A method, system, device and medium for identifying user association in a game platform based on a gift code

Abstract

The application is suitable for the field of game operation, and discloses a user association identification method in a game platform based on a gift package code, which comprises the following steps: a game platform pushes a gift package code to a plurality of application programs; after a user receives the gift package code in the application program, the user enters a game to request to exchange the gift package; the game platform responds to the request of the user, queries the application program receiving the gift package code, and operates the user through the application program. By pushing the gift package code to the plurality of application programs, when the user requests to exchange after receiving, the platform can identify and associate the account of the user in different applications, so that the gift package is not repeatedly issued; meanwhile, the platform establishes a credit sharing mechanism according to the credit data of the user in the plurality of application programs, issues a higher level gift package to the user with good credit, and limits the exchange of the user with poor credit or risk behavior. This method not only improves the fairness and efficiency of the gift package issuing, but also optimizes the platform operation strategy through credit evaluation, and enhances the user experience and platform value.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of game operation, in particular to a game platform user association identification method and system based on gift package codes, equipment and medium. BACKGROUND

[0002] In recent years, with the rapid development of the Internet, user operation mode plays an increasingly important role in the game industry. Game platforms send gift package codes and other incentives to attract and retain users, improve user stickiness and game activity; however, the current technical system still faces many challenges in the issuance and management of gift package codes, especially when users register in multiple applications and may obtain multiple gift package codes, the existing technology is difficult to effectively identify and associate the user's account in different applications, leading to the problem of repeated gift package codes being taken. This not only causes 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 cross-application user reputation sharing mechanism and combine it with the gift package issuance strategy is a problem that needs to be solved. SUMMARY

[0003] The present application aims to at least solve one of the technical problems existing in the prior art, for this purpose, the present application proposes a game platform user association identification method and system based on gift package codes, equipment and medium.

[0004] In a first aspect, the present application proposes a game platform user association identification method based on gift package codes, applied to a game platform, comprising: pushing a gift package code to a plurality 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] Operating the user through the application includes: identifying and associating multiple accounts of the same user in different applications, if the user obtains different gift package codes in different applications to 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, a reputation sharing mechanism is established based on the reputation data, if the reputation data of the user in multiple applications is good, a higher level gift package code is sent to the application of the user, if the reputation data of the user is poor or there is a risk behavior, the user is limited to redeem the gift package code.

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

[0008] According to the characteristics of the user in different application programs in multiple dimensions, a feature vector of the user is obtained, the similarity of the feature vectors of different application program accounts is calculated, and if the similarity of multiple accounts exceeds a preset threshold, it is determined that the multiple accounts are of the same user, wherein the dimensions include behavior data, device information and network characteristics;

[0009] The accounts of the user in different application programs are stored, each account is taken as a node, the association relationship 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] Further specifically, in the above technical solution, the reputation data of the user in different application programs is obtained, and a reputation sharing mechanism is established based on the reputation data, including:

[0011] The reputation data of the user in different application programs is obtained through a distributed data collection system;

[0012] A user reputation feature vector in a unified format is obtained from the reputation data through a multi-source heterogeneous data fusion algorithm;

[0013] A reputation sharing mechanism of different application programs is constructed based on the user reputation feature vector through a reputation propagation model;

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

[0015] Further specifically, in the above technical solution, after the reputation sharing mechanism is established based on the reputation data, the trend of the reputation data is predicted according to a time series analysis method, the time sequence characteristics of the reputation change of the user are captured through a long short-term memory network model, and the reputation trend of the user is predicted based on the time sequence characteristics, and the gift package distribution strategy is adjusted according to the reputation trend.

[0016] Further specifically, in the above technical solution, the gift package code is pushed to several application programs, including:

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

[0018] Further specifically, in the above technical solution, after receiving the gift package code request of the user, the corresponding gift package code record is found, and the gift package content information of the gift package code record is obtained;

[0019] According to the content information, inventory checking is performed on the items in the gift package content information, and it is determined whether the items exist in the inventory;

[0020] If the inventory is insufficient, a substitute item is selected to perform the delivery;

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

[0022] Further specifically, in the above technical solution, the operation of the user through the application program further includes:

[0023] According to the behavior data of the user in the application program, the user is classified through the associated account information, and a differentiated operation strategy is adopted for the users of different categories.

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

[0025] A first processing module: push a gift package code to a plurality of application programs;

[0026] A second processing module: when the application program receives the gift package code and initiates a gift package redemption request according to the gift package code, respond to the request for redeeming the gift package;

[0027] A third processing module: query the application program that receives the gift package code, and operate the user through the application program;

[0028] The operation of the user through the application program includes: the game platform identifies and associates a plurality of account numbers of the same user in different application programs, if the user obtains different gift package codes in different application programs to enter the game and requests to redeem a plurality of gift packages, the game platform only delivers one game gift package;

[0029] After the game platform identifies and associates a plurality of account numbers of the same user in different application programs, the game platform obtains the reputation data of the user in different application programs, establishes a reputation sharing mechanism based on the reputation data, if the reputation data of the user in a plurality of application programs is good, the game platform sends a higher level gift package code to the application program of the user, if the reputation data of the user is poor or there is a risk behavior, the game platform limits the user to redeem the gift package code.

