Student exercise management supervision method and system based on wearable smart watch

Abstract

The invention relates to a student exercise management supervision method and system based on a wearable smart watch, and relates to the field of student exercise management, and the method comprises the steps: collecting body indexes and exercise data of equipment; the exercise step number, the exercise speed and the exercise heart rate during exercise are extracted according to the exercise data; determining an exercise intensity grade according to the exercise speed and the exercise heart rate; determining a motion efficiency index according to the motion step number and the motion speed; determining a physical quality level based on the exercise intensity level and the exercise efficiency index; subtracting a preset personal quality grade from the physical quality grade to obtain a grade interval; when the grade interval is larger than a preset interval threshold value, the device position of the device is collected; determining equipment use conditions according to the body indexes and the equipment positions; identifying and determining whether an error condition exists or not based on the equipment use condition, and outputting error information; and processing the error information and transmitting the error information to a management end. The method has the effect of accurately monitoring and managing the exercise condition of the student.

Description

Technical Field

[0001] This invention relates to the field of student sports management, and in particular to a student sports management and supervision method and system based on a wearable smartwatch. Background Technology

[0002] With the construction of sports campuses, it is necessary to monitor and supervise students' physical fitness and sports performance through student physical health monitoring.

[0003] Within the school, external devices collect students' real-time physical indicators and exercise data, which is then uploaded to a management platform for analysis. The system can also determine whether students' exercise status meets the standards by setting reasonable exercise goals and thresholds, and provide reminders or guidance to students who have not completed their tasks. Simultaneously, the system allows teachers or parents to view relevant data through their management terminals, enabling comprehensive supervision and management of students' exercise behavior, thereby improving students' physical fitness and health.

[0004] In daily life, physical fitness tests for students rely on human visual acuity to time the data, which leads to inaccurate data and further misjudgments of students' true physical condition and athletic ability. This ultimately makes it difficult to track, evaluate and manage students' physical fitness in the future. Summary of the Invention

[0005] To accurately monitor and manage students' exercise, this invention provides a student exercise management and supervision method and system based on a wearable smartwatch.

[0006] In a first aspect, the present invention provides a student sports management and supervision method based on a wearable smartwatch, employing the following technical solution: A student sports management and supervision method based on wearable smartwatches includes: Connection protocol of the data acquisition device; Configure the associated parameters of the device according to the connection protocol, and determine the type of physical test item; Define the data collection dimensions based on the type of physical fitness test items; Based on the data collection dimensions, and verify the collected physical test data; The physical fitness test data will be uploaded to the cloud platform simultaneously. Configure data sharing permissions between the cloud platform and preset service providers, parent terminals, and teacher terminals to achieve multi-terminal data linkage; Verify the consistency of data transmission across all terminals to confirm that the body measurement data collection is complete.

[0007] By adopting the above technical solution, the physical test type is automatically bound after the connection protocol is collected and the associated parameters are configured. The dimension setting and real-time verification ensure that the physical test data such as heart rate and walking speed are accurate from the source. The synchronous upload to the cloud platform immediately triggers the sharing of permissions among service providers, parents and teachers. Consistency verification ensures zero difference in data across multiple terminals, realizing accurate and panoramic monitoring of students' exercise status.

[0008] Optionally, the verification of collected physical examination data includes: Extract the physical test item data from the physical test data; Collect the full score thresholds for each physical fitness test item from the teacher's end; The difference in physical fitness test scores is calculated using project data and the full score threshold. Based on project data and pre-set basic physical data of adolescents, analyze the shortcomings in physical fitness; Based on the differences in physical fitness test results and shortcomings in physical fitness, a physical fitness test analysis report is generated and synchronized to the cloud platform.

[0009] Optionally, after generating the physical fitness analysis report, it includes: Identify physical fitness issues based on the physical fitness test analysis report; Based on the pre-set physical fitness improvement model and physical fitness problems, determine the matching training type; Based on the project data, set a baseline value for training intensity; The training plan is determined based on the training type and training intensity baseline, and then synchronized to the cloud platform; Conduct training according to the training plan and make optimizations and adjustments.

[0010] Optional, also includes: Collect body metrics and motion data from the device; Extract the number of steps, speed, and heart rate during exercise from the exercise data; Exercise intensity levels are determined based on exercise speed and heart rate. The exercise efficiency index is determined based on the number of steps and the speed of movement. Physical fitness levels are determined based on exercise intensity levels and exercise efficiency index. Subtract the preset individual fitness level from the physical fitness level to obtain the level range; When the level range exceeds the preset range threshold, the device location of the data acquisition device is recorded. Determine equipment usage based on physical indicators and equipment location; Based on device usage, identify and determine if an error exists, and output error information; The incorrect information is processed and transmitted to the management terminal.

[0011] By adopting the above technical solution, comprehensive collection of physical indicators and exercise data is carried out to determine the exercise intensity level, exercise efficiency index and physical fitness level, and compare them with the individual's actual situation; when the level range exceeds the limit, the location is triggered to collect data and perform error identification, generate error information and send it back to the management terminal. By quantifying exercise indicators, accurate monitoring and management of students' exercise status can be achieved.

[0012] Optional methods for processing based on erroneous information include: The device user's collected account data is matched according to their physical fitness level; Retrieve device account data within the device; Based on the collected account data, the actual device users are matched and defined as wearers, and the collection activity areas of the wearers are obtained. Based on the device account data, the original device user of the device is matched and defined as the owner, and the device activity area is matched. The rendezvous point is determined based on the data collection activity area and the equipment activity area; When a rendezvous point exists, matching and exchanging data is performed based on the collected account data and the device account data.

[0013] By adopting the above technical solution, when incorrect information is found, the activity areas of the collected account data and the device account data are compared simultaneously to lock the rendezvous point and automatically initiate account matching and exchange. Spatial trajectory cross-verification is used to quickly clarify the ownership of the device, reduce the time for manual investigation, improve the accuracy of sports data collection, ensure that the score records correspond one-to-one with the students, and maintain the fairness of the assessment.

[0014] Optionally, methods for determining equipment usage include: The current location of the equipment is determined based on its location, and historical data is retrieved to determine the current activity area of ​​the equipment. Determine the distance difference based on the current location and the activity area; When the distance difference exceeds the preset distance threshold, the system matches the corresponding device user based on the physical fitness level and the preset personal fitness level, and determines the device usage status as replacement use. When the distance difference is not greater than the preset distance threshold, the risk of disease is determined based on the body data; When there is a risk of illness, the device usage is considered abnormal. If there is no risk of illness, the device is considered to be in misuse.

[0015] By adopting the above technical solution, the distance difference between the current location of the device and the historical activity area is calculated, and the physical fitness level is combined to determine the replacement, abnormal or incorrect use status. In addition, a disease risk identification mechanism is introduced to incorporate location drift and health abnormalities into a unified judgment framework, so as to achieve dual monitoring of the compliance of device use and the health risks of students, reduce the misjudgment rate, and facilitate the determination of students' exercise status.

[0016] Optional, when used incorrectly, includes: The results of the basic exercise conditions are determined based on the exercise efficiency index and the preset exercise baseline threshold. Data collected during the swing arm movement is used to plot the swing arm motion curve, which serves as the swing arm motion feature. The swing arm feature judgment result is determined based on the swing arm motion feature and the preset swing arm motion feature. The duration of the data acquisition device's movement, the amplitude of the device's swing arm, and the acceleration of the swing arm; The exercise load is determined based on the duration of exercise and the fluctuation of heart rate during exercise. Based on the exercise load, combined with the judgment results of the basic exercise conditions and the judgment results of the arm swing characteristics, it is comprehensively determined whether the wearer is in an exercise state. If it is determined that the wearer is in motion, the device is activated and the wearer is photographed to determine the wearing status; The collected exercise data will be retained if the wearer is the individual wearing the device. If the person wearing the device is not the wearer, the collected exercise data will be deleted.

