Method and system for quickly generating game gift bag

Abstract

The invention relates to a game gift bag rapid generation method and system, and the method comprises the steps: building a game gift bag if a developer wants to generate the game gift bag in a customized manner, and carrying out the customized configuration of the game gift bag; if the developer needs to generate the game gift bag according to the player demand, generating a personalized recommended game gift bag according to the player behavior data by collecting the player behavior data; checking and testing the game gift bag; the game gift bag is released, and game gift bag sales data are collected; player behavior data and gift bag use data are collected, a machine learning algorithm is used for analyzing the player behavior data and the gift bag use data, game gift bags are recommended to players, and the content and price of the game gift bags are dynamically adjusted. And related attributes of the game gift bag can be dynamically adjusted according to real-time data.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of game development, and particularly to a game gift package rapid generation method and system. BACKGROUND

[0002] Commonly, the game gift package production system commonly seen in the market mainly includes manual configuration and simple automatic verification tools. These systems usually have the following functions: 1. Manual configuration of gift package content: manually select props, resources and other content through the management interface; 2. Basic verification function: provide basic configuration format verification and numerical verification; 3. Release and management: support the release and simple use data monitoring of the gift package.

[0003] The above system can only meet the basic needs, and is low in efficiency and lacks intelligent recommendation: the existing system relies on manual experience to select gift package content, lacks intelligent recommendation mechanism, and cannot make personalized recommendations according to player behavior data; the existing system has low automation, and the gift package configuration and verification process is time-consuming and prone to errors; the existing system cannot dynamically adjust the gift package content and price according to real-time data, and is difficult to respond to market changes and player needs. SUMMARY

[0004] In order to overcome the problems of lack of intelligent recommendation, low automation and lack of dynamic adjustment in the prior art, the purpose of the present application is to provide a game gift package rapid generation method and system, which can intelligently generate game gift packages, has high automation, and can dynamically adjust the related attributes of the game gift package according to real-time data.

[0005] The present application adopts the following scheme to realize:

[0006] A game gift package rapid generation method, the method steps are as follows:

[0007] Step 1: If the developer wants to customize the generation of the game gift package, create the game gift package and customize the configuration of the game gift package. After the configuration is completed, go to step 3; if the developer needs to quickly generate the game gift package according to the player's needs, go to step 2;

[0008] Step 2: Generate a personalized recommended game gift package according to the player behavior data by collecting the player behavior data;

[0009] Step 3: Verify and test the game gift package;

[0010] Step 4: Release the game gift package and collect the game gift package sales data;

[0011] Step 5: Collect player behavior data and gift package usage data, analyze player behavior data and gift package usage data using machine learning algorithms, recommend game gift packages to players, and dynamically adjust the content and price of the game gift package.

[0012] Furthermore, step 1 specifically involves: customizing the game gift pack configuration, including selecting a game gift pack template or creating a custom game gift pack template, configuring the contents of the game gift pack, configuring the acquisition conditions and validity period of the gift pack, and setting the price;

[0013] The options include selecting a pre-created game gift pack template, or entering basic game gift pack information to create a custom template.

[0014] Furthermore, step 2 specifically involves: continuously collecting player behavior data, including login frequency, spending records, game duration, and task status; cleaning and preprocessing the player behavior data; and using collaborative filtering and deep learning algorithms to generate personalized game gift packs based on the cleaned and preprocessed player behavior data.

[0015] Furthermore, step 3 specifically involves: automatically verifying the format, values, and dependencies of the gift pack configuration file, checking the rationality and validity of the gift pack content, and simulating the process of players receiving and using the gift pack.

[0016] Furthermore, step 4 specifically involves: the release of game gift packs includes pre-release and official release, with pre-release used for testing and also includes setting up a release review process; real-time monitoring of gift pack sales data and player feedback, and optimization of gift pack strategies through data reports and trend analysis.

[0017] Furthermore, step 5 specifically involves: after collecting player behavior data and gift pack usage data, performing data cleaning and preprocessing on the collected player behavior data and gift pack usage data. Data cleaning includes removing outliers and missing values ​​from the data, and preprocessing includes normalizing and standardizing the player behavior data and gift pack usage data.

[0018] After data processing, players are grouped using algorithms such as K-means clustering, and different gift packs are recommended for different player groups. Finally, regression analysis and neural network models are used to predict players' demand for different gift packs and optimize the content and price of the gift packs.

