Basketball training analysis method, system and storage medium

Abstract

The present invention discloses a basketball training analysis method, system and storage medium. The basketball training analysis method comprises: obtaining the player's court position data in real time through ultra-wideband wireless communication positioning technology; collecting the player's posture data through an RGB camera; formulating a basketball training method according to the player's court position data and the player's motion posture data. The present invention obtains the player's court position data in real time through ultra-wideband wireless communication positioning technology and collects the player's posture data through an RGB camera, and combines deep learning and machine vision technology to mine and analyze the data, establish a basketball tactical mathematical model, and on this basis, use an expert system to predict and analyze the player's tactics, thereby improving the tactical execution, improving the basketball ability of the players and the team, and solving the problems of lack of technical support for basketball training, low training efficiency, and lack of pertinence.

Description

Technical Field

[0001] The present invention relates to the technical field of basketball training analysis, and in particular to a basketball training analysis method, system and computer-readable storage medium. Background Art

[0002] With the development of basketball, there is an urgent need for accurate tactical guidance to plan the direction of basketball sports. At present, the basketball training industry has problems such as high cost of auxiliary equipment, lack of professionalism, and low degree of intelligence, which cannot meet the needs of players and coaches.

[0003] Therefore, the prior art still needs to be improved and developed. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the purpose of the present invention is to provide a basketball training analysis method, system and computer-readable storage medium, aiming to solve the problem in the prior art that basketball training has a low degree of intelligence and cannot meet the needs of players and coaches.

[0005] A basketball training analysis method, comprising:

[0006] Obtain players’ court position data in real time through ultra-wideband wireless communication positioning technology;

[0007] Collect player posture data through RGB camera;

[0008] A basketball training method is formulated according to the player's court position data and the player's motion posture data.

[0009] The basketball training analysis method, wherein the real-time acquisition of player court position data through ultra-wideband wireless communication positioning technology comprises:

[0010] Obtaining coordinates of at least three ultra-wideband wireless communication base stations;

[0011] Communicate with the ultra-wideband wireless communication tags carried by the players through the at least three ultra-wideband wireless communication base stations, and obtain the distance between the players and each ultra-wideband wireless communication base station based on the two-way time-of-flight ranging principle;

[0012] The player's court position data is obtained by using a three-sided positioning algorithm based on the coordinates of the base station and the distance between the player and the ultra-wideband wireless communication base station.

[0013] The basketball training analysis method, wherein the process of obtaining the distance between the player and each ultra-wideband wireless communication base station based on the two-way flight time ranging principle, further includes: calculating the flight time error;

[0014] The formula for calculating the flight time error is:

[0015] Where error is the flight time error, is the measured flight time, k a , k b They are the clock accuracy of the ultra-wideband wireless communication base station and the ultra-wideband wireless communication tag respectively.

[0016] The basketball training analysis method, after obtaining the player's court position data, further includes: performing Kalman filtering on the player's court position data.

[0017] The basketball training analysis method, wherein collecting player posture data through an RGB camera comprises:

[0018] Get the player image captured by the RGB camera;

[0019] Detect the player's human skeleton features based on the player's image;

[0020] The player's posture is identified and judged based on the player's human skeleton characteristics to obtain the player's posture data.

[0021] The basketball training analysis method, wherein the formulating of the basketball training method according to the player's court position data and the player's motion posture data comprises:

[0022] Obtaining the player's physical fitness score and movement standard score according to the player's court position data and the player's movement posture data;

[0023] Develop tactical training methods and posture training methods based on physical fitness scores and movement standard scores.

[0024] A basketball training analysis system, comprising:

[0025] A player court position data acquisition module is used to acquire the player court position data in real time through ultra-wideband wireless communication positioning technology;

[0026] A player posture data acquisition module is used to collect player posture data through an RGB camera;

[0027] The data analysis module is used to formulate a basketball training method according to the player's court position data and the player's movement posture data.

[0028] In the basketball training analysis system, the player court position data acquisition module includes: at least three ultra-wideband wireless communication base stations arranged around the court, and ultra-wideband wireless communication tags communicatively connected to the ultra-wideband wireless communication base stations.

[0029] In the basketball training analysis system, the ultra-wideband wireless communication tag includes: a flexible circuit board, a power supply arranged on the flexible circuit board, and an ultra-wideband wireless communication chip arranged on the flexible circuit board and connected to the power supply.

