User association identification method, system and equipment in game platform based on gift bag code and medium

Abstract

The invention is applicable to the field of game operation, and discloses a gift bag code-based game platform user association identification method, which comprises the following steps that: a game platform pushes gift bag codes to a plurality of application programs, a user enters a game request for gift bag exchange after receiving the gift bag codes in the application programs, and the game platform responds to the request of the user and performs gift bag exchange; and querying the application program receiving the gift bag code, and operating the user through the application program. By pushing gift bag codes to a plurality of application programs, when a user requests for exchange after receiving the gift bag codes, the platform can identify and associate account numbers of the user in different applications, and it is ensured that gift bags are not repeatedly issued; meanwhile, the platform establishes a reputation sharing mechanism according to the reputation data of the users in multiple applications, issues higher-level gift bags to the users with good reputation, and restricts exchange for the users with poor reputation or risk behaviors. According to the method, the fairness and efficiency of gift bag issuing are improved, the platform operation strategy is optimized through reputation evaluation, and the user experience and the platform value are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of game operation, and in particular to a method, system, device and medium for identifying user association in a game platform based on gift package codes. Background Art

[0002] In recent years, with the rapid development of the Internet, user operation models have played an increasingly important role in the gaming industry. Game platforms aim to attract and retain users, improve user stickiness and game activity by sending incentives such as gift codes; however, the current technical system still faces many challenges in the issuance and management of gift codes. In particular, when users register in multiple applications and may obtain multiple gift codes, it is difficult for existing technologies to effectively identify and associate these users' accounts in different applications, resulting in the frequent problem of repeated receipt of gift codes. This not only causes a waste of resources, but also damages the interests of other users, affecting the fairness of the game and user experience. In addition, how to establish a user reputation sharing mechanism across applications and combine it with the gift package distribution strategy needs to be solved urgently. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a method, system, device and medium for identifying user associations in a game platform based on gift package codes.

[0004] In the first aspect, the present application proposes a method for user association identification in a game platform based on a gift package code, which is applied to a game platform, including: pushing a gift package code to a number of applications, when the application receives the gift package code and initiates a gift package redemption request according to the gift package code, responding to the gift package redemption request, querying the application that receives the gift package code, and operating the user through the application;

[0005] The operation of the user through the application includes: identifying and associating multiple accounts of the same user in different applications, and if the user obtains different gift package codes in different applications to enter the game and request to redeem multiple gift packages, only one game gift package is issued;

[0006] After identifying and associating multiple accounts of the same user in different applications, the reputation data of the user in different applications is obtained, and a reputation sharing mechanism is established based on the reputation data. If the reputation data of the user in multiple applications are good, a higher-level gift package code is sent to the user's application. If the user's reputation data is poor or there is risky behavior, the user is restricted from redeeming gift package codes.

[0007] More specifically, in the above technical solution, identifying and associating multiple accounts of the same user in different applications includes:

[0008] According to the features of the user in multiple dimensions in different applications, a feature vector of the user is obtained, and the similarity of the feature vectors of different application accounts is calculated. If the similarity of multiple accounts exceeds a preset threshold, they are determined to be multiple accounts of the same user, wherein the dimensions include behavior data, device information, and network features;

[0009] The user's accounts in different applications are stored, each account is taken as a node, the association between each account is taken as an edge, and a set of all accounts of the same user is obtained based on the nodes and edges.

[0010] More specifically, in the above technical solution, obtaining the reputation data of the user in different applications and establishing a reputation sharing mechanism based on the reputation data includes:

[0011] Acquire the user's reputation data in different applications through a distributed data collection system;

[0012] Obtaining a user reputation feature vector in a unified format through a multi-source heterogeneous data fusion algorithm based on the reputation data;

[0013] Building a reputation sharing mechanism for different applications through a reputation propagation model based on the user reputation feature vector;

[0014] The reputation data includes at least one of behavior logs, transaction records and social interactions.

[0015] To be more specific, in the above technical solution, after establishing a reputation sharing mechanism based on the reputation data, the reputation data is trend predicted according to the time series analysis method, the time series characteristics of the user's reputation changes are captured through the long short-term memory network model, and the user's reputation trend is predicted based on the time series characteristics, and the gift package distribution strategy is adjusted according to the reputation trend.

[0016] More specifically, in the above technical solution, the gift package code is pushed to several applications, including:

[0017] The gift package code is pushed to several applications through a distributed message queue system. The applications are used to receive the gift package code and check the validity of the gift package code. If the gift package code is valid, the application is used to store the gift package code in a local cache.

[0018] More specifically, in the above technical solution, after receiving the gift package code request from the user, the corresponding gift package code record is searched to obtain the gift package content information of the gift package code record;

[0019] Performing an inventory check on the items in the gift package content information according to the content information to determine whether the items exist in the inventory;

[0020] If the inventory is insufficient, select alternative items for distribution;

[0021] The gift package code that has been used is marked as invalid.

[0022] More specifically, in the above technical solution, operating the user through the application further includes:

[0023] Through the associated account information, the users are classified according to their behavioral data in the application, and differentiated operation strategies are adopted for different categories of users.

