Intelligent stopwatch interaction method and system special for physical education based on mobile terminal APP

Abstract

The invention relates to the technical field of physical exercise timing measurement. The physical education special-purpose intelligent stopwatch interaction method and system based on the mobile terminal APP are provided, and the method comprises the following steps: establishing a communication connection between a hardware stopwatch and the mobile terminal APP through Bluetooth, carrying out multistage pressure induction calibration on the hardware stopwatch, and generating calibrated pressure threshold data; transmitting the effective operation instruction set and the motion data to a mobile terminal APP through a dual-mode communication module, preprocessing the motion data, and generating classified motion state data; switching a display mode of the mobile terminal APP according to a motion type recognition result, rendering a timing state and motion analysis data in real time, and generating a dynamic visual interface; and superposing the virtual guidance to a motion picture captured by a mobile terminal APP, generating an AR enhanced picture and a training report, and synchronizing the training report to a teacher terminal and a student terminal, so as to solve the problems of mistaken touch caused by solidification of a pressure sensing threshold, lack of AR fusion guidance in a dynamic interface and low efficiency of multi-terminal data collaboration in related technologies.

Description

Technical Field

[0001] The present invention relates to the technical field of sports timing measurement, and in particular to a mobile terminal APP-based smart stopwatch interaction method and system for sports teaching. Background Art

[0002] With the increasing demand for intelligent physical education, the deep integration of traditional timing tools and teaching scenarios has become key to improving teaching quality. In physical education classrooms, accurate timing, real-time analysis of movement data, and multi-terminal collaborative feedback are core elements for teachers to understand students' training effects and are also an important foundation for building a digital teaching system.

[0003] However, the relevant technologies have the following problems: the multi-level pressure sensing mechanism has threshold fixation and insufficient scene adaptation, resulting in frequent misoperations in intense exercise scenes; the dynamic visualization interface lacks the AR virtual-reality fusion capability driven by the type of exercise, making it difficult to achieve standardized guidance for multi-modal movements; multi-terminal data collaboration is limited by general communication protocols and cannot support end-to-end automatic synchronization of teaching instructions and training reports. Summary of the Invention

[0004] Based on this, it is necessary to provide a smart stopwatch interaction method and system for physical education based on a mobile APP to address the above technical problems, so as to solve the problems in related technologies such as the solidification of pressure sensing thresholds leading to false touches, lack of AR fusion guidance in dynamic interfaces, and inefficient multi-terminal data collaboration.

[0005] In a first aspect, the present application provides a method for interacting with a smart stopwatch for physical education based on a mobile APP, the method comprising:

[0006] Establish a communication connection between the hardware stopwatch and the mobile app via Bluetooth, perform multi-level pressure sensing calibration on the hardware stopwatch, generate calibrated pressure threshold data, and load the motion sensor anti-mistouch algorithm based on the calibrated pressure threshold data;

[0007] Generates operation instructions based on the three-level pressure detection results of the hardware stopwatch's capacitive pressure sensor. Combined with the hardware stopwatch's built-in accelerometer data and the motion sensor's anti-false touch algorithm, it filters out non-human false touch operations and generates a valid operation instruction set.

[0008] The effective operation instruction set and motion data are transmitted to the mobile APP through the dual-mode communication module, and the motion data is pre-processed through edge computing to generate classified motion status data;

[0009] Switch the display mode of the mobile app based on the results of the exercise type recognition, and render the timing status and exercise analysis data in real time based on the classified exercise status data to generate a dynamic visualization interface;

[0010] Based on augmented reality technology, virtual guidance is superimposed on the motion images captured by the mobile APP to generate AR enhanced images, and a training report is generated based on the classified motion status data. The training report is synchronized to the teacher and student terminals through a private communication protocol.

[0011] Furthermore, the display mode of the mobile app is switched according to the results of the exercise type recognition, and the timing status and exercise analysis data are rendered in real time based on the classified exercise status data to generate a dynamic visualization interface, including:

[0012] Based on the user selection instructions or sensor data features in the sports type recognition results, the display rule library in the preset sports project database is matched to generate a display template that dynamically adapts to the current sports scene;

[0013] The timing status and motion analysis data in the classified motion status data are processed in parallel through independent threads to generate dynamic timing components and multidimensional analysis charts respectively. The dynamic timing components and multidimensional analysis charts are synthesized in real time according to the spatial layout rules of the display template to generate a dynamic visualization interface.

[0014] Furthermore, the dynamic timing component and the multi-dimensional analysis chart are synthesized in real time according to the spatial layout rules of the display template to generate a dynamic visualization interface, including:

[0015] Based on the spatial layout rules of the display template, the coordinate range and size ratio of the timing component area, analysis chart area and auxiliary information area are analyzed to generate dynamic interface area division data;

[0016] Based on the data divided by the dynamic interface area, the display size and position of the dynamic timing components and multi-dimensional analysis charts are adaptively scaled and aligned to generate standardized components that match the display template;

[0017] The graphics rendering engine overlays standardized components onto corresponding areas of the display template according to preset transparency and hierarchical relationships to generate a dynamic visual interface.

[0018] Furthermore, based on the data divided by the dynamic interface area, the display size and position of the dynamic timing component and the multi-dimensional analysis chart are adaptively scaled and aligned to generate standardized components that match the display template, including:

[0019] Based on the coordinate range and size ratio in the dynamic interface area division data, the target display area boundaries of the dynamic timing components and multi-dimensional analysis charts are calculated, and the component size adaptation rules are generated;

[0020] According to the component size adaptation rules, the display ratio of the dynamic timing component and the multi-dimensional analysis chart is proportionally scaled, and the component position is adjusted according to the preset alignment strategy to generate an initial standardized component that matches the display template;

[0021] Detect the display area overlap or overflow problems of the initial standardized components, perform adaptive corrections by dynamically compressing non-core areas or increasing scrolling tolerance, and generate the final adapted standardized components.

