WO2017143814A1

WO2017143814A1 - Method, device and system for ball game data statistics, smart basketball and wrist band

Info

Publication number: WO2017143814A1
Application number: PCT/CN2016/107174
Authority: WO
Inventors: 许润民
Original assignee: 深圳未网科技有限公司
Priority date: 2016-02-23
Filing date: 2016-11-25
Publication date: 2017-08-31

Abstract

A method for ball game data statistics, comprising: when it is determined that the current state is a set state in which a motion data match is required, receiving first motion data (S101, S202); comparing the first motion data with locally stored second motion data in terms of motion feature(s) to determine the degree of similarity (S102, S203); determining, according to the results of the determined degree of similarity, the first motion data and second motion data that have the highest degree of similarity; and generating motion data statistics according to the first motion data and second motion data that have the highest degree of similarity (S103, S204). Further disclosed are a device and a system for ball game data statistics, a smart basketball and a smart wrist band.

Description

Ball sports data statistics method, device, system, intelligent basketball and wristband

Technical field

The present invention relates to a smart device, and in particular to a ball sports data statistical method, apparatus, system, and smart basketball and smart wristband for including basketball.

Background technique

Basketball is one of the most extensive sports in the world. Daily practice of amateurs, basketball clubs or professional teams' normal training and competitions all hope to record the player's sports information and provide a reference for subsequent sports.

With the development of smart device technology, this technology has gradually been introduced in basketball. For example, the patent application with the publication number CN104043237A discloses a basketball shooting determination system for use with a basket and a portable electronic device including a processing unit, a memory and an output device, the system including a basketball, a plurality of sensors carried by the basketball, And a non-transitory computer readable medium. The medium contains code to direct the processor to obtain multiple attributes of the basketball shot toward the basket. The plurality of attributes are sensed by a plurality of sensors or derived from signal outputs of the plurality of sensors. The code also directs the processor to determine whether the shot is a basket by comparing the plurality of attributes of the shot with one or more predetermined indicia features of the incoming basket, and presenting an output to the person based on the determination of whether the shot is a basket or not .

However, this technology can only record the status information of basketball, and can only record single-person sports information, and can not effectively record the effective scores of multiple players in multi-player sports.

Summary of the invention

It is an object of the present invention to provide a sports data statistical method, apparatus, system, smart basketball and smart wristband for ball sports to solve the problem that it is currently impossible to effectively record the effective scores of multiple players in multiplayer sports.

According to an aspect of the present invention, a ball sports data statistical method is provided. A plurality of athletes participating in a ball sport are respectively dressed with a smart wearable device. The statistical method includes: determining a current state as a need to perform motion data matching. Receiving, in a fixed state, the first motion data, wherein the first motion data is motion object motion data obtained by a sensor disposed in the moving object; and the second motion data is stored locally with the smart wearable device The motion data is compared with a motion feature to determine a degree of matching, wherein the second motion data is a sensor that is disposed in the smart wearable device The wearer's wearer motion data obtained by the device; transmitting the matching degree, the second motion data, and the identifier of the smart wearable device to the moving object, so that the moving object is determined with the first The wearer's wearer's motion data with the highest degree of motion data matching and motion data statistics.

According to a second aspect of the present invention, a ball sports data statistical method is provided, in which a plurality of athletes participating in the ball sports are respectively dressed with smart wearable devices, and the statistical method includes: determining the current state as requiring exercise data Receiving, in the matched setting state, the second motion data sent by each smart wearable device, wherein the second motion data is wearer motion data obtained by the sensor in the smart wearable device; The second motion data is compared with the first motion data stored locally by the moving object to determine a plurality of matching degrees, wherein the first motion data is motion object motion data obtained by a sensor disposed in the moving object. And determining, from the plurality of matching degrees, the first motion data and the second motion data with the highest matching degree; and performing motion data statistics according to the first motion data and the second motion data with the highest matching degree.

According to a third aspect of the present invention, there is provided a motion data statistical device, wherein a plurality of athletes participating in the ball sports are respectively dressed with smart wearable devices, the statistical device comprising: a receiving module, configured to determine a current state as needed Receiving first motion data in a set state in which motion data matching is performed, wherein the first motion data is motion object motion data obtained by a sensor disposed in the motion object; and a comparison module, configured to The motion data is compared with the second motion data stored locally by the smart wearable device to determine a matching degree, wherein the second motion data is a wearer's wearer motion obtained by a sensor disposed in the smart wearable device. a sending module, configured to send the matching degree, the second motion data, and an identifier of the smart wearable device to the moving object, so that the moving object determines a degree of matching with the first motion data The highest wearer's wearer motion data and athletic statistics.

According to a fourth aspect of the present invention, there is provided a motion data statistical device, wherein a plurality of athletes participating in a ball sport are respectively dressed with a smart wearable device, the statistical device comprising: a receiving module, configured to determine that a current state is required for exercise Receiving, in the setting state of the data matching, the second motion data sent by each smart wearable device, wherein the second motion data is wearer motion data obtained by the sensor in the smart wearable device; the comparison module is used And comparing the motion characteristics of each of the second motion data and the first motion data stored locally by the moving object to determine a plurality of matching degrees, wherein the first motion data is a sensor that is disposed in the moving object. Obtaining moving object motion data; a statistic module, configured to determine first motion data and second motion data with the highest matching degree from the plurality of matching degrees, and according to the first motion data with the highest matching degree and the second The motion data is used for motion data statistics.

According to a fifth aspect of the invention, a smart basketball is provided. a microchip is disposed in the basketball, and the microchip is configured to receive a plurality of second motion data in a setting state that determines that a current state is required to perform motion data matching, wherein the plurality of second motion data is Wearer motion data of a plurality of wearers, each wearer's wearer motion data is obtained by sensors disposed in one or more smart wristbands, and each of the second motion data and the first motion data are subjected to motion characteristics Comparing, determining a plurality of matching degrees, and determining from the second motion data having the highest degree of matching with the first motion data, wherein the first motion data is obtained by a sensor disposed in the basketball Basketball data and send identification information to a terminal.

According to a sixth aspect of the invention, a smart wristband is provided. a microchip is disposed in the smart wristband, and the microchip is configured to receive first motion data in a setting state that determines that a current state is required to perform motion data matching, where the first motion data is set by using Moving object motion data obtained by a sensor in the moving object, and comparing the first motion data with the second motion data to determine a matching degree, wherein the second motion data is set by the smart a wearer motion data obtained by a sensor of the wristband, and transmitting the matching degree, the second motion data, and the identification of the smart wristband to the moving object to determine the moving object A sports data with the highest degree of motion data matching and motion statistics.

According to a seventh aspect of the invention, another smart wristband is provided. A microchip is disposed in the smart wristband, and the microchip is configured to identify whether a predetermined motion action occurs according to the obtained second motion data, wherein the second motion data is a sensor that is disposed in the smart wristband Obtaining wearer motion data, and transmitting the second motion data to a moving object, and receiving, storing, and displaying valid motion identification information sent by the moving object, when the predetermined motion motion is recognized, wherein The identification information is that the moving object compares a first motion data with second motion data from one or more smart wristbands, and selects a second motion data with the best matching degree, and obtains the best matching degree. The smart wristband of the second motion data is emitted, wherein the first motion data is motion object motion data obtained by a sensor disposed in the moving object.

According to an eighth aspect of the present invention, a ball sports data statistics system is provided, the system comprising: a first statistical device disposed in a smart wearable device worn by an athlete; and a second statistical device disposed in the moving object, The first statistical device is configured to receive the first motion data and determine the first motion data and the second motion locally stored by the smart wearable device in a setting state that determines that the current state is that motion data matching is required. The data is compared with the motion feature, the matching degree is determined, and then the matching degree, the second motion data, and the identifier of the smart wearable device are sent to the second statistical device, where the first motion data is passed Moving object motion data obtained by a sensor disposed in the moving object, the second motion data being a sensing device disposed in the smart wearable device The wearer's wearer motion data obtained by the device; the second statistical device is configured to send the first motion data to the first statistical device, and receive the smart wearable device worn by each athlete participating in the ball sports The matching degree sent by the first statistical device, the second motion data, and the identifier of the smart wearable device, and determining the second motion data with the highest degree of matching with the first motion data and performing motion data statistics .

According to a ninth aspect of the present invention, there is provided another ball sports data statistics system, the system comprising: a first statistical device disposed in the smart wearable device worn by the athlete and a second statistical device disposed in the moving object , wherein: the first statistical device is configured to send second motion data to the second statistical device, wherein the second athletic data is wearer motion data obtained by a sensor within the smart wearable device The second statistical device is configured to receive the second motion data sent by the first statistical device in each smart wearable device in a setting state that determines that the current state is that motion data matching is required, and each of the The second motion data is compared with the first motion data stored locally by the moving object, and the plurality of matching degrees are determined, and the first motion data and the second motion data with the highest matching degree are determined from the plurality of matching degrees; The first motion data and the second motion data with the highest matching degree perform motion data statistics, wherein the first motion data is a pass Is provided to the motion data of the motion sensor in the moving object obtained by the object.

With the solution of the present invention, a sensor is disposed in each of the moving object and the plurality of smart wearable devices, and the motion data of the moving object and the motion data of the plurality of wearers of the smart wearable device are collected in time; the comparison of the motion data of the two is determined by comparison The sender of the motion action and its corresponding motion data; through the statistics of the motion data, the athletic characteristics of multiple athletes can be obtained during multi-person exercise, and the effective scores of multiple players are effectively recorded, thereby providing reference for subsequent sports training. And basis. Taking basketball as an example, a corresponding sensor is set in a smart wristband worn by basketball and a plurality of basketball players to collect current sports data of the basketball and current sports data of a plurality of basketball players, and then determine the current issuance of the basketball action by comparison. The sports data in the field is counted to obtain its sports characteristics, such as scores, rebounds, assists, steals, blocks, and hit percentages, so that the athletes can be trained according to the characteristics of the sport. It can be seen that the solution of the present invention solves the problem that it is currently impossible to effectively record the effective scores of a plurality of players in a multi-player exercise.

DRAWINGS

1 is a flow chart showing the steps of a method for counting ball motion data according to a first embodiment of the present invention;

2 is a flow chart showing the steps of a method for counting ball motion data according to a second embodiment of the present invention;

3 is a flow chart showing the steps of a method for counting ball motion data according to Embodiment 3 of the present invention;

4 is a schematic diagram showing an example of a change in the acceleration of a basketball during a basketball flight;

5 is a schematic diagram of an example of a change in the acceleration of a basketball during a basketball holding process;

6 is a schematic diagram showing an example of a change in the acceleration of a basketball during a basketball dribble;

7 is a schematic diagram showing an example of a change in angular velocity of a basketball during a basketball shooting process;

8 is a schematic diagram showing an example of a change in the acceleration of a basketball during a basketball pass;

9 is a schematic diagram showing an example of a change in the acceleration of a basketball during a basketball shooting goal;

Figure 10 is a schematic view showing an example of a change in angular velocity of a wristband during dribbling;

Figure 11 is a schematic view showing an example of a change in a pitch angle of a wristband during dribbling;

Figure 12 is a schematic illustration of an example of a change in angular velocity of a wristband during a shot;

Figure 13 is a schematic diagram showing an example of a change in a pitch angle of a wristband during a shooting process;

14 is a flow chart showing the steps of a method for counting ball motion data according to Embodiment 4 of the present invention;

15 is a structural block diagram of a ball type motion data statistical device according to Embodiment 5 of the present invention;

16 is a block diagram showing the structure of a ball type motion data statistical device according to Embodiment 6 of the present invention;

Figure 17 is a block diagram showing the structure of a ball type motion data statistical apparatus according to a tenth embodiment of the present invention.

detailed description

The objects, technical solutions, and advantages of the present invention will become more apparent. The technical solutions of the present invention are clearly and completely described below, and it is obvious that the described embodiments are a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention without creative efforts are within the scope of the present invention.

