Motion data-based team forming method and device, storage medium and electronic equipment

Abstract

The invention discloses a team forming method and device based on motion data, a storage medium and electronic equipment, and relates to the technical field of software and the field of artificial intelligence. The method comprises the following steps: acquiring motion data of a user, time information of expected team formation, user portrait features of the user and teammate portrait features expected by the user; according to the motion data of the users, the time information that the users expect to form the team, the user portrait features of the users and the teammate portrait features that the users expect to form the team, determining a plurality of users meeting preset conditions as members to form the team; and establishing a communication connection between the terminal devices of the members to be grouped, and adding the accounts of the members to be grouped into the same team group. The problems that in the prior art, the team forming effect is poor and the team member matching degree is low due to the fact that the users are randomly distributed for team forming in the exercise courses, and the conditions that the completion rate of team forming training courses is low and the user experience effect is poor are solved.

Description

Technical Field

[0001] The present application relates to the fields of software technology and artificial intelligence, and more specifically, to a team formation method, device, storage medium and electronic device based on motion data. Background Art

[0002] With the development of society, people's living standards are gradually improving, and their requirements for health and physical fitness are getting higher and higher. However, due to the accelerated pace of work and life, the phenomenon of lack of exercise or sports is relatively common, which is not good for physical health and also affects the quality of life. Therefore, more and more people have begun to realize the importance of sports and participate in sports and exercises in various forms and ways to maintain health and improve physical fitness. At the same time, people pay more and more attention to the fun and social attributes of sports, and team sports have become the choice of more and more people.

[0003] Participants in team sports can encourage, collaborate and compete with each other during sports, which not only enhances team spirit but also promotes physical and mental health. However, traditional team sports methods often have some inconveniences and limitations. For example, traditional team formation methods always randomly assign users to teams during sports courses, which often leads to poor team formation results and low team matching, which in turn affects the low completion rate of team training courses and poor user experience.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The main purpose of the present application is to provide a team formation method, device, storage medium and electronic device based on sports data, so as to at least solve the technical problems in the prior art of randomly assigning users to teams in sports courses, resulting in poor team formation effect and low team member matching.

[0006] In order to achieve the above-mentioned purpose, according to one aspect of an embodiment of the present application, a team forming method based on motion data is provided, including: obtaining the user's motion data, the time information of the expected team forming, the user's user portrait characteristics and the user's expected teammate portrait characteristics; according to the user's motion data, the user's expected time information of the team forming, the user's user portrait characteristics and the user's expected teammate portrait characteristics, multiple users who meet preset conditions are determined as members to be teamed, wherein the preset conditions are used to determine whether two users become teammates by detecting user characteristics between the two users; establishing a communication connection between the terminal devices of the members to be teamed, and adding the accounts of the members to be teamed to the same team forming group.

[0007] Optionally, the preset conditions include at least one of the following conditions; a first condition, used to determine whether the i-th user and the j-th user are members to be teamed based on the length of the interval between the times at which the i-th user and the j-th user expect to team up, wherein the i-th user and the j-th user are any two different users; a second condition, used to determine whether the i-th user and the j-th user are members to be teamed based on the data similarity or data statistical distribution information between the motion data of the i-th user and the motion data of the j-th user; a third condition, used to determine whether the i-th user and the j-th user are members to be teamed based on the feature similarity between the user portrait features of the i-th user and the user portrait features of the j-th user; a fourth condition, used to determine whether the i-th user and the j-th user are members to be teamed based on the similarity between the user portrait features of the i-th user and the teammate portrait features of the j-th user, and / or the similarity between the teammate portrait features of the i-th user and the user portrait features of the j-th user.

[0008] Optionally, judging whether the i-th user and the j-th user are members to be teamed together is performed based on the data similarity or data statistical distribution information between the motion data of the i-th user and the motion data of the j-th user, including: when it is detected that the data similarity between the motion data of the i-th user and the motion data of the j-th user is greater than a first threshold, determining the i-th user and the j-th user as members to be teamed together; or, when it is detected that the data statistical distribution information between the motion data of the j-th user and the motion data of the j-th user conforms to a preset distribution trend, determining the i-th user and the j-th user as members to be teamed together.

[0009] Optionally, judging whether the i-th user and the j-th user are members to be teamed together is based on the similarity between the user portrait features of the i-th user and the teammate portrait features of the j-th user, and / or the similarity between the teammate portrait features of the i-th user and the user portrait features of the j-th user, including: determining the i-th user and the j-th user as members to be teamed together when it is detected that the similarity between the teammate portrait features of the i-th user and the user portrait features of the j-th user is greater than a third threshold, and / or the similarity between the teammate portrait features of the j-th user and the user portrait features of the i-th user is greater than the third threshold.

[0010] Optionally, the motion data-based team formation method also includes: judging whether the i-th user and the j-th user are members to be teamed based on the interval duration between the times when the i-th user and the j-th user expect to team up, including: when it is detected that the interval duration is less than a preset duration, determining that the i-th user and the j-th user are members to be teamed; when it is detected that the interval duration is greater than or equal to the preset duration, determining that the i-th user and the j-th user are not members to be teamed; judging whether the i-th user and the j-th user are members to be teamed based on the feature similarity between the static portrait features of the i-th user and the static portrait features of the j-th user, including: when it is detected that the feature similarity is greater than a preset similarity, determining that the i-th user and the j-th user are members to be teamed; when it is detected that the feature similarity is less than or equal to the preset similarity, determining that the i-th user and the j-th user are not members to be teamed.