[0017] By adopting the above technical solution, after the error is determined, the system compares the arm swing motion curve with preset features, and verifies the real exercise state by combining exercise duration and heart rate fluctuations. It also triggers shooting at the fixed point of the arm swing, uses overlapping images to restore facial features, and decides whether to keep or discard data by comparison, ensuring that the uploaded exercise records all come from the wearer, while reducing redundant data storage and improving the platform's operating efficiency.

[0018] Optional methods for plotting the swing arm motion curve include: Collect the swing arm motion time during swing arm operation; Calculate the distance from the end point of the swing arm to the waist based on the swing arm amplitude and the preset effective length of the swing arm; The armpit swing angle is determined based on the arm swing amplitude and preset torso verticality parameters; Calculate the instantaneous velocity of the arm based on the acceleration and duration of the arm swing motion; With the swing arm movement time as the horizontal axis and the distance from the swing arm endpoint to the waist, the armpit swing arm angle, and the instantaneous speed of the swing arm as the vertical axes, sub-curves are generated to show how each parameter changes over time. By integrating the individual curves and marking the distance from the arm endpoint to the waist, the arm angle under the armpit, and the instantaneous velocity of the arm at key time points, the arm motion curve is obtained.

[0019] By adopting the above technical solution, the arm swing motion curve is synthesized by three sub-curves: the distance from the end point of the swing arm to the waist, the angle under the armpit, and the instantaneous velocity. This transforms the three-dimensional motion characteristics into a quantifiable curve, enabling simultaneous control of the standardization of the action and the timing of the shooting, and providing high-reliability data for subsequent analysis.

[0020] Optional methods for taking the photos include: The swing arm movement distance is determined based on the equipment amplitude; The vertical distance of the maximum vertical height is determined based on the distance the swing arm moves. When the acceleration of the swing arm is 0, determine whether the motion state of the swing arm tends to be stable; Collect the horizontal displacement of the swing arm and combine it with the vertical distance to determine the current spatial position of the swing arm; Retrieve the preset swing arm fixed point position parameters and compare the current spatial position with the fixed point position parameters; If the current spatial position matches the fixed point position parameters, confirm that the swing arm has reached the fixed point and send a shooting signal; In response to the shooting signal, the preset shooting device on the equipment is activated, and when the swing arm reaches the fixed point, the shooting is carried out to obtain a fixed-point shooting image.

[0021] By adopting the above technical solution, the moment when the acceleration of the swing arm is zero is used to determine the stable point of motion. The current spatial position is calculated by combining the horizontal displacement and vertical distance. After comparing with the preset point parameters, the shooting is triggered. This method ensures that the image acquisition time strictly corresponds to the highest point of the swing arm. In special cases, it can directly confirm whether it is the person and facilitate identity verification.

[0022] Secondly, this application provides a student sports supervision and management system, which adopts the following technical solution: A student sports supervision and management system, comprising: The acquisition module is used to acquire body metrics, exercise data, and device location. The memory is used to store a program that implements a student sports management and supervision method based on a wearable smartwatch; The processor loads and executes programs from memory.

[0023] In summary, this application includes at least one of the following beneficial technical effects: 1. Collect physical indicators and exercise data to determine exercise intensity level, exercise efficiency index and physical fitness level, and compare them with the individual's actual situation; when the level range exceeds the limit, the location is collected and error is identified, and the error information is sent back to the management terminal. By quantifying exercise indicators, abnormalities can be automatically detected and reported, ensuring the authenticity and effectiveness of physical fitness assessment and accurately monitoring and managing students' exercise. 2. After misuse is detected, the system compares the arm swing motion curve with preset features, and verifies the real exercise state by combining exercise duration and heart rate fluctuations. It also triggers shooting at the fixed point of the arm swing, uses overlapping images to restore facial features, and decides whether to keep or discard data by comparison, ensuring that the uploaded exercise records are all from the wearer, while reducing redundant data storage and improving the platform's operating efficiency. 3. The moment when the acceleration of the swing arm is zero is used to determine the stable point of motion. The current spatial position is calculated by combining the horizontal displacement and vertical distance. The image is then triggered after being compared with the preset point parameters. This method ensures that the moment of image acquisition strictly corresponds to the highest point of the swing arm. In special cases, it can also be used to confirm whether the person is the same person, which is convenient for identity verification. Attached Figure Description

[0024] Figure 1 This is a flowchart of a student sports management and supervision method based on a wearable smartwatch, according to an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0026] This application discloses a student sports management and supervision method based on a wearable smartwatch.

[0027] Reference Figure 1 A student sports management and supervision method based on wearable smartwatches includes the following steps: Step 10: Connection protocol of the acquisition device.

[0028] The device is a smartwatch, which contains a communication module, temperature sensor, gyroscope, health monitoring sensor, and optical sensors such as infrared sensors. Body indicators and motion data are collected through the watch's temperature and infrared sensors, which are pre-set by technicians according to actual conditions and will not be elaborated upon here. The connection protocol refers to the protocol specification used for communication; the data acquisition method is common knowledge to those skilled in the art and will not be elaborated upon here.

[0029] Step 11: Configure the associated parameters of the device according to the connection protocol and determine the type of physical test item.

[0030] Associated parameters refer to the configuration parameters required during device connection, such as baud rate and data format. The physical fitness test item type refers to the specific items in the student's physical fitness test, such as running, long jump, and sit-ups. These are pre-set by technicians according to the actual situation and will not be elaborated upon here.

[0031] The methods for determining the types of physical fitness test items are common knowledge to those skilled in the art and will not be elaborated here.

[0032] Step 12: Set the data collection dimensions according to the type of physical test items.

[0033] Data collection dimensions refer to the types of data collected for different physical test items, such as heart rate, steps, time, speed, etc. These are preset by technicians according to the actual situation and will not be elaborated here.

[0034] Based on different physical fitness test items such as running, long jump, and sit-ups, and combined with the data collection capabilities of the hardware such as temperature sensors, gyroscopes, health monitoring sensors, and infrared sensors on the equipment, specific data types are set as data collection dimensions to suit the physical fitness assessment needs of each item. For example, for running, heart rate, steps, time, and speed are set as data collection dimensions; for long jump, flight time and acceleration are set as data collection dimensions; and for sit-ups, the number of repetitions and heart rate changes are set as data collection dimensions.

[0035] Step 13: Based on the data collection dimensions, verify the collected physical test data.

[0036] Physical fitness test data refers to the raw data of students' physical fitness tests collected by equipment.

[0037] Combining the data collection dimensions set for different physical test items (such as heart rate, speed, and time for running, and number of sit-ups, duration, and heart rate changes), the system collects raw student physical test data for the corresponding dimensions through temperature sensors, gyroscopes, health monitoring sensors, and optical sensors. At the same time, the collected physical test data is verified for validity and completeness (such as checking whether the data format is compliant, whether key dimension data is missing, and whether the values ​​are within a reasonable range) to ensure that the collected physical test data is accurate and usable.

[0038] Step 14: Synchronize and upload the physical test data to the cloud platform.

[0039] A cloud platform refers to a remote server system used for data storage and processing.

[0040] The physical test data is synchronously transmitted to the cloud platform, laying the data foundation for subsequent multi-terminal data linkage analysis.

[0041] Step 15: Configure data sharing permissions between the cloud platform and the preset service providers, parent terminals, and teacher terminals to complete multi-terminal data linkage.

[0042] The service provider refers to a third-party platform that provides data processing and analysis services. The parent's end refers to the terminal interface used by parents to receive students' physical test data. The teacher's end refers to the terminal interface used by teachers to receive students' physical test data.

[0043] Data sharing permissions refer to the data access permissions that the cloud platform assigns to different endpoints.

[0044] Multi-terminal data linkage refers to the synchronization and sharing of data between the cloud platform, the parent's terminal, and the teacher's terminal.

[0045] The cloud platform has a pre-set permission management module that configures different data access permissions for pre-defined service providers, parents, and teachers, and enables a real-time data synchronization mechanism between the cloud platform and each terminal, so that student physical test data can be accurately transferred and shared between multiple interfaces, ultimately achieving multi-terminal data linkage.