[0019] A game gift pack rapid generation system, the system comprising: a game gift pack custom creation module, a game gift pack intelligent generation module, a verification and testing module, a release management module, and a data analysis module;

[0020] The game gift pack customization module is used to create game gift packs and customize their configurations.

[0021] The game gift pack intelligent generation module is used to collect player behavior data and generate personalized recommended game gift packs based on the player behavior data.

[0022] The verification and testing module is used to verify and test game gift packs;

[0023] The release management module is used to release game gift packs and collect game gift pack sales data;

[0024] The data analysis module is used to collect player behavior data and gift pack usage data, use machine learning algorithms to analyze player behavior data and gift pack usage data, recommend game gift packs to players, and dynamically adjust the content and price of game gift packs.

[0025] Furthermore, the custom game gift pack creation module specifically includes: custom configuration of game gift packs, which includes selecting a game gift pack template or creating a custom game gift pack template, configuring the contents of the game gift pack, configuring the acquisition conditions and validity period of the gift pack, and pricing;

[0026] The options include selecting a pre-created game gift pack template, or entering basic game gift pack information to create a custom template.

[0027] Furthermore, the intelligent game gift pack generation module specifically involves: continuously collecting player behavior data, including login frequency, spending records, game duration, and task status; cleaning and preprocessing the player behavior data; and using collaborative filtering and deep learning algorithms to generate personalized recommended game gift packs based on the cleaned and preprocessed player behavior data.

[0028] Furthermore, the verification and testing module specifically performs the following: automatically verifies the format, values, and dependencies of the gift pack configuration file, checks the rationality and validity of the gift pack content, and simulates the process of players receiving and using the gift pack.

[0029] Furthermore, the release management module specifically includes: game gift pack release includes pre-release and official release, with pre-release used for testing and also includes setting up the release review process; real-time monitoring of gift pack sales data and player feedback, and optimization of gift pack strategies through data reports and trend analysis.

[0030] Furthermore, the data analysis module specifically involves: after collecting player behavior data and gift pack usage data, performing data cleaning and preprocessing on the collected player behavior data and gift pack usage data. Data cleaning includes removing outliers and missing values ​​from the data, and preprocessing includes normalizing and standardizing the player behavior data and gift pack usage data.

[0031] After data processing, players are grouped using algorithms such as K-means clustering, and different gift packs are recommended for different player groups. Finally, regression analysis and neural network models are used to predict players' demand for different gift packs and optimize the content and price of the gift packs.

[0032] The beneficial effects of this invention are as follows:

[0033] This invention provides a method and system for rapidly generating game gift packs. By introducing intelligent recommendation algorithms and multi-dimensional automated verification mechanisms, it constructs an efficient, intelligent, and secure method and system for rapidly creating game gift packs. This enables the rapid creation, configuration, release, and management of gift packs, and improves the economic benefits and player satisfaction of gift packs through real-time data analysis and automated optimization. Attached Figure Description

[0034] Figure 1 This is a flowchart of the method of the present invention;

[0035] Figure 2 This is a structural block diagram of the system of the present invention. Detailed Implementation

[0036] The invention will now be further described with reference to the accompanying drawings.

[0037] See Figure 1 A method for quickly generating game gift packs, the steps of which are as follows:

[0038] Step 1: If the developer wants to generate a custom game gift pack, create the game gift pack and customize its configuration. After configuration, proceed to Step 3; if the developer needs to quickly generate game gift packs based on player needs, proceed to Step 2.

[0039] Step 2: By collecting player behavior data, generate personalized game gift pack recommendations based on the player behavior data;

[0040] Step 3: Verify and test the game gift pack;

[0041] Step 4: Release the game gift packs and collect sales data.

[0042] Step 5: Collect player behavior data and gift pack usage data, use machine learning algorithms to analyze player behavior data and gift pack usage data, recommend game gift packs to players, and dynamically adjust the contents and prices of game gift packs.

[0043] The present invention will be further described below with reference to a specific embodiment:

[0044] A method for quickly generating game gift packs, the method comprising the following steps:

[0045] Step 1: When developers want to create custom game gift packs, they can choose to select a recommended gift pack template suitable for the current event or choose to create a custom gift pack. After choosing to create a custom gift pack, developers can enter basic information such as the gift pack name, description, and icon. After the game gift pack is created, the developers can configure the contents of the game gift pack, the conditions for obtaining the gift pack, its validity period, and its pricing. When configuring the game gift pack, the developers can flexibly add and configure the contents of the game gift pack through drag-and-drop operations.

