Online operation activity configuration generation method and device, computing device, and storage medium

Abstract

The application provides an online operation activity configuration generation method and device, a computing device and a storage medium. The method comprises the following steps: in response to a received operation activity configuration generation instruction, collecting activity configuration information, wherein the activity configuration information comprises operation activity information, activity package information, a draw configuration, prize pool information, device adaptation parameters and multi-language configuration; based on the activity configuration information, generating a standardized activity data model; querying the device type of a target device, and extracting UI elements according to the device type to obtain target UI elements; based on the activity data model and the target UI elements, generating an activity instance adapted to the target device through Docker containerization technology in combination with the code repository version label characteristics; and arranging the activity instance in a target running environment corresponding to the target device. The application realizes automatic generation of activities, improves the development efficiency of online operation activities, reduces maintenance costs, and simultaneously supports rapid expansion of new device types and product types.

Description

Technical Field

[0001] This application relates to the field of data configuration technology, and in particular to a method, apparatus, computing device and storage medium for generating online operation activity configurations. Background Technology

[0002] With the rapid development of the accelerator industry, online operation activities have become the core means for accelerators to attract users, increase user activity, and promote user payment. Common accelerator operation activities include package discounts, lucky draws, and sign-in gifts.

[0003] Currently, the configuration generation of online operational activities for the accelerator mainly relies on manual development. Operations personnel need to manually write activity code and configure activity parameters, and develop separate adaptation versions for different devices. This results in a long development cycle, an inability to quickly respond to operational needs, and low configuration efficiency. Furthermore, due to the diverse types of devices and system versions used by accelerator users, and the fact that some users come from different countries and regions, multi-language support is required. Manual adaptation suffers from poor compatibility, easily leading to UI errors, functional abnormalities, and language display errors, impacting user experience. During the actual deployment of activities, inconsistencies in environments and version conflicts can easily occur, causing activities to malfunction. Moreover, the lack of effective monitoring and rollback mechanisms after deployment can result in operational losses if anomalies occur. Finally, the high coupling between activity configuration information and code means that subsequent modifications to activity parameters and updates to activity versions require extensive code modifications, leading to high maintenance costs and a high risk of errors. Summary of the Invention

[0004] In view of this, embodiments of this application provide a method for generating online operational activity configurations to address the technical deficiencies in the prior art. Embodiments of this application also provide an apparatus for generating online operational activity configurations, a computing device, and a computer-readable storage medium.

[0005] According to a first aspect of the embodiments of this application, a method for generating online operation activity configurations is provided, including: In response to the received operation activity configuration generation instruction, the activity configuration information is collected, wherein the activity configuration information includes operation activity information, activity package information, lottery configuration, prize pool information, device adaptation parameters and multi-language configuration; Based on the activity configuration information, a standardized activity data model is generated; Query the device type of the target device, and extract the UI elements based on the device type to obtain the target UI elements; Based on the activity data model and the target UI elements, an activity instance adapted to the target device is generated using Docker containerization technology and combined with code repository version tag characteristics. The activity instance is deployed in the target operating environment corresponding to the target device.

[0006] Optionally, the operational activity information includes activity type, activity time period, activity participation rules, and user access conditions; The activity package information includes package tiers, package discount percentages, package activation conditions, and package stacking rules; The lottery configuration includes a limit on the number of draws, a lottery probability weight, a lottery cooldown time, and rules for verifying user eligibility to participate in the lottery. The prize pool information includes prize type, prize quantity, prize distribution threshold, and prize pool refresh strategy; The device adaptation parameters include device hardware type parameters, device hardware performance parameters, network bandwidth adaptation parameters, and system version compatibility parameters. The multilingual configuration includes a list of languages, text translation rules, time zone adaptation rules, and currency unit display rules.

[0007] Optionally, after collecting activity configuration information in response to the received operation activity configuration generation instruction, the method further includes: The activity configuration information is validated for legality. If the validation fails, a configuration error message is returned. The validity validation includes parameter integrity validation, data format validation, numerical range validation, and conflict rule validation.

[0008] Optionally, generating a standardized activity data model based on the activity configuration information includes: The activity configuration information is input into a pre-trained neural network model for feature extraction, and the activity data model is generated based on the extraction results.

[0009] Optionally, the device type of the target device being queried includes: The device type of the target device can be queried by using one or more of the device fingerprint information, system interface information, or user agent information associated with the target device.

[0010] Optionally, the step of generating an activity instance adapted to the target device based on the activity data model and the target UI element, using Docker containerization technology and combining code repository version tag characteristics, includes: Based on the code repository version tag characteristics, the corresponding version configuration script, rendering engine and dependency library files are pulled. The code repository version tags include stable version tags, test version tags and device-specific adaptation version tags. Based on the activity data model, the target UI element, the configuration script, the rendering engine, and the dependency library files, an activity instance adapted to the target device is generated using Docker containerization technology. The activity instance has a corresponding independent container resource quota and running permissions.

[0011] Optionally, after generating an activity instance adapted to the target device, the method further includes: Perform functional testing, compatibility testing, and performance stress testing on the activity instance. If any test result fails, execute the step of generating a standardized activity data model based on the activity configuration information. Once all test results are passed, proceed with the step of deploying the activity instance in the target operating environment corresponding to the target device.

[0012] According to a second aspect of the embodiments of this application, an online operation activity configuration generation apparatus is provided, comprising: The data collection module is configured to collect activity configuration information in response to a received operation activity configuration generation instruction. The activity configuration information includes operation activity information, activity package information, lottery configuration, prize pool information, device adaptation parameters, and multi-language configuration. The first generation module is configured to generate a standardized activity data model based on the activity configuration information. The UI element extraction module is configured to query the device type of the target device and extract UI elements based on the device type to obtain the target UI elements. The second generation module is configured to generate an activity instance adapted to the target device based on the activity data model and the target UI element, using Docker containerization technology and combined with code repository version tag characteristics. The deployment module is configured to deploy the activity instance in the target runtime environment corresponding to the target device.

[0013] According to a third aspect of the embodiments of this application, a computing device is provided, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor executes the computer-executable instructions to implement the steps of the online operation activity configuration generation method.

[0014] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided that stores computer-executable instructions, which, when executed by a processor, implement the steps of the online operation activity configuration generation method.

[0015] According to a fifth aspect of the present application, a chip is provided that stores a computer program, which, when executed by the chip, implements the steps of the online operation activity configuration generation method.

[0016] The online operational activity configuration generation method provided in this application, in response to a received operational activity configuration generation instruction, collects activity configuration information, including operational activity information, activity package information, lottery configuration, prize pool information, device adaptation parameters, and multi-language configuration. Based on the activity configuration information, a standardized activity data model is generated; the device type of the target device is queried, and UI elements are extracted according to the device type to obtain target UI elements; based on the activity data model and the target UI elements, an activity instance adapted to the target device is generated using Docker containerization technology combined with code repository version tag characteristics; and the activity instance is deployed in the target runtime environment corresponding to the target device. This method solves the problem of repeatedly implementing the same operational activity on different devices and the application challenge of the same activity system on different products. Through standardization and modularization, it automates activity generation, significantly improves the development efficiency of online operational activities, reduces maintenance costs, and supports rapid expansion to new device and product types, providing enterprises with an efficient and unified operational activity solution. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating an online operational activity configuration generation method provided in one embodiment of this application; Figure 2 This is a schematic diagram of the structure of an online operation activity configuration generation device provided in an embodiment of this application; Figure 3 This is a structural block diagram of a computing device provided in one embodiment of this application. Detailed Implementation

[0019] Many specific details are set forth in the following description to provide a full understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this application; therefore, this application is not limited to the specific embodiments disclosed below.

