The intelligent terminal integrates a Beidou positioning module, an UWB ultra-wideband ranging unit, and a six-axis inertial navigation sensor to collect multiple raw position signals at a fixed sampling period. The original position signal includes BeiDou positioning signal, UWB short-range high-precision ranging signal and inertial navigation data; Using the BeiDou positioning signal as the coordinate reference; and based on the UWB short-range high-precision ranging signal, the coordinate reference is locally corrected to obtain the corrected position information; Identify the location sequence of the signal blockage period of the corrected location information; Based on the inertial navigation data, trajectory continuity compensation is performed on the position sequence during the signal obstruction period to output the dynamic position sequence of each student.

[0050] In this embodiment, the smart terminal worn by the students on the research trip has a built-in Beidou positioning module, UWB ultra-wideband ranging unit, and six-axis inertial navigation sensor to achieve synchronous acquisition of three types of heterogeneous signals. The Beidou module is used to provide global absolute coordinate information, the UWB unit is used to provide local high-precision relative distance measurement information, and the inertial navigation unit is used to record information on changes in human motion state. All sensor data are synchronously sampled at a fixed sampling period, which is set to 200ms, that is, 5 sets of complete multi-source data are collected per second.

[0051] The BeiDou positioning signal is defined as the three-dimensional spatial absolute coordinates (X, Y, Z) output based on the CGCS2000 coordinate system. Its horizontal positioning error is controlled within ±3cm, and its elevation error is controlled within ±5cm. It is used to provide a global position reference benchmark. The UWB short-range high-precision ranging signal is defined as the distance data between terminals calculated based on 3-10GHz ultra-wideband pulse signals. The ranging accuracy is better than ±10cm, and the effective radius is controlled within 30m. It is used to provide local spatial constraint information. The six-axis inertial navigation data includes three-axis acceleration and three-axis angular velocity. The acceleration sampling frequency is 100Hz. It is used to reflect changes in human motion state, and the angular velocity is used to calculate attitude changes and short-term displacement trends.

[0052] Using the BeiDou positioning signal as the global coordinate reference, which is defined as the absolute spatial reference coordinate system for students in the ski resort environment, UWB short-range ranging data is introduced on this basis to correct local spatial constraints, thereby eliminating multipath errors caused by BeiDou signals under conditions of snow track obstruction, reflection from cable car steel structures, and mountain obstruction.

[0053] Using the BeiDou output coordinates (Xb, Yb, Zb) as the initial position reference, a UWB distance constraint model was constructed. When a student communicates with at least three UWB base stations simultaneously, a trilateration positioning constraint equation is formed. Error optimization calculations are performed using the least squares method to bring the position correction error to within 0.1m. The spacing between UWB base stations is set to 50m–80m, with a coverage radius of approximately 30m, to ensure continuous coverage within the ski slope and teaching area.

[0054] The corrected location information is defined as a high-precision location result obtained by fusing BeiDou absolute coordinates with UWB local constraints. Its horizontal positioning error is controlled within ±5cm, and its elevation error is controlled within ±8cm. It is used to construct local high-reliability spatial trajectory nodes. When UWB signal coverage is effective, its weighting coefficient is set to 0.7, and the BeiDou weighting is 0.3, thereby enhancing local accuracy. When UWB signal coverage is weak, the BeiDou weighting is automatically increased to ensure global stability.

[0055] Because the ice and snow research and study scene has areas of obstruction below the cable car passage, dense forest areas, and snow slope reflection areas, which may cause short-term failure of Beidou and UWB signals, it is necessary to perform signal integrity detection on the corrected location information and identify the signal obstruction period.

[0056] The signal obstruction period is defined as the time interval during which continuous positioning data is lost or the UWB base station is invisible for more than 500ms. When the continuous loss time exceeds 2 seconds, it is judged as a strong obstruction state. During the identification process, the obstruction interval is determined by timestamp continuity analysis and signal strength RSSI threshold detection. The RSSI threshold is set to -85dBm. When the signal strength is lower than this threshold, the obstruction judgment state is entered.