[0030] A computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps in the basketball training analysis method as described above.

[0031] Beneficial effect: The basketball training analysis method of the present invention obtains the player's court position data in real time through ultra-wideband wireless communication positioning technology and collects the player's posture data through an RGB camera, and then combines deep learning and machine vision technology to mine and analyze the data, establish a basketball tactical mathematical model, and on this basis, predict and analyze the player's tactics, thereby improving the tactical execution, improving the basketball ability of players and teams, and solving the problems of lack of technical support for basketball training, low training efficiency, and poor pertinence. It can be seen that the present invention has great market potential, is easy to install, low cost, has strong operability and practicality, and is expected to fill the gap in the field of professional basketball tactical sports. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 The figure is a flow chart of the basketball training analysis method of the present invention.

[0033] Figure 2 This is a schematic diagram of the ranging principle based on two-way flight time in the ultra-wideband wireless communication positioning technology described in the present invention.

[0034] Figure 3 It is a schematic diagram of the positioning communication process in the ultra-wideband wireless communication positioning technology described in the present invention.

[0035] Figure 4 This is a schematic diagram of the principle of the three-side positioning method in the ultra-wideband wireless communication positioning technology described in the present invention.

[0036] Figure 5 This is a comparison diagram between the measured value and the actual value in the ultra-wideband wireless communication positioning technology described in the present invention without using Kalman filtering processing.

[0037] Figure 6 This is a comparison diagram between the measured value and the actual value processed by Kalman filtering in the ultra-wideband wireless communication positioning technology described in the present invention.

[0038] Figure 7 This is a schematic diagram of the web page interactive interface in the basketball training analysis method of the present invention.

[0039] Figure 8This is a structural diagram of the basketball training analysis system described in the present invention.

[0040] Fig. 9 This is a schematic diagram of the ultra-wideband wireless communication tag jersey of the present invention.

[0041] Fig.10 This is a schematic diagram of the ultra-wideband wireless communication tag ball socks of the present invention.

[0042] Fig.11 This is a schematic diagram of the ultra-wideband wireless communication tag sneakers of the present invention.

[0043] Fig.12 This is a data transmission flow chart of the basketball training analysis method of the present invention. DETAILED DESCRIPTION

[0044] The present invention provides a basketball training analysis method, system and computer-readable storage medium. To make the purpose, technical solution and effect of the present invention clearer and more specific, the present invention is further described in detail below. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0045] In order to improve the level of basketball training, the current development of basketball was investigated and analyzed, as follows:

[0046] (1) Pain points in the development of basketball

[0047] We collected questionnaires from professional players, coaches, and amateur basketball enthusiasts. For details, see Table 1 for pain points in basketball. We analyzed the pain points with the highest percentage:

[0048] A Lack of professional coaching and tactical guidance

[0049] As the development of basketball is still in its rising stage, there is a lack of professional basketball tactical knowledge, coaches and related course training opportunities. Repeatedly watching the end-of-game video to analyze tactics seriously lacks real-time performance, making it difficult for players to improve their technical movements in the training environment at the time. The present invention can achieve accurate tactical analysis of each round through real-time tactical expert system analysis, providing more authoritative tactical guidance.

[0050] Player B's positioning is not correct

[0051] The players themselves lack professional basketball knowledge, which makes it difficult to comprehensively analyze their own status based on physical fitness, technical ability, etc. The present invention detects various indicators of players and uses machine learning algorithms to comprehensively evaluate players' athletic ability, tactical execution ability, etc., and intelligently matches the best tactical lineup for the entire team, perfectly solving the technical pain point of players' inaccurate self-positioning.

[0052] Table 1 Pain points of basketball

[0053] Basketball pain points Proportion Players’ positioning is not correct 22.2％ Lack of professional coaching and tactical guidance 35.5％ Lack of convenient and easy-to-use professional supporting facilities 23.9％ Basketball atmosphere in general 5.3％ Suffering from injuries 13.1％

[0054] (2) Lack of application of foreign basketball training

[0055] There are two main types of basketball training apps. One is a simple basketball training plan app, in which players conduct simple training such as shooting and physical fitness according to the plan. However, its simplicity makes it impossible to use it in the professional tactical field and it can only be limited to personal training. The other is that users watch training videos of professional athletes, but there is a lack of an interactive bridge between the trainer and the software. The main users are still amateurs, and the purpose of professional training cannot be achieved.