[0046] Step 16: Verify the consistency of data transmission at each end to confirm that the body measurement data collection is complete.

[0047] Data transmission consistency refers to the consistency between the data content received by each end and the original data on the cloud platform.

[0048] The method for verifying data transmission at each end is common knowledge to those skilled in the art and will not be elaborated here, thus completing the data collection.

[0049] Verification of collected physical examination data includes the following steps: Step 20: Extract the physical test item data based on the physical test data.

[0050] Project data refers to the data content under a specific physical test item, such as the 50-meter sprint time, heart rate changes, etc.

[0051] The physical fitness test data was extracted according to the scores of different items to obtain the project data.

[0052] Step 21: Collect the full score thresholds for each physical test item from the teacher's end.

[0053] The full score threshold refers to the minimum standard value required to obtain a full score in a physical fitness test item.

[0054] When collecting the full score thresholds for each physical test item from the teacher's end, first locate the module in the teacher's end system that stores the physical test scoring standards, and retrieve the minimum standard value for the full score for each physical test item such as running, long jump, and sit-ups (e.g., the full score threshold for girls in the 800-meter run is 3 minutes and 20 seconds). Complete the extraction and collection of the full score threshold data for each item, and provide a benchmark for subsequent calculation of the physical test difference.

[0055] Step 22: Calculate the difference in physical fitness test scores based on the project data and the full score threshold.

[0056] The physical fitness test difference refers to the gap between a student's actual score and the full score standard.

[0057] First, extract the student's actual performance data for a specific physical test item (e.g., 8 seconds for a 50-meter sprint, 40 sit-ups per minute). Then, retrieve the corresponding full score threshold for that item (e.g., 7 seconds for a 50-meter sprint, 45 sit-ups per minute). Through numerical calculations (selecting the subtraction direction based on the item type, such as subtracting the full score threshold from the actual performance for speed-related items, and subtracting the actual performance from the full score threshold for counting-related items), calculate the specific difference between the student's actual performance and the full score standard for that item, which is the physical test difference.

[0058] Step 23: Analyze the shortcomings in physical fitness based on project data and preset basic physical data of adolescents.

[0059] Basic physical data for adolescents refers to a reference data model used to assess students' physical fitness. It is pre-set by technicians according to actual conditions and will not be elaborated on here.

[0060] Physical fitness weaknesses refer to students' relatively weaker physical abilities or skills.

[0061] The methods for analyzing physical shortcomings are common knowledge in the field and will not be elaborated here.

[0062] Step 24: Based on the physical test differences and physical fitness shortcomings, generate a physical test analysis report and synchronize it to the cloud platform.

[0063] A physical fitness test analysis report is a report generated after comprehensively analyzing students' physical fitness test data, which includes scores, gaps, shortcomings, and suggestions.

[0064] The physical fitness test difference (the gap between the actual score and the full score threshold) is calculated based on the student's physical fitness test data, and the physical fitness shortcomings (such as poor endurance and weak explosive power) are analyzed based on the project data and the basic physical fitness data of adolescents. The results are sorted out, including the students' performance in each physical fitness test, the specific gap with the full score standard, the core physical fitness weaknesses, and corresponding improvement suggestions. The physical fitness test analysis report containing this core information is then uploaded to the cloud platform for data storage and processing through the preset communication link, completing the basic preparation for the cloud storage of the report and subsequent multi-terminal sharing.

[0065] After generating the physical fitness analysis report, the following steps are included: Step 30: Determine physical fitness issues based on the physical fitness analysis report.

[0066] Physical fitness issues refer to specific problems students have in terms of physical fitness, such as poor endurance and weak explosive power.

[0067] First, we review the scores of each physical test item presented in the physical test analysis report, the difference between the actual score and the full score threshold, and the identified physical fitness shortcomings. Then, we compare these shortcomings with the basic physical data of adolescents and analyze the corresponding physical fitness dimension deficiencies. Finally, we summarize them into specific physical fitness problems, such as poor endurance, weak explosive power, insufficient core strength, and poor flexibility.

[0068] Step 31: Based on the preset physical fitness improvement model and physical fitness problems, determine the matching training type.

[0069] Physical fitness improvement models refer to data analysis models used to recommend training types.

[0070] Training type refers to the type of exercise recommended for specific physical fitness problems, such as endurance training and strength training.

[0071] First, the preset physical fitness improvement model is retrieved. This model has built-in matching rules between different physical fitness problems and corresponding training types. Then, the specific physical fitness problems (such as poor endurance, weak explosive power, etc.) obtained from the analysis of the physical test report are input into the model, and the model performs matching according to the built-in rules. Finally, the training type that is highly suitable for the physical fitness problem is selected from the model's preset training type library (such as poor endurance matching long-distance running endurance training, weak explosive power matching sprinting or jumping strength training).

[0072] Step 32: Based on the project data, set the training intensity baseline value.

[0073] The training intensity baseline value refers to the intensity reference value set in the training plan.

[0074] First, extract the core data corresponding to the physical test items (such as running speed / heart rate, number of sit-ups, etc.), combine the full score threshold of the item and the student's actual physical test difference, and refer to the preset basic physical data of adolescents to determine the initial reference value of training intensity that is suitable for the student's current physical fitness level; then, based on the initial value, match the intensity adaptation range of the corresponding training type in the physical fitness improvement model, and finally determine the benchmark value of training intensity to provide an intensity reference for developing personalized training plans.

[0075] Step 33: Determine the training plan based on the training type and training intensity baseline, and synchronize it to the cloud platform.

[0076] A training plan refers to a personalized training program developed for students, including training content, frequency, intensity, etc.

[0077] First, based on the determined training type (such as endurance training and strength training), and taking the training intensity benchmark as the core reference, and considering the physical fitness problems revealed by the student's physical test (such as poor endurance and weak explosive power), a personalized training plan is developed, which includes training content, frequency, single training duration, and intensity gradient. Then, the training plan is packaged according to the preset data format and uploaded to the cloud platform through the connection protocol between the device and the cloud platform to complete the synchronous storage of the training plan, so that parents and teachers can retrieve and view it.

[0078] Step 34: Conduct training according to the training plan and make optimizations and adjustments.

[0079] Optimization and adjustment refers to dynamically adjusting the content of the training plan based on training feedback data.

[0080] The specific methods for optimization and adjustment are common knowledge to those skilled in the art and will not be elaborated here.

[0081] Step 100: Collect the device's body indicators and motion data.

[0082] Body metrics refer to physiological data obtained through sensors on a watch, such as heart rate, body temperature, and blood oxygen.

[0083] Motion data refers to data generated by students during exercise, such as steps, speed, and acceleration.

[0084] Step 101: Extract the number of steps, speed, and heart rate during exercise based on the exercise data.

[0085] Step count refers to the number of steps a student takes during exercise. Exercise speed refers to the student's movement speed during exercise. Exercise heart rate refers to the student's heart rate during exercise.

[0086] The exercise data includes the number of steps taken, speed, and heart rate during exercise, which can be retrieved directly.

[0087] Step 102: Determine the exercise intensity level based on exercise speed and heart rate.

[0088] Exercise intensity levels refer to the levels of exercise intensity based on exercise speed and heart rate, such as low, medium, and high intensity.

[0089] Different exercise speeds and heart rates correspond to different exercise intensity levels. The exercise intensity level is obtained by inputting the exercise speed and heart rate into a preset exercise intensity level database. The exercise intensity level database is a database that is preset by technicians according to the actual situation. The exercise intensity level database contains a table of correspondence between exercise speed and heart rate and exercise intensity level. The table of correspondence is preset by technicians according to the actual situation and will not be described in detail here.

[0090] Step 103: Determine the exercise efficiency index based on the number of steps and the speed of movement.

[0091] The sports efficiency index is an indicator that measures students' sports efficiency, calculated by combining the number of steps and speed.

[0092] Different numbers of steps and movement speeds correspond to different exercise efficiency indices. The exercise efficiency index is obtained by inputting the number of steps and movement speed into a preset exercise efficiency index database. The exercise efficiency index database is a database that is preset by technicians according to the actual situation. The exercise efficiency index database contains a table of correspondence between the number of steps and movement speed and the exercise efficiency index. The table of correspondence is preset by technicians according to the actual situation and will not be described in detail here.

