A motion data analysis method, system, device and readable storage medium

Abstract

The embodiment of the present application discloses a motion data analysis method, system, device and readable storage medium, which can generate trajectory change data of relative behavior motion dynamics in human activities by installing dynamic sensors and solid-state sensors at set points on human limbs, and the data acquired by the sensors are collected in real time and sent to the background data processing system; the motion data analysis system acquires the trajectory change data of relative differences generated by the set action trigger, and determines the reference value of the standard trajectory phenomenon of each test set point; the motion data analysis system determines the difference parameters of each point by comparing with the background database standard; the motion data analysis system evaluates the degree of late damage caused by human limb activities according to the difference parameters of each point and provides effective rehabilitation guidance suggestions. By setting detection points on human limbs and collecting sensor data, human motion data can be analyzed efficiently, conveniently and accurately.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of data analysis technology, and specifically to a motion data analysis method, system, device, and readable storage medium. Background Art

[0002] When the human body makes any dynamic movements, the interrelationships, coordination, correspondence, regularity, cooperation, and systematicity of the joints are first perceived through vision, then analyzed and judged by the brain, and then the nervous system transmits instructions to produce physical behavior results.

[0003] Various sports events related to the dynamics of limb movements and any dynamic phenomena related to limb activities, including various animal activity detection, human activity detection, sports event detection, competitive sports detection, how to formulate and supervise the detection training of technical behavior standards for manual work, and provide more standardized, specific and scientifically based reference data, is an urgent problem to be solved. Summary of the invention

[0004] To this end, the embodiments of the present application provide a motion data analysis method, system, device and readable storage medium, which analyzes human motion data efficiently, conveniently and accurately by setting detection points on human limbs and collecting sensor data.

[0005] In order to achieve the above objectives, the embodiments of the present application provide the following technical solutions:

[0006] According to a first aspect of an embodiment of the present application, a motion data analysis method is provided, the method comprising:

[0007] Dynamic sensors and solid-state sensors are installed at set points on human limbs to generate trajectory change data of relative behavioral movement dynamics during human activities. The data obtained by the sensors are collected in real time and sent to the background data processing system;

[0008] The motion data analysis system obtains the trajectory change data of the relative difference caused by the set action trigger, and determines the reference value of the standard trajectory phenomenon of each test set point;

[0009] The motion data analysis system determines the difference parameters of each point by comparing with the background database standard;

[0010] The motion data analysis system evaluates the degree of subsequent damage caused by human limb activities based on the difference parameters of each point.

[0011] Optionally, the installation of dynamic sensors and solid-state sensors at set points on human limbs includes:

[0012] Dynamic sensors are installed at movable and monitorable points on human limbs, and / or solid-state sensors are installed at solid-state points where the dynamic movements of body behavior are relatively small.

[0013] Optionally, by comparing with the backend database courseware standard, the difference parameters of each point are determined, including:

[0014] Extract the number of trajectory detections that meet the conditions;

[0015] The distance dimension is optimized by comparing the maximum bending distance dimension data in the middle of the arc line with the standard arc dimension at the same position in the database, and the difference parameters are determined.

[0016] Optionally, the background database standard is to select projects in advance, design and evaluate relatively correct and standardized recording and production groups of various dynamic actions, set up a set of detection courseware, obtain the behavior trajectory arc of each person through unified behavior action courseware testing and imitation, save repeated data phenomena in the background database, and so on to obtain this kind of group behavior difference big data for everyone, which is used as the background database standard courseware.

[0017] According to a second aspect of an embodiment of the present application, a motion data analysis system is provided, the system comprising:

[0018] The sensor installation guidance module is used to guide the installation of dynamic sensors and solid-state sensors at set points on human limbs, so as to generate trajectory change data of relative behavioral movement dynamics during human activities. The data obtained by the sensors are collected in real time and sent to the background data processing system;

[0019] A data acquisition module is used to acquire the trajectory change data of the relative difference generated by the set action trigger, and determine the reference value of the standard trajectory phenomenon of each test set point;

[0020] The difference parameter confirmation module is used for the background data processing system to determine the difference parameters of each point by comparing with the background database standard;

[0021] The result evaluation module is used for the background data processing system to evaluate the degree of subsequent damage caused by human limb activities according to the difference parameters of each point.

