A method and terminal for game team management

Abstract

The present application relates to the field of game teaming, and particularly relates to a game teaming management method and terminal, existence of a player exiting a team is detected, network data and hardware running data of a target player exiting the team are collected; whether the exit reason of the target player is non-malicious exit caused by network fluctuation or equipment failure is judged according to the network data and the hardware running data; a digital portrait of the target player is constructed according to historical game data through a deep learning network; re-entry application of the target player is detected, and logical judgment of whether to allow entry is performed according to the exit reason and the digital portrait; if the judgment result is to allow entry, team adjustment is performed according to current team composition information; the specific situation of a team member exiting can be distinguished, for a player exiting due to non-malicious reasons such as network fluctuation, equipment failure or misoperation, the player can be prevented from losing the original team position unjustly, the management of game teaming is optimized, and the game experience is improved.

Description

Technical Field

[0001] This invention relates to the field of game team formation, and in particular to a method and terminal for game team management. Background Technology

[0002] In existing multiplayer online game team systems, the handling of online team members leaving the team is extremely crude. Under traditional technical architectures, once a team member leaves voluntarily or the team is disbanded by the team leader, the system often simply deletes the team association record of that member from the database, completely disregarding the specific context of the member's departure and the potential need for them to rejoin. This simplistic approach has many serious problems.

[0003] On the one hand, in today's complex and ever-changing network environment, many team members may be forced to leave the team due to momentary network fluctuations, temporary equipment failures, or unintentional misoperations. However, the existing system cannot distinguish between these situations and malicious exits, causing players to lose their original team positions innocently. They have to spend a lot of time and energy to find teammates again or start the game from scratch, which greatly damages the smoothness of the game and the player's immersion.

[0004] On the other hand, the existing processing mechanism is almost non-existent in terms of dynamic management of team members. It does not take into account the subsequent impact of team members leaving on the overall team structure and game progress. It cannot flexibly adjust the team composition according to the real-time status of the game and the actual needs of the team, which seriously affects the fairness and stability of the game and falls far short of meeting players' urgent expectations for an intelligent and efficient team environment. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method and terminal for game team management, optimize game team management, and improve the game experience.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for managing game teams, including the following steps: S1. A player is detected leaving the team. Network data and hardware operation data are collected for the target player who left the team. S2. Based on the network data and the hardware operation data, determine whether the reason for the target player's exit is a non-malicious exit caused by network fluctuations or device failure; A digital profile of the target player is constructed using a deep learning network based on historical play data. S3. Detect the target player's re-entry request, and make a logical judgment on whether to allow re-entry based on the reason for withdrawal and the digital profile; S4. If the judgment result is that the team can join, then the team will be adjusted according to the current team composition information.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A game team management terminal includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the game team management method described above.

[0008] The beneficial effects of this invention are as follows: The game team management method and terminal of this invention can distinguish the specific circumstances of team member exits. For players who exit due to non-malicious reasons such as network fluctuations, equipment failures, or accidental operations, it prevents them from unjustly losing their original team positions. Players do not need to spend a lot of time and energy finding teammates again or starting the game from scratch, reducing the inconvenience caused by game interruptions, thereby greatly improving the smoothness of the game and the player's immersion. Simultaneously, through intelligent analysis and digital profile construction, the system can comprehensively understand the abilities and reliability of team members. When determining re-entry into the team, it comprehensively considers factors such as team member abilities, team needs, and reasons for offline departure, using machine learning algorithms to assist in decision-making, improving the accuracy and scientific nature of the judgment. If re-entry is allowed, the team can also be readjusted to maintain high combat effectiveness and collaborative efficiency, enhancing the scientific nature and flexibility of team management. Attached Figure Description

[0009] Figure 1 This is a flowchart illustrating a game team management method according to an embodiment of the present invention; Figure 2 This is a structural diagram of a game team management terminal according to an embodiment of the present invention; Label Explanation: 1. A terminal for managing game teams; 2. A processor; 3. A memory. Detailed Implementation

[0010] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0011] Please refer to Figure 1 A method for managing game teams, including the following steps: S1. A player is detected leaving the team. Network data and hardware operation data are collected for the target player who left the team. S2. Based on the network data and the hardware operation data, determine whether the reason for the target player's exit is a non-malicious exit caused by network fluctuations or device failure; A digital profile of the target player is constructed using a deep learning network based on historical play data. S3. Detect the target player's re-entry request, and make a logical judgment on whether to allow re-entry based on the reason for withdrawal and the digital profile; S4. If the judgment result is that the team can join, then the team will be adjusted according to the current team composition information.

