Processing method, system and equipment for game online process and medium

Abstract

The invention discloses a processing method, system and device for a game online process and a medium, and the method specifically comprises the steps: obtaining basic information of a game, detecting whether the basic information of the game accords with a specification or not according to a preset game configuration standard rule, and executing domain name configuration through the basic information of the game; according to the basic game information and a preset game resource library, adopting a game resource generation algorithm to generate an initial game resource file, and optimizing the initial game resource file through an image processing technology to obtain a target game resource file; generating an nginx configuration file according to the target game resource file based on a preset game configuration template; and packaging the target game resource file and the nginx configuration file, and automatically deploying the packaged target game resource file and nginx configuration file to a target game server through a continuous integration tool. Automatic and intelligent management of the game online process is achieved, the game online speed and efficiency are improved, and consistency of game configuration and effective utilization of resources are guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of Internet technologies, and particularly to a processing method, system, device and medium for a game launch process. Background Art

[0002] In the modern game industry, the launch of a new game is a complex and crucial process, which involves multiple technical aspects such as domain name resolution, code configuration, resource preparation, etc. However, many current game companies (such as Tencent, NetEase, etc.) still highly rely on manual operations when dealing with these processes. Although this method provides a certain degree of flexibility to some extent, it brings many problems and challenges.

[0003] First of all, manually processing steps such as domain name resolution, nginx configuration and front-end code modification takes a long time and is error-prone. Especially when multiple games need to be launched simultaneously, the workload is huge, seriously affecting the speed and efficiency of game launch. In the traditional manual processing method, technical personnel need to manually modify, which not only takes a long time, but is also easy to make mistakes. Once the configuration is incorrect, it may cause the game to be unable to be accessed normally, seriously affecting the user experience.

[0004] Secondly, the update of domain name information in the front-end code is also an important link. During the game launch process, the domain name information in the front-end code needs to be consistent with the back-end server to ensure that the game can be correctly loaded and run. However, in the existing manual operation process, the update of domain name information in the front-end code depends on manual operation, with low efficiency and is prone to errors due to human negligence.

[0005] In addition, the production of ICON (icon) and the update of copyright information for new games also face the problem of insufficient automation. ICON and copyright information are important components of the game. They not only affect the brand image of the game, but also involve legal issues such as copyright protection. However, in the traditional processing method, the production and update of this information lack a systematic and automated process, and often rely on the manual operation of designers, which is not only inefficient, but also prone to information inconsistency or omission.

[0006] More importantly, the entire game launch process lacks a unified management and monitoring mechanism. Due to the lack of a unified management platform, there may be differences in the configurations between different games, making it difficult to ensure that all games follow unified best practices. At the same time, due to the lack of effective monitoring means, once a problem occurs, it is often difficult to quickly locate and solve, bringing great risks to the stable operation of the game. Summary of the Invention

[0007] The purpose of the present invention is to provide a processing method, system, device and medium for the game online process, which realizes the automated and intelligent management of the game online process, improves the speed and efficiency of game online, ensures the consistency of game configuration and the effective utilization of resources, and at the same time enhances the traceability and scalability of the game online process, so as to solve at least one of the above-mentioned prior art problems.

[0008] In the first aspect, the present invention provides a processing method for the game online process, which specifically includes:

[0009] Obtain the basic game information, detect whether the basic game information meets the specifications according to the preset game configuration standard rules, and perform domain name configuration through the basic game information;

[0010] According to the basic game information and the preset game resource library, use the game resource generation algorithm to generate the initial game resource file, and perform optimization processing on the initial game resource file through image processing technology to obtain the target game resource file;

[0011] Based on the preset game configuration template, generate the nginx configuration file according to the target game resource file;

[0012] After packing the target game resource file and the nginx configuration file, automatically deploy them to the target game server through the continuous integration tool, and continuously load the latest target game resource file and nginx configuration file according to the continuous integration tool;

[0013] Use the Prometheus monitoring system to monitor each link in the process of the continuous integration tool loading the latest target game resource file and nginx configuration file in real time, obtain the monitoring data, and determine whether to perform version rollback and exception warning through the monitoring data.

[0014] In the second aspect, the present invention provides a processing system for the game online process, which specifically includes:

[0015] The first processing module is used to obtain the basic game information, detect whether the basic game information meets the specifications according to the preset game configuration standard rules, and perform domain name configuration through the basic game information;

[0016] The second processing module is used to generate the initial game resource file according to the basic game information and the preset game resource library, and perform optimization processing on the initial game resource file through image processing technology to obtain the target game resource file;

[0017] The third processing module is used to generate an nginx configuration file based on a preset game configuration template according to the target game resource file;

[0018] The fourth processing module is used to package the target game resource file and the nginx configuration file, and then automatically deploy them to the target game server through a continuous integration tool, and continuously load the latest target game resource file and nginx configuration file according to the continuous integration tool;

[0019] The fifth processing module is used to use the Prometheus monitoring system to monitor each link in the process of continuously loading the latest target game resource file and nginx configuration file by the continuous integration tool, obtain monitoring data, and determine whether to perform version rollback and exception warning through the monitoring data.

[0020] In a third aspect, the present invention provides a computer device, including: a memory, a processor, and a computer program stored on the memory. When the computer program is executed on the processor, it implements the processing method for the game online process described in any one of the above methods.

[0021] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it implements the processing method for the game online process described in any one of the above methods.

[0022] Compared with the prior art, the present invention has at least one of the following technical effects:

[0023] 1. The present invention realizes the automated and intelligent management of the game online process, improves the speed and efficiency of game online, ensures the consistency of game configuration and the effective utilization of resources, and at the same time enhances the traceability and scalability of the game online process.

[0024] 2. The present invention realizes the automation and intelligence of the game online process, improves the online efficiency, and reduces human errors. Through the real-time monitoring and version rollback mechanism, the stability and reliability of game online are ensured.

[0025] 3. The present invention can automatically generate an initial game resource file that meets the game style and setting requirements, improves the efficiency and accuracy of resource generation. The application of the GAN model further enriches the style and diversity of game icons and copyright images.

[0026] 4. The present invention ensures the compatibility and standardization of game resource files on different platforms, improves the quality of game resources and the user experience.

[0027] 5. The present invention can accurately predict the types and quantities of servers required for each release area, provide a reasonable resource allocation plan for game launch, reduce operating costs, and improve resource utilization.

[0028] 6. The present invention can dynamically adjust server resources according to the actual needs of game operation, ensure the stability and smoothness of the game, and improve the user experience.

[0029] 7. The present invention can optimize the organizational structure and association relationship of game resource files, reduce redundant resources, and improve the loading speed and operation efficiency of the game.