[0020] The terminology used in one or more embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the scope of one or more embodiments of this application. The singular forms “a,” “the,” and “the” used in one or more embodiments of this application and in the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” used in one or more embodiments of this application refers to and includes any or all possible combinations of one or more associated listed items.

[0021] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this application, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this application, and similarly, second may also be referred to as first.

[0022] First, the terminology used in one or more embodiments of the present invention will be explained.

[0023] Docker containerization technology: a lightweight virtualization technology that can package active instances and their dependent runtime environments, such as configuration scripts, rendering engines, and dependency libraries, into independent containers. The containers are isolated from each other and share the host machine's system kernel, enabling rapid deployment of active instances and environment consistency. This avoids abnormal operation of activities due to environment differences and greatly improves deployment efficiency and stability.

[0024] Code repository version tag feature: For different versions of active resources in the code repository, such as configuration scripts, rendering engines, and dependency libraries, set corresponding version tags to facilitate the quick retrieval of the required version of resources. It supports version backtracking and iteration. For example, set exclusive tags for different device adaptation versions to ensure that the retrieved resources match the target device.

[0025] This application provides a method for generating online operational activity configurations. This application also relates to an apparatus for generating online operational activity configurations, a computing device, and a computer-readable storage medium, which will be described in detail in the following embodiments.

[0026] Figure 1 The flowchart illustrates a method for generating online operational activity configurations according to an embodiment of this application, which specifically includes the following steps: Step S102: In response to the received operation activity configuration generation instruction, collect activity configuration information, wherein the activity configuration information includes operation activity information, activity package information, lottery configuration, prize pool information, device adaptation parameters and multi-language configuration; Step S104: Based on the activity configuration information, generate a standardized activity data model; Step S106: Query the device type of the target device, and extract UI elements according to the device type to obtain the target UI elements; Step S108: Based on the activity data model and the target UI element, generate an activity instance adapted to the target device using Docker containerization technology and combined with code repository version tag characteristics; Step S110: Deploy the activity instance in the target operating environment corresponding to the target device.

[0027] The operation activity configuration generation command is triggered by the operations personnel through the accelerator operation management platform. The command may contain the basic identifier of the activity, such as the activity ID and activity name, which is used to trigger the subsequent configuration information collection process. The activity configuration information is the core parameter set of the accelerator operation activities, covering all kinds of sub-configurations required for the entire activity process, ensuring that the activity can run normally and meet the operational needs. Among the sub-configurations, the operation activity information is used to define the basic attributes and participation rules of the activity, the activity package information is used to configure the discount strategies related to the accelerator paid packages, the lottery configuration and prize pool information are used to implement the lottery function in the activity, the device adaptation parameters are used to ensure that the activity is displayed and run normally on different devices, and the multi-language configuration is used to adapt to users of different languages.

[0028] Furthermore, a standardized activity data model involves structuring and standardizing the collected, scattered activity configuration information to form a unified data model. This eliminates format differences between different configuration information, facilitating subsequent UI element matching, activity instance generation, and data retrieval. For example, parameters such as activity time, package price, and lottery probability are uniformly converted into a preset format to ensure data consistency and reusability. Target device types include, but are not limited to, Android devices of different brands and system versions, iOS devices of different models and system versions, Windows and macOS devices with different hardware and / or different software systems, and web browsers of different types and versions. The purpose of querying device types is to extract UI elements adapted to that device to ensure the activity... The dynamic interface conforms to the device's interaction habits and display specifications; target UI elements refer to the interface interaction elements adapted to the target device, including controls, layouts, icons, etc., to ensure that the activity interface is clearly displayed and interacts smoothly on the target device. For example, extracting touchscreen-compatible button controls on mobile devices and mouse-compatible menu controls on PCs; the activity instance is an accelerator operation activity application that can run directly on the target device, containing all the functions and interfaces of the activity. It is generated by combining the activity data model and target UI elements and has an independent running environment; the target running environment corresponds to the software running environment of the target device, including but not limited to Android, iOS, Windows, and web browser environments, to ensure that the activity instance can start and run normally on the target device.

[0029] It should be noted that the target UI elements include control types, control layouts, pop-up styles, and icon resources. Control types, as the core interactive carriers of the activity interface, specifically include, but are not limited to, operation buttons, input controls, selection controls, and display controls. Operation buttons include core buttons such as "Participate Now," "Win a Prize," "Purchase an Acceleration Package," and "View Activity Rules." Input controls include input boxes for users to enter invitation codes and mobile phone numbers. Selection controls include drop-down menus for selecting package tiers and checkboxes for checking the activity agreement. Display controls include text controls displaying activity progress, prize information, and package prices. Control types indicate different control specifications for different devices. For example, mobile controls use larger sizes and wider spacing to adapt to touchscreen clicks; desktop controls are of moderate size to adapt to mouse clicks, ensuring ease of operation.

[0030] The control layout represents the arrangement and position distribution of various UI controls in the activity interface. It is crucial to adapt to the screen size and resolution of the target device. For example, on mobile devices in portrait mode, a vertical flow layout is used, placing core operation buttons at the bottom of the screen for easy one-handed operation. On desktop devices in landscape mode, a combination of horizontal and vertical layouts is used, arranging activity rules, package information, and the lottery area in separate sections to improve interface utilization. On tablet devices, the control layout is adaptively adjusted to meet the needs of switching between portrait and landscape modes, ensuring interface rationality in different screen states. Pop-up styles encompass the visual design and interaction logic of all pop-ups in the accelerator operation activities, including activity rule pop-ups, prize-winning notification pop-ups, error notification pop-ups, and confirmation pop-ups. Pop-up styles must adapt to the screen size of the target device. For example, mobile pop-ups use a full-screen or half-screen design, while desktop pop-ups use a centered small window design. At the same time, the color, font, rounded corners, and closing method of pop-ups are unified to ensure clear display and convenient operation, avoiding cluttered pop-up styles that negatively impact user experience.

[0031] Icon resources are visual elements used in the activity interface to assist in display and guide operations. These include the activity logo, prize icons, function icons, and status icons. Specifically, prize icons include icons for acceleration time, membership benefits, and physical prizes; function icons include icons for back, help, share, and refresh; and status icons include icons for activity countdown and remaining draw attempts. Icon resources need to provide multiple size adaptation versions based on the target device's resolution. For example, 2x and 3x resolution icons should be provided for high-definition screens to avoid blurry or stretched icons. At the same time, the icon style should be consistent with the accelerator client to enhance the overall interface coherence.

[0032] In addition, the target UI elements can also have new accelerator-specific controls, including acceleration time deduction controls, node adaptation selection controls, and acceleration status display controls. Simultaneously, the control layout is optimized based on the user's operation priority, setting acceleration-related controls as high priority and activity operation controls as secondary priority to ensure that acceleration services and activity operations do not interfere with each other. Specifically, the acceleration time deduction control is used to directly deduct the user's remaining acceleration time from the activity, the node adaptation selection control is used for users to select the acceleration node associated with the activity, and the acceleration status display control displays the user's current acceleration status in real time, avoiding conflicts between activity operations and acceleration operations.