Embodiment 1

Referring to FIG. 1, a flow chart of steps of a method for counting ball motion data according to a first embodiment of the present invention is shown.

The ball sports data statistics method of this embodiment includes the following steps:

Step S101: Receiving the first motion data in a setting state that determines that the current state is that motion data matching is required.

The first motion data is motion object motion data obtained by a sensor provided in the moving object, or wearer motion data obtained by a sensor provided in the smart wearable device. In general, a plurality of athletes participating in a ball sport are respectively dressed with smart wearable devices, and in the present invention, "a plurality" means two or more.

Trigger conditions can be set for the motion data statistics scheme, such as the setting state in which motion data matching is required. The setting state can be appropriately set by a person skilled in the art according to actual needs, such as receiving a certain command to open the setting state, and if the moving object is in a certain dribbling state, such as holding the ball state, pitching state, etc., and setting state as well. The default is real time and so on.

Whether the first motion data is the moving object motion data or the wearer motion data depends on the implementation of the motion data statistical method of the embodiment, and if the implementing party is the smart wearable device side, the first motion data is the moving object motion data; The implementer is a moving object side, and the first motion data is wearer motion data.

Step S102: comparing the first motion data with the locally stored second motion data to determine a matching degree.

The matching degree indicates the degree of matching of the first motion data and the second motion data. When the first motion data is moving object motion data, the second motion data is wearer's wearer motion data; when the first motion data is wearer's wearer motion data, the second motion data is motion of the moving object data.

If the implementation of the method in this embodiment is a smart wearable device side, in this step, the smart wearable device will receive the received moving object motion data, and the locally stored wearer obtained through the sensor disposed in the smart wearable device. The wearer motion data is compared to determine a matching degree, and after the plurality of smart wearable devices participating in the ball sports respectively perform the step, a plurality of matching degrees may be determined.

If the embodiment of the method of the embodiment is a moving object side, in this step, the moving object (for example, basketball, soccer, etc.) will receive the wearer's movement data of each wearer of the track, and the local stored pass is set in the motion. The moving object motion data obtained by the sensors in the object are compared to determine a plurality of matching degrees.

Step S103: Determine first motion data and second motion data with the highest matching degree according to the matching degree result; and perform motion data statistics according to the first motion data and the second motion data with the highest matching degree.

Specifically, if the implementation method of the method in this embodiment is a smart wearable device side, the matching degree, the second motion data, and the identifier of the smart wearable device are sent to the moving object, so that the moving object determines the highest matching degree with the first motion data. The wearer's wearer's athletic data and athletic statistics.

If the implementer of the method of the embodiment is a moving object side, the first motion data and the second motion data with the highest matching degree are determined from the plurality of matching degrees, and the first motion data and the second motion data with the highest matching degree are performed. Sports data statistics.

With the embodiment, a sensor is disposed in each of the moving object and the plurality of smart wearable devices, and the motion data of the moving object and the motion data of the plurality of wearers of the smart wearable device are collected in time; the motion is determined by comparing the motion data of the two. The sender of the action and its corresponding motion data; through the statistics of the exercise data, the athletic characteristics of multiple athletes can be obtained during multi-person exercise, and the effective scores of multiple players are effectively recorded, thereby providing reference for subsequent sports training and in accordance with. Taking basketball as an example, a corresponding sensor is set in a smart wristband worn by basketball and a plurality of basketball players to collect current sports data of the basketball and current sports data of a plurality of basketball players, and then determine the current issuance of the basketball action by comparison. The sports data in the field is counted to obtain its sports characteristics, such as scores, rebounds, assists, steals, blocks, and hit percentages, so that the athletes can be trained according to the characteristics of the sport. It can be seen that with the present embodiment, the problem that the effective scores of a plurality of players cannot be effectively recorded at the time of multi-person exercise can be solved.

Embodiment 2

Referring to FIG. 2, a flow chart of steps of a method for counting ball motion data according to a second embodiment of the present invention is shown.

The motion data statistical method of this embodiment includes:

Step S201: Detect the current state, and determine whether the current state is a set state in which motion data matching is required.

The detection of the current state may be performed in real time, or may be performed at intervals, or may be performed when the motion data changes satisfy certain conditions, and the current state may be determined by the motion data.

Step S202: Receiving the first motion data in a setting state that determines that the current state is that motion data matching is required.

The first motion data is motion object motion data obtained by a sensor disposed in the moving object, or is wearer motion data of a plurality of wearers obtained by a plurality of sensors disposed in the smart wearable device.

Preferably, the first motion data and the second motion data each comprise at least one of the following: ball holding data, dribbling data, and pitching data. That is, when the first motion data includes ball-holding data, and/or dribble data, and/or pitching data, correspondingly, the second motion data includes corresponding ball-holding data, and/or dribble data, and/or Or pitching data.

among them,

The ball holding data includes: the average value of the acceleration of the moving object, the maximum value of the acceleration of the moving object, the minimum value of the acceleration of the moving object, the acceleration peak time of the moving object, the average value of the acceleration of the smart wearable device, the maximum value of the acceleration of the smart wearable device, and the intelligence The minimum acceleration of the wearable device and the acceleration peak time of the smart wearable device. That is, when the first motion data includes an average value of the acceleration of the moving object, an acceleration maximum value of the moving object, an acceleration minimum value of the moving object, and an acceleration peak time of the moving object, correspondingly, the second motion data includes the smart wearable device The average of the acceleration, the maximum acceleration of the smart wearable device, the minimum acceleration of the smart wearable device, and the acceleration peak time of the smart wearable device. Or vice versa.

The dribble data includes: the action time and the number of actions of the moving object, the action time and the number of actions of the smart wearable device. That is, when the first motion data includes the action time and the number of actions of the moving object, the second motion data includes the action time and the number of actions of the smart wearable device. Or vice versa.

The pitching data includes: the average value of the acceleration of the moving object, the maximum value of the acceleration of the moving object, the minimum value of the acceleration of the moving object, the pitching time of the moving object, the average value of the acceleration of the smart wearable device, the maximum value of the acceleration of the smart wearable device, and the smart wearable device. The minimum acceleration, and the pitching time of the smart wearable device. That is, when the first motion data includes an average value of the acceleration of the animal body, a maximum value of the acceleration of the moving object, a minimum value of the acceleration of the moving object, and a pitching time of the moving object, the second motion data includes: an average value of the acceleration of the smart wearable device. The maximum acceleration of the smart wearable device, the minimum acceleration of the smart wearable device, and the pitching time of the smart wearable device. Or vice versa.

Step S203: Comparing the first motion data with the locally stored second motion data to determine a matching degree. Wherein, when the first motion data is the moving object motion data, the second motion data is the wearer's wearer motion data; when the first motion data is the wearer's wearer motion data, the second motion data is the moving object motion data.

This step specifically includes:

The average value of the acceleration of the moving object, the maximum value of the acceleration of the moving object, the minimum value of the acceleration of the moving object, and the acceleration peak time of the moving object, and the average value of the acceleration of the corresponding smart wearable device, the maximum value of the acceleration of the smart wearable device, Comparing the minimum value of the acceleration of the smart wearable device with the acceleration peak time of the smart wearable device; determining the matching degree of the first motion data and the second motion data according to each comparison result;

and / or,

Comparing the action time and the number of actions of the moving object with the action time and the number of actions of the corresponding smart wearable device respectively; determining the matching degree of the first motion data and the second motion data according to each comparison result;

and / or,

The average value of the acceleration of the animal body, the maximum value of the acceleration of the moving object, the minimum value of the acceleration of the moving object, the pitching time of the moving object, the average value of the acceleration of the corresponding smart wearable device, the maximum value of the acceleration of the smart wearable device, smart wear The minimum value of the acceleration of the device is compared with the pitching time of the smart wearable device; and the matching degree of the first motion data and the second motion data is determined according to each comparison result.

Step S204: Determine first motion data and second motion data with the highest matching degree according to the matching degree result; and perform motion data statistics according to the first motion data and the second motion data with the highest matching degree.

It should be noted that when the first motion data is the moving object motion data and the second motion data is the wearer's wearer motion data, the smart wearable device compares the first motion data with the locally stored second motion data. Determining a matching degree; determining the first motion data and the second motion data with the highest matching degree according to the matching degree result, comprising: comparing the first motion data with the locally stored second motion data, and obtaining the first motion data and a matching degree of the second motion data, and determining whether the obtained matching degree satisfies the set matching degree; if yes, transmitting the second motion data and the identifier of the corresponding smart wearing device to the moving object, and determining by the moving object The first motion data and the second motion data with the highest matching degree. In most ball games, the ratio of the ball to the player is usually 1:N, that is, there is 1 moving object and N wearing devices. In this case, the matching degree is obtained by the moving object to determine the matching degree. The highest first motion data and second motion data greatly improve data processing efficiency and speed.

When the first motion data is wearer motion data of the wearer, and the second motion data is motion object motion data, the motion object compares the first motion data with the locally stored second motion data to determine a matching degree; The matching result determines the first motion data and the second motion data with the highest matching degree. Specifically, the moving object compares the received first motion data with the second motion data stored locally by the moving object to obtain a plurality of matching degrees, and determines the first motion data with the highest matching degree from the plurality of matching degrees. Second exercise data. In this way, data transmission and interaction can be reduced, and the data transmission burden can be reduced.

When the motion data statistics are performed according to the first motion data and the second motion data with the highest matching degree, the motion feature data may be obtained according to the first motion data and the second motion data with the highest matching degree (eg, score, rebound, assist, Snatch, block, hit rate, etc.; statistics and send motion feature data The moving object or the smart wearable device is uploaded to the mobile terminal through the moving object or the smart wearable device. Data can be shared and displayed by uploading data to the mobile terminal.

With the embodiment, a sensor is disposed in each of the moving object and the plurality of smart wearable devices, and the motion data generated by itself is collected in time; and the sender of the motion action and the corresponding event are determined by comparing the first motion data and the second motion data. The sports data can be used to obtain the sports data of multiple athletes during multi-person sports, record the effective scores of multiple players, and provide reference and basis for the subsequent sports training. It can be seen that with the present embodiment, the problem that the effective scores of a plurality of players cannot be effectively recorded at the time of multi-person exercise can be solved.

Hereinafter, the exercise data statistical scheme of the present invention will be described by taking basketball as an example. However, it should be apparent to those skilled in the art that the principles and features of the embodiments shown in the third embodiment and the fourth embodiment are also applicable to other similar sports, such as soccer, volleyball, baseball, softball, rugby, hockey, golf, tennis. , badminton, table tennis, etc.

Referring to FIG. 3, a flow chart of steps of a method for counting ball motion data according to a third embodiment of the present invention is shown.

