A competitive game performance evaluation method and terminal

Abstract

The application discloses a kind of competitor game performance evaluation method and terminal, obtains the performance influence factor combination corresponding to competitor game and the performance index conventional value corresponding to performance influence factor combination, and performance influence factor combination is composed of game type, hardware configuration information and user group characteristics;According to performance influence factor combination and performance index conventional value, set the performance index recommended value corresponding to performance influence factor combination;According to the hardware configuration information of influence factor combination, run competitor game, and obtain performance index test result;According to performance index test result and performance index recommended value, obtain the competitor performance evaluation result of competitor game.The application sets performance index recommended value to reflect the influence of performance influence factor combination on performance index when running competitor game, and comprehensively, objectively and finely evaluates the competitor performance of competitor game with performance index recommended value as reference, to provide accurate and valuable reference data for game product optimization.

Description

Technical Field

[0001] This invention relates to the field of game evaluation technology, and in particular to a method and terminal for evaluating the performance of competing games. Background Technology

[0002] In the PC gaming industry, competitor performance analysis is crucial for product optimization and strategic decision-making. Existing competitor performance analysis methods primarily focus on comparing several basic performance metrics, such as frame rate, screen resolution, and loading speed. The typical approach involves running competitor games on specific hardware using professional testing software, recording the data for these basic performance metrics, and then performing simple numerical comparisons and chart visualizations.

[0003] However, existing methods only consider a limited set of performance metrics, making competitor performance susceptible to the decisive influence of one or two fundamental performance indicators, thus failing to provide a comprehensive and objective performance evaluation. Furthermore, many games today exhibit similar performance characteristics. Once a popular game emerges, numerous similar games quickly follow. Focusing solely on comparing a few basic performance metrics fails to highlight the subtle differences between games, reducing the guiding role of competitor game performance evaluations in the future development of games. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and terminal for evaluating the performance of competing games, providing a comprehensive, objective and detailed evaluation of the performance of competing games, and providing accurate and valuable reference data for game product optimization.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for evaluating the performance of competing games includes the following steps: S1. Obtain the combination of performance influencing factors for competitor games and the traditional values ​​of performance indicators corresponding to the combination of performance influencing factors. The combination of performance influencing factors consists of game type, hardware configuration information and user group characteristics. S2. Set recommended values ​​for performance indicators corresponding to the combination of performance influencing factors based on the combination of performance influencing factors and the traditional values ​​of performance indicators; S3. Run the competitor's game based on the hardware configuration information of the combination of the influencing factors, and obtain the performance index test results; Based on the test results of the performance indicators and the recommended values ​​of the performance indicators, the competitive performance evaluation results of the competing games are obtained.

[0006] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows: A competitor game performance evaluation terminal includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor runs the computer program, it performs the following steps: S1. Obtain the combination of performance influencing factors for competitor games and the traditional values ​​of performance indicators corresponding to the combination of performance influencing factors. The combination of performance influencing factors consists of game type, hardware configuration information and user group characteristics. S2. Set recommended values ​​for performance indicators corresponding to the combination of performance influencing factors based on the combination of performance influencing factors and the traditional values ​​of performance indicators; S3. Run the competitor's game based on the hardware configuration information of the combination of the influencing factors, and obtain the performance index test results; Based on the test results of the performance indicators and the recommended values ​​of the performance indicators, the competitive performance evaluation results of the competing games are obtained.

[0007] The beneficial effects of this invention are as follows: It provides a method and terminal for evaluating the performance of competing games, introducing a combination of performance influencing factors composed of game type, hardware configuration information, and user group characteristics. Each competing game has a traditional value for the performance influencing factor combination. By setting recommended values ​​for performance indicators, the impact of the performance influencing factor combination on the performance indicators during the operation of the competing game is reflected. Then, with the recommended values ​​for performance indicators as a reference, the performance of competing games is comprehensively, objectively, and meticulously evaluated, providing accurate and valuable reference data for game product optimization. Attached Figure Description

[0008] Figure 1 This is a schematic diagram illustrating the steps of a competitive game performance evaluation method according to the present invention; Figure 2 This is a system block diagram of a competitor game performance evaluation terminal according to the present invention.