[0024] In the second aspect, the present application proposes a user association identification system in a game platform based on a gift package code, which is applied to the game platform and includes:

[0025] The first processing module: pushes the gift package code to several applications;

[0026] The second processing module is: when the application receives the gift package code and initiates a gift package redemption request according to the gift package code, responding to the gift package redemption request;

[0027] The third processing module: querying the application that receives the gift package code, and operating the user through the application;

[0028] The operation of the user through the application includes: the game platform identifies and associates multiple accounts of the same user in different applications, and if the user obtains different gift package codes in different applications to enter the game and request to redeem multiple gift packages, the game platform only issues one game gift package;

[0029] After the game platform identifies and associates multiple accounts of the same user in different applications, the game platform obtains the user's reputation data in different applications and establishes a reputation sharing mechanism based on the reputation data. If the user's reputation data in multiple applications is good, the game platform sends a higher-level gift package code to the user's application. If the user's reputation data is poor or there is risky behavior, the game platform restricts the user from redeeming gift package codes.

[0030] In a third aspect, the present application further proposes a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the methods described above when executing the computer program.

[0031] In a fourth aspect, the present application further proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method as described in any one of the above items is implemented.

[0032] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0033] Gift package codes and gift package contents are usually resources invested by the platform to attract users and increase activity. If the above resources are repeatedly claimed by the same user, other users may not be able to obtain sufficient rewards, resulting in a waste of resources. Reducing repeated issuance can ensure that these resources can be distributed to all users more fairly and effectively, thereby improving the efficiency of resource use. In addition, in multi-player games or applications, it is very important to ensure that each user has an equal opportunity to receive rewards. If some users can repeatedly receive gift packages for some reason (such as unrecognized account association, system vulnerabilities, etc.), this will undermine fairness and make other users dissatisfied. Reducing repeated issuance can maintain this fairness and allow each user to obtain the gift packages they deserve.

[0034] In addition, the establishment of a reputation sharing mechanism enables the platform to uniformly manage the reputation status of users across multiple applications, avoiding the problem of information islands, simplifying the management process, improving management efficiency, and enabling the platform to have a more comprehensive understanding of user behavior patterns, thereby formulating more accurate management strategies; restricting users with poor reputation data or risky behaviors from redeeming gift codes helps maintain a healthy community environment for games and applications. Through negative incentives, the platform can effectively curb the occurrence of malicious behavior, protect the legitimate rights and interests of the majority of users, and improve the overall user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 It is a flowchart of a method for identifying user association in a game platform based on a gift package code provided by an embodiment of the present invention;

[0037] Figure 2 It is a structural diagram of a user association identification system in a game platform based on gift package codes provided by an embodiment of the present invention;

[0038] Figure 3 It is a schematic diagram of the structure of the terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0039] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0040] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0041] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0042] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0043] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0044] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0045] See also Figure 1, the present application proposes a method for user association identification in a game platform based on a gift package code, including: S101, the game platform pushes the gift package code to several applications, S102, when the application receives the gift package code and initiates a gift package redemption request according to the gift package code, responding to the gift package redemption request; S103, querying the application that receives the gift package code, and operating the user through the application;

[0046] The operation of the user through the application includes: the game platform identifies and associates multiple accounts of the same user in different applications, and if the user obtains different gift package codes in different applications to enter the game and request to redeem multiple gift packages, the game platform only issues one game gift package;

[0047] After the game platform identifies and associates multiple accounts of the same user in different applications, the game platform obtains the user's reputation data in different applications and establishes a reputation sharing mechanism based on the reputation data. If the user's reputation data in multiple applications is good, the game platform sends a higher-level gift package code to the user's application. If the user's reputation data is poor or there is risky behavior, the game platform restricts the user from redeeming gift package codes.

[0048] The game platform obtains the gift package code received by the user in the specified application according to the user's request, performs preliminary verification and records the received data. If the verification is successful, the game platform confirms and associates the user's account in the application with the game account to ensure that the account information is consistent. Subsequently, the game platform checks the number of gift package codes obtained by the user in different applications and determines whether there is a risk of repeated redemption. If it is found that the same user obtains multiple gift package codes, the game platform only selects a valid gift package code for redemption. The game platform obtains the user's reputation data in all associated applications and adopts a reputation sharing mechanism to comprehensively evaluate the user's overall reputation. If the evaluation result shows that the user has a good reputation, the game platform generates and issues a high-level gift package code to the user's corresponding application. If the user's reputation is poor or there is abnormal behavior, the game platform restricts the user from redeeming the gift package code based on the reputation data. The game platform monitors the user's redemption behavior in real time, records the details of the redemption success and failure, and maintains the user's redemption history. Based on the redemption record and user feedback, the game platform updates the user's reputation data to provide a basis for the subsequent gift package distribution. By analyzing the user's redemption history and behavior patterns, the game platform adjusts the future gift package distribution and reputation evaluation strategies to more accurately identify the user's reputation status.