[0022] Optionally, the sensor installation guidance module is specifically used to:

[0023] Dynamic sensors are installed at movable and monitorable points on human limbs, and / or solid-state sensors are installed at solid-state points where the dynamic movements of body behavior are relatively small.

[0024] Optionally, the difference parameter confirmation module is specifically used to:

[0025] Extract the number of trajectory detections that meet the conditions;

[0026] The distance dimension is optimized by comparing the maximum bending distance dimension data in the middle of the arc line with the standard arc dimension at the same position in the database, and the difference parameters are determined.

[0027] Optionally, the background database standard is to select projects in advance, design and evaluate relatively correct and standardized recording and production groups of various dynamic actions, set up a set of detection courseware, obtain the behavior trajectory arc of each person through unified behavior action courseware testing and imitation, save repeated data phenomena in the background database, and so on to obtain this kind of group behavior difference big data for everyone, which is used as the background database standard courseware.

[0028] According to a third aspect of an embodiment of the present application, a device is provided, comprising: a data acquisition device, a processor and a memory; the data acquisition device is used to acquire data; the memory is used to store one or more program instructions; the processor is used to execute one or more program instructions to execute any method described in the first aspect.

[0029] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, wherein the computer storage medium contains one or more program instructions, and the one or more program instructions are used to execute the method as described in any one of the first aspects.

[0030] In summary, the embodiments of the present application provide a motion data analysis method, system, device and readable storage medium, which installs dynamic sensors and solid-state sensors at set points on human limbs to generate trajectory change data of relative behavioral motion dynamics in human activities, and the data acquired by the sensors are collected in real time and sent to the background data processing system; the motion data analysis system acquires the trajectory change data of the relative difference generated by the set action trigger, and determines the reference value of the standard trajectory phenomenon of each test set point; the motion data analysis system determines the difference parameters of each point by comparing with the background database standard; the motion data analysis system evaluates the degree of later damage caused by human limb activities according to the difference parameters of each point. By setting detection points on human limbs and collecting sensor data, human motion data can be analyzed efficiently, conveniently and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0032] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size, and their corresponding relationships shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0033] Figure 1 A schematic diagram of a motion data analysis method provided in an embodiment of the present application;

[0034] Figure 2 One of the schematic diagrams of sensor installation positions provided in the embodiment of the present application;

[0035] Figure 3 The second schematic diagram of the sensor installation position provided in the embodiment of the present application;

[0036] Figure 4 A block diagram of a motion data analysis system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are 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.

[0038] In the prior art, similar sports detection patents are used to obtain special testing technology methods for similar domestic sports-related tests. The method provided in the embodiment of this application is different from the main technical testing principle. In the prior art, images, pictures, and coordinate positioning are used to collect a large number of tester behaviors and actions, and they are compared with the background standard courseware in real time.

[0039] This technology has many disadvantages. For example: the collection point is relatively fixed and dependent, many collection devices are used, the collection angle distance light image quality and the background courseware comparison results may cause serious inaccuracy in the obtained courseware comparison data due to differences in human reading levels. The technology of detection in the form of coordinates will have serious morphological docking error values ​​in the collection distance light, the size and shape differences of the human body and the comparison of database courseware. This error value will bring serious recommendation deviations to the rectification and analysis suggestions obtained in the later comparison, and directly affect the formulation and implementation of the later training plan. The above collection detection and comparison methods use many devices, have high collection difficulty requirements, and are limited in promotion and popularization. The collection process of the collected person using the above methods is complicated, and the collection behavior activities will be repeated many times due to collection quality reasons.

[0040] When the human body performs any dynamic action, the interrelationship, coordination, correspondence, regularity, cooperation, and systemicity of each joint are first perceived visually, then analyzed and judged by the brain, and then the nervous system transmits instructions to produce physical behavior results. In this process, when the body is used to complete each behavioral action, the static fixed area position will be relatively around the lower Dantian A in front of the middle axis of the body, the Mingmen acupoint B point behind the body, and the area line within 20 cm below the transverse line. According to various behavioral detection requirements, a solid-state sensor is selectively installed at a certain position. Through the monitoring technology equipment and positioning monitoring method provided by the embodiment of the present application, real-time monitoring is performed as the body swings up and down, left and right, forward and backward, rotates, twists, stretches, and changes in various forms and changes in strength and speed behavioral activities.

