A method and terminal for detecting a weakness of a game virtual object

Abstract

The application discloses a kind of game virtual object weak point detection method and terminal, obtain the game virtual object game match data index, according to the index analysis game virtual object game match evaluation data;Game match evaluation data is compared and analyzed with the game match evaluation data of other game virtual objects of the same type, to determine the weak point of game virtual object, so as to consider the game virtual object's own match data and the match data of virtual objects of the same type, more accurately analyze weak point;Collect the mood data of the game virtual object corresponding player client, generate operation suggestion in combination with mood data and the weak point of game virtual object, in this way improve the generation flexibility and accuracy of weak point operation suggestion.

Description

Technical Field

[0001] This invention relates to the technical field of game data analysis, and in particular to a method and terminal for detecting weaknesses in virtual game objects. Background Technology

[0002] In the current gaming industry, most games' combat results are limited to simple data presentation, with relatively singular data dimensions. The analysis of player weaknesses and operational suggestions are usually rigid, lacking flexibility and accuracy. Therefore, there is an urgent need for a method to detect the weaknesses of virtual game objects to solve the above problems. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method and terminal for detecting weaknesses in virtual game objects, which can improve the flexibility and accuracy of analyzing game weaknesses and generating operation suggestions.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for detecting weaknesses in virtual objects in a game includes the following steps: S1. Obtain game match data indicators of the game virtual object, and analyze the game match evaluation data of the game virtual object based on the game match data indicators; S2. Compare and analyze the game match evaluation data with the game match evaluation data of other virtual objects of the same type to determine the weaknesses of the virtual object; S3. Collect the emotional data of the player client corresponding to the game virtual object, and combine the emotional data with the weaknesses of the game virtual object to generate operation suggestions.

[0005] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows: A terminal for detecting weaknesses in virtual game objects includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of the aforementioned method for detecting weaknesses in virtual game objects.

[0006] The beneficial effects of this invention are as follows: It acquires game performance data indicators of virtual game objects, analyzes the game performance evaluation data of these virtual objects based on these indicators, compares and analyzes the game performance evaluation data with that of other virtual game objects of the same type to determine the weaknesses of the virtual game objects. Therefore, it can consider both the virtual game object's own performance data and the performance data of similar virtual objects, resulting in more accurate weakness analysis. Furthermore, it collects emotional data from the player's client corresponding to the virtual game object, and combines this emotional data with the virtual game object's weaknesses to generate operational suggestions, thereby improving the flexibility and accuracy of generating weakness operation suggestions. Attached Figure Description

[0007] Figure 1 This is a flowchart of a method for detecting weaknesses in virtual game objects according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a terminal for detecting weaknesses in virtual game objects according to an embodiment of the present invention; Label Explanation: 1. A terminal for detecting weaknesses in virtual game objects; 2. A memory; 3. A processor. Detailed Implementation

[0008] 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.

[0009] Please refer to Figure 1 This invention provides a method for detecting weaknesses in virtual objects in games, comprising the following steps: S1. Obtain game match data indicators of the game virtual object, and analyze the game match evaluation data of the game virtual object based on the game match data indicators; S2. Compare and analyze the game match evaluation data with the game match evaluation data of other virtual objects of the same type to determine the weaknesses of the virtual object; S3. Collect the emotional data of the player client corresponding to the game virtual object, and combine the emotional data with the weaknesses of the game virtual object to generate operation suggestions.

[0010] As can be seen from the above description, the beneficial effects of the present invention are as follows: It acquires game match data indicators of virtual game objects, analyzes the game match evaluation data of virtual game objects based on these indicators, compares and analyzes the game match evaluation data with the game match evaluation data of other virtual game objects of the same type, and determines the weaknesses of the virtual game objects. Therefore, it can take into account both the game match data of the virtual game objects themselves and the game match data of virtual game objects of the same type, and perform more accurate weakness analysis; it collects the emotional data of the player client corresponding to the virtual game objects, and combines the emotional data with the weaknesses of the virtual game objects to generate operation suggestions, thereby improving the flexibility and accuracy of generating weakness operation suggestions.