[0057] A position sequence is defined as a set of discrete spatial coordinates arranged in chronological order. During the occlusion period, this sequence is represented by breakpoints or low-confidence points. The system segments and marks each student's trajectory sequence, distinguishing between normal segments and occluded segments. The occluded segments are marked as low-confidence trajectory segments, with the confidence parameter set between 0.2 and 0.5 for subsequent inertial compensation processing.

[0058] During the signal obstruction period, the student's trajectory is continuously compensated using six-axis inertial navigation data. Acceleration data is used to estimate short-term displacement changes, angular velocity data is used to calculate changes in motion direction, and recursive integral calculations are performed in conjunction with the initial corrected position.

[0059] Inertial compensation employs a combination of zero-velocity correction constraints and a drift suppression model. When a student is detected to be in a gliding state, the displacement increment is calculated through acceleration integration; when a stationary state is detected, a zero-velocity update mechanism is used to suppress accumulated errors. Inertial navigation drift error is defined as the cumulative position deviation that increases over time, with its growth rate controlled within 0.15 m / min.

[0060] During the compensation process, a time window of 2–5 seconds is set, and continuous interpolation reconstruction is performed on the occluded trajectory segments to maintain the continuity of the trajectory in the time dimension. The trajectory continuity parameter is defined as the proportion of the compensated trajectory without breaks, with a target value of no less than 97%; the position confidence parameter is defined as the credibility of the compensated trajectory, ranging from 0.3 to 0.9, and is updated over time with decay.

[0061] In the event of a signal loss of approximately 3 seconds in the obstructed area at the bottom of the cable car, the displacement of approximately 2.1m was calculated through IMU integration and corrected by combining the preceding and following UWB constraint points, thereby controlling the trajectory error within 0.15m and restoring the complete motion path.

[0062] After completing BeiDou reference positioning, UWB local correction and inertial compensation, all processing results are fused into a time series to generate a complete dynamic position sequence for each student. This sequence consists of continuous three-dimensional spatial coordinate points and is strictly sorted by timestamp to achieve full traceability of the trajectory.

[0063] The dynamic position sequence is defined as a continuous spatial trajectory set after fusing multi-source positioning and inertial estimation, with a uniform time resolution of 200ms and spatial accuracy controlled at the 0.1m level. Each trajectory point includes position coordinates, velocity vector, acceleration state, and confidence parameters, where the velocity parameter describes the change in motion state and the acceleration parameter describes the change in motion intensity.

[0064] The final output dynamic location sequence can be used for subsequent digital twin mapping, crowd congestion analysis, teaching route planning and emergency evacuation decision-making, enabling high-precision and continuous dynamic location perception and management of 300 students in complex ice and snow environments.

[0065] In this embodiment, step S3 includes the following steps: The body condition monitoring parameters are subjected to time-series calculations to extract a time-series state feature set; the time-series state feature set includes heart rate, respiratory rate, blood pressure, and body temperature; Retrieve pre-stored student age, past athletic ability level, and historical physiological baseline data from the cloud platform to construct a personal profile baseline; Based on the personal profile benchmark, the deviation values ​​of heart rate change amplitude, respiratory rate fluctuation rate, blood pressure trend slope and body temperature change value are calculated frame by frame for the time-series state feature set to generate four deviation indicators. A comprehensive assessment of physical load and exercise risk is conducted based on four deviation indicators to generate a safety status score.