[0056] like Figure 1 As shown, the present invention provides a basketball training analysis method, which includes:

[0057] S100, real-time acquisition of players’ court position data through ultra-wideband wireless communication positioning technology;

[0058] S200, collects player posture data through RGB camera;

[0059] S300: Formulate a basketball training method according to the player's court position data and the player's motion posture data.

[0060] The basketball training analysis method of the present invention obtains the player's court position data in real time through ultra-wideband wireless communication positioning technology and collects the player's posture data through an RGB camera, and formulates a basketball training method through data analysis. It can be seen that the present invention has great market potential, is easy to install, has low cost, has strong operability and practicality, and is expected to fill the gap in the field of professional basketball tactical sports.

[0061] In S100, the ultra-wideband wireless communication positioning technology is also called UWB positioning technology. Ultra-wideband wireless communication technology (UWB) is a carrier-free communication technology. UWB does not use a carrier, but uses a short energy pulse sequence and extends the pulse to a frequency range through orthogonal frequency division modulation or direct sequencing. The main features of UWB are high transmission rate, large spatial capacity, low cost, low power consumption, etc.

[0062] In S100, the player's court position data may specifically refer to the coordinates of the player at a certain moment in a preset coordinate system, wherein the preset coordinate system may be pre-established based on a basketball court, for example, a coordinate system is established using the sidelines of a basketball court as coordinate axes.

[0063] In one embodiment of the present invention, the real-time acquisition of the player's court position data by ultra-wideband wireless communication positioning technology includes:

[0064] S101, obtaining coordinates of at least three ultra-wideband wireless communication base stations;

[0065] S102, communicating with the ultra-wideband wireless communication tags carried by the players through the at least three ultra-wideband wireless communication base stations, and obtaining the distance between the players and each ultra-wideband wireless communication base station based on the two-way time-of-flight ranging principle;

[0066] S103, obtaining the player's court position data by using a three-sided positioning algorithm according to the coordinates of the base station and the distance between the player and the ultra-wideband wireless communication base station.

[0067] Specifically, the distance measurement of the ultra-wideband wireless communication positioning technology. The commonly used distance measurement method based on flight time has strict requirements on the clock synchronization between base stations, otherwise it will cause large errors. DS-TWR is a distance measurement method based on two-way flight time, and its purpose is to compensate for the errors introduced by clock offset.

[0068] See also Figure 2 The present invention adopts DS-TWR (Double-sided Two-way Ranging) ranging, and its principle is as follows:

[0069] 1. Measure flight time

[0070] Since the clock deviation of base station A and tag B will affect T prop The value (the electromagnetic wave flight time to be measured) is affected, and the flight speed of the electromagnetic wave is about 30cm / ns, that is, a small clock deviation will also have a great impact on the measurement result. Therefore, the time of two communications is used to compensate for the error introduced by the clock offset. At this time, the base station sums the flight time from the tag to the base station obtained above and takes the average value to obtain the one-way flight time TOF. In this way, the base station and the tag use their own clocks to time, avoiding the problem of using synchronous clocks.

[0071] Figure 2 In the example, the tag can be regarded as device A, and the base station can be regarded as device B. Device A actively initiates the first ranging message, and device B responds to it, obtaining four timestamps. Device A waits for T reply2 Then it is initiated again, and device B receives it, and then two timestamps are obtained. Therefore, the following four time differences can be obtained: T round1 , T reply1 , T round2 , T reply2 .

[0072] The flight time required above The calculation formula is:

[0073]

[0074] in, is the flight time, T round1 、T reply1 、T round2 、T reply2 The four time differences are obtained.

[0075] The flight time multiplied by the speed of light gives the distance between devices A and B.

