A data monitoring method and system

Abstract

The present invention relates to the field of data communication technology, and particularly to a method and system for monitoring health data in a sports program of a mover, including: obtaining the completion of a sports behavior during use; using sensor technology to synchronously locate and detect the fault data of the sports equipment during use, and analyzing the fault state of the sports equipment during use within a cycle based on the positioning result to obtain the synchronous performance data of the sports equipment during use; for the sports equipment with sequentially fixed actions during use, obtaining the big data of the movement actions of the sports equipment during historical use, generating a general action line, collecting the sports behavior during the use of the sports scenario during use, and completing high-frequency prediction and alert; identifying the potential high-frequency processing behavior of the sports equipment during use based on the sensor data of the sports equipment and the movement actions. The present invention has real-time processing capabilities and can accurately and real-time measure movements and adapt to complex environmental changes.

Description

Technical Field

[0001] The present invention relates to the field of data communication technology, and particularly to a data monitoring method and system. Background Art

[0002] In recent years, with the rapid development of information technology, dynamic-based remote motion measurement technology has been widely applied to the fields of remote medical monitoring and health management. This technology allows for the estimation of an individual's health condition by analyzing dynamic images, providing a convenient and effective way of health monitoring.

[0003] However, although existing dynamic-based motion monitoring systems have made progress in many aspects, they still face some important challenges in practical applications. First, these systems often lack sufficient real-time processing capabilities when dealing with high-resolution dynamic data and are unable to output measurement results in a timely manner, which directly affects the user experience and the practicality of the system. Second, the system has difficulty accurately measuring motion in a dynamic environment, especially when the user makes small movements, and the accuracy will be affected accordingly. In addition, when the system faces complex environmental changes, such as lighting conditions and background interference, it often fails to 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 are problems 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:

[0006] According to a first aspect of the present invention, the present invention claims a data monitoring system, including a motion behavior snapshot unit, a mover snapshot unit, a sports equipment maintenance unit, a motion action storage unit, a metadata correspondence and quality inspection matching unit, a reminder and verification unit, and a standard score control unit;

[0007] The motion behavior snapshot unit stores the metadata features of the sports equipment in use and forms a set of sports equipment in use;

[0008] The mover snapshot unit stores the movers corresponding to the sports equipment in use and forms a set of movers;

[0009] The sports equipment maintenance unit includes sensors, synchronizes and detects the fault data of the sports equipment in use, and sends the fault data to the sports equipment maintenance unit;

[0010] The movement action storage unit receives the fault data sent by the sports equipment maintenance unit and stores the action forms and sequence data of the sports equipment in use;

[0011] The metadata correspondence and quality inspection matching unit corresponds to the sports occasion image sensor. For the sports equipment in use, the sports person needs to submit the current expression video to the quality inspection matching unit every day. In the action program of the sports equipment in use, the sports occasion image sensor is used to query the sports equipment identifier in the picture. Referring to the set of sports equipment in use and the current expression video, it judges the legitimacy of the sports equipment in use and sends a high-frequency signal to the reminder and verification unit;

[0012] The reminder and verification unit receives the high-frequency signal data and gives a reminder, and has the functions of verification and processing;

[0013] The standard score control unit sets the standard score of the sports equipment in use, and based on the data of the sports equipment maintenance unit, the movement 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, increases or decreases 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.

[0014] Furthermore, the movement behavior snapshot unit stores the metadata features of the sports equipment in use, including the identifier of the sports equipment in use, the appearance video of the sports equipment in use, the age of the sports equipment in use, the model of the sports equipment in use, and forms a set of sports equipment in use, and creates a file for storage for each piece of sports equipment in use;

[0015] The sports person snapshot unit stores the sports person corresponding to the sports equipment in use, including the basic data of the sports person corresponding to the sports equipment in use, and forms a set of sports persons, which corresponds to the label of the sports equipment in use;

[0016] The sports equipment maintenance unit includes sensors, synchronously locates and detects the fault data of the sports equipment in use, analyzes the fault state of the sports equipment in use within the cycle based on the positioning result, obtains the synchronous performance data of the sports equipment in use, and submits it to the standard score control unit;

[0017] The movement action storage unit receives the fault data sent by the sports equipment maintenance unit and stores the action forms and sequence data of the sports equipment in use. Using big data and referring to the normal movement action data, it collects the deformation data in the action program of the sports equipment in use and submits it to the standard score control unit;

[0018] The reminder and verification unit includes a voice reminder. It receives the high-frequency signal from the metadata correspondence and quality inspection matching unit, and uses the voice reminder to remind the maintainer to complete the verification for the high-frequency data.