[0041] Figure 1 The following is a flow chart of a motion data analysis method provided by an embodiment of the present application, wherein the method comprises the following steps:

[0042] Step 101: Dynamic sensors and solid-state sensors are installed at set points on human limbs to generate trajectory change data of relative behavioral motion dynamics during human activities. The data obtained by the sensors are collected in real time and sent to a background data processing system.

[0043] Step 102: The motion data analysis system obtains the trajectory change data of the relative difference generated by the set action trigger, and determines the reference value of the standard trajectory phenomenon of each test set point.

[0044] Step 103: The motion data analysis system determines the difference parameters of each point by comparing with the background database standard.

[0045] Step 104: The motion data analysis system evaluates the degree of subsequent damage caused by human limb activities based on the difference parameters of each point.

[0046] In a possible implementation, in step 101, the step of installing a dynamic sensor and a solid-state sensor at a set point on a human limb includes:

[0047] Dynamic sensors are installed at movable and monitorable points on human limbs, and / or solid-state sensors are installed at solid-state points where the dynamic movements of body behavior are relatively small.

[0048] It should be noted that the number of sensors installed and used is based on the requirements of different test items. The number of sensors used at the installed test points is different. The number of track detections can be selectively extracted from the collected background results, and the number of extractions varies for different projects. The dynamic track line change curve data collection method is to obtain the difference parameters by comparing the maximum bending distance dimension data in the middle of the arc line with the standard arc dimension at the same position in the database, making distance dimensions, and optimizing them.

[0049] In a possible implementation, in step 103, by comparing with the background database courseware standard, the difference parameters of each point are determined, including:

[0050] Extract the number of trajectory detections that meet the conditions; optimize the distance size through the maximum bending distance size data in the middle of the arc line and the standard arc size at the same position in the database, and compare and determine the difference parameters.

[0051] In one possible implementation, the background database standard is to select projects in advance, design a relatively correct and standardized courseware production group for evaluating various dynamic actions, set up a set of test courseware, obtain the behavior trajectory arc of each person through unified behavior action courseware imitation, save the repeated data phenomena in the background database, and so on to obtain such behavior data of everyone to obtain the background database standard.

[0052] The various dynamic project standard courseware in the behavior database is to make unified courseware subject behavior operations for the selected behavior standard groups in advance. Big data synthesis optimization selects the only standard behavior courseware, and obtains standardized technical actions through optimization as the background database standard.

[0053] In a possible implementation, the method may also include: providing a later behavioral action imitation courseware training method, providing a training plan and specific program suggestions. After personal training rectification, the same courseware test is performed again to obtain a new test behavioral action problem data report, and new rectification training suggestions are obtained by entering the background database for intelligent query detection and analysis.

[0054] Therefore, in the method provided in the embodiment of the present application, when the human body's limbs perform various dynamic movements, dynamic sensors are installed at each movable and monitorable point of the limbs, and solid-state sensors are installed at relatively small solid-state points of the body's dynamic movements, so that various relative dynamic trajectory change data of the human body are naturally generated during any activity. The solid-state sensor is used as a reference to test the trajectory difference data of the position trajectory arc change of each limb activity point to obtain the result of the real-time monitoring method. Further, the information of each sensor is read and collected in real time into the background data device. Further, the database optimizes the standard courseware device through intelligent comparison. Further, a result report device is printed. Further, the database guides the training courseware device.

[0055] The technical key points of the embodiments of the present application are to obtain a relatively static position point of the body by monitoring the laws of dynamic movement behaviors of various movable points of human limb organs and related acupuncture points of the human body as solid points, and to obtain the position of the Shenque point A in the front and above the body, the Mingmen point B in the middle of the vertical central axis of the body, the Qihai point (also called the lower Dantian point C) in the front and the Yaoshu point in the back; among them, the position of the area line within the upper and lower transverse lines is selected according to the requirements of the test items and the technical requirements to install the solid-state sensor in this area.

