Cross-platform game asset unified management analysis method and device, and storage medium

Abstract

The application provides a cross-platform game asset unified management analysis method and device and a storage medium. The method comprises the following steps: obtaining game asset association data and terminal device configuration data of a target user from multiple game platforms; performing field analysis, caliber conversion, credibility marking and evidence fingerprint generation on the obtained data according to a preset cross-platform data model to obtain standardized game asset data; performing semantic matching, rule checking and right coverage relationship identification on game entities and right entities in the standardized game asset data to generate entity mapping results; constructing a user unique game asset archive according to the entity mapping results; identifying abnormal consumption states, repeated right states, configuration inadaptation states and account risk states according to various representation and account state data to generate professional analysis results. The application can improve data integration accuracy, enhance right identification consistency and improve asset risk identification efficiency.

Description

Technical Field

[0001] This application relates to the field of game asset management technology, and in particular to a cross-platform game asset unified management and analysis method, device and storage medium. Background Technology

[0002] With the development of digital game distribution methods and game platform ecosystems, users typically purchase, claim, subscribe to, or use game assets across multiple game platforms. Different game platforms record data such as users' game library, spending history, playtime, achievement progress, additional content, and account login status. However, the data interfaces, field structures, asset identifiers, and statistical standards of each platform are inconsistent, resulting in users' game assets being managed in a decentralized manner for a long time.

[0003] Existing game community platforms or game platform clients can typically only display the game library, spending records, or play statistics within a single platform. While some tools can list the games a user already owns, they mainly rely on the platform's raw data for synchronization and aggregation. They lack a unified identification mechanism for cross-platform game entities, version rights, additional content, subscription rights, and duplicate assets, and it is also difficult to correlate and analyze spending costs, actual play status, achievement completion status, terminal device configuration, and account security status.

[0004] Therefore, existing technologies struggle to create unique game asset profiles for each user, failing to accurately reflect the overall status of a user's cross-platform game assets. When users prepare to purchase new games or manage existing game assets, current methods typically cannot identify issues such as duplicate purchases, underutilized purchases, purchases with incompatible configurations, abnormal spending, account risks, and changes in asset availability. This results in users lacking professional analytical support based on their actual asset status and usage behavior. Therefore, it is necessary to provide a method that can uniformly organize, correlate, model, and comprehensively analyze cross-platform game assets. Summary of the Invention

[0005] In view of this, embodiments of this application provide a cross-platform game asset unified management and analysis method, device and storage medium to solve the problems of cross-platform data dispersion, difficulty in unifying rights and interests, and difficulty in identifying asset risks in the prior art.

[0006] A first aspect of this application provides a method for unified management and analysis of cross-platform game assets, comprising: in response to user authorization, obtaining game asset association data and terminal device configuration data of a target user from multiple game platforms; according to a preset cross-platform data model, performing field parsing, caliber conversion, credibility marking, and evidence fingerprint generation on the obtained data to obtain standardized game asset data; based on a game entity matching model, performing semantic matching, rule verification, and rights coverage relationship identification on game entities and rights entities in the standardized game asset data to generate entity mapping results; according to the entity mapping results, associating asset records, transaction evidence, rights status, usage status, and device adaptation status in different game platforms with the user asset graph to construct a unique game asset profile for the user; based on the unique game asset profile for the user, generating asset value representation, consumption structure representation, play efficiency representation, cost-effectiveness representation, and device adaptation representation; and identifying abnormal consumption status, duplicate rights status, mismatched configuration status, and account risk status based on various representations and account status data to generate professional analysis results for unified management and analysis of cross-platform game assets.

[0007] A second aspect of this application provides a cross-platform game asset unified management and analysis device, comprising: an acquisition module, configured to acquire, in response to user authorization, game asset association data and terminal device configuration data of a target user from multiple game platforms; a standardization module, configured to perform field parsing, caliber conversion, credibility marking, and evidence fingerprint generation on the acquired data according to a preset cross-platform data model to obtain standardized game asset data; an identification module, configured to perform semantic matching, rule verification, and rights coverage relationship identification on game entities and rights entities in the standardized game asset data based on a game entity matching model to generate entity mapping results; an association module, configured to associate asset records, transaction evidence, rights status, usage status, and device adaptation status in different game platforms with the user asset graph according to the entity mapping results to construct a unique game asset profile for the user; a representation module, configured to generate asset value representation, consumption structure representation, play efficiency representation, cost-effectiveness representation, and device adaptation representation based on the unique game asset profile for the user; and a generation module, configured to identify abnormal consumption status, duplicate rights status, mismatched configuration status, and account risk status according to various representations and account status data to generate professional analysis results for cross-platform game asset unified management and analysis.