[0046] The options include selecting a pre-created game gift pack template, or entering basic game gift pack information to create a custom template.

[0047] Configure game gift pack contents as follows:

[0048] Users can select items, resources, virtual currency, etc. in the gift pack and set the quantity and attributes;

[0049] Item selection: Select items from the system database, supporting search and category browsing;

[0050] Quantity and attribute settings: Set the quantity, quality, validity period, and other attributes of each item to ensure the diversity and attractiveness of the game gift pack content;

[0051] The conditions for obtaining the configuration gift pack and its validity period are as follows:

[0052] Conditions for obtaining the gift pack include: task completion, level restrictions, VIP level, etc.

[0053] Validity period setting: Specify the start and end dates of the gift pack to ensure that the gift pack is valid for the specified period.

[0054] Step 2: Continuously collect player behavior data, including login frequency, spending records, game duration, and task status. Clean and preprocess the player behavior data, and use collaborative filtering and deep learning algorithms to generate personalized game gift packs based on the cleaned and preprocessed player behavior data. Collaborative filtering and deep learning algorithms for personalized recommendations are existing technologies and will not be discussed further here.

[0055] Data cleaning, such as:

[0056] 1. Handling missing values: If the proportion of missing values ​​is small, you can delete, fill in missing values, and fill in the mean, median, mode, etc.

[0057] 2. Handling duplicate values: Delete, or merge and retain, etc., according to business rules;

[0058] 3. Handling outliers: Delete or modify outliers, or retain them.

[0059] 4. Consistency check: Data types are consistent, and date, time, currency, and other formats are uniform;

[0060] 5. Data standardization: such as removing spaces, standardizing encoding, etc.

[0061] Preprocessing is as follows:

[0062] 1. Data Conversion: Data type conversion, date conversion;

[0063] 2. Feature Engineering: Generate features based on existing data, taking into account data relevance and business requirements;

[0064] 3. Data standardization and normalization:

[0065] Standard; convert the data into a standard normal distribution with a mean of 0 and a standard deviation of 1;

[0066] Normalization: Scaling data to a specific range (usually 0 to 1);

[0067] 4. Data Splitting: Divide the dataset into training and testing sets to ensure that different data are used for model training and evaluation. Use cross-validation to further split the dataset and enhance the model's generalization ability.

[0068] 5. Dimensional reduction

[0069] Principal Component Analysis (PCA): Reduces the dimensionality of data while retaining key features.

[0070] Feature selection: Select the features that are most useful to the model based on their importance or relevance.

[0071] Collaborative filtering: Analyzes players' historical behavior and recommends gift packs that match the preferences of similar players.

[0072] Deep learning: Utilize neural network models to predict players' preferences for different gift pack contents and generate personalized recommendations.

[0073] This solution can adjust the contents of gift packs and recommendation strategies in real time based on the latest player behavior data, ensuring the timeliness and accuracy of the recommendation results and the continued attractiveness of the gift packs. Alternatively, it can modify the generated gift packs based on game balance. This step can also be customized.

[0074] Step 3: Automatically verify the format, values, and dependencies of the gift pack configuration file, check the rationality and validity of the gift pack content, and simulate the process of players receiving and using the gift pack.

[0075] Format validation: Ensures the configuration file is in the correct format to avoid gift pack generation failure due to incorrect format;

[0076] Numerical verification: Checks whether the values ​​in the configuration are within a reasonable range to prevent abnormal values ​​from affecting game balance;

[0077] Dependency verification: Verify the dependencies between various items and resources in the gift pack to avoid conflicts and duplication.

[0078] Reasonableness check: Checks the reasonableness of the gift pack contents through preset rules, such as restrictions on the quantity and quality of items.

[0079] Validity check: Verify the validity of items to ensure that all items can be used in the game.

[0080] Simulate the player's action of claiming a gift pack to verify the correctness of the gift pack claiming function.

[0081] Simulate the process of players using gift packs to check the correctness of the gift pack contents and their functional effects.

[0082] Step 4: The release of game gift packs includes pre-release and official release. Pre-release is used for testing and also includes setting up a release review process; real-time monitoring of gift pack sales data and player feedback, and optimization of gift pack strategies through data reports and trend analysis.