[0056] This area can be used as the core static coordinate point for detecting dynamic changes in various limb behaviors. Figure 2 and 3 As shown, two test sensors A and B are installed at the lower Dantian point A in front of the body and the Mingmen point B behind the body and the transverse line. The limb organ points that change in various behaviors and actions are installed with behavioral track sensors C+1.2.3.4..., collectively referred to as the variable posture sensor group C. The correlation between this dynamic sensor group C+ and the static sensor AB in the dynamic detection of limbs can use existing technology to achieve real-time and accurate detection data. This detection method is not limited by the environment or temperature. Whether it is a fixed or variable environment, no matter how much the difference in human body shape is, the data obtained by monitoring is the same comparison method.

[0057] The data obtained by this detection method is relatively regular, and the difference data compared with the standard courseware in the database through artificial intelligence analysis software is very clear and accurate. The above technology can obtain valuable reference data reports. It is helpful for later behavior correction. This detection principle does not affect the real-time detection and collection results of this principle regardless of the environment, any body movements of the human body, and the detection courseware activities performed in a fixed position or in a dynamic environment. The difference results of similar behaviors and actions obtained by repeated imitation of courseware activities will be compared in the database later.

[0058] Using static points A and B as point-line benchmarks, the sensor point C+ group on the limb points will produce relatively regular behavior trajectory phenomena in dynamics. By imitating the prescribed actions of the courseware, the difference results of similar but different trajectory behavior lines will be produced. The difference report suggestions are obtained through the comparison of the pre-made courseware in the background, and the relative problem rectification training guidance plan is given. The test result data report is saved in the background for the comparison of the results of later tests. Through this test, we can find out what problems the test person has, and what destructive effects and harms will be caused to the body in the later stage if they are not solved. Furthermore, the rectification suggestions for the difference problems at each point and the prediction of the later effects are given.

[0059] This detection method compares the limb dynamic movement courseware in the background of technical collection to obtain the maximum difference value of the arc. The larger the difference value, the more problems there are. Using this arc detection technology method, the coach can quickly use the personal physical examination data obtained by this special detection system to take a more scientific, effective and standardized way to help guide the problem person to do the correct dynamic movement courseware arc imitation and repeated training in accordance with the system guidance report requirements, and obtain the standardized behavior results of the correct arc dynamic movement. During the training process, the video pictures collected in real time by the mobile phone and the camera can be used to help the problem person see more clearly and intuitively what kind of problem exists and feel the phenomenon, which is of great substantive help to the problem person's rapid understanding, absorption, digestion and mastery.

[0060] The database prepares in advance various dynamic irregular behavior cases and related harmful and destructive consequences, and also prompts the direct and indirect health impact indicators caused by the differences in the behavior trajectories of each test point.

[0061] Among them, the changes in the interlaced differences of each point and the subsequent behavioral hazards are different, and the corresponding solutions are also different. The larger the difference size data between the arc point in the middle of the arc line and the arc point in the same position of the standard courseware, the greater the problem, and the smaller the relative difference size data, the fewer the problem. The arc difference method can easily determine and solve the problem of behavioral dynamic action differences. The arc data difference also proves the size of the problem difference. When the arc difference data is equal to the courseware, it means that the problem has been basically solved. From the perspective of the difference in the data of the microscopic phenomenon of limb movement, the slight difference will not produce a large difference in the dynamic action reaction process of the body.

[0062] It can be seen that by using the method provided in the embodiment of the present application, it is possible to quickly complete the arc repeated acquisition technology work in the trajectories of various behavioral dynamic actions in various fields, and simultaneously obtain the arc trajectory phenomenon and data for each time.

[0063] Among them, the collected technical courseware methods are: through the early selection of projects, design a relatively correct and standardized courseware production group for evaluating various dynamic actions, set up a set of test courseware, let these people imitate through the unified behavior action courseware, obtain the behavior trajectory arc of each person, put the repeated data phenomenon into the background database for storage, and so on to obtain the behavior data of everyone. Do optimization processing in the background to obtain the only correct and standardized template for courseware. The design of this standard courseware production is reasonable and has the authoritative persuasiveness of the same industry. This standard can also be used to guide the production of training courseware.