[0011] Optionally, establishing a communication connection between terminal devices of the members to be teamed, and adding the accounts of the members to be teamed to the same team group includes: by generating a target code, when it is detected that the terminal devices of the members to be teamed input the same target code, establishing a communication connection between the terminal devices of the members to be teamed, and adding the accounts of the members to be teamed to the same team group; or, by generating a team link, when it is detected that the members to be teamed click to enter the team link through the terminal device, establishing a communication connection between the terminal devices of the members to be teamed, and adding the accounts of the members to be teamed to the same team group; or, when it is detected that the distance between the terminal devices of different members to be teamed is less than a preset distance, establishing a communication connection between the terminal devices of the members to be teamed, and adding the accounts of the members to be teamed to the same team group.

[0012] Optionally, after establishing a communication connection between terminal devices of the members to be teamed and adding the accounts of the members to be teamed to the same team group, the teaming method based on motion data also includes: collecting target motion data generated by each team member in the team group when following the target course training; adjusting the course content of the target course in a future time period according to the target motion data of each team member, and / or determining the motion goal of each team member in the future time period, wherein the motion goal includes at least exercise pace and exercise duration, and the motion goals of different team members in the team group are interrelated.

[0013] In order to achieve the above-mentioned purpose, according to another aspect of an embodiment of the present application, a team forming device based on motion data is also provided, including: an acquisition unit, which acquires the user's motion data, the time information of the expected team forming, the user's user portrait characteristics and the user's expected teammate portrait characteristics; a determination unit, which determines multiple users who meet preset conditions as members to be teamed according to the user's motion data, the time information of the user's expected team forming, the user's user portrait characteristics and the user's expected teammate portrait characteristics, wherein the preset conditions are used to determine whether two users become teammates by detecting user characteristics between the two users; a processing unit, which establishes a communication connection between the terminal devices of the members to be teamed, and adds the accounts of the members to be teamed to the same teaming group.

[0014] According to another aspect of an embodiment of the present application, an electronic device is also provided, comprising one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the above-mentioned motion data-based teaming method.

[0015] According to another aspect of an embodiment of the present application, a computer-readable storage medium is also provided, and the computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned motion data-based teaming method.

[0016] According to another aspect of an embodiment of the present application, a computer program product is also provided, including computer instructions, which implement the steps of the above-mentioned motion data-based team formation method when executed by a processor.

[0017] In an embodiment of the present application, the user's motion data, the time information of the expected team formation, the user's user portrait characteristics, and the user's expected teammate portrait characteristics are first obtained; secondly, based on the user's motion data, the user's expected team formation time information, the user's user portrait characteristics, and the user's expected teammate portrait characteristics, multiple users who meet preset conditions are determined as members to be teamed; wherein the preset conditions are used to determine whether two users become teammates by detecting user characteristics between the two users; then, a communication connection is established between the terminal devices of the members to be teamed, and the accounts of the members to be teamed are added to the same team group.

[0018] From the above content, it can be seen that the present application first collects the user's sports data automatically or triggered by the user, which includes but is not limited to specific indicators such as exercise type, frequency, distance, speed, etc.; at the same time, obtains the time information when the user expects to form a team, for example, the user hopes to perform team sports within a specific time period; then collects the user's user portrait features and the user's input expectations for teammates' portrait features, where the user portrait features cover personalized information such as the user's age, gender, occupation, interest preferences, etc. The user's input expectations for teammates' portrait features include requirements for teammates' athletic ability, interest matching, etc.

[0019] Secondly, based on the information collected above, a series of preset conditions are used to determine which users can be identified as team members. These conditions not only take into account the similarity of user exercise data, but also combine the user's time requirements, personalized portraits, and specific expectations of teammates to ensure the efficiency and satisfaction of team formation. For example, if two users have the same exercise type, similar exercise frequency, and expect to exercise in the same time period, the system will automatically match and identify them as potential team members.

[0020] In addition, once the team members are determined, this application will establish a real-time communication connection between these members. This connection allows users to share data and communicate during exercise, enhancing team spirit and sports fun. Subsequently, the system adds the accounts of these members to the same team group to form a virtual sports team, which facilitates information sharing and real-time interaction between members.

[0021] It can be seen that through the technical solution of the present application, the purpose of realizing personalized and efficient sports partner matching is achieved, thereby realizing the technical effect of adapting to the needs of users in different scenarios through a variety of implementation methods and ensuring the convenience and safety of the team formation process, thereby solving the technical problems in the prior art of randomly assigning users to team in sports courses, resulting in poor team formation effect and low team member matching. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0023] Figure 1 A hardware structure block diagram of a computer terminal for implementing a team formation method based on sports data is shown;

[0024] Figure 2 is a flowchart of an optional team formation method based on sports data according to an embodiment of the present application;

[0025] Figure 3is a system flow chart of an optional team formation method based on sports data according to an embodiment of the present application;

[0026] Figure 4 is a system principle diagram of an optional team formation method based on sports data according to an embodiment of the present application;

[0027] Figure 5 is a schematic diagram of a team forming device based on motion data provided according to an embodiment of the present application;

[0028] Figure 6 It is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] It should also be noted that the collected information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this application are information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data are in compliance with relevant laws, regulations and standards, necessary confidentiality measures are taken, and public order and good customs are not violated, and corresponding operation entrances are provided for users to choose to authorize or refuse. For example, an interface is set up between this system and relevant users or institutions to provide users with corresponding operation entrances for users to choose to agree or refuse the results of automated decision-making; if the user chooses to refuse, the expert decision-making process will be entered.