[0012] As described above, the beneficial effects of this invention are as follows: The game team management method of this invention can distinguish the specific circumstances of team member departures. For players who leave due to non-malicious reasons such as network fluctuations, equipment failures, or accidental operations, it prevents them from unjustly losing their original team positions. Players do not need to spend a lot of time and energy finding teammates again or starting the game from scratch, reducing the inconvenience caused by game interruptions, thereby greatly improving the smoothness of the game and the player's immersion. Simultaneously, through intelligent analysis and digital profile construction, the system can comprehensively understand the abilities and reliability of team members. When determining re-entry into the team, it comprehensively considers factors such as team member abilities, team needs, and reasons for offline departure, using machine learning algorithms to assist in decision-making, improving the accuracy and scientific nature of the judgment. If re-entry is allowed, the team can also be readjusted to maintain high combat effectiveness and collaborative efficiency, enhancing the scientific nature and flexibility of team management.

[0013] Furthermore, step S1 also includes the following steps: The network data and operational data are uploaded to the blockchain using the blockchain's API interface. Step S2 includes: Based on blockchain-based smart contract technology, once the network data and the runtime data are stored on the blockchain, the analysis program in the smart contract is automatically triggered. Based on the network data and the hardware runtime data, it is determined whether the target player's exit was not malicious due to network fluctuations or equipment failure.

[0014] As described above, blockchain API interfaces are used to upload network and operational data to the blockchain, and analysis programs are triggered based on blockchain smart contract technology. The application of blockchain technology ensures data security and immutability, making the data more credible and providing a solid basis for subsequent judgments. The automatic triggering of analysis programs by smart contracts automates the process, improves processing efficiency, reduces manual intervention, and avoids human error.

[0015] Furthermore, the network data includes transmission rate, packet loss rate, and latency information; The hardware operation data includes device temperature, memory usage, and CPU load; Based on the network data and the hardware operation data, determining whether the target player's non-malicious exit was due to network fluctuations or device malfunction involves the following steps: Based on the network data, determine whether the preset conditions of large packet loss or high latency are met; if so, it is determined that there is a network fluctuation. Based on the hardware operating data, determine whether the preset conditions of excessive temperature or memory overflow are met; if so, it is determined that there is a device malfunction. If the aforementioned network fluctuations or device malfunctions are present, the target player is deemed to have exited without malicious intent.

[0016] As described above, the specific content of network data and hardware operation data, as well as the criteria for determining non-malicious exits, are defined in detail. This unified data standard makes the judgment process more standardized and accurate, enabling the system to accurately identify the reasons for player exits, distinguish between malicious and non-malicious exits, provide precise evidence for subsequent decisions, and protect player rights.

[0017] Furthermore, constructing a digital profile of the target player based on historical play data using a deep learning network includes the following steps: Obtain the target player's historical play data; Based on the historical gameplay data, deep learning convolutional neural networks are used for analysis to generate scores and ratings for the target player in each preset ability dimension, thereby obtaining a player ability assessment.

[0018] As described above, the specific steps for constructing a digital profile using deep learning networks involve acquiring historical gameplay data and using convolutional neural networks to analyze and generate scores and ratings. This approach can systematically and comprehensively evaluate player abilities, deeply analyze player performance in the game, provide rich and accurate information on player capabilities for team selection, and enhance the scientific rigor of the judgment.

[0019] Furthermore, generating the target player's scores and ratings across various preset ability dimensions includes: Assess tactical decision-making ability by observing movement paths and skill release timing during combat; Calculate resource management efficiency using data from the in-game economic system; The interaction between the target player and his / her teammates is quantified using social network analysis algorithms to generate a team collaboration influence index.