[0066] In this embodiment, approximately 300 students wearing smart terminals continuously output physiological data streams with a fixed sampling period of 200ms, forming a high-frequency time-series sequence of physical states. The physical state monitoring parameters include heart rate, respiratory rate, blood pressure, and body temperature. Heart rate is measured in bpm, reflecting cardiovascular load levels; respiratory rate is measured in breaths per minute, reflecting respiratory system oxygen supply efficiency; blood pressure includes systolic and diastolic blood pressure (mmHg), reflecting circulatory system pressure status; and body temperature is measured in °C, reflecting body surface thermal balance. In the time dimension, a 60-second sliding time window is established for each student, with a sliding step of 5 seconds. Within each time window, statistical and time-series modeling processing is performed on the four types of physiological data, extracting features such as mean, standard deviation, maximum value, and rate of change to form a time-series state feature set. Specifically, the heart rate variation amplitude characterizes the intensity of short-term cardiac load fluctuations; the respiratory rate fluctuation rate is defined as the ratio of standard deviation to mean to measure respiratory stability; the blood pressure trend slope is calculated using linear regression to describe the direction of circulatory system pressure changes; and the body temperature change value characterizes the amplitude of thermometabolic changes. This time-series state feature set is continuously updated in units of time windows, enabling each student to form a continuous and computable dynamic physiological feature sequence, providing a basic input for subsequent risk assessment.

[0067] The cloud platform establishes an independent health record for each student participating in the study tour. The data sources for these records include pre-camp physical fitness test data, historical athletic ability assessment results, and a basic physiological benchmark database. The personal record benchmarks include age, previous athletic ability levels, and historical physiological benchmark data. Age is used to characterize differences in basic physiological metabolic capacity. Athletic ability levels are divided into five grades, L1 to L5, to represent a student's adaptability to skiing and high-intensity exercise. Historical physiological benchmark data includes resting heart rate, standard respiratory rate, and baseline blood pressure range, serving as an individual health control baseline. For example, a 13-year-old student at the L2 athletic level has a baseline resting heart rate of 78 bpm, a baseline respiratory rate of 18 breaths / minute, and a baseline blood pressure range of 110 / 70 mmHg. The purpose of these personal record benchmarks is to provide an individualized reference model, enabling students with different physiological conditions to have differentiated assessment standards under the same exercise intensity, thereby avoiding misjudgments caused by uniform thresholds.

[0068] After obtaining the time-series state feature set, the physiological data within each time window are compared frame-by-frame with the individual's baseline to generate four deviation indicators to quantify the degree of deviation from the physiological state. These include: heart rate variation deviation, defined as the relative deviation between the current mean heart rate and the resting heart rate baseline, reflecting the degree of increase in cardiovascular load; respiratory rate fluctuation, defined as the ratio of the current window's respiratory standard deviation to the baseline mean, reflecting the degree of decrease in respiratory system stability; blood pressure trend slope, defined as the linear regression coefficient of systolic blood pressure over time, characterizing the trend of circulatory system pressure changes; and body temperature change, defined as the difference between the current mean body temperature and the baseline body temperature, reflecting the degree of abnormal thermometabolism. All deviation indicators are standardized from 0 to 1 to eliminate the influence of different physiological dimensions. For example, if a student's heart rate increases from 78 bpm to 132 bpm during gliding, the heart rate deviation is approximately 0.69, while the respiratory fluctuation reaches 0.42, the blood pressure trend slope is 0.80 mmHg / min, and the body temperature change is 0.6℃. This forms a complete four-dimensional deviation indicator vector for subsequent risk assessment input.

[0069] After obtaining the four deviation indicators, a multidimensional weighted fusion model is used to comprehensively assess the students' physical load status and generate a safety status score. The optimized weights in the assessment model are: heart rate deviation weight 0.35, respiratory fluctuation weight 0.25, blood pressure trend weight 0.25, and body temperature change weight 0.15, to reflect the dominant influence of the cardiovascular system on exercise risk. The safety status score is a continuous value from 0 to 100, where 90-100 indicates a low-risk state, 70-89 indicates a moderate load state, 50-69 indicates a high-load warning state, and below 50 indicates a high-risk intervention state. For example, when a student's four deviation indicators are 0.72, 0.40, 0.80, and 0.60 respectively, the weighted safety score is approximately 62, corresponding to a high-load warning level, indicating that the student is under high exercise stress and needs to reduce exercise intensity or adjust the teaching schedule. The safety status score is used to uniformly characterize the physiological safety level of 300 students participating in ice and snow sports, enabling data-driven real-time health risk classification and dynamic intervention management.

