Monitoring method and system based on health data in exercise program of sporter

Abstract

The invention relates to the technical field of data communication, in particular to a monitoring method and system based on health data in an exercise program of an exerciser, and the method comprises the steps: completing the obtaining of an exercise behavior in use; synchronously positioning and detecting fault data of the sports equipment in use by adopting a sensor technology, analyzing a fault state of the sports equipment in use in a period according to a positioning result, and obtaining synchronous performance data of the sports equipment in use; for the in-use sports equipment with sequential fixed actions, completing big data acquisition of sports actions of the sports equipment in historical use, generating a common action line, collecting in-use sports behaviors in a sports scene in use, and completing high-frequency prediction and warning; and completing identification of potential high-frequency processing behaviors of the sports equipment in use according to the sports equipment sensor data and the sports actions. The method has a real-time processing capability, can accurately measure motion in real time, and adapts to complex environment changes.

Description

Technical Field

[0001] The present invention relates to the field of data communication technology, and in particular to a method and system for monitoring health data in an athlete's exercise program. Background Art

[0002] In recent years, with the rapid development of information technology, dynamic remote motion measurement technology has been widely used in the field of remote medical monitoring and health management. This technology allows the estimation of an individual's health status by analyzing dynamic images, providing a convenient and effective way to monitor health.

[0003] However, despite the progress made in many aspects, existing dynamic-based motion monitoring systems still face some important challenges in practical applications. First, these systems often lack sufficient real-time processing capabilities when processing high-resolution dynamic data and cannot output measurement results in a timely manner, which directly affects the user experience and practicality of the system. Second, the system has difficulty in accurately measuring motion in dynamic environments, especially when the user makes small movements, and the accuracy is affected. In addition, when the system faces complex environmental changes, such as lighting conditions and background interference, it often cannot maintain stable and accurate output. These environmental factors greatly limit the reliability and accuracy of the system in a wider range of application scenarios.

[0004] In the prior art, due to insufficient real-time processing capabilities and sensitivity to dynamic environments and environmental changes, there is a problem of being unable to accurately measure motion in real time and adapt to complex environmental changes. Summary of the invention

[0005] To achieve the above object, the present invention has the following technical solutions: According to a first aspect of the present invention, the present invention claims protection for a health data monitoring system based on an athlete's exercise program, comprising an exercise behavior snapshot unit, an athlete snapshot unit, an exercise equipment maintenance unit, an exercise action storage unit, a metadata correspondence and quality inspection matching unit, a reminder and verification unit, and a standard score control unit; The sports behavior snapshot unit stores metadata features of the sports equipment in use and forms a set of sports equipment in use; The athlete snapshot unit stores athletes corresponding to the sports equipment in use and forms an athlete set; The sports equipment maintenance unit includes a sensor that synchronously locates and detects fault data of the sports equipment in use, and sends the fault data to the sports equipment maintenance unit; The sports action storage unit receives the fault data sent by the sports equipment maintenance unit and stores the action form and sequence data of the sports equipment in use; The metadata corresponding and quality inspection and matching unit corresponds to the sports scene image sensor. For the sports equipment in use, the corresponding athlete needs to submit the current expression video to the quality inspection and matching unit every day. In the sports equipment action program in use, the sports scene image sensor is used to complete the query of the sports equipment identification in the picture, and the legitimacy of the sports equipment in use is judged by referring to the sports equipment set in use and the current expression video, and a high-frequency signal is sent to the reminder and verification unit; The reminder and verification unit receives high-frequency signal data and gives reminders, and has verification and processing functions; The standard score control unit sets the standard score for the sports equipment in use, and uses artificial intelligence to identify potential high-frequency processing behaviors of the sports equipment in use based on the data from the sports equipment maintenance unit, the sports action storage unit, and the metadata correspondence and quality inspection matching unit, lowers or increases the standard score, sets the level of the sports equipment in use based on the score range, and sends it to the metadata correspondence and quality inspection matching unit.