[0030] In a third aspect, the application further provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to realize the method of any one of the above.

[0031] In a fourth aspect, the present application also provides a computer readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method according to any one of the preceding aspects.

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

[0033] The gift package code and the gift package content are usually resources invested by the platform to attract users and improve the activity level. If the above resources are repeatedly obtained by the same user, other users may not be able to obtain sufficient rewards, resulting in resource waste. Reducing the repeated distribution can ensure that these resources can be more fairly and effectively distributed to all users, thereby improving the use efficiency of the resources. In a game or application involving multiple people, it is very important to ensure that each user has an equal opportunity to obtain rewards. If some users can repeatedly obtain the gift package due to some reasons (such as unrecognized account association, system vulnerabilities, etc.), this will destroy the fairness and make other users feel dissatisfied. Reducing the repeated distribution can maintain this fairness and enable each user to obtain the deserved gift package.

[0034] In addition, establishing the reputation sharing mechanism enables the platform to uniformly manage the reputation status of the user across multiple application programs, avoids the information silo problem, simplifies the management process, improves the management efficiency, and also enables the platform to more comprehensively understand the behavior pattern of the user, thereby formulating a more accurate management strategy. Limiting the user with poor reputation data or risk behavior to exchange the gift package code helps to maintain the healthy community environment of the game and the application program. Through negative incentive measures, the platform can effectively curb the occurrence of malicious behavior, protect the legitimate rights and interests of most users, and improve the overall user experience. BRIEF DESCRIPTION OF DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

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

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

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

[0039] In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular architectures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known methods, devices, circuits, and

[0040] It will be understood that the terms "comprises" and / or "comprising," when used in this specification, include the presence of one or more features, integers, steps, operations, elements, and / or components but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0041] It will be understood that the term "and / or," when used in this specification, includes the possibility of both there being a combination of features, integers, steps, operations, elements, and / or components and there being only one of either feature, integer, step, operation, element, component, or group thereof.

[0042] As used in this specification and claims, the terms "if" and "when" can each be interpreted to mean "when" or "if," depending on the context. Similarly, the phrase "if it is determined" or "if [a described condition or event] is detected" can each be interpreted to mean "if it is determined that," or "if [the described condition or event] is detected," depending on the context.

[0043] In addition, the terms "first," "second," "third," etc. are used herein only to distinguish one element from another, and do not imply a relative importance or a given order.

[0044] Reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," and the like in various places throughout this specification are not necessarily referring to the same embodiment, unless otherwise specified. Furthermore, the terms "comprise," "comprises," "comprising," "include," "includes," "including," and the like are synonymous with the term "contain," "contains," "containing," and the like, and are used in the sense of "including but not limited to," unless otherwise specified.

[0045] See Figure 1The application provides a user association recognition method in a game platform based on a gift code, which comprises the following steps: S101, the game platform pushes a gift code to several application programs; S102, when the application program receives the gift code and initiates a gift package exchange request according to the gift code, the game platform responds to the request for exchanging the gift package; S103, the game platform queries the application program receiving the gift code and operates the user through the application program.

[0046] The operation of the user through the application program comprises the following steps: the game platform identifies and associates multiple accounts of the same user in different application programs, if the user obtains different gift codes in different application programs to enter the game and request to exchange 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 application programs, the game platform obtains the reputation data of the user in different application programs, establishes a reputation sharing mechanism based on the reputation data, if the reputation data of the user in multiple application programs is good, the game platform sends a higher level gift code to the application program of the user, if the reputation data of the user is poor or there is a risk behavior, the game platform limits the user to exchange the gift code.

[0048] The game platform obtains the gift code received by the user in the specified application program according to the user's request, performs preliminary verification and records the receiving data. If the verification is passed, the game platform confirms and associates the account of the user in the application program with the game account, and ensures that the account information is consistent. Then, the game platform checks the number of gift codes obtained by the user in different application programs, and judges whether there is a risk of repeated exchange. If it is found that the same user obtains multiple gift codes, the game platform only selects one valid gift code for exchange processing. The game platform obtains the reputation data of the user in all associated application programs, and adopts a reputation sharing mechanism to comprehensively evaluate the overall reputation of the user. If the evaluation result shows that the user's reputation is good, the game platform generates and issues a high-level gift code to the application program corresponding to the user. If the user's reputation is poor or there is an abnormal behavior, the game platform limits the user to exchange the gift code according to the reputation data. The game platform monitors the user's exchange behavior in real time, records the detailed information of successful and failed exchange, and maintains the exchange history of the user. According to the exchange record and user feedback, the game platform updates the reputation data of the user, which provides a basis for subsequent gift issuance. The game platform analyzes the exchange history and behavior mode of the user, adjusts the future gift issuance and reputation evaluation strategy, so as to more accurately identify the reputation of the user.