[0020] As described above, the specific methods for generating scores and ratings across each preset ability dimension have been further clarified. Player abilities are quantitatively evaluated from multiple dimensions, including tactical decision-making, resource management, and teamwork, comprehensively showcasing players' overall performance in the game. This makes the digital profiles more accurate and detailed, improving the accuracy of team selection and facilitating the formation of more optimized teams.

[0021] Further, step S3 includes the following steps: S31. Detect the target player's re-entry request, and obtain the reason for leaving and the digital profile; S32. Determine whether the exit reason is a non-malicious exit. If so: Obtain the digital profiles of the team members. Based on the digital profiles of the target player and the team members, determine whether the target player's ability dimensions meet the team's ability requirements. If so, allow the target player to join the team.

[0022] As described above, the detailed process for rejoining a team involves first determining the reason for leaving, and then comparing the player's profile with the team members' digital profiles to assess their skill requirements. This process makes the team joining decision more logical and reasonable, prioritizing players who left without malicious intent, and considering the team's actual needs to determine whether to allow them to join, thus improving the scientific rigor and adaptability of team formation.

[0023] Furthermore, the pre-trained machine learning model is used to assist in the judgment of steps S31 and S32.

[0024] As described above, a pre-trained machine learning model is used to assist in the judgment. This model, trained on a large amount of data, can more accurately analyze complex factors, assist the judgment process, reduce subjective errors, improve the accuracy and scientific rigor of the entry judgment, and make the decision more consistent with the actual game situation.

[0025] Furthermore, team adjustments based on the current team composition information include: Obtain the digital profiles of the current team members, and based on the digital profiles of the target player and the team members, use optimization algorithms such as dynamic programming or greedy algorithms to readjust the positions and responsibilities of the team members.

[0026] As described above, team adjustments are made based on player profiles, using optimization algorithms to readjust member positions and responsibilities. These algorithms identify better team configurations, fully leveraging each player's strengths, improving team collaboration efficiency and combat effectiveness, and enhancing the game's competitiveness and enjoyment.

[0027] Furthermore, it also includes the following steps: S5. Update the team's new status information to the game database and blockchain.

[0028] As described above, the process involves adding steps to update the team's new status information to the game database and blockchain. This ensures the consistency and real-time nature of game data, facilitating subsequent management and analysis by the game system; blockchain storage ensures data traceability, facilitates review and statistics, and provides data support for game operation and improvement.

[0029] Please refer to Figure 2 A game team management terminal includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the game team management method described above.

[0030] As described above, the beneficial effects of this invention are as follows: The game team management terminal of this invention can distinguish the specific circumstances of team member exits. For players who exit due to non-malicious reasons such as network fluctuations, equipment failures, or accidental operations, it prevents them from unjustly losing their original team positions. Players do not need to spend a lot of time and energy searching for teammates again or starting the game from scratch, reducing the inconvenience caused by game interruptions and thus greatly improving the smoothness of the game and the player's immersion. Simultaneously, through intelligent analysis and digital profile construction, the system can comprehensively understand the abilities and reliability of team members. When determining re-entry into the team, it comprehensively considers factors such as team member abilities, team needs, and reasons for offline status, using machine learning algorithms to assist in decision-making, improving the accuracy and scientific nature of the judgment. If re-entry is allowed, the team can also be readjusted to maintain high combat effectiveness and collaborative efficiency, enhancing the scientific nature and flexibility of team management.

[0031] The present invention provides a method and terminal for game team management, which is applicable to various multiplayer online game scenarios, effectively solves the problems of traditional team systems, and enhances the player's gaming experience.

[0032] Please refer to Figure 1 Embodiment 1 of the present invention is as follows: A method for managing game teams, including the following steps: S1. A player is detected leaving the team. Network data and hardware operation data are collected for the target player who left the team. Step S1 also includes the following steps: The network data and operational data are uploaded to the blockchain using the blockchain's API interface. The network data includes transmission rate, packet loss rate, and latency information; The hardware operating data includes device temperature, memory usage, and CPU load.