[0070] In this embodiment, step S4 includes the following steps: The state is classified based on the safety status score to obtain the state classification results; The state classification results include normal state, fatigue state, dangerous state, and emergency state; the state classification results are then subjected to differentiated color rendering to obtain differentiated rendering nodes. The differentiated rendering nodes are as follows: normal state is rendered as a green node, fatigue state is rendered as a yellow node, dangerous state is rendered as an orange node, and emergency state is rendered as a flashing red node. The differentiated rendering nodes are mapped to the synchronized ski resort twin model in real time.

[0071] In this embodiment, each student's score is used as an input variable for hierarchical mapping to form a standardized state classification system. The safety state score is defined as a continuous value from 0 to 100, representing the student's current physiological load and exercise risk level, with lower scores indicating higher risk. Based on this score range, a four-level state classification rule is established: 90-100 points correspond to a normal state, indicating stable physiological indicators and exercise load within a safe range; 70-89 points correspond to a fatigue state, indicating moderate physical exertion but still the ability to participate in activities; 50-69 points correspond to a dangerous state, indicating significant physiological deviation and a risk accumulation trend; and below 50 points correspond to an emergency state, indicating high physiological risk requiring immediate intervention or cessation of exercise. During the state classification process, a threshold piecewise function is used to discretize the continuous scores, ensuring that each student's score uniquely corresponds to a state label, thus forming a structured state classification result set for subsequent visualization and risk-linked control.

[0072] Each student's status label is mapped to a visual rendering node, enabling hierarchical visual representation in the digital twin scenario. A rendering node is defined as a visual identifier unit in the 3D twin space used to represent an individual's state. Each node is bound to a student's unique identifier, spatial coordinates, and state level information. The color mapping rule adopts a four-level differentiated coding system: a normal state is mapped to a green node, representing a low-risk, stable state; a fatigued state is mapped to a yellow node, representing a moderate workload and mild fatigue; a dangerous state is mapped to an orange node, representing a high workload and risk accumulation; and an emergency state is mapped to a flashing red node, representing a high-risk state that triggers a real-time warning. The flashing mechanism uses a 1Hz flashing frequency to enhance the visual warning effect. The color rendering process takes the status label as input and generates corresponding visual codes through a rule mapping engine, ensuring that each student has a unique color state representation in the twin model, thus forming a differentiated set of rendering nodes.

[0073] After generating differentiated rendering nodes, they are spatially mapped in real time to the existing digital twin base of the ski resort, allowing student status to be updated synchronously in the virtual ski resort environment. During the mapping process, the rendering nodes are bound to corresponding 3D spatial grid units based on the students' real-time position coordinates, achieving a unified relationship between position, status, and visualization. The mapping refresh rate is set to 2Hz, meaning node status and spatial position changes are updated every 0.5 seconds, ensuring that the virtual-real synchronization delay is controlled within 0.2 seconds. Normal status nodes are displayed as stable green point clouds on the ski slope or teaching area; fatigue status nodes are presented with a yellow semi-transparent diffusion effect; danger status nodes are marked with orange highlighted boundaries indicating their activity range; and emergency status nodes are continuously indicated by high-frequency red flashing, overlaid with a dynamic warning circle to emphasize the risk's spread. All rendering nodes are updated in real time along with the students' movement trajectories, enabling dynamic visualization of the status of 300 students in the ski resort. This constructs a synchronously linked ski resort twin operation model to support teaching scheduling optimization and safety emergency response decisions.

[0074] In this embodiment, the real-time status detection specifically refers to: Real-time status detection is performed based on a synchronized ski resort twin model. When a red flashing node or an orange node is detected, the corresponding instructor is identified, and the student's spatial coordinates and the instructor's location information are extracted. Calculate the real-time distance and direction of movement of the student's spatial coordinates and the instructor's location information; Based on the real-time distance and direction of movement, a path is planned to generate the arrival path. The arrival path and student spatial coordinates are sent to the instructor's smart terminal, and a safety warning signal is generated.