[0006] Furthermore, the sports behavior snapshot unit stores metadata features of the sports equipment in use, including the identification of the sports equipment in use, the appearance video of the sports equipment in use, the age of the sports equipment in use, and the model of the sports equipment in use, and forms a set of sports equipment in use, and completes file creation and storage for each sports equipment in use; The athlete snapshot unit stores the athlete corresponding to the sports equipment in use, including basic data of the athlete corresponding to the sports equipment in use, and forms an athlete set corresponding to the sports equipment in use; The sports equipment maintenance unit includes a sensor, which synchronously locates and detects fault data of the sports equipment in use, and analyzes the fault state of the sports equipment in use within a cycle based on the positioning result, obtains the synchronous performance data of the sports equipment in use, and submits it to the standard sub-control unit; The sports action storage unit receives the fault data sent by the sports equipment maintenance unit, stores the action form and sequence data of the sports equipment in use, uses big data to refer to the normal sports action data, collects the deformation data in the action program of the sports equipment in use, and submits it to the standard sub-control unit; The reminder and verification unit includes a voice reminder, which receives the high-frequency signal of the metadata correspondence and quality inspection matching unit, uses the voice reminder to remind the maintainer, and completes the verification for the high-frequency data.

[0007] Furthermore, the metadata corresponding and quality inspection and matching unit corresponds to the sports scene image sensor. For the sports equipment in use, the corresponding athlete needs to submit the current expression video to the quality inspection and matching unit every day. In the sports equipment action program in use, the sports scene image sensor is used to complete the query for the sports equipment identification in the picture, and the legitimacy of the sports equipment in use is judged by referring to the sports equipment set in use and the current expression video, and a high-frequency signal is sent to the reminder and verification unit. The specific steps are: Corresponding to the sports scene image sensor, the sports scene image sensor collects the monitoring video frames of the sports equipment in the sports scene, and detects the identification of the sports equipment, and completes the query for the identification of the sports equipment in the monitoring video frame with reference to the set of sports equipment in use, collects the sports behavior in use in the monitoring video frame, and adopts random inspection to obtain the sports equipment in use to be checked; The corresponding athletes who are using the sports equipment submit their current expression videos to the metadata correspondence and detection spot check matching unit every day, and the current expression videos are matched to the set of sports equipment in use based on the sports behavior in use; Complete maintenance history detection for the sports equipment in use queried in the surveillance video frame, collect the corresponding current expression video in the set of sports equipment in use, and match and identify legitimacy; Using the test strategy, a secondary test is completed for matching and identifying improperly used sports equipment. If the secondary test fails, a high-frequency signal is sent to the reminder and verification unit.

[0008] Furthermore, the corresponding athlete of the sports equipment in use submits the current expression video to the metadata correspondence and detection spot check matching unit every day. Based on the sports behavior in use, the current expression video is matched to the sports equipment set in use, and the maintenance history detection is completed for the sports equipment in use queried in the monitoring video frame, and the corresponding current expression video in the sports equipment set in use is collected, and the legitimacy of matching recognition is matched. The specific steps are: Image submission and matching: The corresponding athletes of the sports equipment in use submit the current expression video to the metadata matching and detection spot check matching unit every day, and the current expression video corresponds to the sports equipment identification of the sports equipment in use; Image acquisition: Based on the acquired sports equipment in use, current expression video is collected based on effective motion data, and maintenance history video of the sports equipment in use is collected for monitoring video frames; Reference and recognition: For the original surveillance video frames, analyze the expression characteristics of the collected facial parts.

[0009] Furthermore, the use test strategy performs a secondary test on the sports equipment that is improperly used, and if the secondary test fails, a high-frequency signal is sent to the reminder and verification unit. The specific steps are: For sports equipment that is initially in improper use, the sports equipment maintenance unit will perform maintenance and location for the sports equipment that is initially in improper use; According to the image sensor in the sports scene, three different faults are used in turn, and three supplementary matches are completed for the sports equipment that is not in use initially; Three supplementary references are used to identify the preliminary improper use of sports equipment. When the three results are all improper, the sports equipment in use is marked and a high-frequency signal is sent to the reminder and verification unit to complete the manual matching and identification of the final legitimacy.