[0022] Furthermore, based on the three-level pressure detection results of the hardware stopwatch's capacitive pressure sensor, operation instructions are generated. Combined with the hardware stopwatch's built-in accelerometer data and the motion sensor's anti-mistouch algorithm to filter out non-human accidental touch operations, a valid operation instruction set is generated, including:

[0023] Based on the three-level pressure detection results of the capacitive pressure sensor, the preset first-level, second-level, and third-level operation instruction mapping tables are matched to generate an initial operation instruction set;

[0024] Perform real-time frequency domain feature extraction on accelerometer data, identify high-frequency vibration interference signals, and generate interference feature markers;

[0025] Combining the interference feature markers with the dynamic threshold rules in the motion sensor anti-false touch algorithm, the initial operation instruction set is filtered for false touch operations to generate a valid operation instruction set.

[0026] Furthermore, the interference feature markers are combined with the dynamic threshold rules in the motion sensor anti-false touch algorithm to filter the false touch operations on the initial operation instruction set and generate a valid operation instruction set, including:

[0027] Based on the high-frequency vibration intensity and duration in the interference signature, the dynamic threshold rule in the motion sensor's anti-false touch algorithm is called to generate a false touch determination threshold that matches the current usage scenario;

[0028] According to the trigger timestamp and pressure level of each instruction in the initial operation instruction set, the instructions are sorted according to the preset priority rules to generate a candidate instruction set after priority sorting;

[0029] Combining the false touch judgment threshold with the priority-sorted candidate instruction set, the vibration interference intensity and instruction trigger rationality are synchronously verified through logical AND operations, conflicting instructions are filtered out, and a valid operation instruction set is generated.

[0030] Furthermore, the effective operation instruction set and motion data are transmitted to the mobile APP through the dual-mode communication module, and the motion data is pre-processed through edge computing to generate classified motion status data, including:

[0031] Dynamically activate Bluetooth mode or Wi-Fi transmission mode based on the distance between the mobile app and the hardware stopwatch and the network signal strength to generate an optimized real-time transmission link;

[0032] Based on the optimized real-time transmission link, the motion data is analyzed by multi-threaded frame division through the edge computing node, and the motion state classification and action trajectory feature extraction are performed synchronously to generate classified motion state data with timestamp marks.

[0033] In a second aspect, the present application also provides a smart stopwatch interactive system for physical education based on a mobile APP, the system comprising:

[0034] The communication connection and calibration module is used to establish a communication connection between the hardware stopwatch and the mobile app via Bluetooth, perform multi-level pressure sensing calibration on the hardware stopwatch, generate calibrated pressure threshold data, and load the motion sensor anti-mistouch algorithm based on the calibrated pressure threshold data;

[0035] The operation instruction generation and filtering module is used to generate operation instructions based on the three-level pressure detection results of the hardware stopwatch's capacitive pressure sensor. It combines the hardware stopwatch's built-in accelerometer data and the motion sensor's anti-accidental touch algorithm to filter out non-human accidental touch operations and generate a valid operation instruction set;

[0036] The dual-mode communication and edge computing module is used to transmit the valid operation instruction set and motion data to the mobile app through the dual-mode communication module, and pre-process the motion data through edge computing to generate classified motion status data;

[0037] The dynamic interface adaptation and rendering module is used to switch the display mode of the mobile app according to the results of the exercise type recognition, and to render the timing status and exercise analysis data in real time based on the classified exercise status data to generate a dynamic visual interface;

[0038] The AR enhancement and data collaboration module is used to superimpose virtual guidance on the motion images captured by the mobile app based on augmented reality technology, generate AR enhanced images, and generate training reports based on the classified motion status data. The training reports are synchronized to the teacher and student terminals through a private communication protocol.

[0039] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any method in the first aspect of the present application are implemented.

[0040] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any method in the first aspect of the present application when the computer program is executed by a processor.

[0041] The technical solution provided by the present application includes the following technical effects: by providing a method and system for interacting with a smart stopwatch dedicated to physical education based on a mobile APP, the method includes: establishing a communication connection between a hardware stopwatch and a mobile APP via Bluetooth, performing multi-level pressure sensing calibration on the hardware stopwatch, generating calibrated pressure threshold data, and loading a motion sensor anti-false touch algorithm based on the calibrated pressure threshold data; generating operation instructions based on the three-level pressure detection results of the capacitive pressure sensor of the hardware stopwatch, combining the built-in accelerometer data of the hardware stopwatch and the motion sensor anti-false touch algorithm to filter non-human false touch operations, and generating an effective operation instruction set; transmitting the effective operation instruction set and the motion data through the dual-mode communication module The data is input to the mobile APP, and the motion data is pre-processed through edge computing to generate classified motion status data; the display mode of the mobile APP is switched according to the motion type recognition result, and the timing status and motion analysis data are rendered in real time based on the classified motion status data to generate a dynamic visualization interface; based on augmented reality technology, virtual guidance is superimposed on the motion picture captured by the mobile APP to generate an AR enhanced picture, and a training report is generated based on the classified motion status data, and the training report is synchronized to the teacher and student ends through a private communication protocol to solve the problems in related technologies such as the solidification of pressure sensing thresholds leading to false touches, the lack of AR fusion guidance in dynamic interfaces, and inefficient multi-terminal data collaboration. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 This is a flow chart of a method for interacting with a smart stopwatch for physical education based on a mobile APP in one embodiment of the present invention;