[0009] Label Explanation: 1. A competitor game performance evaluation terminal; 2. Memory; 3. Processor. 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 evaluating the performance of competing games includes the following steps: S1. Obtain the combination of performance influencing factors for competitor games and the traditional values ​​of performance indicators corresponding to the combination of performance influencing factors. The combination of performance influencing factors consists of game type, hardware configuration information and user group characteristics. S2. Set recommended values ​​for performance indicators corresponding to the combination of performance influencing factors based on the combination of performance influencing factors and the traditional values ​​of performance indicators; S3. Run the competitor's game based on the hardware configuration information of the combination of the influencing factors, and obtain the performance index test results; Based on the test results of the performance indicators and the recommended values ​​of the performance indicators, the competitive performance evaluation results of the competing games are obtained.

[0012] As can be seen from the above description, the beneficial effects of the present invention are as follows: It provides a method and terminal for evaluating the performance of competing games, introduces a combination of performance influencing factors composed of game type, hardware configuration information and user group characteristics, and each competing game has a traditional value for the performance index combination of performance influencing factors. By setting recommended values ​​for performance indexes, the impact of the performance influencing factor combination on the performance index during the operation of the competing game is reflected. Then, with the recommended values ​​for performance indexes as a reference, the performance of competing games is comprehensively, objectively and meticulously evaluated, providing accurate and valuable reference data for game product optimization.

[0013] Further, step S2 includes: S21. Select at least two sample games that have the same combination of performance influencing factors as the competing games, and obtain the performance index data generated by running each sample game on the hardware configuration information of the combination of performance influencing factors. S22. Based on all the performance index data and traditional values ​​of the performance indexes, calculate the recommended values ​​of the performance indexes corresponding to the combination of performance influencing factors.

[0014] As described above, when determining the recommended values ​​for performance indicators, a large number of sample games with the same combination of performance influencing factors as the competing games are selected. The performance indicator data generated by running each sample game on the hardware configuration information of the combination of performance influencing factors is used as case data. Then, reasonable recommended values ​​for performance indicators are analyzed and used to evaluate the performance of competing games, thereby improving the accuracy of the evaluation results.

[0015] Furthermore, step 21 also includes: If there is a missing hardware configuration information for the combination of performance influencing factors corresponding to a game to be selected, then the hardware configuration range is obtained based on the hardware configuration information and performance index data of other games. Based on the hardware configuration range, the hardware configuration information of the games to be selected is obtained, and the games to be selected are used as the sample games; The games to be selected, other games, and competing games are of the same type and share the same user group characteristics.

[0016] As can be seen from the above description, in cases of abnormal data collection, the games to be selected may lack suitable hardware configuration information. By comparing and analyzing the hardware configuration information and performance data of other games of the same type and with the same user group characteristics, a reasonable range of hardware configurations can be obtained, thereby determining the hardware configuration information of the games to be selected so that they can be used as sample data.

[0017] Furthermore, it also includes: S4. Based on the historical performance index test results, historical user reputation data, and historical market share of the competing games, analyze the relationship between the performance changes and market response of the competing games.

[0018] As described above, considering data from multiple dimensions, performance metrics reflect the game's technical performance and quality, such as smoothness, loading speed, and stability. User reviews reflect players' subjective feelings and evaluations, including gameplay, storyline, and social interaction. Market share directly demonstrates the game's competitiveness and popularity in the market. Analyzing these data together provides a more comprehensive understanding of the game's overall situation. Conclusions drawn from this comprehensive analysis can offer valuable decision support for game developers and operators. For example, when deciding whether to update the game, invest more resources in performance optimization, or adjust marketing strategies, the relationship between performance changes and market response can be referenced to make more informed decisions, improve resource utilization efficiency, and increase return on investment.

[0019] Furthermore, it also includes: S5. Create a competitor game performance prediction model based on time series analysis and market trend forecasting. Input the historical performance index test results of the competitor game into the competitor game performance prediction model to obtain the performance index development trend of the competitor game.