In this embodiment, the moving object is a basketball, and the smart wearing device is a wristband. A device for performing corresponding data processing, such as a microprocessor or a microchip, is provided in the basketball and the smart wearable device. For convenience of description, the chip is exemplified in the present invention. Among them, the basketball chip set in the basketball is used to identify the state of the basketball, such as flying, holding the ball, shooting, rebounding, scoring, passing, assisting, etc.; the wristband chip set in the wristband is used to identify the player's arm. The state, such as dribbling, shooting, holding the ball and so on. The basketball chip and the wristband chip can choose a pattern matching function to match the basketball state with the player's wristband status, find the player who caused each basketball state, and separately count the data of individual players (such as score, hit rate, rebounding, etc.). Wait). Of course, the basketball chip and the wristband chip can also set the pattern matching function at the same time. One party is in the active state at the same time, and the other party is in the inactive state, and the active state transition can also be performed when needed.

In addition, basketball is also provided with a basketball sensor, which is a sensor packaged inside the basketball, and may include: a three-axis acceleration sensor, and a three-axis gyroscope sensor. Among them, the three-axis acceleration sensor can collect the acceleration of the basketball in the three-dimensional coordinate system, and the three-axis gyroscope sensor can collect the rotational angular velocity of the basketball in the three-dimensional coordinate system. Of course, those skilled in the art can also set other sensors, such as a three-axis magnetometer, a pressure gauge, etc. when needed, the three-axis magnetometer can collect the magnetic field strength of the basketball in the three-dimensional coordinate system, and the pressure gauge can collect the pressure of the basketball. size.

The smart wristband is a wearable device that fits on the player. The smart wristband is equipped with a player sensor, including a three-axis acceleration sensor and a three-axis gyro sensor. Among them, the three-axis acceleration sensor can The acceleration of the wristband in the three-dimensional coordinate system is acquired, and the three-axis gyro sensor can acquire the rotational angular velocity of the wristband in the three-dimensional coordinate system. Optionally, those skilled in the art can also set other devices according to actual needs, such as a three-axis magnetometer that collects the magnetic field strength of the wristband in a three-dimensional coordinate system, and a pressure gauge that collects the pressure of the wristband, and prompts by vibration. A vibrator for player-specific technical statistics, a display screen showing the player's technical statistics, and a buzzer that alerts the player to specific technical statistics by beeping. The embodiment of the invention does not limit this.

Based on this, the motion data statistics method of this embodiment includes:

Step S301: The basketball chip and the wristband chip respectively identify the motion state of the basketball and the player.

In this embodiment, the athletic state of the basketball includes: flying, holding the ball, dribbling, shooting, passing, scoring, shooting, assists, and rebounding.

Hereinafter, the recognition of the state of motion in basketball will be described.

(1) Flight status

Detecting the flight status of the basketball is the basis of the whole plan. Only knowing whether the basketball is flying or not can identify the shooting, passing, dribbling and so on. When detecting the flight state of the basketball, at least one feature during the flight of the basketball may be extracted, for example, the feature data of the basketball performing free fall motion and rotating at a constant speed during flight.

During the flight, the ball rotates at a constant speed, and the acceleration collected by the sensor does not change. Suppose a(t) is a three-axis accelerometer sensor that records the acceleration of the basketball at time t. If the acceleration change is less than a certain value within a range of not shorter than T(fly) time, ie |a(t)-a( T-1)|<M, determine that the basketball is in a "flight" state. Preferably, 0.2 s < T (fly) < 2.0 s, 0.1 g < M < 0.3 g. Where s represents "second", g is the unit of acceleration, and 1g is approximately equal to 9.8m/s^2.

FIG. 4 is a diagram showing an example of a change in the acceleration of a basketball during a basketball flight. As can be seen from the figure:

When t<396.5s: the basketball is in the hands of the person, and the acceleration fluctuates up and down;

When 396.5s<t<396.9s: the ball flies in the air, the sphere rotates at a constant speed, and the acceleration of the basketball holds the ball constant;

When t>397s: Basketball hits something, stops flying, and the acceleration peaks.

Where t is the time and s is the "second".

(2) Holding the ball

The passing in basketball is actually changed by "ball->flight->ball holding". Therefore, the detection of the ball-holding state is the basic and important detection. Suppose a(t) is a three-axis accelerometer sensor that records the acceleration of the basketball at time t. If the range of time is not shorter than the T (hold) time, the variation of the acceleration of the basketball's three-axis acceleration sensor is greater than a certain value. That is, |a(t)-a(t-1)|>M, and the basketball is judged to be in a "ball holding" state. Preferably, T(hold) > 0.2 s, M > 0.3 g. Where s represents "second", g is the unit of acceleration, and 1g is approximately equal to 9.8m/s^2.

FIG. 5 is a diagram showing an example of a change in the acceleration of a basketball during a basketball holding process. As can be seen from the figure:

When t < 395.7s: the basketball is flying, has not been taken;

When 395.7s<t<396.5s: In the ball, the acceleration of the basketball is unstable and changes smoothly;

When t>396.5s: The basketball leaves the hand and enters the flight state.

Where t is the time and s is the "second".

(3) Dribbling status

When the basketball continuously appears in the following states in sequence, it is determined that the basketball is in a dribbling state: a "ball holding" state, and the ball holding state is shorter than T1; a "flying" state, and the flight state is shorter than T2 a "impact" state (short acceleration spike, as shown in Figure 6), and the impact state is shorter than T3; a "flight" state, and the flight state is shorter than T4; "State, and the time of the ball holding state is shorter than T5. Preferably, 0.1 s < T1 < 1.0 s, 0.1 s < T2 < 1.0 s, 0 s < T3 < 0.2 s, 0.1 s < T4 < 1.0 s, 0.1 s < T5 < 1.0 s. Where s means "second".

When the basketball state appears as follows: holding the ball => flight => hit the ground => flight => holding the ball, and repeating multiple times, can be determined as the dribble state. Fig. 6 is a diagram showing an example of a change in the acceleration of a basketball during a basketball dribble. As can be seen from the figure:

t<0.3s: holding the ball, the acceleration is unstable;

0.3s<t<0.41s: flight, constant acceleration;

0.41s<t<4.9s.: The ground is exploding, and the acceleration has a sharp peak;

4.9s<t<5.6s: flight, constant acceleration;

t>5.6s: Holding the ball, the acceleration is unstable.

Where t is the time and s is the "second".

Using the characteristic data in the dribble, you can calculate the frequency and strength of the dribble, which is very helpful for training.

(4) Shooting status

When the angular velocity of the gyro collected by the basketball continuously exhibits the following characteristics, the basketball is determined to be in a shooting state:

The player lifts the basketball, and the angular velocity of the gyroscope is “first increase, then decrease”. The peak value is at least greater than W1, and the basketball is almost stationary at the end of the process. The angular velocity of the module should be less than W2; the player will vote for the basketball. The angular velocity of the gyroscope is increased; the basketball flies to the hoop and enters the "flight" state. Wherein, 200 deg/s < W1 < 400 deg/s, 50 deg/s < W2 < 180 deg/s, and deg/s is a unit of angular velocity (degrees per second).

Decomposing the shooting action, you can find that the shooting action can be divided into three steps:

The first step: lift the ball to the highest point;

Step 2: Throw the ball out and get a feature of the process at this step;

The third step: basketball flies to the basket.

FIG. 7 is a diagram showing an example of a change in the angular velocity of a basketball during a basketball shooting. As can be seen from the figure:

When 326.8s < t < 327.5s: the athlete lifts the ball over the top of the head: the basketball is first an accelerated rotation, then slows down and rotates, and finally stays at the top of the head;

When 327.5s < t < 327.7s: throw the ball;

When t>327.7s: The basketball flies to the basket and enters the “flight” state.

Where t is the time and s is the "second".

(5) Passing status

When the following characteristics of the basketball accelerating continuously, it is determined that the basketball is in a passing state: a "ball holding" state; a "flying" state; and a "ball holding" state.

When passing the ball, the movement of the basketball is "holding the ball -> flying -> catching the ball". FIG. 8 is a diagram showing an example of a change in the acceleration of the basketball during a basketball pass. As can be seen from the figure:

When t<107.1s: holding the ball, the acceleration is unstable;

When 107.1s < t < 107.8s: flight, constant acceleration;

When t>107.8s: catching the ball, the acceleration is unstable.

Where t is the time and s is the "second".

(6) Goal status

When the acceleration of the basketball continuously appears as follows, it is determined that the basketball is in a scoring state: a "flying" state; the acceleration continuously fluctuates, |a(t)-a(t-1)|>A, and the duration T is within a certain range. Inside, ie T(min)<T<T(max); a flight state. Preferably, 0.2 g < A < 10 g, 0.01 s < T (min) < 0.1 s, 0.1 s < T (max) < 0.4 s. Where g is the unit of acceleration, 1g is approximately equal to 9.8m/s^2, and s is "second".

Basketball goals need to pass the Nets. By capturing the characteristic data of basketball and the Nets interaction, you can identify whether the goal is scored or not. FIG. 9 is a diagram showing an example of a change in the acceleration of a basketball during a basketball shooting goal. As can be seen from the figure:

When t<393s: the ball holding process;

When 393s < t < 394s: the basketball leaves the hand and flies to the hoop for the "flying" state;

When 394s<t<394.2s: basketball interacts with the basket and the net;

When t>394.2s: Basketball flies away from the ring.

Where t is the time and s is the "second".

(7) Shooting does not enter the state

When the basketball detects the following state, it is determined that the shooting is not in progress: the shooting state; the state of the shooting "goal" does not occur.

(8) assist status

Assist is a special kind of "passing". Its essential characteristics are the same as passing. The difference is that if the passing player receives a quick shot after catching the ball, the player who delivers the pass is successfully performed once. Assist.

When the basketball first passes the ball and then the goal state occurs, an assist is determined. Specifically, basketball continues to appear in the following states: Player A holds the ball; Player A passes the ball to Player B; Player B shoots; Player B scores.

(9) rebound status

Rebounding refers to a basketball player who grabs a basket and flies off the basket after a missed shot. When the basketball recognizes a "shooting is not going" state, if the basketball is caught by the player, a rebound is generated.

When the basketball continues to appear in the following states, it is determined that the basketball is in a rebounding state: the shooting state; the shooting is not in the state; the ball is in the state.

It should be noted that the shooting position can also be determined by the above state and data.

When the "shooting" status is recognized, the basketball can also identify the position of the player's shot, requiring three conditions: basketball flight speed; basketball flight time; shooting angle.

First, the flight speed of the basketball is multiplied by the flight time. The product is the distance from the shooting point to the basket circle. Then, the arctangent value of the x-axis acceleration and the y-axis acceleration of the basketball on the horizontal plane when calculating the shooting is calculated, that is, the angle of the shooting; Finally, the shooting position can be uniquely determined by the shooting distance and the shooting angle. It can be seen that the distance between the shooting point and the rim is measured by the speed and time of the basketball flight, and the angle of the shooting, that is, the position of the shooting can be uniquely determined.

In this embodiment, the motion state of the wristband includes: dribbling and shooting.

Hereinafter, the recognition of the motion state in the wristband (player) movement will be described.

(1) Dribbling status

In basketball, dribbling is an important movement. The characteristics of the player's dribble (dribbling frequency, strength) is the basis for the subsequent matching of basketball and player's athletic data.

When the wristband has the following regularity, it is judged as the dribble state: the angular velocity of the wristband's gyroscope is positively and negatively alternated, and the peak represents the downward bounce. The trough represents the rebound of the basketball and returns to the hand. Process; the pitch angle of the wristband: the arm swings up and down, and the corresponding pitch angle varies by about 90 degrees.