[0019] Further, the metadata correspondence and quality inspection matching unit corresponds to the image sensor in the sports occasion. For the sports equipment in use, the corresponding athlete needs to submit the current facial expression video to the quality inspection matching unit every day. In the action program of the sports equipment in use, the image sensor in the sports occasion is used to query the identification of the sports equipment in the picture. Referring to the set of sports equipment in use and the current facial expression video, it judges the legitimacy of the sports equipment in use and sends a high-frequency signal to the reminder and verification unit. The specific steps are as follows:

[0020] Corresponding to the image sensor in the sports occasion, it collects the monitoring video frames of the sports equipment in the sports occasion by the image sensor in the sports occasion, and detects the identification of the sports equipment. Referring to the set of sports equipment in use, it queries the identification of the sports equipment in the monitoring video frames, collects the sports behaviors in use in the monitoring video frames, and uses spot checks to obtain the sports equipment in use to be checked;

[0021] The corresponding athlete of the sports equipment in use submits the current facial expression video to the metadata correspondence and inspection spot check matching unit every day. Based on the sports behaviors in use, the current facial expression video is corresponded to the set of sports equipment in use;

[0022] It completes the maintenance history detection for the sports equipment in use queried in the monitoring video frames, and collects the current facial expression video corresponding to the sports equipment in use in the set of sports equipment in use to match and identify the legitimacy;

[0023] Using the test strategy, it completes the secondary test for the sports equipment in use that is matched and identified as improper. If the secondary test fails, it sends a high-frequency signal to the reminder and verification unit.

[0024] Further, the corresponding athlete of the sports equipment in use submits the current facial expression video to the metadata correspondence and inspection spot check matching unit every day. Based on the sports behaviors in use, the current facial expression video is corresponded to the set of sports equipment in use. It completes the maintenance history detection for the sports equipment in use queried in the monitoring video frames, and collects the current facial expression video corresponding to the sports equipment in use in the set of sports equipment in use to match and identify the legitimacy. The specific steps are as follows:

[0025] Image submission and correspondence: The corresponding athlete of the sports equipment in use submits the current facial expression video to the metadata correspondence and inspection spot check matching unit every day, and corresponds the current facial expression video to the identification of the sports equipment of the sports equipment in use;

[0026] Image acquisition: Based on the obtained in-use sports equipment to be inspected, collect the current facial expression video based on the effective motion data, and for the surveillance video frames, collect the maintenance history video of the in-use sports equipment to be inspected;

[0027] Reference and recognition: For the original surveillance video frames, analyze the facial expression features of the collected facial parts.

[0028] Further, for the in-use sports equipment whose matching and recognition is improper, the usage test strategy performs a secondary test. If the secondary test fails, a high-frequency signal is sent to the reminder and verification unit. The specific steps are as follows:

[0029] For the initially improperly used in-use sports equipment, according to the sports equipment maintenance unit, perform maintenance and positioning on the initially improperly used in-use sports equipment;

[0030] According to the motion scene image sensor, successively adopt three different faults to perform three supplementary matches on the initially failed in-use sports equipment;

[0031] Adopt three supplementary references to identify the initially improperly used in-use sports equipment. When all three results are improper, mark the in-use sports equipment and send a high-frequency signal to the reminder and verification unit to complete the final legitimacy of manual matching recognition.