[0044] Figure 2 In one embodiment of the present invention, a dynamic timing component and a multi-dimensional analysis chart are synthesized in real time according to the spatial layout rules of a display template to generate a flow chart of a dynamic visualization interface;

[0045] Figure 3 This is a structural diagram of a smart stopwatch interactive system for physical education based on a mobile APP in one embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the above-mentioned purposes, features and advantages of the present application more clearly understood, the specific implementation methods of the present application are described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to fully understand the present application. However, the present application can be implemented in many other ways than those described herein, and those skilled in the art can make similar improvements without violating the connotation of the application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0047] like Figure 1 As shown, the present application provides a method for interacting with a smart stopwatch for physical education based on a mobile APP, the method comprising:

[0048] S101: Establish a communication connection between the hardware stopwatch and the mobile app via Bluetooth, perform multi-level pressure sensing calibration on the hardware stopwatch, generate calibrated pressure threshold data, and load a motion sensor anti-false touch algorithm based on the calibrated pressure threshold data.

[0049] Specifically, the mobile APP scans the Bluetooth broadcast signal of the hardware stopwatch, completes identity authentication and secure pairing based on the device's unique identifier, and establishes a two-way communication channel; calibration instructions are issued through the mobile APP to guide users to apply three levels of standard pressure, namely light press, medium press, and heavy press, to the capacitive pressure sensor of the hardware stopwatch in turn, and simultaneously collect the capacitance signal amplitude corresponding to each pressure level; the collected original signal is filtered and denoised, and the mean and variance of each level of pressure signal are calculated to generate a dynamic threshold matrix containing the pressure level-signal interval, which is stored as calibrated pressure threshold data; a pressure-operation instruction mapping relationship table is constructed based on the calibration data, and the accelerometer sampling frequency and vibration intensity tolerance parameters are integrated into the motion sensor anti-mistouch algorithm to complete the algorithm configuration and hardware loading.

[0050] S102: Generate an operation instruction based on the three-level pressure detection results of the capacitive pressure sensor of the hardware stopwatch, combine the built-in accelerometer data of the hardware stopwatch and the motion sensor anti-false touch algorithm to filter non-human false touch operations, and generate a valid operation instruction set.

[0051] Specifically, the capacitive pressure sensor of the hardware stopwatch detects the pressure signal in real time, and divides the detected pressure signal into three levels of pressure detection results according to the preset pressure level standard. According to the three-level pressure detection results, the preset operation instruction mapping rules are matched to generate the corresponding initial operation instruction set. At the same time, the real-time data of the built-in accelerometer of the hardware stopwatch is obtained and used as the motion state reference information. The initial operation instruction set and the accelerometer data are input into the motion sensor anti-false touch algorithm model together. Through the comprehensive analysis of pressure operation and motion state, the false touch operations caused by non-human factors such as device shaking and external collisions are filtered out, and the valid instructions that conform to the human operation logic and motion scenes are retained to generate a more accurate and effective operation instruction set.

[0052] S103: Transmit the valid operation instruction set and motion data to the mobile APP through the dual-mode communication module, and pre-process the motion data through edge computing to generate classified motion status data.

[0053] Specifically, the hardware stopwatch integrates the generated valid operation instruction set with the collected motion data and transmits it to the mobile app via a dual-mode communication module. After receiving the data, the app uses edge computing technology to pre-process the motion data, including data cleaning and feature extraction, removing noise and redundant information, and extracting key features. The data is then classified into different motion states based on the motion characteristics, such as running, jumping, and throwing. The classified motion state data is then generated, providing a foundation for subsequent motion analysis and visualization.

[0054] S104: Switch the display mode of the mobile APP according to the result of the exercise type recognition, and render the timing status and exercise analysis data in real time based on the classified exercise status data to generate a dynamic visualization interface.

[0055] Specifically, the mobile app switches to a display mode that matches the current exercise based on the results of the exercise type identification. Afterwards, the app obtains the classified exercise status data from the background data processing module. The data includes detailed information and characteristics of the current exercise. Using the above data, the front-end interface rendering engine of the app updates the interface elements in real time, and displays the timing information and exercise analysis data to the user in the form of dynamic charts, real-time updated numbers and visual graphics, generating a dynamic visual interface. This interface can not only reflect the time progress of the exercise in real time, but also provide analytical data on key exercise indicators such as speed, distance, heart rate, etc., to help users intuitively understand their exercise status and results.

[0056] S105: Based on augmented reality technology, virtual guidance is superimposed on the motion picture captured by the mobile APP to generate an AR enhanced picture, and a training report is generated based on the classified motion status data. The training report is synchronized to the teacher and student terminals through a private communication protocol.

[0057] Specifically, the mobile app uses its camera to capture motion images and simultaneously receives classified motion status data. Based on the motion status data, the backend, using augmented reality technology, determines the content and location of virtual guidance, such as displaying posture adjustment prompt arrows on the running screen. The virtual guidance is integrated with the real-time image to generate an AR-enhanced image. The motion status data is then used to generate a training report, which includes details such as exercise duration and intensity. This report is then securely and quickly synchronized to the teacher and student terminals via a proprietary communication protocol.