[0049] Specifically, in the game platform, when a user requests a gift code, the system will perform preliminary verification through the user ID and application ID to ensure the legitimacy of the gift code, and use a hash algorithm such as SHA256 to encrypt and store the gift code to ensure data security. The system will match the user's account information in the application with the game account, and use algorithms such as Jaccard similarity to compare the association between the two accounts to ensure information consistency. Next, the platform will count the number of gift codes obtained by the user in all applications, and use a hash table to store user data of different applications to avoid repeated redemption. If it is detected that the user has multiple gift codes in multiple applications, the system selects a valid gift code for redemption through a random number algorithm. The platform obtains the user's reputation data in all associated applications and can use a weighted average algorithm (such as using a weight value of 5) to calculate its overall reputation score. If the user's overall reputation score is higher than 80 points, the platform will generate a high-level gift code for the user and send the gift code to the user's application via encrypted email. If the user's reputation score is lower than 50 points, or abnormal behavior is detected, the system will use a logistic regression model to analyze its behavior pattern and limit the user's gift code redemption rights. The platform monitors users' redemption behavior in real time through a logging system, and the success and failure of each redemption operation are recorded in detail in the database. By analyzing these records and user feedback, the system will regularly update the user's credit data and use the Kmeans clustering algorithm to classify users, thereby providing a basis for subsequent gift package distribution decisions. The platform predicts user behavior patterns through machine learning models and adjusts future gift package distribution and credit assessment strategies, enabling the system to more accurately identify and manage users' credit status.

[0050] In some embodiments, the game platform identifies and associates multiple accounts of the same user in different applications. If the user obtains different gift package codes in different applications to enter the game and request to redeem multiple gift packages, the game platform only issues one game gift package.

[0051] According to the characteristics of multiple dimensions such as user behavior data, device information, network characteristics, etc. in different applications, the principal component analysis (PCA) is used to reduce the dimension to obtain the user feature vector. The similarity between the user feature vectors of different accounts is calculated by the cosine similarity algorithm. If the cosine similarity of the user feature vectors of two accounts exceeds the preset threshold, they are determined to be multiple accounts of the same user. The game platform uses a graph database to store the association relationship of user accounts. Each user account is a node in the graph, and the association relationship between accounts is an edge. The depth-first search algorithm is used to traverse the graph structure to obtain all the associated account sets of the same user. When a user requests to redeem a gift package, the game platform queries the graph database according to the user's currently logged-in account ID, and traverses all account nodes associated with the account through the breadth-first search algorithm to obtain the gift package information that the user has redeemed in all applications. The game platform uses a Bloom filter to quickly check whether the user has redeemed the gift package, and maps the string composed of the user ID and the gift package ID to the Bloom filter. If the Bloom filter returns that the user has redeemed the gift package, the repeated redemption request is rejected.

[0052] It should be noted that if the user has not redeemed the gift package, the game platform uses the token bucket algorithm to control the gift package issuance frequency. The system adds tokens to the token bucket according to the preset token generation rate. Each time a gift package is issued, a token is consumed. If there is no available token in the token bucket, the issuance request is rejected. The game platform uses the consistent hashing algorithm to distribute the gift package data to multiple cache nodes, calculates the hash value according to the gift package ID, and stores the gift package data in the corresponding node on the hash ring. When the cache capacity needs to be expanded, only a small part of the data needs to be reallocated to avoid large-scale data migration. The time-based sliding window algorithm is used to count the frequency of user gift package redemption. The number of times the user redeems the gift package within a fixed time window is recorded. If it exceeds the preset threshold, the risk control mechanism is triggered to temporarily freeze the user's redemption rights. The game platform uses a directed acyclic graph (DAG) to model the gift package redemption process, taking each step in the redemption process as a node in the graph, and the dependency relationship between the steps as a directed edge. The execution order of each step is determined by the topological sorting algorithm to ensure the correctness of the gift package redemption process.

[0053] Optionally, the game platform uses an asynchronous message queue to process gift package distribution requests, encapsulates the user's redemption request into a message and delivers it to the message queue. The consumer process obtains the message from the queue and processes it asynchronously to prevent the gift package distribution process from blocking the main business process. A cache elimination algorithm based on the LRU (least recently used) strategy is used to manage the gift package data cache, and a bidirectional linked list is maintained to record the access order of the gift package data. When the cache capacity reaches the upper limit, the data at the end of the linked list is eliminated, and the frequently accessed hot gift package data will be retained in the cache to improve data reading efficiency.

[0054] Optionally, the game platform uses a cuckoo filter to detect repeated gift package redemption requests, maps the combination of user ID and gift package ID to multiple locations, and reduces the probability of conflict through multiple hash function calculations. If all locations are occupied, it is determined to be a repeated redemption request and rejected. The cardinality estimation method based on the HyperLogLog algorithm is used to count the number of types of gift packages redeemed by users, and multiple hash functions are used to map the gift package ID to different buckets. The cardinality of the gift package types is estimated based on the maximum number of zero digits in each bucket. This method can maintain a high accuracy even when the data volume is large. The game platform uses a Bloom cascade filter to optimize the gift package query performance and constructs a multi-level Bloom filter. The misjudgment rate of each level is gradually reduced. When querying, start from the first level with a higher misjudgment rate. If a hit is found, continue to query the next level until the last level obtains an accurate result. This method can effectively reduce the number of accesses to the backend storage.