[0076] 2. Label positioning principle

[0077] The positioning algorithm used in the present invention is a three-sided positioning algorithm. First, it is necessary to know the location coordinates of each base station installation (need to be input in advance) and the distance to the tag respectively. The communication process is shown in Table 2 and Figure 3 As shown:

[0078] Table 2 Description of positioning communication process

[0079] Behavior Active side Passive side illustrate 1 A base station Label A base station and tag distance measurement 2 A base station B Base Station Base station A issues a ranging command to base station B 3 B Base Station Label B base station and tag distance measurement 4 B Base Station A base station Base station B returns the ranging information to base station A 5 A base station C Base Station Base station A issues a ranging command to base station C 6 C Base Station Label C base station and tag distance measurement 7 C Base Station A base station Base station C returns the ranging information to base station A

[0080] Among them, base station A is the master base station (A), and base stations B and C are slave base stations.

[0081] Through the above communication, the data of each base station and tag are summarized in the main base station (base station A), and the main base station uses a three-sided positioning algorithm. The principle is as follows Figure 4 As shown in the figure, the main calculation idea is: given the positions of three points (x1, y1), (x2, y2), (x3, y3), assume the unknown point (x0, y0) and its distances to the three points d1, d2, d3. Draw three circles with d1, d2, d3 as the radius, and according to the Pythagorean theorem, the calculation formula for the intersection point, i.e. the position of the unknown point, is obtained:

[0082] (x1-x0) 2 +(y1-y0) 2 =d1 2

[0083] (x2-x0) 2 +(y2-y0) 2 =d2 2

[0084] (X3-x0) 2 +(y3-y0) 2 =d3 2

[0085] The unknown point is located at (x, y), and the center coordinates of the first circle P1 are set to (0, 0). P2 is at the same ordinate, with the center coordinates (d, 0). The center coordinates of P3 are set to (i, j). The radii of the three circles are r1, r2, and r3, respectively. Then:

[0086] r1 2 =x 2 +y 2

[0087] r2 2 =(xd) 2 +y 2

[0088] r3 2 =(xi) 2 +(yj) 2

[0089] That is, the three circles intersect at one point on the horizontal plane, and the x and y values ​​corresponding to the position point can be solved. The formula is as follows:

[0090] x=(r1 2 -r2 2 +d 2 ) / 2d

[0091] y=(r1 2 –r3 2 -x 2 +(x–i) 2 +j2) / 2j

[0092] Measuring flight time When there is still an error, the error is determined by calculating the flight time error to determine whether it is a negligible small error. In one embodiment of the present invention, the process of obtaining the distance between the player and each ultra-wideband wireless communication base station based on the two-way flight time ranging principle also includes: calculating the flight time error;

[0093] The formula for calculating the flight time error is:

[0094] Where error is the flight time error, T ^ prop* is the measured flight time, k a , k b They are the clock accuracy of the ultra-wideband wireless communication base station and the ultra-wideband wireless communication tag respectively.

[0095] Generally speaking, the clock accuracy of device A and device B is 20ppm (very poor), 1ppm is one part per million, so k a and k bare 0.99998 or 1.00002 respectively, k a and k b are the ratios of the actual and expected frequencies of the clocks of devices A and B, respectively. If the distance between devices A and B is 100m, and the flight time of electromagnetic waves is 333ns, then the error introduced by the clock is 20×333× 10 -9 The distance measurement error is 2.2 mm, which is negligible compared to the size of a basketball court.

[0096] In one embodiment of the present invention, after acquiring the player's court position data, the method further includes: performing Kalman filtering on the player's court position data.

[0097] like Figure 5 and Figure 6 As shown in the figure, in the actual measurement process, due to random interference caused by non-line-of-sight, multipath propagation and human body, the observed information is often mixed with random noise, resulting in errors in the observed positioning results. By comparison, Kalman filtering is the best linear filter, which is simple to implement and is a pure time domain filter with low computational complexity, making it very suitable for mobile devices. When applying, the obtained tag coordinates are first processed by Kalman filtering, and then the final result is obtained. Figure 6 The difference between the measured value and the actual value before and after error processing can be seen in the figure. The average error before Kalman filter processing is 19.67cm, and the average error after Kalman filter processing is 9.16cm, which is a decrease of 53.38%.

[0098] In S200, the player is photographed through an RGB camera, the photographed image is analyzed, and the posture of the player in the image is identified.

[0099] In one embodiment of the present invention, collecting player posture data through an RGB camera includes:

[0100] S201, obtaining the player image captured by the RGB camera;

[0101] S202, detecting the player's human skeleton features according to the player's image;

[0102] S203: Identify and judge the player's posture according to the player's human skeleton features to obtain the player's posture data.