[0093] Step 104: Determine the physical fitness level based on the exercise intensity level and exercise efficiency index.

[0094] Physical fitness level refers to the student's current physical fitness rating, expressed as a numerical value.

[0095] Different weights are preset for exercise intensity levels and exercise efficiency indices. The corresponding physical fitness level is obtained through weighted calculation. The weights are preset by technicians according to the actual situation, and will not be elaborated here.

[0096] Step 105: Subtract the preset personal fitness level from the physical fitness level to obtain the level range.

[0097] Individual fitness level refers to the numerical value of a student's basic physical fitness level, which is pre-set data based on the student's situation. Level range refers to the range of difference between the physical fitness level and the individual fitness level.

[0098] The difference between the physical fitness level and the individual fitness level is the level range.

[0099] Step 106: When the level range is greater than the preset range threshold, the device location of the acquisition device is collected.

[0100] Interval thresholds refer to the critical values ​​of grade intervals, used to determine whether a student's physical fitness is abnormal. They are preset by technicians according to the actual situation and will not be elaborated here.

[0101] Device location refers to the device's geographical coordinates, including its real-time location and historical activity locations, which are collected through the positioning system on the watch.

[0102] When the grade range is greater than the range threshold, it indicates that the student may have an abnormality, such as being sick, cheating by having someone else run for them, or bringing the wrong watch. The location of the data collection device should be checked.

[0103] Step 107: Determine the equipment usage status based on physical indicators and equipment location.

[0104] Equipment usage status refers to the usage status of the equipment, including whether it has been used abnormally (malfunction), swapped (wrong watch), or misused (cheating by having someone else run the runner).

[0105] The specific methods are described in steps 300 to 305, and will not be repeated here.

[0106] Step 108: Based on device usage, identify and determine if there are any errors, and output error information.

[0107] Error situations refer to situations where the equipment is used improperly or the data is abnormal.

[0108] Misplaced information refers to watches being mistakenly paired with each other, such as students exchanging the wrong identification information.

[0109] When an error is detected, it means that two people are wearing the wrong watches, resulting in a mismatch between the watch device and its actual owner. Therefore, the information of both people will be retrieved and output through the platform.

[0110] If it is determined that there is no error, then it is another situation, and it should be handled according to the equipment usage.

[0111] Step 109: Process the incorrect information and transmit it to the management terminal.

[0112] The management end refers to the remote management platform or system that receives and processes erroneous information, such as the teacher's end and the parent's end.

[0113] When errors occur, refer to steps 200 to 205 for specific methods, which will not be elaborated here.

[0114] When no error is found, proceed according to the device usage.

[0115] The specific methods for processing based on erroneous information include the following steps: Step 200: Match the user's collected account data based on their physical fitness level.

[0116] Collecting account data refers to retrieving user account information, including identity, historical data, etc.

[0117] The physical fitness level is entered into a preset account database to obtain the collected account data. The account database stores the account data corresponding to all device users. It is preset by technicians according to the actual situation and will not be described in detail here.

[0118] Step 201: Obtain device account data within the device.

[0119] Device account data refers to the account information stored inside the device, which is usually the information of the person to whom the device is bound.

[0120] The device stores information about the bound user, such as device account data and personal physical fitness level, which can be directly accessed.

[0121] Step 202: Match the actual device user based on the collected account data, define them as the wearer, and obtain the collection activity area of ​​the wearer.

[0122] The wearer refers to the student currently using the device. The data collection activity area refers to the geographical range of the wearer's daily activities. The matching method is common knowledge known to those skilled in the art and will not be elaborated here.

[0123] By identifying the actual wearer, the collection activity area can be determined based on the daily activity range within the collected account data for subsequent screening.

[0124] Step 203: Match the original user of the device based on the device account data, define them as the owner, and match the device activity area.

[0125] The owner refers to the original user who registered the device. The device activity area refers to the geographical range of the owner's daily activities. The device activity area is obtained by matching the device account data in the same way, which will not be elaborated here.

[0126] The original user bound to the device is determined based on the device account data, thereby matching the device's activity area, which facilitates further analysis and filtering.

[0127] Step 204: Identify and determine the rendezvous point based on the data collection activity area and the equipment activity area.

[0128] A meeting point refers to a geographical location where two activity areas overlap or are close to each other.

[0129] By matching the activity area and the activity area of ​​the equipment, a precise rendezvous point can be obtained, narrowing the overlap to a dormitory or a household. This matching is common knowledge known to those skilled in the art and will not be elaborated here.

[0130] Step 205: When a rendezvous point exists, match and exchange data based on the collected account data and the device account data.

[0131] Matching and exchanging refers to the system allowing temporary exchange of device usage rights or updating of binding information after identity verification.

[0132] When a rendezvous point exists, it indicates that roommates or family members have brought the wrong devices to each other, and an exchange will be conducted based on the collected account data and the device account data.

[0133] When multiple people bring the wrong data, the data is matched and exchanged together by multiple sets of collected account data and device account data. The matching and exchange method is common knowledge known to those skilled in the art and will not be described in detail here.

[0134] The method for determining equipment usage includes the following steps: Step 300: Determine the current location of the device based on its location, and retrieve historical data to determine the current activity area of ​​the device.

[0135] Current location refers to the device's current real-time geographical location.

[0136] The activity area refers to the geographical range of a user's daily activities. It is generated based on historical location data. The method for retrieving and determining this data is common knowledge known to those skilled in the art and will not be elaborated here.

[0137] By determining the current location of the device and retrieving the identified activity area, it is possible to subsequently determine whether there are any other usage scenarios.

[0138] Step 301: Determine the distance difference based on the current location and the activity area.

[0139] Distance difference refers to the distance between the current location and the center or boundary of the activity area.

[0140] The difference between the current location and the active area is calculated, which is the distance difference. The calculation method is common knowledge known to those skilled in the art and will not be elaborated here.

[0141] By determining the distance difference, it can be determined whether to use a replacement.

[0142] Step 302: When the distance difference is greater than the preset distance threshold, match the corresponding device user according to the physical fitness level and the preset personal fitness level, and determine the device usage status as replacement use.

[0143] Distance threshold refers to the critical distance value used to determine whether a device is being used by someone else.

[0144] Replacement use refers to situations where the equipment is used by someone other than the original owner.

[0145] When the distance difference is greater than the distance threshold, it indicates that the range is outside the normal activity range. For example, the device for a junior high school student is located in a primary school, which helps determine whether it is a replacement situation.

[0146] Step 303: When the distance difference is not greater than the preset distance threshold, determine whether there is a risk of disease based on the body data.

[0147] Disease risk refers to the risk that a user's data may be abnormal due to health problems.

[0148] The specific methods for determining whether there is a risk of disease are described in steps 400 to 405, and will not be repeated here.

[0149] When the distance difference is not greater than the distance threshold, it means that it is not out of range and there are other usage situations, such as abnormal usage and incorrect usage.

[0150] Step 304: When there is a risk of disease, the device usage is considered abnormal.

[0151] Abnormal use refers to abnormal device data caused by health problems.

[0152] When there is a risk of illness, it indicates that the student may be experiencing physical discomfort, and abnormal use will be considered as equipment usage.

[0153] Step 305: If there is no risk of disease, the device is considered to be in misuse.

[0154] Misuse refers to situations where equipment is misused or operated improperly.

[0155] If there is no risk of illness, it indicates that the student may have cheated by having someone else run the race for them, and the misuse will be considered as a device usage situation.

[0156] The methods for determining the risk of disease include the following steps: Step 400: Compare the exercise efficiency index with the preset historical exercise efficiency to determine whether the two are within the preset range.

[0157] Historical motion efficiency refers to the motion efficiency data of the equipment user in the past. It is determined in advance based on historical motion data and will not be elaborated here. Amplitude range refers to the allowable range of efficiency fluctuations.