[0032] According to an embodiment of the present application, a method embodiment of a team formation method based on motion data is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0033] It should be noted that a team formation system based on motion data can be used as the execution subject of the team formation method based on motion data in the embodiment of the present application. It is understandable that the team formation method based on motion data provided in the embodiment of the present application can also be executed by other systems or devices, and the embodiment of the present application does not specifically limit this.

[0034] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 FIG. 1 shows a hardware structure block diagram of a computer terminal (or mobile device) for implementing a team formation method based on sports data. Figure 1 As shown, the computer terminal 10 (or mobile device) may include one or more (102a, 102b, ..., 102n are used to illustrate) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0035] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuit may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be fully or partially integrated into any of the other components in the computer terminal 10 (or mobile device). As in the team formation method based on motion data involved in the embodiments of the present application, the data processing circuit serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0036] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the team formation method based on motion data in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, realizing the above-mentioned team formation method based on motion data. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0037] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0038] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 (or mobile device).

[0039] Under the above operating environment, this application provides Figure 2 The team formation method based on sports data is shown. Figure 2 FIG. 1 is a flow chart of an optional team formation method based on motion data according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0040] Step S201, obtaining the user's sports data, the time information of the desired team formation, the user's user portrait characteristics, and the user's desired teammate portrait characteristics.

[0041] Optionally, the team formation system based on sports data collects the user's sports data in real time, including but not limited to sports type (such as running, cycling, swimming, etc.), sports frequency, sports distance, average speed, heart rate and other physiological indicators. These data not only reflect the user's sports preferences and habits, but also provide an objective quantitative basis for team formation and matching.

[0042] Optionally, the team formation system based on sports data accurately obtains the specific time information that the user expects to participate in the team sports. This data ensures that the user can accurately express his or her sports intentions in the schedule in the system, thereby improving the synchronization and efficiency of the team formation.

[0043] Optionally, the sports data-based team formation system builds a comprehensive and dynamic user portrait based on the user's behavior data, self-description information and social network analysis on the platform. The portrait features include but are not limited to age, gender, professional background, hobbies, sports goals, health status, etc. These features provide rich dimensions for achieving personalized team matching.

[0044] Optionally, users can set expected characteristics of ideal teammates in the team formation interface, such as pace range, sports preference, incentive method, interaction preference, etc. This application allows users to express their personalized needs for the team formation environment, so that they can more accurately find sports partners that meet their expectations and enhance the satisfaction and effect of team formation.

[0045] Step S202, based on the user's sports data, the time information when the user expects to form a team, the user's user portrait characteristics and the user's expected teammate portrait characteristics, multiple users who meet preset conditions are determined as members to be formed into a team.

[0046] In step S202, the preset condition is used to determine whether the two users become teammates by detecting user features between the two users.

[0047] Optionally, the motion data-based team formation system conducts in-depth analysis of the motion data submitted by the user, the time information of the desired team formation, the user portrait characteristics, and the desired characteristics of teammates according to preset logical rules and algorithm models, accurately identifies and determines multiple users who meet all or part of the preset conditions as a candidate list of team members to be formed, thereby achieving efficient and personalized team matching.

[0048] Optionally, the purpose of the preset conditions is to determine whether two users are suitable to be teammates by quantitatively and logically judging the similarity of user characteristics between them, thereby promoting the vigorous development of sports social activities, including: time synchronization conditions, sports data similarity conditions, user portrait compatibility conditions, teammates' expected characteristics matching conditions, etc.

[0049] Step S203, establish a communication connection between the terminal devices of the members to be teamed, and add the accounts of the members to be teamed to the same teaming group.

[0050] Optionally, the motion data-based team formation system establishes a communication connection between the terminal devices of the team members to be formed, adds the accounts of the team members to the same team formation group, ensures that the terminal devices of each team member to be formed can be interconnected in real time, realizes barrier-free transmission of information, and provides a stable and efficient communication environment for team members.

[0051] It can be seen from the contents of steps S201 to S203 that in an embodiment of the present application, the user's motion data, the time information of the expected team formation, the user's user portrait characteristics, and the user's expected teammate portrait characteristics are first obtained, and then based on the user's motion data, the user's expected team formation time information, the user's user portrait characteristics, and the user's expected teammate portrait characteristics, multiple users who meet preset conditions are determined as members to be teamed up, wherein the preset conditions are used to determine whether two users become teammates by detecting user characteristics between the two users, and then a communication connection is established between the terminal devices of the members to be teamed up, and the accounts of the members to be teamed up are added to the same team group.

[0052] From the above content, it can be seen that the present application first collects the user's sports data automatically or triggered by the user, which includes but is not limited to specific indicators such as exercise type, frequency, distance, speed, etc.; at the same time, obtains the time information when the user expects to form a team, for example, the user hopes to perform team sports within a specific time period; then collects the user's user portrait features and the user's input expectations for teammates' portrait features, where the user portrait features cover personalized information such as the user's age, gender, occupation, interest preferences, etc. The user's input expectations for teammates' portrait features include requirements for teammates' athletic ability, interest matching, etc.

[0053] Secondly, based on the information collected above, a series of preset conditions are used to determine which users can be identified as team members. These conditions not only take into account the similarity of user exercise data, but also combine the user's time requirements, personalized portraits, and specific expectations of teammates to ensure the efficiency and satisfaction of team formation. For example, if two users have the same exercise type, similar exercise frequency, and expect to exercise in the same time period, the system will automatically match and identify them as potential team members.