[0055] Specifically, the user's behavior patterns in different applications can be used to identify multiple cross-platform accounts of the same user. Machine learning algorithms, such as Kmeans in clustering algorithms, can be used to perform cluster analysis on the user's behavior characteristics. Assuming that in application A and application B, the user's login time, usage time, active frequency, etc. form a set of feature vectors, by calculating the Euclidean distance of these vectors, it is possible to identify accounts with high similarity that may belong to the same user. In order to manage the gift codes received by users in different applications, a relational database, such as MySQL, can be used to establish a table containing user ID, application identifier, gift code, and collection time, and the user's gift collection records on various platforms can be quickly retrieved through SQL queries. Assuming that the user receives gift code X in application A and gift code Y in application B, the data is then merged through a JOIN operation to determine whether the user has obtained it repeatedly on multiple platforms. In order to prevent repeated collection, a hash algorithm is required to generate a unique user identifier, and all requests are quickly compared through a Bloom Filter, so that users with the same identifier are only allowed to receive the gift package once. Use RESTful API interface for unified management, receive the user's gift code collection operation through GET and POST requests, and judge the legitimacy of the request based on the OAuth 0 authorization framework to ensure that each request carries a valid access token. Dynamic allocation algorithms, such as greedy algorithms, manage a limited number of game gift packages, and prioritize user gift package redemption requests through priority queues. When resources are limited, high-priority requests are given priority. Assuming that there are 1,000 rare gift packages in a game, a priority queue is used to sort them according to the user's VIP level and activity to ensure reasonable allocation of resources. After integrating the information of each step, use message queue tools such as Apache Kafka to transmit and process the user's gift package collection and account identification information in real time to ensure efficient operation of the entire system and data consistency.

[0056] In some embodiments, the game platform obtains the reputation data of the user in different applications and establishes a reputation sharing mechanism based on the reputation data. If the reputation data of the user in multiple applications are good, the game platform sends a higher-level gift package code to the user's application. If the user's reputation data is poor or there is risky behavior, the game platform restricts the user from redeeming gift package codes.

[0057] The game platform uses a distributed data collection system to obtain user reputation data in different applications, including multi-dimensional information such as user behavior logs, transaction records, and social interactions. The collected raw data is cleaned, standardized, and integrated through a multi-source heterogeneous data fusion algorithm to obtain a user reputation feature vector in a unified format. Based on the processed user reputation feature vector, the game platform uses a reputation propagation model based on a graph neural network to build a cross-application reputation sharing mechanism. The model uses users as nodes and reputation relationships between applications as edges. Information flows on a heterogeneous graph structure through a message passing mechanism to obtain a comprehensive reputation score that integrates multiple applications. The game platform uses a rule-based decision tree algorithm to determine the gift package distribution based on the user's comprehensive reputation score output by the graph neural network model. If the user's reputation score in multiple applications is higher than the preset threshold, the user is judged to be a user with good reputation. For users with good reputation, the game platform creates a higher-level personalized gift package code through a dynamic gift package generation algorithm. The game platform uses a secure transmission protocol to send the generated high-level gift package code to the relevant applications of the user with good reputation. If the user's comprehensive credit score is lower than the threshold or there are risky behavior markers, the game platform will activate a credit recovery mechanism based on time decay, limiting the user's right to redeem gift code for a certain period of time. The game platform uses reinforcement learning algorithms to continuously optimize the credit evaluation model and decision rules. By collecting user behavior data after obtaining high-level gift packages, as well as feedback information such as gift package usage, the model parameters and decision thresholds are continuously adjusted to make the credit sharing mechanism more accurate and effective.

[0058] Preferably, the game platform predicts the trend of user reputation data based on the time series analysis method. The long short-term memory network model is used to capture the time series characteristics of user reputation changes, and the user's reputation trend in the future is predicted. According to the prediction results, the game platform adjusts the gift package distribution strategy in advance to achieve forward-looking reputation management. The game platform uses federated learning technology to train a global reputation evaluation model with multiple applications under the premise of protecting user privacy. Each application trains a sub-model locally and only shares model parameters instead of raw data. The game platform aggregates parameters as a central node and updates the global model, thereby realizing cross-application collaborative learning and reputation knowledge sharing.