[0032] Further, a high-frequency prediction detection system is also used in the metadata correspondence and quality inspection matching unit to perform high-frequency prediction of the in-use sports equipment for high-frequency motion scenes. The specific steps are as follows:

[0033] For high-frequency motion scenes, including gyms, homes, and outdoor population POIs, use the motion action storage unit to obtain the big data of the motion actions of the in-use sports equipment for the sequentially fixed actions and generate a normal action route;

[0034] Use the sports equipment maintenance unit to collect the in-use motion behaviors in the in-use motion scene according to the positioning result, including performance and faults, and set performance threshold values and time threshold values;

[0035] In high-frequency motion scenes, store the performance and motion actions of the in-use sports equipment. When the performance of the in-use sports equipment exceeds the performance threshold value for 15 minutes, or when the motion actions of the in-use sports equipment are misaligned with the normal motion actions in the historical big data by 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 maintainer to pay manual attention to the high-frequency in-use sports equipment.

[0036] According to the second aspect of the present invention, the present invention claims protection for a data monitoring method, wherein this method uses the above-mentioned data monitoring system and includes the following steps:

[0037] For the acquisition of motion behaviors during use, including the identification of the exercise equipment in use, the appearance video of the exercise equipment in use, the age of the exercise equipment in use, and the model of the exercise equipment in use, and form a set of exercise equipment in use for filing and storage. At the same time, obtain the corresponding exercisers of the exercise equipment in use, including the basic data of the exercisers corresponding to the exercise equipment in use, and form a set of exercisers, which corresponds to the label of the exercise equipment in use;

[0038] Adopt sensor technology to synchronously locate and detect the fault data of the exercise equipment in use, and analyze the fault status of the exercise equipment in use within the cycle based on the positioning results to obtain the synchronous performance data of the exercise equipment in use;

[0039] Adopt receiving the fault data of the exercise equipment, store the action form and sequence data of the exercise equipment in use, and use big data to refer to the normal exercise action data to collect the deformation data in the action program of the exercise equipment in use;

[0040] For the exercisers corresponding to the exercise equipment in use who need to submit the current facial expression video every day, use the corresponding motion scene image sensor, collect the effective motion data of the monitoring video frame according to the external rectangle detection algorithm and NLP detection technology, and complete spot checks based on the set of exercise equipment in use to obtain the maintenance history video of the exercise equipment in use to be checked. Use the YOLO algorithm to collect the maintenance history video of the exercise equipment in use to be checked. According to the color recognition result, use the analysis of Euclidean distance to refer to the set of exercise equipment in use and the current facial expression video to judge the legitimacy of the exercise equipment in use and send high-frequency signals;

[0041] Based on the POI of gyms, families, and outdoor populations, for the exercise equipment with fixed-sequence actions in use, complete the acquisition of big data on the exercise actions of the exercise equipment in use in history, generate ordinary action routes, and collect the motion scenarios and motion behaviors during use of the exercise equipment in use according to sensor technology, including performance and faults, and set performance threshold values and time threshold values. For the exercise equipment in use in high-frequency motion scenarios, complete high-frequency prediction and alert;

[0042] Set the standard score of the exercise equipment in use, and based on the sensor data and motion actions of the exercise equipment, use the LSTM model to complete the identification of potential high-frequency processing behaviors of the exercise equipment in use, detect the behaviors of the exercise equipment in use that become faster, slower, and frequently deformed, increase or decrease the corresponding standard score of the exercise equipment, and set the level of the exercise equipment in use based on the score range to assist in the extraction of the exercise equipment in use to be checked.

[0043] The present invention relates to the field of data communication technologies, and particularly to a data monitoring method and system. It acquires the motion behaviors during use; uses sensor technology to synchronously locate and detect the fault data of the exercise equipment during use, and analyzes the fault status of the exercise equipment during use within a cycle based on the positioning results to obtain the synchronization performance data of the exercise equipment during use; for the exercise equipment with sequentially fixed actions, it acquires the big data of the motion actions of the exercise equipment during historical use, generates an ordinary action line, collects the motion scenarios and motion behaviors during use, and completes high-frequency prediction and alert; and identifies the potential high-frequency processing behaviors of the exercise equipment during use based on the sensor data of the exercise equipment and the motion actions. The present invention has real-time processing capabilities and can accurately and real-time measure motion and adapt to complex environmental changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a system structure diagram of a data monitoring system claimed and protected in an embodiment of the present invention;

[0045] Figure 2 It is a working flowchart of a data monitoring method claimed and protected in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described based on the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0047] The terms "first", "second", and "third" in the present invention are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first", "second", and "third" may explicitly or implicitly include at least one of these features. In the description of the present invention, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically and clearly defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only for explaining the relative fault relationship and motion conditions between components in a specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. 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 further includes steps or units not listed, or optionally further includes other steps or units inherent to these programs, methods, products, or devices.