[0011] Further, step S1 includes: Obtain game match data metrics for virtual game objects, and calculate the difference between each game match data metric and the corresponding metric mean. Establish regression models for all game match data indicators and game match results, and analyze the correlation between each game match data indicator and game match results; By combining the differences between each game match data indicator and the mean of the corresponding indicator, as well as the correlation between each game match data indicator and the game match result, the game virtual object is evaluated using a preset number of game match data indicators with the highest correlation as the dimension, thus obtaining game match evaluation data.

[0012] As described above, by calculating the mean difference of game match data indicators and establishing a regression model to calculate the correlation between game match data indicators and match results, the virtual objects in the game can be evaluated using a preset number of game match data indicators with the highest correlation, which can improve the accuracy and rationality of the evaluation.

[0013] Further, step S2 includes: The game match evaluation data is compared and analyzed with the average game match evaluation data and excellent game match evaluation data of other virtual objects of the same type. The average difference value and excellent difference value of the game match evaluation data are obtained, and the weaknesses of the virtual objects of the game are determined.

[0014] As described above, by comparing multi-dimensional data and combining it with the analysis of the performance of excellent virtual objects, we can more accurately identify the weaknesses of virtual objects, avoid missing important problem areas, and improve the accuracy of weakness diagnosis.

[0015] Furthermore, step S2 also includes: Obtain game skill data corresponding to the weaknesses of the game virtual object, and analyze the reasons for the weaknesses of the game virtual object by combining the game skill data with the skill effects of different game scenarios, release timing and skill targets.

[0016] As described above, after identifying weaknesses, an in-depth analysis of the reasons for those weaknesses is conducted, including discussions on relevant factors such as usage scenarios, release timing, and target types, in order to provide more targeted operational suggestions in the future.

[0017] Further, step S3 includes: Based on the weaknesses and their causes, operational recommendations are determined; Collect emotional data of the player client corresponding to the virtual game object, including player voice characteristics and operation behavior data; Establish a correlation model between the emotional data and the weakness, adjust the operation suggestions based on the correlation between the emotional data and the weakness, and determine the priority of the operation suggestions.

[0018] As described above, the operational suggestions and their priorities are adjusted based on the player's emotional data and weaknesses. If the player's emotions are significantly low and related to a certain weakness, assistance will be provided to address that weakness more frequently, effectively improving the flexibility and rationality of the operational suggestions.

[0019] Please refer to Figure 2 Another embodiment of the present invention provides a terminal for detecting weaknesses in game virtual objects, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of the above-described method for detecting weaknesses in game virtual objects.

[0020] The method and terminal for detecting weaknesses in virtual game objects described above are applicable to improving the flexibility and accuracy of analyzing game weaknesses and generating operation suggestions. The following is a detailed description of the implementation methods: Please refer to Figure 1 Embodiment 1 of the present invention is as follows: A method for detecting weaknesses in virtual objects in a game includes the following steps: S1. Obtain game match data indicators of the game virtual object, and analyze the game match evaluation data of the game virtual object based on the game match data indicators.

[0021] S11. Obtain the game match data indicators of the game virtual object, and calculate the difference between each game match data indicator and the corresponding indicator mean.

[0022] In this embodiment, after a player finishes a game, various game data indicators of the corresponding virtual game object are collected, including but not limited to damage output, damage taken, number of kills, skill hit rate, skill release timing, movement distance, decision accuracy, and resource utilization efficiency.

[0023] For each game performance metric, calculate the difference between the game's virtual character's metric and the average level of similar virtual characters. For example, calculate the difference between the virtual character's damage output and the average damage output of similar virtual characters, and the difference between its skill hit rate and the average skill hit rate of similar virtual characters. These differences identify which metrics the virtual character lags behind the average level of similar virtual characters, potentially revealing its weaknesses.

[0024] S12. Establish regression models for all game match data indicators and game match results, and analyze the correlation between each game match data indicator and game match results.

[0025] In this embodiment, a regression model is established between game performance data metrics and game outcomes (such as victory or defeat). Various data metrics, including skill hit rate, movement distance, decision accuracy, and resource utilization efficiency, are used as independent variables, while the game outcome is included as the dependent variable. Regression analysis is used to determine the degree of influence of each metric on the game outcome. For example, the model analyzes the impact of a certain percentage increase in skill hit rate on the probability of final victory, and the correlation between the rationality of movement distance and victory. This approach further clarifies which metrics are more critical to game performance, providing a basis for prioritizing the identification of weaknesses.