[0058] An embodiment of the present application provides an interactive method for a smart stopwatch dedicated to physical education based on a mobile APP, comprising: establishing a communication connection between a hardware stopwatch and a mobile APP via Bluetooth, performing multi-level pressure sensing calibration on the hardware stopwatch, generating calibrated pressure threshold data, and loading a motion sensor anti-false touch algorithm based on the calibrated pressure threshold data; generating an operation instruction based on the three-level pressure detection results of the capacitive pressure sensor of the hardware stopwatch, filtering non-human false touch operations in combination with the built-in accelerometer data of the hardware stopwatch and the motion sensor anti-false touch algorithm, and generating a valid operation instruction set; transmitting the valid operation instruction set and the motion data to the mobile APP through a dual-mode communication module, and Motion data is preprocessed through edge computing to generate classified motion status data; the display mode of the mobile APP is switched according to the motion type recognition results, and the timing status and motion analysis data are rendered in real time based on the classified motion status data to generate a dynamic visualization interface; virtual guidance is superimposed on the motion picture captured by the mobile APP based on augmented reality technology to generate an AR enhanced picture, and a training report is generated based on the classified motion status data. The training report is synchronized to the teacher and student terminals through a private communication protocol to solve the problems in related technologies such as the solidification of pressure sensing thresholds leading to false touches, the lack of AR fusion guidance in dynamic interfaces, and inefficient multi-terminal data collaboration.

[0059] Furthermore, the display mode of the mobile app is switched according to the results of the exercise type recognition, and the timing status and exercise analysis data are rendered in real time based on the classified exercise status data to generate a dynamic visualization interface, including:

[0060] Based on the user selection instructions or sensor data features in the sports type recognition results, the display rule library in the preset sports project database is matched to generate a display template that dynamically adapts to the current sports scene;

[0061] The timing status and motion analysis data in the classified motion status data are processed in parallel through independent threads to generate dynamic timing components and multidimensional analysis charts respectively. The dynamic timing components and multidimensional analysis charts are synthesized in real time according to the spatial layout rules of the display template to generate a dynamic visualization interface.

[0062] Specifically, the mobile app recognizes the type of exercise and switches to the display mode for the corresponding exercise scene if it detects a user selection instruction or sensor data feature that matches a rule in the database. The app processes the classified exercise state data in parallel through independent threads, extracting timing status and exercise analysis data from it. This data is used to generate dynamic timing components and multi-dimensional analysis charts. Subsequently, these components are synthesized in real time according to the preset display template spatial layout rules, forming a dynamic visualization interface. This process ensures that the interface can display exercise-related information in real time and intuitively, providing users with a better user experience.

[0063] like Figure 2 As shown, the dynamic timing component and the multi-dimensional analysis chart are synthesized in real time according to the spatial layout rules of the display template to generate a dynamic visualization interface, including:

[0064] S201: Based on the spatial layout rules of the display template, the coordinate ranges and size ratios of the timing component area, the analysis chart area, and the auxiliary information area are analyzed to generate dynamic interface area division data;

[0065] S202: Based on the dynamic interface area division data, adaptively scale and align the display size and position of the dynamic timing component and the multi-dimensional analysis chart to generate a standardized component that matches the display template;

[0066] S203: Using a graphics rendering engine, the standardized components are superimposed on corresponding areas of the display template according to preset transparency and hierarchical relationships to generate a dynamic visual interface.

[0067] Specifically, based on the spatial layout rules of the display template, the coordinate range and size ratio of the timing component area, analysis chart area and auxiliary information area are determined, thereby generating dynamic interface area division data. Afterwards, the display size and position of the dynamic timing component and the multi-dimensional analysis chart are adaptively scaled and aligned to meet the layout requirements of the display template, thereby forming standardized components that precisely match the template. Afterwards, using the graphics rendering engine, the above-mentioned standardized components are superimposed on the designated area of the display template according to the preset transparency and hierarchical relationship to achieve the generation of a dynamic visual interface. This process involves close collaboration of multiple steps to ensure that the interface elements are reasonably laid out and displayed more accurately, providing users with an intuitive and highly interactive dynamic visual interface, so that sports data can be presented clearly and effectively, helping users better understand and analyze their sports status.

[0068] Furthermore, based on the data divided by the dynamic interface area, the display size and position of the dynamic timing component and the multi-dimensional analysis chart are adaptively scaled and aligned to generate standardized components that match the display template, including:

[0069] Based on the coordinate range and size ratio in the dynamic interface area division data, the target display area boundaries of the dynamic timing components and multi-dimensional analysis charts are calculated, and the component size adaptation rules are generated;

[0070] According to the component size adaptation rules, the display ratio of the dynamic timing component and the multi-dimensional analysis chart is proportionally scaled, and the component position is adjusted according to the preset alignment strategy to generate an initial standardized component that matches the display template;

[0071] Detect the display area overlap or overflow problems of the initial standardized components, perform adaptive corrections by dynamically compressing non-core areas or increasing scrolling tolerance, and generate the final adapted standardized components.

[0072] Specifically, based on the coordinate range and size ratios in the dynamic interface area partitioning data, the target display area boundaries for the dynamic timing component and multi-dimensional analysis chart are calculated, and component size adaptation rules are generated based on the calculation results. Then, according to the component size adaptation rules, the dynamic timing component and multi-dimensional analysis chart are proportionally scaled to ensure that the components are fully and harmoniously displayed in display areas of different sizes. Simultaneously, based on a pre-set alignment strategy, the scaled component positions are precisely adjusted to align with the layout requirements of the display template, thereby generating an initial standardized component that initially matches the display template. The initial standardized component is then tested for display quality. If issues such as display area overlap or overflow are detected, adaptive corrections are made by dynamically compressing non-core areas or increasing scrolling tolerance to optimize the component's display quality, generating a standardized component that fully matches the display template. Through these calculations and adjustments, the dynamic timing component and multi-dimensional analysis chart are presented in the mobile app interface with the optimal layout and proportions, providing users with an intuitive and clear visual experience and facilitating real-time viewing of timing status and motion analysis data.