[0008] A third aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0009] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects: By responding to user authorization, this application acquires game asset association data and terminal device configuration data of target users from multiple game platforms. Following a pre-defined cross-platform data model, the acquired data undergoes field parsing, definition conversion, credibility marking, and evidence fingerprint generation to obtain standardized game asset data. Based on a game entity matching model, semantic matching, rule verification, and rights coverage relationship identification are performed on game entities and rights entities within the standardized game asset data to generate entity mapping results. Based on these entity mapping results, asset records, transaction evidence, rights status, usage status, and device compatibility status from different game platforms are linked to the user's asset graph, constructing a unique game asset profile for each user. Based on this unique user game asset profile, asset value representation, consumption structure representation, gameplay efficiency representation, cost-effectiveness representation, and device compatibility representation are generated. Based on these representations and account status data, abnormal consumption status, duplicate rights status, mismatched configuration status, and account risk status are identified, generating professional analysis results for unified cross-platform game asset management and analysis. This application improves data integration accuracy, enhances the consistency of rights identification, and increases the efficiency of asset risk identification. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating the cross-platform game asset unified management and analysis method provided in the embodiments of this application; Figure 2 This is a schematic diagram of the cross-platform game asset unified management and analysis device provided in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0012] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0013] In existing technologies, users' game assets are typically stored across multiple game platforms, each recording data such as game library, purchase history, playtime, achievement progress, additional content, subscription benefits, and account status. Due to differences in data interfaces, field structures, asset identifiers, benefit types, and statistical methods among different game platforms, existing game community platforms or game platform clients can usually only display or synchronize data within a single platform, making it difficult to uniformly organize and correlate game assets across platforms.

[0014] Under the aforementioned existing technological conditions, it is difficult to accurately correspond game entities, version rights, and additional content of the same game across different gaming platforms. There is a lack of a unified identification mechanism between subscription rights, permanent rights, gift pack rights, and duplicate rights. Furthermore, existing methods typically do not jointly model consumption records, gameplay behavior, achievement completion status, terminal device configuration, and account status, resulting in users being unable to accurately grasp the overall status of their cross-platform game assets and struggling to promptly identify issues such as duplicate purchases, underutilized purchases, incompatible configurations, abnormal consumption, and account risks.

[0015] Based on this, this application provides a cross-platform game asset unified management and analysis method, mainly addressing the problems of fragmented cross-platform data, difficulty in unifying rights and interests, and difficulty in identifying asset risks in existing technologies. This problem is not simply a matter of consumer advice, but rather a data processing and status identification problem caused by the difficulty in unified modeling of multi-source heterogeneous data, the difficulty in accurately mapping game asset entities, and the difficulty in correlated analysis of asset usage status and device compatibility status.

[0016] To address the aforementioned technical issues, this application, in response to user authorization, obtains game asset association data and terminal device configuration data of target users from multiple game platforms. Following a pre-defined cross-platform data model, the obtained data undergoes field parsing, caliber conversion, credibility marking, and evidence fingerprint generation to obtain standardized game asset data. Subsequently, based on a game entity matching model, semantic matching, rule verification, and rights coverage relationship identification are performed on game entities and rights entities within the standardized game asset data to generate entity mapping results. This achieves unified mapping of rights and related rights for the same game across different game platforms.

[0017] Based on this, this application, according to the entity mapping results, links asset records, transaction evidence, rights status, usage status, and device compatibility status from different game platforms to the user asset graph, constructing a unique game asset profile for each user. Furthermore, based on this unique user game asset profile, it generates asset value representations, consumption structure representations, gameplay efficiency representations, cost-effectiveness representations, and device compatibility representations. Combined with account status data, it identifies abnormal consumption states, duplicate rights states, mismatched configuration states, and account risk states, ultimately generating professional analysis results for unified management and analysis of cross-platform game assets.

[0018] Through the above technical solutions, this application can improve the accuracy of cross-platform game asset data integration, enhance the consistency of game entity and rights relationship identification, and improve the identification efficiency of asset risk status such as abnormal consumption, duplicate rights, mismatched configuration and account risk, so that game assets scattered in multiple game platforms can be uniformly modeled, dynamically associated and comprehensively analyzed.