[0048] As used herein, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment can be included in at least one embodiment of the present invention. The phrase does not necessarily refer to the same embodiment every time it appears in the specification, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0049] As Figure 1 shown, a data monitoring system includes a motion behavior snapshot unit, a mover snapshot unit, a sports equipment maintenance unit, a motion action storage unit, a metadata correspondence and quality inspection matching unit, a reminder and verification unit, and a standard score control unit;

[0050] The motion behavior snapshot unit stores the metadata features of 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 the filing and storage for each piece of sports equipment in use;

[0051] The mover snapshot unit stores the movers corresponding to the sports equipment in use, including the basic data of the movers corresponding to the sports equipment in use, and forms a set of movers, which corresponds to the labels of the sports equipment in use.

[0052] It should be noted that the databases of the data of the sports equipment in use and the corresponding mover data are established and stored to facilitate subsequent data collection and reference.

[0053] The sports equipment maintenance unit includes sensors, synchronously locates and detects the fault data of the sports equipment in use, analyzes the fault status of the sports equipment in use within a cycle according to the positioning results, obtains the synchronous performance data of the sports equipment in use, and submits it to the standard score control unit;

[0054] The motion action 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, refers to the normal motion action data using big data, collects the deformation data in the motion program of the sports equipment in use, and submits it to the standard score control unit;

[0055] The reminder and verification unit includes a voice reminder, receives the high-frequency signals from the metadata correspondence and quality inspection matching unit, and uses the voice reminder to remind the maintainer to complete the verification for the high-frequency data.

[0056] It should be noted that based on the sensor satellite maintenance and repair system, it is possible to complete the maintenance and repair of synchronous faults of sports equipment in use. According to the positioning results of the collected fault data, the performance of the sports equipment in use is analyzed, and the fault status of the sports equipment in use is judged based on the stored motion actions.

[0057] The metadata correspondence and quality inspection matching unit corresponds to the motion occasion image sensor. For the sports equipment in use, the corresponding athlete needs to submit the current facial expression video to this unit every day. In the motion program of the sports equipment in use, the motion occasion image sensor is used to query the identification of the sports equipment in the picture. Referring to the set of sports equipment in use and the current facial expression video, the legitimacy of the sports equipment in use is judged, and a high-frequency signal is sent to the reminder and verification unit;

[0058] The corresponding motion occasion image sensor collects the monitoring video frames of the sports equipment in the motion occasion by the motion occasion image sensor, and detects the identification of the sports equipment. Referring to the set of sports equipment in use, the identification of the sports equipment in the monitoring video frames is queried, the motion behavior in use in the monitoring video frames is collected, and sampling is used to obtain the sports equipment in use to be inspected;

[0059] The corresponding athlete of the sports equipment in use submits the current facial expression video to the metadata correspondence and detection sampling matching unit every day. Based on the motion behavior in use, the current facial expression video is corresponded to the set of sports equipment in use;

[0060] The maintenance history of the sports equipment in use queried in the monitoring video frames is detected, and the current facial expression video corresponding to the set of sports equipment in use is collected to match and identify the legitimacy;

[0061] Using the test strategy, the sports equipment in use that is identified as improper after matching is subjected to a secondary test. If the secondary test fails, a high-frequency signal is sent to the reminder and verification unit;

[0062] The corresponding motion occasion image sensor collects the monitoring video frames of the sports equipment in the motion occasion by the motion occasion image sensor, and detects the identification of the sports equipment. Referring to the set of sports equipment in use, the identification of the sports equipment in the monitoring video frames is queried, and the motion behavior in use in the monitoring video frames is collected.