[0030] 8. The present invention can monitor the running state of the game in real time, discover and solve abnormal problems in a timely manner, improve the stability and reliability of the game. At the same time, through clustering analysis and health calculation, it provides strong data support for game optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0032] Figure 1 is a schematic flowchart of a processing method for game launch process provided by the first embodiment of the present invention;

[0033] Figure 2 is a schematic flowchart of a processing method for game launch process provided by the second embodiment of the present invention;

[0034] Figure 3 is a schematic flowchart of a processing method for game launch process provided by the third embodiment of the present invention;

[0035] Figure 4 is a schematic flowchart of a processing method for game launch process provided by the fourth embodiment of the present invention;

[0036] Figure 5 is a schematic flowchart of a processing method for game launch process provided by the fifth embodiment of the present invention;

[0037] Figure 6 is a schematic flowchart of a processing method for game launch process provided by the sixth embodiment of the present invention;

[0038] Figure 7 is a schematic flowchart of a processing method for game launch process provided by the seventh embodiment of the present invention;

[0039] Figure 8 It is a schematic structural diagram of a processing system for a game online process provided by an embodiment of the present invention;

[0040] Figure 9 It is a schematic structural diagram of a computer device provided by an embodiment of the present invention. Specific Embodiments

[0041] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are presented in order to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0042] It should be understood that when used in the specification and claims of the present application, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0043] It should also be understood that the term "and / or" used in the specification and claims of the present application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0044] As used in the specification and claims of the present application, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" depending on the context. Similarly, the phrase "if determined" or "if detected [the described condition or event]" can be interpreted as meaning "once determined", "in response to determining", "once detected [the described condition or event]", or "in response to detecting [the described condition or event]" depending on the context.

[0045] In addition, in the description of the specification and claims of the present application, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0046] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that specific features, structures, or characteristics described in connection with that embodiment are included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized.

[0047] In the embodiments of this application, the execution subject of the process includes a terminal device. The terminal device includes, but is not limited to: devices such as servers, computers, smart phones, and tablet computers that can execute the methods disclosed in this application. Figure 1 The flowchart showing the processing method for the game online process disclosed in the first embodiment of the present invention is shown below and is described in detail as follows:

[0048] S101, obtain the basic game information, detect whether the basic game information conforms to the specification according to the preset game configuration standard rules, and perform domain name configuration through the basic game information.

[0049] In this embodiment, obtain the basic information of the game, including the game name, type, developer, etc., and store this information in a structured data format, such as JSON or XML. According to the preset game configuration standard specification, detect the obtained basic game information item by item, judge whether each piece of information meets the corresponding standard requirements, and record the detection results. For the game information that does not meet the standard specification, generate a corresponding error report, point out the specific non-conforming items and reasons, and provide modification suggestions until all information conforms to the specification. Format the basic game information that conforms to the standard specification according to the predefined data structure to generate a standardized game information data set for subsequent storage and invocation. According to the standardized game information data set, automatically generate corresponding domain name configuration parameters, such as domain name prefix, suffix, DNS resolution, etc., to ensure that the domain name matches the game information. Apply the generated domain name configuration parameters to the DNS server, and realize the dynamic configuration and resolution of the game domain name by adding or updating DNS records to ensure the accessibility of the domain name. Establish a mapping relationship database between the game information and the domain name configuration, record the domain name parameters corresponding to each game, and set a regular synchronization mechanism to ensure that when the game information changes, the domain name configuration can be automatically updated.

[0050] S102. Generate an initial game resource file according to the basic game information and a preset game resource library, and perform optimization processing on the initial game resource file through an image processing technology to obtain a target game resource file.

[0051] In this embodiment, according to the basic game information and a preset game resource library, a game resource generation algorithm based on deep learning is used to automatically generate an initial game resource file that meets the requirements of the basic game information. For the generated initial game resource file, key elements in the game resource file are extracted through an image segmentation technology, and the extracted key elements are screened and optimized according to the requirements in the basic game information. The screened and optimized game resource elements are recombined through an image synthesis technology to generate a new game resource file, obtaining a preliminarily optimized game resource file. An image super-resolution technology based on a convolutional neural network is used to enhance details and improve the quality of the preliminarily optimized game resource file to generate a high-definition game resource file. According to the art style requirements in the basic game information, a style transfer algorithm is used to perform artistic processing on the high-definition game resource file to make it meet specific art style requirements. Through operations such as scaling and cropping on the processed game resource file, multi-size game resource files adapted to different device resolutions are generated. Finally, a series of generated target game resource files are integrated and packaged to form a complete game resource package, providing high-quality game resources for subsequent game development.

[0052] S103. Based on a preset game configuration template, generate an nginx configuration file according to the target game resource file.

[0053] In this embodiment, obtain a preset game configuration template to determine the basic structure and parameters of the configuration file. For the target game, obtain the corresponding game resource file and extract key information. According to the information in the game resource file, judge the parameters and instructions that need to be set in the nginx configuration file. Map information such as the path and type of the game resource file to the corresponding positions in the nginx configuration file. Through a template engine, fuse the game configuration template with the extracted game resource information to generate an initial nginx configuration file. Perform syntax checking and optimization on the generated nginx configuration file to ensure the correctness and efficiency of the configuration. Output the finally generated nginx configuration file for deployment to a game server to realize the access and loading of game resources.

[0054] S104. After packaging the target game resource file and the nginx configuration file, automatically deploy them to a target game server through a continuous integration tool, and continuously load the latest target game resource file and nginx configuration file according to the continuous integration tool.

[0055] In this embodiment, the target game resource file and the nginx configuration file are packaged to obtain a packaged target game resource file package and an nginx configuration file package. The packaged target game resource file package and the nginx configuration file package are obtained through a continuous integration tool and automatically deployed to a specified directory of the target game server. On the target game server, according to the configuration information of the deployed nginx configuration file, the storage path of the target game resource file package is determined. When the target game server receives a resource request from a game client, the request is forwarded to the storage path of the corresponding target game resource file package through the routing rules in the nginx configuration file to obtain the requested resource file. The continuous integration tool regularly obtains the latest target game resource file and nginx configuration file from the code repository, packages them to obtain the latest target game resource file package and nginx configuration file package. The continuous integration tool automatically deploys the latest target game resource file package and nginx configuration file package to the specified directory of the target game server, overwriting the old version of the file package. The target game server automatically loads the latest version of the target game resource file package according to the configuration information of the updated nginx configuration file, ensuring that the game client can obtain the latest game resource file.

[0056] S105. Use the Prometheus monitoring system to monitor each link in the process of the continuous integration tool loading the latest target game resource file and nginx configuration file in real time, obtain monitoring data, and determine whether to perform version rollback and exception warning through the monitoring data.

[0057] In this embodiment, the Prometheus monitoring system is used to monitor the running status of the continuous integration tool in real time, obtain performance metric data for each link, such as CPU occupancy, memory usage, network latency, etc., and store the data in a time series database. The AlertManager component of Prometheus is used to set warning rules, and it is judged whether an abnormal situation occurs according to the thresholds of the monitoring metrics, such as resource update failure, configuration file parsing error, etc. If the warning condition is triggered, an alarm notification is sent by means of email, text message, etc. Monitoring probes of Prometheus are embedded in the build pipeline of the continuous integration tool to collect the update status of game resource files and nginx configuration files in real time and report the data to the Prometheus server. The PromQL query language is used to perform aggregation analysis on the monitoring data. By comparing the differences between historical data and current data, it is judged whether the resource update meets the expectations. If an abnormal deviation occurs, a rollback operation is triggered. In combination with the Grafana visualization tool, the monitoring data collected by Prometheus is presented in the form of a dashboard, which is convenient for operation and maintenance personnel to view the various metrics in the continuous integration process in real time and discover and locate problems in time. Different monitoring strategies and warning rules are set for different types of game resource files and configuration files to improve the accuracy and reliability of monitoring and avoid situations such as missed reports or false reports. The Prometheus monitoring system is integrated with components such as the continuous integration tool, game server, and nginx server to form a complete DevOps closed loop, realizing full-process automated management from code submission, building, testing, deployment to monitoring and alarming, and improving the efficiency and quality of game development and operation and maintenance.