[0019] The technical solution of this application will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0020] Figure 1 This is a flowchart illustrating the cross-platform game asset unified management and analysis method provided in this application embodiment. Figure 1 As shown, the method may specifically include: S101, in response to user authorization, obtains game asset association data and terminal device configuration data of the target user from multiple game platforms; S102, according to the preset cross-platform data model, the acquired data is parsed, the definition is converted, the credibility is marked and the evidence fingerprint is generated to obtain standardized game asset data; S103, based on the game entity matching model, performs semantic matching, rule verification and equity coverage relationship identification on game entities and equity entities in standardized game asset data, and generates entity mapping results; S104. Based on the entity mapping results, the asset records, transaction evidence, rights status, usage status and device compatibility status in different game platforms are associated with the user asset graph to construct a unique game asset profile for each user. S105 generates asset value representation, consumption structure representation, gameplay efficiency representation, cost-effectiveness representation, and device compatibility representation based on the user's unique game asset profile. S106 identifies abnormal consumption status, duplicate rights status, mismatched configuration status, and account risk status based on various characteristics and account status data, and generates professional analysis results for unified management and analysis of cross-platform game assets.

[0021] It should be noted that the concept of rights coverage refers to the relationship of rights inclusion, substitution, or overlap established between a unified game entity or a unified rights entity. It is used to indicate whether one game right covers all or part of the content of another game right. Rights coverage can be determined based on the content scope, availability period, and acquisition method of the basic game version, upgraded versions, additional content, bundled rights, subscription rights, shared rights, and limited-time rights. For example, if a user already owns the standard version of a game and then obtains bundled rights that include the standard version and additional content through a subscription service, then there is a partial or complete coverage relationship between the standard version rights and the bundled rights. Rights coverage is used to determine duplicate rights, missing rights, and substitutable rights, and does not indicate an actual control relationship between the game platforms.

[0022] Evidence fingerprints are unique or quasi-unique verification identifiers generated by the system based on the source information and key business fields corresponding to game asset data. They are used for deduplication, verification, and tracing of data records. Evidence fingerprints can be generated based on at least some fields from the following: platform source identifier, platform account identifier, game identifier, rights identifier, transaction time, transaction amount, collection time, order number, or data summary. Evidence fingerprints do not alter the original data content; rather, they serve as an index linking standardized game asset data to the original platform records, enabling subsequent entity matching, asset value calculation, and risk identification to trace back to the corresponding data source.

[0023] Credibility tags refer to the data reliability status configured by the system for data records based on data source, collection method, field completeness, field consistency, and verification results. Credibility tags characterize the reference weight or verification priority of the data record in subsequent calculations. Data obtained through open interfaces with complete fields and consistent with other data sources can be configured with a high credibility tag; data obtained through file parsing or local caching but with mostly complete fields can be configured with a medium credibility tag; data with missing fields, conflicting definitions, or whose source cannot be fully verified can be configured with a credibility tag pending verification. Credibility tags are used to reduce the impact of anomalous data on analysis results and do not represent a credit rating for user accounts or the game platform itself.

[0024] Asset value representation refers to structured analytical data generated based on transaction evidence, rights status, and rights coverage relationships within a user's unique game asset profile. This data reflects the acquisition cost, current acquireable cost, and rights availability of game assets. Asset value representation may include parameters such as asset acquisition cost, current alternative acquisition cost, rights validity status, rights duration, refund impact, cost breakdown of gift packs, and subscription cost allocation. Asset value representation is used to support asset management analysis and does not constitute financial investment value or transaction price assessment.

[0025] Consumption structure representation refers to structured analytical data generated from the consumption records of target users across different time periods, gaming platforms, game types, and acquisition methods of benefits. It reflects the distribution of target users' gaming consumption behavior. Consumption structure representation can show information such as purchase frequency, consumption amount range, platform distribution, benefit type distribution, discount purchase ratio, and cyclical consumption changes. This representation is used to compare with the target user's historical purchase baseline to identify deviations in purchasing behavior.

[0026] Play efficiency metrics refer to structured analytical data generated based on the usage status of each asset node in a user's unique game asset profile. This data reflects the actual degree to which game assets are used. Play efficiency metrics can be generated based on parameters such as cumulative playtime, recent launch frequency, most recent launch interval, first launch time after purchase, achievement completion rate, and long-term inactivity time. For different game types, the system can apply different weights to normalize these parameters to avoid evaluating games of different gameplay types using the same usage standard.