[0054] In addition, once the team members are determined, this application will establish a real-time communication connection between these members. This connection allows users to share data and communicate during exercise, enhancing team spirit and sports fun. Subsequently, the system adds the accounts of these members to the same team group to form a virtual sports team, which facilitates information sharing and real-time interaction between members.

[0055] It can be seen that through the technical solution of the present application, the purpose of realizing personalized and efficient sports partner matching is achieved, thereby realizing the technical effect of adapting to the needs of users in different scenarios through a variety of implementation methods and ensuring the convenience and safety of the team formation process, thereby solving the technical problems in the prior art of randomly assigning users to team in sports courses, resulting in poor team formation effect and low team member matching.

[0056] In an optional embodiment, the preset conditions include at least one of the following conditions: a first condition, used to determine whether the i-th user and the j-th user are members to be teamed based on the interval between the times when the i-th user and the j-th user expect to team up, wherein the i-th user and the j-th user are any two different users; a second condition, used to determine whether the i-th user and the j-th user are members to be teamed based on the data similarity or data statistical distribution information between the motion data of the i-th user and the motion data of the j-th user; a third condition, used to determine whether the i-th user and the j-th user are members to be teamed based on the feature similarity between the user portrait features of the i-th user and the user portrait features of the j-th user; a fourth condition, used to determine whether the i-th user and the j-th user are members to be teamed based on the similarity between the user portrait features of the i-th user and the teammate portrait features of the j-th user, and / or the similarity between the teammate portrait features of the i-th user and the user portrait features of the j-th user.

[0057] Optionally, the time synchronization evaluation condition (i.e., the first condition) is used to quantify and analyze the interval between the expected teaming times of any two users (recorded as the i-th user and the j-th user). The teaming system based on motion data can accurately determine whether two users have the teaming conditions in time by setting a reasonable synchronization threshold, thereby ensuring the synchronization and convenience of motion.

[0058] Optionally, the motion data similarity evaluation condition (i.e., the second condition) is used to identify and match users with similar exercise habits and physical fitness levels to ensure the harmony and efficiency of team exercise. The team formation system based on motion data compares the motion data of the i-th user and the j-th user, including indicators such as exercise type, speed, distance, heart rate, etc., to evaluate the similarity or statistical distribution information of the two data.

[0059] Optionally, the user portrait feature matching condition (i.e., the third condition) can promote social compatibility between users. The team formation system based on sports data evaluates whether the two users have social compatibility for team formation by calculating the feature similarity between the i-th user and the j-th user in terms of age, gender, occupation, hobbies, etc., and can quickly screen out potential teammates with similar lifestyles and values ​​from massive user information, thereby enhancing the social fun and team cohesion of team sports.

[0060] Optionally, the personalized teammate expectation matching logic condition (i.e., the fourth condition) can ensure that team members can meet each other's personalized needs. The team formation system based on sports data evaluates the matching degree of the two users in terms of sports preferences, incentive methods, interaction modes, etc. by comparing the user portrait features of the i-th user with the teammate portrait features of the j-th user, and the reverse matching process, and accurately identifies and matches teammates who are most likely to meet the specific needs of users, thereby improving the satisfaction and sustainability of team sports.

[0061] From the above content, it can be seen that the team formation system based on sports data has constructed a multi-dimensional and personalized team matching system through the setting of the first condition, the second condition, the third condition, and the fourth condition. It not only promotes the socialization of sports, but also ensures efficient collaboration and in-depth interaction between sports partners.

[0062] In an optional embodiment, the motion data-based team forming system determines the i-th user and the j-th user as members to be teamed when it is detected that the data similarity between the motion data of the i-th user and the motion data of the j-th user is greater than a first threshold, or determines the i-th user and the j-th user as members to be teamed when it is detected that the data statistical distribution information of the motion data of the j-th user and the motion data of the j-th user conforms to a preset distribution trend.

[0063] Optionally, when the motion data-based team forming system analyzes and determines that the similarity between the motion data of the i-th user and the motion data of the j-th user in key indicators such as type, frequency, intensity, and duration exceeds a preset first threshold, the two users will be regarded as potential team forming partners whose exercise habits and physical fitness levels are highly matched.

[0064] Optionally, the team formation system based on motion data is also equipped with a statistical motion data distribution trend analysis mechanism. If the motion data of the i-th user and the motion data of the j-th user show a consistent trend in statistical distribution, that is, the motion data distribution curves of the two users show similar shapes within the preset motion index range, this application will determine the two users as members to be teamed.

[0065] Optionally, if the motion data of the ith user and the motion data of the jth user show a complementary trend in statistical distribution, the ith user and the jth user can also be determined as members to be teamed. This can achieve a relatively balanced effect of comprehensive capabilities among multiple groups, without concentrating all users with strong athletic abilities in one group or all users with weak athletic abilities in one group. For example, assuming there are 100 users, they need to be divided into 25 groups for competition, and each group includes 4 users. For the sake of fairness, the motion data distribution trend among the four members of each group needs to conform to the preset distribution trend. For example, each group includes two users with strong running abilities and two users with weak running abilities. In this way, the suspense of the competition will not be lost among the 25 groups, which ensures the fairness and fun of the competition and improves the user experience.

[0066] From the above content, it can be seen that the team formation system based on sports data can screen and match team members based on the similarity of user sports data and the consistency of statistical distribution by comparing the similarity of two users with a first threshold or by comparing whether the data statistical distribution information of the two users conforms to a preset distribution trend, thereby meeting the basic needs of users in finding sports partners.