[0049] Specifically, in the game platform, when the user requests a gift code, the system will perform preliminary verification through the user ID and application program ID to ensure the legality of the gift code. The gift code is encrypted using a hash algorithm such as SHA256 and stored, ensuring data security. The system matches the user's account information in the application program with the game account, uses an algorithm such as Jaccard similarity to compare the relevance of the two accounts, and ensures information consistency. Next, the platform will count the number of gift codes obtained by the user in all application programs, use a hash table to store user data for different application programs, and avoid duplicate redemption. If it is detected that the user has multiple gift codes in multiple application programs, the system selects an effective gift code for redemption through a random number algorithm. The platform obtains the user's reputation data in all associated application programs and can use a weighted average algorithm (such as using a weight value of 5) to calculate the 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 program through an 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 patterns and restrict the user's gift code redemption rights. The platform monitors the user's redemption behavior in real time through a log recording 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 periodically update the user's reputation data, use a Kmeans clustering algorithm to classify users, and provide a basis for subsequent gift distribution decisions. The platform predicts user behavior patterns through machine learning models, adjusts future gift distribution and reputation evaluation strategies, and enables the system to more accurately identify and manage user reputation status.

[0050] In some embodiments, the game platform identifies and associates multiple accounts of the same user in different application programs, and if the user obtains different gift codes in different application programs to enter the game and requests to redeem multiple gifts, the game platform only issues one game gift.

[0051] According to the user behavior data in different application programs, device information, network characteristics and other multi-dimensional features, the principal component analysis (PCA) is used for dimension reduction processing to obtain the user feature vector, the cosine similarity algorithm is used to calculate the similarity between the user feature vectors of different accounts, and if the cosine similarity of the user feature vectors of two accounts exceeds the preset threshold, it is determined that they are multiple accounts of the same user. The game platform uses a graph database to store the user account association relationship, each user account is a node in the graph, and the association relationship between the accounts is an edge. The depth-first search algorithm is used to traverse the graph structure to obtain the set of all associated accounts of the same user. When the user requests to exchange the gift package, the game platform queries the graph database according to the account ID currently logged in by the user, traverses all account nodes associated with the account by using the breadth-first search algorithm, and obtains the gift package information exchanged by the user in all applications. The game platform uses a Bloom filter to quickly check whether the user has exchanged the gift package, maps the string composed of the user ID and the gift package ID into the Bloom filter, and if the Bloom filter returns that the user has exchanged the gift package, the repeated exchange request is rejected.

[0052] It should be noted that if the user has not exchanged the gift package, the game platform uses a token bucket algorithm to control the gift package distribution frequency. The system adds tokens to the token bucket at a preset token generation rate, consumes one token each time the gift package is distributed, and rejects the distribution request if there is no available token in the token bucket. The game platform uses a consistent hash 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 on 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 redistributed, avoiding large-scale data migration. A time-based sliding window algorithm is used to count the frequency of user exchange of the gift package. The number of times the user exchanges the gift package is recorded within a fixed time window, and if it exceeds the preset threshold, the risk control mechanism is triggered to temporarily freeze the exchange permission of the user. The game platform uses a directed acyclic graph (DAG) to model the gift package exchange process, each step in the exchange process as a node in the graph, and the dependency relationship between the steps as a directed edge. The topological sorting algorithm is used to determine the execution order of each step to ensure the correctness of the gift package exchange process.

[0053] Optionally, the game platform uses an asynchronous message queue to process the gift package distribution request, encapsulates the user's exchange request into a message and delivers it to the message queue. The consumer process obtains the message from the queue and processes it asynchronously, avoiding blocking the main business process during the gift package distribution process. A cache eviction algorithm based on the LRU (Least Recently Used) strategy is used to manage the gift package data cache, a double-linked list is maintained to record the access order of the gift package data, and when the cache capacity reaches the upper limit, the data at the tail of the list is evicted. The hot gift package data frequently accessed will be retained in the cache, improving the data reading efficiency.

[0054] Optionally, the game platform uses a cuckoo filter to detect repeated gift package exchange requests, maps the combination of user ID and gift package ID to multiple locations, reduces the collision probability through multiple hash function calculations, and if all locations are occupied, it is determined that it is a repeated exchange request and is rejected. The HyperLogLog algorithm-based cardinality estimation method is used to count the number of types of user gift packages, multiple hash functions are used to map the gift package ID to different buckets, and the maximum number of zeros in each bucket is used to estimate the cardinality of the gift package type. This method can still maintain high accuracy even in large data volumes. The game platform uses a Bloom filter to optimize gift package query performance, builds a multi-level Bloom filter, and the false negative rate of each level gradually decreases. When querying, start from the first level with a higher false negative rate. If it hits, continue to query the next level until the last level gets the accurate result. This method can effectively reduce the number of accesses to the backend storage.