[0158] By comparing the exercise efficiency index with historical exercise efficiency, it can be determined whether the fluctuations in exercise data are normal, so as to facilitate subsequent analysis of whether there is a risk of disease.

[0159] Step 401: When the range is not within the preset range, determine the various body coefficients based on the body data.

[0160] The body coefficients refer to the comprehensive coefficients of physiological indicators including body temperature, blood oxygen, pulse rate, and skin resistance.

[0161] When the body is outside the preset range, it indicates that the body is indeed unwell, and thus the various body coefficients are determined based on the body data.

[0162] The specific methods for determining various body coefficients are described in steps 500 to 505, and will not be repeated here.

[0163] Step 402: Determine whether the physical fitness level is greater than the preset historical average physical fitness level.

[0164] The historical average physical fitness level refers to the average of the user's past physical fitness levels.

[0165] By determining whether the physical fitness level is greater than the historical average, we can identify whether there has been a decline or abnormality in physical fitness, which will facilitate subsequent analysis.

[0166] Step 403: When the physical fitness level is not greater than the average value, the comprehensive risk coefficient is calculated based on the weighted average of various physical coefficients.

[0167] The comprehensive risk coefficient refers to the health risk value calculated by combining various physiological indicators.

[0168] The overall risk coefficient is obtained by weighting various body factors. The weights of each factor are preset by technicians according to the actual situation, and will not be elaborated here.

[0169] When a physical fitness level is not higher than the average, it indicates that the body may be ill or unwell, leading to a decline in academic performance.

[0170] When a person's physical fitness level is above average, it is determined that there is no risk of developing a disease.

[0171] Step 404: If the overall risk coefficient is higher than the preset risk warning threshold, then it is determined that there is a risk of disease.

[0172] Risk warning thresholds refer to the critical values ​​for judging health risks. They are preset by technical personnel based on actual conditions and will not be elaborated on here.

[0173] If the overall risk coefficient is higher than the preset risk warning threshold, it indicates that the currently collected physical data deviates significantly from the physical data under normal conditions, and the possibility of illness is high, thus confirming the existence of disease risk.

[0174] Step 405: If the overall risk coefficient is lower than the preset risk warning threshold, then it is determined that there is no risk of disease.

[0175] If the overall risk coefficient is lower than the preset risk warning threshold, it indicates that the possibility of getting sick is low, and therefore there is no risk of getting sick.

[0176] The methods for determining various body coefficients include the following steps: Step 500: Determine body temperature, blood oxygen concentration, pulse rate, and skin resistance based on body data.

[0177] Body temperature refers to the user's body temperature. Blood oxygen saturation refers to the oxygen saturation in the blood. Pulse rate refers to the number of pulses per minute. Skin resistance refers to the resistance value of the skin surface, reflecting the amount of sweat.

[0178] The body data includes body temperature, blood oxygen concentration, pulse rate, and skin resistance, which are collected by various types of sensors preset on the watch, and will not be elaborated here.

[0179] Step 501: Determine the blood oxygen coefficient based on the blood oxygen concentration and the preset blood oxygen threshold.

[0180] Blood oxygen threshold refers to the critical value for determining whether blood oxygenation is normal. Blood oxygen coefficient is a comprehensive indicator reflecting blood oxygenation status and is used in subsequent calculations of comprehensive risk factors.

[0181] The difference between blood oxygen concentration and blood oxygen threshold is called the blood oxygen coefficient.

[0182] Step 502: Combine pulse rate and skin resistance, and determine the state coefficient with reference to the preset circulatory state judgment criteria.

[0183] The criteria for judging the state of circulation refer to the basis for judging the state of blood circulation. They are pulse rate and skin resistance data under normal conditions, which are preset by technicians according to the actual situation and will not be elaborated here.

[0184] State coefficients are indicators that reflect the state of a cyclic system.

[0185] The state coefficient is obtained by matching the pulse rate and skin resistance to a preset state database. The state database contains a lookup table for determining the state coefficient based on the pulse rate and skin resistance. This table is preset by technicians according to the actual situation and will not be elaborated here.

[0186] Step 503: Determine the temperature deviation based on the body temperature and the preset normal body temperature range.

[0187] Normal body temperature range refers to the range within which the human body can normally maintain its temperature.

[0188] Body temperature deviation refers to the degree to which the current body temperature deviates from the normal range.

[0189] The difference between body temperature and the normal body temperature range is calculated, and then multiplied by a body temperature coefficient. This multiplier gives the body temperature deviation. The body temperature coefficient is preset by technicians based on actual conditions and will not be elaborated upon here.

[0190] Step 504: Match the exercise intensity level to the exercise heart rate, retrieve the normal heart rate range under that exercise intensity level, and determine whether the exercise heart rate is within the normal heart rate range to obtain the heart rate coefficient.

[0191] The normal heart rate zone refers to the normal heart rate range under this exercise intensity. It is preset by technicians according to the actual situation and will not be elaborated here. The heart rate coefficient is an indicator that reflects the heart rate status.

[0192] Based on the retrieved normal heart rate range for the exercise intensity level, the actual exercise heart rate is compared with the normal range. If it is within the range, the heart rate coefficient is a standard value of 1. If it exceeds or falls below the range, a correction coefficient is calculated based on the deviation (e.g., if the deviation is 10%, the coefficient is 0.8). Finally, the heart rate coefficient that quantifies the fit between the heart rate and the normal range is obtained.

[0193] Step 505: Use blood oxygen coefficient, status coefficient, body temperature deviation, and heart rate coefficient as various body coefficients.

[0194] The various body coefficients refer to a set of indicators that comprehensively reflect the user's current physiological state.

[0195] The blood oxygen coefficient, state coefficient, body temperature deviation, and heart rate coefficient are used as various body coefficients to facilitate the determination of the comprehensive risk coefficient.

[0196] When used incorrectly, the following steps are included: Step 600: Determine the basic exercise conditions based on the exercise efficiency index and the preset exercise baseline threshold.

[0197] The baseline exercise threshold refers to the preset minimum exercise efficiency standard.

[0198] The basic exercise condition result refers to the result used to determine whether the user is exercising. If the exercise efficiency index is greater than the basic exercise threshold, the basic exercise condition result is met; otherwise, it is not met. If the basic exercise condition is met, the operation continues; if it is not met, the operation is paused until it is met and restarted.

[0199] Step 601: Collect data during arm swing and plot the arm swing motion curve as the arm swing motion feature. Determine the arm swing feature judgment result based on the arm swing motion feature and the preset arm swing motion feature.

[0200] The swing arm motion curve refers to the curve showing how the swing arm amplitude and acceleration change over time. The swing arm motion characteristics refer to the characteristic curves of the swing arm. The motion swing arm characteristics refer to the standard swing arm motion pattern.

[0201] The swing arm feature judgment result indicates whether the swing arm is in motion. If the swing arm motion feature is consistent with the swing arm motion feature, the result is that the swing arm is in motion; otherwise, the swing arm is not in motion.

[0202] The specific drawing method is described in steps 700 to 705, and will not be repeated here.

[0203] Step 602: Collect the movement duration of the device, the amplitude of the device during the swing arm movement, and the acceleration of the swing arm.

[0204] Exercise duration refers to the duration of the user's exercise recorded by the device. Device amplitude refers to the amplitude of the arm swing detected by the device. Arm swing acceleration refers to the change in acceleration of the arm swing. Exercise duration is obtained through a preset timer on the watch.

[0205] The amplitude of the device and the acceleration of the swing arm are collected by sensors on the watch, which will not be elaborated here.

[0206] Step 603: Determine the exercise load based on the duration of exercise and the fluctuation of heart rate.

[0207] Exercise load refers to the degree of physiological burden on a user during exercise.

[0208] The greater the fluctuation of the heart rate within a unit of exercise duration, the greater the exercise load. The exercise load is obtained by matching the exercise duration and heart rate into a preset exercise load database. The exercise load database is a database that is preset by technicians according to the actual situation. The exercise load database contains the relationship between exercise duration, heart rate and exercise load. The actual parameters are preset by technicians according to the actual situation, which will not be elaborated here.