[0033] Therefore, by clearly defining the core components of the target UI elements, a specific and executable basis is provided for the UI element extraction process, avoiding blindness in the extraction process and ensuring that the extracted UI elements meet the interface requirements of the accelerator operation activities and the adaptation requirements of the target devices. It covers four core UI elements: controls, layout, pop-ups, and icons, comprehensively covering the display and interaction requirements of the activity interface, ensuring that the activity interface is reasonably laid out on the target devices, controls are adapted, pop-ups are clear, and icons are standardized, avoiding UI confusion and inconvenience. It unifies the core types and design specifications of UI elements, ensuring that the style of the accelerator operation activity interface on different devices is consistent with the accelerator client, improving user awareness and comfort, and enhancing user stickiness. It clarifies the extraction scope and content, avoiding invalid and duplicate extraction, shortening the UI element extraction cycle, providing reliable support for the rapid generation of subsequent activity instances, and further improving the efficiency of the entire activity configuration generation process.

[0034] In summary, by automatically collecting activity configuration information and generating standardized activity data models, the configuration cycle for accelerator operation activities is significantly shortened, eliminating the need for extensive manual coding and enabling rapid response to operational needs. By querying target device types and extracting suitable UI elements, combined with Docker containerization technology, activity instances adapted to different devices are generated, solving the adaptation challenges caused by the diverse device types of accelerator users and avoiding issues such as UI errors and functional abnormalities. The activity configuration information includes multi-language configurations, meeting the usage needs of users in different regions of the accelerator and improving international operation capabilities and user experience. Through Docker containerization technology, activity instances and their dependent environments are packaged, resolving environment inconsistencies. Combined with the version tag features of the code repository, resources of the corresponding version can be quickly pulled, avoiding version conflicts. The standardized activity data model decouples activity configuration information from code, eliminating the need to modify extensive code when modifying activity parameters or updating activity versions, thus reducing maintenance costs.

[0035] Since the core service of the accelerator is network acceleration, operational activities can easily consume excessive network bandwidth and CPU resources, leading to service lag and increased latency. Furthermore, activity configurations need to be deeply integrated with accelerator node resources, acceleration duration, and package plans. Conventional configuration methods cannot achieve this coordination, easily resulting in conflicts between the activity and the acceleration service, and mismatches between configuration and acceleration benefits. Therefore, when generating the activity data model, we added a reserved configuration for acceleration service resources, preset resource consumption thresholds for activity operation, and added acceleration service association parameters to the activity configuration information, including node adaptation range, acceleration duration deduction rules, and package benefit association identifiers. When generating the data model, it automatically links with the accelerator core service database to achieve automatic matching of activity configurations and acceleration benefits, ensuring that activity operation does not affect the core acceleration service.

[0036] Furthermore, the operational activity information includes activity type, activity time period, activity participation rules, and user access conditions; the activity package information includes package tiers, package discount percentages, package activation conditions, and package stacking rules; the lottery configuration includes lottery draw limit, lottery draw probability weight, lottery draw cooldown time, and user lottery eligibility verification rules; the prize pool information includes prize type, prize quantity, prize distribution threshold, and prize pool refresh strategy; the device adaptation parameters include device hardware type parameters, device hardware performance parameters, network bandwidth adaptation parameters, and system version compatibility parameters; the multilingual configuration includes a language list, text translation rules, time zone adaptation rules, and currency unit display rules.

[0037] The operational activity information includes activity types such as package discount activities, lucky draw activities, check-in activities, friend invitation activities, and new user exclusive activities; activity time periods include activity start time, end time, daily activity time slots, and activity duration, accurate to the second to ensure timely start and end of activities; activity participation rules include participation methods such as logging in or completing designated tasks, and limits on the number of daily participations and the total number of participations during the activity period; user access conditions include user types such as new users, old users, and paid users, user level, user region, and the duration or data usage of the accelerator, for example, only allowing new users to participate in new user exclusive activities.

[0038] The activity package information includes package tiers corresponding to the accelerator's paid package levels, such as monthly packages, quarterly packages, and annual packages. Different tiers correspond to different acceleration durations, acceleration nodes, and other benefits. Package discount percentages correspond to the discount levels of different packages, such as 10% off for monthly packages and 30% off for annual packages. This package discount percentage can be set with tiered discounts. Package activation conditions include immediate activation after purchase, activation at a specified time, activation after meeting a certain consumption amount, etc. Package stacking rules refer to whether users are allowed to purchase multiple packages at the same time, whether package durations are stacked, and whether discounts are stacked. For example, it is prohibited to purchase two monthly packages at the same time, but annual packages can be used in combination with limited-time coupons.

[0039] The lottery configuration includes a limit on the number of draws, corresponding to the user's total number of draws per day, week, or event period, such as 3 times per day or 10 times during the event period; lottery probability weights, indicating the winning probability of different prizes, such as 1% for first prize and 5% for second prize, ensuring reasonable consumption of prize pool resources through weight allocation; lottery cooldown time, referring to the interval between two draws, such as 10 minutes, to avoid users drawing frequently and reduce system pressure; and user lottery eligibility verification rules, used to verify whether users meet the lottery conditions, such as whether they are logged in, have completed specified tasks (such as watching ads or inviting friends), or are paying users.

[0040] The prize pool information includes prize types, including but not limited to accelerator time, acceleration traffic, membership benefits, physical prizes, coupons, etc.; the prize quantity corresponds to the total inventory of each prize, and a maximum limit can be set for a single user to win, such as a maximum of 1 win per user for the same prize; the prize distribution threshold refers to the distribution of specific prizes when the cumulative number of draws reaches a certain value or the user level reaches a specified requirement; the prize pool refresh strategy includes the refresh time, refresh method, and refresh conditions.

[0041] The device adaptation parameters include device hardware type parameters, such as device hardware model, screen size, resolution, and processor model, like a 6.7-inch mobile phone screen with a resolution of 2400×1080; device hardware performance parameters, such as CPU performance, memory size, and graphics card performance, used to adapt the rendering of the activity interface and the operation of functions on devices with different performance levels, such as simplifying the activity interface animation on low-performance devices; network bandwidth adaptation parameters, used to adjust the loading speed of activity resources according to the user's network bandwidth, such as prioritizing the loading of lightweight interface resources in low-bandwidth environments; and system version compatibility parameters, corresponding to the device's operating system version, such as Android 11 and above, iOS 15 and above, ensuring that the activity instance is compatible with the system version and avoiding functional abnormalities.

[0042] The multilingual configuration includes a list of supported languages, such as Chinese, English, Japanese, and Korean, covering the accelerator's target user group. Text translation rules indicate the text correspondence between different languages, ensuring accurate translation of promotional texts while conforming to local language habits. Time zone adaptation rules adjust the display of promotional times based on the user's time zone; for example, if the promotion starts at 10:00 AM Beijing time, it corresponds to 9:00 PM the previous day in New York time, ensuring users see the accurate promotional time. Currency unit display rules show the corresponding currency unit based on the user's region, such as displaying RMB for China and USD for the United States, making it easier for users to understand information such as package prices.

[0043] Therefore, by clearly defining configuration standards and providing specific parameters for each sub-configuration of the activity configuration information, we offer clear configuration guidelines to operators, avoiding issues such as missing or non-standard configuration parameters and improving the completeness and accuracy of activity configuration information. Furthermore, by refining various configuration parameters, such as package tiers and lottery probabilities, tailored to the characteristics of accelerator operation activities, we can achieve refined configuration of the activities, enhancing their attractiveness and conversion rates. Detailed device compatibility parameters and multi-language configuration parameters ensure that the activities can better adapt to devices with different hardware performance and system versions, as well as users in different languages ​​and time zones, further improving the user experience. Clearly defined rules for lottery eligibility verification and package activation conditions prevent problems such as rule confusion and user disputes during activity operation, ensuring the orderly conduct of the operation activities.