[0067] In an optional embodiment, when the motion data-based team forming system detects that the similarity between the teammate portrait features of the ith user and the user portrait features of the jth user is greater than a third threshold, and / or the similarity between the teammate portrait features of the jth user and the user portrait features of the ith user is greater than a third threshold, the ith user and the jth user are determined as members to be teamed together.

[0068] Optionally, when the sports data-based team formation system detects that the similarity between the teammate portrait features of the i-th user and the user portrait features of the j-th user exceeds a set third threshold, this indicates a high degree of synergy between the two users in social attributes such as sports preferences, incentive methods, and interaction habits.

[0069] Optionally, when the similarity between the teammate portrait features of the j-th user and the user portrait features of the ith user also exceeds the third threshold, this indicates that the two users not only meet each other's sports needs, but also achieve an equal match at the social level, thereby ensuring two-way benefits of team sports.

[0070] From the above content, it can be seen that the team-forming system based on sports data determines the team members by comparing the similarity of the teammate portrait features of two users and the third threshold, thereby achieving deep personalized matching between users. It not only meets the needs of users to find ideal sports partners, but also effectively promotes the diversification and high-quality development of sports social activities.

[0071] In an optional embodiment, the motion data-based team formation system first determines whether the i-th user and the j-th user are members to be teamed based on the interval duration between the times when the i-th user and the j-th user expect to team up, including: when it is detected that the interval duration is less than a preset duration, determining that the i-th user and the j-th user are members to be teamed; when it is detected that the interval duration is greater than or equal to the preset duration, determining that the i-th user and the j-th user are not members to be teamed, and then determining whether the i-th user and the j-th user are members to be teamed based on the feature similarity between the static portrait features of the i-th user and the static portrait features of the j-th user, including: when it is detected that the feature similarity is greater than a preset similarity, determining that the i-th user and the j-th user are members to be teamed; when it is detected that the feature similarity is less than or equal to the preset similarity, determining that the i-th user and the j-th user are not members to be teamed.

[0072] Optionally, the team formation system based on motion data analyzes and compares the time when the i-th user and the j-th user expect to form a team, and accurately measures the interval between the two schedules. When this interval is detected to be shorter than the time threshold preset by the system, the two users will be regarded as potential team formation partners with high temporal coordination, and thus determined by the system as members to be formed into a team. On the other hand, if the interval is equal to or exceeds the preset time, the system will consider that the time arrangements of the two users do not match, thereby excluding the possibility of them joining the same team.

[0073] Optionally, the team formation system based on motion data first evaluates the similarity between the static portrait features (such as age, gender, occupation, hobbies, etc.) of the i-th user and the static portrait features of the j-th user. Then, the feature similarity is compared with the preset similarity threshold. When the system detects that the feature similarity exceeds the preset similarity threshold, the two users will be regarded as potential team formation partners with high matching in social attributes, and thus will be determined by the system as members to be formed into a team. On the contrary, if the feature similarity is lower than or equal to the preset similarity, the system will consider that the two users are not sufficiently matched at the social level, and thus will not recommend them to form a team.

[0074] From the above content, it can be seen that the team formation system based on sports data determines whether two users are members to be teamed up by the length of the interval between the times when the two users expect to team up and the feature similarity of the static portrait features between the two users. A sports team matching system based on time and social attributes is constructed to meet the needs of users to find sports partners with coordinated time and social compatibility.

[0075] In an optional embodiment, the motion data-based team formation system first generates a target code, and when it is detected that the terminal devices of the team members input the same target code, a communication connection is established between the terminal devices of the team members to be formed, and the accounts of the team members to be formed are added to the same team group. Alternatively, a team formation link is generated, and when it is detected that the team members to be formed click to enter the team formation link through the terminal device, a communication connection is established between the terminal devices of the team members to be formed, and the accounts of the team members to be formed are added to the same team group. Alternatively, when it is detected that the distance between the terminal devices of different team members to be formed is less than a preset distance, a communication connection is established between the terminal devices of the team members to be formed, and the accounts of the team members to be formed are added to the same team group.

[0076] Optionally, for establishing a communication connection between the terminal devices of the members to be teamed up, the accounts of the members to be teamed up are added to the same teaming group. This application mainly provides three methods, including: a method of generating a target code, a method of generating a teaming link, and a method of teaming based on geographic location.

[0077] Optionally, regarding the method of generating target codes, the team forming system based on motion data generates a set of unique and easy-to-remember target codes, and when two or more team members to be formed enter the same target code on their respective terminal devices, a matching mechanism can be immediately triggered.

[0078] It should be noted that the target code includes but is not limited to digital code, letter code, special symbol code and mixed code generated by any combination of codes (for example, mixed code containing both numbers and letters, mixed code containing both numbers and special symbols).

[0079] Optionally, for the method of generating a team link, the team system based on the sports data automatically generates a team invitation link and shares it with potential team members through social tools. Once the team member clicks on the link through his terminal device, the system will respond immediately, using instant messaging technology to build a communication bridge between user terminal devices, and automatically add the invited member's account to the same team group.

[0080] Optionally, for the team formation method based on geographic location, when the team formation system based on motion data detects that the distance between the terminal devices of different team members is less than a preset safety distance threshold, it will automatically activate the communication connection establishment and group management mechanism to integrate these close-range user accounts into the same team group.