[0083] Phased release: Supports pre-release and official release of gift packs, facilitating testing and official launch;

[0084] Release Review: Set up a release review process to ensure that the contents of the gift pack are checked by multiple parties before being launched;

[0085] Real-time monitoring of gift pack sales data is used to understand sales trends and player purchasing behavior;

[0086] Player feedback collection: Collect player feedback on gift packs, analyze feedback data, and optimize gift pack content and strategies.

[0087] Step 5: After collecting player behavior data and gift pack usage data, perform data cleaning and preprocessing on the collected player behavior data and gift pack usage data. Data cleaning includes removing outliers and missing values ​​from the data, and preprocessing includes normalizing and standardizing the player behavior data and gift pack usage data.

[0088] After data processing, players are grouped using algorithms such as K-means clustering, and different gift packs are recommended for different player groups. Finally, regression analysis and neural network models are used to predict players' demand for different gift packs and optimize the content and price of the gift packs.

[0089] Player behavior data includes login frequency, game duration, spending history, and task completion status.

[0090] Gift pack usage data: including the number of times the gift pack was claimed, its usage, and the amount of items consumed.

[0091] Data cleaning: Cleaning up outliers and missing values ​​in the data to ensure its accuracy and consistency.

[0092] Data preprocessing: Normalizing and standardizing the data to facilitate subsequent analysis and modeling.

[0093] See Figure 2 A game gift pack rapid generation system, the system comprising: a game gift pack custom creation module, a game gift pack intelligent generation module, a verification and testing module, a release management module, and a data analysis module;

[0094] The game gift pack customization module is used to create game gift packs and customize their configurations.

[0095] The game gift pack intelligent generation module is used to collect player behavior data and generate personalized recommended game gift packs based on the player behavior data.

[0096] The verification and testing module is used to verify and test game gift packs;

[0097] The release management module is used to release game gift packs and collect game gift pack sales data;

[0098] The data analysis module is used to collect player behavior data and gift pack usage data, use machine learning algorithms to analyze player behavior data and gift pack usage data, recommend game gift packs to players, and dynamically adjust the content and price of game gift packs.

[0099] In one embodiment of the present invention, the game gift pack customization creation module specifically includes: custom configuration of game gift packs, including selecting a game gift pack template or creating a custom game gift pack template, configuring the content of the game gift pack, configuring the acquisition conditions and validity period of the gift pack, and pricing;

[0100] The options include selecting a pre-created game gift pack template, or entering basic game gift pack information to create a custom template.

[0101] In one embodiment of the present invention, the intelligent game gift pack generation module specifically involves: continuously collecting player behavior data, including login frequency, consumption records, game duration, and task status; cleaning and preprocessing the player behavior data; and using collaborative filtering and deep learning algorithms to generate personalized recommended game gift packs based on the cleaned and preprocessed player behavior data.

[0102] In one embodiment of the present invention, the verification and testing module specifically performs the following functions: automatically verifies the format, values, and dependencies of the gift pack configuration file, checks the rationality and validity of the gift pack content, and simulates the process of players receiving and using the gift pack.

[0103] In one embodiment of the present invention, the release management module specifically includes: game gift pack release includes pre-release and official release, wherein the pre-release is used for testing and also includes setting up a release review process; real-time monitoring of gift pack sales data and player feedback, and optimization of gift pack strategies through data reports and trend analysis.

[0104] In one embodiment of the present invention, the data analysis module specifically includes: after collecting player behavior data and gift pack usage data, performing data cleaning and preprocessing on the collected player behavior data and gift pack usage data. Data cleaning includes cleaning outliers and missing values ​​in the data, and preprocessing includes normalizing and standardizing the player behavior data and gift pack usage data.

[0105] After data processing, players are grouped using algorithms such as K-means clustering, and different gift packs are recommended for different player groups. Finally, regression analysis and neural network models are used to predict players' demand for different gift packs and optimize the content and price of the gift packs.

[0106] In one specific embodiment of the present invention, the process of generating the newbie gift pack is as follows:

[0107] Application scenario: Provide welcome gift packs to newly registered players to enhance their initial experience.

[0108] Implementation steps:

[0109] 1. The system has detected a new player registration.

[0110] 2. Analyze the initial behavioral data of new players, such as the duration of their first login and the duration of their first day of gameplay.

[0111] 3. Based on data analysis results, recommend the most suitable newbie gift pack content.