[0044] Furthermore, since most accelerator users are cross-border internet users, the compliance requirements and network environments vary greatly across different regions, making standard configuration parameters unsuitable for regional characteristics. Additionally, when acceleration packages are linked to promotional offers, issues such as incorrect deduction of package duration and cross-border users being unable to participate in domestic package discounts can easily arise. Therefore, the activity configuration information has been updated with regional adaptation parameters, including regional identifiers, cross-border network adaptation thresholds, and local compliance rules. Specifically, operational activity information now includes regional participation restrictions, activity package information includes region-specific discounts and cross-border package adaptation rules, and multi-language configurations include regional compliance text verification rules. The prize pool information now includes accelerator cross-border node usage rights and cross-border acceleration duration, linked to regional node resources to ensure prize distribution is compatible with regional nodes.

[0045] Furthermore, after collecting the activity configuration information in response to the received operation activity configuration generation instruction, a legality verification is also required. In this embodiment, the specific implementation method is as follows: The activity configuration information is validated for legality. If the validation fails, a configuration error message is returned. The validity validation includes parameter integrity validation, data format validation, numerical range validation, and conflict rule validation.

[0046] Because the legality of the configuration information is not verified after the activity configuration information is collected, operators are prone to problems such as missing parameters, incorrect data format, unreasonable numerical range, and rule conflicts during the configuration process. If these problems are not detected in time, they will lead to the failure of subsequent activity data model generation, abnormal operation of activity instances, and even affect the stability of the entire accelerator system, increasing operational risks and maintenance costs.

[0047] Therefore, by verifying the legitimacy of activity configuration information, issues such as missing parameters, incorrect formats, abnormal values, and rule conflicts can be detected in a timely manner, preventing incorrect configurations from entering subsequent processes. This, in turn, avoids failures in generating activity data models and abnormal operation of activity instances due to incorrect configurations, reducing operational losses and ensuring the stability of the accelerator system. By returning clear error messages, it helps operations personnel quickly locate and correct configuration errors without waiting for subsequent process failures to investigate, saving investigation and modification time. Establishing a standardized legitimacy verification mechanism ensures the standardization and accuracy of activity configuration information, providing a reliable foundation for the subsequent generation of activity data models and activity instances.

[0048] It should be noted that the legality verification is performed after the activity configuration information is collected but before the standardized activity data model is generated, ensuring that all configuration information entering the data model generation stage is legal and valid. The parameter completeness verification includes checking whether the activity configuration information contains all necessary parameters, such as whether the activity time period is missing or the activity package tier is missing; if any is missing, the verification fails. The data format verification includes checking whether the data format of each configuration parameter meets the preset requirements, such as whether the activity time is in "year-month-day hour:minute:second" format, whether the package price is in numerical format, and whether the user access conditions are preset enumerated values; if the format does not match, the verification fails. The numerical range verification includes checking whether the configuration parameters of numerical types are within a reasonable range. For example, the verification process includes checking if the total probability weight of the lottery is 100%, if the package discount ratio is between 0 and 1, and if the activity period is reasonable (e.g., the activity duration is not less than 1 day). If these conditions are exceeded, the verification will fail. The content of the conflict rule verification includes checking for rule conflicts between different configuration parameters. For example, the activity participation rule "participate 3 times per day" conflicts with the lottery configuration "draw 5 times per day," and the package stacking rule "prohibit package stacking" conflicts with the package activation condition "multiple packages can be effective simultaneously." If a conflict exists, the verification will fail. Error messages will be returned when the verification fails, such as "Incorrect activity time format, please enter 'year-month-day hour:minute:second' format" or "The total probability weight of the lottery is not 100%, please reconfigure," to facilitate quick error correction by operations personnel.

[0049] Furthermore, due to the industry-specific nature of the validity verification of parameters such as acceleration duration, node benefits, and package discounts in accelerator activity configurations, conventional verification rules cannot cover all aspects. For example, if the acceleration duration configuration exceeds the user's remaining time, the node benefit configuration exceeds the accelerator's existing node resources, or the package discount ratio conflicts with the accelerator's pricing system, it can easily lead to issues where benefits cannot be redeemed during the activity. Therefore, a new accelerator-specific verification item has been added to the validity verification process, including acceleration resource verification, package discount verification, and user benefit verification. During verification, it is linked in real time with the accelerator resource management database and the user benefit database. When the verification fails, specific prompts such as insufficient resources or rule conflicts are returned. Specifically, acceleration resource verification includes verifying whether the prize pool acceleration duration and node benefits match the accelerator's existing resources; package discount verification includes verifying whether the discount ratio and discount rules comply with the accelerator's pricing system; and user benefit verification includes verifying the match between the activity's entry conditions and the user's acceleration membership level and remaining time.

[0050] Furthermore, the process of generating a standardized activity data model based on the activity configuration information is specifically implemented in this embodiment as follows: The activity configuration information is input into a pre-trained neural network model for feature extraction, and the activity data model is generated based on the extraction results.

[0051] The pre-trained neural network model is trained on a large amount of accelerator operation activity configuration data. It possesses features and data structuring capabilities, and can employ models such as CNN and RNN. It can automatically identify various sub-configurations, parameters, and their relationships within the activity configuration information. The feature extraction process involves the neural network model performing deep analysis of the activity configuration information to extract core features, including the type, value, rules, and relationships of various configuration parameters. For example, it extracts core features such as the activity type being "package discount," the package tier being "annual package," and the discount ratio being "70% off." The process of generating the activity data model involves generating the model based on the extracted core features, following a pre-defined standardized format. The model contains all core configuration parameters with a unified format and clear relationships, facilitating subsequent matching with target UI elements and generating activity instances. Furthermore, the neural network model can be continuously trained and optimized based on the actual configuration requirements and generation results of operational activities, improving the accuracy of feature extraction and the quality of the generated data model to adapt to constantly changing accelerator operation needs.

[0052] It should be noted that before processing the activity configuration information through the pre-trained neural network model to obtain the activity data model, the activity configuration information can undergo preprocessing such as structured decomposition, field standardization mapping, and redundant data removal. The preprocessing results serve as the input to the neural network model, ultimately yielding the activity data model. Furthermore, the pre-trained neural network model includes an accelerator-specific feature extraction layer to specifically extract acceleration-related features from the activity configuration information, such as node identifiers, duration parameters, package benefits, and regional adaptation information. The training data incorporates historical accelerator activity configuration data, acceleration resource data, and user benefit data to ensure the model can accurately identify the correlation between acceleration parameters and activity configurations. The generated activity data model can directly interface with the accelerator's core service interface.

[0053] Therefore, by automatically extracting the core features of activity configuration information through a pre-trained neural network model, no manual processing is required, significantly shortening the generation time of the activity data model. The neural network model can accurately identify various parameters and their relationships in the activity configuration information, extract core features, and generate a more standardized and accurate activity data model, providing reliable support for the generation of subsequent activity instances. The pre-trained neural network model can adapt to different types and parameters of accelerator operation activity configuration information, and can quickly adapt to the configuration requirements of various operation activities without the need to develop separate processing logic for different activities. Finally, it reduces the number of manual data processing steps, avoids errors caused by manual processing, and reduces operation and maintenance costs.

[0054] Furthermore, the process of querying the device type of the target device is specifically implemented in this embodiment as follows: The device type of the target device can be queried by using one or more of the device fingerprint information, system interface information, or user agent information associated with the target device.