[0063] Morphological processing is used to maintain and collect the effective motion parts in the video, and the external rectangle detection algorithm is used to detect the effective motion parts. The specific steps are as follows:

[0064] For each external rectangle, area analysis is completed. The number of grids is used to determine the size of the external rectangle, and the aspect ratio of the external rectangle is analyzed based on the external rectangle of the external rectangle, that is, the ratio of the width to the height of the rectangle;

[0065] Set filtering parameters based on the area range and aspect ratio range of effective motion, and filter out the circumscribed rectangle parts that meet the parameters based on the filtering parameters;

[0066] Adopt NLP detection technology to collect the video semantics of the circumscribed rectangle parts that meet the parameters, match the collected semantic content with the identifiers of the in-use sports equipment in the in-use sports equipment set, filter out the in-use sports equipment that appears in the surveillance video frames, and conduct spot checks to obtain the in-use sports equipment to be checked.

[0067] The corresponding exercisers of the in-use sports equipment submit the current facial expression video to the metadata correspondence and detection spot check matching unit every day. Based on the in-use motion behavior, the current facial expression video is corresponded to the in-use sports equipment set, and the maintenance history of the in-use sports equipment queried in the surveillance video frames is detected, and the current facial expression video corresponding to the in-use sports equipment set is collected to match and identify the legitimacy. The specific steps are as follows:

[0068] Image submission and correspondence: The corresponding exercisers of the in-use sports equipment submit the current facial expression video to the metadata correspondence and detection spot check matching unit every day, and correspond the current facial expression video to the sports equipment identifier of the in-use sports equipment;

[0069] Image acquisition: Based on the in-use sports equipment to be checked obtained, collect the current facial expression video based on the effective motion data, and collect the maintenance history video of the in-use sports equipment to be checked for the surveillance video frames;

[0070] Reference and recognition: Analyze the facial expression features of the collected facial parts for the original surveillance video frames.

[0071] Use the test strategy to conduct a second test on the in-use sports equipment with improper matching recognition. If the second test fails, send a high-frequency signal to the reminder and verification unit. The specific steps are as follows:

[0072] For the initially improper in-use sports equipment, based on the sports equipment maintenance unit, perform maintenance and positioning on the initially improper in-use sports equipment;

[0073] According to the image sensor of the sports occasion, adopt three different faults in turn to perform three supplementary matches on the initially failed in-use sports equipment;

[0074] Adopt three supplementary references to identify the initially improper in-use sports equipment. When all three results are improper, mark the in-use sports equipment and send a high-frequency signal to the reminder and verification unit to complete the final legitimacy identification of manual matching.

[0075] It should be noted that when three different faults are successively used and three supplementary matches are completed for the initially failed in-use sports equipment, fault types such as the sliding fault of the contact surface of the equipment, the device connection fault, and the gear fault can be used as the verification method for the passing match of the sports equipment;

[0076] The three supplementary references are respectively based on the above three faults to make corresponding supplementary reference to the control samples, making the monitoring of the improper use of sports equipment more comprehensive.

[0077] Adopting three supplementary references reduces the probability of misjudgment and reduces unnecessary manual inspection links.

[0078] A high-frequency prediction detection system is also used in the metadata correspondence and quality inspection matching unit to complete the high-frequency prediction of in-use sports equipment for high-frequency motion scenarios. The specific steps are as follows:

[0079] For high-frequency motion scenarios, including gyms, homes, and outdoor population POIs, a motion action storage unit is used to obtain the big data of the motion actions of in-use sports equipment for the fixed-sequence actions, and generate ordinary action lines;

[0080] A sports equipment maintenance unit is used to collect the in-use motion behaviors in the in-use motion scenarios according to the positioning results, including performance and faults, and set performance threshold values and time threshold values;

[0081] In high-frequency motion scenarios, the performance and motion actions of in-use sports equipment are stored. When the performance of in-use sports equipment exceeds the performance threshold value for 15 minutes, or when the motion actions of in-use sports equipment are misaligned with the ordinary motion actions of historical big data by 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 maintainer to pay attention to the high-frequency in-use sports equipment manually.