[0033] In this embodiment, when a team member is detected leaving the team, the multi-source information acquisition program is immediately initiated. The current system time is obtained using a high-precision timestamp function and accurate to milliseconds, and recorded as an offline timestamp.

[0034] The network monitoring submodule is activated to capture and analyze network data packets in real time using deep packet inspection (DPI) technology. It records key indicators such as data packet transmission rate, packet loss rate, and latency in detail, and organizes them into a network status log according to a specific data format.

[0035] At the same time, the device hardware monitoring interface is invoked to communicate with the device's sensors, obtain real-time hardware monitoring data such as device temperature, memory usage, and CPU load, and convert them into standard data structures.

[0036] If the game has an operation recording function, the video analysis module based on artificial intelligence image recognition will be activated to extract and preliminarily process the video clips of the player's operation before exiting, in preparation for subsequent analysis of misoperations.

[0037] The collected offline timestamps, network status logs, device hardware monitoring data, and operation video clips are encrypted. Asymmetric encryption algorithms (such as RSA or elliptic curve cryptography) are used to encrypt the data to ensure its security and privacy.

[0038] The encrypted data is uploaded to the blockchain network using the blockchain's API interface. A dedicated data structure is then created on the blockchain to store this exit information. For example, a blockchain data block with multiple fields can be defined, each field corresponding to a type of exit information. These data blocks are then linked together using hash pointers to form an immutable information chain.

[0039] S2. Based on the network data and the hardware operation data, determine whether the reason for the target player's exit is a non-malicious exit caused by network fluctuations or device failure; Step S2 includes: Based on blockchain-based smart contract technology, once the network data and the runtime data are stored on the blockchain, the analysis program in the smart contract is automatically triggered to determine whether the target player's exit was not malicious due to network fluctuations or equipment failure, based on the network data and the hardware runtime data. Based on the network data and the hardware operation data, determining whether the target player's non-malicious exit was due to network fluctuations or device malfunction involves the following steps: Based on the network data, determine whether the preset conditions of large packet loss or high latency are met; if so, it is determined that there is a network fluctuation. Based on the hardware operating data, determine whether the preset conditions of excessive temperature or memory overflow are met; if so, it is determined that there is a device malfunction. If the aforementioned network fluctuations or device malfunctions are present, the target player is deemed to have exited without malicious intent.

[0040] In this embodiment, based on blockchain smart contract technology, once the exit information is successfully stored on the blockchain, the analysis program in the smart contract is automatically triggered. The smart contract can be designed as an automated script with specific logic and rules. It listens for specific events on the blockchain (such as a team member exit event), and once the event is detected, it initiates the subsequent analysis process.

[0041] The analysis program first reads the team member's withdrawal information from the blockchain and decrypts it back to the original data format. Then, based on this data, it determines the possible reasons for the team member's withdrawal. For example, if the network status log shows a large amount of packet loss and high latency before the withdrawal, and the device hardware monitoring data is normal, the initial judgment is that the withdrawal was caused by network fluctuations; if there are obvious hardware problems such as excessive device temperature or memory overflow, it is determined to be due to device failure.

[0042] A digital profile of the target player is constructed using a deep learning network based on historical play data. Constructing a digital profile of the target player using a deep learning network based on historical play data includes the following steps: Obtain the target player's historical play data; Based on the historical play data, deep learning convolutional neural networks are used for analysis to generate scores and ratings for the target player in each preset ability dimension, thereby obtaining a player ability assessment. Generating the target player's scores and ratings across various preset ability dimensions includes: Assess tactical decision-making ability by observing movement paths and skill release timing during combat; Calculate resource management efficiency using data from the in-game economic system; The interaction between the target player and his / her teammates is quantified using social network analysis algorithms to generate a team collaboration influence index.

[0043] In this embodiment, for the long-term performance data of team members in the team, the analysis program retrieves relevant data from the game database through database query statements, including all game records in which the team members participated, operation data in battle, resource acquisition and consumption data, and interaction records with teammates, etc.