[0010] Furthermore, the metadata correspondence and quality inspection matching unit also uses a high-frequency prediction detection system to complete the high-frequency prediction of the sports equipment in use for high-frequency sports scenes. The specific steps are: For high-frequency sports scenes, including gyms, homes, and outdoor population POIs, a sports action storage unit is used to obtain the big data of sports actions of sports equipment in use with fixed sequential actions, and generate common action routes; Use sports equipment maintenance units to collect sports behaviors in sports scenes in use, including performance and faults, based on positioning results, and set performance thresholds and time thresholds; In high-frequency sports scenarios, the performance and sports movements of the sports equipment in use are stored. When the performance of the sports equipment in use exceeds the performance threshold for 15 minutes, or the sports movements of the sports equipment in use and the ordinary sports movements in the historical big data are misaligned for more than 40 minutes, a high-frequency signal is submitted to the reminder and verification unit, and a voice reminder is used to remind the maintenance personnel to pay attention to the high-frequency sports equipment in use.

[0011] According to a second aspect of the present invention, the present invention claims protection for a method for monitoring health data based on an athlete's exercise program, wherein the method adopts the above-mentioned system for monitoring health data based on an athlete's exercise program, and comprises the following steps: Complete acquisition of sports behavior in use, including the identification of the sports equipment in use, the appearance video of the sports equipment in use, the age of the sports equipment in use, and the model of the sports equipment in use, and form a collection of sports equipment in use for archiving and preservation. At the same time, obtain the corresponding athletes of the sports equipment in use, including the basic data of the corresponding athletes of the sports equipment in use, and form an athlete collection, which corresponds to the label of the sports equipment in use; Use sensor technology to synchronously locate and detect fault data of sports equipment in use, and analyze the fault status of the sports equipment in use within a cycle based on the positioning results to obtain synchronous performance data of the sports equipment in use; By receiving sports equipment fault data, storing the motion form and sequence data of the sports equipment in use, using big data to refer to the normal motion data, and collecting the deformation data in the motion program of the sports equipment in use; For the corresponding athletes of the sports equipment in use, they need to submit the current expression video every day. The corresponding sports scene image sensor is used to collect the effective motion data of the monitoring video frame based on the circumscribed rectangle detection algorithm and NLP detection technology. According to the set of sports equipment in use, the spot check is completed to obtain the maintenance history video of the sports equipment in use to be checked. The YOLO algorithm is used to collect the maintenance history video of the sports equipment in use to be checked. According to the color recognition result, the Euclidean distance is analyzed to refer to the set of sports equipment in use and the current expression video to judge the legitimacy of the sports equipment in use, and send a high-frequency signal; Based on the POIs of gyms, homes, and outdoor populations, the big data of sports movements of sports equipment in use with fixed sequential movements is acquired, and common movement routes are generated. The sports behaviors in sports scenes in use, including performance and faults, are collected based on sensor technology, and performance thresholds and time thresholds are set. High-frequency predictions and alerts are completed for sports equipment in high-frequency sports scenes. Set standard scores for sports equipment in use, and identify potential high-frequency processing behaviors of sports equipment in use based on the LSTM model according to the sports equipment sensor data and sports movements. Detect the speed-up, slow-down and frequent deformation behaviors of sports equipment in use, lower or increase the standard scores of the corresponding sports equipment, and set the level of sports equipment in use based on the score range to assist in the extraction of sports equipment in use to be checked.

[0012] The present invention relates to the field of data communication technology, and in particular to a method and system for monitoring health data in a sports program of an athlete, which completes the acquisition of sports behavior in use; uses sensor technology to synchronously locate and detect fault data of sports equipment in use, and analyzes the fault state of the sports equipment in use within a cycle based on the positioning result, and obtains the synchronous performance data of the sports equipment in use; for sports equipment in use with sequential fixed actions, completes the acquisition of big data of sports actions of sports equipment in use in history, generates common action lines, collects sports behaviors in use in sports scenes, completes high-frequency prediction and alerts; and completes the identification of potential high-frequency processing behaviors of sports equipment in use based on sports equipment sensor data and sports actions. The present invention has real-time processing capabilities, can accurately measure movements in real time and adapt to complex environmental changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A system structure diagram of a health data monitoring system based on an athlete's exercise program claimed for protection in an embodiment of the present invention; Figure 2A workflow diagram of a method for monitoring health data in an athlete's exercise program as claimed in an embodiment of the present invention. DETAILED DESCRIPTION

[0014] The following will clearly and completely describe the technical solutions in the embodiments of the present invention based on the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0015] The terms "first", "second" and "third" in the present invention only describe the purpose, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention only explain the relative fault relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a program, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these programs, methods, products or devices.