[0059] Specifically, in order to evaluate the user's reputation data in various applications, we first need to collect the user's reputation score and reputation level in these applications. Suppose a user's reputation score in application A is 85, with a level of "good", in application B it is 90, with a level of "excellent", and in application C it is 70, with a level of "ordinary". We use the weighted average method, and the weights are set according to the importance of the application and the user's activity. The weight of application A is 3, the weight of application B is 5, and the weight of application C is 2. The overall reputation score is calculated as 853+905+702=85. According to the set rules, 8089 is "good", so the user's overall reputation level is "good". Next, through the gift package code generation and distribution mechanism, we determine whether the user is eligible to obtain a higher level gift package code. Assume that only users with "excellent" and above levels can obtain advanced gift package codes. Since the user's level is "good", it does not meet the conditions for obtaining it and can only receive ordinary level gift package codes. In response to the user's reward and punishment measures, the system will automatically adjust the gift package code distribution strategy. At the same time, we analyze the user's behavior records in each application, compare them with the defined risk behaviors (such as frequent malicious refunds, abnormal logins, etc.), and use specific risk identification algorithms, such as time series-based anomaly detection algorithms, to determine whether the user has risky behavior. Assume that the user has an abnormal login record in application A, but it does not reach the threshold of risky behavior, so no restrictive measures are taken. By analyzing the user's historical behavior and reputation changes, the system adopts a dynamic adjustment mechanism and uses machine learning algorithms to predict the user's future reputation trends. Assuming that the model predicts that the user's reputation may increase by 5% in the next three months, the system will adjust the reputation assessment on this basis and update the user's gift code distribution strategy accordingly. Such a mechanism ensures that the user's reputation assessment is more accurate, and the system can adaptively adjust the strategy to respond to changes in user behavior.

[0060] In some embodiments, the game platform pushes gift package codes to several applications, and the user enters the game to request to redeem the gift package after the application receives the gift package code.

[0061] The game platform uses a distributed message queue system to push gift codes to several applications. The K-means clustering algorithm is used to group applications according to the application type and user group characteristics. A unique hash value is assigned to each application through a consistent hashing algorithm to determine the storage location of the gift code in the message queue. After the application receives the gift code, it uses a Bloom filter to quickly check the validity of the gift code. If the gift code is valid, the application stores it in the local cache and uses the least recently used (LRU) cache eviction algorithm to manage the cache space.

[0062] Optionally, after receiving the redemption request, the game server uses a token bucket algorithm to control the request frequency to prevent malicious redemption behavior. The game server uses a distributed lock mechanism to ensure the uniqueness and concurrent security of the gift code. The consistent hashing algorithm is used to locate the data shard where the gift code is located, and the optimistic lock strategy is used to update the gift code status. If the gift code has been redeemed, the redemption failure information is returned; if the gift code has not been redeemed, it is marked as redeemed. The game server uses a directed acyclic graph (DAG) algorithm to calculate the order of reward issuance according to the reward content corresponding to the gift code. The reward is added to the user account in batch processing, and the two-phase commit protocol is used to ensure the consistency of the transaction. In order to cope with large-scale concurrent redemption requests, the game server uses a load balancing algorithm to distribute the requests to multiple processing nodes. The consistent hashing algorithm is used to determine the mapping relationship between user requests and processing nodes, ensuring that the requests of the same user are always routed to the same node, thereby improving the cache hit rate. The game server uses a time-based sliding window algorithm to count the redemption of gift codes, and combines the exponential smoothing method to predict the redemption volume in the future. According to the prediction results, the dynamic programming algorithm is used to adjust the server resource allocation and optimize the system performance. During the entire redemption process, the game platform uses a distributed tracking system to record the processing path and time of each request. It uses an anomaly detection algorithm to monitor the system operation status in real time, and combines the decision tree algorithm to automatically diagnose and solve potential problems.

[0063] Specifically, when a user submits a gift package code, the system will first generate a unique identifier, such as encrypting the gift package code through the SHA256 algorithm to generate a 64-bit string as the unique identifier of the gift package. Then, the system will query the database and find the pre-configured gift package content, including detailed information such as game props, resources, and currency, through this unique identifier. For example, a gift package may contain 1,000 gold, 50 gems, and a legendary weapon "Flame Sword". The system will further check the validity period of the gift package based on this unique identifier, usually by comparing the current timestamp with the start and end timestamps in the gift package record to determine whether the gift package is within the validity period. For example, the current time is October 1, 2023, and if the validity period of the gift package is from September 1, 2023 to December 31, 2023, the gift package is still within the validity period. Next, the system will receive the user's unique identifier, such as user ID "U12345", to verify whether the user has the right to redeem this gift package. This process may involve querying the user's permission level or purchase record, etc. In order to check whether the user has reached the upper limit of the number of redemptions, the system will search the redemption record based on the user ID and gift code, and count the number of redemptions the user has made in the past. For example, if a user has redeemed 3 times and the upper limit is 5 times, the user can still continue to redeem. In addition, the system will verify whether the application the user is in is within the scope of the gift package through the list of target applications. For example, the gift package is only applicable to "Game A" and "Game B", and the user is currently in "Game A", so it meets the redemption conditions. When the user successfully redeems the gift package, the system will record the redemption time, such as recording "20231001 12:30:45" through the timestamp, and store this information in the log file. Finally, the system will update the status of the gift code, marking it as "redeemed" to ensure that the same gift code will not be reused, and return the redemption result to the user to confirm that the redemption is successful. The entire process does not require manual intervention and relies entirely on automated program logic execution.

[0064] In some embodiments, the gaming platform queries the application program that receives the gift package code in response to the user's request.