[0209] Step 604: Based on the exercise load, combined with the judgment results of the basic exercise conditions and the judgment results of the arm swing characteristics, comprehensively determine whether the wearer is in an exercise state.

[0210] The wearer refers to the person who actually wears the device.

[0211] When the basic exercise conditions are met, the exercise load is greater than the exercise load threshold, and the arm swing characteristics are consistent with the exercise arm swing characteristics, the wearer is determined to be in an exercise state; otherwise, they are not in an ideal exercise state.

[0212] Step 605: If it is determined that the wearer is in motion, start the device and take a picture of the wearer to determine the wearing status.

[0213] Wearing status refers to whether it is worn by the wearer, which is determined by taking a picture with the camera preset on the watch.

[0214] If it is determined that the wearer is in motion, then the wearer will be photographed as appropriate.

[0215] If it is determined that the wearer is not in motion, then no photos will be taken.

[0216] Step 606: If the wearer is the individual, retain the collected motion data.

[0217] "I" refers to the user bound to the device.

[0218] If the person wearing the device is themselves, it indicates that they are running, and the exercise data will be retained.

[0219] Step 607: Delete the collected motion data if the wearer is not the person wearing the device. If the person wearing the device is not the one who ran the race, it indicates that someone else is running on their behalf, and the collected exercise data will be deleted and invalidated.

[0220] The method for drawing the motion curve of the swing arm includes the following steps: Step 700: Collect the arm swing time during the arm swing.

[0221] The arm swing time refers to the duration of the arm swing motion.

[0222] The data collection method is the same as the method for collecting motion duration, and will not be repeated here.

[0223] Step 701: Calculate the distance from the end point of the swing arm to the waist based on the swing arm amplitude and the preset effective length of the swing arm.

[0224] The effective arm swing length refers to the effective swing radius of the arm, which is the average arm length of an adult.

[0225] The distance from the end point of the arm to the waist refers to the horizontal distance between the end point of the arm and the waist.

[0226] When calculating, the arm swing motion is regarded as "circular arc motion with the waist as the center and the effective length L of the arm swing as the radius". At this time, the distance between the end point of the arm swing and the waist can be derived by the cosine theorem of the isosceles triangle: the two legs of the isosceles triangle formed after the arm swing are L, the vertex angle is the arm swing amplitude θ, and the base is the distance D to be found. The formula is D=L×√[2(1-cosθ)].

[0227] For example: If L=60cm and θ=30°, substituting them, we get D≈60×√[2(1-0.866)]≈15.5cm, which means that the end point of the swing arm is about 15.5cm away from the waist.

[0228] Step 702: Determine the arm swing angle under the arm based on the arm swing amplitude and the preset torso verticality parameters.

[0229] The trunk verticality parameter refers to the angle between the body trunk and the vertical direction.

[0230] The armpit angle refers to the angle between the arm and the torso.

[0231] The calculation uses the "torso axis" as a reference: First, determine the initial angle under the armpit when the arm is hanging naturally (a preset value, such as about 15° between the arm and the torso axis when hanging naturally, since the torso's verticality has fixed the torso direction, this initial angle is a fixed reference); then adjust according to the arm swing amplitude - if the arm swing direction is far away from the torso (such as swinging forward / to the side), "armpit angle = initial angle + arm swing amplitude"; if the arm swing direction is close to the torso (such as swinging backward and the amplitude does not exceed the initial angle), armpit angle = initial angle - arm swing amplitude (if the result is negative, take 0° to indicate that the arm is close to the torso).

[0232] Example: If the torso verticality is preset to 0° (upright), the initial arm angle is 15°, and the forward arm swing amplitude is 30°, then "the arm swing angle under the arm = 15° + 30° = 45°"; if the torso is tilted forward by 10° (verticality parameter 10°) and the backward arm swing amplitude is 20°, then "the armpit angle under the arm = 15° - 20° = 0°" (arm close to the torso).

[0233] Step 703: Calculate the instantaneous velocity of the arm based on the arm's acceleration and motion time.

[0234] Instantaneous arm swing speed refers to the arm swing speed at a certain moment.

[0235] The instantaneous velocity of the arm is calculated by multiplying the acceleration of the arm movement by the arm movement time.

[0236] Step 704: Using the swing arm movement time as the horizontal axis, and the distance from the swing arm endpoint to the waist, the armpit swing arm angle, and the instantaneous speed of the swing arm as the vertical axes, generate sub-curves showing the changes of each parameter over time.

[0237] A sub-curve refers to a curve in which a single parameter changes over time.

[0238] Using the arm swing time as the horizontal axis and the distance from the arm swing end point to the waist, the arm swing angle under the armpit, and the instantaneous velocity of the arm swing as the vertical axes, a corresponding coordinate system is drawn to determine the arm swing motion curve.

[0239] Step 705: Integrate the individual curves, mark the distance from the end point of the swing arm to the waist, the arm angle under the armpit, and the instantaneous velocity of the swing arm at key time points, and draw the swing arm motion curve.

[0240] The arm swing motion curve refers to the motion trajectory curve that comprehensively reflects the characteristics of the arm swing motion.

[0241] The three sub-curves are integrated into the same time axis, and the corresponding parameter values ​​are marked at each key time point. Finally, a complete swing arm motion curve is drawn, which intuitively presents the dynamic changes of position, angle and speed during the swing arm process (such as marking the highest point of the forward swing as "18cm from waist, angle 45°, speed 0m / s", and the midpoint of the cycle as "10cm from waist, angle 30°, speed 0.8m / s").

[0242] The specific methods for taking photos include the following steps: Step 800: Determine the swing arm movement distance based on the equipment amplitude.

[0243] The arm swing distance refers to the horizontal displacement distance of the arm during swing.

[0244] If the device amplitude directly outputs a "linear range" (such as a watch labeled "forward and backward arm swing amplitude 40cm"): then the device amplitude corresponds to the "single-way movement distance" of the arm. This is because the device amplitude has been directly calculated by the sensor to determine the linear span of the arm from the "initial position" (such as the end position when the arm hangs naturally) to the "maximum swing position" (such as the farthest point of the forward swing). The device amplitude value can be directly used as the single-way movement distance of the arm (if a round-trip movement distance is required, then the device amplitude is multiplied by 2).

[0245] If the device outputs an "angle range" (e.g., a watch indicates "front and rear swing arm angle 30°"), it needs to be combined with the preset "effective swing arm length" (e.g., the distance from the waist reference point to the wrist, preset to 60cm) and converted into a linear movement distance using the arc length formula. The formula is: Swing arm movement distance = device amplitude (angle) × π × effective swing arm length ÷ 180.

[0246] Example: With a device amplitude of 30° and an effective swing arm length of 60cm, the moving distance is calculated as 30×π×60÷180≈31.4cm, meaning the swing arm moves approximately 31.4cm in a single trip.

[0247] Step 801: Determine the vertical distance of the maximum vertical height based on the distance the swing arm moves.

[0248] Vertical distance refers to the vertical height difference between the highest and lowest points of an arm swing.

[0249] First, determine the distance the swing arm moves (the one-way linear distance from the endpoint to the maximum swing position), and obtain the angle between the swing arm trajectory and the horizontal direction (direction angle, which can be determined by the watch gyroscope or preset scene angle); then multiply the distance the swing arm moves by the sine of this direction angle, and the result is the vertical distance of the maximum vertical height (essentially, extracting the vertical component from the total moving distance).

[0250] For example, if the moving distance is 40cm and the direction angle is 20°, then the vertical distance is approximately 40 × sin20° ≈ 13.7cm.

[0251] Step 802: When the acceleration of the swing arm is 0, determine whether the motion state of the swing arm tends to be stable.

[0252] The arm swing motion becomes stable when the arm swing enters a state of uniform speed or stillness.

[0253] When the arm swing acceleration is 0, it means that the arm and the wearer's limbs (mainly the head) are relatively still. This allows us to determine whether the arm swing motion is stable and whether the position where the arm swing acceleration is 0 is suitable for taking pictures of the wearer.