[0044] These data are fed into a deep learning-based convolutional neural network (CNN) model for training and analysis. Within the CNN model, specialized convolutional and pooling layers are designed to extract key features of team members in different game scenarios. For example, tactical decision-making ability is assessed by analyzing movement paths and skill release timing in battle replays, resource management efficiency is calculated using in-game economic system data, and social network analysis algorithms are applied to quantify the interaction effects with teammates, thereby constructing a team collaboration influence index for each player.

[0045] After multiple iterations of training and optimization, the CNN model finally outputs a digital profile of the team members' comprehensive abilities and reliability. This profile is represented in the form of vectors or structured data, including the team members' scores and ratings in various key ability dimensions, providing data support for subsequent re-entry decisions.

[0046] S3. Detect the target player's re-entry request, and make a logical judgment on whether to allow re-entry based on the reason for withdrawal and the digital profile; Step S3 includes the following steps: S31. Detect the target player's re-entry request, and obtain the reason for leaving and the digital profile; S32. Determine whether the exit reason is a non-malicious exit. If so: Obtain the digital profiles of the team members. Based on the digital profiles of the target player and the team members, determine whether the target player's ability dimensions meet the team's ability requirements. If so, allow the target player to join the team.

[0047] The pre-trained machine learning model is used to assist in the judgment of steps S31 and S32.

[0048] In this embodiment, when a team member attempts to rejoin the team, the system first receives the member's rejoining request and verifies their identity information. The system then interacts with the game's user authentication system to confirm the legitimacy of the requesting member.

[0049] Subsequently, the system retrieves the player's withdrawal information and previously constructed digital profile from the blockchain, and performs a comprehensive analysis in conjunction with the current team status information. Team status information includes the current team members' skill configurations, level distribution, and ongoing game tasks or levels, which can be obtained through database queries and real-time game data collection.

[0050] Based on pre-set re-entry rules and thresholds, as well as the results of intelligent analysis, the system makes re-entry judgments. For example, if a member's offline reason is determined to be a non-malicious exit (such as network fluctuations or equipment failure), and their digital profile shows a high score in the key capabilities required by the current team, while the current team has a weakness in that capability dimension, the system tends to allow the member to rejoin.

[0051] During the decision-making process, machine learning algorithms such as decision trees or Bayesian classification can be used to assist in decision-making. These algorithms are trained based on a large amount of historical data and game rules, and can output a reasonable decision result (allow or deny re-entry) based on various input factors (such as team member abilities, team needs, reasons for offline status, etc.), thereby improving the accuracy and scientific nature of the judgment.

[0052] S4. If the judgment result is that the team can join the team, then the team will be adjusted according to the current team composition information. Adjustments to the team based on the current team composition information include: Obtain the digital profiles of the current team members, and based on the digital profiles of the target player and the team members, use optimization algorithms such as dynamic programming or greedy algorithms to readjust the positions and responsibilities of the team members.

[0053] In this embodiment, if the system decides to allow a team member to rejoin the team, it first sends a notification message to the member confirming successful rejoining. This can be achieved through in-game push notifications or email. Simultaneously, based on the team's current situation and the members' abilities, the system uses optimization algorithms such as dynamic programming or greedy algorithms to readjust the team. For example, it determines the optimal positions and responsibilities of team members, updates the team's collaboration strategies and task allocation schemes, ensuring the team maintains high combat effectiveness and collaborative efficiency after regrouping.

[0054] S5. Update the team's new status information to the game database and blockchain.

[0055] In this embodiment, the team's new status information is updated in the game database and on the blockchain to maintain data consistency and traceability. By executing database update statements and blockchain transaction operations, the latest team composition, member positions, and responsibilities are recorded, providing accurate data for subsequent game progress and team management.

[0056] Please refer to Figure 2 Embodiment two of the present invention is as follows: A game team management terminal 1 includes a processor 2, a memory 3, and a computer program stored in the memory 3 and executable on the processor 2. When the processor 2 executes the computer program, it implements the steps in the game team management method described above.