[0020] As described above, the ability to predict the performance trends of competitor games based on historical data allows game developers, operators, and other stakeholders to anticipate changes in competitor performance before they actually occur. For example, if a competitor's game is predicted to significantly improve its graphics rendering performance, the developer can plan accordingly, such as increasing investment in graphics optimization to avoid being at a disadvantage in market competition; this also provides direction for game development and improvement. Based on the performance trends of competitor games, companies can clarify the key areas for improvement and innovation in their own games. For instance, if a competitor's game shows a trend towards virtual reality (VR) or augmented reality (AR) technology applications, the company can proactively build up its technological reserves and conduct research in this area to enhance the competitiveness of its own game.

[0021] Please refer to Figure 2 A competitor game performance evaluation terminal 1 includes a memory 2, a processor 3, and a computer program stored on the memory 2 and executable on the processor 3. When the processor 3 runs the computer program, it performs the following steps: S1. Obtain the combination of performance influencing factors for competitor games and the traditional values ​​of performance indicators corresponding to the combination of performance influencing factors. The combination of performance influencing factors consists of game type, hardware configuration information and user group characteristics. S2. Set recommended values ​​for performance indicators corresponding to the combination of performance influencing factors based on the combination of performance influencing factors and the traditional values ​​of performance indicators; S3. Run the competitor's game based on the hardware configuration information of the combination of the influencing factors, and obtain the performance index test results; Based on the test results of the performance indicators and the recommended values ​​of the performance indicators, the competitive performance evaluation results of the competing games are obtained.

[0022] As can be seen from the above description, the beneficial effects of the present invention are as follows: It provides a method and terminal for evaluating the performance of competing games, introduces a combination of performance influencing factors composed of game type, hardware configuration information and user group characteristics, and each competing game has a traditional value for the performance index combination of performance influencing factors. By setting recommended values ​​for performance indexes, the impact of the performance influencing factor combination on the performance index during the operation of the competing game is reflected. Then, with the recommended values ​​for performance indexes as a reference, the performance of competing games is comprehensively, objectively and meticulously evaluated, providing accurate and valuable reference data for game product optimization.

[0023] Further, step S2 includes: S21. Select at least two sample games that have the same combination of performance influencing factors as the competing games, and obtain the performance index data generated by running each sample game on the hardware configuration information of the combination of performance influencing factors. S22. Based on all the performance index data and traditional values ​​of the performance indexes, calculate the recommended values ​​of the performance indexes corresponding to the combination of performance influencing factors.

[0024] As described above, when determining the recommended values ​​for performance indicators, a large number of sample games with the same combination of performance influencing factors as the competing games are selected. The performance indicator data generated by running each sample game on the hardware configuration information of the combination of performance influencing factors is used as case data. Then, reasonable recommended values ​​for performance indicators are analyzed and used to evaluate the performance of competing games, thereby improving the accuracy of the evaluation results.

[0025] Furthermore, step 21 also includes: If there is a missing hardware configuration information for the combination of performance influencing factors corresponding to a game to be selected, then the hardware configuration range is obtained based on the hardware configuration information and performance index data of other games. Based on the hardware configuration range, the hardware configuration information of the games to be selected is obtained, and the games to be selected are used as the sample games; The games to be selected, other games, and competing games are of the same type and share the same user group characteristics.

[0026] As can be seen from the above description, in cases of abnormal data collection, the games to be selected may lack suitable hardware configuration information. By comparing and analyzing the hardware configuration information and performance data of other games of the same type and with the same user group characteristics, a reasonable range of hardware configurations can be obtained, thereby determining the hardware configuration information of the games to be selected so that they can be used as sample data.

[0027] Furthermore, it also includes: S4. Based on the historical performance index test results, historical user reputation data, and historical market share of the competing games, analyze the relationship between the performance changes and market response of the competing games.

[0028] As described above, considering data from multiple dimensions, performance metrics reflect the game's technical performance and quality, such as smoothness, loading speed, and stability. User reviews reflect players' subjective feelings and evaluations, including gameplay, storyline, and social interaction. Market share directly demonstrates the game's competitiveness and popularity in the market. Analyzing these data together provides a more comprehensive understanding of the game's overall situation. Conclusions drawn from this comprehensive analysis can offer valuable decision support for game developers and operators. For example, when deciding whether to update the game, invest more resources in performance optimization, or adjust marketing strategies, the relationship between performance changes and market response can be referenced to make more informed decisions, improve resource utilization efficiency, and increase return on investment.