[0064] Each industry has different characteristics for behavior detection. Everyone imitates the same courseware. The result is that there is no completely identical behavior dynamic action data. According to the above characteristics of the behavior law, a set of such detection technology is needed to complete the detection task in this test field. Through the above phenomenon law, through the intelligent analysis, filtering, synthesis and optimization of the behavior trajectory phenomenon big data, we can make the standard courseware for the behavior test project set by various industries.

[0065] In terms of courseware development, we extract the number of groups with recognized relatively standard dynamic behavior movements from each project industry that needs to be tested, and do the courseware development and testing input work. According to the courseware design requirements, we uniformly perform the same dynamic behavior movements within a unit of time, extract and collect the relevant data of each person's behavior trajectory and save it in the background database. When the courseware uniformly entered by these people completes the results, the background will do big data comparison and analysis to obtain the arc difference data value of the best dynamic behavior specification as the standard authoritative courseware.

[0066] In the later use of this test method, due to various test conditions and all the needs of the tester, some collection-related auxiliary instruments, tools and equipment will be selectively used. Among them, including network platforms, mobile phones, hard drives, cameras, monitors, cameras, computers, display screens, time controllers, voice control equipment, test houses, special clothing and equipment supplies for testing, template courseware test sites used for special intelligent data control training, and other related supplies required for normal use. The above behavior operations are determined by the requirements of the personnel participating in the test and the service principles of the courseware system function setting of this method. The necessary collection, input, comparison and analysis of difference results, rectification suggestions and system background storage and transmission process functions should be given for the training rectification measures. The person being tested needs to voluntarily apply for the test and fill in the relevant personal real-name information, including: name, gender, age, height, weight, sports hobbies, best results, professional equipment required for the behavior standard courseware test of specific work types, and each person's work and living place, WeChat account, and mobile phone number.

[0067] It can be seen that the method provided in the embodiment of the present application can perform collection work of the same principle difficulty in any environment. The use of this method in various harsh environments will not be limited by the collection technology, and will not be limited by the collection of body type difference technology. The audience group is wide and the test items are wide. Moreover, the background collection optimization and comparison courseware method is simple, practical, and clear to read. The analysis results of the report data are easy to understand, which plays a lot of convenient roles in the later reference comparison guidance and suggestions. And the collection method using the variable points of human limbs and the detection sensors in the embodiment of the present application is innovative and unique, with few collection equipment and instruments, low investment cost, high technology promotion and popularization rate, wide beneficiaries, and wide testing fields.

[0068] It should be noted that this detection method can be selectively arranged in segments, partial limb positions, or a single limb position can be tested according to the specific needs of the project to be tested; a variety of selective installations use the number of sensors to detect the difference results of dynamic action behavior trajectory phenomena, including upper limbs, lower limbs, left and right limbs, and single limbs. It can detect various dynamic animal behavior data and tangible dynamic phenomenon monitoring data in real time. First, find a relatively solid point as a test benchmark, install the sensor C+ group at the behavior point to be tested according to the data collection requirements, monitor the behavior courseware activities, and obtain the corresponding data to complete the collection work. Provide users with reference data reports.

[0069] In summary, the embodiments of the present application provide a motion data analysis method, system, device and readable storage medium, which installs dynamic sensors and solid-state sensors at set points on human limbs to generate trajectory change data of relative behavioral motion dynamics in human activities, and the data acquired by the sensors are collected in real time and sent to the background data processing system; the motion data analysis system acquires the trajectory change data of the relative difference generated by the set action trigger, and determines the reference value of the standard trajectory phenomenon of each test set point; the motion data analysis system determines the difference parameters of each point by comparing with the background database standard; the motion data analysis system evaluates the degree of later damage caused by human limb activities according to the difference parameters of each point. By setting detection points on human limbs and collecting sensor data, human motion data can be analyzed efficiently, conveniently and accurately.

[0070] Based on the same technical concept, the present application also provides a motion data analysis system. Figure 4 As shown, the system comprises:

[0071] The sensor installation guidance module 401 is used to guide the installation of dynamic sensors and solid-state sensors at set points on human limbs to generate trajectory change data of relative behavioral movement dynamics during human activities. The data obtained by the sensors are collected in real time and sent to the background data processing system.

[0072] The data acquisition module 402 is used to acquire the trajectory change data of the relative difference generated by the set action trigger, and determine the reference value of the standard trajectory phenomenon of each test set point.