[0112] 4. The system automatically configures and generates a newbie gift pack, which is then distributed through newbie tasks or login rewards.

[0113] In one specific embodiment of the present invention, the process of generating a holiday gift package is as follows:

[0114] Application scenario: Launch limited-time promotional gift packs during holidays to increase the attractiveness and participation of holiday activities.

[0115] Implementation steps:

[0116] 1. The system generates holiday gift packs based on predefined holiday templates.

[0117] 2. Analyze historical data to understand players' purchasing behavior and preferences during similar holidays.

[0118] 3. Based on the analysis results, optimize the contents and price of the holiday gift packs.

[0119] 4. Release holiday gift packs and notify players through announcements and push notifications.

[0120] 5. Monitor sales data and player feedback in real time, and dynamically adjust gift pack contents and promotional strategies.

[0121] In one specific embodiment of the present invention, the process of generating a VIP player gift pack is as follows:

[0122] Application scenario: Provide exclusive gift packs for high-value players (VIP players) to enhance their loyalty and satisfaction.

[0123] Implementation steps:

[0124] 1. The system identifies VIP players and collects their spending records and game behavior data.

[0125] 2. Utilize deep learning algorithms to analyze the preferences and needs of VIP players.

[0126] 3. Based on the analysis results, generate high-value personalized VIP gift packs.

[0127] 4. Push gift packs to VIP players through exclusive channels (such as the VIP exclusive store and email).

[0128] 5. Monitor the purchase and usage of VIP players and optimize subsequent VIP gift pack strategies.

[0129] In one specific embodiment of the present invention, the process of generating the activity gift pack is as follows:

[0130] Application scenario: Launch exclusive gift packs during specific in-game events to increase event participation and player activity.

[0131] Implementation steps:

[0132] 1. The system generates event gift packs based on the event theme and objectives.

[0133] 2. Analyze player participation in the event and optimize the gift pack content to increase participation.

[0134] 3. During the event, gift packs will be pushed out through the event page, announcements, and other means.

[0135] 4. Monitor the receipt and use of event gift packs in real time, and adjust the contents of the gift packs to improve the event's effectiveness.

[0136] In a specific embodiment of the present invention, the process of generating a limited-time promotional gift pack is as follows:

[0137] Application scenario: Launch limited-time promotional packages within a specific period to increase sales and players' purchasing enthusiasm.

[0138] Implementation steps:

[0139] 1. The system generates limited-time promotional gift packs based on market data and player purchasing behavior.

[0140] 2. Analyze players' purchasing behavior and historical promotional data to optimize the content and price of promotional packages.

[0141] 3. Set promotional periods and promote the gift packs through limited-time event pages, push notifications, and other means.

[0142] 4. Monitor promotional data and player feedback in real time, and dynamically adjust gift pack contents and promotional strategies to improve sales performance.

[0143] In one specific embodiment of the present invention, the process of generating a region-specific gift pack is as follows:

[0144] Application scenario: Launch exclusive gift packs based on the preferences and needs of players in different regions to enhance the localized experience for players.

[0145] Implementation steps:

[0146] 1. The system identifies the player's location and collects player behavior data for that region.

[0147] 2. Analyze the preferences and purchasing habits of players in a region to generate exclusive gift packs suitable for that region.

[0148] 3. Promote the gift pack to players through localized promotional channels (such as regional announcements and social media).

[0149] 4. Monitor the sales and usage of gift packs in real time, and dynamically adjust the content and strategies of the gift packs based on feedback.

[0150] The above description is only a preferred embodiment of the present invention. All equivalent changes and modifications made within the scope of the claims of the present invention should be included in the scope of the present invention.

Claims

1. A method for quickly generating game gift packs, characterized in that, The method steps are as follows: Step 1: If the developer wants to generate a custom game gift pack, create the game gift pack and customize its configuration. After configuration, proceed to Step 3; if the developer needs to quickly generate game gift packs based on player needs, proceed to Step 2. Step 2: By collecting player behavior data, generate personalized game gift pack recommendations based on the player behavior data; Step 3: Verify and test the game gift pack; Step 4: Release the game gift packs and collect sales data. Step 5: Collect player behavior data and gift pack usage data, use machine learning algorithms to analyze player behavior data and gift pack usage data, recommend game gift packs to players, and dynamically adjust the contents and prices of game gift packs.