Fig. 10 is a view showing an example of a change in the angular velocity of the wristband during the dribbling, in which the abscissa indicates time and the ordinate indicates angular velocity. Fig. 11 is a view showing an example of a change in the pitch angle of the wristband during the dribbling, in which the abscissa indicates time and the ordinate indicates angle.

(2) Shooting status

During the shooting process, the angular velocity and pitch angle of the wristband are regularly changed. When the wristband has the following regularity, it is determined as the shooting state: the angular velocity w<0, and the time is not shorter than T1; the angular velocity w is changed from negative to positive, and the time is not shorter than T2; the angular velocity is changed from negative to negative. At the positive critical point, the rotation angle of the wristband should be within a certain range, that is, roll(min)<roll<roll(max). Preferably, 0.2 s < T1 < 0.8 s, 0 s < T2 < 0.2 s, - 80 degrees < roll < 0 degrees.

It should be noted that the positive and negative relationship here is related to the wearing direction of the smart wearable device. In the wearing manner opposite to the above, the positive and negative relationship is reversed, and when the wristband has the following regularity, the shooting state is determined: The angular velocity w>0, and maintain a time not shorter than T1; the angular velocity w changes from positive to negative, and maintains a time not shorter than T2; at the critical point where the angular velocity is positively negative, the rotation angle of the wristband should be Within a certain range, that is, roll(min)<roll<roll(max). Preferably, 0.2 s < T1 < 0.8 s, 0 s < T2 < 0.2 s, and 0 degree < roll < 80 degrees.

Fig. 12 is a view showing an example of a change in the angular velocity of the wristband during the shooting, in which the abscissa indicates time and the ordinate indicates angular velocity. Fig. 13 is a view showing an example of a change in the pitch angle of the wristband during the shooting, in which the abscissa indicates time and the ordinate indicates angle. As can be seen from the figure:

When 336.8s<t<337.5s: the process of raising the hand, the angular velocity is <0, and the pitch angle is increased;

When t>337.5s: the process of shooting, the angular velocity is >0.

Where t is the time and s is the "second".

Step S302: The basketball chip determines whether the current motion state is a setting state in which motion data matching is required; if yes, step S303 is performed; if not, returning to step S301.

When the basketball chip detects that it is in a set state (such as shooting, dribbling, holding the ball, etc.), it automatically turns on the "search mode" to find the wristband that matches the state. For example, when the basketball detects that it is in a "shooting" state, the basketball will look for a matching "shooter" and update the "shooter" data record, such as the number of shots, the hit rate, and so on.

Step S303: The basketball chip establishes communication with all the wristband chips, and transmits the motion data to each wristband chip.

In this embodiment, a Bluetooth communication method is adopted between the basketball chip and the wristband chip. As a system center, basketball can communicate with two wristbands in both directions.

As described in the foregoing description of the state of motion, when the basketball is in a different state, the motion data transmitted to the wristband is also different.

Step S304: The wristband chip matches the received motion data with the self-motion data, and the successful communication is established with the basketball chip, and the matching result is sent to the basketball chip.

When the basketball opens the "search mode", the basketball sends some motion data of its own state to all wristbands, such as acceleration, angular velocity, etc. After the wristband receives the motion data, it matches the motion data with its own motion data. Determine whether the exercise data and basketball data match, and feed the results back to the basketball. The feedback data carries the logo of the wristband itself, so that the basketball can distinguish the data of different wristbands.

When the basketball is in the state of holding the ball, in order to identify the current "ball holder", in addition to the independent analysis of the motion data acquired by the basketball chip and the wristband chip, the motion data of the two chips is also needed. Make a match. For example, the following data characteristics can be used to determine the ball player in a period of time (T1, T2): the average of the acceleration of the basketball and wristband chips a_mean (ball), a_mean (wrist); the maximum acceleration of the basketball and wristband chips The values a_max(ball), a_max(wrist); the minimum acceleration of the basketball and wristband chips a_min(ball), a_min(wrist); the peak time of the basketball chip and the wristband chip T_peak(ball), T_peak(wrist). Among them, the meaning of "ball" means basketball data, and the meaning of "wrist" means wristband data. If the above data characteristics of the basketball and wristband chips have a certain degree of matching, it is determined that the player wearing the wristband is a ball-holding player.

For example, if the following four conditions are met simultaneously: |a_mean(ball)-a_mean(wrist)|<A1;|a_max(ball)-a_max(wrist)|<A2;|a_min(ball)-a_min(wrist)| A3;|T_peak(ball)-T_peak(wrist)|<A4; The data of the basketball chip and the wristband chip are considered to match. Wherein A1 is 1.5 g, preferably 1.0 g, further preferably 0.5 g; A2 is 2.0 g, preferably 1.2 g, more preferably 0.6 g; A3 is 2.0 g, preferably 1.2 g, and further preferably 0.6 g; A4 is 0.8 s, preferably 0.5 s, and further preferably 0.3 s. "||" means absolute value, g is the unit of acceleration, 1g is approximately equal to 9.8m/s^2, and s means "second".

When the basketball is in the dribble state or when the wristband is in the dribble state, the rules of the up and down movement of the basketball and the wristband should be consistent. The following data characteristics can be used to determine the dribble player within a certain period of time (T1, T2): (T1 , T2) time, record the time of each ball shot obtained by the basketball chip and the wristband chip Ti_ball, Ti_wirst, where i = 1, 2, ... N, N is N_ball, the smaller of N_wrist; (T1, T2) In the time, record the number of shots N_ball, N_wrist obtained by the basketball chip and the wristband chip. If both of the following conditions are met, the wristband wearer is considered to be dribbling: |N_ball-N_wrist|<N1, Sum(|Ti_ball-Ti_wrist|)<N2.

When the basketball is in dribble or when the wristband is in the shooting state, the basketball has never left the player's hand during the shooting. The motion laws of the motion data obtained by the basketball chip and the wristband chip should be strictly matched. During the time period corresponding to the shooting action, the basketball player can be determined according to the following data characteristics: the average value of the acceleration of the basketball chip and the wristband chip a_mean (ball), a_mean (wrist); the maximum acceleration a_max of the basketball chip and the wristband chip (ball), a_max (wrist); the minimum acceleration of the basketball chip and the wristband chip a_min (ball), a_min (wrist); the time of the basketball chip and the wristband chip raising the hand T_up (ball), T_up (wrist). Among them, the above refers to "ball" means basketball data, and "wrist" means wristband data.

If the above data characteristics of the basketball chip and the wristband chip have a certain degree of matching, it is determined that the player wearing the wristband is a basketball player. For example, if |a_mean(ball)-a_mean(wrist)|<A1;|a_max(ball)-a_max(wrist)|<A2;|a_min(ball)-a_min(wrist)|<A3;T_up(ball)- T_up(wrist)|<A4; It is considered that the data of the basketball chip and the wristband chip match. Wherein: A1 is 1.5 g, preferably 1.0 g, more preferably 0.5 gg; A2 is 2.0 g, preferably 1.2 g, further preferably 0.6 g; A3 is 2.0 g, preferably 1.2 g, further preferably 0.6 g; A4 is 0.8 s Preferably, it is 0.5 s, and further preferably 0.3 s. "||" means absolute value, g is the unit of acceleration, 1g is approximately equal to 9.8m/s^2, and s means "second".

Step S305: The basketball chip selects the best matching wristband from the received one or more matching wristbands.

Further, the basketball chip can notify the best matching wristband of the matching result, and the best matching wristband performs motion data statistics according to the first motion data and the second motion data.

Step S306: Establish Bluetooth communication between the basketball chip and the mobile terminal, and send the motion feature data and the matching result to the mobile terminal.

The motion feature data may be the result of processing and statistics on the motion data, such as the number of shots, the number of rebounds, the number of assists, and the hit rate.

In this embodiment, the mobile terminal is a mobile phone, and one basketball can communicate with multiple mobile phones. Basketball transmits the data on the field to the phone via Bluetooth and displays it on the phone. Among them, the data transmitted by the basketball to the mobile phone may include: the number of shots corresponding to each wristband, the number of rebounds, the number of assists, the hit rate, the shooting position of each shot, and the like.

In addition, in addition to transmitting data, basketball also has a storage function. When the basketball does not search for a mobile phone nearby, the data will be stored in the basketball local area, and the untransmitted data will be transmitted to the mobile phone after the mobile phone is found again.

Step S307: The mobile terminal uploads the received motion state data to the cloud through the mobile network.

Mobile terminals can share data through the cloud. Even if a player does not have a mobile terminal such as a mobile phone when playing, his data can be transmitted to the cloud through other people's mobile terminals and synchronized to his mobile terminal.

Step S308: The cloud performs the next processing.

The processing includes, but is not limited to, data storage, statistics and analysis of the athletic character data of each player, statistics and analysis of the overall player's motion feature data, and training recommendations according to the set model, etc. limit.

Through this embodiment, not only can each technician on the field be provided with technical statistical services, but also can assist the athletes in scientific training, improve the level of competition, and can also record the daily sports information of the athletes and improve the physical quality. Moreover, a large amount of motion data that cannot be obtained manually can be obtained by the electronic device, thereby saving labor costs; further, through the Internet platform, sports enthusiasts from all over the world can share the sports experience on the Internet, compare the exercise data, and improve the use experience.

Embodiment 4

Referring to Figure 14, there is shown a flow chart of the steps of a ball sports data statistics method in accordance with a fourth embodiment of the present invention.

This embodiment is still based on the description of the basketball and wristbands in the third embodiment, as well as the various states of motion of the basketball and wristbands.

The motion data statistical method of this embodiment includes the following steps:

Step S401: The basketball chip and the wristband chip respectively identify the movement state of the basketball and the player.

In this embodiment, the sports state of the basketball includes: flying, holding the ball, dribbling, shooting, passing, scoring, shooting, assisting, and rebounding; the sports state of the wristband includes: dribbling and shooting. A detailed description of each state of motion is as described in the third embodiment.

Step S402: The wristband chip determines whether the current motion state is a setting state in which motion data matching is required; if yes, step S403 is performed; if not, returning to step S401.

Among them, the setting state includes a shooting state or a dribbling state.

Step S403: The wristband chip establishes Bluetooth communication with the basketball chip, and transmits the motion data to the basketball chip.

When the wristband is in a different state, the motion data sent to the basketball is also different.

Step S404: The basketball chip sequentially matches the motion data of the received one or more wristband chips with the self-motion data to select the optimal matching wristband.

For example, the matching process of the motion data of the received one or more wristband chips to the self-motion data may be referred to the matching process described in step S304 in the third embodiment, and details are not described herein again.

Step S405: Establish Bluetooth communication between the basketball chip and the mobile terminal, and send the motion feature data and the matching result to the mobile terminal.

Step S406: The mobile terminal uploads the received motion state data to the cloud through the mobile network.

Step S407: The cloud performs the next processing.

It should be noted that the wristband can also transmit its own data to the mobile terminal such as a mobile phone via Bluetooth. Each athlete's wristband stores its own data and can be transmitted to its mobile phone in real time. The transmitted data includes: the wristband With the corresponding number of shots, rebounds, assists, hit rate, shooting position for each shot.

Embodiment 5

Referring to Figure 15, there is shown a block diagram of a structure of a ball type motion data statistical apparatus according to a fifth embodiment of the present invention.