[0027] Cost-effectiveness representation refers to structured analytical data generated by correlating asset value representation with play efficiency representation. It is used to characterize the match between the acquisition cost and actual usage of game assets. Cost-effectiveness representation can be generated based on parameters such as playtime per unit acquisition cost, achievement completion rate per unit acquisition cost, post-purchase activation status, subscription benefit usage distribution results, and long-term inactivity status. Cost-effectiveness representation is used to identify asset states such as high cost with low usage, low cost with high usage, or long-term inactivity, and does not represent an absolute evaluation of the quality of the game product itself.

[0028] Device compatibility characterization refers to structured analysis data generated by matching terminal device configuration data with the corresponding runtime configuration requirements of asset nodes. It characterizes the degree of compatibility between the target user's current terminal device and the corresponding game assets. Device compatibility characterization can be generated based on the matching results between processor, graphics processor, memory, video memory, storage space, operating system version, driver version, display resolution, and target runtime conditions, and can be corrected by incorporating historical startup failures, runtime crashes, or user feedback. Device compatibility characterization is used to identify configuration incompatibility states and generate configuration prompts; it does not represent a certification conclusion made by the game platform regarding device performance.

[0029] In some embodiments, in response to user authorization, game asset association data and terminal device configuration data of a target user are obtained from multiple game platforms, including: Receive platform binding requests from target users for multiple game platforms, establish corresponding authorization sessions based on the platform binding requests, and configure platform adapters for each authorization session; By identifying the data access capabilities and data field definitions of the corresponding game platform through the platform adapter, a data collection strategy that matches the game platform is generated. According to the data collection strategy, the target user's asset status record, transaction behavior record, usage behavior record and account status record in the corresponding game platform are obtained, and the device configuration record of the target user's corresponding terminal device is also obtained. The acquired data is associated with the platform source identifier, authorization time identifier, and collection method identifier to generate game asset association data and terminal device configuration data to be standardized.

[0030] Specifically, when a target user uses the cross-platform game asset unified management and analysis function for the first time, the system displays a list of game platforms that can be bound and receives platform binding requests initiated by the target user for different game platforms. The platform binding request carries the platform account identifier, authorization scope, authorization validity period, data synchronization frequency, and terminal device collection permission. The system establishes an authorization session based on each platform binding request and generates a unique session identifier, token validity period identifier, and permission scope identifier for each platform. For game platforms that support interface authorization, the system establishes an authorization session using an interface token; for game platforms that only support file export or client local record reading, the system establishes a file parsing session or a local reading session and records the data source and collection method.

[0031] After establishing an authorization session, the system configures a platform adapter for each session. The platform adapter includes an interface call unit, a field parsing unit, a caliber identification unit, and a data collection strategy generation unit. The interface call unit identifies whether the game platform has opened data interfaces related to the game library, orders, playtime, achievements, and account status. The field parsing unit parses the asset, transaction, behavior, and status fields in the data returned by the platform. The caliber identification unit identifies the data statistics rules used by the game platform for playtime, achievement progress, refund status, additional content benefits, and subscription benefits. Based on the above identification results, the system generates a data collection strategy, which includes the scope of the collection target, collection path, pagination rules, incremental synchronization conditions, exception retry rules, and field completion methods.

[0032] For example, a target user may be linked to game platform A, game platform B, and game platform C simultaneously. Game platform A provides a full API, allowing the system to access the game library, purchase orders, playtime, and achievement progress via API calls. Game platform B only provides the game library and achievement APIs; the system supplements transaction records by exporting files based on user-uploaded orders. Game platform C can only read the client's local cache; the system extracts recent launch records and account login status by reading local sessions and assigns a lower credibility level to this data. When the same game has records on multiple game platforms, the system retains the original platform records at this stage without merging them, ensuring a complete source of evidence for subsequent standardization and entity mapping.

[0033] During data collection, the system acquires the target user's asset status records, transaction behavior records, usage behavior records, and account status records across various game platforms according to the data collection strategy. Asset status records represent the user's ownership, subscription, sharing, or temporary availability of game rights; transaction behavior records represent the asset formation process, such as purchases, refunds, acquisition of gift packs, or acquisition of additional content; usage behavior records represent cumulative playtime, most recent launch time, activity cycle, and achievement progress; and account status records represent the login device, login location, number of authorized devices, and security verification status. Simultaneously, the system acquires terminal device configuration records based on the target user's authorization. These records include runtime environment data such as processor, graphics processor, memory, storage space, operating system version, display parameters, and driver version.