[0103] Specifically, the player's posture is recognized and judged by detecting the human skeleton features in the player's image taken by the RGB camera. In order to pursue higher real-time performance and accuracy, the bottom-up (first obtain the key point position and then obtain the skeleton) human posture estimation algorithm Openpose using Part Affinity Fields (PAFs) is used. The top-down algorithm (first detect people and then regress to key points) is not selected because the latter's calculation time will increase significantly with the number of people in the image, while the calculation time required for the bottom-up algorithm remains basically unchanged.

[0104] Optionally, the human skeleton features include one or more skeleton features of eye sockets, shoulders, elbows, wrists, hips, knees, ankles, and cervical and lumbar vertebrae on the spine. Specifically, multiple corresponding feature points in the picture are extracted based on the eye sockets, shoulders, elbows, wrists, hips, knees, ankles, and cervical and lumbar vertebrae on the spine of the human body in the picture, and the player posture data is determined based on the relative position relationship between the feature points. In particular, for pictures where it is difficult to determine the human skeleton features, such as pictures of the side or back of the player, the human skeleton features that are difficult to determine can be obtained by analyzing the human skeleton features in the previous and subsequent continuous pictures and combining partial human features of the back or side of the player to obtain the player's full human skeleton features.

[0105] The player's action is determined by extracting the feature points corresponding to the human skeleton features, and the motion trend of the feature points is determined by combining the relative relationship of the corresponding feature point positions in the previous and next images to obtain the player's posture data; then the player's posture score is obtained by comparing the measured player's posture data with the standard player's posture data, and the player's posture score can be determined according to the overlap or similarity between the player's posture data and the standard player's posture data. For example, the higher the overlap or similarity, the higher the player's posture score. Optionally, the player's body shape (including: height, shoulder width, etc.) is determined according to the player's skeleton features, and the standard posture data of the player is calculated in combination with the preset standard action data (different players correspond to different standard posture data); then the feature point positions and motion trend data in the measured player's posture data are compared with the feature point positions and motion trend data corresponding to the standard player's posture data to obtain the player's posture score.

[0106] Deploy the Openpose model algorithm to the locally installed camera to collect and judge the players' motion information, obtain more comprehensive sports data of the players, and thus better understand the players' strengths and weaknesses and tap their potential.

[0107] In S300, by analyzing the collected data, deep learning and machine vision technology can be combined to mine and analyze the data, and a mathematical model of basketball tactics can be established. On this basis, an expert system can be used to predict and analyze the players' tactics, thereby improving the execution of tactics, improving the basketball ability of players and teams, and solving the problems of lack of technical support for basketball training, low training efficiency, and lack of targetedness.

[0108] In one embodiment of the present invention, formulating a basketball training method according to the player's court position data and the player's motion posture data includes:

[0109] S301, obtaining a player's physical fitness score and a movement standard score according to the player's court position data and the player's movement posture data;

[0110] S302. Formulate tactical training methods and posture training methods based on the physical fitness scores and movement standard scores.

[0111] Specifically, the player's court position data can extract the player's movement trajectory and shooting accuracy at different coordinate positions, so as to determine the tactical training method; the player's posture information collection function extracts the player's posture and movement characteristics, which can be used to judge the player's various actions (shooting, passing, etc.); further, the present invention can also score the player's various actions and make suggestions for posture improvement, and can process and obtain the player's shooting time, jumping height and other important sports indicators. Finally, information exchange is carried out through the cloud, and interaction is formed with the user through the web page. The web page specifically includes: real-time recording, historical records, posture collection, smart coaching board, display of player hot zones, cancellation of player hot zones, player status, user center, forum, home page, training and other display areas. Figure 7 It is an interface of the web page of the present invention.

[0112] Optionally, the basketball training analysis method can provide information including: player status, display of player hot spots, exercise duration, average speed, running distance, heart rate / BPM, body temperature / °C. Among them, the heart rate and body temperature can be realized by setting corresponding sensors in the wearable device.

[0113] The present invention also provides a basketball training analysis system, which includes:

[0114] A player court position data acquisition module is used to acquire the player court position data in real time through ultra-wideband wireless communication positioning technology;

[0115] A player posture data acquisition module is used to collect player posture data through an RGB camera;

[0116] The data analysis module is used to formulate a basketball training method according to the player's court position data and the player's movement posture data.