[0254] Step 803: Collect the horizontal displacement of the swing arm and combine it with the vertical distance to determine the current spatial position of the swing arm.

[0255] The current spatial position of the swing arm refers to the real-time position coordinates of the arm in three-dimensional space.

[0256] First, establish a three-dimensional coordinate system with the waist as the reference origin (X-axis: front-back direction, Y-axis: left-right direction, Z-axis: vertical direction); collect the front-back displacement (X-axis) and left-right displacement (Y-axis) of the swing arm in the horizontal plane to obtain the horizontal displacement coordinates (X,Y); then combine the determined vertical distance (Z-axis, positive upward) with the horizontal displacement coordinates and vertical distance to integrate the three-dimensional coordinates (X,Y,Z), and the current spatial position of the swing arm endpoint can be determined.

[0257] Step 804: Retrieve the preset swing arm fixed point position parameters and compare the current spatial position with the fixed point position parameters.

[0258] The swing arm fixed position parameters refer to the preset standard swing arm position parameters.

[0259] First, retrieve the preset key position parameters of the swing arm from the system or device (usually three-dimensional coordinates, such as the X-axis forward / backward displacement, Y-axis left / right displacement, and Z-axis vertical height of the highest point of the forward swing and the lowest point of the backward swing, and the parameters are preset based on the individual arm length and the motion scenario). For example: front swing fixed point: X=28cm, Y=6cm, Z=20cm); then, using the same three-dimensional coordinate system as a reference, compare the real-time calculated current spatial position coordinates of the swing arm (X current, Y current, Z current) with the preset point coordinates one by one, calculate the difference of each axis coordinate (ΔX=X current-X preset, ΔY=Y current-Y preset, ΔZ=Z current-Z preset), and determine whether the difference is within the preset allowable error range (such as ±3cm), thereby determining whether the current swing arm position meets the expected fixed point requirements.

[0260] Step 805: If the current spatial position is consistent with the fixed point position parameters, confirm that the swing arm has reached the fixed point and send out a shooting signal.

[0261] The shooting signal refers to the electrical signal that triggers the device to take a picture.

[0262] If the current spatial position matches the fixed position parameters, it means that the arm and the wearer's limbs (mainly the head) are relatively still. Once the arm swing reaches the fixed point, a shooting signal is sent to take a picture.

[0263] If the current spatial position is inconsistent with the fixed point position parameters, and the arm has not reached the fixed point, no shooting will be performed.

[0264] Step 806: In response to the shooting signal, the preset shooting device on the device is activated, and a shot is taken when the swing arm reaches the fixed point to obtain a fixed-point shooting image.

[0265] The shooting device refers to the camera component on the equipment.

[0266] When a shooting signal is received, the device is activated to capture images from a fixed point.

[0267] The method for determining whether a garment is being worn includes the following steps: Step 900: Extract sharpness data from the fixed-point captured image.

[0268] Sharpness data refers to the quantitative value of the sharpness of an image.

[0269] First, the images captured at fixed points are preprocessed (e.g., noise removal, grayscale unification, and avoiding interference from color or speckle interference with sharpness assessment). Then, image sharpness quantification algorithms are used to extract key data—common methods include calculating the grayscale gradient of image edges using the Sobel operator (the higher the gradient value, the sharper the edge and the better the sharpness), or extracting the proportion of high-frequency components using Fourier transform (the more high-frequency components, the more complete the image details are preserved, and the higher the sharpness). Finally, the calculated gradient mean, high-frequency proportion, and other values ​​are used as the image sharpness data, which can be further compared with a preset sharpness threshold (e.g., a gradient mean ≥30 indicates sharpness, <20 indicates blurriness).

[0270] Step 901: Identify blurred areas in the image based on resolution data and fixed-point captured images.

[0271] Blurred areas refer to regions in an image that lack sufficient clarity.

[0272] First, the sharpness data (such as gradient values ​​and the proportion of high-frequency components) is mapped one-to-one with the pixel positions of the fixed-point captured image to generate a "sharpness heatmap"—the darker the color (or the lower the value) in the heatmap, the lower the sharpness of the corresponding area in the image; then, a sharpness threshold is set (such as gradient mean < 20), and pixel areas in the heatmap below the threshold are marked as "suspected blurry areas"; then, edge detection (such as the Canny algorithm) is used to verify the suspected areas: if the edges in the area are broken or lack details (no obvious texture or outline), the false judgment of "solid color area without details" is excluded, and it is confirmed as a real blurry area; finally, adjacent small blurry areas are integrated, and the position and range of the blurry area are marked on the original image with a selection box or color block to complete the recognition.

[0273] Step 902: If there are blurred areas, extract the color distribution and edge contour parameters of the blurred areas.

[0274] Color distribution refers to the color composition of a blurred area.

[0275] Edge contour parameters refer to the shape parameters of the boundary of the fuzzy region.

[0276] If there are blurred areas, it will lead to inaccurate recognition. Image correction is used to process the image and extract the color distribution and edge contour parameters of the blurred areas.

[0277] Step 903: Retrieve images of the corresponding area taken within the same time period.

[0278] The corresponding region image refers to other images located at the same position as the blurred region.

[0279] Once started, the camera takes continuous shots, allowing for the retrieval of multiple images for image restoration.

[0280] Step 904: Based on the color distribution of the blurred area, overlap the corresponding area image with the blurred area.

[0281] Overlap refers to the process of image superimposition and alignment.

[0282] By obtaining the RGB channel mean, hue (H) distribution ratio, saturation (S), and brightness (V) value range of the blurred area through pixel statistics, a "color feature template" for the area is generated (e.g., the main color is light gray, the RGB mean values ​​are 220, 220, and 220 respectively, and the hue is concentrated in 200-220°). Then, in the corresponding area image (the clear image used to fill the blur), sub-regions that match the "color feature template" are selected. By calculating the color similarity between the sub-region and the blurred area (e.g., RGB mean error ≤ ±15, hue overlap ≥ 80%), the appropriate filling sub-region is determined. Based on the edge coordinates of the blurred area, the color-matching sub-region is translated and scaled to the position of the blurred area. Pixel-level alignment is used to achieve overlap, and color gradient fusion (e.g., Gaussian blur transition) is applied to the overlap boundary to eliminate splicing marks and complete the accurate filling of the blurred area.

[0283] Step 905: Adjust the overlapping areas by combining edge contour parameters to create a clear overlapping image.

[0284] Overlapping images refer to composite images whose clarity has been improved after processing.

[0285] Edge contour parameters (including contour coordinates, gradient values, and curvature features) of the images to be overlapped are extracted using edge detection algorithms (such as the Canny algorithm) and used as the structural alignment benchmark. Then, the edge contours of the two images in the overlapping area are compared. For contour misalignment (such as gradient direction deviation or coordinate offset), the image position is finely adjusted according to the principle of contour curvature consistency (such as translation or rotation of the overlapping area) to ensure accurate alignment of key contours (such as object edges and structural lines). Then, combined with sharpness data, the blurry parts in the overlapping area are filled with the contour details of the sharp image (such as replacing the blurry edges of the low-sharpness area with contour textures of high gradient values). Finally, pixel-weighted fusion (gradual transition at the overlapping boundary) is used to eliminate stitching marks, resulting in a structurally aligned and detailed overlapping image.

[0286] Step 906: Obtain facial feature points based on overlapping image recognition.

[0287] Facial feature points refer to the key identification points of a face, such as the eyes and nose.

[0288] First, eliminate color / brightness differences at the splicing boundaries (e.g., Gaussian smoothing transition) to ensure uniform grayscale / color across the image and avoid splicing marks interfering with face detection. Then, use a face detection algorithm (e.g., MTCNN) to locate the face region in the image and select an effective area containing the complete face (from forehead to chin, between the ears). Next, call a face feature point extraction model (e.g., a deep learning-based 68 / 106-point feature point regression model) to identify and output the coordinates of key feature points within the face region—covering core points such as eyebrows (brow peak, eyebrow tail), eyes (corner of eye, pupil), nose (tip of nose, alar of nose), mouth (corner of mouth, cupid's bow), and facial contours (jaw angle, cheekbone). Finally, verify the continuity and rationality of the feature points (e.g., whether the line connecting the feature points of both eyes is horizontal, and whether the tip of the nose is located on the midline of the face), eliminate abnormal points caused by overlapping area errors, and obtain an accurate set of face feature points.