[0065] After receiving the gift code application request from the user, the game platform uses a hash table data structure to store the gift code information, calculates the hash value of the gift code as an index through a hash function, and searches for the corresponding gift code record in the hash table according to the calculated hash value. If a matching gift code record is found, the gift package content information contained in the record is obtained. The depth-first search algorithm is used to traverse the list of items in the gift package content information, and an inventory check is performed on each item. The Bloom filter is used to quickly determine whether the item exists in the inventory. If the Bloom filter returns that the item may exist, the database is further queried to confirm the actual inventory quantity. A directed acyclic graph is constructed based on the inventory check results, and the dependencies between items are represented as edges in the graph. The directed acyclic graph is sorted using a topological sorting algorithm to obtain the order in which the items are issued. The issuance logic of each item is processed in turn according to the topological sorting results. For each item to be issued, a suitable server node is selected through a consistent hashing algorithm to perform the issuance operation. The optimistic locking mechanism is used to update the item inventory, and concurrent access is controlled by the version number. If the optimistic lock update fails, the backoff algorithm is used to retry the operation. The token bucket algorithm is used to control the rate of item issuance to prevent system overload. The sliding window algorithm is used to count the number of distributions in a short period of time to determine whether the flow limit is triggered. If the flow limit is triggered, the request is placed in the priority queue for processing. The asynchronous message queue is used to asynchronously notify the user of the distribution results. The publish-subscribe mode is used to push the distribution results to multiple terminals that subscribe to the user. The longest common subsequence algorithm is used to compare the distribution results with the original gift package content to determine whether there are items that have failed to be distributed. If there are items that have failed to be distributed, the backtracking algorithm is used to try to find replacement items. The cosine similarity algorithm is used to calculate the similarity between items, and the item with the highest similarity is selected as a replacement. The aforementioned distribution process is repeated for the replacement items. The finite state machine is used to record the state changes of the entire gift package code application process. The decision tree algorithm is used to select the next processing logic according to the current state. The dynamic programming algorithm is used to optimize the depth of the decision tree and reduce the number of state transitions. Finally, the Bloom filter is used to mark the used gift package code as invalid. The invalid gift package code is synchronized to the distributed cache regularly using incremental updates to improve the efficiency of subsequent gift package code verification. The consistent hashing algorithm is used to evenly distribute the gift package code data among multiple cache nodes.

[0066] Specifically, after obtaining the gift code, the corresponding gift content can be retrieved by calling the database query interface based on the input gift code, which includes in-game virtual items and game currency, etc. For example, the gift content data can be obtained through the SQL query statement "SELECT FROM gift_packages WHERE codeABC123". After confirming the gift content, further query the database to obtain the gift expiration date. The expiration date information can be obtained using the SQL query statement "SELECT expiration_date FROM gift_packages WHERE codeABC123", and then compare the current date with the expiration date. For example, use the datetime module in Python for comparison "datetime.now() < expiration_date" to determine whether the gift code is within the expiration date. Next, check whether the game server zone where the user is currently located matches the zone applicable to the gift code. Assume that the user server information obtained through the API is "user_server server1", and the applicable server zones for the gift are obtained through the query "SELECT applicable_servers FROM gift_packages WHERE codeABC123" with the result "server1,server2". Use string operations to determine "user_server in applicable_servers.split" to ensure a match. Then, query the gift redemption record based on the user account information to determine whether the current account exceeds the gift redemption limit. Assume the user account is "user_id user123", and the redemption count is obtained as 1 through the query "SELECT COUNT() FROM redemption_records WHERE user_id user123 AND codeABC123". If the limit is 1 time, the user cannot redeem again. In addition, check the status of the gift code. Through the query "SELECT status FROM gift_packages WHERE codeABC123", if the status is "redeemed", it means it has been redeemed, and a corresponding prompt is returned; if the status is "available", the redemption process continues. Obtain the generation time of the gift code. The generation time is obtained through the query "SELECT creation_date FROM gift_packages WHERE codeABC123", and compare it with the current time "creation_date < datetime.now()" to ensure that the generation time is reasonable.After confirming that all conditions are met, get the current time "exchange_time = datetime.now()" as the exchange time, update the database status and exchange time "UPDATE gift_packages SET statusredeemed exchange_time exchange_time WHERE codeABC123" and return the exchange result to the user. This ensures that each step is logically rigorous and information processing is automated.

[0067] In some embodiments, the user is operated through the application.