[0055] Specifically, the behavior patterns of users in different applications are used to identify multiple cross-platform accounts of the same user. Machine learning algorithms such as Kmeans clustering algorithm can be used to cluster and analyze user behavior characteristics. Assuming that in application A and application B, the user's login time, usage duration, and active frequency form a set of feature vectors, by calculating the Euclidean distance of these vectors, the account with high similarity may belong to the same user. In order to manage the gift package codes that users receive in different applications, a relational database such as MySQL can be used to create a table containing user ID, application identifier, gift package code, and redemption time. Through SQL queries, the user's gift package redemption records on each platform can be quickly retrieved. Assuming that the user has received gift package code X in application A and gift package code Y in application B, then through the JOIN operation, it is determined whether the user has repeatedly obtained the gift package on multiple platforms. In order to prevent repeated redemption, a hash algorithm is used to generate a unique user identifier, and all requests are compared through a Bloom filter to allow only one user with the same identifier to receive a gift package. RESTful API interface is used for unified management, and GET and POST requests are used to receive user gift package redemption operations. The legality of the request can be based on the OAuth 0 authorization framework to ensure that each request carries a valid access token. Dynamic allocation algorithms such as greedy algorithm are used to manage a limited number of game gifts. Through a priority queue, the user's gift exchange request is sorted by priority. In the case of limited resources, high-priority requests are satisfied first. Assuming that there are 1000 rare gifts in a game, the priority queue is used to sort users by VIP level and activity, ensuring that resources are allocated reasonably. After integrating the information of each step, Apache Kafka and other message queue tools are used to deliver and process user gift redemption and account identification information in real time, ensuring efficient operation and data consistency of the entire system.

[0056] In some embodiments, the game platform obtains the reputation data of the user in different applications, establishes a reputation sharing mechanism based on the reputation data, if the reputation data of the user in multiple applications is good, the game platform sends a higher level of gift code to the application of the user, if the user's reputation data is poor or there is a risk behavior, the game platform limits the user to exchange the gift code.

[0057] The game platform uses a distributed data collection system to obtain the reputation data of the user in different applications, including user behavior logs, transaction records, social interactions and other multi-dimensional information. Through multi-source heterogeneous data fusion algorithm, the collected raw data is cleaned, standardized and integrated to obtain a unified format of user reputation feature vector. Based on the processed user reputation feature vector, the game platform uses a reputation propagation model based on graph neural network to build a cross-application reputation sharing mechanism. The model takes the user as the node and the reputation relationship between the applications as the edge, and through the message passing mechanism, the information flows on the heterogeneous graph structure, thereby obtaining a comprehensive reputation score integrating multiple applications. The game platform uses a rule-based decision tree algorithm to determine the gift distribution according to 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, it is determined as a good reputation user. For good reputation users, the game platform creates a higher level of personalized gift code through a dynamic gift generation algorithm. The game platform sends the generated high-level gift code to the related application of the good reputation user using a secure transmission protocol. If the user's comprehensive reputation score is lower than the threshold or there is a risk behavior label, the game platform starts a reputation recovery mechanism based on time decay, limiting the user's permission to exchange gift codes within a certain time. The game platform uses reinforcement learning algorithm to continuously optimize the reputation evaluation model and decision rules, by collecting user behavior data after obtaining high-level gift, as well as feedback information such as gift usage, continuously adjusting model parameters and decision thresholds, making the reputation sharing mechanism more accurate and effective.

[0058] Preferably, the game platform uses time series analysis method to predict the trend of user reputation data. Through the long short-term memory network model, the time sequence characteristics of user reputation change are captured, and the reputation trend of the user in the future period of time is predicted. According to the prediction result, the game platform adjusts the gift distribution strategy in advance, realizing the foresight of reputation management. The game platform uses federated learning technology to train a global reputation evaluation model with multiple applications while protecting user privacy. Each application trains a local model, only shares model parameters but not raw data, and the game platform as the center node aggregates parameters and updates the global model, thereby realizing collaborative learning and reputation knowledge sharing across applications.

[0059] Specifically, to evaluate the user's reputation data in various applications, we first need to collect the user's reputation scores and levels in these applications. Assume that a user has a reputation score of 85 in application A and a level of "good", a score of 90 in application B and a level of "excellent", and a score of 70 in application C and a level of "ordinary". We use a weighted average method, with weights set according to the importance of the application and the user's activity level. Application A has a weight of 3, application B has a weight of 5, and application C has a weight of 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 code generation and distribution mechanism, we determine whether the user is eligible to obtain a higher level of gift code. Assume that only users with a level of "excellent" or above can obtain a high-level gift code. Since the user's level is "good", he does not meet the conditions and can only receive a normal-level gift code. For the user's reward and punishment measures, the system will automatically adjust the gift code distribution strategy. At the same time, we analyze the user's behavior records in each application program, compare them with the defined risk behaviors (such as frequent malicious refunds, abnormal login, etc.), and use specific risk identification algorithms, such as time series-based anomaly detection algorithms, to determine whether the user has risk behaviors. Assume that the user has had an abnormal login record in application A, but has not reached the threshold of risk behavior, so no restrictive measures are taken. Through the analysis of the user's historical behavior and reputation changes, the system uses a dynamic adjustment mechanism to predict the user's future reputation trend using machine learning algorithms. Assume that the model predicts that the user's reputation may increase by 5% in the next three months, then the system adjusts the reputation evaluation based on this and updates the user's gift code distribution strategy accordingly. Such a mechanism ensures that the user's reputation evaluation 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 the gift code to several applications, and the user receives the gift code in the application and enters the game to request the exchange of the gift.