[0057] In summary, the game team management method and terminal provided by this invention can distinguish the specific circumstances of team member departures. For players who leave due to non-malicious reasons such as network fluctuations, equipment failures, or accidental operations, it prevents them from unjustly losing their original team positions. Players do not need to spend a lot of time and energy finding teammates again or starting the game from scratch, reducing the inconvenience caused by game interruptions, thereby greatly improving the smoothness of the game and the player's immersion. At the same time, with the help of intelligent analysis and digital profile construction, the system can comprehensively understand the abilities and reliability of team members. When judging re-entry into the team, it comprehensively considers multiple factors such as team member abilities, team needs, and reasons for offline departure, and uses machine learning algorithms to assist in decision-making, improving the accuracy and scientific nature of the judgment. If re-entry is allowed, the team can also be readjusted to maintain high combat effectiveness and collaborative efficiency, enhancing the scientific nature and flexibility of team management.

[0058] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for managing game teams, characterized in that, Including the following steps: S1. A player is detected leaving the team. Network data and hardware operation data are collected for the target player who left the team. S2. Based on the network data and the hardware operation data, determine whether the reason for the target player's exit is a non-malicious exit caused by network fluctuations or device failure; A digital profile of the target player is constructed using a deep learning network based on historical play data. S3. Detect the target player's re-entry request, and make a logical judgment on whether to allow re-entry based on the reason for withdrawal and the digital profile; S4. If the judgment result is that the team can join, then the team will be adjusted according to the current team composition information.

2. The method for managing game teams according to claim 1, characterized in that, Step S1 also includes the following steps: The network data and operational data are uploaded to the blockchain using the blockchain's API interface. Step S2 includes: Based on blockchain-based smart contract technology, once the network data and the runtime data are stored on the blockchain, the analysis program in the smart contract is automatically triggered. Based on the network data and the hardware runtime data, it is determined whether the target player's exit was not malicious due to network fluctuations or equipment failure.

3. A method for managing game teams according to claim 1 or 2, characterized in that, The network data includes transmission rate, packet loss rate, and latency information; The hardware operation data includes device temperature, memory usage, and CPU load; Based on the network data and the hardware operation data, determining whether the target player's non-malicious exit was due to network fluctuations or device malfunction involves the following steps: Based on the network data, determine whether the preset conditions of large packet loss or high latency are met; if so, it is determined that there is a network fluctuation. Based on the hardware operating data, determine whether the preset conditions of excessive temperature or memory overflow are met; if so, it is determined that there is a device malfunction. If the aforementioned network fluctuations or device malfunctions are present, the target player is deemed to have exited without malicious intent.

4. The method for managing game teams according to claim 1, characterized in that, Constructing a digital profile of the target player using a deep learning network based on historical play data includes the following steps: Obtain the target player's historical play data; Based on the historical gameplay data, deep learning convolutional neural networks are used for analysis to generate scores and ratings for the target player in each preset ability dimension, thereby obtaining a player ability assessment.

5. A method for managing game teams according to claim 4, characterized in that, Generating the target player's scores and ratings across various preset ability dimensions includes: Assess tactical decision-making ability by observing movement paths and skill release timing during combat; Calculate resource management efficiency using data from the in-game economic system; The interaction between the target player and his / her teammates is quantified using social network analysis algorithms to generate a team collaboration influence index.

6. The method for managing game teams according to claim 1, characterized in that, Step S3 includes the following steps: S31. Detect the target player's re-entry request, and obtain the reason for leaving and the digital profile; S32. Determine whether the exit reason is a non-malicious exit. If so: Obtain the digital profiles of the team members. Based on the digital profiles of the target player and the team members, determine whether the target player's ability dimensions meet the team's ability requirements. If so, allow the target player to join the team.

7. A method for managing game teams according to claim 6, characterized in that, The pre-trained machine learning model is used to assist in the judgment of steps S31 and S32.

8. A method for managing game teams according to claim 1, characterized in that, Adjustments to the team based on the current team composition information include: Obtain the digital profiles of the current team members, and based on the digital profiles of the target player and the team members, use optimization algorithms such as dynamic programming or greedy algorithms to readjust the positions and responsibilities of the team members.

9. A method for managing game teams according to claim 1, characterized in that, It also includes the following steps: S5. Update the team's new status information to the game database and blockchain.

10. A terminal for game team management, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the game team management method according to any one of claims 1-9.

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