[0016] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in accordance with the embodiments may be included in at least one embodiment of the present invention. Each occurrence of the phrase in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive of other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be dependent on other embodiments.

[0017] like Figure 1 As shown, the health data monitoring system based on the athlete's exercise program includes a sports behavior snapshot unit, an athlete snapshot unit, a sports equipment maintenance unit, a sports action storage unit, a metadata correspondence and quality inspection matching unit, a reminder and verification unit, and a standard score control unit; The sports behavior snapshot unit stores metadata features of the sports equipment in use, including the identification of the sports equipment in use, the appearance video of the sports equipment in use, the age of the sports equipment in use, and the model of the sports equipment in use, and forms a collection of sports equipment in use, and completes file storage for each sports equipment in use; The athlete snapshot unit stores the athletes corresponding to the sports equipment in use, including basic data of the athletes corresponding to the sports equipment in use, and forms an athlete set corresponding to the tags of the sports equipment in use.

[0018] It should be noted that the data of the sports equipment in use and the corresponding athlete data are stored in a database to facilitate subsequent data collection and reference.

[0019] The sports equipment maintenance unit includes a sensor, which synchronously locates and detects the fault data of the sports equipment in use, and analyzes the fault status of the sports equipment in use within a cycle based on the positioning results, obtains the synchronous performance data of the sports equipment in use, and submits it to the standard sub-control unit; The motion storage unit receives the fault data sent by the sports equipment maintenance unit, stores the motion form and sequence data of the sports equipment in use, uses big data to refer to the normal motion data, collects the deformation data in the motion program of the sports equipment in use, and submits it to the standard sub-control unit; The reminder and verification unit includes a voice reminder, which receives the high-frequency signal of the metadata correspondence and quality inspection matching unit, uses the voice reminder to remind the maintenance person, and completes the verification for the high-frequency data.

[0020] It should be noted that based on the sensor satellite maintenance system, maintenance can be completed for synchronous faults of sports equipment in use. The performance of the sports equipment in use can be analyzed based on the positioning results of the collected fault data, and the fault status of the sports equipment in use can be judged based on the motion storage.

[0021] The metadata correspondence and quality inspection matching unit corresponds to the sports scene image sensor. For the sports equipment in use, the corresponding athlete needs to submit the current expression video to the unit every day. In the sports equipment action program in use, the sports scene image sensor is used to complete the query of the sports equipment identification in the picture, and the legitimacy of the sports equipment in use is judged by referring to the sports equipment set in use and the current expression video, and a high-frequency signal is sent to the reminder and verification unit; Corresponding to the sports scene image sensor, the sports scene image sensor collects the monitoring video frames of the sports equipment in the sports scene, and detects the identification of the sports equipment, and completes the query for the identification of the sports equipment in the monitoring video frame with reference to the set of sports equipment in use, collects the sports behavior in use in the monitoring video frame, and adopts random inspection to obtain the sports equipment in use to be checked; The corresponding athletes who are using the sports equipment submit their current expression videos to the metadata correspondence and detection spot check matching unit every day, and the current expression videos are matched to the set of sports equipment in use based on the sports behavior in use; Complete maintenance history detection for the sports equipment in use queried in the surveillance video frame, collect the corresponding current expression video in the set of sports equipment in use, and match and identify legitimacy; Using the test strategy, a secondary test is performed on the sports equipment that is improperly used and matched. If the secondary test fails, a high-frequency signal is sent to the reminder and verification unit; Corresponding to the sports venue image sensor, the sports venue image sensor collects monitoring video frames of sports equipment in the sports venue, detects the sports equipment identification, and completes the query for the sports equipment identification in the monitoring video frame with reference to the set of sports equipment in use, and collects the sports behavior in use in the monitoring video frame.