[0026] S13. Combining the difference between each game match data indicator and the mean of the corresponding indicator, as well as the correlation between each game match data indicator and the game match result, the game virtual object is evaluated using the preset number of game match data indicators with the highest correlation as the dimension, and game match evaluation data is obtained.

[0027] In this embodiment, clustering is performed based on the data indicators of virtual objects, using a preset number of game match data indicators with the highest correlation as the dimension. For example, virtual objects are divided into different categories such as a high skill hit rate group and a low resource utilization efficiency group. Specifically, during the clustering process, the results of mean comparison algorithms and regression analysis algorithms can be considered comprehensively. For example, virtual objects whose skill hit rate is significantly lower than the average level in mean comparison and whose skill hit rate has a significant impact on game results in regression analysis can be more finely clustered into a specific skill weakness group.

[0028] Therefore, in this embodiment, the relative lagging indicators found by the mean comparison algorithm, the key influencing indicators determined by the regression analysis algorithm, and the virtual character category features obtained by cluster analysis can be combined to evaluate the virtual characters.

[0029] S2. Compare and analyze the game match evaluation data with the game match evaluation data of other virtual objects of the same type to determine the weaknesses of the virtual object.

[0030] S21. The game match evaluation data is compared and analyzed with the average game match evaluation data and excellent game match evaluation data of other virtual objects of the same type. The average difference value and excellent difference value of the game match evaluation data are obtained, and the weaknesses of the virtual object of the game are determined.

[0031] In this embodiment, the game performance evaluation data of the virtual game objects is compared and analyzed with the average level of virtual game objects of the same type and the game performance evaluation data of excellent virtual game objects.

[0032] Specifically, for each data metric, the differences between the game's virtual object and the average level of similar game virtual objects, as well as the differences between outstanding game virtual objects, are calculated. For example, the difference between the game virtual object's skill hit rate and the average skill hit rate of similar game virtual objects, as well as the gap with the skill hit rate of outstanding game virtual objects, are calculated using a mean comparison algorithm. Based on the results of the comparative analysis, aspects where the game virtual object has significant gaps are identified and determined as weaknesses. For example, if the game virtual object's skill hit rate is significantly lower than the average level of similar game virtual objects and outstanding game virtual objects, then a low skill hit rate is a weakness.

[0033] In some embodiments, regression analysis results can be used to categorize weaknesses based on their impact on the performance of virtual game objects, such as skill-related, decision-making-related, and resource management-related weaknesses. These weaknesses are then prioritized, focusing on those with a greater impact on the game outcome, allowing players to concentrate on solving key problems and gradually improve their game skills.

[0034] S22. Obtain the game skill data corresponding to the weakness of the game virtual object, and analyze the reasons for the weakness of the game virtual object in different game scenarios, release timing and skill effects of the skill target based on the game skill data.

[0035] Specifically, for each weakness, an in-depth analysis is conducted on its usage scenarios, timing of release, target type, and other related factors. For example, for a weakness like low skill hit rate, the applicability of the skill in different game scenarios, the optimal timing of release, and its effectiveness against different target types are analyzed. Therefore, it not only considers the skill itself but also comprehensively takes into account factors such as usage scenarios, timing of release, target type, player operating habits, and the game environment, enabling a more comprehensive analysis of the causes of weaknesses.

[0036] The scenario adaptability analysis can be divided into several parts: 1) Analyzing the applicability of skills in a given game scenario, identifying special elements such as terrain, obstacles, and enemy distribution, and assessing their impact on skill effectiveness. 2) Analyzing the timing of skill release, examining the differences in effectiveness at different release times, such as whether an opening skill can effectively control enemies, to determine the optimal release time. 3) Analyzing the target's characteristics, examining the skill's impact on that target, identifying the target's weaknesses such as defensive vulnerabilities and attack blind spots, and assessing whether the skill can effectively target these weaknesses.

[0037] Weaknesses are analyzed using specialized analytical models. For example, for skill-related weaknesses, a skill operation model can be applied, which considers the mechanical principles of skills, the release process, and the player's operational feedback mechanisms. For decision-making weaknesses, decision tree models or game theory models can be used to analyze the player's decision-making logic and optimal strategies in different game situations. The collected and analyzed data is input into the specialized model, and the model's calculations and reasoning identify the specific reasons for the weaknesses. For example, a skill operation model might conclude that the low hit rate of virtual object skills is due to the player's unfamiliarity with the pre-release actions, leading to inaccurate timing of release; a decision tree model might show that the player chose a suboptimal strategy at certain key decision points because of inaccurate judgment of the game situation.