[0081] From the above content, it can be seen that the team formation system based on sports data establishes communication connections between the terminal devices of the team members through three methods: generating target codes, generating team links, and team formation based on geographic location, and adds the accounts of the team members to the same team group, which meets the users' team formation needs in different scenarios and promotes the widespread dissemination and in-depth interaction of sports social activities.

[0082] In an optional embodiment, the motion data-based team system first collects the target motion data generated by each team member in the team group when following the target course training, and then adjusts the course content of the target course in the future time period based on the target motion data of each team member, and / or determines the motion goal of each team member in the future time period, wherein the motion goal includes at least exercise pace and exercise duration, and the motion goals of different team members in the team group are interrelated.

[0083] Optionally, while team members are training following customized target courses, the team formation system based on sports data collects real-time sports data of each team member, including but not limited to key indicators such as exercise pace, exercise duration, heart rate changes, route selection, etc., which not only provides real-time monitoring of the team's sports status, but also provides a detailed data basis for subsequent personalized course design and sports goal setting.

[0084] Optionally, the sports data-based team formation system analyzes the target sports data of each team member, including exercise habits, physical fitness level, improvement trends, etc., and adjusts the content of the target course in the future time period accordingly to ensure that the course difficulty matches the team members' athletic ability and promotes maximization of the exercise effect.

[0085] Optionally, the team formation system based on sports data will also comprehensively consider the overall sports performance of the team and determine the sports goals of each team member. These goals include but are not limited to sports pace and exercise duration. The sports goals of different team members will be interrelated to form a collaborative sports mode.

[0086] From the above content, it can be seen that the team formation system based on sports data collects the target sports data generated by each team member when following the target course training, adjusts the course content of the target course in the future time period, and determines the sports goals of each team member in the future time period. It not only meets the diversified needs of team members in sports guidance and team interaction, but also promotes the scientific and intelligent development of sports course design and sports goal setting.

[0087] In an optional embodiment, Figure 3A system flow chart of an optional team formation method based on motion data according to an embodiment of the present application is shown, which can more clearly understand the system flow of the team formation method based on motion data: for the formation of an outdoor team, it mainly includes two scenarios: online team formation and offline team formation. Among them, online team formation includes three core gameplays: team formation of acquaintances and strangers, team formation of acquaintances, and team formation of strangers. The necessary strategy for team formation of acquaintances and strangers is to form a team based on a designated user group and people with the same frequency at the same time. The non-essential strategy is to judge mainly based on the POI (Point of Interest), pace, age, gender and other characteristics of users in the group through the designated user group. The necessary strategy can also be based on the designated user group and the designated event or operation activity to form a team; for team formation of acquaintances, the team is mainly based on the designated user group; for team formation of strangers, the team is mainly based on people with the same frequency at the same time, and it can also be judged based on the POI, pace, age, gender and other characteristics of the user group and then form a team. Then the offline team formation mainly includes two core gameplays: team formation of acquaintances and team formation of strangers. For teaming up with acquaintances, the team is mainly formed based on the designated user group; for teaming up with strangers, the team is mainly formed based on the same event or activity. By forming a running or cycling team, the fun, companionship, motivation, and atmosphere of the user's exercise can be increased, and the user's need for a complete experience of outdoor sports partners before, during, and after training can also be met. Among them, by forming a team of acquaintances to run, it can better form a temporary social circle for running users, find partners for easy running, and increase the fun of running.

[0088] In an optional embodiment, Figure 4 A system principle diagram of an optional team formation method based on motion data according to an embodiment of the present application is shown, which can more clearly understand the system principle of the team formation method based on motion data: in the team formation process based on motion data, the team leader first initiates the team formation, and then the team members join the team formation by invitation to form a sports team. Then the movement process begins, the team leader first establishes a long connection with the server, and then the server returns and establishes a long connection with the team members. In the subsequent movement process, the team leader and the team members report the motion data to the server according to the long connection with the server, and then the server forwards the motion data to the team leader and the team members respectively according to the long connection.

[0089] According to another aspect of the embodiment of the present application, a team forming device based on motion data is also provided, wherein: Figure 5 is a schematic diagram of an optional team formation device based on motion data according to an embodiment of the present application, such as Figure 5 As shown, the team forming device based on motion data includes: an acquisition unit 501, a determination unit 502, and a processing unit 503.

[0090] An acquisition unit 501 acquires the user's motion data, the time information of the desired team formation, the user's user portrait characteristics, and the user's desired teammate portrait characteristics; a determination unit 502 determines multiple users that meet preset conditions as members to be teamed up based on the user's motion data, the user's desired team formation time information, the user's user portrait characteristics, and the user's desired teammate portrait characteristics, wherein the preset conditions are used to determine whether two users become teammates by detecting user characteristics between the two users; a processing unit 503 establishes a communication connection between the terminal devices of the members to be teamed up, and adds the accounts of the members to be teamed up to the same team group.

[0091] Optionally, the preset conditions include at least one of the following conditions; a first condition, used to determine whether the i-th user and the j-th user are members to be teamed based on the length of the interval between the times at which the i-th user and the j-th user expect to team up, wherein the i-th user and the j-th user are any two different users; a second condition, used to determine whether the i-th user and the j-th user are members to be teamed based on the data similarity or data statistical distribution information between the motion data of the i-th user and the motion data of the j-th user; a third condition, used to determine whether the i-th user and the j-th user are members to be teamed based on the feature similarity between the user portrait features of the i-th user and the user portrait features of the j-th user; a fourth condition, used to determine whether the i-th user and the j-th user are members to be teamed based on the similarity between the user portrait features of the i-th user and the teammate portrait features of the j-th user, and / or the similarity between the teammate portrait features of the i-th user and the user portrait features of the j-th user.