[0073] Furthermore, based on the three-level pressure detection results of the hardware stopwatch's capacitive pressure sensor, operation instructions are generated. Combined with the hardware stopwatch's built-in accelerometer data and the motion sensor's anti-mistouch algorithm to filter out non-human accidental touch operations, a valid operation instruction set is generated, including:

[0074] Based on the three-level pressure detection results of the capacitive pressure sensor, the preset first-level, second-level, and third-level operation instruction mapping tables are matched to generate an initial operation instruction set;

[0075] Perform real-time frequency domain feature extraction on accelerometer data, identify high-frequency vibration interference signals, and generate interference feature markers;

[0076] Combining the interference feature markers with the dynamic threshold rules in the motion sensor anti-false touch algorithm, the initial operation instruction set is filtered for false touch operations to generate a valid operation instruction set.

[0077] Specifically, on the hardware stopwatch side, the capacitive pressure sensor detects the pressure signal in real time and divides it into three levels of pressure detection results. According to the above three-level pressure detection results, the preset first-level, second-level, and third-level operation instruction mapping tables are matched to generate the corresponding initial operation instruction set. At the same time, the real-time data of the built-in accelerometer of the hardware stopwatch is obtained, and the accelerometer data is subjected to real-time frequency domain feature extraction to identify the high-frequency vibration interference signal and generate an interference feature marker. The initial operation instruction set is combined with the interference feature marker, and the dynamic threshold rule in the motion sensor anti-false touch algorithm is used to filter the false touch operations of the initial operation instruction set, and then a more accurate and effective operation instruction set is generated. The above process ensures the accuracy and reliability of the operation instructions through multi-step collaborative processing, providing a solid foundation for subsequent motion timing and data analysis.

[0078] Furthermore, the interference feature markers are combined with the dynamic threshold rules in the motion sensor anti-false touch algorithm to filter the false touch operations on the initial operation instruction set and generate a valid operation instruction set, including:

[0079] Based on the high-frequency vibration intensity and duration in the interference signature, the dynamic threshold rule in the motion sensor's anti-false touch algorithm is called to generate a false touch determination threshold that matches the current usage scenario;

[0080] According to the trigger timestamp and pressure level of each instruction in the initial operation instruction set, the instructions are sorted according to the preset priority rules to generate a candidate instruction set after priority sorting;

[0081] Combining the false touch judgment threshold with the priority-sorted candidate instruction set, the vibration interference intensity and instruction trigger rationality are synchronously verified through logical AND operations, conflicting instructions are filtered out, and a valid operation instruction set is generated.

[0082] Specifically, based on the intensity and duration of the high-frequency vibration in the interference feature mark, the dynamic threshold rules in the motion sensor's anti-false touch algorithm are called to generate a false touch judgment threshold that fits the current usage scenario. Next, according to the trigger timestamp and pressure level of each instruction in the initial operation instruction set, the instructions are sorted according to the preset priority rules to generate a prioritized candidate instruction set. After that, combining the false touch judgment threshold with the sorted candidate instruction set, logical and operational operations are used to synchronously verify the vibration interference intensity and the rationality of the instruction trigger, filter out conflicting instructions, and generate an accurate and effective operation instruction set. The above process ensures the accuracy and reliability of the operation instruction set through layer-by-layer screening and verification, providing a solid foundation for subsequent motion timing and data analysis.

[0083] Furthermore, the effective operation instruction set and motion data are transmitted to the mobile APP through the dual-mode communication module, and the motion data is pre-processed through edge computing to generate classified motion status data, including:

[0084] Dynamically activate Bluetooth mode or Wi-Fi transmission mode based on the distance between the mobile app and the hardware stopwatch and the network signal strength to generate an optimized real-time transmission link;

[0085] Based on the optimized real-time transmission link, the motion data is analyzed by multi-threaded frame division through the edge computing node, and the motion state classification and action trajectory feature extraction are performed synchronously to generate classified motion state data with timestamp marks.

[0086] Specifically, based on the distance between the mobile APP and the hardware stopwatch and the network signal strength, the Bluetooth mode or Wi-Fi transmission mode is dynamically selected to establish an optimized real-time transmission link. During the data transmission process, in order to ensure the accuracy and real-time nature of the data, multi-threaded frame parsing technology is used to process the motion data. At the same time, edge computing nodes are used to pre-process the motion data, including data cleaning, feature extraction and other operations to remove noise and redundant information and extract key features. In the above process, the motion state classification and motion trajectory feature extraction tasks are also performed synchronously. By analyzing the key parameters in the motion data, such as speed, acceleration, trajectory, etc., the motion state is classified, and the feature points and feature vectors of the motion trajectory are extracted. Then, the classified motion state data with timestamps are generated to provide a basis for subsequent motion analysis and visualization.