[0038] Therefore, the weakness identification method in this embodiment covers multiple dimensions of data indicators, including not only traditional data such as damage output, but also more specific indicators such as skill hit rate, skill release timing, positioning distance, decision accuracy, and resource utilization efficiency. Through multi-dimensional data comparison, the weaknesses of virtual game objects can be discovered more comprehensively and accurately. Furthermore, this embodiment, in its comparative analysis, not only compares with the average level of similar virtual game objects but also considers the performance of outstanding virtual game objects. This allows for a clearer definition of the weaknesses of virtual game objects, as the performance of outstanding virtual game objects serves as a higher standard and reference, helping players understand the gap between themselves and top-level players, thus enabling more targeted improvements. After identifying weaknesses, a deeper analysis of the causes is conducted, including discussions of relevant factors such as usage scenarios, release timing, and target type. Professional analysis models are used to identify specific reasons, providing accurate weakness diagnosis and effective improvement suggestions. This helps players gradually improve their game skills, thereby enhancing their gaming experience and increasing player satisfaction.

[0039] S3. Collect the emotional data of the player client corresponding to the game virtual object, and combine the emotional data with the weaknesses of the game virtual object to generate operation suggestions.

[0040] S31. Determine operational recommendations based on the weaknesses and their causes.

[0041] Specifically, the system automatically generates personalized operation suggestions and training scenario recommendations based on the player's weaknesses. If a player has a low hit rate for a certain skill, a dedicated skill training level is recommended. This level sets specific objectives and environments to help the player improve the hit rate of that skill. Alternatively, detailed guidance and prompts are provided within the training scenarios. For example, in skill training levels, when a player releases a skill, a visual display of the skill trajectory is shown to help the player adjust the aiming angle; in movement training scenarios, warnings of danger zones are displayed to help players better predict attacks.

[0042] Training scenarios of varying difficulty levels can be designed to meet the needs of players of different skill levels. Players can choose the training scenario that suits their own situation.

[0043] During player training, performance is monitored in real time and provided with timely feedback. Feedback includes the gap between current performance and goals, and directions for improvement. A progress tracking system is established to record in detail the player's improvements in each weakness, allowing players to review their progress at any time.

[0044] S32. Collect the emotional data of the player client corresponding to the game virtual object. The emotional data includes the player's voice characteristics and operation behavior data.

[0045] Specifically, during gameplay, player emotional data is collected through various methods. For example, the game's built-in voice chat function can be used to analyze characteristics such as tone, speed, and volume of the player's voice to infer their emotional state. Simultaneously, player actions in the game, such as key pressure and frequency, can also indirectly reflect their emotions. Additionally, simple emotion feedback buttons can be added to the game interface, allowing players to actively express their current emotions, such as happiness, frustration, or anger.

[0046] S33. Establish a correlation model between the emotion data and the weakness, adjust the operation suggestions based on the correlation between the emotion data and the weakness, and determine the priority of the operation suggestions.

[0047] In this embodiment, a correlation model between emotional fluctuations and weaknesses is established. When a player frequently makes mistakes in a certain skill, their emotional changes are observed. For example, if a player's tone of voice becomes hurried and their actions become flustered after repeatedly failing to unleash a specific skill, and they click the frustration feedback button, that skill will be marked as a potential weakness, and the associated emotional characteristics will be recorded. Through analysis of a large amount of player data, general patterns between emotional fluctuations and different weaknesses are identified. For instance, it was found that when players make mistakes in decision-making skills, they often exhibit hesitant emotional characteristics, such as prolonged pauses and low voice volume.

[0048] The presentation and priority of operational suggestions are adjusted based on the player's emotional state and weaknesses. If a player's mood is significantly low and related to a specific weakness, assistance will be provided more prioritized for that weakness. The operational suggestions not only offer technical guidance but also include encouraging words. For example, for skills with low accuracy, in addition to suggestions on aiming angles and timing, encouraging statements such as "Don't be discouraged, you're very close to success! Try a few more times, and you'll definitely improve your accuracy!" are displayed. Simultaneously, the difficulty of training scenarios is adjusted based on the player's emotional state. If a player is depressed, the difficulty of training scenarios can be appropriately reduced, allowing the player to build confidence in an environment where success is easier and gradually improve their skills.