[0061] The game platform uses a distributed message queue system to push the gift code to several applications. According to the type of application and the characteristics of the user group, the K-means clustering algorithm is used to group the applications. Through the consistent hash algorithm, a unique hash value is assigned to each application to determine the storage location of the gift code in the message queue. After the application receives the gift code, it uses the 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 controls the request frequency using the token bucket algorithm to prevent malicious redemption behavior. The game server uses a distributed lock mechanism to ensure the uniqueness and concurrent safety of the gift code. Through the consistent hash algorithm, the data shard where the gift code is located is located, and the optimistic lock strategy is used to update the gift code state. If the gift code has been redeemed, return the redemption failure information; if the gift code has not been redeemed, mark it as redeemed. The game server calculates the reward distribution order using the directed acyclic graph (DAG) algorithm according to the reward content corresponding to the gift code. Adopting batch processing method to add rewards to user accounts, using two-phase commit protocol to ensure the consistency of transactions. In order to cope with large-scale concurrent redemption requests, the game server uses a load balancing algorithm to distribute requests to multiple processing nodes. Adopting consistent hash algorithm to determine the mapping relationship between user requests and processing nodes, ensure that the same user's request is always routed to the same node, improve cache hit rate. The game server uses a time-based sliding window algorithm to count the redemption of the gift code, and combines the exponential smoothing method to predict the redemption amount in the future period. According to the prediction result, a dynamic programming algorithm is used to adjust the server resource allocation, and the system performance is optimized. In the whole redemption process, the game platform uses a distributed tracking system to record the processing path and time consumption of each request. Adopting anomaly detection algorithm to monitor the system running state in real time, combining decision tree algorithm to automatically diagnose and solve potential problems.

[0063] Specifically, when a user submits a gift code, the system will first generate a unique identifier, such as encrypting the gift code through the SHA256 algorithm to generate a 64-bit string as the unique identifier of the gift. Then, the system will query the database to find the pre-configured gift content through this unique identifier, including detailed information such as game props, resources, and currency. For example, a gift may contain 1000 gold, 50 gems, and a legendary weapon "Flame Sword". The system will further check the validity period of the gift according to this unique identifier, usually by comparing the current timestamp with the start and end timestamps in the gift record to determine whether the gift is within the valid period. For example, if the current time is October 1, 2023, and the validity period of the gift is from September 1, 2023 to December 31, 2023, then the gift is still within the validity period. Then, 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. 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 redemption times, the system will retrieve and count the user's past redemption times in the redemption record according to the user identifier and gift code, for example, a user has redeemed 3 times, and the upper limit of redemption is 5 times, so the user can continue to redeem. In addition, the system will verify whether the application program where the user is located is within the scope of the gift through the list of target applications, such as the gift 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, the system will record the redemption time, such as through the timestamp "20231001 12:30:45", and store this information in the log file. Finally, the system will update the status of the gift code by marking it as "redeemed" to ensure that the same gift code cannot be reused, and return the redemption result to the user to confirm the successful redemption. The entire process does not require human intervention and relies entirely on automated program logic execution.

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

[0065] After the game platform receives the user's gift code application request, it stores the gift code information using a hash table data structure, calculates the hash value of the gift code as an index using a hash function, and looks up 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 content information contained in the record is obtained. A depth-first search algorithm is used to traverse the item list in the gift content information, and inventory checking is performed for each item. A 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 actual inventory quantity is further queried to confirm. According to the inventory checking result, a directed acyclic graph is constructed, and the dependency relationship between items is represented as an edge in the graph. A topological sorting algorithm is used to sort the directed acyclic graph to obtain the order of item distribution. The distribution logic of each item is processed in turn according to the topological sorting result. For each item to be distributed, a consistent hashing algorithm is used to select a suitable server node to perform the distribution operation. An optimistic locking mechanism is used to update the item inventory, and a version number is used to control concurrent access. If the optimistic locking update fails, a backoff algorithm is used to retry the operation. A token bucket algorithm is used to control the rate of item distribution to prevent system overload. A sliding window algorithm is used to count the number of distributions in a short period of time to determine whether to trigger flow limiting. If flow limiting is triggered, the request is placed in a priority queue for processing. An asynchronous message queue is used to asynchronously notify the user of the distribution result. A publish-subscribe pattern is used to push the distribution result to multiple terminals that subscribe to the user. A longest common subsequence algorithm is used to compare the distribution result with the original gift content to determine whether there are any items that failed to be distributed. If there are any items that failed to be distributed, a backtracking algorithm is used to try to find a replacement item. A cosine similarity algorithm is used to calculate the similarity between items, and the item with the highest similarity is selected as the replacement. The replacement item is repeatedly executed through the aforementioned distribution process. A finite state machine is used to record the state changes of the entire gift code application process. A decision tree algorithm is used to select the next processing logic based on the current state. A dynamic programming algorithm is used to optimize the depth of the decision tree to reduce the number of state transitions. Finally, a Bloom filter is used to mark the used gift codes as invalid. An incremental update method is used to periodically synchronize the invalid gift codes to the distributed cache to improve the efficiency of subsequent gift code verification. A consistent hashing algorithm is used to evenly distribute the gift code data among multiple cache nodes.