[0075] In this embodiment, continuous state scanning and event trigger detection are performed on all rendered nodes. The scanning frequency is set to 2Hz, meaning a global state refresh is completed every 0.5 seconds. When any student node is detected to be in an orange danger state or a red flashing emergency state, the node is marked as a high-priority risk target, and the teacher matching mechanism is triggered. Instructors and students are pre-bound according to teaching group relationships, with each teacher responsible for an average of 20-25 students, forming a fixed responsibility domain. The locking mechanism first determines the set of responsible teachers based on their respective teaching zones, then performs optimal responsibility matching based on the student's current spatial grid position, ultimately determining a unique instructor as the response target.

[0076] During this process, two types of core spatial information are extracted simultaneously: student spatial coordinates are defined as their real-time three-dimensional position (X, Y, Z) in the CGCS2000 unified coordinate system, used to represent their precise location on the ski slope or in the teaching area; instructor location information is defined as the real-time spatial coordinates output by the teacher's wearing terminal, used to represent their current patrol or teaching location. All coordinate data are accompanied by a 100ms-level timestamp to ensure spatiotemporal consistency, thus forming a dual-object spatial association structure of at-risk student and responsible teacher.

[0077] After obtaining the real-time spatial coordinates of the student and the supervising teacher, the straight-line distance between them is calculated based on the Euclidean distance model, and the relative direction of movement is calculated by combining the time series coordinate changes. The real-time distance is defined as the shortest spatial distance between two points in three-dimensional space. It is calculated based on the square root of the sum of the squares of the differences between the X, Y, and Z coordinates, and is used to measure the spatial proximity between the teacher and the student at risk. The unit is meters, and the accuracy is controlled within 0.1m.

[0078] Movement direction calculation is based on the position change vectors of two consecutive time sampling points. The student's movement direction is defined as the tangent direction of their trajectory per unit time, and the teacher's movement direction is defined as their current movement path vector. The angle between these vectors is used to determine whether they are approaching each other. An angle less than 90° indicates a consistent approach direction, while an angle greater than 90° indicates relative distance. The distance parameter is used to determine the urgency of the response; for example, a distance less than 30m triggers a rapid response level, and less than 10m triggers an immediate intervention level.

[0079] The real-time distance parameter is defined as the physical spatial interval between the teacher and the student; the movement direction parameter is defined as the motion trend characteristic based on the trajectory vector; and the approach speed parameter is defined as the rate of change of distance per unit time, used to measure the urgency of the rescue response. After obtaining the real-time distance and movement direction information, a path planning graph model is constructed based on the ski resort's 3D grid index, and spatial constraints are modeled for the passable areas, teaching areas, and restricted areas of the ski slope. The path planning objective is for the instructor to quickly reach the location of the student at risk from the current position, and the A* path search algorithm is used to calculate the optimal path.

[0080] During the path calculation process, the snow slope parameter, current personnel density parameter, and environmental risk level parameter are comprehensively considered. Among them, the snow slope parameter is defined as the local terrain inclination angle, used to assess the difficulty of movement; the personnel density parameter is defined as the number of people per unit grid, used to avoid congested paths; and the environmental risk level parameter is defined as a comprehensive index of wind speed, visibility, and snow conditions, used to avoid dangerous areas.

[0081] The path cost function is composed of a weighted average of distance cost, gradient cost, and congestion cost, with distance cost having a weight of 0.5, gradient cost 0.3, and congestion cost 0.2, thus generating a comprehensive optimal arrival path. This path is output as a continuous grid node sequence, ensuring that teachers can quickly approach the target students along a safe path while avoiding high-risk areas.

[0082] After path planning is completed, the generated arrival path sequence and the real-time spatial coordinates of the target students are synchronously sent to the corresponding instructor's smart terminal device. The terminal device includes a handheld teaching terminal or a wearable display terminal, used to receive scheduling instructions and spatial navigation information in real time. The path data is presented in the form of segmented coordinate sequences and is overlaid in real time on the ski resort's 3D map interface, providing dynamic navigation guidance for the instructor.