[0058] In some embodiments, such as Figure 2 shown, in the above step S102, the initial game resource file is generated by using a game resource generation algorithm according to the game basic information and a preset game resource library; the initial game resource file includes initial game icon information and initial copyright image information, specifically including:

[0059] S1021, extract the material feature information from the preset game resource library, and obtain the initial game resource image file by matching the material feature information with the game basic information;

[0060] S1022, use an image recognition model based on ResNet to detect whether the game content in the initial game resource image file meets the preset game style requirements, and obtain the first detection result, where the game content includes game scenes, game characters, and game props;

[0061] S1023, use optical character recognition (OCR) technology to detect whether the text description in the initial game resource image file meets the preset game setting requirements, and obtain a second detection result;

[0062] S1024, based on the initial game resource image file, use a GAN model to perform image style transfer to generate initial game icon information and initial copyright image information.

[0063] In this embodiment, according to the basic game information, retrieve materials matching the basic game information from a preset game resource library; for each retrieved material, extract the feature information of the material to obtain material feature information; calculate the similarity between the extracted material feature information and the basic game information to obtain the similarity between each material and the basic game information; according to a preset similarity threshold, determine whether the similarity of each material exceeds the threshold; if the similarity of the material exceeds the threshold, use the material as a candidate game resource, and obtain the image file corresponding to the material; perform duplicate removal processing on the image files of the candidate game resources to obtain a set of initial game resource image files.

[0064] Identify and extract game content such as game scenes, game characters, and game props in the image file. Use a pre-trained ResNet-based image recognition model to perform feature extraction and representation learning on the extracted game content images to obtain feature vectors of the game content. According to the preset game style requirements, establish a corresponding style feature vector library, and judge whether the game content meets the preset game style requirements by calculating the similarity between the game content feature vector and the style feature vector. If the similarity between the game content feature vector and the preset style feature vector is greater than the set threshold, it is determined that the game content meets the preset game style requirements; otherwise, it is determined that the game content does not meet the preset game style requirements.

[0065] Use OCR technology to process the initial game resource image file, and extract the text description content contained therein; compare the extracted text description content with the preset game setting requirements to determine whether the description content meets the setting requirements; if the text description content meets the preset game setting requirements, mark the initial game resource image file as meeting the requirements; if the text description content does not meet the preset game setting requirements, mark the initial game resource image file as not meeting the requirements; according to the compliance judgment results of each initial game resource image file, statistically obtain a second detection result; use the second detection result as the basis for whether the game resource image content meets the game setting requirements.

[0066] According to the initial game resource image file, obtain the image pixel matrix data and the image style features; input the obtained image pixel matrix data into the pre-trained GAN generation model, and generate the first game image after style transfer through the generator network; input the first game image into the pre-trained GAN discriminant model, and judge the authenticity of the first game image through the discriminator network; if the authenticity of the first game image is greater than the preset threshold, determine the first game image as the initial game icon information; otherwise, return to the generator network, adjust the parameters of the generator network, and regenerate the first game image; according to the initial game resource image file, obtain the copyright logo features; input the copyright logo features into the pre-trained GAN generation model, and generate the second game image through the generator network; determine the second game image as the initial copyright image information.

[0067] Exemplarily, assume there is a role-playing game (RPG) whose resource library contains various game scene, character, and prop materials. Through the feature extraction algorithm, the key attributes of each material can be extracted, such as the color tone of the scene, the action postures of the characters, the materials of the props, etc. These feature information will be matched with the basic game information to ensure that the selected materials conform to the overall style and setting of the game. By matching the material feature information and the basic game information, an initial game resource image file can be obtained. For example, if the basic game information specifies that the game background is ancient mythology, then the matching algorithm will preferentially select those materials with classical color tones and mythological elements to generate an initial game scene image file. This step ensures the preliminary screening of game resources and lays a foundation for subsequent optimization and detection. Use an image recognition model based on ResNet to detect whether the game content in the initial game resource image file meets the preset game style requirements. ResNet is a deep residual network with powerful feature extraction and classification capabilities. For example, for the initial game scene image, the ResNet model can identify elements such as the architectural style and vegetation type in it and compare them with the preset ancient mythology style. If the detection result shows that the architectural style in the scene is too modern and does not conform to the ancient mythology setting, it will be marked as not meeting the requirements and generate a first detection result. Use optical character recognition (OCR) technology to detect whether the text description in the initial game resource image file meets the preset game setting requirements. OCR technology can convert the text in the image into an editable text format. For example, the text information such as the prop names and skill descriptions in the game needs to be consistent with the game setting. Through OCR technology, it can be detected whether the text content is accurate, such as whether "flame sword" is misrecognized as "fire flame sword", so as to obtain a second detection result. Based on the initial game resource image file, use a generative adversarial network (GAN) model for image style transfer to generate initial game icon information and initial copyright image information. The GAN model consists of a generator and a discriminator and can generate high-quality images through adversarial training. For example, assume the game requires a unique icon style, the GAN model can convert the initial icon image into an icon that conforms to this style. Similarly, the copyright image can also be style-transferred through the GAN model to ensure its consistency with the overall game style.

[0068] In this embodiment, through feature matching and image recognition, it is possible to ensure that the preliminary screening of game resources conforms to the overall style and setting of the game, avoiding the costs of large-scale modifications in the later stage. The application of OCR technology ensures the accuracy of in-game text information, enhancing the gaming experience of players. The application of the GAN model not only improves the visual effects of game resources but also generates unique icons and copyright images through style transfer, enhancing the game's recognition and copyright protection. The comprehensive application of these technologies brings significant technical effects. The automated detection and generation process greatly improve the efficiency of game development and shorten the development cycle. Through multi-level detection and optimization, the quality and consistency of game resources are ensured, enhancing the overall quality of the game. The application of real-time monitoring and continuous integration tools ensures the continuous update and stable operation of game resources, reducing the operation and maintenance costs. For example, in an actual game development project, through the above process, the development team successfully transformed a game with a modern urban theme into an ancient mythology style. The initial resource library contained a large number of modern building and character materials. Through feature matching and detection by the ResNet model, scenes and characters that conform to the ancient style were screened out. OCR technology ensured the accuracy of in-game text descriptions, such as "immortal magic" not being misrecognized as "magic". The application of the GAN model generated unique game icons and copyright images, making the game highly recognizable in the market. Through this systematic resource management and optimization process, game development not only achieved efficient resource utilization but also ensured the consistency and high quality of the game style, ultimately enhancing the gaming experience of players and the competitiveness in the market.