[0066] Specifically, after obtaining the gift package code, the corresponding gift package content can be obtained by calling the database query interface according to the input gift package code, which includes in-game virtual items and game coins, etc. For example, the gift package content data can be obtained by the SQL query statement "SELECT FROM gift_packages WHERE codeABC123". After confirming the gift package content, the database is further queried to obtain the gift package validity period, and the SQL query statement "SELECT expiration_date FROM gift_packages WHERE codeABC123" can be used to obtain the validity period information, and then the current date is compared with the validity period, for example, the comparison "datetime.now()<expiration_date" is performed using the datetime module in Python to determine whether the gift package code is within the validity period. Next, it is checked whether the game area server currently used by the user matches the area server applicable to the gift package code. Assuming that the user area server information obtained through the API is "user_serverserver1", and the applicable area server of the gift package is obtained by querying "SELECT applicable_servers FROM gift_packages WHERE codeABC123" to obtain the result "server1,server2". By string operation, it is determined whether "user_server in applicable_servers.split" to ensure matching. Then, the gift package redemption record is queried according to the user account information to determine whether the current account exceeds the gift package redemption limit. Assuming that the user account is "user_iduser123", the redemption times are obtained by querying "SELECT COUNT() FROM redemption_records WHERE user_iduser123 AND codeABC123" to be 1, and if the limit is 1 time, the user cannot redeem again. In addition, the gift package code state is checked by querying "SELECT status FROM gift_packages WHERE codeABC123", and if the state is "redeemed", it indicates that it has been redeemed, and the corresponding prompt is returned; if the state is "available", the redemption process continues. The generation time of the gift package code is obtained by querying "SELECT creation_date FROM gift_packages WHERE codeABC123", and compared with the current time "creation_date<datetime.now()" to ensure that the generation time is reasonable.After confirming that all conditions are met, the current time "exchange_time = datetime. now()" is obtained as the exchange time, the database state and exchange time are updated "UPDATE gift_packages SET status = redeemed exchange_time exchange_time WHERE code = ABC123", and the exchange result is returned to the user. This ensures that each step of the logic is rigorous and information processing is automated.

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

[0068] The application program obtains the account information and game account information of the user in the application program. According to the obtained account information, the cosine similarity algorithm is used to calculate the similarity score between two accounts. If the similarity score exceeds the preset threshold, it is determined that the two accounts are associated. Through the associated account information, a user portrait model is constructed. A collaborative filtering recommendation algorithm is used to recommend personalized content to the user based on the user portrait model. According to the user's behavior data in the application program, a decision tree algorithm is used to classify the user. Different operation strategies are used for different categories of users. Through the Naive Bayes classifier, the user's published content is analyzed for sentiment. If the sentiment is positive, the user is pushed to the reward. A K-means clustering algorithm is used to subdivide the user group. According to the subdivision results, targeted operation plans are developed. A support vector machine algorithm is used to predict the user's risk of loss. If the prediction result shows that the user has a risk of loss, the retention mechanism is triggered. Through a deep learning neural network model, the user's usage habits and preferences are analyzed. According to the analysis results, the application program interface is customized. Reinforcement learning algorithm is used to optimize the operation strategy. Through user feedback data, the operation plan is continuously adjusted and improved. Time series analysis method is used to predict the trend of user activity, and according to the prediction result, the corresponding operation activities are deployed in advance. Association rule mining algorithm is used to find the association between user behavior patterns, and cross-marketing strategies are designed according to the mined association rules. Through principal component analysis method, the dimension of user features is reduced, and the feature data after dimension reduction is used to improve the efficiency of subsequent analysis. Random forest algorithm is used to evaluate the importance of each operation measure, and according to the evaluation result, the resource allocation scheme is optimized. Gradient boosting decision tree algorithm is used to predict the user's willingness to pay, and if the prediction result shows that the user has a high willingness to pay, targeted preferential treatment is pushed to the user. Through the recurrent neural network model, the user's behavior sequence is analyzed, and according to the sequence analysis result, the user's next step behavior is predicted. Anomaly detection algorithm is used to identify abnormal behavior in the user group. If abnormal behavior is detected, the corresponding risk control measures are triggered. Graph neural network model is used to analyze the structural characteristics of the user's social network. According to the network structure characteristics, key users with high influence are identified.