[0029] Furthermore, it also includes: S5. Create a competitor game performance prediction model based on time series analysis and market trend forecasting. Input the historical performance index test results of the competitor game into the competitor game performance prediction model to obtain the performance index development trend of the competitor game.

[0030] As described above, the ability to predict the performance trends of competitor games based on historical data allows game developers, operators, and other stakeholders to anticipate changes in competitor performance before they actually occur. For example, if a competitor's game is predicted to significantly improve its graphics rendering performance, the developer can plan accordingly, such as increasing investment in graphics optimization to avoid being at a disadvantage in market competition; this also provides direction for game development and improvement. Based on the performance trends of competitor games, companies can clarify the key areas for improvement and innovation in their own games. For instance, if a competitor's game shows a trend towards virtual reality (VR) or augmented reality (AR) technology applications, the company can proactively build up its technological reserves and conduct research in this area to enhance the competitiveness of its own game.

[0031] Please refer to Figure 1 Embodiment 1 of the present invention is as follows: A method for evaluating the performance of competing games includes the following steps: S1. Obtain the performance influencing factor combination and the traditional performance index value corresponding to the performance influencing factor combination for the competitor's game. The performance influencing factor combination consists of game type, hardware configuration information and user group characteristics. In this embodiment, the game type in the performance influencing factors combination includes different categories such as role-playing, shooting, strategy, and e-sports shooting games. User group characteristics include information constructed through user profiling technology, such as age, gender, game duration, and consumption habits. Hardware configuration includes CPU model, GPU model, memory size, hard drive type, and read / write speed. Traditional performance indicators specifically include conventional values ​​such as frame rate, resolution, image quality parameters, and network latency. In addition, the performance influencing factors combination may also include other factors such as the duration of a single game session.

[0032] This study delves into the combined impact of multiple factors, including game genre, user demographics, and hardware configuration, on the performance of PC games. For example, for massively multiplayer online role-playing games (MMORPGs), the focus is on the performance pressure exerted by large-scale scene rendering and multiplayer interaction; for esports shooting games, the emphasis is on analyzing high frame rates, low latency, and precise mouse and keyboard response performance. Different user groups may prioritize extreme graphics quality and extremely low latency for hardcore esports players, while casual players may prioritize smooth gameplay on mid-to-low-end hardware.

[0033] Furthermore, it should be noted that competing games may have more than one combination of performance influencing factors. Different combinations of performance influencing factors will differ due to differences in the specific value or content of a particular factor. For example, for shooting games with the same hardware configuration, there are two different combinations of performance influencing factors depending on whether the user group is teenagers or middle-aged adults. The traditional values ​​of performance indicators for shooting games may differ under these two different combinations of performance influencing factors, and the impact of different user group characteristics on the traditional values ​​of performance indicators will also be different.

[0034] S2. Set recommended values ​​for performance indicators corresponding to the combination of performance influencing factors based on the combination of performance influencing factors and the traditional values ​​of performance indicators; In this embodiment, at least two sample games with the same combination of performance influencing factors as competing games are selected, and the performance index data generated by running each sample game on the hardware configuration information of the combination of performance influencing factors is obtained. Based on all performance index data and traditional values, calculate the recommended performance index values ​​corresponding to the combination of performance influencing factors.

[0035] In this embodiment, in addition to constructing a method for calculating recommended performance metrics for different games, the process is as follows: First, a large amount of historical data was collected, covering the performance of different games across various user groups and hardware configurations. Then, association rule mining algorithms (such as Apriori or FP-Growth) were used to uncover frequent itemsets and association rules between different combinations of performance-influencing factors and different types of performance metrics. For example, a strong correlation was found between "MMORPG game type, high-end hardware configuration, and hardcore esports player group" and the performance metric "high frame rate, low latency". Next, regression algorithms (such as linear regression and decision tree regression) were used in machine learning to train a model based on these association rules, using different combinations of performance-influencing factors and traditional performance metric values ​​as input features, and recommended performance metric values ​​as output targets.