[0082] The gyms, homes, and outdoor population POIs mentioned above represent the specific interest location points of gyms, homes, and outdoor populations.

[0083] It should be noted that based on historical big data, the motion action data of the same type of in-use sports equipment in high-frequency motion scenarios is obtained, including the performance, action form, and residence time data of the sports equipment. Based on the obtained historical data, analysis and processing are completed to generate ordinary action lines.

[0084] The standard score control unit sets the standard score of in-use sports equipment, and based on the data of the sports equipment maintenance unit, the motion action storage unit, and the metadata correspondence and quality inspection matching unit, uses artificial intelligence to identify the potential high-frequency processing behaviors of in-use sports equipment, increase or decrease the standard score, and set the level of in-use sports equipment based on the score range and send it to the metadata correspondence and quality inspection matching unit.

[0085] Refer to Figure 2 , a data monitoring method, comprising the following steps:

[0086] 101. For the acquisition of in-use exercise behavior completion, including the identification of in-use exercise equipment, the appearance video of in-use exercise equipment, the age of in-use exercise equipment, the model of in-use exercise equipment, and form a set of in-use exercise equipment for filing and storage. At the same time, obtain the exercisers corresponding to the in-use exercise equipment, including the basic data of the exercisers corresponding to the in-use exercise equipment, and form a set of exercisers, and correspond to the labels of the in-use exercise equipment;

[0087] 102. Adopt sensor technology to synchronously locate and detect the fault data of in-use exercise equipment, and analyze the fault status of in-use exercise equipment within a cycle according to the positioning results to obtain the synchronous performance data of in-use exercise equipment;

[0088] 103. Receive the fault data of exercise equipment, store the action forms and sequence data of in-use exercise equipment, and use big data to refer to normal exercise action data to collect the deformation data in the action program of in-use exercise equipment;

[0089] 104. For the exercisers corresponding to the in-use exercise equipment, they need to submit the current expression video every day. Adopt the corresponding motion occasion image sensor, collect the effective motion data of the monitoring video frame according to the external rectangle detection algorithm and NLP detection technology, and complete spot checks according to the set of in-use exercise equipment to obtain the maintenance history video of the in-use exercise equipment to be checked. Adopt the YOLO algorithm to collect the maintenance history video of the in-use exercise equipment to be checked. According to the color recognition result, use the analysis of Euclidean distance to refer to the set of in-use exercise equipment and the current expression video to judge the legitimacy of the in-use exercise equipment and send high-frequency signals;

[0090] 105. Based on the POI of gyms, families, and outdoor populations, for the in-use exercise equipment with fixed sequential actions, complete the acquisition of big data on the exercise actions of historical in-use exercise equipment, generate ordinary action routes, collect the in-use exercise behavior in the in-use exercise scenario according to sensor technology, including performance and faults, and set performance threshold values and time threshold values. For the in-use exercise equipment in high-frequency exercise scenarios, complete high-frequency prediction and alert;

[0091] 106. Set the standard score of in-use exercise equipment, and based on the sensor data and exercise actions of exercise equipment, complete the identification of potential high-frequency processing behaviors of in-use exercise equipment based on the LSTM model, detect the behaviors of in-use exercise equipment becoming faster, slower, and frequently deforming, increase or decrease the corresponding standard score of exercise equipment, and set the level of in-use exercise equipment based on the score range to assist in the extraction of in-use exercise equipment to be checked.

[0092] In 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 merely illustrative. For example, the setting of the intervals of the units is only a logical function interval setting. In actual implementation, there may be other interval setting methods. For example, multiple units or components can be based on or integrated into another system, or some features can be ignored or not executed. Another point is that the couplings or direct couplings or communication connections shown or discussed between each other can be indirect couplings or communication connections using some interfaces, devices, or units, and can be in electrical, mechanical, or other forms.

[0093] In addition, each functional unit in various embodiments of the present invention can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units. The above is only the implementation manner of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or equivalent process transformation made using the content of the specification and drawings of the present invention, or directly or indirectly applied in other corresponding technical fields, is equally included in the patent protection scope of the present invention.