[0069] In some embodiments, such as Figure 3 shown, in the above step S102, the optimization process of the initial game resource file through image processing technology to obtain the target game resource file specifically includes:

[0070] S1025, using the Canny edge detection algorithm to extract the image contours of the initial game icon information and the initial copyright image information, and using the Gaussian filtering algorithm to smooth the image contours;

[0071] S1026, obtaining the image material requirement parameters of different platforms, and correcting the smoothed initial game icon information and the initial copyright image information according to the image material requirement parameters to obtain the target game icon information and the target copyright image information;

[0072] S1027, respectively extracting the image feature information of the target game icon information and the target copyright image information, and calculating the hash value according to the image feature information;

[0073] S1028. Compare the hash value with the standard hash value of the corresponding platform to determine whether the target game icon information and the target copyright image information meet the specification requirements of the platform.

[0074] In this embodiment, the Canny edge detection algorithm is used to process the input initial game icon information and initial copyright image information to extract the contour information of the image. According to the contour information extracted by the Canny algorithm, the edge feature data of the image is obtained. Gaussian filtering processing is performed on the contour information extracted by the Canny algorithm, and the image contour is smoothed through the Gaussian filtering algorithm to reduce image noise. After Gaussian filtering processing, the smoothed image contour data is obtained as the optimized image edge feature. The smoothed image contour data is used as the edge feature of the game icon and the copyright image for subsequent image analysis and recognition.

[0075] Obtain the image material requirement parameters of different platforms, and store the image material requirement parameters in the parameter database; read the image material requirement parameters from the parameter database, and determine the image correction rules according to the image material requirement parameters; obtain the initial game icon information and initial copyright image information after smoothing processing; according to the image correction rules, use the convolutional neural network algorithm to correct the initial game icon information to obtain the target game icon information; according to the image correction rules, use the generative adversarial network algorithm to correct the initial copyright image information to obtain the target copyright image information; fuse the target game icon information and the target copyright image information to obtain a target game icon that meets the requirements of different platforms; store the target game icon in the icon database, and return the storage path of the target game icon to the requesting terminal to complete the generation and application of the icon.

[0076] For the target game icon information and the target copyright image information, use the image feature extraction algorithm to extract the feature information such as the color, texture, and shape of the image; according to the extracted image feature information, use the hash algorithm to calculate the hash value of the image; obtain the standard hash values of the game icon and the copyright image of the corresponding platform; compare the hash values of the target game icon and the copyright image with the standard hash values of the corresponding platform, and calculate the similarity between the hash values; if the similarity is greater than the preset threshold, it is determined that the target game icon and the copyright image meet the specification requirements of the platform; if the similarity is less than or equal to the preset threshold, it is determined that the target game icon and the copyright image do not meet the specification requirements of the platform; according to the judgment result, determine whether the target game icon and the copyright image meet the platform specifications, and output the judgment result.

[0077] Exemplarily, for the initial game icon information, the Canny algorithm can clearly outline the contour of the icon, such as the character contour, weapon shape, etc. Suppose the initial icon is a knight holding a sword. The Canny algorithm will extract the body contour of the knight and the edge of the sword to form a binary edge image. The Gaussian filtering algorithm is used to smooth the image, reducing the interference of noise and details. Its core is to use the Gaussian function to perform weighted averaging on the image, making the image smoother. For example, after extracting the edge of the knight icon, Gaussian filtering can remove the fine noise points and jagged edges in the edge image, making the edge smoother and facilitating subsequent correction and processing. The parameter requirements for image materials on different platforms vary, including resolution, color mode, file format, etc. For example, the icon requirements for the iOS platform are 1024x1024 pixels, in PNG format, while the Android platform may require 512x512 pixels, also in PNG format. According to these parameters, the initial game icon information and the initial copyright image information after smoothing are corrected to ensure that they meet the specifications of each platform. Suppose the initial icon resolution is 800x800 pixels. Through scaling and cropping, it is adjusted to 1024x1024 pixels to meet the requirements of the iOS platform. Extracting image feature information is an important step in image recognition and processing, usually including color features, texture features, shape features, etc. For the target game icon information, features such as its color histogram and edge direction histogram can be extracted. Suppose the main colors of the icon are blue and gold. The distribution of these two colors can be quantified through the color histogram. The edge direction histogram can describe the direction distribution of the icon edges, such as the ratio of vertical edges to horizontal edges. Hash value calculation is a commonly used image fingerprint technology. By converting the image feature information into a hash value of a fixed length, it is convenient for quick comparison and retrieval. Use the MD5 or SHA-1 algorithm to calculate the hash value of the extracted image feature information to obtain a unique hash value. Suppose the hash value of the target game icon is "abc123def456". This hash value will be used as its unique identifier. Comparing the hash value with the standard hash value of the corresponding platform is to ensure that the image material meets the specification requirements of the platform. For example, the iOS platform may have specific requirements for the quality and style of the icon and has pre-set a standard hash value range. Through comparison, it can be quickly determined whether the target game icon meets these requirements. Suppose the standard hash value range for the iOS platform is from "abc000def000" to "abc999def999". If the hash value "abc123def456" of the target icon falls within this range, it is determined to meet the requirements.

[0078] In this embodiment, Canny edge detection and Gaussian filtering ensure the clarity and smoothness of the image contour, laying a foundation for subsequent correction and feature extraction. Correction based on platform parameters ensures the compatibility of image materials and avoids display problems on different platforms. The extraction of image feature information and the calculation of hash values provide technical support for the rapid comparison and retrieval of images, improving work efficiency. Through hash value comparison, it is ensured that the image materials comply with platform specifications, enhancing the user experience of the game on different platforms. For example, in an actual game development project, the initial game icon is a sprite holding a bow and arrow. The edges of the sprite and the bow and arrow are extracted through the Canny algorithm and then smoothed through Gaussian filtering to obtain a clear contour image. According to the parameter requirements of the iOS platform, the icon resolution is adjusted to 1024x1024 pixels and converted to the PNG format. The main color features and edge direction features of the icon are extracted, and the calculated hash value is "xyz789uvw123". By comparing with the standard hash value range of the iOS platform, it is confirmed that the icon complies with platform specifications and is finally successfully launched. Through this systematic image processing and detection process, not only the visual effect of the game icon is improved, but also its compatibility and standardization on different platforms are ensured, ultimately enhancing the overall quality and market competitiveness of the game.

[0079] In some embodiments, such as Figure 4 shown, the method further includes:

[0080] According to the estimated number of players in each release area of the game, select the corresponding server type and quantity from a pre-established server resource pool;

[0081] Perform correlation analysis on the estimated number of players and the corresponding server type and quantity to form a first training data set;

[0082] Use the first training data set as input and adopt a decision tree algorithm for modeling training to form a server resource demand prediction model, which is used to predict the server type and quantity required for each release area;

[0083] Obtain the historical number of players in each release area after the game is launched, and optimize the server resource demand prediction model through the historical number of players to obtain an optimized server resource demand prediction model.