[0068] Obtain the user's account information and game account information in the application. Based on the obtained account information, use the cosine similarity algorithm to calculate the similarity score between the two accounts. If the similarity score exceeds the preset threshold, it is determined that the two accounts are associated. Use the associated account information to build a user portrait model. Use the collaborative filtering recommendation algorithm to recommend personalized content to users based on the user portrait model. Use the decision tree algorithm to classify users based on their behavior data in the application. Use differentiated operation strategies for different categories of users. Use the naive Bayes classifier to perform sentiment analysis on the content posted by users. If the sentiment tendency is positive, push rewards to the user. Use the K-means clustering algorithm to segment the user group. According to the segmentation results, formulate targeted operation plans. Use the support vector machine algorithm to predict the user's churn risk. If the prediction results show that the user has a churn risk, trigger the retention mechanism. Use the deep learning neural network model to analyze the user's usage habits and preferences. According to the analysis results, personalize the application interface. Use the reinforcement learning algorithm to optimize the operation strategy. Continuously adjust and improve the operation plan through user feedback data. Use time series analysis methods to predict the changing trend of user activity and deploy corresponding operational activities in advance based on the prediction results. Use association rule mining algorithms to discover the associations between user behavior patterns and design cross-marketing strategies based on the mined association rules. Use principal component analysis methods to reduce the dimensions of user features and use the reduced feature data to improve the efficiency of subsequent analysis. Use random forest algorithms to evaluate the importance of various operational measures and optimize resource allocation plans based on the evaluation results. Use gradient boosting decision tree algorithms to predict users' willingness to pay. If the prediction results show that users have a high willingness to pay, push targeted discounts to them. Use recurrent neural network models to analyze user behavior sequences and predict users' next behavior based on the sequence analysis results. Use anomaly detection algorithms to identify abnormal behaviors in user groups. If abnormal behaviors are detected, trigger corresponding risk control measures. Use graph neural network models to analyze the structural characteristics of user social networks. Identify key users with high influence based on network structural characteristics.

[0069] Specifically, in applications and games, the unique identification of the user is obtained through the user ID, for example, the SHA256 algorithm can be used for encryption to generate a unique hash value. Then, the name used by the user in the application and game is obtained through the user name, and a database query statement such as a SELECT statement is used to find a matching user name record to determine the personal identity of the user. In the judgment of the account binding status, the association status of the application account and the game account can be judged by whether there is a record in the database. For example, it is queried whether there is a record of a specific user ID in the user binding table. If it exists, it means that the account has been associated. The specific time point of the association can be obtained through the binding time field of the database record, for example, using the TIMESTAMP data type of SQL. When a user tries to log in, the login credentials provided by the user, such as a user token, can be used to verify the token through the verification server to determine the legitimacy of the user's identity. The permissions that users have in applications and games can be obtained through the permission management system, and the role permission mapping table is used to calculate the user's permission level in different scenarios. User data such as game level, virtual currency and props can be obtained by retrieving relevant fields from the user data table, such as SELECT level, virtual currency, props FROM user data table WHERE user ID = 12345. Tracking of user behavior data can be done by analyzing the login time and game duration fields in the server log file to calculate the user's activity, such as by counting the number of daily logins and the average game duration. Using these data for trend analysis can determine the user's usage tendencies and preferences. Combining the analysis results of user permissions and user data, machine learning algorithms such as decision trees or random forests are used to determine the possible changes in the user's V IP level, thereby predicting the user's potential development direction in the game. By analyzing user behavior patterns through algorithms, such as using the KMeans clustering algorithm to group user behavior data, the user's interest in specific functions or activities can be determined. Combining the results of user data and behavior analysis, a weighted scoring model is used to score the user's overall performance in applications and games, such as taking into account factors such as activity, consumption amount, and game performance to form a comprehensive evaluation of the user. Through these technical means, user data can be automatically processed to provide deep insights into user behavior.

[0070] See also Figure 2 , the application also proposes a user association identification system in a game platform based on a gift package code, including:

[0071] First processing module 201: Pushing gift package codes to several applications;

[0072] The second processing module 202: when the application receives the gift package code and initiates a gift package redemption request according to the gift package code, responds to the gift package redemption request;

[0073] The third processing module 203: querying the application that receives the gift package code, and operating the user through the application;

[0074] The operation of the user through the application includes: the game platform identifies and associates multiple accounts of the same user in different applications, and if the user obtains different gift package codes in different applications to enter the game and request to redeem multiple gift packages, the game platform only issues one game gift package;

[0075] After the game platform identifies and associates multiple accounts of the same user in different applications, the game platform obtains the user's reputation data in different applications and establishes a reputation sharing mechanism based on the reputation data. If the user's reputation data in multiple applications is good, the game platform sends a higher-level gift package code to the user's application. If the user's reputation data is poor or there is risky behavior, the game platform restricts the user from redeeming gift package codes.

[0076] It is understandable that if Figure 1 The contents of the embodiment of the method for associating and identifying users in a game platform based on gift package codes are applicable to the embodiment of the system for associating and identifying users in a game platform based on gift package codes. The functions specifically implemented by the embodiment of the system for associating and identifying users in a game platform based on gift package codes are similar to those in the embodiment of the method for associating and identifying users in a game platform based on gift package codes. Figure 1 The embodiment of the method for identifying user association in a game platform based on a gift package code is the same as that shown in the embodiment, and the beneficial effects achieved are the same as those of the embodiment shown in the embodiment. Figure 1 The beneficial effects achieved by the embodiment of the method for user association identification within a game platform based on gift package codes are also the same.

[0077] It should be noted that the information interaction, execution process and other contents between the above-mentioned systems are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0078] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0079] See also Figure 3 The embodiment of the present invention further provides a computer device 3, comprising: a memory 302 and a processor 301 and a computer program 303 stored in the memory 302. When the computer program 303 is executed on the processor 301, a user association identification method based on a gift package code in a game platform as described in any one of the above methods is implemented.