[0036] During training, model parameters are continuously adjusted to ensure the model accurately predicts performance metrics under different combinations of factors. Finally, based on the model's training results, the contribution of each factor to the performance metrics under different combinations is analyzed to determine its overall influence weight. For example, the trained model might determine that under a specific combination, game type has a weight of 0.3 for frame rate, hardware configuration has a weight of 0.4, user group characteristics have a weight of 0.2, and other factors have a weight of 0.1. In this way, a highly accurate and comprehensive performance evaluation model is built, enabling more accurate assessment of the performance of PC games. When collecting data, web crawling technology is used, with dedicated crawling tasks set up for social media platforms (such as Weibo and Twitter), game review websites, user review platforms (such as Steam reviews and App Store reviews), and industry databases. Each crawling task is customized to crawl information related to competing PC games based on the platform's structure and data acquisition rules, including but not limited to game name, version number, release date, user reviews, ratings, hardware configuration requirements, and game genre tags. Furthermore, regarding hardware configuration information, we collaborate with hardware detection software developers to obtain detailed information about users' hardware devices when running games through application programming interfaces (APIs), such as CPU model, GPU model, memory size, hard drive type and read / write speed, as well as corresponding game performance data, such as frame rate, loading time, and real-time monitoring data of CPU and GPU utilization.

[0037] Simultaneously, the collected multi-source heterogeneous data is imported into the data processing engine. Data format conversion tools are used to uniformly convert data of different formats (such as JSON, XML, CSV, etc.) into structured data formats suitable for subsequent processing, such as DataFrame structures. Data cleaning algorithms are used to identify and handle missing values, outliers, and duplicate data, for example: If there is a missing hardware configuration information for the combination of performance influencing factors for a game to be selected, the hardware configuration range will be obtained based on the hardware configuration information and performance data of other games. Based on the hardware configuration range, the hardware configuration information of the games to be selected is obtained, and the games to be selected are used as sample games; The games to be selected, other games, and competing games are of the same type and share the same user group characteristics.

[0038] Then, feature vectors related to game performance are extracted from the cleaned data. Game type, user group characteristics (such as age, gender, game duration, and consumption habits constructed using user profiling techniques), hardware configuration characteristics, and traditional performance metrics (such as frame rate, resolution, image quality parameters, and network latency) are encoded and quantified. Dimensionality reduction techniques such as Principal Component Analysis (PCA) are used to reduce the dimensionality of high-dimensional feature vectors, reducing data processing complexity and improving model training efficiency while retaining key information. Simultaneously, new composite features are created through feature cross-combination techniques to capture the potential interactive effects between different features. For example, game type can be cross-combined with performance metrics under specific hardware configurations to form composite features such as "large-scale 3D games - high-end GPU frame rate performance".

[0039] S3. Run competitor games based on hardware configuration information that combines performance influencing factors, and obtain performance indicator test results; Based on the performance indicator test results and recommended performance indicator values, the competitive performance evaluation results of competing games are obtained.

[0040] In this embodiment, since there may be more than one combination of performance influencing factors for the competing game, different performance index test results will be obtained in different tests. When generating the performance evaluation results of the competing game, the performance evaluation score for each combination of performance influencing factors is calculated, and then the comprehensive score is calculated.

[0041] During computation, a multi-dimensional performance evaluation model is constructed using deep learning frameworks (such as TensorFlow or PyTorch). A multi-layer neural network architecture is employed, where the input layer receives feature vectors after feature engineering, the hidden layers learn complex non-linear relationships between features by setting different numbers of neurons and activation functions (such as ReLU, Sigmoid, etc.), and the output layer generates a comprehensive performance score for each competing game. A large number of labeled game data samples (i.e., those with known comprehensive performance evaluation results) are divided into training, validation, and test sets. The model is trained using the training set, employing backpropagation and an optimizer (such as the Adam optimizer) to continuously adjust the model's weights and biases to minimize the loss function (such as the mean squared error loss function). The training effect of the model is monitored by performance evaluation metrics (such as accuracy, recall, F1 score, etc.) on the validation set to prevent overfitting. Once the model's performance on the validation set no longer improves, training is stopped, and the final model is evaluated using the test set to ensure the model's generalization ability and accuracy.

[0042] S4. Based on the historical performance index test results, historical user reputation data, and historical market share of competitor games, analyze the relationship between the performance changes of competitor games and market response.