[0083] Simultaneously, a safety warning signal is generated. This signal is defined as a multimodal alert triggered based on risk level, including visual, vibration, and audible cues. A flashing red node triggers the highest level warning, with the vibration intensity set to the maximum and the alert frequency at 1Hz. An orange node triggers a medium-to-high level warning, with an alert frequency of 0.5Hz. The warning signal also includes the student's ID, current location, risk level, and recommended arrival time to assist teachers in making quick decisions.

[0084] In this embodiment, the specific steps for generating the traceable trajectory playback and constructing a complete activity trajectory, and uploading the complete activity trajectory to the smart cloud platform for storage management, are as follows: Based on the synchronous and linked ski resort twin model, the movement coordinates, stopping areas, movement speed and physical status scores of students during the study tour are collected to generate a full-cycle movement trajectory chain and physical status change curve. The entire lifecycle movement trajectory chain and body state change curve are traceable trajectory playback generated to construct a complete activity trajectory; The complete activity trajectory is uploaded to the smart cloud platform for storage and management.

[0085] In this embodiment, students' spatial behavior and physiological state are continuously collected and structured modeled throughout the entire study tour. The collected data includes movement coordinates, dwelling areas, movement speed, and body condition scores. Movement coordinates are defined as a continuous three-dimensional spatial position sequence (X, Y, Z) of the student in the CGCS2000 unified coordinate system, with a sampling period of 200ms, used to describe high-precision spatial movement trajectories. Dwelling areas are defined as functional area units in a three-dimensional spatial grid where the student remains continuously for more than 30 seconds, used to characterize the distribution characteristics of teaching behaviors. Movement speed is defined as the rate of change of spatial distance between adjacent time points, in m / s, used to reflect gliding intensity and movement rhythm. Body condition scores are defined as a comprehensive health score after fusing the aforementioned multi-dimensional physiological deviation indicators, ranging from 0 to 100, used to describe changes in physiological load during the exercise process.

[0086] In terms of data organization, all continuous movement coordinates are connected sequentially over time to form a full-cycle movement trajectory chain. This trajectory chain is represented by a three-element structure of trajectory point—timestamp—spatial grid ID, realizing a continuous trajectory representation from entering the ski resort to leaving. Body state scores are serialized along the same time axis to form a body state change curve, used to describe the fluctuation patterns of physiological load in students at different stages of exercise. The trajectory chain and the state curve are strictly aligned in the time dimension, with the time synchronization error controlled within 100ms, thus ensuring a consistent mapping relationship between spatial behavior and physiological changes.

[0087] The trajectory chain is standardized on the time axis, and all trajectory points are reordered according to a unified time base, with missing segments due to signal loss or occlusion being filled in. For missing trajectory segments, interpolation reconstruction is performed by combining inertial estimation and adjacent spatial constraints to maintain trajectory continuity above 98%. Subsequently, the body state change curve is synchronously superimposed on the trajectory chain to form a two-dimensional joint trajectory model of spatial behavior and physiological state.

[0088] A complete activity trajectory is defined as a multi-dimensional temporal fusion result that includes spatial location sequences, dwelling behavior nodes, movement speed change curves, and body state change curves. Dwelling behavior nodes are used to mark teaching points, rest points, and risk points; movement speed curves reflect changes in exercise intensity at different stages; and body state curves reflect trends in physiological load. This method enables the full, traceable reconstruction of the behavioral trajectories and health status of 300 students throughout the entire study tour.

[0089] After the complete activity trajectory is constructed, it is uniformly packaged into a structured trajectory data package and uploaded to the smart cloud platform for centralized storage and management. The data package structure includes four core dimensions: spatial trajectory data dimension, physiological state data dimension, behavioral event data dimension, and time index data dimension. Among them, spatial trajectory data is used to record the student's location changes throughout the entire cycle, physiological state data is used to record changes in physical state scores, behavioral event data is used to record the areas where the student stayed, teaching behaviors, and abnormal events, and time index data is used to achieve end-to-end time alignment and retrieval.