[0073] The difference parameter confirmation module 403 is used for the background data processing system to determine the difference parameters of each point by comparing with the background database standard.

[0074] The result evaluation module 404 is used for the background data processing system to evaluate the degree of subsequent damage caused by human limb activities according to the difference parameters of each point.

[0075] Optionally, the sensor installation guidance module 401 is specifically used to: install dynamic sensors at movable and monitorable points on human limbs, and / or install solid-state sensors at solid-state points where the dynamic movements of body behavior are relatively small.

[0076] Optionally, the difference parameter confirmation module 403 is specifically used to: extract the number of trajectory detections that meet the conditions; optimize the distance size by comparing the maximum bending distance size data in the middle of the arc line and the standard arc size at the same position in the database, and determine the difference parameters.

[0077] Optionally, the background database standard is achieved by selecting projects in the early stage, designing a relatively correct and standardized courseware production group for evaluating various dynamic actions, setting up a set of detection courseware, obtaining the behavior trajectory arc of each person through unified behavior action courseware imitation, and saving repeated data phenomena in the background database, and so on to obtain such behavior data of everyone to obtain the background database standard.

[0078] Based on the same technical concept, an embodiment of the present application also provides a device, which includes: a data acquisition device, a processor and a memory; the data acquisition device is used to collect data; the memory is used to store one or more program instructions; the processor is used to execute one or more program instructions to execute any one of the methods described.

[0079] Based on the same technical concept, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer storage medium contains one or more program instructions, and the one or more program instructions are used to execute any of the methods described.

[0080] In this specification, each embodiment of the above method is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. For relevant parts, refer to the partial description of the method embodiment.

[0081] It should be noted that, although the operations of the method of the present invention are described in a specific order in the drawings, this does not require or imply that the operations must be performed in the specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0082] Although the present application provides method operation steps such as embodiments or flow charts, more or less operation steps may be included based on conventional or non-creative means. The order of steps listed in the embodiment is only one way of executing the order of many steps and does not represent a unique execution order. When the device or client product in practice is executed, it can be executed in sequence or in parallel according to the method shown in the embodiment or the accompanying drawings (for example, a parallel processor or a multi-threaded processing environment, or even a distributed data processing environment). The term "includes", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, product or equipment including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such process, method, product or equipment. In the absence of more restrictions, it is not excluded that there are other identical or equivalent elements in the process, method, product or equipment including the elements.

[0083] The units, devices or modules described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. For the convenience of description, the above devices are described separately by functions divided into various modules. Of course, when implementing this application, the functions of each module can be implemented in the same or more software and / or hardware, or the modules that implement the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0084] Those skilled in the art also know that, in addition to implementing the controller in a purely computer-readable program code, the controller can be made to implement the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules for implementing the method and structures within the hardware component.

[0085] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0086] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a mobile terminal, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present application or certain parts of the embodiments.

[0087] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, cloud storage, including distributed computing environments of any of the above systems or devices, etc.