[0117] The basketball training analysis system of the present invention corresponds to the basketball training analysis method and is used to implement the steps in the basketball training analysis method.

[0118] like Figure 8 As shown, in one embodiment of the present invention, the player court position data acquisition module includes: at least three ultra-wideband wireless communication base stations set around the court, the three ultra-wideband wireless communication base stations include: ultra-wideband wireless communication base station 101, ultra-wideband wireless communication base station 102, ultra-wideband wireless communication base station 103, wherein ultra-wideband wireless communication base station 101 is used as the main ultra-wideband wireless communication base station, and ultra-wideband wireless communication base station 102 and ultra-wideband wireless communication base station 103 are used as slave ultra-wideband wireless communication base stations; ultra-wideband wireless communication tag 2 connected to the ultra-wideband wireless communication base station. Specifically, three ultra-wideband wireless communication base stations can be set at three corners of the basketball court, and the ultra-wideband wireless communication tag can be set in a wearable device. The ultra-wideband wireless communication tag is located by communicating with the ultra-wideband wireless communication base station through the ultra-wideband wireless communication tag.

[0119] The player posture data acquisition module mainly includes an RGB camera 3. The RGB camera is used to capture the player's motion picture and extract the player's posture from the motion picture.

[0120] In one embodiment of the present invention, the ultra-wideband wireless communication tag includes: a flexible circuit board, a power source disposed on the flexible circuit board, and an ultra-wideband wireless communication chip disposed on the flexible circuit board and connected to the power source. The circuit board of the present invention is a flexible circuit board, and a wearable ultra-wideband wireless communication tag can be obtained, and the player wears sports equipment or clothing with the ultra-wideband wireless communication tag.

[0121] like Fig. 9 As shown, the ultra-wideband wireless communication tag jersey is a jersey body 501 in which an ultra-wideband wireless communication tag 502 is set. The ultra-wideband wireless communication tag jersey can fully avoid and alleviate the impact of exercise on the equipment. The goal of the ultra-wideband wireless communication tag jersey is to make it easier for the equipment to integrate into the professional training market, so that more players can experience professional basketball tactical services. Under the premise of increasing the popularity rate, it is also easier to obtain the training data of professional players. We can let all players experience high-level training based on big data.

[0122] In addition, the present invention provides an ultra-wideband wireless communication tag ball socks and an ultra-wideband wireless communication tag ball shoe, wherein, as Fig.10As shown, the ultra-wideband wireless communication tag ball socks is an ultra-wideband wireless communication tag 602 set on the ball socks body 601; Fig.11 As shown, the ultra-wideband wireless communication tag sneakers are those in which the ultra-wideband wireless communication tag 702 is set inside the sneaker body 701 . Optionally, the ultra-wideband wireless communication tag 702 is set on a special pressure-resistant insole in the sneaker body 701 .

[0123] The ultra-wideband wireless communication tag foot strap and the ultra-wideband wireless communication tag sneaker are made by embedding the ultra-wideband wireless communication chip and power supply in the flexible circuit board into the foot strap and the sneaker respectively. The foot strap allows players to wear daily training equipment without a great sense of burden, and the sneaker is a professional modification of the insole, using a pressure-resistant insole to allow the ultra-wideband wireless communication tag to run perfectly in the sneaker. The chip in the ultra-wideband wireless communication tag can be a DW1000 chip.

[0124] The ultra-wideband wireless communication base station structure of the present invention uses the STM32F103 chip as the main microcontroller, and uses the low power consumption of the chip to carry the DW1000 chip, thereby meeting the functional requirements of positioning and ranging. The following are the two chips and their main performance indicators as shown in Table 3:

[0125] Table 3 Performance indicators of STM32F103 chip and DW1000 chip

[0126]

[0127] The basketball training and analysis system of the present invention further comprises a smart basket, which is provided with a plurality of sensors and / or RGB cameras, wherein the sensors can be used to detect the pressure of the backboard when it is contacted by the basketball, and transmit the pressure to the data analysis module for data analysis. The camera can be used to collect the sports images of the players, and transmit the images to the data analysis module.