[0022] Morphological processing is used to maintain and collect effective moving parts in the video, and the bounding rectangle detection algorithm is used to detect effective moving parts. The specific steps are: Complete area analysis for each bounding rectangle, use the number of grid cells to determine the size of the bounding rectangle, and analyze the aspect ratio, i.e., the ratio of the width to the height of the rectangle, based on the bounding rectangle of the bounding rectangle; Based on the effective motion area range and aspect ratio range, filter parameters are set, and based on the filter parameters, circumscribed rectangular parts that meet the parameters are filtered out; By adopting NLP detection technology, the video semantics of the circumscribed rectangular area that meets the parameters are collected, and the collected semantic content is matched with the identification of the sports equipment in use in the set of sports equipment in use, so as to filter out the sports equipment in use that appears in the monitoring video frame, complete the spot check, and obtain the sports equipment in use to be checked.

[0023] The corresponding athletes of the sports equipment in use submit the current expression video to the metadata correspondence and detection spot check matching unit every day. Based on the sports behavior in use, the current expression video is matched to the set of sports equipment in use. The maintenance history detection is completed for the sports equipment in use queried in the monitoring video frame, and the corresponding current expression video in the set of sports equipment in use is collected to match and identify legitimacy. The specific steps are as follows: Image submission and matching: The corresponding athletes of the sports equipment in use submit the current expression video to the metadata matching and detection spot check matching unit every day, and the current expression video corresponds to the sports equipment identification of the sports equipment in use; Image acquisition: Based on the acquired sports equipment in use, current expression video is collected based on effective motion data, and maintenance history video of the sports equipment in use is collected for monitoring video frames; Reference and recognition: For the original surveillance video frames, analyze the expression characteristics of the collected facial parts.

[0024] Using the test strategy, a secondary test is performed on the sports equipment that is improperly used. If the secondary test fails, a high-frequency signal is sent to the reminder and verification unit. The specific steps are as follows: For sports equipment that is initially in improper use, the sports equipment maintenance unit shall perform maintenance and location for the sports equipment that is initially in improper use; According to the image sensor in the sports scene, three different faults are used in turn, and three supplementary matches are completed for the initial non-use sports equipment; Three supplementary references are used to identify the preliminary improper use of the sports equipment. When the three results are all improper, the sports equipment in use is marked and a high-frequency signal is sent to the reminder and verification unit to complete the manual matching and identification of the final legitimacy.

[0025] It should be noted that, when three different faults are used in sequence and three supplementary matches are completed for the initially unused sports equipment, the fault types such as the contact surface sliding fault, the device connection fault and the gear fault can be used as the matching verification method for the sports equipment; The three supplementary references are used to make references to corresponding supplementary control samples based on the three types of faults mentioned above, making the monitoring of improper use of sports equipment more comprehensive.

[0026] Three supplementary references are used to reduce the probability of misjudgment and unnecessary manual inspection steps.

[0027] The metadata correspondence and quality inspection matching unit also uses a high-frequency prediction detection system to complete the high-frequency prediction of sports equipment in use for high-frequency sports scenes. The specific steps are as follows: For high-frequency sports scenes, including gyms, homes, and outdoor population POIs, a sports action storage unit is used to obtain the big data of sports actions of sports equipment in use with fixed sequential actions, and generate common action routes; Use sports equipment maintenance units to collect sports behaviors in sports scenes in use, including performance and faults, based on positioning results, and set performance thresholds and time thresholds; In high-frequency sports scenarios, the performance and sports movements of the sports equipment in use are stored. When the performance of the sports equipment in use exceeds the performance threshold for 15 minutes, or the sports movements of the sports equipment in use and the ordinary sports movements in the historical big data are misaligned for more than 40 minutes, a high-frequency signal is submitted to the reminder and verification unit, and a voice reminder is used to remind the maintenance personnel to pay attention to the high-frequency sports equipment in use.

[0028] The gym, home, and outdoor population POIs represent specific gym, home, and outdoor population interest locations.

[0029] It should be noted that based on historical big data, the motion data of similar sports equipment in use in high-frequency sports scenes are obtained, including the performance, motion form, and dwell time data of the sports equipment. The analysis and processing are completed based on the acquired historical data to generate a common motion route.

[0030] The standard score control unit sets the standard score for the sports equipment in use, and based on the data from the sports equipment maintenance unit, the sports action storage unit, and the metadata correspondence and quality inspection matching unit, uses artificial intelligence to identify the potential high-frequency processing behaviors of the sports equipment in use, lowers or increases the standard score, sets the level of the sports equipment in use based on the score range, and sends it to the metadata correspondence and quality inspection matching unit.