[0087] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0088] In one embodiment, if Figure 3 As shown, the present application also provides a smart stopwatch interactive system 300 for physical education based on a mobile APP, the system 300 including:

[0089] The communication connection and calibration module 301 is used to establish a communication connection between the hardware stopwatch and the mobile app via Bluetooth, perform multi-level pressure sensing calibration on the hardware stopwatch, generate calibrated pressure threshold data, and load the motion sensor anti-mistouch algorithm based on the calibrated pressure threshold data;

[0090] The operation instruction generation and filtering module 302 is used to generate operation instructions based on the three-level pressure detection results of the capacitive pressure sensor of the hardware stopwatch, and filter non-human accidental touch operations by combining the accelerometer data built into the hardware stopwatch and the motion sensor anti-accidental touch algorithm to generate a valid operation instruction set;

[0091] The dual-mode communication and edge computing module 303 is used to transmit the valid operation instruction set and motion data to the mobile terminal APP through the dual-mode communication module, and pre-process the motion data through edge computing to generate classified motion state data;

[0092] Dynamic interface adaptation and rendering module 304, used to switch the display mode of the mobile app according to the exercise type recognition result, and render the timing status and exercise analysis data in real time based on the classified exercise status data to generate a dynamic visual interface;

[0093] The AR enhancement and data collaboration module 305 is used to superimpose virtual guidance on the motion picture captured by the mobile APP based on augmented reality technology, generate an AR enhanced picture, and generate a training report based on the classified motion status data, and synchronize the training report to the teacher and student ends through a private communication protocol.

[0094] Specifically, the communication connection and calibration module 301 is responsible for establishing a Bluetooth communication connection between the hardware stopwatch and the mobile APP to ensure the stability and reliability of data transmission. At the same time, the module performs multi-level pressure sensing calibration on the hardware stopwatch, generates accurate calibrated pressure threshold data, and loads the motion sensor anti-false touch algorithm based on this data, laying the foundation for subsequent accurate timing and operation control. Afterwards, the operation instruction generation and filtering module 302 generates an initial operation instruction set based on the three-level pressure detection results of the hardware stopwatch capacitive pressure sensor and the preset mapping table. Then, the built-in accelerometer data of the hardware stopwatch and the motion sensor anti-false touch algorithm are used to filter out non-human false touch operations, generate accurate and effective operation instruction sets, and ensure the accuracy and reliability of timing operations.

[0095] The dual-mode communication and edge computing module 303 transmits the effective operation instruction set and motion data to the mobile APP through the dual-mode communication module. During the transmission process, the Bluetooth or Wi-Fi mode is dynamically selected according to the actual distance and network signal strength to optimize the transmission link. After the mobile APP receives the data, it uses edge computing technology to pre-process the motion data in real time, including data cleaning, feature extraction and other operations to remove noise and redundant information, extract key features, and classify the motion state and extract motion trajectory features. It generates classified motion state data with timestamps to provide clear data support for subsequent motion analysis and visualization. The dynamic interface adaptation and rendering module 304 switches the display mode of the mobile APP according to the results of motion type recognition. Based on the classified motion state data, the timing status and motion analysis data are rendered in real time to generate a dynamic visualization interface. The interface displays key indicators such as exercise time, speed, distance, heart rate, etc. in the form of intuitive charts and graphics, making it convenient for users to understand their own exercise status and results in real time.

[0096] The AR Enhancement and Data Collaboration Module 305 then deeply integrates augmented reality technology, overlaying virtual guidance elements onto the real-life motion footage captured by the mobile app. This generates AR-enhanced images, providing students with intuitive, standardized movement guidance. Simultaneously, detailed training reports are generated based on the classified motion status data. A proprietary communication protocol ensures timely and secure synchronization of these reports between the teacher and student terminals, enabling efficient feedback and data collaboration during the teaching process, helping to improve the quality of physical education and optimize student athletic performance.

[0097] The dynamic interface adaptation and rendering module 304 is further used to:

[0098] Based on the user selection instructions or sensor data features in the sports type recognition results, the display rule library in the preset sports project database is matched to generate a display template that dynamically adapts to the current sports scene;

[0099] The timing status and motion analysis data in the classified motion status data are processed in parallel through independent threads to generate dynamic timing components and multidimensional analysis charts respectively. The dynamic timing components and multidimensional analysis charts are synthesized in real time according to the spatial layout rules of the display template to generate a dynamic visualization interface.

[0100] The dynamic interface adaptation and rendering module 304 is further used to:

[0101] Based on the spatial layout rules of the display template, the coordinate range and size ratio of the timing component area, analysis chart area and auxiliary information area are analyzed to generate dynamic interface area division data;

[0102] Based on the data divided by the dynamic interface area, the display size and position of the dynamic timing components and multi-dimensional analysis charts are adaptively scaled and aligned to generate standardized components that match the display template;

[0103] The graphics rendering engine overlays standardized components onto corresponding areas of the display template according to preset transparency and hierarchical relationships to generate a dynamic visual interface.

[0104] The dynamic interface adaptation and rendering module 304 is further used to:

[0105] Based on the coordinate range and size ratio in the dynamic interface area division data, the target display area boundaries of the dynamic timing components and multi-dimensional analysis charts are calculated, and the component size adaptation rules are generated;

[0106] According to the component size adaptation rules, the display ratio of the dynamic timing component and the multi-dimensional analysis chart is proportionally scaled, and the component position is adjusted according to the preset alignment strategy to generate an initial standardized component that matches the display template;

[0107] Detect the display area overlap or overflow problems of the initial standardized components, perform adaptive corrections by dynamically compressing non-core areas or increasing scrolling tolerance, and generate the final adapted standardized components.

[0108] The operation instruction generation and filtering module 302 is further used to:

[0109] Based on the three-level pressure detection results of the capacitive pressure sensor, the preset first-level, second-level, and third-level operation instruction mapping tables are matched to generate an initial operation instruction set;

[0110] Perform real-time frequency domain feature extraction on accelerometer data, identify high-frequency vibration interference signals, and generate interference feature markers;

[0111] Combining the interference feature markers with the dynamic threshold rules in the motion sensor anti-false touch algorithm, the initial operation instruction set is filtered for false touch operations to generate a valid operation instruction set.