2. The method for quickly generating game gift packs according to claim 1, characterized in that, Step 1 specifically involves: customizing the game gift pack configuration, including selecting a game gift pack template or creating a custom game gift pack template, configuring the contents of the game gift pack, configuring the acquisition conditions and validity period of the gift pack, and setting the price; The options include selecting a pre-created game gift pack template, or entering basic game gift pack information to create a custom template.

3. The method for rapidly generating game gift packs according to claim 2, characterized in that, Step 2 specifically involves: continuously collecting player behavior data, including login frequency, spending records, game duration, and task status; cleaning and preprocessing the player behavior data; and using collaborative filtering and deep learning algorithms to generate personalized game gift packs based on the cleaned and preprocessed player behavior data.

4. The method for quickly generating game gift packs according to claim 1, characterized in that, Step 3 specifically involves: automatically verifying the format, values, and dependencies of the gift pack configuration file, checking the rationality and validity of the gift pack content, and simulating the process of players receiving and using the gift pack.

5. The method for rapidly generating game gift packs according to claim 1, characterized in that, Step 4 specifically involves: the release of game gift packs, which includes pre-release and official release. Pre-release is used for testing and also includes setting up a release review process; real-time monitoring of gift pack sales data and player feedback, and optimization of gift pack strategies through data reports and trend analysis.

6. The method for rapidly generating game gift packs according to claim 1, characterized in that, Step 5 specifically involves: after collecting player behavior data and gift pack usage data, performing data cleaning and preprocessing on the collected player behavior data and gift pack usage data. Data cleaning includes removing outliers and missing values ​​from the data, and preprocessing includes normalizing and standardizing the player behavior data and gift pack usage data. After data processing, players are grouped using algorithms such as K-means clustering, and different gift packs are recommended for different player groups. Finally, regression analysis and neural network models are used to predict players' demand for different gift packs and optimize the content and price of the gift packs.

7. A system for rapidly generating game gift packs, characterized in that, The system includes: a custom game gift pack creation module, a smart game gift pack generation module, a verification and testing module, a release management module, and a data analysis module; The game gift pack customization module is used to create game gift packs and customize their configurations. The game gift pack intelligent generation module is used to collect player behavior data and generate personalized recommended game gift packs based on the player behavior data. The verification and testing module is used to verify and test game gift packs; The release management module is used to release game gift packs and collect game gift pack sales data; The data analysis module is used to collect player behavior data and gift pack usage data, use machine learning algorithms to analyze player behavior data and gift pack usage data, recommend game gift packs to players, and dynamically adjust the content and price of game gift packs.

8. A game gift pack rapid generation system according to claim 7, characterized in that, The custom game gift pack creation module specifically includes: custom configuration of game gift packs, including selecting a game gift pack template or creating a custom game gift pack template, configuring the contents of the game gift pack, configuring the acquisition conditions and validity period of the gift pack, and pricing; The options include selecting a pre-created game gift pack template, or entering basic game gift pack information to create a custom template.

9. A game gift pack rapid generation system according to claim 7, characterized in that, The intelligent game gift pack generation module works by continuously collecting player behavior data, including login frequency, spending records, game duration, and task status. It also cleans and preprocesses the player behavior data, and uses collaborative filtering and deep learning algorithms to generate personalized recommended game gift packs based on the cleaned and preprocessed player behavior data.

10. A game gift pack rapid generation system according to claim 7, characterized in that, The verification and testing module specifically verifies the format, values, and dependencies of the gift pack configuration file, checks the rationality and validity of the gift pack content, and simulates the process of players receiving and using the gift pack.

11. A game gift pack rapid generation system according to claim 7, characterized in that, The release management module specifically includes: game gift pack release, which includes pre-release and official release. Pre-release is used for testing and also includes setting up the release review process; real-time monitoring of gift pack sales data and player feedback, and optimization of gift pack strategies through data reports and trend analysis.

12. A game gift pack rapid generation system according to claim 7, characterized in that, The data analysis module specifically involves: after collecting player behavior data and gift pack usage data, performing data cleaning and preprocessing on the collected player behavior data and gift pack usage data. Data cleaning includes removing outliers and missing values ​​from the data, and preprocessing includes normalizing and standardizing the player behavior data and gift pack usage data. After data processing, players are grouped using algorithms such as K-means clustering, and different gift packs are recommended for different player groups. Finally, regression analysis and neural network models are used to predict players' demand for different gift packs and optimize the content and price of the gift packs.

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