[0092] Optionally, the team formation device based on motion data further includes: a second determination unit and a third determination unit. The second determination unit is used to determine the i-th user and the j-th user as members to be formed into a team when it is detected that the data similarity between the motion data of the i-th user and the motion data of the j-th user is greater than a first threshold; the third determination unit is used to determine the i-th user and the j-th user as members to be formed into a team when it is detected that the data statistical distribution information of the motion data of the j-th user and the motion data of the j-th user conforms to a preset distribution trend.

[0093] Optionally, the motion data-based team forming device further includes: a fourth determination unit, for determining the i-th user and the j-th user as members to be formed into a team when it is detected that the similarity between the teammate portrait features of the i-th user and the user portrait features of the j-th user is greater than a third threshold, and / or the similarity between the teammate portrait features of the j-th user and the user portrait features of the i-th user is greater than a third threshold.

[0094] Optionally, the team formation device based on motion data further includes: a second processing unit and a third processing unit. The second processing unit is used to determine whether the i-th user and the j-th user are members to be formed into a team based on the interval between the times when the i-th user and the j-th user expect to form a team, including: when it is detected that the interval is less than a preset time, the i-th user and the j-th user are members to be formed into a team; when it is detected that the interval is greater than or equal to the preset time, the i-th user and the j-th user are not members to be formed into a team; the third processing unit is used to determine whether the i-th user and the j-th user are members to be formed into a team based on the feature similarity between the static portrait feature of the i-th user and the static portrait feature of the j-th user, including: when it is detected that the feature similarity is greater than the preset similarity, the i-th user and the j-th user are members to be formed into a team; when it is detected that the feature similarity is less than or equal to the preset similarity, the i-th user and the j-th user are not members to be formed into a team.

[0095] Optionally, the processing unit 503 includes: a first processing subunit, a second processing subunit, and a third processing subunit. The first processing subunit is used to establish a communication connection between the terminal devices of the members to be teamed and add the accounts of the members to be teamed to the same team group by generating a target code when it is detected that the terminal devices of the members to be teamed input the same target code; the second processing subunit is used to establish a communication connection between the terminal devices of the members to be teamed and add the accounts of the members to be teamed to the same team group by generating a team link when it is detected that the members to be teamed click to enter the team link through the terminal device; the third processing subunit is used to establish a communication connection between the terminal devices of the members to be teamed and add the accounts of the members to be teamed to the same team group when it is detected that the distance between the terminal devices of different members to be teamed is less than a preset distance.

[0096] Optionally, the team formation device based on motion data further includes: a collection unit and a fourth processing unit. The collection unit is used to collect the target motion data generated by each team member in the team formation group when following the target course training; the fourth processing unit is used to adjust the course content of the target course in the future time period according to the target motion data of each team member, and / or determine the motion goal of each team member in the future time period, wherein the motion goal at least includes the motion pace and the motion duration, and the motion goals of different team members in the team formation group are interrelated.

[0097] An embodiment of the present application may provide an electronic device, Figure 6 is a structural block diagram of an electronic device according to an embodiment of the present application. Figure 6 As shown, the electronic device may include: one or more ( Figure 6Only one is shown) a processor, a memory, a storage controller, and a peripheral interface, wherein the peripheral interface is connected to a radio frequency module, an audio module, and a display.

[0098] Among them, the memory can be used to store software programs and modules, such as program instructions / modules corresponding to the methods and devices in the embodiments of the present application, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0099] It can be understood by those skilled in the art that Figure 6 The structure shown is for illustration only, and the electronic device may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (Mobile Internet Devices, MID), a PAD, or other terminal devices. Figure 6 The structure of the electronic device is not limited. Figure 6 More or fewer components (such as network interfaces, display devices, etc.) shown in, or having Figure 6 Different configurations shown.

[0100] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0101] According to another aspect of the present application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer-readable storage medium, wherein when the computer program runs, the device where the computer-readable storage medium is located executes the above-mentioned team formation method based on motion data.

[0102] According to another aspect of the present application, a computer program product is also provided, wherein the computer program product includes computer instructions, wherein when the computer instructions are executed, the device where the computer program product is located executes the above-mentioned team formation method based on motion data.

[0103] The above-mentioned embodiments or examples disclosed in the present application are not exhaustive, but are only illustrative of some embodiments or examples, and are not intended to be specific limitations on the scope of protection disclosed in the present application. In the absence of contradiction, each step in a certain embodiment or example in the present application can be implemented as an independent example, and the steps can be combined arbitrarily. For example, the scheme after removing some steps in a certain embodiment or example can also be implemented as an independent example, and the order of the steps in a certain embodiment or example can be arbitrarily exchanged. In addition, the optional methods or optional examples in a certain embodiment or example can be combined arbitrarily; in addition, the various embodiments or examples can be combined arbitrarily, for example, some or all steps of different embodiments or examples can be combined arbitrarily, and a certain embodiment or example can be combined arbitrarily with the optional methods or optional examples of other embodiments or examples.