The motion data statistical device of the embodiment is disposed in the ball type moving object, and the device includes:

The receiving module 501 is configured to receive second motion data sent by each smart wearable device in a setting state that determines that the current state is that motion data matching is required, where the second motion data is obtained by a sensor disposed in the smart wearable device. Wearer's wearer motion data;

The comparison module 502 is configured to compare the first motion data with the second motion data stored locally by the moving object to determine a plurality of matching degrees, wherein the first motion data is a motion obtained by a sensor disposed in the moving object. Object motion data;

The statistic module 503 is configured to determine first motion data and second motion data with the highest matching degree from the plurality of matching degree results, and perform motion data statistics according to the first motion data and the second motion data with the highest matching degree.

The motion data statistic device of the embodiment is used to implement the corresponding motion data statistic method in the foregoing method embodiment, and has the beneficial effects of the corresponding method embodiment, and details are not described herein again.

The comparison module includes: a first comparison module, configured to respectively average the acceleration of the moving object, the maximum value of the acceleration of the moving object, the minimum value of the acceleration of the moving object, and the acceleration peak time of the moving object, and the acceleration of the smart wearable device Comparing the average value, the maximum value of the acceleration of the smart wearable device, the minimum value of the acceleration of the smart wearable device, and the acceleration peak time of the smart wearable device, and determining the matching degree of the first motion data and the second motion data according to each comparison result; or , the first a comparison module, configured to compare the action time and the number of actions of the moving object with the action time and the number of actions of the smart wearable device, and determine the matching degree of the first motion data and the second motion data according to each comparison result; or a third comparison module, configured to respectively average an acceleration of the moving object, a maximum value of the acceleration of the moving object, a minimum value of the acceleration of the moving object, a pitching time of the moving object, and an average value of the acceleration of the smart wearable device, the smart wearable device The acceleration maximum value, the minimum acceleration of the smart wearable device, and the pitching time of the smart wearable device are compared, and the matching degree of the first motion data and the second motion data is determined according to each comparison result.

The statistic module performs motion data statistics according to the first motion data and the second motion data with the highest matching degree: obtaining motion feature data according to the first motion data and the second motion data with the highest matching degree; and performing motion feature data on the motion feature data Statistics and upload to mobile terminals.

Embodiment 6

Referring to FIG. 16, there is shown a structural block diagram of a ball type motion data statistical device according to Embodiment 6 of the present invention, wherein the device can be disposed in a smart wristband, and the ball sport in this embodiment can be a basketball sport. . Or the device in this embodiment may be other smart wearable devices, and the ball sports may also be sports such as soccer, volleyball and the like.

The motion data statistical device of this embodiment includes:

The receiving module 601 is configured to receive first motion data in a setting state that determines that the current state is that motion data matching is required, where the first motion data is motion obtained by a sensor disposed in a moving object (eg, a basketball) Object motion data.

The comparison module 602 is configured to compare the first motion data with the second motion data stored locally by the smart wearable device to determine a matching degree, where the second motion data is the wear obtained by the sensor disposed in the smart wearable device. Wearer motion data.

The sending module 603 is configured to send the matching degree, the second motion data, and the identifier of the smart wearable device to the moving object, so that the moving object determines wearer motion data of the wearer with the highest degree of matching with the first motion data, and performs motion data. statistics.

Preferably, the first motion data and the second motion data each include at least one of: ball holding data, dribble data, and pitching data;

among them,

The ball holding data includes: the average value of the acceleration of the moving object, the maximum value of the acceleration of the moving object, the minimum value of the acceleration of the moving object, the acceleration peak time of the moving object, and the acceleration of the smart wearable device. Average value, maximum acceleration of the smart wearable device, minimum acceleration of the smart wearable device, and acceleration peak time of the smart wearable device;

The dribble data includes: the action time and the number of actions of the moving object, the action time and the number of actions of the smart wearable device;

The pitching data includes: the average value of the acceleration of the moving object, the maximum value of the acceleration of the moving object, the minimum value of the acceleration of the moving object, the pitching time of the moving object, the average value of the acceleration of the smart wearable device, the maximum value of the acceleration of the smart wearable device, and the smart wearable device. The minimum acceleration, and the pitching time of the smart wearable device.

Preferably, the comparison module 602 comprises:

The first comparison module 6021 is configured to respectively average the acceleration of the moving object, the maximum value of the acceleration of the moving object, the minimum value of the acceleration of the moving object, and the acceleration peak time of the moving object, and the average value of the acceleration of the corresponding smart wearable device, Comparing the maximum value of the acceleration of the smart wearable device, the minimum value of the acceleration of the smart wearable device, and the acceleration peak time of the smart wearable device; determining the matching degree of the first motion data and the second motion data according to each comparison result;

and / or,

The second comparison module 6022 is configured to compare the action time and the number of actions of the moving object with the action time and the action time of the corresponding smart wearable device respectively; and determine the match between the first motion data and the second motion data according to each comparison result. degree;

and / or,

The third comparison module 6023 is configured to separately average the acceleration of the moving object, the maximum value of the acceleration of the moving object, the minimum value of the acceleration of the moving object, the pitching time of the moving object, and the average value of the acceleration of the corresponding smart wearing device, and smart wear. The maximum value of the acceleration of the device, the minimum value of the acceleration of the smart wearable device, and the pitching time of the smart wearable device are compared; and the matching degree of the first motion data and the second motion data is determined according to each comparison result.

Preferably, the first comparison module 6021, the absolute value of the difference between the average value of the acceleration of the basketball and the average value of the acceleration of the smart wristband is greater than 0 g and less than 1.5 g, preferably greater than 0 g and less than 1.0 g, and further preferably greater than 0 g and Less than 0.5g; the absolute value of the difference between the maximum acceleration of the basketball and the acceleration maximum of the smart wristband is greater than 0g and less than 2.0g, preferably greater than 0g and less than 1.2g, and further preferably greater than 0g and less than 0.6g, the minimum acceleration of the basketball The absolute value of the difference between the value and the minimum value of the acceleration of the smart wristband is greater than 0g and less than 2.0g, preferably greater than 0g and less than 1.2g, and more preferably greater than 0g and less than 0.6g, the acceleration peak time of the basketball and the acceleration peak of the smart wristband The absolute value of the difference between time is greater than 0s and less than 0.8s, excellent Determining that the first motion data and the second motion data match when greater than 0 s and less than 0.5 s, and further preferably greater than 0 s and less than 0.3 s;

and / or,

The second comparison module 6022 is configured to: the sum of the absolute values of the difference between the action time of the basketball and the action time of the smart wristband is greater than 0 s and less than 10 s, preferably greater than 0 s and less than 5 s, and the number of actions of the basketball and the smart wristband Determining that the first motion data and the second motion data match when the absolute value of the difference in the number of actions is greater than 0 times and less than 5 times, preferably greater than 0 s and less than 3 s;

and / or,

The third comparison module 6023, the absolute value of the difference between the average value of the acceleration of the basketball and the average value of the acceleration of the smart wristband is greater than 0 g and less than 1.5 g, preferably greater than 0 g and less than 1.0 g, and further preferably greater than 0 g and less than 0.5 g. The absolute value of the difference between the maximum acceleration of the basketball and the acceleration maximum of the smart wristband is greater than 0 g and less than 2.0 g, preferably greater than 0 g and less than 1.2 g, and more preferably greater than 0 g and less than 0.6 g, and the minimum acceleration and intelligence of the basketball The absolute value of the difference between the acceleration minimums of the wristband is greater than 0g and less than 2.0g, preferably greater than 0g and less than 1.2g, and more preferably greater than 0g and less than 0.6g, the difference between the projection time of the basketball and the projection time of the smart wristband The absolute value is greater than 0 s and less than 0.8 s, preferably greater than 0 s and less than 0.5 s, and further preferably greater than 0 s and less than 0.3 s, determining that the first motion data and the second motion data match. Where g is the unit of acceleration, 1g is approximately equal to 9.8m/s^2, and s is the time unit second.

Specifically, when the basketball is in the state of holding the ball, in order to identify the current "ball holder", in addition to separately analyzing the motion data acquired by the basketball chip and the wristband chip, it is also necessary to perform motion data of the two chips. match. For example, the following data characteristics can be used to determine the ball player in a period of time (T1, T2): the average of the acceleration of the basketball and wristband chips a_mean (ball), a_mean (wrist); the maximum acceleration of the basketball and wristband chips The values a_max(ball), a_max(wrist); the minimum acceleration of the basketball and wristband chips a_min(ball), a_min(wrist); the peak time of the basketball chip and the wristband chip T_peak(ball), T_peak(wrist). Among them, the meaning of "ball" means basketball data, and the meaning of "wrist" means wristband data. If the above data characteristics of the basketball and wristband chips have a certain degree of matching, it is determined that the player wearing the wristband is a ball-holding player.

When the basketball is in the dribble state or when the wristband is in the dribble state, the rules of the up and down movement of the basketball and the wristband should be consistent. The following data characteristics can be used to determine the dribble player within a certain period of time (T1, T2): (T1 , T2) time, record the time of each ball shot obtained by the basketball chip and the wristband chip Ti_ball, Ti_wirst, where i = 1, 2, ... N, N is N_ball, the smaller of N_wrist; (T1, T2) In the time, record the number of shots N_ball, N_wrist obtained by the basketball chip and the wristband chip. If both of the following are met In both cases, the wristband wearer is said to be dribbling: |N_ball-N_wrist|<N1,Sum(|Ti_ball-Ti_wrist|)<N2.

If the above data characteristics of the basketball chip and the wristband chip have a certain degree of matching, it is determined that the player wearing the wristband is a basketball player. For example, if |a_mean(ball)-a_mean(wrist)|<A1;|a_max(ball)-a_max(wrist)|<A2;|a_min(ball)-a_min(wrist)|<A3;T_up(ball)- T_up(wrist)|<A4; It is considered that the data of the basketball chip and the wristband chip match.

Preferably, the set state in which the motion data matching is required includes: a ball holding state, a dribbling state, or a pitching state.

Preferably, the ball sports data statistics device further includes: a determining module, configured to determine, after the comparing module determines the matching degree, whether the determined matching degree satisfies the set matching degree; the sending module is configured to satisfy the setting match in the determined matching degree The degree of matching, the second motion data, and the identification of the smart wearable device are transmitted to the moving object.

Preferably, the statistics module 603: when performing motion data statistics according to the first motion data and the second motion data: obtaining motion feature data according to the first motion data and the second motion data; performing statistics on the motion feature data and transmitting the motion feature data to the moving object And upload to the mobile terminal through moving objects.

The motion data statistic apparatus of the present embodiment is used to implement the corresponding motion data statistic method in the foregoing multiple method embodiments, and has the beneficial effects of the corresponding method embodiments, and details are not described herein again.

Example 7

The embodiment provides a smart basketball in which a microchip is disposed, and the microchip is configured to receive a plurality of second motion data in a setting state in which the current state is determined to be required for motion data matching, wherein The second motion data is wearer motion data of the plurality of wearers, and the wearer motion data of each wearer is obtained by sensors disposed in one or more smart wristbands, and the second motion data and the first motion are respectively The data is compared with the motion characteristics, and multiple matching degrees are determined, and the A second motion data with the highest degree of motion data matching is identified, wherein the first motion data is basketball motion data obtained by a sensor disposed in the basketball, and the identification information is transmitted to a terminal.