[0112] The operation instruction generation and filtering module 302 is further used to:

[0113] Based on the high-frequency vibration intensity and duration in the interference signature, the dynamic threshold rule in the motion sensor's anti-false touch algorithm is called to generate a false touch determination threshold that matches the current usage scenario;

[0114] According to the trigger timestamp and pressure level of each instruction in the initial operation instruction set, the instructions are sorted according to the preset priority rules to generate a candidate instruction set after priority sorting;

[0115] Combining the false touch judgment threshold with the priority-sorted candidate instruction set, the vibration interference intensity and instruction trigger rationality are synchronously verified through logical AND operations, conflicting instructions are filtered out, and a valid operation instruction set is generated.

[0116] The dual-mode communication and edge computing module 303 is also used to:

[0117] Dynamically activate Bluetooth mode or Wi-Fi transmission mode based on the distance between the mobile app and the hardware stopwatch and the network signal strength to generate an optimized real-time transmission link;

[0118] Based on the optimized real-time transmission link, the motion data is analyzed by multi-threaded frame division through the edge computing node, and the motion state classification and action trajectory feature extraction are performed synchronously to generate classified motion state data with timestamp marks.

[0119] In one embodiment, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0120] In one embodiment, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps in the above-mentioned method embodiments when the computer program is executed by a processor.

[0121] In one embodiment, an embedded real-time operating system (RTOS) runs through the full-link collaborative interaction between the hardware and the mobile terminal, including: the RTOS uses a dynamic priority management mechanism to parallelly schedule the multi-level calibration tasks of the capacitive pressure sensor and the accelerometer signal acquisition tasks on the hardware stopwatch side to ensure real-time updates of the pressure threshold data and high-frequency monitoring of the motion status; at the same time, based on the interrupt nesting technology, the sensor signal is analyzed in the millisecond frequency domain to instantly trigger the dynamic threshold filtering of the anti-false touch algorithm to eliminate erroneous operation instructions caused by device shaking or collision. The network protocol stack module of the RTOS dynamically switches the dual-mode communication link according to the signal quality of the Bluetooth and wireless networks, and coordinates the multi-threaded frame parsing tasks of the data transmission and edge computing nodes to realize real-time classification and feature extraction of motion data, providing a low-latency data stream for mobile interface rendering. On the mobile side, the RTOS task scheduler simultaneously manages the resource allocation of the interface rendering thread and the augmented reality (AR) engine, ensuring that the dynamic visual interface is adaptively laid out according to the type of exercise, while also completing the real-time generation of AR guidance overlays and training reports. Through the lightweight RTOS kernel's Tickless mode, it collaboratively controls the low-power state switching of the hardware stopwatch, ensuring long-term stable operation of the device in teaching scenarios. Based on the RTOS's real-time task synchronization mechanism, a private communication protocol distributes instructions and data according to priority between the teacher, student, and cloud terminals, achieving millisecond-level responses to cross-terminal operation instructions and encrypted synchronization of training reports, building a complete sports teaching data closed loop. Through a unified real-time task scheduling framework, the RTOS implements a complete link from pressure sensing, false touch filtering, data transmission to AR enhanced display, solving key issues such as multi-sensor parallel processing, communication delays, and interface freezes. It provides highly reliable and real-time interactive support for sports teaching scenarios, adapting to the intelligent teaching needs of diverse sports such as running, swimming, and ball games.

[0122] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the components described as separate parts may or may not be physically separated, and the parts displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0123] The above-described embodiments merely represent several implementation methods of the embodiments of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the concept of the embodiments of the present application, and these modifications and improvements fall within the scope of protection of the embodiments of the present application.

Claims

1. A smart stopwatch interaction method for physical education based on a mobile APP, characterized in that: The method comprises: Establishing a communication connection between the hardware stopwatch and the mobile app via Bluetooth, performing multi-level pressure sensing calibration on the hardware stopwatch to generate calibrated pressure threshold data, and loading a motion sensor anti-mistouch algorithm based on the calibrated pressure threshold data; Generate an operation instruction based on the three-level pressure detection results of the capacitive pressure sensor of the hardware stopwatch, combine the accelerometer data built into the hardware stopwatch and the motion sensor anti-mistouch algorithm to filter non-human mistouch operations, and generate a valid operation instruction set; The valid operation instruction set and motion data are transmitted to the mobile terminal APP through the dual-mode communication module, and the motion data is pre-processed through edge computing to generate classified motion state data; Switching the display mode of the mobile APP according to the result of the exercise type recognition, and rendering the timing status and exercise analysis data in real time based on the classified exercise status data to generate a dynamic visual interface; Based on augmented reality technology, virtual guidance is superimposed on the motion picture captured by the mobile APP to generate an AR enhanced picture, and a training report is generated based on the classified motion status data, and the training report is synchronized to the teacher and student ends through a private communication protocol.

2. The method for interacting with a smart stopwatch for physical education based on a mobile APP according to claim 1, characterized in that: The method of switching the display mode of the mobile APP according to the result of the exercise type recognition, and rendering the timing status and exercise analysis data in real time based on the classified exercise status data to generate a dynamic visualization interface includes: Based on the user selection instruction or sensor data features in the sports type recognition result, matching the display rule library in the preset sports project database to generate a display template dynamically adapted to the current sports scene; The timing status and motion analysis data in the classified motion status data are processed in parallel through independent threads to generate dynamic timing components and multidimensional analysis charts respectively. The dynamic timing components and the multidimensional analysis charts are synthesized in real time according to the spatial layout rules of the display template to generate a dynamic visualization interface.