[0043] In this embodiment, big data analytics algorithms are used to deeply mine and correlate the integrated data, uncovering hidden relationships and potential trends among competitors in terms of performance, user reputation, market share, and technological innovation. For example, the causal relationship between a competitor's increased market share in a specific region and recent performance optimizations can be analyzed, or the impact path of a new technology applied to a competitor on user reputation can be determined. Based on the big data analysis results, artificial intelligence algorithms (such as reinforcement learning algorithms) are used to provide intelligent decision support for enterprises. For example, based on the performance advantages of competitors and the enterprise's own resources, the most suitable product feature improvement directions and priorities can be recommended; based on the market competition situation and competitor performance trends, the optimal marketing strategy can be formulated, such as determining the best advertising channels and promotional timing, and selecting the most promising partners (such as hardware manufacturers jointly optimizing performance, game content providers enriching the game ecosystem, etc.), comprehensively improving the scientific nature and competitiveness of the enterprise's strategic decision-making in the PC game market.

[0044] S5. Create a competitor game performance prediction model based on time series analysis and market trend forecasting. Input the historical performance index test results of competitor games into the competitor game performance prediction model to obtain the performance index development trend of competitor games.

[0045] In this embodiment, a data sequence of historical performance indicator test results for competitor games is extracted from the database, including performance indicator data corresponding to different version release times. The time series data is tested for stationarity, such as using the unit root test (ADF test). If the data is not stationary, techniques such as differencing or seasonal decomposition are used to transform it into a stationary sequence. An Autoregressive Moving Average (ARIMA) model or a Seasonal ARIMA (SARIMA) model is constructed, and the model order (p, d, q) and seasonal parameters (P, D, Q, s) are determined based on the autocorrelation function (ACF) and partial autocorrelation function (PACF) of the time series data. The model parameters are estimated using methods such as maximum likelihood estimation, and the model is tested for residuals to ensure its effectiveness and rationality. The trained ARIMA or SARIMA model is used to predict the performance indicators of future versions of competitor games, obtaining performance trend prediction results based on the time series. Meanwhile, by combining market trend data (such as market share change trends, user demand survey reports, etc.) and technology development trend data (such as hardware technology development roadmaps, application predictions of emerging game technologies, etc.), multi-factor fusion algorithms (such as weighted fusion or Bayesian network-based fusion methods) are used to integrate trend information from different sources to obtain more comprehensive and accurate prediction results of competitor performance development trends.

[0046] Please refer to Figure 2 Embodiment two of the present invention is as follows: A competitor game performance evaluation terminal 1 includes a memory 2, a processor 3, and a computer program stored on the memory 2 and executable on the processor 3. When the processor 3 runs the computer program, it implements a competitor game performance evaluation method according to Embodiment 1.

[0047] In summary, this invention provides a method and terminal for evaluating the performance of competing games. It introduces a combination of performance influencing factors, comprised of game type, hardware configuration information, and user group characteristics. Each competing game's corresponding performance influencing factor combination has a traditional performance index value. By setting recommended performance index values, the impact of the performance influencing factor combination on the performance index during the competing game's operation is reflected. Then, using the recommended performance index value as a reference, combined with performance index test results, the competing game's performance is evaluated, achieving a comprehensive, objective, and detailed competitive performance assessment. This provides accurate and valuable reference data for game product optimization. Simultaneously, it comprehensively considers data from multiple dimensions. Performance index test results reflect the game's technical performance and quality, such as screen smoothness, loading speed, and stability; user reputation data reflects players' subjective feelings and evaluations of the game, including gameplay, storyline, and social interaction; and market share directly demonstrates the game's competitiveness and popularity in the market. Combining and analyzing these data allows for a more comprehensive understanding of the game's overall situation. The conclusions drawn from this comprehensive analysis can provide valuable decision support for game developers and operators. For example, when deciding whether to update a game, invest more resources in performance optimization, or adjust marketing strategies, the relationship between performance changes and market response can be referenced to make more informed decisions, improve resource utilization efficiency and return on investment, and predict the performance trend of competing games in advance based on historical data, so that game developers, operators and other stakeholders can be aware of the actual performance changes of competitors.