[0084] In this embodiment, the estimated number of players is obtained according to the release area; the pre-established server resource pool is matched using the estimated number of players to obtain the eligible server types and quantities; through correlation analysis of the estimated number of players and the server type quantities, a first training data set is constructed; a decision tree algorithm is used to perform modeling training on the first training data set to generate a server resource requirement prediction model; through the server resource requirement prediction model, it is determined whether the server types and quantities of each release area meet the preset threshold; if they do not meet the preset threshold, the server resource pool configuration is adjusted and the server resource requirement prediction model is updated; the prediction results of the server types and quantities required for each release area by the updated server resource requirement prediction model are obtained. The historical player number data of the game in each release area is obtained and used as the training data set. According to the historical player number data, a time series prediction algorithm is used to establish an initial server resource requirement prediction model. For the initial prediction model, a cross-validation method is used for model evaluation to obtain the model accuracy rate and generalization ability indicators. If the model accuracy rate and generalization ability indicators are lower than the preset threshold, the initial model is optimized by means such as increasing the training data volume, optimizing the model hyperparameters, and introducing regularization terms. The above steps are repeated until the model accuracy rate and generalization ability indicators meet the preset requirements to obtain an optimized server resource requirement prediction model. The optimized prediction model is applied to the game operation process, and according to the real-time player data of each release area, the server resource quantity required in the future for a period of time is dynamically predicted. According to the server resource requirement prediction results, the corresponding server resources are allocated and prepared in advance to ensure the smooth operation of the game and the player experience.

[0085] Exemplarily, according to the estimated number of players in each release region, the corresponding server types and quantities are screened from a pre-established server resource pool. Suppose a certain game plans to be released in five regions globally, namely North America, Europe, Asia, South America, and Africa. Through market research and data analysis, the estimated number of players in each region is as follows: 1 million in North America, 800,000 in Europe, 2 million in Asia, 500,000 in South America, and 300,000 in Africa. The server resource pool contains various types of servers, such as high-performance servers, medium-performance servers, and basic-performance servers. According to the estimated number of players, high-performance servers are required in the North American and Asian regions, medium-performance servers are needed in Europe, while basic-performance servers can be configured in South America and Africa. The specific quantity is matched according to the number of players and the carrying capacity of the server. For example, each high-performance server can carry 100,000 players, so 10 are needed in North America and 20 in Asia. Next, the estimated number of players is correlated with the corresponding server types and quantities for analysis to form the first training dataset. Taking the North American region as an example, a record in the dataset may include: region name "North America", estimated number of players "1 million", server type "high-performance", and server quantity "10 units". In this way, the estimated data and server configuration data of all regions are integrated into a complete dataset. Then, the decision tree algorithm is used to model and train the first training dataset to form a server resource demand prediction model. The decision tree algorithm recursively divides the dataset to find the optimal feature division point, thereby establishing a prediction model. For example, the model may recommend different types and quantities of servers according to different intervals of the number of players. Through training, the model can learn the association rules between the number of players in different regions and server configurations. After the game is launched, the historical number of players in each release region is obtained, and the server resource demand prediction model is optimized with these data. Suppose after the game is launched, there is a deviation between the actual number of players and the estimate. For example, the actual number of players in North America is 1.2 million, and in Asia is 1.8 million. These actual data are input into the model for iterative optimization, adjusting the branch nodes and weights of the decision tree to make the model more accurately reflect the actual demand. The optimized model can more accurately predict the server resources required for future release regions. Specific implementation method example: In the initial stage of the game launch, the actual number of players in the North American region exceeded the estimate, resulting in high server load and a decline in the player experience. By collecting data such as the daily active player count and the peak online number, it is found that the actual number of players stabilizes at around 1.2 million. This data is fed back into the prediction model, and after optimization, the model recommends adding 2 high-performance servers to meet the actual demand. Similarly, the actual number of players in the Asian region is lower than the estimate. After model optimization, it is recommended to reduce 2 high-performance servers to avoid resource waste.

[0086] In this embodiment, a reasonable initial server configuration can ensure the stability of the player experience in the initial stage of the game launch, avoiding problems such as lag and disconnection caused by insufficient servers. Through correlation analysis and model training, a scientific prediction mechanism can be established to improve the efficiency and accuracy of resource allocation. Using actual data for model optimization can enable the prediction model to continuously adapt to actual changes and maintain the accuracy of prediction. For example, in a large MMORPG, when the number of players in the North American region increased sharply in the initial stage of the launch, servers were added in a timely manner through model optimization, ensuring the stability of the game operation; in the Asian region, the server configuration was adjusted according to the actual number of players, avoiding resource waste. Eventually, the game gained a good reputation among players and stable operation performance globally. Through this systematic server resource allocation and prediction method, not only the efficiency and effect of game operation are improved, but also valuable experience and data support are provided for subsequent game releases and resource allocation, forming a virtuous cycle of the technology chain.

[0087] In some embodiments, as Figure 5 shown, the method further includes:

[0088] Setting a server expansion threshold and a server contraction threshold for each release area;

[0089] When the output result of the optimized server resource demand prediction model is greater than the server expansion threshold of any release area, perform an expansion process on the server resources of that release area;

[0090] When the output result of the optimized server resource demand prediction model is less than the server contraction threshold of any release area, perform a contraction process on the server resources of that release area.

[0091] In this embodiment, reasonable server expansion thresholds and contraction thresholds are set according to the business characteristics and resource usage of each release area. When the predicted resource demand exceeds the expansion threshold, an expansion operation is triggered; when the predicted resource demand is lower than the contraction threshold, a contraction operation is triggered. During expansion, the number of servers to be added is calculated based on the predicted resource demand. The newly added server resources can be reasonably allocated to different service nodes through a load balancing algorithm. During contraction, the number of servers to be reduced is calculated based on the predicted resource demand. The dynamic migration technology is used to smoothly migrate the traffic of the servers to be contracted to other servers to avoid service interruption. Continuously monitor the actual server resource usage of each release area, compare the actual data with the predicted data, and continuously optimize the parameters and algorithms of the prediction model to improve the accuracy of the prediction. Personalized scaling strategies are set according to the business characteristics of different release areas. For areas with large business fluctuations, more sensitive scaling thresholds can be set; for areas with relatively stable business, relatively loose thresholds can be set to reduce unnecessary scaling operations and save costs.

[0092] Exemplarily, setting the server expansion threshold and contraction threshold for each release area is based on the anticipation of the fluctuation of the number of players and the analysis of actual operation data. Suppose a certain game is released in five regions globally, namely North America, Europe, Asia, South America, and Africa. Through market research and data analysis, the estimated number of players in each region is as follows: 1 million in North America, 800,000 in Europe, 2 million in Asia, 500,000 in South America, and 300,000 in Africa. Based on these data, corresponding thresholds can be set for each region. For example, the expansion threshold for the North American region is set at 1.1 million players, and the contraction threshold is set at 900,000 players. This means that when the number of players output by the prediction model exceeds 1.1 million, the system will automatically trigger the expansion mechanism to increase server resources; when the number of players output by the prediction model is lower than 900,000, the system will trigger the contraction mechanism to reduce server resources. Suppose in the initial stage of the game launch, the actual number of players in the North American region is 1.2 million, exceeding the set expansion threshold of 1.1 million. At this time, the system will automatically increase the number of high-performance servers according to the output result of the prediction model. Suppose each high-performance server can carry 100,000 players, then the system will recommend adding 2 high-performance servers to meet the actual demand and ensure the stability and smoothness of the game operation. Suppose the expansion threshold for the Asian region is set at 2.1 million players, and the contraction threshold is set at 1.9 million players. After the game is launched, the actual number of players is 1.8 million, lower than the contraction threshold of 1.9 million. At this time, the system will automatically reduce the number of high-performance servers according to the output result of the prediction model. Suppose each high-performance server can carry 100,000 players, then the system will recommend reducing 2 high-performance servers to avoid resource waste and reduce operating costs.