[0031] Reference Figure 2 , a method for monitoring health data in an athlete's exercise program, comprising the following steps: 101. Complete the acquisition of the sports behavior in use, including the identification of the sports equipment in use, the appearance video of the sports equipment in use, the age of the sports equipment in use, and the model of the sports equipment in use, and form a collection of sports equipment in use for archiving and preservation. At the same time, obtain the corresponding athletes of the sports equipment in use, including the basic data of the corresponding athletes of the sports equipment in use, and form an athlete collection, which corresponds to the label of the sports equipment in use; 102. Use sensor technology to synchronously locate and detect fault data of the sports equipment in use, and analyze the fault status of the sports equipment in use within a cycle based on the positioning results to obtain synchronous performance data of the sports equipment in use; 103, receiving sports equipment fault data, storing motion form and sequence data of the sports equipment in use, using big data to refer to normal motion data, and collecting deformation data in the motion program of the sports equipment in use; 104. For the corresponding athletes of the sports equipment in use, they need to submit the current expression video every day. The corresponding sports scene image sensor is used to collect the effective motion data of the monitoring video frame according to the circumscribed rectangle detection algorithm and the NLP detection technology. According to the set of sports equipment in use, the spot check is completed to obtain the maintenance history video of the sports equipment in use to be checked. The YOLO algorithm is used to collect the maintenance history video of the sports equipment in use to be checked. According to the color recognition result, the Euclidean distance is analyzed to refer to the set of sports equipment in use and the current expression video to judge the legitimacy of the sports equipment in use, and send a high-frequency signal; 105. Based on the POIs of gyms, homes, and outdoor populations, for the sports equipment in use with fixed sequential actions, complete the acquisition of big data on the sports actions of the sports equipment in use in history, generate common action routes, collect the sports behaviors in the sports scenes in use based on sensor technology, including performance and faults, and set performance thresholds and time thresholds, and complete high-frequency prediction and alerts for the sports equipment in use in high-frequency sports scenes; 106. Set the standard score for sports equipment in use, and identify the potential high-frequency processing behaviors of the sports equipment in use based on the LSTM model according to the sports equipment sensor data and sports movements, detect the speed-up, slow-down and frequent deformation behaviors of the sports equipment in use, lower or increase the standard score of the corresponding sports equipment, and set the level of the sports equipment in use based on the score range, to assist in the extraction of the sports equipment in use to be checked.

[0032] In the several embodiments of the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the interval setting of the unit is only a logical function interval setting. There may be other interval setting methods in actual implementation. For example, multiple units or components can be based on or can be integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection of some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0033] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional units. The above is only an implementation mode of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the specification and drawings of the present invention, or directly or indirectly used in other corresponding technical fields, is also included in the patent protection scope of the present invention.

[0034] The above detailed description of the specific implementation of the invention is only used as an example, and the present invention is not limited to the specific implementation described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the present invention. Therefore, the equalization, modification, improvement, etc. made without departing from the spirit and principle of the present invention should be included in the scope of the present invention.

Claims

1. A health data monitoring system based on an athlete's exercise program, characterized by: It includes a sports behavior snapshot unit, a sportsperson snapshot unit, a sports equipment maintenance unit, a sports action storage unit, a metadata correspondence and quality inspection matching unit, a reminder and verification unit, and a standard score control unit; The sports behavior snapshot unit stores metadata features of the sports equipment in use and forms a set of sports equipment in use; The athlete snapshot unit stores athletes corresponding to the sports equipment in use and forms an athlete set; The sports equipment maintenance unit includes a sensor that synchronously locates and detects fault data of the sports equipment in use, and sends the fault data to the sports equipment maintenance unit; The sports action storage unit receives the fault data sent by the sports equipment maintenance unit and stores the action form and sequence data of the sports equipment in use; The metadata corresponding and quality inspection and matching unit corresponds to the sports scene image sensor. For the sports equipment in use, the corresponding athlete needs to submit the current expression video to the quality inspection and matching unit every day. In the sports equipment action program in use, the sports scene image sensor is used to complete the query of the sports equipment identification in the picture, and the legitimacy of the sports equipment in use is judged by referring to the sports equipment set in use and the current expression video, and a high-frequency signal is sent to the reminder and verification unit; The reminder and verification unit receives high-frequency signal data and gives reminders, and has verification and processing functions; The standard score control unit sets the standard score for the sports equipment in use, and uses artificial intelligence to identify potential high-frequency processing behaviors of the sports equipment in use based on the data from the sports equipment maintenance unit, the sports action storage unit, and the metadata correspondence and quality inspection matching unit, lowers or increases the standard score, sets the level of the sports equipment in use based on the score range, and sends it to the metadata correspondence and quality inspection matching unit.