[0094] The above has completed a detailed description of the specific implementation manner of the invention, but it is only an example, and the present invention is not limited to the specific implementation manner 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, equal transformation, modification, improvement, etc. made without departing from the spirit and principle of the present invention should all be covered by the scope of the present invention.

Claims

1. A data monitoring system, characterized in that: It includes a sports behavior snapshot unit, a sports person 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 the metadata features of the sports equipment in use and forms a set of sports equipment in use; The sports behavior includes 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; The sports person snapshot unit stores the sports persons corresponding to the sports equipment in use and forms a set of sports persons; The sports equipment maintenance unit includes sensors, synchronously locates and detects the 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 correspondence and quality inspection matching unit corresponds to the sports occasion image sensor, collects the monitoring video frames of the sports equipment in the sports occasion, detects the identification of the sports equipment, completes the query for the sports equipment identification in the monitoring video frames with reference to the set of sports equipment in use, filters out the sports equipment in use that appears in the monitoring video frames, collects the sports behavior in use in the monitoring video frames, and uses spot checks to obtain the sports equipment in use to be checked; The sports persons corresponding to the sports equipment in use submit the current facial expression video to the metadata correspondence and detection spot check matching unit every day, and based on the sports behavior in use, correspond the current facial expression video to the set of sports equipment in use; Complete the maintenance history detection for the sports equipment in use to be checked in the monitoring video frames, and collect the current facial expression video corresponding to the set of sports equipment in use to match and identify the legitimacy; For the initially improperly used sports equipment, according to the sports equipment maintenance unit, complete the maintenance and positioning for the initially improperly used sports equipment; According to the sports occasion image sensor, successively use three different faults to complete three supplementary matches for the initially unqualified sports equipment in use; Adopt three supplementary references to identify the initially improperly used sports equipment. When all three results are improper, mark the sports equipment in use and send a high-frequency signal to the reminder and verification unit to complete the artificial matching and identify the final legitimacy; The reminder and verification unit receives the high-frequency signal data and gives a reminder, and has the functions of verification and processing; The standard score control unit sets the standard score of the sports equipment in use, and based on the data of the sports equipment maintenance unit, the sports action storage unit, and the metadata correspondence and quality inspection matching unit, uses artificial intelligence to complete the identification of the potential high-frequency processing behaviors of the sports equipment in use, raises or lowers 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; The sports action storage unit uses big data to refer to the normal sports action data and collects the deformation data in the action program of the sports equipment in use; The data monitoring system is based on the POIs of gyms, homes, and outdoor populations. For the exercise equipment in use with fixed-sequence actions, it acquires the big data of the exercise actions of the exercise equipment in historical use, generates ordinary action lines, and collects the exercise scenarios and exercise behaviors in use, including performance and faults, according to sensor technology. It also sets performance threshold values and time threshold values to complete high-frequency prediction and alert for the exercise equipment in high-frequency exercise scenarios. Set the standard score for the exercise equipment in use. Based on the sensor data and exercise actions of the exercise equipment, use the LSTM model to identify the potential high-frequency processing behaviors of the exercise equipment in use, detect the behaviors of the exercise equipment in use becoming faster, slower, and deforming frequently, increase or decrease the corresponding standard score of the exercise equipment, and set the level of the exercise equipment in use based on the score range to assist in the extraction of the exercise equipment in use to be inspected.

2. The data monitoring system according to claim 1, wherein: The exercise behavior snapshot unit stores the metadata features of the exercise equipment in use, including the identification of the exercise equipment in use, the appearance video of the exercise equipment in use, the age of the exercise equipment in use, and the model of the exercise equipment in use, and forms a set of exercise equipment in use, and completes the filing and storage for each exercise equipment in use. The exerciser snapshot unit stores the exercisers corresponding to the exercise equipment in use, including the basic data of the exercisers corresponding to the exercise equipment in use, and forms a set of exercisers, which corresponds to the labels of the exercise equipment in use. The exercise equipment maintenance unit includes sensors, synchronously locates and detects the fault data of the exercise equipment in use, analyzes the fault status of the exercise equipment in use within the cycle according to the positioning result, obtains the synchronous performance data of the exercise equipment in use, and submits it to the standard score control unit. The exercise action storage unit receives the fault data sent by the exercise equipment maintenance unit, stores the action forms and sequence data of the exercise equipment in use, collects the deformation data in the action program of the exercise equipment in use by referring to the normal exercise action data with big data, and submits it to the standard score control unit. The reminder and verification unit includes a voice reminder. It receives the high-frequency signals from the metadata correspondence and quality inspection matching unit, and uses the voice reminder to remind the maintainer to complete the verification for the high-frequency data.