[0055] Among them, device fingerprint information is the unique identifier of the target device, generated by a combination of the device's hardware parameters and system parameters. It is unique, and the hardware parameters include processor model, memory size, network card address, etc. The system parameters include system version, device name, etc. The device fingerprint information is collected by the accelerator client to identify the device type. System interface information refers to the interfaces provided by the target device's operating system. By calling these interfaces, information such as device type, system version, and hardware configuration can be obtained. For example, the System interface of Android devices and the UIDevice interface of iOS devices can be used to obtain information. The information obtained by calling the interfaces is accurate and reliable. User agent information is the identification information sent by the target device's browser or client, which includes device type, operating system version, browser type, etc. For example, the web client can identify the device type by parsing the user agent information, and the mobile client can identify the specific device model and system version by the client's user agent information. However, it should be noted that some browsers may forge user agent information, and other methods need to be combined to improve accuracy.

[0056] Furthermore, since most accelerator user devices are dedicated acceleration devices, such as gaming PCs and cross-border terminals, some devices have their device fingerprints and system interface information encrypted or hidden due to the activation of acceleration services, making it impossible to accurately identify the device type using conventional query methods. Moreover, the hardware performance and network compatibility capabilities of different acceleration devices vary significantly, and inaccurate device type identification can lead to failures in matching activity instances with acceleration devices. Therefore, when querying the target device type, an accelerator client-assisted identification mechanism is added. This mechanism obtains device acceleration adaptation parameters, including acceleration mode and node adaptation type, through the accelerator client and combines them with device fingerprints and system interface information for joint identification. For encrypted devices, basic device information is decrypted through a dedicated accelerator interface to ensure accurate identification of device type, hardware performance, and network adaptation capabilities, providing a basis for UI element extraction and activity instance adaptation.

[0057] Therefore, by combining multiple query methods, the limitations of a single query method are avoided, ensuring accurate identification of the target device type, system version, hardware parameters, and other information. Accurate device type query is the foundation for extracting compatible UI elements and generating compatible activity instances, preventing activity instance adaptation failures due to incorrect device type identification and improving user experience. Furthermore, since it supports queries for different types of devices, whether Android, iOS, Windows devices, or web-based, the corresponding query method can accurately identify the device type, meeting the needs of multi-device users of the accelerator. Multiple query methods can be backed up, so if one query method fails, other methods can be used to query, ensuring the stability of the device type query process.

[0058] Furthermore, the process of generating an activity instance adapted to the target device based on the activity data model and the target UI element, using Docker containerization technology and combining code repository version tag characteristics, is specifically implemented as follows in this embodiment: Based on the code repository version tag characteristics, the corresponding version's configuration script, rendering engine, and dependency library files are retrieved. The code repository version tags include stable version tags, test version tags, and device-specific adaptation version tags. Based on the activity data model, the target UI element, the configuration script, the rendering engine, and the dependency library files, the activity instance adapted to the target device is generated using Docker containerization technology. The activity instance has corresponding independent container resource quotas and running permissions.

[0059] The code repository includes tags for different versions of resources to distinguish configuration scripts, rendering engines, and dependency library files. These tags include stable version tags, beta version tags, and device-specific adaptation tags. Specifically, the stable version tag indicates a fully tested and stable version used to generate official event instances and ensure the event runs correctly. The beta version tag indicates a version used for testing, containing new features or incompletely optimized content, used to generate test event instances for functional and compatibility testing. The device-specific adaptation tag indicates a version optimized for specific device types, ensuring that the pulled resources are highly compatible with the target device and improving the performance of the event instances.

[0060] Based on this, according to the target device type and activity requirements, the system automatically pulls configuration scripts for configuring activity function logic, rendering engines for activity interface rendering, and dependency library files for supporting the operation of various functions through code repository version tags, avoiding resource missing or version errors. Then, the activity data model, target UI elements, and pulled resources are packaged into independent Docker containers. Each container contains all the environment required for the activity instance to run, resolving environment inconsistencies. Containers are also isolated from each other, ensuring independent operation of activity instances. Furthermore, each activity instance is allocated independent CPU, memory, and disk resource quotas, rationally allocating resources according to the scale and requirements of the activity to avoid resource waste or insufficiency. Each container is also assigned independent running permissions, limiting the operational scope of the containers and preventing interference between activity instances, thus improving system security and stability. Moreover, when generating activity instances using Docker containerization technology, an accelerator core service interface adaptation module is pre-set in the container, ensuring seamless communication between the container and the accelerator interface. The code repository version tags now include node adaptation tags for cross-border and domestic nodes. Based on the acceleration node type of the target device, the system pulls resources for the corresponding node adaptation version, ensuring stable operation of activity instances in different acceleration node environments.

[0061] Therefore, by standardizing the activity instance generation process and clarifying the specific steps involved, the generation process is standardized, avoiding issues such as process confusion and resource shortages. By pulling resources of the corresponding version through code repository version tags, the appropriate version can be selected based on the target device type and activity requirements, avoiding resource conflicts between different versions and improving the stability of activity instances. Pre-set configuration scripts, rendering engines, and dependency library files can be quickly pulled without rewriting or configuring them. Combined with Docker containerization technology, this enables rapid generation of activity instances. Each activity instance is allocated an independent container resource quota and running permissions to avoid interference between different activity instances. At the same time, resources are allocated reasonably to avoid resource waste or insufficient resources leading to activity runtime anomalies. The lightweight nature of Docker containers also significantly improves the startup and running speed of activity instances. Test version tags can be used to generate test activity instances for functional and compatibility testing, while stable version tags ensure the stability of official activity instances and support rapid iteration of activity versions.

[0062] It should be noted that when generating an activity instance, the corresponding language text resources are automatically loaded and the interface is rendered based on the multilingual configuration. Specifically, the multilingual configuration in the activity configuration information is linked to the text resource library, which stores text content in all supported languages. Each text entry has a unique identifier that corresponds one-to-one with the text translation rules in the multilingual configuration.

[0063] When generating activity instances, the system automatically identifies the user's target language based on the target device's system language settings, the user's language preferences in the accelerator, or the user's location. For example, if the device's system language is set to English and the user's location is the United States, the target language is identified as English; if the user manually sets the language to Japanese in the accelerator, that manually set language is prioritized. Based on the identified target language, the system automatically loads corresponding text resources from the text resource library, including all text content in the activity interface such as the activity title, activity rules, button text, prize descriptions, package information, and prompts. The loading process is automated and requires no manual intervention, ensuring high loading efficiency.

[0064] After loading the corresponding language text resources, the system automatically renders the event interface based on the target UI elements, ensuring that the text and UI elements are compatible. For example, since text lengths differ between languages, the system automatically adjusts text layout and font size during rendering to avoid issues such as text overflow or layout errors. After the event instance is generated, users can manually switch languages ​​on the event interface. After switching, the system automatically reloads the corresponding language text resources and re-renders the interface to meet users' personalized language needs. Furthermore, the text resource library supports dynamic updates, allowing for the addition of new languages ​​and modification of text content based on operational needs without regenerating the event instance, thus improving maintenance flexibility.

[0065] Therefore, based on the multilingual configuration and the user's language, the corresponding language copy resources are automatically loaded to ensure that the copy on the activity interface is consistent with the user's language, solving the language adaptation problem in international operations. Users of different languages ​​can clearly understand the activity rules, package information, operation instructions, etc., lowering the threshold for users to participate in the activity and improving the satisfaction and participation rate of international users. There is no need to generate activity instances separately for different languages. Multilingual adaptation is achieved by automatically loading copy resources, reducing manual development and maintenance costs and improving the efficiency of the accelerator's international operations. The copy resources for all languages ​​are generated based on a unified multilingual configuration, ensuring that the copy content in different languages ​​is consistent and the information is accurate, avoiding ambiguity and information deviation, and ensuring the standardization of the operation activities.