[0069] Specifically, in the application and game, the unique identification of the user is obtained through the user ID, for example, a unique hash value can be generated by using the SHA256 algorithm for encryption. Then, the name used by the user in the application and game is obtained through the username, and the database query statement such as the SELECT statement is used to find the matching username record to determine the personal identity of the user. In the judgment of the account binding state, whether the application account and the game account are associated can be judged by whether there is a record in the database. For example, query 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 association can be obtained through the binding time field of the database record, for example, using the TIMESTAMP data type of SQL. When the user tries to log in, the user's identity legitimacy can be judged by verifying the token provided by the user, such as the user token, through the token verification of the verification server. The user's authority in the application and game can be obtained through the authority management system, using the role authority mapping table, and calculating the user's authority level in different situations. User data such as game level, virtual currency and prop information can be obtained by retrieving related fields from the user data table, for example, SELECT level, virtual currency, prop FROM user data table WHERE user ID = 12345. The tracking of user behavior data can be achieved by analyzing the login time and game duration fields in the server log file to calculate the user's activity, for example, by counting the daily login times and average game duration. Trend analysis using these data can determine the user's usage tendency and preference. Combined with the analysis results of user authority and user data, machine learning algorithms such as decision tree or random forest are used to judge the possible VIP level change of the user, so as to predict the potential development direction of the user in the game. By analyzing the user behavior pattern through algorithm, for example, using KMeans clustering algorithm to group user behavior data, the interest degree of the user to a specific function or activity can be judged. Combined with the results of user data and behavior analysis, a weighted scoring model is used to score the overall performance of the user in the application and game, for example, considering factors such as activity, consumption amount, game performance, etc., to form a comprehensive evaluation of the user. Through these technical means, user data can be automatically processed to provide deep insight into user behavior.

[0070] Please refer to Figure 2 The application also proposes a user association recognition system in a game platform based on gift package code, comprising:

[0071] The first processing module 201 pushes the gift package code to a plurality of applications.

[0072] The second processing module 202 responds to the request for exchanging the gift package when the application receives the gift package code and initiates the request for exchanging the gift package according to the gift package code.

[0073] The third processing module 203: query the application program receiving the gift package code, and operate the user through the application program;

[0074] Operating the user through the application program includes: the game platform identifies and associates multiple accounts of the same user in different application programs, and if the user obtains different gift package codes in different application programs to enter the game and requests to exchange multiple gifts, the game platform only issues one game gift;

[0075] After the game platform identifies and associates multiple accounts of the same user in different application programs, the game platform obtains the credit data of the user in different application programs, establishes a credit sharing mechanism based on the credit data, if the credit data of the user in multiple application programs is good, the game platform sends a higher level gift package code to the application program of the user, if the credit data of the user is poor or there is a risk behavior, the game platform limits the user to exchange the gift package code.

[0076] It can be understood that the contents in the user association identification method embodiment in the game platform based on the gift package code as shown in the above are all applicable to the user association identification system embodiment in the game platform based on the gift package code, the functions realized by the user association identification system embodiment in the game platform based on the gift package code are the same as the user association identification method embodiment in the game platform based on the gift package code as shown in the above, and the beneficial effects achieved are also the same as the user association identification method embodiment in the game platform based on the gift package code as shown in the above. Figure 1 Figure 1 Figure 1 The functions realized by the user association identification system embodiment in the game platform based on the gift package code are the same as the user association identification method embodiment in the game platform based on the gift package code as shown in the above, and the beneficial effects achieved are also the same as the user association identification method embodiment in the game platform based on the gift package code as shown in the above.

[0077] It should be noted that the information interaction, execution process and the like between the above systems, since the same concept, its specific functions and the technical effects brought about, specific can refer to the method embodiment part, here will not be repeated.

[0078] ​​Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the above described functions. The functional units and modules in the embodiments can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit, and the integrated unit can be realized in the form of hardware or software. In addition, the specific names of the functional units and modules are only for mutual distinction, and do not limit the protection scope of the application. The specific working process of the units and modules in the system can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0079] Please refer to Figure 3 The embodiment of the application 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, the method for identifying user association in a gift code-based game platform is realized.

[0080] The computer device 3 can be a desktop computer, a notebook computer, a palm computer and a cloud server and the like. The computer device 3 can include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art can understand that, Figure 3 The computer device 3 is only an example and does not limit the computer device 3, which can include more or fewer components than shown, or combine certain components, or different components, for example, can also include input and output devices, network access devices and the like.

[0081] The processor 301 can be a central processing unit (CPU), and the processor 301 can also be 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. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

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

[0083] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program. When the computer program is run by a processor, the method for identifying user association in a gift code-based game platform is implemented.