3. A data monitoring system according to claim 2, characterized in that: The exerciser corresponding to the exercise equipment in use submits the current facial expression video to the metadata correspondence and inspection sampling matching unit every day. Based on the exercise behaviors in use, the current facial expression video is corresponded to the set of exercise equipment in use. For the exercise equipment in use to be inspected in the monitoring video frames, the maintenance history detection is completed, and the current facial expression video corresponding to the set of exercise equipment in use is collected to match and identify the legitimacy. The specific steps are as follows: Image submission and correspondence: The exerciser corresponding to the exercise equipment in use submits the current facial expression video to the metadata correspondence and inspection sampling matching unit every day, and corresponds the current facial expression video to the identification of the exercise equipment of the exercise equipment in use. Image collection: Based on the obtained exercise equipment in use to be inspected, collect the current facial expression video based on the effective exercise data, and collect the maintenance history video of the exercise equipment in use to be inspected for the monitoring video frames. Reference and recognition: For the original monitored video frames, analyze the expression features of the collected facial parts.

4. A data monitoring system according to claim 3, characterized in that: A high-frequency prediction detection system is also used in the metadata correspondence and quality inspection matching unit to complete the high-frequency prediction of the in-use exercise equipment for high-frequency motion scenarios. The specific steps are as follows: For high-frequency motion scenarios, including gyms, homes, and outdoor population POIs, an exercise action storage unit is used to obtain the big data of the exercise actions of the in-use exercise equipment with fixed sequential actions, and generate a normal action route. An exercise equipment maintenance unit is used to collect the in-use exercise behaviors in the in-use exercise scenario according to the positioning result, including performance and faults, and set performance threshold values and time threshold values. In high-frequency motion scenarios, store the performance and exercise actions of the in-use exercise equipment. When the performance of the in-use exercise equipment exceeds the performance threshold value for 15 minutes, or when the exercise actions of the in-use exercise equipment are misaligned with the normal exercise actions in the historical big data by 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 maintainer to pay attention to the high-frequency in-use exercise equipment manually.

5. A data monitoring method, characterized in that, This method uses a data monitoring system as described in any one of claims 1-4, and includes the following steps: Obtain the in-use exercise behaviors and form a set of in-use exercise equipment for filing and storage. At the same time, obtain the corresponding exercisers of the in-use exercise equipment, including the basic data of the corresponding exercisers of the in-use exercise equipment, and form a set of exercisers, which corresponds to the labels of the in-use exercise equipment. Use sensor technology to synchronously locate and detect the fault data of the in-use exercise equipment, and analyze the fault status of the in-use exercise equipment within the cycle according to the positioning result to obtain the synchronous performance data of the in-use exercise equipment. Receive the fault data of the exercise equipment and store the action forms and sequential data of the in-use exercise equipment. For the exercisers corresponding to the in-use exercise equipment, they need to submit the current expression video every day. Use the corresponding exercise scenario image sensor to collect the effective exercise data of the monitored video frames according to the external rectangle detection algorithm and NLP detection technology. According to the set of in-use exercise equipment, randomly select and obtain the historical maintenance videos of the in-use exercise equipment to be inspected. Use the YOLO algorithm to collect the historical maintenance videos of the in-use exercise equipment to be inspected. According to the color recognition result, analyze the Euclidean distance with reference to the set of in-use exercise equipment and the current expression video to judge the legitimacy of the in-use exercise equipment and send a high-frequency signal.

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