[0104] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0105] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0106] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0107] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0108] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0109] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0110] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A team formation method based on sports data, characterized in that: include: Obtaining the user's sports data, the time information of the desired team formation, the user's user portrait characteristics, and the user's desired teammate portrait characteristics; According to the user's sports data, the time information of the user's desired team formation, the user's user portrait characteristics and the user's desired teammate portrait characteristics, multiple users who meet preset conditions are determined as members to be teamed, wherein the preset conditions are used to determine whether the two users become teammates by detecting the user characteristics between the two users; Establish a communication connection between the terminal devices of the members to be teamed up, and add the accounts of the members to be teamed up to the same teaming group.

2. The team formation method based on sports data according to claim 1, characterized in that: The preset conditions include at least one of the following conditions: The first condition is used to determine whether the i-th user and the j-th user are the members to be teamed according to the interval between the times when the i-th user and the j-th user expect to team up, wherein the i-th user and the j-th user are any two different users; The second condition is used to determine whether the i-th user and the j-th user are the members to be teamed according to the data similarity or data statistical distribution information between the motion data of the i-th user and the motion data of the j-th user; The third condition is used to determine whether the i-th user and the j-th user are the members to be teamed according to the feature similarity between the user portrait feature of the i-th user and the user portrait feature of the j-th user; The fourth condition is used to determine whether the i-th user and the j-th user are the members to be teamed together based on the similarity between the user portrait features of the i-th user and the teammate portrait features of the j-th user, and / or the similarity between the teammate portrait features of the i-th user and the user portrait features of the j-th user.

3. The team formation method based on sports data according to claim 2, characterized in that: Judging whether the i-th user and the j-th user are members to be formed into a team according to data similarity or data statistical distribution information between the motion data of the i-th user and the motion data of the j-th user, including: When it is detected that the data similarity between the motion data of the i-th user and the motion data of the j-th user is greater than a first threshold, determining the i-th user and the j-th user as the members to be teamed; or, When it is detected that the motion data of the j-th user and the data statistical distribution information of the motion data of the j-th user meet the preset distribution trend, the i-th user and the j-th user are determined as the members to be teamed.

4. The team formation method based on sports data according to claim 2, characterized in that: Judging whether the i-th user and the j-th user are the members to be teamed according to the similarity between the user portrait feature of the i-th user and the teammate portrait feature of the j-th user, and / or the similarity between the teammate portrait feature of the i-th user and the user portrait feature of the j-th user, comprises: When it is detected that the similarity between the teammate portrait features of the i-th user and the user portrait features of the j-th user is greater than a third threshold, and / or the similarity between the teammate portrait features of the j-th user and the user portrait features of the i-th user is greater than the third threshold, the i-th user and the j-th user are determined as the members to be teamed.

5. The team formation method based on sports data according to claim 2, characterized in that: The team formation method based on sports data also includes: According to the interval between the times when the i-th user and the j-th user expect to form a team, judging whether the i-th user and the j-th user are the members to be formed into a team, comprising: when it is detected that the interval is less than a preset time, determining that the i-th user and the j-th user are the members to be formed into a team; when it is detected that the interval is greater than or equal to the preset time, determining that the i-th user and the j-th user are not the members to be formed into a team; Judging whether the i-th user and the j-th user are the members to be teamed according to the feature similarity between the static portrait feature of the i-th user and the static portrait feature of the j-th user, includes: When it is detected that the feature similarity is greater than a preset similarity, the i-th user and the j-th user are determined to be the members to be teamed; when it is detected that the feature similarity is less than or equal to the preset similarity, it is determined that the i-th user and the j-th user are not the members to be teamed.

6. The team formation method based on sports data according to claim 1, characterized in that: Establishing a communication connection between the terminal devices of the members to be teamed, and adding the accounts of the members to be teamed to the same teaming group, including: By generating a target code, when it is detected that the terminal device of the member to be teamed has input the same target code, a communication connection is established between the terminal devices of the member to be teamed, and the account of the member to be teamed is added to the same teaming group; or, By generating a team link, when it is detected that the team member to be teamed up clicks on the team link through a terminal device, a communication connection is established between the terminal devices of the team member to be teamed up, and the account of the team member to be teamed up is added to the same team group; or, When it is detected that the distance between the terminal devices of different members to be teamed is less than the preset distance, a communication connection is established between the terminal devices of the members to be teamed, and the accounts of the members to be teamed are added to the same teaming group.

7. The team formation method based on sports data according to claim 1, characterized in that: After establishing a communication connection between the terminal devices of the members to be teamed, and adding the accounts of the members to be teamed to the same teaming group, the teaming method based on sports data further includes: Collecting target motion data generated by each team member in the team group when following the target course training; According to the target exercise data of each team member, the course content of the target course in the future time period is adjusted, and / or the exercise goal of each team member in the future time period is determined, wherein the exercise goal includes at least exercise pace and exercise duration, and the exercise goals of different team members in the team group are interrelated.

8. A team formation device based on sports data, characterized in that: include: An acquisition unit, which acquires the user's sports data, the time information of the desired team formation, the user's user portrait characteristics, and the user's desired teammate portrait characteristics; a determination unit, which determines a plurality of users who meet preset conditions as members to be teamed up according to the user's motion data, the time information of the user's desired teaming, the user's portrait characteristics, and the user's desired teammate portrait characteristics, wherein the preset conditions are used to determine whether the two users become teammates by detecting user characteristics between the two users; The processing unit establishes a communication connection between the terminal devices of the members to be teamed, and adds the accounts of the members to be teamed to the same teaming group.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located executes the team formation method based on motion data as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the motion data-based team formation method described in any one of claims 1 to 7.

11. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the motion data-based team formation method described in any one of claims 1 to 7 are implemented.

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