[0066] Furthermore, after generating the activity instance adapted to the target device, testing is also required. In this embodiment, the specific implementation method is as follows: The activity instance is subjected to functional testing, compatibility testing, and performance stress testing. If any test fails, the step of generating a standardized activity data model based on the activity configuration information is executed. If all test results pass, the step of deploying the activity instance in the target operating environment corresponding to the target device is executed.

[0067] The functional testing includes verifying the normal operation of all functions of the activity instance and whether they meet the activity configuration requirements. This includes checking if the package discount function works correctly, the lottery function functions properly, prize distribution is accurate, and multilingual display is correct, ensuring the core functions of the activity can be used normally. The compatibility testing includes testing the adaptation of the activity instance on target devices, including whether the interface display is normal (e.g., whether UI elements are misaligned, whether text is clear), whether the functions are compatible (e.g., compatibility with the accelerator client, compatibility with the device system), and compatibility between different device models, ensuring the activity instance can run normally on various target devices.

[0068] The performance stress test assesses the performance of the activity instance under high concurrency and high load conditions. For example, it measures the activity's response speed, loading speed, and stability when a large number of users access the activity simultaneously, ensuring the instance can handle the expected user traffic and avoid issues such as lag or crashes. Additionally, targeted test items specific to the accelerator scenario can be added, including acceleration collaboration tests, node switching tests, and benefit redemption tests. The test environment simulates acceleration scenarios with different nodes, bandwidths, and acceleration modes to ensure the activity instance is compatible with all accelerator scenarios. The acceleration collaboration test includes testing the resource consumption and response speed of the activity instance and acceleration service running simultaneously; the node switching test includes testing the stability of the activity instance during cross-border / domestic node switching; and the benefit redemption test includes testing the acceleration duration and the synchronization between the node benefit distribution and the accelerator user database.

[0069] Therefore, through multi-dimensional testing, we can promptly identify functional defects, compatibility issues, and performance deficiencies in activity instances, preventing problematic activity instances from going live. This ensures that the launched activity instances function correctly, are compatible with target devices, meet performance standards, and run stably, improving user experience and reducing operational losses. When tests fail, we regenerate the activity data model and then regenerate the activity instance, forming a closed-loop optimization to ensure that the activity instance meets operational needs. By resolving issues in activity instances in advance, we avoid fixing problems after launch, reducing subsequent maintenance costs and operational risks.

[0070] After generating an activity instance adapted to the target device, the process of deploying the activity instance in the target runtime environment corresponding to the target device adopts a canary release strategy to gradually push the activity instance to the target device.

[0071] The canary release strategy is a phased and scoped deployment method. Its core is to gradually increase the number of users. Specifically, it can be divided into three phases. The first phase pushes the deployment to 10%-20% of the target device users, mainly focusing on users with low activity levels, to initially test the operational stability of the active instances. The second phase, based on the operational data and user feedback from the first phase, fixes the issues and then pushes the deployment to 50%-60% of the target device users to expand the testing scope. The third phase, after confirming that the active instances are normal, pushes the deployment to 100% of the target device users, completing the full deployment.

[0072] The rollout strategy allows for flexible selection of the push scope based on accelerator user attributes such as user level, region, device type, and activity level. For example, the first phase prioritizes pushes to existing and low-activity users to avoid impacting the user experience of new and highly active users. Separate rollouts can be conducted for different device types to ensure compatibility and stability. Furthermore, at each stage of the rollout, the operational status and user behavior data of the activity instances are monitored in real time, establishing an early warning mechanism. If any anomalies occur, the push for that stage is immediately paused, and troubleshooting and repair are initiated. The rollout resumes only after the issue is resolved. Operational status data includes response speed, crash rate, and number of errors; user behavior data includes participation rate, operation success rate, and feedback content. Moreover, by linking the rollout strategy with the accelerator's user management system and device management system, target devices and user groups are accurately identified, enabling precise pushes of activity instances. The push scope can also be flexibly adjusted, allowing for faster or slower rollout speeds to adapt to different operational scenarios.

[0073] Furthermore, the canary release strategy needs to be combined with the acceleration node type of the target device, the user acceleration mode, and the user region to divide the push scope. In the first stage, priority should be given to pushing to a small group of users with the same node type and acceleration mode. The running status of the activity instance in the corresponding acceleration scenario should be monitored to ensure that the activity instance is compatible with the acceleration node and acceleration mode before gradually expanding the push scope.

[0074] Therefore, by gradually expanding the scope of the activity instances through canary releases, initially pushing them to a small number of target device users, any problems discovered can be stopped in time to prevent the issues from spreading to all users, minimizing operational losses caused by deployment errors. After a small-scale rollout, user feedback and activity operation data can be collected in real time to promptly identify and fix any undetected defects. Once the activity instances are stable, they can be pushed to a wider range of users, ensuring that most users experience stable and smooth activity instances, thus improving the user experience. During the canary release process, activity parameters and interface styles can be flexibly adjusted based on user feedback and operation data without stopping the activity deployment, improving the flexibility and efficiency of activity optimization. Gradually pushing out activity instances avoids peak system pressure caused by a large number of users accessing the activity simultaneously, ensuring the operational stability of the accelerator system and activity instances, and preventing system lag, crashes, and other problems.

[0075] After the activity instances are deployed, the system also includes real-time collection of activity instance runtime logs and monitoring of instance runtime status. This includes real-time collection of various data during the activity instance's operation, including but not limited to device information, user operation logs, activity runtime logs, and resource usage logs. Log collection employs a lightweight approach to avoid excessive device resource consumption and ensure uninterrupted user experience of the activity and accelerator functions. Device information includes device model, system version, and network status; user operation logs include clicked controls, operation time, and operation results; activity runtime logs include interface loading time, function execution status, error messages, and prize distribution records; and resource usage logs include CPU utilization, memory usage, and network bandwidth consumption.

[0076] It's important to note that the core monitoring metrics include: activity instance availability (whether it can start and load normally); response speed (including interface loading time and operation response time); stability (including crash rate, number of errors, and number of abnormal exits); and resource consumption (CPU, memory, and network usage). Preset thresholds are set, and alerts are automatically triggered when monitoring metrics exceed these thresholds. Collected operational logs are stored on a dedicated log server, supporting log classification, retrieval, and analysis. Technical personnel can use log analysis tools to quickly filter abnormal logs and pinpoint the causes of anomalies. Statistical analysis of log data can also be performed to generate activity operation reports, providing data references for activity optimization and subsequent operational configuration. The monitoring scope covers all target devices with deployed activity instances, including users with different device types, regions, and network environments, ensuring comprehensive monitoring and avoiding omissions of anomalies from any users.

[0077] Furthermore, while collecting the running logs of the activity instances in real time, the system also collects the status logs of the accelerator core service, which include node running status, bandwidth fluctuations, and acceleration latency. When monitoring the running status of the instances, a correlation monitoring mechanism between the activity instances and the acceleration service is established to distinguish between anomalies in the activity itself and anomalies in the acceleration service, generating corresponding monitoring alerts for each. At the same time, the system records the impact of acceleration service anomalies on the activity instances, providing accurate evidence for anomaly investigation.