Preferably, the microchip compares the motion characteristics of the second motion data with the first motion data to determine the matching degree, and specifically performs the following steps: respectively, the average value of the acceleration of the basketball, the maximum value of the acceleration of the basketball, and the acceleration of the basketball respectively. The value, and the acceleration peak time of the basketball, compare with the average value of the acceleration of the corresponding smart wristband, the maximum acceleration of the smart wristband, the minimum acceleration of the smart wristband, and the acceleration peak time of the smart wristband, and get the first Comparing the result set, determining a matching degree of the first motion data and the second motion data according to each comparison result in the first comparison result set; and/or respectively, respectively, an action time and an action number of the basketball, and an action of the corresponding smart wristband Comparing the time and the number of actions, obtaining a second comparison result set, determining a matching degree of the first motion data and the second motion data according to each comparison result in the second comparison result set; and/or respectively calculating an average value of the basketball acceleration The maximum acceleration of basketball, the minimum acceleration of basketball, and the projection time of basketball. The average value of the acceleration of the corresponding smart wristband, the maximum acceleration of the smart wristband, the minimum acceleration of the smart wristband, and the projection time of the smart wristband are compared to obtain a third comparison result set, which is concentrated according to the third comparison result. The respective comparison results determine the degree of matching of the first motion data and the second motion data.

Preferably, the absolute value of the difference between the average value of the acceleration of the microchip and the average value of the acceleration of the smart wristband is greater than 0 g and less than 1.5 g, and the absolute value of the difference between the maximum value of the acceleration of the basketball and the maximum value of the acceleration of the smart wristband is greater than 0g and less than 2.0g, the absolute value of the difference between the minimum acceleration of the basketball and the minimum acceleration of the smart wristband is greater than 0g and less than 2.0g, and the absolute difference between the acceleration peak time of the basketball and the acceleration peak time of the smart wristband When the value is greater than 0 s and less than 0.8 s, determining that the matching degree of the first motion data and the second motion data is a match; and/or, the sum of the absolute values of the difference between the action timing of the microchip and the action time of the smart wristband If the absolute value of the difference between the number of actions of the basketball and the number of actions of the smart wristband is greater than 0 and less than 5 times, determining that the matching degree of the first motion data and the second motion data is a match; and / or, the absolute value of the microchip's difference between the average value of the acceleration of the basketball and the average of the acceleration of the smart wristband is greater than 0g and less than 1.5g, the maximum acceleration of the basketball and the acceleration of the smart wristband The absolute value of the difference between the maximum values is greater than 0g and less than 2.0g, and the absolute value of the difference between the minimum value of the acceleration of the basketball and the minimum value of the acceleration of the smart wristband is greater than 0g and less than 2.0g, and the projection time of the basketball and the wristband of the smart wristband When the absolute value of the difference between the projection times is greater than 0 s and less than 0.8 s, it is determined that the matching degree of the first motion data and the second motion data is a match; wherein g represents an acceleration unit, and s represents a time unit second.

Preferably, the microchip is further configured to perform data statistics according to the second motion data with the highest matching degree to obtain the number of shots, the number of goals, the number of rebounds, the number of assists, and the number of dribbles of each player.

Preferably, the set states required to perform the motion data matching include: a ball holding state, a dribbling state, and a pitching state.

Preferably, the terminal is at least one of a smart wristband, a mobile phone, and a computer.

Preferably, the microchip includes a memory for storing the statistical motion feature data.

Preferably, the microchip is further configured to acquire the flight speed, the flight time, and the shooting angle of the basketball in the current state of the shooting state, and determine the shooting position according to the flight speed, the flight time, and the shooting angle.

Preferably, when determining the shooting position, the microchip specifically performs the following steps: calculating the product of the flying speed and the flight time to determine the shooting distance of the shooting point from the basket; and calculating the arctangent of the x-axis acceleration of the basketball in the horizontal plane when shooting. The value and the arctangent of the acceleration in the y-axis direction determine the shooting angle; and determine the shooting position based on the shooting distance and the shooting angle.

Preferably, the microchip is further configured to identify whether a predetermined motion action occurs according to the obtained first motion data, and send the first motion data to one or more smart wristbands after the confirmation of the occurrence of the predetermined motion, the smart wristband The second motion data is sent to the basketball after receiving the first motion data.

Example eight

The embodiment provides a smart wristband, wherein the smart wristband is provided with a microchip, and the microchip is configured to receive the first motion data in a setting state that determines that the current state is required to perform motion data matching, where A motion data is motion object motion data obtained by a sensor disposed in the moving object, and the first motion data is compared with the second motion data to determine a matching degree, wherein the second motion data is set by the smart The wearer motion data obtained by the sensor of the wristband transmits the matching degree, the second motion data, and the identification of the smart wristband to the moving object, so that the moving object determines the wearer motion data with the highest matching degree with the first motion data. Perform motion statistics.

Preferably, the microchip compares the motion characteristics of the first motion data with the second motion data to determine the matching degree, and specifically performs the following steps: respectively, the average value of the acceleration of the moving object, the maximum value of the acceleration of the moving object, and the moving object. The acceleration minimum value and the acceleration peak time of the moving object are compared with the acceleration average value, the acceleration maximum value, the acceleration minimum value, and the acceleration peak time of the smart wristband to obtain a fourth comparison result set, according to each of the fourth comparison result sets. The comparison result determines the matching degree of the first motion data and the second motion data; and/or, respectively, compares the action time and the number of actions of the moving object with the action time and the number of actions of the smart wristband to obtain a fifth comparison result set. And determining, according to each comparison result in the fifth comparison result set, a matching degree of the first motion data and the second motion data; and/or respectively, respectively, an average value of the acceleration of the moving object, a maximum value of the acceleration of the moving object, and The minimum acceleration of the animal body, the projection time of the moving object, and the acceleration average value, the acceleration maximum value, the acceleration minimum value, and the projection time of the smart wristband are compared to obtain a sixth comparison result set, according to each of the sixth comparison result sets. The comparison result determines the degree of matching of the first motion data and the second motion data.

Preferably, the microchip is further configured to determine whether the determined matching degree satisfies the set matching degree, and only send the matching degree, the second motion data, and the identifier of the smart wristband to the determined matching degree to satisfy the preset matching degree. Moving objects.

Preferably, the set states required to perform the motion data matching include: a dribbling state, a ball holding state, and a pitching state.

Example nine

This embodiment provides another smart wristband. The smart wristband is provided with a microchip for identifying whether a predetermined motion action occurs according to the obtained second motion data, wherein the second motion data is wearer motion data obtained by a sensor disposed in the smart wristband And, when identifying the predetermined motion action, transmitting second motion data to a moving object, and receiving, storing, and displaying valid motion identification information sent by the moving object, wherein the identification information is that the moving object has a first motion data and Comparing the second motion data from the one or more smart wristbands, selecting a second motion data with the best matching degree, and then sending the smart wristband to obtain the second motion data with the best matching degree, wherein, A motion data is motion object motion data obtained by a sensor provided in a moving object.

Preferably, the moving object is a basketball, and the predetermined motion action includes at least one of shooting, passing, and dribbling.

Preferably, the second motion data comprises at least one of: maintenance data, operation data and projection data of the smart wristband; wherein the retention data of the smart wristband comprises: an acceleration average, an acceleration maximum, an acceleration minimum, and an acceleration peak Time; the operating data of the smart wristband includes the action time and the number of actions; the projection data of the smart wristband includes: the acceleration average, the acceleration maximum, the acceleration minimum, and the projection time.

Preferably, the microchip identifies a predetermined motion action according to the following method: determining, according to the obtained second motion data, that the angular velocity w<0, and maintaining a time that is not shorter than T1; the angular velocity w is changed from negative to positive, and is maintained for a period of not less than The time of T2; when the angular velocity of the wristband is within the preset angle range, the shooting angle is determined when the angular rotation of the wristband is within the preset angle range, wherein 0.2s<T1<0.8s, 0s<T2<0.2s, -80 degrees <roll<0 degrees; according to the obtained second motion data, it is determined that the angular velocity of the module is positively and negatively alternating, and when the pitch angle of the module changes by 90 degrees, it is determined that the dribble occurs.

Example ten

The tenth embodiment provides a ball sports data statistics system, and the system includes: a first statistical device disposed in the smart wearable device worn by the athlete and a second statistical device disposed in the moving object, for example In the ball sports data statistics system for data statistics of a basketball game, the first statistical device disposed in the smart wearable device worn by each player, and the second statistical device disposed in the basketball Thus, the system includes a total of ten first statistical devices and one second statistical device.

In the statistical system, the first motion data is motion object motion data obtained by a sensor provided in the moving object, and the second motion data is wearer motion data obtained by a sensor provided in the smart wearable device.

The second statistical device is configured to send the first motion data to the first statistical device, where the first statistical device is configured to receive the first motion data and determine the current state that is required to perform motion data matching, and the first motion data The data is compared with the second motion data stored locally by the smart wearable device to determine a matching degree, and then the matching degree, the second motion data, and the identifier of the smart wearable device are sent to the second statistical device, where the second statistical device is further used for Receiving, by the first statistical device in the smart wearable device worn by each athlete participating in the ball sports, the matching degree, the second motion data, and the identifier of the smart wearable device, and determining the second sport having the highest matching degree with the first motion data. Data and statistics of exercise data.

Preferably, the second statistical device is further configured to perform data statistics according to the second motion data with the highest matching degree to obtain the number of shots, the number of goals, the number of rebounds, the number of assists, and the number of dribbles of each player.

Preferably, the ball sports data statistics system further includes: a mobile terminal, wherein the second statistical device is further configured to send the identifier of the smart wearable device corresponding to the second motion data with the highest matching degree to the mobile terminal.

Preferably, the mobile terminal is at least one of a smart wristband, a mobile phone and a computer.

Preferably, the second statistical device is further configured to identify whether a predetermined motion action occurs according to the obtained first motion data, and send the first motion data to the first statistical device after confirming that the predetermined action occurs.

Preferably, the first statistical device is further configured to determine whether the determined matching degree satisfies the set matching degree, and only the matching degree, the second motion data, and the identifier of the smart wristband are determined only when the determined matching degree satisfies the preset matching degree. Send to the second statistical device.

Embodiment 11

The eleventh embodiment provides another ball sports data statistics system, and the system includes: a first statistical device disposed in the smart wearable device worn by the athlete and a second statistical device disposed in the moving object For example, in a ball sports data statistics system that performs data statistics on a basketball game sport, the first statistical device disposed in the smart wearable device worn by each player includes a second set in the basketball. The statistical device, therefore, comprises a total of ten first statistical devices and one second statistical device.

The first statistic device is configured to send the second statistic data to the second statistic device, and the second statistic device is configured to receive the first statistic in each smart wearable device in a setting state that determines that the current state is that the motion data needs to be matched. And second motion data sent by the device, and comparing each of the second motion data with the first motion data stored locally by the moving object, determining a plurality of matching degrees, and determining the first matching degree from the plurality of matching degrees The motion data and the second motion data; the motion data statistics are performed according to the first motion data and the second motion data having the highest matching degree, wherein the first motion data is motion object motion data obtained by a sensor disposed in the moving object.

Preferably, the first statistical device is further configured to identify whether a predetermined motion action occurs according to the obtained second motion data, and send the second motion data to the second statistical device only when the predetermined motion motion is recognized.