[0128] like Fig.12 As shown, the basketball training analysis system, wherein the data analysis module includes:

[0129] A Raspberry Pi gateway 4, the Raspberry Pi gateway is connected to the player court position data acquisition module and the player posture data acquisition module;

[0130] A cloud server 5, the cloud server 5 is connected to the Raspberry Pi gateway 4, and is used to formulate a basketball training method according to the player's court position data and the player's motion posture data;

[0131] A web page display terminal is connected to the cloud server.

[0132] The web page display terminal may specifically be a display terminal, such as a display, which displays corresponding analysis results and data through a WebApp client.

[0133] The data transmission process of the basketball training analysis method can be specifically that the main ultra-wideband wireless communication base station 101 obtains the player's location information (location data) by communicating with the player's ultra-wideband wireless communication tag and the UWB of the secondary ultra-wideband wireless communication base station (such as the secondary ultra-wideband wireless communication 102 and the secondary ultra-wideband wireless communication 103), and transmits the location information to the Raspberry Pi gateway 4 through Zigbee, and finally the Raspberry Pi gateway 4 integrates and encrypts the data and interacts with the cloud server 5 through WIFI. Further, the cloud server 5 can be a model based on a neural network algorithm for data processing and analysis. The cloud server 5 transmits the processed data to the WebApp client, and the WebApp client displays relevant visualized training data, real-time tactical analysis, real-time player status and other information.

[0134] The present invention is developed for the application of player data collection and data mining technology in basketball training. The player's court position data is obtained in real time through ultra-wideband wireless communication positioning technology. A flexible circuit board based on the integrated development of the DW1000 chip is embedded in sports equipment to obtain a basketball training system based on the construction of the Internet of Things. The RGB camera 3 and other sensors are used to collect various sports indicators of the players, and the data is sent to the Raspberry Pi gateway 4 through the main ultra-wideband wireless communication base station 101 through the Zigbee, thereby exchanging information with the cloud server 5, and finally interacting with the user through the web page to realize a fully functional basketball training system.

[0135] A computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps in the basketball training analysis method as described above.

[0136] In one embodiment of the present invention, the data analysis module includes: a processor and a memory; the memory stores a computer-readable program that can be executed by the processor; when the processor executes the computer-readable program, the steps in the basketball training analysis method described above are implemented.

[0137] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. The memory, as a computer-readable storage medium, can be configured to store software programs, computer executable programs, such as program instructions or modules corresponding to the methods in the embodiments of the present disclosure. The processor executes functional applications and data processing by running the software programs, instructions or modules stored in the memory, that is, implementing the methods in the above-mentioned embodiments.

[0138] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A basketball training analysis method, characterized in that: include: Obtaining coordinates of at least three ultra-wideband wireless communication base stations; Communicate with the ultra-wideband wireless communication tags carried by the players through the at least three ultra-wideband wireless communication base stations, and use the distance measurement principle of bilateral two-way flight time to calculate the flight time error and obtain the distance between the players and each ultra-wideband wireless communication base station; Acquire the player's court position data according to the coordinates of the base station and the distance between the player and the ultra-wideband wireless communication base station through a three-sided positioning algorithm; Collect player posture data through RGB camera; Formulate a basketball training method based on the player's court position data and the player's motion posture data; Wherein, the formula for calculating the flight time error is: Where error is the flight time error, is the measured flight time, k a , k b They are the clock accuracy of the ultra-wideband wireless communication base station and the ultra-wideband wireless communication tag respectively.

2. The basketball training analysis method according to claim 1, characterized in that: After obtaining the player's court position data, the method further includes: performing Kalman filtering on the player's court position data.

3. The basketball training analysis method according to claim 1, characterized in that: The collecting of player posture data by the RGB camera includes: Get the player image captured by the RGB camera; Detect the player's human skeleton features based on the player's image; The player's posture is identified and judged according to the player's human skeleton characteristics to obtain the player's posture data.

4. The basketball training analysis method according to claim 1, characterized in that: The formulating of a basketball training method according to the player's court position data and the player's motion posture data comprises: Obtaining the player's physical fitness score and movement standard score according to the player's court position data and the player's movement posture data; Develop tactical training methods and posture training methods based on physical fitness scores and movement standard scores.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps in the basketball training analysis method according to any one of claims 1 to 4.

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