2. The health data monitoring system based on the exercise program of the athlete as claimed in claim 1, characterized in that: The sports behavior snapshot unit stores metadata features of the sports equipment in use, including the identification of the sports equipment in use, the appearance video of the sports equipment in use, the age of the sports equipment in use, and the model of the sports equipment in use, and forms a set of sports equipment in use, and completes file creation and storage for each sports equipment in use; The athlete snapshot unit stores the athlete corresponding to the sports equipment in use, including basic data of the athlete corresponding to the sports equipment in use, and forms an athlete set corresponding to the sports equipment in use; The sports equipment maintenance unit includes a sensor, which synchronously locates and detects fault data of the sports equipment in use, and analyzes the fault state of the sports equipment in use within a cycle based on the positioning result, obtains the synchronous performance data of the sports equipment in use, and submits it to the standard sub-control unit; The sports action storage unit receives the fault data sent by the sports equipment maintenance unit, stores the action form and sequence data of the sports equipment in use, uses big data to refer to the normal sports action data, collects the deformation data in the action program of the sports equipment in use, and submits it to the standard sub-control unit; The reminder and verification unit includes a voice reminder, which receives the high-frequency signal of the metadata correspondence and quality inspection matching unit, uses the voice reminder to remind the maintainer, and completes the verification for the high-frequency data.

3. The health data monitoring system based on the exercise program of the athlete as claimed in claim 1, characterized in that: The metadata corresponding and quality inspection and matching unit corresponds to the sports scene image sensor. For the sports equipment in use, the corresponding athlete needs to submit the current expression video to the quality inspection and matching unit every day. In the sports equipment action program in use, the sports scene image sensor is used to complete the query for the sports equipment identification in the picture, and the legitimacy of the sports equipment in use is judged by referring to the sports equipment set in use and the current expression video, and a high-frequency signal is sent to the reminder and verification unit. The specific steps are: Corresponding to the sports scene image sensor, the sports scene image sensor collects the monitoring video frames of the sports equipment in the sports scene, and detects the identification of the sports equipment, and completes the query for the identification of the sports equipment in the monitoring video frame with reference to the set of sports equipment in use, collects the sports behavior in use in the monitoring video frame, and adopts random inspection to obtain the sports equipment in use to be checked; The corresponding athletes who are using the sports equipment submit their current expression videos to the metadata correspondence and detection spot check matching unit every day, and the current expression videos are matched to the set of sports equipment in use based on the sports behavior in use; Complete maintenance history detection for the sports equipment in use queried in the surveillance video frame, collect the corresponding current expression video in the set of sports equipment in use, and match and identify legitimacy; Using the test strategy, a secondary test is completed for matching and identifying improperly used sports equipment. If the secondary test fails, a high-frequency signal is sent to the reminder and verification unit.

4. The health data monitoring system based on the exercise program of the athlete as claimed in claim 3, characterized in that: The corresponding athlete of the sports equipment in use submits the current expression video to the metadata correspondence and detection spot check matching unit every day. Based on the sports behavior in use, the current expression video is matched to the sports equipment set in use, and the maintenance history detection is completed for the sports equipment in use queried in the monitoring video frame, and the corresponding current expression video in the sports equipment set in use is collected, and the legitimacy of matching recognition is matched. The specific steps are: Image submission and matching: The corresponding athletes of the sports equipment in use submit the current expression video to the metadata matching and detection spot check matching unit every day, and the current expression video corresponds to the sports equipment identification of the sports equipment in use; Image acquisition: Based on the acquired sports equipment in use, current expression videos are collected based on effective motion data, and maintenance history videos of the sports equipment in use are collected for surveillance video frames; Reference and recognition: For the original surveillance video frames, analyze the expression characteristics of the collected facial parts.