It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not limited thereto; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that The technical solutions described in the foregoing embodiments are modified, or the equivalents of the technical features are replaced. The modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

A method for statistically counting ball sports data, characterized in that a plurality of athletes participating in the ball sports are respectively dressed with smart wearable devices, and the statistical methods include:

Receiving first motion data in a setting state that determines that the current state is that motion data matching is required, wherein the first motion data is motion object motion data obtained by a sensor disposed in the moving object;

Comparing the first motion data with the second motion data stored locally by the smart wearable device to determine a matching degree, wherein the second motion data is a wear obtained by a sensor disposed in the smart wearable device Wearer motion data;

Transmitting the matching degree, the second motion data, and the identifier of the smart wearable device to the moving object, so that the moving object determines a wearer of a wearer that has the highest degree of matching with the first motion data Motion data and statistics of exercise data.
The method for statistically counting ball motion data according to claim 1, wherein

The first motion data includes at least one of: first ball holding data, first dribble data, and first pitching data, wherein the first ball holding data includes: an average value of an acceleration of the moving object, and a moving object The acceleration maximum value, the acceleration minimum value of the moving object, and the acceleration peak time of the moving object; the first dribble data includes: an action moment and an action number of the moving object; and the pitching data includes: an acceleration average of the moving object , the maximum acceleration of the moving object, the minimum value of the acceleration of the moving object, and the pitching time of the moving object,

The second motion data includes at least one of: second ball-holding data, second dribble data, and second pitching data, wherein the second ball-holding data includes: an average value of acceleration of the smart wearable device, and intelligence An acceleration maximum value of the wearable device, an acceleration minimum value of the smart wearable device, and an acceleration peak time of the smart wearable device; the second dribble data includes: an action time and an action count of the smart wearable device; and the second pitching data includes : the average value of the acceleration of the smart wearable device, the maximum value of the acceleration of the smart wearable device, the minimum value of the acceleration of the smart wearable device, and the pitching time of the smart wearable device.
The ball sports data statistical method according to claim 1, wherein after the matching degree is determined, the matching degree, the second motion data, and the identifier of the smart wearable device are transmitted to the moving object. Previously, the method further includes:

Determining whether the determined matching degree satisfies the set matching degree,

The step of transmitting the matching degree, the second motion data, and the identification of the smart wearable device to the moving object is performed only when the determined matching degree satisfies the set matching degree.
A method for statistically counting ball sports data, characterized in that a plurality of athletes participating in the ball sports are respectively dressed with smart wearable devices, and the statistical methods include:

Receiving second motion data sent by each smart wearable device in a setting state that determines that the current state is that motion data matching is required, wherein the second motion data is a wearer's wear obtained by a sensor in the smart wearable device. Motion data;

Comparing each of the second motion data with the first motion data stored locally by the moving object, and determining a plurality of matching degrees, wherein the first motion data is obtained by a sensor disposed in the moving object Motion data of moving objects;

Determining, from the plurality of matching degrees, first motion data and second motion data having the highest matching degree;

The motion data statistics are performed according to the first motion data and the second motion data with the highest matching degree.
The method for statistically counting ball motion data according to claim 4, characterized in that

The first motion data includes at least one of: first ball holding data, first dribble data, and first pitching data, wherein the first ball holding data includes: an average value of an acceleration of the moving object, and a moving object The acceleration maximum value, the acceleration minimum value of the moving object, and the acceleration peak time of the moving object; the first dribble data includes: an action moment and an action number of the moving object; and the pitching data includes: an acceleration average of the moving object , the maximum acceleration of the moving object, the minimum value of the acceleration of the moving object, and the pitching time of the moving object,

The second motion data includes at least one of: second ball-holding data, second dribble data, and second pitching data, wherein the second ball-holding data includes: an average value of acceleration of the smart wearable device, and intelligence An acceleration maximum value of the wearable device, an acceleration minimum value of the smart wearable device, and an acceleration peak time of the smart wearable device; the second dribble data includes: an action time and an action count of the smart wearable device; and the second pitching data includes : the average value of the acceleration of the smart wearable device, the maximum value of the acceleration of the smart wearable device, the minimum value of the acceleration of the smart wearable device, and the pitching time of the smart wearable device.
The ball sports data statistical method according to claim 4, wherein the performing motion data statistics according to the first motion data and the second motion data having the highest matching degree comprises:

Obtaining motion feature data according to the first motion data and the second motion data with the highest matching degree;

The motion feature data is counted and uploaded to the mobile terminal.
A ball sports data statistics device, wherein a plurality of athletes participating in the ball sports are respectively dressed with smart wearable devices, and the statistical device includes:

a receiving module, configured to receive first motion data in a setting state that determines that the current state is that motion data matching is required, wherein the first motion data is motion object motion data obtained by a sensor disposed in the moving object ;

a comparison module, configured to compare the first motion data with the second motion data stored locally by the smart wearable device to determine a matching degree, where the second motion data is set in the smart wearable device The wearer's wearer movement data obtained by the sensor;

a sending module, configured to send the matching degree, the second motion data, and the identifier of the smart wearable device to the moving object, so that the moving object determines the highest matching degree with the first motion data The wearer's wearer's exercise data and athletic statistics.
The ball sports data statistics device according to claim 7, wherein the comparison module comprises:

a first comparison module, configured to respectively average an acceleration of the moving object, a maximum value of the acceleration of the moving object, a minimum value of the acceleration of the moving object, and an acceleration peak time of the moving object, and an average value of the acceleration of the smart wearable device Comparing the maximum value of the acceleration of the smart wearable device, the minimum value of the acceleration of the smart wearable device, and the acceleration peak time of the smart wearable device, and determining the first motion data and the second motion data according to each comparison result. Matching degree;

or,

a second comparison module, configured to compare the action time and the number of actions of the moving object with the action time and the number of actions of the smart wearable device, and determine the first motion data and the second motion according to each comparison result The matching degree of the data;

or,

a third comparison module, configured to respectively average an acceleration of the moving object, a maximum value of the acceleration of the moving object, a minimum value of the acceleration of the moving object, a pitching time of the moving object, and the smart wearing Comparing the average value of the acceleration of the wearing device, the maximum value of the acceleration of the smart wearable device, the minimum value of the acceleration of the smart wearable device, and the pitching time of the smart wearable device, and determining the first motion data according to each comparison result. The degree of matching with the second motion data.
The ball motion data statistical device according to claim 7, wherein:

The device further includes: a determining module, configured to determine, after the comparing module determines the matching degree, whether the determined matching degree satisfies the set matching degree;

The sending module is configured to send the matching degree, the second motion data, and the identifier of the smart wearable device to the moving object when the determined matching degree satisfies the set matching degree.
A ball sports data statistics device, wherein a plurality of athletes participating in the ball sports are respectively dressed with smart wearable devices, and the statistical device includes:

a receiving module, configured to receive second motion data sent by each smart wearable device in a setting state that determines that the current state is that motion data matching is required, where the second motion data is obtained by using a sensor in the smart wearable device Wearer's wearer motion data;

a comparison module, configured to compare each of the second motion data with the first motion data stored locally by the moving object, and determine a plurality of matching degrees, wherein the first motion data is set by the motion Moving object motion data obtained by sensors within the object;

And a statistic module, configured to determine first motion data and second motion data with the highest matching degree from the plurality of matching degrees, and perform motion data statistics according to the first motion data and the second motion data with the highest matching degree.
The ball sports data statistics device according to claim 10, wherein the comparison module comprises:

a first comparison module, configured to respectively average an acceleration of the moving object, a maximum value of the acceleration of the moving object, a minimum value of the acceleration of the moving object, and an acceleration peak time of the moving object, and an average value of the acceleration of the smart wearable device Comparing the maximum value of the acceleration of the smart wearable device, the minimum value of the acceleration of the smart wearable device, and the acceleration peak time of the smart wearable device, and determining the first motion data and one of the second motions according to each comparison result. The matching degree of the data;

Or,

a second comparison module, configured to compare the action time and the number of actions of the moving object with the action time and the number of actions of the smart wearable device, and determine the first motion data and one of the second according to each comparison result Matching degree of motion data;

or,

a third comparison module, configured to respectively average an acceleration of the moving object, a maximum value of the acceleration of the moving object, a minimum value of the acceleration of the moving object, a pitching time of the moving object, and an average value of the acceleration of the smart wearable device, the smart Comparing the maximum value of the acceleration of the wearable device, the minimum value of the acceleration of the smart wearable device, and the pitching time of the smart wearable device, and determining a match between the first motion data and one of the second motion data according to each comparison result. degree.
The ball sports data statistics device according to claim 10, wherein the statistical module performs motion data statistics according to the first motion data and the second motion data having the highest matching degree:

Obtaining motion feature data according to the first motion data and the second motion data with the highest matching degree;

The motion feature data is counted and uploaded to the mobile terminal.
A smart basketball, wherein the basketball is provided with a microchip, and the microchip is configured to receive a plurality of second motion data in a setting state that determines that the current state is required to perform motion data matching, wherein The plurality of second motion data is wearer motion data of a plurality of wearers, and wearer motion data of each wearer is obtained by sensors disposed in one or more smart wristbands, each of the second motions Comparing the motion characteristics with the first motion data, determining a plurality of matching degrees, and determining a second motion data having the highest matching degree with the first motion data, and performing identification, wherein the first motion data is The basketball data obtained by the sensor set in the basketball is transmitted to the terminal.
The smart basketball according to claim 13, wherein the microchip compares the motion characteristics of the second motion data with the first motion data to determine a matching degree, and specifically performs the following steps:

The average value of the acceleration of the basketball, the maximum value of the acceleration of the basketball, the minimum value of the acceleration of the basketball, and the acceleration peak time of the basketball, the average acceleration of the corresponding smart wristband, the maximum acceleration of the smart wristband, and the intelligent wristband. Minimum acceleration, and acceleration peaks of smart wristbands Comparing, obtaining a first comparison result set, determining a matching degree of the first motion data and the second motion data according to each comparison result in the first comparison result set;

and / or,

Comparing the action time and the number of actions of the basketball with the action time and the number of actions of the corresponding smart wristband respectively, obtaining a second comparison result set, and determining the first motion according to each comparison result in the second comparison result set The degree of matching of the data and the second motion data;

and / or,

The average value of the basketball's acceleration, the maximum value of the basketball's acceleration, the minimum of the basketball's acceleration, the basketball's projection time, and the corresponding acceleration of the smart wristband, the maximum acceleration of the smart wristband, and the acceleration of the smart wristband. The value is compared with the projection time of the smart wristband to obtain a third comparison result set, and the matching degree of the first motion data and the second motion data is determined according to each comparison result in the third comparison result set.
A smart basketball according to claim 14, wherein

The absolute value of the difference between the average value of the acceleration of the basketball and the average value of the acceleration of the smart wristband is greater than 0 g and less than 1.5 g, and the absolute value of the difference between the maximum acceleration of the basketball and the maximum acceleration of the smart wristband is greater than 0 g. And less than 2.0g, the absolute value of the difference between the minimum acceleration of the basketball and the minimum acceleration of the smart wristband is greater than 0g and less than 2.0g, and the absolute value of the difference between the acceleration peak time of the basketball and the acceleration peak time of the smart wristband When the value is greater than 0 s and less than 0.8 s, determining that the matching degree of the first motion data and the second motion data is a match;

and / or,

The sum of the absolute values of the difference between the action time of the basketball and the action time of the smart wristband of the microchip is greater than 0 s and less than 10 s, and the absolute value of the difference between the number of actions of the basketball and the number of movements of the smart wristband is greater than 0 times. And less than 5 times, determining that the matching degree of the first motion data and the second motion data is a match;

and / or,

The absolute value of the difference between the average value of the acceleration of the basketball and the average value of the acceleration of the smart wristband is greater than 0 g and less than 1.5 g, and the absolute value of the difference between the maximum acceleration of the basketball and the maximum acceleration of the smart wristband is greater than 0 g. And less than 2.0g, the absolute value of the difference between the minimum acceleration of the basketball and the minimum acceleration of the smart wristband is greater than 0g and less than 2.0g, and the absolute value of the difference between the projection time of the basketball and the projection time of the smart wristband is greater than 0s. And less than 0.8 s, determining that the matching degree of the first motion data and the second motion data is a match;