[0090] The upload process employs a segmented storage mechanism, dividing the complete activity trajectory into multiple continuous data segments according to time windows. Each segment corresponds to a fixed time interval (e.g., 5 minutes or 10 minutes) and is assigned a unique trajectory identifier code for enabling rapid retrieval and playback. When the trajectory data is stored in the cloud, a structured index is constructed. The spatial index is used to retrieve trajectories by region, the time index by time period, and the individual index by student ID, thus achieving multi-dimensional query capabilities.

[0091] In this embodiment, a full-process management system for ice and snow study tours based on a smart cloud platform is provided, used to execute the full-process management method for ice and snow study tours based on a smart cloud platform as described above, including: Pre-built modules are used to import multiple structural diagrams of ice and snow venues in advance to build a digital twin base; The data acquisition module is used to collect multiple raw location signals and body status monitoring parameters based on the smart terminals worn by students during the study tour; the multiple raw location signals are mapped to the digital twin base to build a synchronized and linked ski resort twin model; The risk assessment module is used to perform time-series calculations and comprehensive assessments of exercise risks on the body status monitoring parameters, and generate a safety status score. The rendering module is used to classify the status based on the safety status score and to perform differentiated rendering of the synchronously linked ski resort twin model. The storage management module is used to perform real-time status detection based on the synchronous linked ski resort twin model, generate traceable trajectory playback, and construct a complete activity trajectory; the complete activity trajectory is then uploaded to the smart cloud platform for storage management.

[0092] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0093] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein are implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for managing the entire process of ice and snow study tours based on a smart cloud platform, characterized in that: Includes the following steps: Step S1: Import multiple structural diagrams of the ice and snow venue in advance to construct a digital twin base; Step S2: Collect multiple raw location signals and body status monitoring parameters based on the smart terminals worn by the students; map the multiple raw location signals to the digital twin base to construct a synchronized ski resort twin model; Step S3: Perform time-series calculations and comprehensive risk assessments on the body status monitoring parameters to generate a safety status score; Step S4: Classify the status based on the safety status score and perform differentiated rendering on the synchronous linked ski resort twin model; Step S5: Based on the synchronized ski resort twin model, perform real-time status detection and generate traceable trajectory playback to construct a complete activity trajectory; upload the complete activity trajectory to the smart cloud platform for storage and management.

2. The method for managing the entire process of ice and snow study tours based on a smart cloud platform according to claim 1, characterized in that, The specific steps of step S1 are as follows: Import the snow track structure map, teaching zone map, cable car operation route map, equipment operation area distribution map and emergency evacuation route map of the ice and snow venue in advance to establish the basic layer architecture of the venue; The three-dimensional spatial coordinates of BeiDou differential positioning base stations, environmental sensing nodes and intelligent camera terminals within the site are collected to form a spatial anchor point coordinate set; A unified coordinate set is obtained by performing a unified coordinate transformation on the spatial anchor point coordinate set; Identify the actual boundaries of the site's basic layer architecture, divide it into spatial units, and construct a three-dimensional spatial grid index. A digital twin foundation is constructed by dynamically mapping a three-dimensional spatial grid index based on a unified coordinate set.

3. The method for managing the entire process of ice and snow study tours based on a smart cloud platform according to claim 1, characterized in that, The specific steps of step S2 are as follows: The system collects multiple raw location signals and body status monitoring parameters based on the smart terminals worn by students during their study tours. Coordinate correction and trajectory continuity compensation are performed on multiple original position signals to generate a dynamic position sequence for each student; The dynamic location sequence is mapped to the digital twin base, and corresponding dynamic virtual personnel identifiers are generated and personnel location changes are updated to construct a synchronized ski resort twin model.

4. The method for managing the entire process of ice and snow study tours based on a smart cloud platform according to claim 3, characterized in that, The specific steps for performing coordinate correction and trajectory continuity compensation on multiple original position signals to generate a dynamic position sequence for each student are as follows: The intelligent terminal integrates a Beidou positioning module, an UWB ultra-wideband ranging unit, and a six-axis inertial navigation sensor to collect multiple raw position signals at a fixed sampling period. The original position signal includes BeiDou positioning signal, UWB short-range high-precision ranging signal and inertial navigation data; Using the BeiDou positioning signal as the coordinate reference; and based on the UWB short-range high-precision ranging signal, the coordinate reference is locally corrected to obtain the corrected position information; Identify the location sequence of the signal blockage period of the corrected location information; Based on the inertial navigation data, trajectory continuity compensation is performed on the position sequence during the signal obstruction period to output the dynamic position sequence of each student.