[0078] Therefore, by collecting operation logs and monitoring the operation status in real time, operational anomalies of activity instances can be detected in a timely manner, preventing the anomalies from continuously affecting users and minimizing the loss of user experience. The operation logs contain key data such as the time of the anomaly, device information, operation steps, and error messages, which can help technicians quickly locate the cause of the anomaly, shorten the troubleshooting and repair time, and reduce maintenance costs. By analyzing the operation logs and operation data, such as user operation paths, participation duration, and function usage frequency, shortcomings in the activity design can be identified, providing data support for activity optimization and improving the attractiveness of the activity and user experience. Real-time monitoring of the operation status of activity instances can promptly detect the impact of activity instances on the accelerator system, preventing the accelerator system from freezing or crashing due to activity instance anomalies, and ensuring the stable operation of the entire accelerator system.

[0079] When an abnormal operation of an activity instance is detected, a rollback mechanism is automatically triggered and a backup activity instance is switched to. An activity instance is considered abnormal if any of the following occurs: activity instance crashes or fails to load normally; interface lag time exceeds a preset threshold; function execution fails; error count exceeds a preset threshold; resource consumption remains excessively high; or network connection abnormalities prevent normal interaction of the activity.

[0080] Furthermore, the rollback mechanism refers to reverting the malfunctioning activity instance to a previously stable version. The rollback process is automatic and requires no manual intervention. During rollback, user activity data, such as participation counts, winning records, and purchased packages, is preserved to prevent data loss and ensure an unaffected user experience. The rollback version can be quickly retrieved from the code repository based on version tags, ensuring efficient rollback. The backup activity instance is a pre-generated version with identical functionality to the current activity instance, based on the activity configuration information. It is deployed in a backup environment identical to the target runtime environment, remaining in standby mode. The backup instance is synchronized with the current activity instance to ensure a consistent user experience after the switch and avoid functional differences.

[0081] Based on this, upon detecting an anomaly, the system automatically interrupts the operation of the current abnormal activity instance, releases its occupied resources, and simultaneously starts a backup activity instance. User requests are seamlessly switched to the backup instance, with the entire switchover process completed in the background, ensuring users are unaware of the change without page refreshes or operational interruptions. After the switchover is complete, the system sends an anomaly warning and switchover notification to technical personnel, facilitating their investigation and repair of the original activity instance's anomaly. Once the anomaly in the original activity instance is repaired, a canary release strategy can be used to gradually switch users back from the backup instance to the repaired original activity instance, ensuring a smooth switchover and preventing recurrence of the anomaly.

[0082] Furthermore, before triggering the rollback mechanism and switching to the standby activity instance, it is necessary to first determine whether the cause of the exception is an acceleration service exception. If it is an acceleration service exception, the accelerator node switching mechanism is triggered simultaneously to switch the user acceleration node to the standby node, and then switch the standby activity instance to ensure that the standby activity instance and the standby acceleration node are compatible. If the exception is the activity instance itself, after switching to the standby instance, the acceleration service status is monitored simultaneously to avoid the activity instance and the acceleration service from conflicting again.

[0083] Therefore, by automatically triggering a handling mechanism upon detecting anomalies, no manual intervention is required, significantly shortening anomaly handling time, preventing anomalies from continuously impacting users, and minimizing user experience and operational losses. After switching to a backup activity instance, users can continue to participate in activities, avoiding activity interruptions due to activity instance anomalies, ensuring the normal operation of accelerator activities, and improving user satisfaction. Automated anomaly handling reduces manual intervention, allowing technical personnel to focus on troubleshooting and fixing problems without real-time monitoring, thus reducing maintenance costs. Through rollback mechanisms and backup instance switching, a closed loop of anomaly handling is formed, ensuring that activity instances can recover quickly when problems occur, improving the reliability and stability of accelerator operations.

[0084] Corresponding to the above method embodiments, this application also provides an embodiment of an online operation activity configuration generation device. Figure 2 A schematic diagram of an online operation activity configuration generation device according to an embodiment of this application is shown. Figure 2 As shown, the device includes: The data acquisition module 202 is configured to collect activity configuration information in response to a received operation activity configuration generation instruction. The activity configuration information includes operation activity information, activity package information, lottery configuration, prize pool information, device adaptation parameters, and multi-language configuration. The first generation module 204 is configured to generate a standardized activity data model based on the activity configuration information; The UI element extraction module 206 is configured to query the device type of the target device and extract UI elements according to the device type to obtain the target UI elements; The second generation module 208 is configured to generate an activity instance adapted to the target device based on the activity data model and the target UI element, using Docker containerization technology and combined with code repository version tag characteristics. The deployment module 210 is configured to deploy the active instance in the target operating environment corresponding to the target device.

[0085] In an optional embodiment, the acquisition module 202 is further configured to: The operational activity information includes activity type, activity time period, activity participation rules, and user access conditions; the activity package information includes package tiers, package discount percentages, package activation conditions, and package stacking rules; the lottery configuration includes lottery draw limit, lottery probability weight, lottery cooldown time, and user lottery eligibility verification rules; the prize pool information includes prize type, prize quantity, prize distribution threshold, and prize pool refresh strategy; the device adaptation parameters include device hardware type parameters, device hardware performance parameters, network bandwidth adaptation parameters, and system version compatibility parameters; the multilingual configuration includes a language list, text translation rules, time zone adaptation rules, and currency unit display rules.

[0086] In an optional embodiment, the online operation activity configuration generation device further includes: The verification module is configured to perform a validity check on the activity configuration information. When the verification fails, it returns a configuration error message. The validity check includes parameter integrity check, data format check, numerical range check, and conflict rule check.

[0087] In an optional embodiment, the first generation module 204 is further configured to: The activity configuration information is input into a pre-trained neural network model for feature extraction, and the activity data model is generated based on the extraction results.

[0088] In an optional embodiment, the UI element extraction module 206 is further configured to: The device type of the target device can be queried by using one or more of the device fingerprint information, system interface information, or user agent information associated with the target device.

[0089] In an optional embodiment, the second generation module 208 is further configured to: Based on the code repository version tag characteristics, the corresponding version's configuration script, rendering engine, and dependency library files are retrieved. The code repository version tags include stable version tags, test version tags, and device-specific adaptation version tags. Based on the activity data model, the target UI element, the configuration script, the rendering engine, and the dependency library files, the activity instance adapted to the target device is generated using Docker containerization technology. The activity instance has corresponding independent container resource quotas and running permissions.

[0090] In an optional embodiment, the online operation activity configuration generation device further includes: The testing module is configured to perform functional testing, compatibility testing, and performance stress testing on the activity instance. If any test result fails, the first generation module 204 is invoked; if all test results pass, the deployment module 210 is invoked.

[0091] The online operational activity configuration generation device provided in this application, in response to a received operational activity configuration generation command, collects activity configuration information, including operational activity information, activity package information, lottery configuration, prize pool information, device adaptation parameters, and multi-language configuration. Based on the activity configuration information, it generates a standardized activity data model; queries the device type of the target device and extracts UI elements according to the device type to obtain target UI elements; based on the activity data model and the target UI elements, it generates an activity instance adapted to the target device using Docker containerization technology combined with code repository version tag characteristics; and deploys the activity instance in the target runtime environment corresponding to the target device. This solves the problem of repeatedly implementing the same operational activity on different devices and the application challenge of the same activity system on different products. Through standardization and modular processing, it automates activity generation, significantly improves the development efficiency of online operational activities, reduces maintenance costs, and supports rapid expansion to new device and product types, providing enterprises with an efficient and unified operational activity solution.