Where g is the unit of acceleration and s is the unit of time in seconds.
The smart basketball according to claim 13, wherein the microchip is further configured to perform data statistics according to the second motion data with the highest matching degree to obtain the number of shots, the number of goals, and the number of rebounds of each player. , the number of assists, the number of dribble.
The smart basketball according to claim 13, wherein the set state in which the motion data matching is required includes: a ball holding state, a dribbling state, and a pitching state.
The smart basketball according to claim 13, wherein said terminal is at least one of said smart wristband, mobile phone and computer.
The smart basketball of claim 18, wherein the microchip comprises a memory for storing the statistical motion feature data.
The smart basketball according to claim 18, wherein the microchip is further configured to acquire a flight speed, a flight time, and a shooting angle of the basketball when the current state is a shooting state, and fly according to the flight speed. Time, and shooting angle, determine the position of the shot.
The smart basketball according to claim 20, wherein the microchip performs the following steps when determining the shooting position:

Calculating the product of flight speed and flight time to determine the distance from the shooting point to the basket;

Calculating the arctangent value of the acceleration of the x-axis direction of the basketball on the horizontal plane and the arctangent value of the acceleration of the y-axis direction when determining the shooting angle, and determining the shooting angle;

The shooting position is determined based on the shooting distance and the shooting angle.
The smart basketball according to claim 13, wherein the microchip is further configured to identify whether a predetermined motion action occurs according to the obtained first motion data, and after confirming that the predetermined action occurs, The first motion data is sent to one or more smart wristbands, and the smart wristband transmits the second motion data to the basketball after receiving the first motion data.
A smart wristband is characterized in that: a microchip is disposed in the smart wristband, and the microchip is configured to receive first motion data in a setting state that determines that a current state is required for motion data matching, where The first motion data is motion object motion data obtained by a sensor disposed in the moving object, and compares the first motion data with the second motion data to determine a matching degree, wherein the first The second motion data is wearer motion data obtained by a sensor provided on the smart wristband, and the matching degree, the second motion data, and the identifier of the smart wristband are transmitted to the moving object, The moving object is determined to have the highest degree of wearer motion data that matches the first motion data and to perform motion data statistics.
The smart wristband according to claim 23, wherein the microchip compares the motion characteristics of the first motion data with the second motion data to determine a matching degree, and specifically performs the following steps:

The average value of the acceleration of the moving object, the maximum value of the acceleration of the moving object, the minimum value of the acceleration of the moving object, and the acceleration peak time of the moving object, respectively, and the acceleration average value, the acceleration maximum value, the acceleration minimum value of the smart wristband, and The acceleration peak time is compared to obtain a fourth comparison result set, and the matching degree of the first motion data and the second motion data is determined according to each comparison result in the fourth comparison result set;

and / or,

Comparing the action time and the number of actions of the moving object with the action time and the number of actions of the smart wristband, respectively, to obtain a fifth comparison result set, and determining the first according to each comparison result in the fifth comparison result set Matching degree of motion data and the second motion data;

and / or,

The average value of the acceleration of the moving object, the maximum value of the acceleration of the moving object, the minimum value of the acceleration of the moving object, the projection time of the moving object, and the average value of the acceleration of the smart wristband, the maximum value of the acceleration, the minimum value of the acceleration, and the projection time The comparison is performed to obtain a sixth comparison result set, and the matching degree of the first motion data and the second motion data is determined according to each comparison result in the sixth comparison result set.
A smart wristband according to claim 24, wherein

The absolute value of the difference between the average value of the acceleration of the basketball and the average value of the acceleration of the smart wristband is greater than 0 g and less than 1.5 g, and the absolute value of the difference between the maximum acceleration of the basketball and the maximum acceleration of the smart wristband is greater than 0 g. And less than 2.0g, the absolute value of the difference between the minimum acceleration of the basketball and the minimum acceleration of the smart wristband is greater than 0g and less than 2.0g, and the absolute value of the difference between the acceleration peak time of the basketball and the acceleration peak time of the smart wristband When the value is greater than 0 s and less than 0.8 s, determining that the matching degree of the first motion data and the second motion data is a match;

and / or,

The sum of the absolute values of the difference between the action time of the basketball and the action time of the smart wristband of the microchip is greater than 0 s and less than 10 s, and the absolute value of the difference between the number of actions of the basketball and the number of movements of the smart wristband is greater than 0 times. And less than 5 times, determining that the matching degree of the first motion data and the second motion data is a match;

and / or,

The absolute value of the difference between the average value of the acceleration of the microchip and the average value of the acceleration of the smart wristband is greater than 0g and less than 1.5g, and the maximum acceleration of the basketball and the maximum acceleration of the smart wristband The absolute value of the difference is greater than 0g and less than 2.0g, and the absolute value of the difference between the minimum acceleration of the basketball and the minimum acceleration of the smart wristband is greater than 0g and less than 2.0g, and the projection time of the basketball and the projection time of the smart wristband When the absolute value of the difference is greater than 0 s and less than 0.8 s, determining that the matching degree of the first motion data and the second motion data is a match;

Where g is the unit of acceleration and s is the unit of time in seconds.
A smart wristband according to any one of claims 23 to 25, wherein

The microchip is further configured to determine whether the determined matching degree satisfies the set matching degree, and only if the determined matching degree satisfies the preset matching degree, the matching degree, the second motion data, and the The identification of the smart wristband is sent to the moving object.
The smart wristband according to claim 23, wherein said setting states for which motion data matching is required include: a dribbling state, a ball holding state, and a pitching state.
A smart wristband, wherein the smart wristband is provided with a microchip for identifying whether a predetermined motion action occurs according to the obtained second motion data, wherein the second motion data is The wearer motion data obtained by the sensor disposed in the smart wristband, and when the predetermined motion motion is recognized, the second motion data is transmitted to a moving object, and the moving object is received, stored, and displayed The valid action identification information sent, wherein the identification information is that the moving object compares a first motion data with second motion data from one or more smart wristbands, and selects a best match degree After the second motion data, the smart wristband that obtains the second motion data with the best matching degree is emitted, wherein the first motion data is motion object motion data obtained by a sensor disposed in the moving object.
A smart wristband according to claim 28, wherein said moving object is a basketball, and said predetermined motion action comprises at least one of shooting, passing and dribbling.
The smart wristband according to claim 29, wherein the second motion data comprises at least one of: retention data, operational data, and projection data of the smart wristband;

among them,

The maintenance data of the smart wristband includes: an acceleration average value, an acceleration maximum value, an acceleration minimum value, and an acceleration peak time; the operation data of the smart wristband includes an action time and an action number; and the projection data of the smart wristband includes : acceleration average, acceleration maximum, acceleration minimum, and projection time.
The smart wristband according to claim 30, wherein said microchip identifies said predetermined motion action according to the following method:

Determining from the obtained second motion data: the angular velocity w<0, and maintaining a period not shorter than T1 Time; the angular velocity w is positively changed from negative to a time that is not shorter than T2; when the angular velocity of the wristband is within a predetermined angle range, the angular rotation is determined to occur. 0.2s<T1<0.8s, 0s<T2<0.2s, -80 degrees<roll<0 degrees;

According to the obtained second motion data, it is determined that the angular velocity of the module is positively and negatively alternating, and when the module pitch angle changes by 90 degrees, it is determined that the dribble occurs.
A ball sports data statistics system, comprising: a first statistical device disposed in a smart wearable device worn by an athlete; and a second statistical device disposed in the moving object, wherein

The first statistical device is configured to receive the first motion data in a setting state that determines that the current state is that motion data matching is required, and perform the first motion data and the second motion data locally stored by the smart wearable device. Comparing the motion features, determining the degree of matching, and then transmitting the matching degree, the second motion data, and the identifier of the smart wearable device to the second statistical device, wherein the first motion data is set by Moving object motion data obtained by a sensor within the moving object, the second motion data being wearer motion data obtained by a sensor disposed in the smart wearable device;

The second statistical device is configured to send the first motion data to the first statistical device, and receive the matching sent by a first statistical device in a smart wearable device worn by each athlete participating in the ball sports And the second motion data and the identifier of the smart wearable device, and determine second motion data with the highest degree of matching with the first motion data and perform motion data statistics.
The ball sports data statistics system according to claim 32, wherein the second statistical device is further configured to perform data statistics according to the second motion data with the highest matching degree to obtain the number of shots of each player, The number of goals, the number of rebounds, the number of assists, and the number of dribbles.
The ball sports data statistics system according to claim 32, further comprising: a mobile terminal, wherein the second statistical device is further configured to: use the smart wearable device corresponding to the second motion data with the highest matching degree The identity is sent to the mobile terminal.
The ball sports data statistics system according to claim 34, wherein the mobile terminal is at least one of the smart wristband, the mobile phone, and the computer.
The ball sports data statistics system according to claim 32, wherein the second statistical means is further configured to: identify whether a predetermined motion action occurs according to the obtained first motion data, and confirm that the occurrence occurs After the predetermined action, the first motion data is transmitted to the first statistical device.
The ball sports data statistics system according to claim 32, wherein the first statistical device is further configured to determine whether the determined matching degree satisfies the set matching degree, and only when the determined matching degree is full. The matching degree, the second motion data, and the identifier of the smart wristband are sent to the second statistical device when the preset matching degree is sufficient.
A ball sports data statistics system, comprising: a first statistical device disposed in a smart wearable device worn by an athlete; and a second statistical device disposed in the moving object, wherein:

The first statistical device is configured to send second motion data to the second statistical device, wherein the second motion data is wearer motion data of a wearer obtained by a sensor in the smart wearable device;

The second statistical device is configured to receive the second motion data sent by the first statistical device in each smart wearable device in a setting state that determines that the current state is that motion data matching is required, and each of the The second motion data is compared with the first motion data stored locally by the moving object, and the plurality of matching degrees are determined, and the first motion data and the second motion data with the highest matching degree are determined from the plurality of matching degrees; The first motion data and the second motion data having the highest matching degree perform motion data statistics, wherein the first motion data is motion object motion data obtained by a sensor provided in the moving object.
The ball sports data statistics system according to claim 38, wherein the second statistical device is further configured to perform data statistics according to the second motion data with the highest matching degree to obtain the number of shots of each player, The number of goals, the number of rebounds, the number of assists, and the number of dribbles.
The ball sports data statistics system according to claim 38, further comprising: a mobile terminal, wherein the second statistical device is further configured to: use the smart wearable device corresponding to the second motion data with the highest matching degree The identity is sent to the mobile terminal.
The ball sports data statistics system according to claim 40, wherein the mobile terminal is at least one of the smart wristband, the mobile phone, and the computer.
The ball sports data statistics system according to claim 38, wherein the first statistical device is further configured to identify whether a predetermined motion action occurs according to the obtained second motion data, only when a predetermined motion motion is recognized. And transmitting the second motion data to the second statistical device.