[0084] In the embodiment, the integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the present application can implement all or part of the processes in the above-mentioned embodiment methods through a computer program to instruct relevant hardware, and the computer program can be stored in a computer readable storage medium. The computer program, when executed by a processor, can implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, an executable file, or some intermediate form. The computer readable medium at least includes any entity or device capable of carrying the computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. For example, a U disk, a mobile hard disk, a magnetic disk or an optical disk, and the like. In some jurisdictions, according to legislation and patent practice, the computer readable medium cannot be an electrical carrier signal and a telecommunications signal.

[0085] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit them; although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for identifying user association in a gift code-based game platform, characterized in that, The application is applied to a game platform, including: pushing a gift package code to a plurality of application programs, when the application programs receive the gift package code and initiate a gift package exchange request according to the gift package code, in response to the request for exchanging the gift package, querying the application programs receiving the gift package code, and operating users through the application programs; Operating users through the application programs includes: identifying and associating a plurality of accounts of the same user in different application programs, and if the user obtains different gift package codes in different application programs to enter a game and request to exchange a plurality of gift packages, only one game gift package is issued; After identifying and associating a plurality of accounts of the same user in different application programs, obtaining reputation data of the user in different application programs, establishing a reputation sharing mechanism based on the reputation data, if the reputation data of the user in a plurality of the application programs is good, sending a higher level gift package code to the application program of the user, and if the reputation data of the user is poor or there is a risk behavior, limiting the user to exchange the gift package code; Wherein, identifying and associating a plurality of accounts of the same user in different application programs includes: According to a plurality of dimensions of the user in different application programs, obtaining a feature vector of the user, calculating the similarity of the feature vectors of different application program accounts, if the similarity of a plurality of accounts exceeds a preset threshold, determining that they are a plurality of accounts of the same user, wherein the dimensions include behavior data, device information and network characteristics; Storing the accounts of the user in different application programs, taking each account as a node, taking the association relationship between each account as an edge, and obtaining a set of all accounts of the same user based on the nodes and edges.

2. The method of claim 1, wherein the method further comprises: Obtaining reputation data of the user in different application programs, and establishing a reputation sharing mechanism based on the reputation data includes: Obtaining the reputation data of the user in different application programs through a distributed data collection system; According to the reputation data, obtaining a user reputation feature vector in a unified format through a multi-source heterogeneous data fusion algorithm; Based on the user reputation feature vector, a reputation sharing mechanism of different application programs is constructed through a reputation propagation model; Wherein, the reputation data includes at least one of behavior logs, transaction records and social interactions.

3. The method of claim 2, wherein the method further comprises: After establishing the reputation sharing mechanism based on the reputation data, the trend of the reputation data is predicted according to a time series analysis method, the time sequence characteristics of the reputation change of the user are captured through a long short-term memory network model, and the reputation trend of the user is predicted based on the time sequence characteristics, and the gift package issuing strategy is adjusted according to the reputation trend.

4. The method of claim 1, wherein the method further comprises: Pushing a gift package code to a plurality of application programs includes: The gift package code is pushed to a plurality of application programs through a distributed message queue system, and the application programs 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 programs are used to store the gift package code in a local cache.

5. The method of claim 4, wherein the method further comprises: After receiving the gift package code request of the user, the corresponding gift package code record is found, and the gift package content information of the gift package code record is obtained; According to the content information, inventory checking is performed on the items in the gift package content information to determine whether the items exist in inventory; If the inventory is insufficient, a substitute item is selected to perform the delivery; The used gift package code is marked as invalid.

6. The method of claim 1, wherein the method further comprises: The operation on the user through the application further includes: According to the behavior data of the user in the application, the user is classified through the associated account information, and different operation strategies are adopted for different categories of users.

7. A system for identifying user associations within a game platform based on a gift code, the system comprising: a gift code database; a game database; a game server; and a game client. Applied to a game platform, comprising: A first processing module: push a gift package code to a plurality of applications; A second processing module: when the application receives the gift package code and initiates a gift package redemption request according to the gift package code, respond to the gift package redemption request; A third processing module: query the application that receives the gift package code, and operate the user through the application; Operating 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 request to redeem multiple gift packages, the game platform only delivers 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 reputation data of the user in different applications, establishes a reputation sharing mechanism based on the reputation data, and if the reputation data of the user in multiple applications is good, the game platform sends a higher level gift package code to the application of the user, and if the reputation data of the user is poor or there is a risk behavior, the game platform limits the user to redeem the gift package code; Wherein, identifying and associating multiple accounts of the same user in different applications includes: According to the characteristics of the user in multiple dimensions in different applications, a feature vector of the user is obtained, the similarity of the feature vectors of different application accounts is calculated, and if the similarity of multiple accounts exceeds a preset threshold, it is determined that multiple accounts of the same user, wherein the dimensions include behavior data, device information and network characteristics; Store the user's account in different applications, take each account as a node, and take the association relationship between each account as an edge, and obtain the set of all accounts of the same user based on the nodes and edges.

8. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the method of any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. The computer program is executed by the processor to realize the method of any one of claims 1-6.

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