3. The method for interacting with a smart stopwatch for physical education based on a mobile APP according to claim 2, characterized in that: The step of synthesizing the dynamic timing component and the multi-dimensional analysis chart in real time according to the spatial layout rule of the display template to generate a dynamic visualization interface includes: Based on the spatial layout rules of the display template, the coordinate ranges and size ratios of the timing component area, the analysis chart area, and the auxiliary information area are analyzed to generate dynamic interface area division data; Adaptively scaling and aligning the display size and position of the dynamic timing component and the multi-dimensional analysis chart according to the dynamic interface area division data to generate a standardized component that matches the display template; The standardized components are superimposed on the corresponding areas of the display template according to preset transparency and hierarchical relationships through a graphics rendering engine to generate the dynamic visual interface.

4. The method for interacting with a smart stopwatch for physical education based on a mobile APP according to claim 3, characterized in that: The step of adaptively scaling and aligning the display size and position of the dynamic timing component and the multi-dimensional analysis chart according to the dynamic interface area division data to generate a standardized component matching the display template includes: Calculating the target display area boundaries of the dynamic timing component and the multi-dimensional analysis chart based on the coordinate range and size ratio in the dynamic interface area division data, and generating component size adaptation rules; According to the component size adaptation rule, the display ratio of the dynamic timing component and the multi-dimensional analysis chart is proportionally scaled, and the component position is adjusted according to a preset alignment strategy to generate an initial standardized component that matches the display template; The display area overlap or overflow problem of the initial standardized component is detected, and adaptive correction is performed by dynamically compressing the non-core area or increasing the scrolling tolerance to generate the final adapted standardized component.

5. The method for interacting with a smart stopwatch for physical education based on a mobile APP according to claim 1, characterized in that: The three-level pressure detection result of the capacitive pressure sensor based on the hardware stopwatch generates an operation instruction, combines the accelerometer data built into the hardware stopwatch and the motion sensor anti-mistouch algorithm to filter non-human mistouch operations, and generates a valid operation instruction set, including: Based on the three-level pressure detection results of the capacitive pressure sensor, matching the preset first-level, second-level, and third-level operation instruction mapping tables to generate an initial operation instruction set; Performing real-time frequency domain feature extraction on the accelerometer data, identifying high-frequency vibration interference signals, and generating interference feature markers; The interference feature mark is combined with the dynamic threshold rule in the motion sensor anti-false touch algorithm to filter the false touch operations on the initial operation instruction set to generate the valid operation instruction set.

6. The method for interacting with a smart stopwatch for physical education based on a mobile APP according to claim 5, characterized in that: The combining of the interference feature mark and the dynamic threshold rule in the motion sensor anti-false touch algorithm to filter false touch operations on the initial operation instruction set to generate the valid operation instruction set includes: Based on the high-frequency vibration intensity and duration in the interference feature mark, calling the dynamic threshold rule in the motion sensor anti-false touch algorithm to generate a false touch determination threshold that matches the current usage scenario; Sorting the instructions according to a preset priority rule based on the trigger timestamp and pressure level of each instruction in the initial operation instruction set to generate a prioritized candidate instruction set; Combining the false touch determination threshold with the priority-sorted candidate instruction set, the vibration interference intensity and instruction trigger rationality are synchronously verified through logical AND operations, conflicting instructions are filtered, and the valid operation instruction set is generated.

7. The method for interacting with a smart stopwatch for physical education based on a mobile APP according to claim 1, characterized in that: The effective operation instruction set and motion data are transmitted to the mobile terminal APP through the dual-mode communication module, and the motion data is pre-processed by edge computing to generate classified motion state data, including: Dynamically activate Bluetooth mode or Wi-Fi transmission mode based on the distance between the mobile app and the hardware stopwatch and the network signal strength to generate an optimized real-time transmission link; Based on the optimized real-time transmission link, the motion data is subjected to multi-threaded frame parsing through edge computing nodes, motion state classification and motion trajectory feature extraction are performed synchronously, and the classified motion state data with a timestamp is generated.

8. The smart stopwatch interactive system for physical education based on mobile APP is characterized by: The system comprises: A communication connection and calibration module, configured to establish a communication connection between the hardware stopwatch and the mobile app via Bluetooth, perform multi-level pressure sensing calibration on the hardware stopwatch, generate calibrated pressure threshold data, and load a motion sensor anti-mistouch algorithm based on the calibrated pressure threshold data; An operation instruction generation and filtering module, configured to generate an operation instruction based on the three-level pressure detection results of the capacitive pressure sensor of the hardware stopwatch, filter non-human accidental touch operations by combining the accelerometer data built into the hardware stopwatch and the motion sensor anti-accidental touch algorithm, and generate a valid operation instruction set; A dual-mode communication and edge computing module is used to transmit the valid operation instruction set and motion data to the mobile terminal APP through the dual-mode communication module, and pre-process the motion data through edge computing to generate classified motion state data; A dynamic interface adaptation and rendering module, configured to switch the display mode of the mobile app according to the results of the motion type recognition, and to render the timing status and motion analysis data in real time based on the classified motion status data to generate a dynamic visual interface; The AR enhancement and data collaboration module is used to superimpose virtual guidance on the motion picture captured by the mobile APP based on augmented reality technology, generate an AR enhanced picture, and generate a training report based on the classified motion status data, and synchronize the training report to the teacher and student ends through a private communication protocol.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the mobile APP-based smart stopwatch interactive method for physical education described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the mobile APP-based smart stopwatch interactive method for physical education described in any one of claims 1 to 7 are implemented.

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