[0092] The above is a schematic scheme of an online operation activity configuration generation device according to this embodiment. It should be noted that the technical solution of this online operation activity configuration generation device and the technical solution of the above-described online operation activity configuration generation method belong to the same concept. Details not described in detail in the technical solution of the online operation activity configuration generation device can be found in the description of the technical solution of the above-described online operation activity configuration generation method. Furthermore, the components in the device embodiment should be understood as functional modules necessary to implement each step of the program flow or each step of the method; these functional modules are not actual functional divisions or separations. A device defined by such a set of functional modules should be understood as primarily implementing the functional module architecture of the solution through the computer program described in the specification, and not as a physical device primarily implementing the solution through hardware.

[0093] Figure 3 A structural block diagram of a computing device 300 according to an embodiment of this application is shown. The components of the computing device 300 include, but are not limited to, a memory 310 and a processor 320. The processor 320 is connected to the memory 310 via a bus 330, and a database 350 is used to store data.

[0094] The computing device 300 also includes an access device 340, which enables the computing device 300 to communicate via one or more networks 360. Examples of these networks include a Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or a combination of communication networks such as the Internet. The access device 340 may include one or more of any type of wired or wireless network interface (e.g., a Network Interface Card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) interface, a Wi-MAX interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, a Near Field Communication (NFC) interface, and so on.

[0095] In one embodiment of this application, the aforementioned components of the computing device 300 and Figure 3 Other components, not shown, can also be connected to each other, for example, via a bus. It should be understood that... Figure 3 The block diagram of the computing device shown is for illustrative purposes only and is not intended to limit the scope of this application. Those skilled in the art can add or replace other components as needed.

[0096] The computing device 300 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or PCs. The computing device 300 can also be a mobile or stationary server.

[0097] The processor 320 is used to execute computer-executable instructions for each step of the online operation activity configuration generation method.

[0098] The above is an illustrative scheme of a computing device according to this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the online operation activity configuration generation method described above belong to the same concept. For details not described in detail in the technical solution of the computing device, please refer to the description of the technical solution of the online operation activity configuration generation method described above.

[0099] An embodiment of this application also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, are used to implement the steps of the online operation activity configuration generation method.

[0100] The above is an illustrative scheme of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium and the technical solution of the online operation activity configuration generation method described above belong to the same concept. For details not described in detail in the technical solution of the storage medium, please refer to the description of the technical solution of the online operation activity configuration generation method described above.

[0101] An embodiment of this application also provides a chip that stores a computer program, which, when executed by the chip, implements the steps of the online operation activity configuration generation method.

[0102] The foregoing has described specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0103] The computer instructions include computer program code, which may be in the form of source code, object code, executable file, or certain intermediate forms. The computer-readable medium may include any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added to or subtracted according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0104] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0105] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0106] The preferred embodiments disclosed above are merely illustrative of this application. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this application. These embodiments are selected and specifically described in this application to better explain the principles and practical applications of this application, thereby enabling those skilled in the art to better understand and utilize this application. This application is limited only by the claims and their full scope and equivalents.

Claims

1. A method for generating online operational activity configurations, characterized in that, include: In response to the received operation activity configuration generation instruction, the activity configuration information is collected, wherein the activity configuration information includes operation activity information, activity package information, lottery configuration, prize pool information, device adaptation parameters and multi-language configuration; Based on the activity configuration information, a standardized activity data model is generated. Specifically, the activity configuration information is input into a pre-trained neural network model for feature extraction, and the activity data model is generated based on the extraction results. The feature extraction process is as follows: the neural network model performs in-depth analysis on the activity configuration information and extracts the core features, including the type, value, rules, and correlation of various configuration parameters. Query the device type of the target device, and extract the UI elements based on the device type to obtain the target UI elements; Based on the activity data model and the target UI element, an activity instance adapted to the target device is generated using Docker containerization technology and combined with code repository version tag characteristics. Specifically, the configuration script, rendering engine, and dependency library files of the corresponding version are pulled according to the code repository version tag characteristics. The code repository version tags include stable version tags, test version tags, and device-specific adaptation version tags. Based on the activity data model, the target UI element, the configuration script, the rendering engine, and the dependency library files, the activity instance adapted to the target device is generated using Docker containerization technology. The activity instance has corresponding independent container resource quotas and running permissions. The activity instance is deployed in the target operating environment corresponding to the target device.

2. The method according to claim 1, characterized in that, The operational activity information includes activity type, activity time period, activity participation rules, and user access conditions; The activity package information includes package tiers, package discount percentages, package activation conditions, and package stacking rules; The lottery configuration includes a limit on the number of draws, a lottery probability weight, a lottery cooldown time, and rules for verifying user eligibility to participate in the lottery. The prize pool information includes prize type, prize quantity, prize distribution threshold, and prize pool refresh strategy; The device adaptation parameters include device hardware type parameters, device hardware performance parameters, network bandwidth adaptation parameters, and system version compatibility parameters. The multilingual configuration includes a list of languages, text translation rules, time zone adaptation rules, and currency unit display rules.

3. The method according to claim 1, characterized in that, After receiving the operational activity configuration generation instruction and collecting the activity configuration information, the process further includes: The activity configuration information is validated for legality. If the validation fails, a configuration error message is returned. The validity validation includes parameter integrity validation, data format validation, numerical range validation, and conflict rule validation.

4. The method according to claim 1, characterized in that, The device type of the target device being queried includes: The device type of the target device can be queried by using one or more of the device fingerprint information, system interface information, or user agent information associated with the target device.

5. The method according to claim 1, characterized in that, After generating the activity instance adapted to the target device, the process further includes: Perform functional testing, compatibility testing, and performance stress testing on the activity instance. If any test result fails, execute the step of generating a standardized activity data model based on the activity configuration information. Once all test results are passed, proceed with the step of deploying the activity instance in the target operating environment corresponding to the target device.

6. An online operational activity configuration generation device, characterized in that, include: The data collection module is configured to collect activity configuration information in response to a received operation activity configuration generation instruction. The activity configuration information includes operation activity information, activity package information, lottery configuration, prize pool information, device adaptation parameters, and multi-language configuration. The first generation module is configured to generate a standardized activity data model based on the activity configuration information. Specifically, the activity configuration information is input into a pre-trained neural network model for feature extraction, and the activity data model is generated based on the extraction results. The feature extraction process is as follows: the neural network model performs in-depth analysis on the activity configuration information and extracts the core features, including the type, value, rules, and correlation of various configuration parameters. The UI element extraction module is configured to query the device type of the target device and extract UI elements based on the device type to obtain the target UI elements. The second generation module is configured to generate an activity instance adapted to the target device based on the activity data model and the target UI element, using Docker containerization technology and code repository version tag characteristics. Specifically, it pulls the corresponding version of the configuration script, rendering engine, and dependency library files according to the code repository version tag characteristics. The code repository version tags include stable version tags, test version tags, and device-specific adaptation version tags. Based on the activity data model, the target UI element, the configuration script, the rendering engine, and the dependency library files, the activity instance adapted to the target device is generated using Docker containerization technology. The activity instance has corresponding independent container resource quotas and running permissions. The deployment module is configured to deploy the activity instance in the target runtime environment corresponding to the target device.

7. A computing device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the steps of the online operation activity configuration generation method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing computer instructions, characterized in that, When executed by the processor, this instruction implements the steps of the online operation activity configuration generation method according to any one of claims 1 to 5.

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