5. The health data monitoring system based on the exercise program of the athlete as claimed in claim 3, characterized in that: The use test strategy is to complete a secondary test for matching and identifying improperly used sports equipment. If the secondary test fails, a high-frequency signal is sent to the reminder and verification unit. The specific steps are as follows: For sports equipment that is initially in improper use, the sports equipment maintenance unit will perform maintenance and location for the sports equipment that is initially in improper use; According to the image sensor in the sports scene, three different faults are used in turn, and three supplementary matches are completed for the sports equipment that is not in use initially; Three supplementary references are used to identify the preliminary improper use of sports equipment. When the three results are all improper, the sports equipment in use is marked and a high-frequency signal is sent to the reminder and verification unit to complete the manual matching and identification of the final legitimacy.

6. The health data monitoring system based on the exercise program of the athlete as claimed in claim 1, characterized in that: The metadata correspondence and quality inspection matching unit also uses a high-frequency prediction detection system to complete the high-frequency prediction of sports equipment in use for high-frequency sports scenes. The specific steps are: For high-frequency sports scenes, including gyms, homes, and outdoor population POIs, a sports action storage unit is used to obtain the big data of sports actions of sports equipment in use with fixed sequential actions, and generate common action routes; Use sports equipment maintenance units to collect sports behaviors in sports scenes in use, including performance and faults, based on positioning results, and set performance thresholds and time thresholds; In high-frequency sports scenarios, the performance and sports movements of the sports equipment in use are stored. When the performance of the sports equipment in use exceeds the performance threshold for 15 minutes, or the sports movements of the sports equipment in use and the ordinary sports movements in the historical big data are misaligned for more than 40 minutes, a high-frequency signal is submitted to the reminder and verification unit, and a voice reminder is used to remind the maintenance personnel to pay attention to the high-frequency sports equipment in use.

7. A method for monitoring health data in an athlete's exercise program, characterized in that: The method adopts the health data monitoring system based on the exercise program of the athlete as described in any one of claims 1 to 6, and comprises the following steps: Complete acquisition of sports behavior in use, including the identification of the sports equipment in use, the appearance video of the sports equipment in use, the age of the sports equipment in use, and the model of the sports equipment in use, and form a collection of sports equipment in use for archiving and preservation. At the same time, obtain the corresponding athletes of the sports equipment in use, including the basic data of the corresponding athletes of the sports equipment in use, and form an athlete collection, which corresponds to the label of the sports equipment in use; Use sensor technology to synchronously locate and detect fault data of sports equipment in use, and analyze the fault status of the sports equipment in use within a cycle based on the positioning results to obtain synchronous performance data of the sports equipment in use; By receiving sports equipment fault data, storing the motion form and sequence data of the sports equipment in use, using big data to refer to the normal motion data, and collecting the deformation data in the motion program of the sports equipment in use; For the corresponding athletes of the sports equipment in use, they need to submit the current expression video every day. The corresponding sports scene image sensor is used to collect the effective motion data of the monitoring video frame based on the circumscribed rectangle detection algorithm and NLP detection technology. According to the set of sports equipment in use, the spot check is completed to obtain the maintenance history video of the sports equipment in use to be checked. The YOLO algorithm is used to collect the maintenance history video of the sports equipment in use to be checked. According to the color recognition result, the Euclidean distance is analyzed to refer to the set of sports equipment in use and the current expression video to judge the legitimacy of the sports equipment in use, and send a high-frequency signal; Based on the POIs of gyms, homes, and outdoor populations, the big data of sports movements of sports equipment in use with fixed sequential movements is acquired, and common movement routes are generated. The sports behaviors in sports scenes in use, including performance and faults, are collected based on sensor technology, and performance thresholds and time thresholds are set. High-frequency predictions and alerts are completed for sports equipment in high-frequency sports scenes. Set standard scores for sports equipment in use, and identify potential high-frequency processing behaviors of sports equipment in use based on the LSTM model according to the sports equipment sensor data and sports movements. Detect the speed-up, slow-down and frequent deformation behaviors of sports equipment in use, lower or increase the standard scores of the corresponding sports equipment, and set the level of sports equipment in use based on the score range to assist in the extraction of sports equipment in use to be checked.

Images