[0093] In this embodiment, setting the expansion and contraction thresholds can ensure that the server resources are adjusted in a timely manner when the number of players fluctuates, avoiding problems such as lag and disconnection caused by insufficient servers and improving the player experience. By dynamically adjusting the server resources, the resource utilization rate can be increased, over-configuration or under-configuration can be avoided, and the operation cost can be reduced. For example, during the global release of a large-scale MMORPG game, by setting the expansion and contraction thresholds, the dynamic adjustment of server resources was successfully achieved. In the early stage of the launch in the North American region, due to the sharp increase in the number of players, the system automatically triggered the expansion mechanism to increase the server resources and ensure the stability of the game operation; in the Asian region, according to the actual number of players, the system automatically triggered the contraction mechanism to reduce the server resources and avoid resource waste. In addition, setting the expansion and contraction thresholds can also enhance the ability to handle emergencies. For example, during a special event, it is estimated that the number of players will increase significantly. The system can, according to the output result of the prediction model, trigger the expansion mechanism in advance to increase the server resources and ensure the player experience during the event. On the contrary, after the event ends and the number of players drops, the system can automatically trigger the contraction mechanism to reduce the server resources and avoid resource idleness. Through this systematic server resource configuration and prediction method, not only the efficiency and effect of game operation are improved, but also valuable experience and data support are provided for subsequent game releases and resource configurations, forming a virtuous cycle of the technology chain. Through these specific implementation methods and examples, it can be seen that setting the server expansion and contraction thresholds, combined with the optimized server resource demand prediction model, can achieve the dynamic adjustment of server resources, ensure the stable operation of the game at different stages and the improvement of the player experience. It not only improves the efficiency and accuracy of resource configuration, but also provides a strong guarantee for the long-term stable operation of the game.

[0094] In some embodiments, such as Figure 6 shown, the method further includes:

[0095] Classifying the target game resource file using a decision tree classification algorithm to obtain game resource files of different categories;

[0096] Constructing a resource dependency graph by analyzing the dependency relationships of the game resource files of different categories;

[0097] Optimizing the target game resource file according to the resource association relationships of the resource dependency graph, removing redundant resources with no association relationships, and obtaining an optimized target game resource file.

[0098] In this embodiment, the resource files of the target game are obtained. For the characteristic attributes of the resource files, the decision tree classification algorithm is used for training to obtain a resource file classification model. The target game resource files are input into the classification model to classify the resource files, and sets of game resource files of different categories are obtained. For each resource file category, the dependency relationships between the resource files are analyzed, and a resource dependency directed graph is constructed, with the dependency relationships as directed edges and the resource files as nodes. In the resource dependency graph, all the nodes in the graph are traversed through the depth-first search algorithm to determine whether there are successor nodes for the node resource files. If not, the resource file is a redundant resource. All the redundant resource nodes in the resource dependency graph are obtained to form a list of redundant resource files. According to the list of redundant resource files, the corresponding redundant resource files are removed from the set of target game resource files. For the optimized game resource files, the unique hash value of each resource file is calculated using the hash algorithm. Through hash value comparison, the repeatability of the resource files is further determined, and the duplicate resources are removed. The game resource files after removing redundant and duplicate resources are used as the optimized set of target game resource files to replace the original resource files, completing the optimization of the game resources.

[0099] Exemplarily, the target game contains various types of resource files, such as texture files, sound effect files, model files, and script files, etc. Through the decision tree classification algorithm, these resource files can be classified according to attributes such as type, size, and usage frequency. For example, the first-level nodes of the decision tree can be the resource type, divided into four categories: texture, sound effect, model, and script; the second-level nodes can be the resource size, divided into three intervals: less than 1MB, 1MB to 10MB, and greater than 10MB; the third-level nodes can be the usage frequency, divided into high frequency, medium frequency, and low frequency. Suppose there is a batch of resource files, after classification by the decision tree, the following classification results are obtained: - Texture files (less than 1MB, high frequency) - Sound effect files (1MB to 10MB, medium frequency) - Model files (greater than 10MB, low frequency) - Script files (less than 1MB, low frequency) Next, perform a dependency analysis on different categories of game resource files to construct a resource dependency graph. The resource dependency graph is a graphical representation method that shows the dependency relationships between various resource files. For example, a certain model file may depend on multiple texture files and sound effect files, while the script file may control the loading and display of the model. Suppose there is a model file "hero_model.obj", which depends on two texture files "hero_skin1.png" and "hero_skin2.png", and a sound effect file "hero_attack.wav". In the resource dependency graph, these files are connected by directed edges to form a dependency relationship network. According to the resource association relationships in the resource dependency graph, optimize the target game resource files and remove redundant resources without association relationships. The purpose of this step is to reduce unnecessary resource loading and improve the game running efficiency. For example, in the resource dependency graph, if it is found that a certain texture file has no other resource files depending on it, then this texture file can be regarded as a redundant resource and deleted. Specifically, suppose in the resource dependency graph, it is found that the texture file "abandoned_texture.png" has no dependencies. After confirmation, this file is indeed no longer used, so it is removed from the resource package. In this way, the size of the game resource package can be significantly reduced and the loading speed can be improved.

[0100] In this embodiment, classifying resource files through a decision tree classification algorithm enables a clear understanding of the characteristics and usage of various resources, facilitating subsequent refined management. Constructing a resource dependency graph helps visually display the association relationships between resources and discover potential redundant resources. Removing redundant resources without association relationships can effectively reduce the size of the resource package, improve the game loading speed and running efficiency. For example, in a large MMORPG game, by using a decision tree classification algorithm, tens of thousands of resource files were classified, and a detailed resource dependency graph was constructed. After analysis, hundreds of redundant texture files and sound effect files were discovered and removed, reducing the size of the game resource package by 15% and increasing the loading speed by 20%. This not only enhances the player's gaming experience but also reduces the server storage and bandwidth costs. Another example is that in a game version update, a large number of models and texture files were added. Through decision tree classification and dependency analysis, the association relationships between the newly added resources and the existing resources were quickly identified, optimizing the resource loading order and ensuring the stability and fluency of the updated game. Using a decision tree classification algorithm to classify game resource files, combined with dependency analysis and redundant resource removal, can significantly improve the utilization efficiency of game resources and the performance of game operation. It is not only applicable to the resource management of large games but also provides an effective technical means for the optimization of small and medium-sized games.

[0101] In some embodiments, as Figure 7 shown, the method further includes:

[0102] Obtaining the operation data metrics of the game, extracting features from the operation data metrics to obtain operation feature vectors, where the operation data metrics include response time, frame rate fluctuation, and memory occupancy rate;

[0103] Performing clustering processing on the operation feature vectors using the K-means clustering algorithm to obtain a clustering result;

[0104] Statistically analyzing the game operation quality metrics of each cluster in the clustering result, and calculating the game operation health and game reliability level of each cluster according to the game operation quality metrics;

[0105] Determining whether the game has an abnormal operation state and the factors causing the abnormal operation state according to the game operation health and game reliability level of each cluster.