[0049] Player emotional feedback can also be used to optimize the system. If players generally express negative emotions, such as anger or frustration, regarding operational suggestions and training scenarios for a particular weakness, it indicates that the current solution may be problematic. The system can then reassess and adjust its analysis methods and operational suggestions for that weakness based on this emotional feedback, thereby improving system effectiveness and player satisfaction.

[0050] In some embodiments, the game difficulty and challenge can be dynamically adjusted continuously based on the player's performance and weaknesses. If a player excels in a certain area, the game difficulty is appropriately increased to provide a greater challenge and encourage continuous improvement. If a player shows significant improvement in a certain weakness, the difficulty in that area is reduced to give the player a greater sense of accomplishment and enhance their motivation to continue improving.

[0051] The method for judging outstanding performance is as follows: For each data metric (such as damage output, skill hit rate, decision accuracy, etc.), an excellent threshold is set based on the game's characteristics and statistical analysis of a large amount of player data. This threshold can be a fixed value or a value dynamically adjusted based on the data distribution of similar players. For example, for skill hit rate, if the average skill hit rate of similar players is 60%, a skill hit rate of 80% or higher can be set as excellent performance. After a player completes a game, various data metrics are collected and analyzed. By comparing them with the set excellent threshold, if a player reaches or exceeds the threshold in one or more data metrics, the player is judged to have performed excellently in that aspect.

[0052] The method for judging significant progress is: A progress model is built for each player's weakness. This model, based on time series analysis, records the changes in the player's data indicators for that weakness over time, enabling dynamic analysis of the player's long-term improvement trend. For example, for a weakness like low skill accuracy, the model records the player's skill accuracy data in each game. Trend analysis is then performed on the data in the progress model. Methods such as linear regression can be used to analyze the changing trends of the data indicators. If, within a certain time frame (e.g., the last 5 games), the data indicator shows a continuous upward trend, and the increase reaches a certain standard (e.g., an increase of more than 5% each time), then the player is considered to have made significant progress in that weakness.

[0053] This approach allows for more accurate assessment of player performance and significant improvement, enabling more precise adjustments to game difficulty to provide appropriate challenges and incentives. When players excel, greater challenges are introduced to encourage continued progress; conversely, when players show significant improvement in their weaknesses, the difficulty is reduced to enhance their motivation to continue improving, thereby enhancing the player's gaming experience and the efficiency of skill development.

[0054] Personalized judgment criteria and dynamic analysis methods can better adapt to the different skill levels and progress speeds of players. Whether you are a novice or an expert, you can get a more suitable gaming experience through dynamic adjustments, avoiding player loss due to excessively high or low difficulty.

[0055] Personalized game tasks and challenges are generated based on the player's weaknesses and game goals. These tasks and challenges are targeted, helping players improve their game skills in a targeted manner while completing them. As the player's game level continues to improve, training scenarios and operation suggestions are continuously updated and optimized to ensure that players always receive the most effective training and improvement.

[0056] Please refer to Figure 2 Embodiment two of the present invention is as follows: A terminal 1 for detecting weaknesses in virtual game objects includes a memory 2, a processor 3, and a computer program stored in the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, it implements the various steps of a method for detecting weaknesses in virtual game objects according to Embodiment 1.

[0057] In summary, this invention provides a method and terminal for detecting weaknesses in virtual game objects. It acquires game performance data indicators for virtual game objects, analyzes their performance evaluation data based on these indicators, compares the performance evaluation data with that of other virtual game objects of the same type to determine the weaknesses of the virtual game objects, and collects emotional data from the player's client corresponding to the virtual game object. This emotional data, combined with the weaknesses of the virtual game object, generates operational suggestions. This invention, by comprehensively considering relevant factors and applying professional models, can accurately pinpoint the specific reasons for the occurrence of weaknesses, avoiding vague judgments and incorrect attributions, allowing players to clearly understand their problems. Accurate cause analysis lays the foundation for providing effective improvement solutions. Players can train and adjust their game strategies in a targeted manner based on the specific reasons, thereby more effectively improving their game skills. When players can clearly understand the reasons for their weaknesses and improve their game skills through effective improvement measures, it will enhance their sense of accomplishment and satisfaction in the game, thereby improving their overall gaming experience.