[0080] The computer device 3 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 3 may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will appreciate that Figure 3 It is only an example of computer device 3 and does not constitute a limitation on computer device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access devices, etc.

[0081] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0082] In some embodiments, the memory 302 may be an internal storage unit of the computer device 3, such as a hard disk or memory of the computer device 3. In other embodiments, the memory 302 may also be an external storage device of the computer device 3, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device 3. Further, the memory 302 may also include both an internal storage unit of the computer device 3 and an external storage device. The memory 302 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 302 may also be used to temporarily store data that has been output or is to be output.

[0083] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for user association identification within a game platform based on a gift package code as described in any one of the above methods is implemented.

[0084] In this embodiment, if the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0085] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for identifying user associations in a game platform based on gift package codes, characterized in that: Applied to the game platform, including: pushing gift package codes to several applications, when the application receives the gift package code and initiates a gift package redemption request according to the gift package code, responding to the gift package redemption request, querying the application that receives the gift package code, and operating the user through the application; The operation of the user through the application includes: identifying and associating multiple accounts of the same user in different applications, and if the user obtains different gift package codes in different applications to enter the game and request to redeem multiple gift packages, only one game gift package is issued; After identifying and associating multiple accounts of the same user in different applications, the reputation data of the user in different applications is obtained, and a reputation sharing mechanism is established based on the reputation data. If the reputation data of the user in multiple applications are good, a higher-level gift package code is sent to the user's application. If the user's reputation data is poor or there is risky behavior, the user is restricted from redeeming gift package codes.

2. According to claim 1, the method for identifying user associations in a game platform based on gift package codes is characterized in that: Identify and associate multiple accounts of the same user across different applications, including: According to the features of the user in multiple dimensions in different applications, a feature vector of the user is obtained, and the similarity of the feature vectors of different application accounts is calculated. If the similarity of multiple accounts exceeds a preset threshold, they are determined to be multiple accounts of the same user, wherein the dimensions include behavior data, device information, and network features; The user's accounts in different applications are stored, each account is taken as a node, the association between each account is taken as an edge, and a set of all accounts of the same user is obtained based on the nodes and edges.

3. According to claim 1, the method for identifying user associations in a game platform based on gift package codes is characterized in that: Acquiring the reputation data of the user in different applications and establishing a reputation sharing mechanism based on the reputation data includes: Acquire the user's reputation data in different applications through a distributed data collection system; Obtaining a user reputation feature vector in a unified format through a multi-source heterogeneous data fusion algorithm based on the reputation data; Building a reputation sharing mechanism for different applications through a reputation propagation model based on the user reputation feature vector; The reputation data includes at least one of behavior logs, transaction records and social interactions.

4. According to claim 3, the method for identifying user associations in a game platform based on gift package codes is characterized in that: After establishing a reputation sharing mechanism based on the reputation data, the reputation data is trend predicted according to the time series analysis method, the time series characteristics of the user's reputation changes are captured through the long short-term memory network model, and the user's reputation trend is predicted based on the time series characteristics, and the gift package distribution strategy is adjusted according to the reputation trend.

5. According to claim 1, the method for identifying user associations in a game platform based on gift package codes is characterized in that: Push gift codes to several apps, including: The gift package code is pushed to several applications through a distributed message queue system. The applications are used to receive the gift package code and check the validity of the gift package code. If the gift package code is valid, the application is used to store the gift package code in a local cache.

6. According to claim 5, the method for identifying user associations in a game platform based on gift package codes is characterized in that: After receiving the gift package code request from the user, search for the corresponding gift package code record and obtain the gift package content information of the gift package code record; Performing an inventory check on the items in the gift package content information according to the content information to determine whether the items exist in the inventory; If the inventory is insufficient, select alternative items for distribution; The gift package code that has been used is marked as invalid.

7. According to claim 1, the method for identifying user associations in a game platform based on gift package codes is characterized in that: Operating the user through the application also includes: Through the associated account information, the users are classified according to their behavioral data in the application, and differentiated operation strategies are adopted for different categories of users.

8. A user association identification system in a game platform based on gift package codes, characterized in that: Applied to gaming platforms, including: The first processing module: pushes gift package codes to several applications; The second processing module is: when the application receives the gift package code and initiates a gift package redemption request according to the gift package code, responding to the gift package redemption request; The third processing module: querying the application that receives the gift package code, and operating the user through the application; The operation of the user through the application includes: the game platform identifies and associates multiple accounts of the same user in different applications, and if the user obtains different gift package codes in different applications to enter the game and request to redeem multiple gift packages, the game platform only issues one game gift package; After the game platform identifies and associates multiple accounts of the same user in different applications, the game platform obtains the user's reputation data in different applications and establishes a reputation sharing mechanism based on the reputation data. If the user's reputation data in multiple applications is good, the game platform sends a higher-level gift package code to the user's application. If the user's reputation data is poor or there is risky behavior, the game platform restricts the user from redeeming gift package codes.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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