[0048] 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 evaluating the performance of competing games, characterized in that, The steps include the following: S1. Obtain the combination of performance influencing factors for competitor games and the traditional values ​​of performance indicators corresponding to the combination of performance influencing factors. The combination of performance influencing factors consists of game type, hardware configuration information and user group characteristics. S2. Set recommended values ​​for performance indicators corresponding to the combination of performance influencing factors based on the combination of performance influencing factors and the traditional values ​​of performance indicators; S3. Run the competitor's game based on the hardware configuration information of the combination of the influencing factors, and obtain the performance index test results; Based on the test results of the performance indicators and the recommended values ​​of the performance indicators, the competitive performance evaluation results of the competing games are obtained.

2. The method for evaluating the performance of competing games according to claim 1, characterized in that, Step S2 includes: S21. Select at least two sample games that have the same combination of performance influencing factors as the competing games, and obtain the performance index data generated by running each sample game on the hardware configuration information of the combination of performance influencing factors. S22. Based on all the performance index data and traditional values ​​of the performance indexes, calculate the recommended values ​​of the performance indexes corresponding to the combination of performance influencing factors.

3. The method for evaluating the performance of competing games according to claim 2, characterized in that, Step 21 further includes: If there is a missing hardware configuration information for the combination of performance influencing factors corresponding to a game to be selected, then the hardware configuration range is obtained based on the hardware configuration information and performance index data of other games. Based on the hardware configuration range, the hardware configuration information of the games to be selected is obtained, and the games to be selected are used as the sample games; The games to be selected, other games, and competing games are of the same type and share the same user group characteristics.

4. The method for evaluating the performance of competing games according to claim 2, characterized in that, Also includes: S4. Based on the historical performance index test results, historical user reputation data, and historical market share of the competing games, analyze the relationship between the performance changes and market response of the competing games.

5. The method for evaluating the performance of competing games according to claim 1, characterized in that, Also includes: S5. Create a competitor game performance prediction model based on time series analysis and market trend forecasting. Input the historical performance index test results of the competitor game into the competitor game performance prediction model to obtain the performance index development trend of the competitor game.

6. A competitor game performance evaluation terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor runs the computer program, it performs the following steps: S1. Obtain the combination of performance influencing factors for competitor games and the traditional values ​​of performance indicators corresponding to the combination of performance influencing factors. The combination of performance influencing factors consists of game type, hardware configuration information and user group characteristics. S2. Set recommended values ​​for performance indicators corresponding to the combination of performance influencing factors based on the combination of performance influencing factors and the traditional values ​​of performance indicators; S3. Run the competitor's game based on the hardware configuration information of the combination of the influencing factors, and obtain the performance index test results; Based on the test results of the performance indicators and the recommended values ​​of the performance indicators, the competitive performance evaluation results of the competing games are obtained.

7. A competitor game performance evaluation terminal according to claim 6, characterized in that, Step S2 includes: S21. Select at least two sample games that have the same combination of performance influencing factors as the competing games, and obtain the performance index data generated by running each sample game on the hardware configuration information of the combination of performance influencing factors. S22. Based on all the performance index data and traditional values ​​of the performance indexes, calculate the recommended values ​​of the performance indexes corresponding to the combination of performance influencing factors.

8. A competitor game performance evaluation terminal according to claim 7, characterized in that, Step 21 further includes: If there is a missing hardware configuration information for the combination of performance influencing factors corresponding to a game to be selected, then the hardware configuration range is obtained based on the hardware configuration information and performance index data of other games. Based on the hardware configuration range, the hardware configuration information of the games to be selected is obtained, and the games to be selected are used as the sample games; The games to be selected, other games, and competing games are of the same type and share the same user group characteristics.

9. A competitor game performance evaluation terminal according to claim 7, characterized in that, Also includes: S4. Based on the historical performance index test results, historical user reputation data, and historical market share of the competing games, analyze the relationship between the performance changes and market response of the competing games.

10. A competitor game performance evaluation terminal according to claim 6, characterized in that, Also includes: S5. Create a competitor game performance prediction model based on time series analysis and market trend forecasting. Input the historical performance index test results of the competitor game into the competitor game performance prediction model to obtain the performance index development trend of the competitor game.

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