[0106] In this embodiment, operation data metrics such as response time, frame rate fluctuation, and memory occupancy rate during game operation are obtained. Feature engineering processing is performed on these metric data to extract key feature parameters reflecting the game operation state, and a game operation feature vector is constructed. Clustering algorithms such as K-means are used to cluster the game operation feature vector. By calculating the distance from each data point to the cluster center, similar game operation states are divided into the same cluster, and a clustering result reflecting different operation states is obtained. For each cluster in the clustering result, the distribution of game operation data metrics within the cluster is statistically analyzed respectively, and game operation quality metrics such as the average response time, average frame rate fluctuation, and average memory occupancy rate of each cluster are calculated. According to the game operation quality metrics of each cluster, the game operation health and reliability level of the cluster are calculated. Among them, the operation health can be judged by the quality of metrics such as response time and frame rate fluctuation, and the reliability level can be judged by the stability of metrics such as memory occupancy rate. For clusters with low game operation health and reliability levels, the game operation state represented by the cluster is determined to be an abnormal state. Further analyze the sample data within the cluster to find the key influencing factors causing the abnormality, such as too long response time or sudden increase in memory occupancy rate, etc. For the key factors triggering the abnormal game operation state, analyze their causes and give improvement measures to optimize game performance and improve operation stability. By means of technical means such as optimizing the game engine, improving the resource loading mechanism, and compressing redundant data, the game operation quality is improved. Continuously monitor the game operation data metrics, perform clustering analysis and anomaly diagnosis regularly, continuously optimize the game performance, and ensure the game experience of players. At the same time, establish an early warning mechanism for the game operation state, timely discover and handle potential abnormal risks, and maintain the stable operation of the game.

[0107] For example, response time refers to the time required from the player issuing an operation instruction to the game responding, such as the time interval from the player pressing the fire button to the bullet being fired in a shooting game. Frame rate fluctuation refers to the range of variation of the game frame rate. Ideally, the frame rate should remain stable. Excessive fluctuation may cause the screen to freeze. Memory occupancy rate reflects the consumption of system memory when the game is running. Excessive memory occupancy may cause the system to freeze or even crash. Feature extraction of these operating data indicators can obtain operating feature vectors. For example, suppose the response time data of a game is, the frame rate fluctuation data is, and the memory occupancy data is. Through standardization, these data are converted into a unified feature vector form, such as [0.9, 0.8, 0.85]. Next, the K-means clustering algorithm is used to cluster the operating feature vector. The K-means algorithm divides the feature vector into K clusters through iterative calculations, and the vectors in each cluster have high similarity. Assuming that the data is divided into 3 clusters, the clustering results may be as follows: Cluster 1: [0.9, 0.8, 0.85]; Cluster 2: [1.0, 0.9, 0.88]; Cluster 3:

[0108] [0.8, 0.7, 0.78]. The game running quality indicators of each cluster are counted, including the average response time, average frame rate fluctuation and average memory occupancy. For example, the average response time of cluster 1 is 110ms, the average frame rate fluctuation is 6.5fps, and the average memory occupancy is 62%. Based on these indicators, the game running health and game reliability level of each cluster are calculated. The running health can be calculated by weighted average, such as: assuming that the health score of cluster 1 is 0.8 and the reliability level is 0.85, which indicates that the game running status in this cluster is relatively good. According to the health and reliability level of each cluster, determine whether the game has an abnormal running status and the factors that cause the abnormality. For example, if the health score of cluster 3 is only 0.5 and the reliability level is 0.6, which is significantly lower than other clusters, it means that there are abnormalities in the game running in this cluster. Further analysis of the data of cluster 3 shows that its average response time is as high as 150ms, the average frame rate fluctuation reaches 10fps, and the memory occupancy is as high as 70%. By comparing the clustering data of normal operation, it can be inferred that the abnormal operation status may be caused by memory leaks or improper resource loading.

[0109] Suppose in a certain test, 1000 sample data are obtained. After feature extraction, 1000 running feature vectors are obtained. The K-means algorithm is used to cluster these vectors into 5 clusters, and the running quality indicators of each cluster are statistically analyzed. It is found that the health score of cluster 4 is only 0.4 and the reliability level is 0.5, which is significantly abnormal. Further analysis reveals that the samples in cluster 4 generally have a high memory occupancy rate and large frame rate fluctuations, inferring that there may be a memory leak problem in a certain section of code. In this way, abnormal states during game operation can be detected and located in a timely manner, and targeted optimization measures can be taken. For example, for the memory leak problem, code review and optimization are carried out to reduce unnecessary memory allocation and release operations. After optimization, the test is carried out again, and it is found that the health score of cluster 4 has increased to 0.7 and the reliability level has increased to 0.8, and the game operation state has been significantly improved. Through a data-driven approach, the game operation state can be objectively and accurately evaluated, and potential problems can be detected in a timely manner. At the same time, cluster analysis can reveal the feature differences under different operation states, providing a basis for refined optimization. Finally, by optimizing abnormal operation states, the overall performance and user experience of the game are improved. For example, after a certain version update, players reported that the game was severely stuck in a specific scenario. By collecting the operation data in this scenario, performing feature extraction and cluster analysis, it is found that the samples in a certain cluster have high response time and high frame rate fluctuations. Further investigation reveals that a large number of high-resolution texture resources are loaded in this scenario, resulting in a sharp increase in memory occupancy. By optimizing the resource loading strategy and loading texture resources in batches, the memory occupancy rate is successfully reduced and the frame rate stability is improved, and the player experience has been significantly improved. Through these specific implementation methods and examples, it can be seen that obtaining game operation data indicators, performing feature extraction and cluster analysis can effectively identify and optimize abnormal game operation states, improve the running health and reliability level of the game, thus ensuring a smooth game experience for players.

[0110] Referring to Figure 8 , an embodiment of the present invention provides a processing system 8 for a game online process, and the system 8 specifically includes:

[0111] A first processing module 801, configured to obtain basic game information, detect whether the basic game information conforms to the specification according to a preset game configuration standard rule, and perform domain name configuration through the basic game information;

[0112] A second processing module 802, configured to generate an initial game resource file according to the basic game information and a preset game resource library, perform optimization processing on the initial game resource file through image processing technology, and obtain a target game resource file;

[0113] The third processing module 803 is used to generate an nginx configuration file based on the preset game configuration template according to the target game resource file;

[0114] The fourth processing module 804 is used to package the target game resource file and the nginx configuration file, and then automatically deploy them to the target game server through a continuous integration tool, and continuously load the latest target game resource file and nginx configuration file according to the continuous integration tool;

[0115] The fifth processing module 805 is used to use the Prometheus monitoring system to monitor all links in the process of continuously loading the latest target game resource file and nginx configuration file by the continuous integration tool, obtain monitoring data, and determine whether to perform version rollback and exception warning through the monitoring data.

[0116] It can be understood that the content in the embodiment of the processing method for the game online process as Figure 1 shown is applicable to the embodiment of the processing system for the game online process. The specific functions implemented by the embodiment of the processing system for the game online process are the same as those in the embodiment of the processing method for the game online process as Figure 1 shown, and the beneficial effects achieved are also the same as those in the embodiment of the processing method for the game online process as Figure 1 shown.