5. The method for managing the entire process of ice and snow study tours based on a smart cloud platform according to claim 1, characterized in that, Step S3 is as follows: The body condition monitoring parameters are subjected to time-series calculations to extract a time-series state feature set; the time-series state feature set includes heart rate, respiratory rate, blood pressure, and body temperature; Retrieve pre-stored student age, past athletic ability level, and historical physiological baseline data from the cloud platform to construct a personal profile baseline; Based on the personal profile benchmark, the deviation values ​​of heart rate change amplitude, respiratory rate fluctuation rate, blood pressure trend slope and body temperature change value are calculated frame by frame for the time-series state feature set to generate four deviation indicators. A comprehensive assessment of physical load and exercise risk is conducted based on four deviation indicators to generate a safety status score.

6. The method for managing the entire process of ice and snow study tours based on a smart cloud platform according to claim 1, characterized in that, The specific steps of step S4 are as follows: The state is classified based on the safety status score to obtain the state classification results; The state classification results include normal state, fatigue state, dangerous state, and emergency state; the state classification results are then subjected to differentiated color rendering to obtain differentiated rendering nodes. The differentiated rendering nodes are as follows: normal state is rendered as a green node, fatigue state is rendered as a yellow node, dangerous state is rendered as an orange node, and emergency state is rendered as a flashing red node. The differentiated rendering nodes are mapped to the synchronized ski resort twin model in real time.

7. The method for managing the entire process of ice and snow study tours based on a smart cloud platform according to claim 1, characterized in that, The real-time status detection specifically refers to: Real-time status detection is performed based on a synchronized ski resort twin model. When a red flashing node or an orange node is detected, the corresponding instructor is identified, and the student's spatial coordinates and the instructor's location information are extracted. Calculate the real-time distance and direction of movement of the student's spatial coordinates and the instructor's location information; Based on the real-time distance and direction of movement, a path is planned to generate the arrival path. The arrival path and student spatial coordinates are sent to the instructor's smart terminal, and a safety warning signal is generated.

8. The method for managing the entire process of ice and snow study tours based on a smart cloud platform according to claim 1, characterized in that, The specific steps for generating the traceable trajectory playback and constructing a complete activity trajectory, and uploading the complete activity trajectory to the smart cloud platform for storage and management, are as follows: Based on the synchronous and linked ski resort twin model, the movement coordinates, stopping areas, movement speed and physical status scores of students during the study tour are collected to generate a full-cycle movement trajectory chain and physical status change curve. The entire lifecycle movement trajectory chain and body state change curve are traceable trajectory playback generated to construct a complete activity trajectory; The complete activity trajectory is uploaded to the smart cloud platform for storage and management.

9. A full-process management system for ice and snow study tours based on a smart cloud platform, characterized in that, The method for managing the entire process of ice and snow study tours based on a smart cloud platform as described in claim 1 includes: Pre-built modules are used to import multiple structural diagrams of ice and snow venues in advance to build a digital twin base; The data acquisition module is used to collect multiple raw location signals and body status monitoring parameters based on the smart terminals worn by students during the study tour; the multiple raw location signals are mapped to the digital twin base to build a synchronized and linked ski resort twin model; The risk assessment module is used to perform time-series calculations and comprehensive assessments of exercise risks on the body status monitoring parameters, and generate a safety status score. The rendering module is used to classify the status based on the safety status score and to perform differentiated rendering of the synchronously linked ski resort twin model. The storage management module is used to perform real-time status detection based on the synchronous linked ski resort twin model, generate traceable trajectory playback, and construct a complete activity trajectory; the complete activity trajectory is then uploaded to the smart cloud platform for storage management.

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