[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 detecting weaknesses in virtual objects in games, characterized in that, Including the following steps: S1. Obtain game match data indicators of the game virtual object, and analyze the game match evaluation data of the game virtual object based on the game match data indicators; S2. Compare and analyze the game match evaluation data with the game match evaluation data of other virtual objects of the same type to determine the weaknesses of the virtual object; S3. Collect the emotional data of the player client corresponding to the game virtual object, and combine the emotional data with the weaknesses of the game virtual object to generate operation suggestions.

2. The method for detecting weaknesses in virtual game objects according to claim 1, characterized in that, Step S1 includes: Obtain game match data metrics for virtual game objects, and calculate the difference between each game match data metric and the corresponding metric mean. Establish regression models between all game match data indicators and game match results, and analyze the correlation between each game match data indicator and game match results; By combining the differences between each game match data indicator and the mean of the corresponding indicator, as well as the correlation between each game match data indicator and the game match result, the game virtual object is evaluated using a preset number of game match data indicators with the highest correlation as the dimension, thus obtaining game match evaluation data.

3. The method for detecting weaknesses in virtual game objects according to claim 1, characterized in that, Step S2 includes: The game match evaluation data is compared and analyzed with the average game match evaluation data and excellent game match evaluation data of other virtual objects of the same type. The average difference value and excellent difference value of the game match evaluation data are obtained, and the weaknesses of the virtual objects of the game are determined.

4. The method for detecting weaknesses in virtual game objects according to claim 3, characterized in that, Step S2 also includes: Obtain game skill data corresponding to the weaknesses of the game virtual object, and analyze the reasons for the weaknesses of the game virtual object by combining the game skill data with the skill effects of different game scenarios, release timing and skill targets.

5. The method for detecting weaknesses in virtual game objects according to claim 4, characterized in that, Step S3 includes: Based on the weaknesses and their causes, operational recommendations are determined; Collect emotional data of the player client corresponding to the virtual game object, including player voice characteristics and operation behavior data; Establish a correlation model between the emotional data and the weakness, adjust the operation suggestions based on the correlation between the emotional data and the weakness, and determine the priority of the operation suggestions.

6. A terminal for detecting weaknesses in virtual game objects, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it performs the following steps: S1. Obtain game match data indicators of the game virtual object, and analyze the game match evaluation data of the game virtual object based on the game match data indicators; S2. Compare and analyze the game match evaluation data with the game match evaluation data of other virtual objects of the same type to determine the weaknesses of the virtual object; S3. Collect the emotional data of the player client corresponding to the game virtual object, and combine the emotional data with the weaknesses of the game virtual object to generate operation suggestions.

7. A terminal for detecting weaknesses in virtual game objects according to claim 6, characterized in that, Step S1 includes: Obtain game match data metrics for virtual game objects, and calculate the difference between each game match data metric and the corresponding metric mean. Establish regression models between all game match data indicators and game match results, and analyze the correlation between each game match data indicator and game match results; By combining the differences between each game match data indicator and the mean of the corresponding indicator, as well as the correlation between each game match data indicator and the game match result, the game virtual object is evaluated using a preset number of game match data indicators with the highest correlation as the dimension, thus obtaining game match evaluation data.

8. A terminal for detecting weaknesses in virtual game objects according to claim 6, characterized in that, Step S2 includes: The game match evaluation data is compared and analyzed with the average game match evaluation data and excellent game match evaluation data of other virtual objects of the same type. The average difference value and excellent difference value of the game match evaluation data are obtained, and the weaknesses of the virtual objects of the game are determined.

9. A terminal for detecting weaknesses in virtual game objects according to claim 8, characterized in that, Step S2 also includes: Obtain game skill data corresponding to the weaknesses of the game virtual object, and analyze the reasons for the weaknesses of the game virtual object by combining the game skill data with the skill effects of different game scenarios, release timing and skill targets.

10. A terminal for detecting weaknesses in virtual game objects according to claim 9, characterized in that, Step S3 includes: Based on the weaknesses and their causes, operational recommendations are determined; Collect emotional data of the player client corresponding to the virtual game object, including player voice characteristics and operation behavior data; Establish a correlation model between the emotional data and the weakness, adjust the operation suggestions based on the correlation between the emotional data and the weakness, and determine the priority of the operation suggestions.

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