[0088] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A method for analyzing sports data, characterized in that: The method comprises: Dynamic point sensors and static point sensors are installed at set points on human limbs to generate trajectory change data of relative behavioral movement dynamics during human activities. The data obtained by the sensors are collected in real time and sent to the background data processing system; The motion data analysis system obtains the trajectory change data of the relative difference caused by the set action trigger, and determines the reference value of the standard trajectory phenomenon of each test set point; The motion data analysis system determines the difference parameters of each point by comparing with the background database courseware standard; The motion data analysis system evaluates the degree of late damage caused by human limb activities based on the difference parameters of each point; The method of installing dynamic point sensors and static point sensors at set points on human limbs includes: Dynamic point sensors are installed at movable and monitorable points on human limbs, and / or static point sensors are installed at static points where the dynamic movements of body behavior are relatively small; By monitoring the movable points of human limb organs and the positions of relevant acupuncture points of the human body as static points, and the laws of dynamic behaviors of movement, it is obtained that there is a relatively static position point in the body, the horizontal section line position of the Shenque point A in the front of the upper body and the Mingmen point B in the back of the body on the vertical central axis in the middle of the body, and the horizontal section line of the Xiadantian point C and the Yaoshu point in the lower body; Among them, the location of the area line within the upper and lower transverse lines is determined according to the requirements of the test project and the characteristics of the technical requirements to select the specific location of the static point sensor in this area; By comparing with the backend database courseware standard, the difference parameters of each point are determined, including: Extract the number of trajectory detections that meet the conditions; The distance dimension is optimized by comparing the maximum bending distance dimension data in the middle of the arc line with the standard arc dimension at the same position in the database, and the difference parameters are determined; The background database courseware standard is to select projects in the early stage, design and evaluate the relatively correct and standardized recording and production groups of various dynamic actions, set a set of test courseware, and obtain the behavior trajectory arc of each person through unified behavior action courseware testing and imitation, and save the repeated data phenomena in the background database, so as to obtain the big data of group behavior differences of the crowd and use it as the background database courseware standard courseware; With the static points of Shenque point A in the upper front of the body and Mingmen point B in the back as the point-line reference, and the sensor points on the limbs as the C+ group, relatively regular behavior trajectories will be produced in dynamics. By imitating the prescribed movements in the courseware, similar but different trajectory behavior line results will be produced. A difference report will be obtained by comparing the pre-prepared courseware in the background, and at the same time, rectification suggestions and later effect predictions will be given for the differences in each point.

2. A sports data analysis system, characterized in that: The system comprises: The sensor installation guidance module is used to guide the installation of dynamic point sensors and static point sensors at set points on human limbs, so as to generate trajectory change data of relative behavior and movement dynamics during human activities. The data obtained by the sensors are collected in real time and sent to the background data processing system; A data acquisition module is used to acquire the trajectory change data of the relative difference generated by the set action trigger, and determine the reference value of the standard trajectory phenomenon of each test set point; The difference parameter confirmation module is used for the background data processing system to determine the difference parameters of each point by comparing with the background database courseware standard; A result evaluation module, which is used for the background data processing system to evaluate the degree of subsequent damage caused by human limb activities according to the difference parameters of each point; The method of installing dynamic point sensors and static point sensors at set points on human limbs includes: Dynamic point sensors are installed at movable and monitorable points on human limbs, and / or static point sensors are installed at static points where the dynamic movements of body behavior are relatively small; By monitoring the movable points of human limb organs and the positions of relevant acupuncture points of the human body as static points, and the laws of dynamic behaviors of movement, it is obtained that there is a relatively static position point in the body, the horizontal section line position of the Shenque point A in the front of the upper body and the Mingmen point B in the back of the body on the vertical central axis in the middle of the body, and the horizontal section line of the Xiadantian point C and the Yaoshu point in the lower body; Among them, the location of the area line within the upper and lower transverse lines is determined according to the requirements of the test project and the characteristics of the technical requirements to select the specific location of the static point sensor in this area; The difference parameter confirmation module is specifically used for: Extract the number of trajectory detections that meet the conditions; The distance dimension is optimized by comparing the maximum bending distance dimension data in the middle of the arc line with the standard arc dimension at the same position in the database, and the difference parameters are determined; The background database courseware standard is to select projects in the early stage, design and evaluate the relatively correct and standardized recording and production groups of various dynamic actions, set a set of test courseware, and obtain the behavior trajectory arc of each person through unified behavior action courseware testing and imitation, and save the repeated data phenomena in the background database, so as to obtain the big data of group behavior differences of the crowd and use it as the background database courseware standard courseware; With the static points of Shenque point A in the upper front of the body and Mingmen point B in the back as the point-line reference, and the sensor points on the limbs as the C+ group, relatively regular behavior trajectories will be produced in dynamics. By imitating the prescribed movements in the courseware, similar but different trajectory behavior line results will be produced. A difference report will be obtained by comparing the pre-prepared courseware in the background, and at the same time, rectification suggestions and later effect predictions will be given for the differences in each point.

3. A sports data analysis device, characterized in that: The device comprises: a data acquisition device, a processor and a memory; The data acquisition device is used to acquire data; the memory is used to store one or more program instructions; and the processor is used to execute one or more program instructions to execute the method according to claim 1.

4. A computer-readable storage medium, characterized in that: The computer-readable storage medium contains one or more program instructions, and the one or more program instructions are used to execute the method according to claim 1.

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