[0117] It should be noted that for the information interaction, execution process, etc. between the above systems, since they are based on the same concept as the method embodiment of the present invention, the specific functions and the technical effects brought by them can be specifically referred to the method embodiment part, and will not be elaborated here.

[0118] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiment, and will not be elaborated here.

[0119] Referring to Figure 9, An embodiment of the present invention further provides a computer device 9, including: a memory 902, a processor 901, and a computer program 903 stored on the memory 902. When the computer program 903 is executed on the processor 901, it implements the processing method for the game online process described in any one of the above methods.

[0120] The computer device 9 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device 9 may include, but is not limited to, a processor 901 and a memory 902. Those skilled in the art can understand that Figure 9 merely examples of the computer device 9, which do not constitute a limitation on the computer device 9, may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0121] The so-called processor 901 may be a central processing unit (CPU), and the processor 901 may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0122] The memory 902 may be an internal storage unit of the computer device 9 in some embodiments, such as the hard disk or memory of the computer device 9. The memory 902 may also be an external storage device of the computer device 9 in other embodiments, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 9. Further, the memory 902 may also include both the internal storage unit and the external storage device of the computer device 9. The memory 902 is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program. The memory 902 may also be used to temporarily store data that has been output or will be output.

[0123] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it implements the processing method for the game online process as described in any one of the above methods.

[0124] In this embodiment, if the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above embodiment methods of this application, a computer program can be used to instruct relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the photographing device / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0125] In the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0126] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0127] In the embodiments disclosed in the present application, it should be understood that the disclosed apparatus / terminal device and method can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the apparatus or unit can be in electrical, mechanical or other forms.

[0128] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

Claims

1. A method for processing a game online process, characterized in that: The method specifically comprises: Obtain basic game information, detect whether the basic game information complies with the specification according to preset game configuration standard rules, and perform domain name configuration through the basic game information; According to the basic game information and the preset game resource library, an initial game resource file is generated by using a game resource generation algorithm, and the initial game resource file is optimized by using an image processing technology to obtain a target game resource file; Based on a preset game configuration template, generate an nginx configuration file according to the target game resource file; After packaging the target game resource files and the nginx configuration file, they are automatically deployed to the target game server through a continuous integration tool, and the latest target game resource files and nginx configuration files are continuously loaded according to the continuous integration tool; Use the Prometheus monitoring system to monitor each link in the process of loading the latest target game resource files and nginx configuration files by the continuous integration tool in real time, obtain monitoring data, and determine whether to perform version rollback and abnormal alarm based on the monitoring data.

2. The method according to claim 1, characterized in that The initial game resource file is generated by using a game resource generation algorithm according to the basic game information and a preset game resource library; The initial game resource file includes initial game icon information and initial copyright image information, specifically including: Extracting material feature information from a preset game resource library, and obtaining an initial game resource image file by matching the material feature information with the basic game information; Using a ResNet-based image recognition model to detect whether the game content in the initial game resource image file meets the preset game style requirements, and obtaining a first detection result, wherein the game content includes game scenes, game characters, and game props; Using optical character recognition (OCR) technology to detect whether the text description in the initial game resource image file meets the preset game setting requirements, and obtain a second detection result; Based on the initial game resource image file, a GAN model is used to perform image style transfer to generate initial game icon information and initial copyright image information.

3. The method according to claim 2, characterized in that The optimizing process of the initial game resource file by using the image processing technology to obtain the target game resource file specifically includes: Using a Canny edge detection algorithm to extract image contours of the initial game icon information and the initial copyright image information, and using a Gaussian filter algorithm to smooth the image contours; Obtaining image material requirement parameters of different platforms, and modifying the smoothed initial game icon information and the initial copyright image information according to the image material requirement parameters to obtain target game icon information and target copyright image information; Respectively extracting image feature information of the target game icon information and the target copyright image information, and calculating a hash value according to the image feature information; The hash value is compared with the standard hash value of the corresponding platform to determine whether the target game icon information and the target copyright image information meet the specification requirements of the platform.

4. The method according to any one of claims 1 to 3, characterized in that The method further comprises: Based on the estimated number of players in each release region of the game, select the corresponding server type and quantity from the pre-established server resource pool; Performing correlation analysis on the estimated number of players and the corresponding server types and numbers to form a first training data set; Taking the first training data set as input, a decision tree algorithm is used for modeling training to form a server resource demand prediction model, where the server resource demand prediction model is used to predict the type and quantity of servers required for each release area; The historical number of players in each release area after the game is launched is obtained, and the server resource demand prediction model is optimized according to the historical number of players to obtain an optimized server resource demand prediction model.

5. The method according to any one of claim 4, characterized in that: The method further comprises: Set server expansion thresholds and server reduction thresholds for each release area; When the output result of the optimized server resource demand prediction model is greater than the server expansion threshold of any release area, the server resources of the release area are expanded; When the output result of the optimized server resource demand prediction model is less than the server capacity reduction threshold of any release area, the server resources of the release area are reduced.

6. The method according to any one of claims 1 to 3, characterized in that The method further comprises: Using a decision tree classification algorithm to classify the target game resource files to obtain game resource files of different categories; By analyzing the dependencies of different categories of game resource files, a resource dependency graph is constructed; According to the resource association relationship of the resource dependency graph, the target game resource file is optimized, and redundant resources without association relationship are removed to obtain an optimized target game resource file.

7. The method according to any one of claims 1 to 3, characterized in that The method further comprises: Obtaining game operation data indicators, performing feature extraction on the operation data indicators, and obtaining an operation feature vector, wherein the operation data indicators include response time, frame rate fluctuation, and memory occupancy rate; Using K-means clustering algorithm to cluster the running feature vector to obtain a clustering result; Counting the game running quality index of each cluster in the clustering results, and calculating the game running health and game reliability level of each cluster according to the game running quality index; Based on the game operation health and game reliability level of each cluster, determine whether the game has an abnormal operation state and the factors that cause the abnormal operation state.

8. A processing system for a game online process, characterized in that: The system specifically comprises: The first processing module is used to obtain basic game information, detect whether the basic game information meets the specification according to the preset game configuration standard rules, and perform domain name configuration according to the basic game information; A second processing module is used to generate an initial game resource file using a game resource generation algorithm according to the basic game information and a preset game resource library, and optimize the initial game resource file using an image processing technology to obtain a target game resource file; A third processing module is used to generate an nginx configuration file according to the target game resource file based on a preset game configuration template; The fourth processing module is used to package the target game resource file and the nginx configuration file, automatically deploy them to the target game server through a continuous integration tool, and continuously load the latest target game resource file and nginx configuration file according to the continuous integration tool; The fifth processing module is used to use the Prometheus monitoring system to monitor in real time each link in the process of loading the latest target game resource files and nginx configuration files by the continuous integration tool, obtain monitoring data, and determine whether to perform version rollback and abnormal alarm through the monitoring data.

9. A computer device, characterized in that: include: A memory, a processor and a computer program stored in the memory, when the computer program is executed on the processor, implements the processing method for the game online process as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the processing method for the game online process as described in any one of claims 1 to 7 is implemented.

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