[0289] Step 907: If there is no blurred area, extract the wearer's facial feature points based on the fixed-point captured image.

[0290] The method for extracting the wearer's facial feature points is the same as step 906, and will not be repeated here.

[0291] Step 908: Compare the facial feature points with the device's preset personal facial feature database to determine the wearing status.

[0292] The "Personal Facial Feature Database" refers to the database of user facial features registered on the device. "Dressing Status" refers to the determination of whether the device is worn by the person in question.

[0293] If the facial feature points match the device's facial feature database, then the person wearing the device is the wearer; otherwise, the person is not the wearer. The comparison method is common knowledge known to those skilled in the art and will not be elaborated here.

[0294] Based on the same inventive concept, embodiments of the present invention provide a student sports supervision and management system, comprising: The acquisition module is used to acquire body indicators, exercise data, device location, device account data, exercise duration, arm swing time, device amplitude, arm swing acceleration, and arm horizontal displacement.

[0295] The memory is used to store a program that implements a student sports management and supervision method based on a wearable smartwatch.

[0296] The processor loads and executes programs from memory.

[0297] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0298] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for student sports management and supervision based on a wearable smartwatch, characterized in that, include: Connection protocol of the data acquisition device; Configure the associated parameters of the device according to the connection protocol, and determine the type of physical test item; Define the data collection dimensions based on the type of physical fitness test items; Based on the data collection dimensions, and verify the collected physical test data; The physical fitness test data will be uploaded to the cloud platform simultaneously. Configure data sharing permissions between the cloud platform and preset service providers, parent terminals, and teacher terminals to achieve multi-terminal data linkage; Verify the consistency of data transmission across all terminals to confirm that the body measurement data collection is complete.

2. The student sports management and supervision method based on a wearable smartwatch according to claim 1, characterized in that, The verification and collection of physical test data includes: Extract the physical test item data from the physical test data; Collect the full score thresholds for each physical fitness test item from the teacher's end; The difference in physical fitness test scores is calculated using project data and the full score threshold. Based on project data and pre-set basic physical data of adolescents, analyze the shortcomings in physical fitness; Based on the differences in physical fitness test scores and shortcomings in physical fitness, a physical fitness test analysis report is generated and synchronized to the cloud platform.

3. The student sports management and supervision method based on a wearable smartwatch according to claim 1, characterized in that, After generating the physical fitness analysis report, it includes: Identify physical fitness issues based on the physical fitness test analysis report; Based on the pre-set physical fitness improvement model and physical fitness problems, determine the matching training type; Based on the project data, set a baseline value for training intensity; The training plan is determined based on the training type and training intensity baseline, and then synchronized to the cloud platform; Conduct training according to the training plan and make optimizations and adjustments.

4. The student sports management and supervision method based on a wearable smartwatch according to claim 1, characterized in that, Also includes: Collect body metrics and motion data from the device; Extract the number of steps, speed, and heart rate during exercise from the exercise data; Exercise intensity levels are determined based on exercise speed and heart rate. The exercise efficiency index is determined based on the number of steps and the speed of movement. Physical fitness levels are determined based on exercise intensity levels and exercise efficiency index. Subtract the preset individual fitness level from the physical fitness level to obtain the level range; When the level range exceeds the preset range threshold, the device location of the data acquisition device is recorded. Determine equipment usage based on physical indicators and equipment location; Based on device usage, identify and determine if an error exists, and output error information; The incorrect information is processed and transmitted to the management terminal.

5. A student sports management and supervision method based on a wearable smartwatch according to claim 4, characterized in that, Specific methods for processing based on erroneous information include: The device user's collected account data is matched according to their physical fitness level; Retrieve device account data within the device; Based on the collected account data, the actual device users are matched and defined as wearers, and the collection activity areas of the wearers are obtained. Based on the device account data, the original device user of the device is matched and defined as the owner, and the device activity area is matched. The rendezvous point is determined based on the data collection activity area and the equipment activity area; When a rendezvous point exists, matching and exchanging data is performed based on the collected account data and the device account data.

6. A student sports management and supervision method based on a wearable smartwatch according to claim 4, characterized in that, Methods for determining equipment usage include: The current location of the equipment is determined based on its location, and historical data is retrieved to determine the current activity area of ​​the equipment. Determine the distance difference based on the current location and the activity area; When the distance difference exceeds the preset distance threshold, the system matches the corresponding device user based on the physical fitness level and the preset personal fitness level, and determines the device usage status as replacement use. When the distance difference is not greater than the preset distance threshold, the risk of disease is determined based on the body data; When there is a risk of illness, the device usage is considered abnormal. If there is no risk of illness, the device is considered to be in misuse.

7. A student sports management and supervision method based on a wearable smartwatch according to claim 6, characterized in that, When used incorrectly, including: The results of the basic exercise conditions are determined based on the exercise efficiency index and the preset exercise baseline threshold. Data collected during the swing arm movement is used to plot the swing arm motion curve, which serves as the swing arm motion feature. The swing arm feature judgment result is determined based on the swing arm motion feature and the preset swing arm motion feature. The duration of the data acquisition device's movement, the amplitude of the device's swing arm, and the acceleration of the swing arm; The exercise load is determined based on the duration of exercise and the fluctuation of heart rate during exercise. Based on the exercise load, combined with the judgment results of the basic exercise conditions and the judgment results of the arm swing characteristics, it is comprehensively determined whether the wearer is in an exercise state. If it is determined that the wearer is in motion, the device is activated and the wearer is photographed to determine the wearing status; The collected exercise data will be retained if the wearer is the individual wearing the device. If the person wearing the device is not the wearer, the collected exercise data will be deleted.

8. A student sports management and supervision method based on a wearable smartwatch according to claim 7, characterized in that, Methods for drawing the motion curve of a swing arm include: Collect the swing arm motion time during swing arm operation; Calculate the distance from the end point of the swing arm to the waist based on the swing arm amplitude and the preset effective length of the swing arm; The armpit swing angle is determined based on the arm swing amplitude and preset torso verticality parameters; Calculate the instantaneous velocity of the arm based on the acceleration and duration of the arm swing motion; With the swing arm movement time as the horizontal axis and the distance from the swing arm endpoint to the waist, the armpit swing arm angle, and the instantaneous speed of the swing arm as the vertical axes, sub-curves are generated to show how each parameter changes over time. By integrating the individual curves and marking the distance from the arm endpoint to the waist, the arm angle under the armpit, and the instantaneous velocity of the arm at key time points, the arm motion curve is obtained.

9. A student sports management and supervision method based on a wearable smartwatch according to claim 8, characterized in that, The specific methods for taking photos include: The swing arm movement distance is determined based on the equipment amplitude; The vertical distance of the maximum vertical height is determined based on the distance the swing arm moves. When the acceleration of the swing arm is 0, determine whether the motion state of the swing arm tends to be stable; Collect the horizontal displacement of the swing arm and combine it with the vertical distance to determine the current spatial position of the swing arm; Retrieve the preset swing arm fixed point position parameters and compare the current spatial position with the fixed point position parameters; If the current spatial position matches the fixed point position parameters, confirm that the swing arm has reached the fixed point and send a shooting signal; In response to the shooting signal, the preset shooting device on the equipment is activated, and when the swing arm reaches the fixed point, the shooting is carried out to obtain a fixed-point shooting image.

10. A student sports supervision and management system, characterized in that, include: The acquisition module is used to acquire body metrics, exercise data, and device location. A memory for storing a program that implements any one of the student sports management and supervision methods based on a wearable smartwatch, as described in any one of claims 1 to 9; The processor loads and executes programs from memory.

Images