[0034] After data collection is complete, the system associates each data record with a platform source identifier, authorization time identifier, and collection method identifier, and generates a collection batch identifier and a raw data summary. The platform source identifier is used to distinguish the game platform to which the data corresponds, the authorization time identifier is used to record the time when the data was acquired, and the collection method identifier is used to distinguish between interface collection, file parsing, and local reading. The system further encapsulates the above data into game asset association data to be standardized according to the user account dimension, and encapsulates the terminal device configuration records into terminal device configuration data to be standardized.

[0035] It should be noted that asset status records refer to data records used to characterize the current holding status of a target user for a specific game asset or game right. The data object is the game asset or right itself, primarily reflecting whether the target user has acquired the corresponding game, version, additional content, subscription rights, or shared rights, and whether these rights are in a state of availability, expiration, time-limited, or unavailable after a refund. Asset status records are not used to characterize account login security status, nor are they used to characterize payment behavior during the asset acquisition process.

[0036] Transaction behavior records refer to data representing the process by which a target user acquires, changes, or terminates game asset rights. The data object is events involving changes in rights, primarily reflecting actions such as purchases, refunds, gifts, exchanges, subscription activations, subscription renewals, acquisition of gift packs, or order modifications that lead to the formation, expansion, reduction, or termination of asset rights. Transaction behavior records emphasize changes in rights and their corresponding time, amount, order source, or change voucher; they are not used to describe actual gameplay after acquiring rights.

[0037] Usage behavior records refer to data records used to characterize the actual use of game assets by a target user after acquiring game asset rights. The data objects are game operation and interaction behaviors, mainly reflecting usage status such as launch, playtime, recent play time, achievement completion, save synchronization, online participation, or content consumption. Usage behavior records do not change whether a user owns the corresponding rights; they are only used to evaluate the degree of asset utilization. If the same raw data contains both purchase time and first launch time, the system will classify the purchase time into transaction behavior records and the first launch time into usage behavior records.

[0038] Account status records refer to data records used to characterize the access, security, and authorization status of a target user at the corresponding game platform account level. The data object is the platform account, not a specific game asset. It primarily reflects the login device, login region, authorized device, security verification, account restrictions, abnormal logins, and risk control status. Account status records are used to determine the asset access environment and account risk, and do not indicate the target user's holding status of a specific game asset.

[0039] When partitioning data, the system categorizes it according to the data object and its function: data objects representing the current state of game rights are classified as asset status records; data objects representing events that cause changes in rights are classified as transaction behavior records; data objects representing operational or interactive behaviors after the rights are acquired are classified as usage behavior records; and data objects representing platform account access and security status are classified as account status records. For mixed fields returned by the game platform, the system splits them into corresponding records according to the above rules and configures record type identifiers for the splitting results, thereby ensuring that the boundaries of the four types of records are clear and their meanings are explicit.

[0040] This embodiment enables the generation of cross-platform data collection results that are clear in origin, have well-defined permissions, and are traceable, without altering the original data structure of each game platform. This provides a data foundation for subsequent field standardization, entity matching, and asset graph construction.

[0041] In some embodiments, according to a preset cross-platform data model, the acquired data undergoes field parsing, caliber conversion, credibility marking, and evidence fingerprint generation to obtain standardized game asset data, including: Based on a pre-defined cross-platform data model, the system identifies platform fields, business fields, and status fields in game asset-related data and terminal device configuration data, and generates field parsing results. In accordance with unified data standards, the game identifier, rights status, transaction records, usage status, and device configuration parameters in the field parsing results are formatted and their units are standardized. Data credibility tags are generated based on data source, collection method, and field completeness, and verification tags are added to data with missing fields or conflicting definitions. Evidence fingerprints are generated based on standardized field content, platform source identifiers, and collection time identifiers. The evidence fingerprints are then associated with the corresponding data records to obtain standardized game asset data.

[0042] Specifically, after completing data collection from multiple game platforms, the system standardizes game asset-related data and terminal device configuration data according to a pre-defined cross-platform data model. The pre-defined cross-platform data model defines the field hierarchy, meaning, and format of various data types within a unified asset archive. The model includes three parsing layers: platform fields, business fields, and status fields. Platform fields record the game platform number, platform account identifier, collection batch, and data interface type; business fields record the game name, version rights, acquisition method, transaction amount, playtime, achievement progress, and terminal operating environment; and status fields record the asset availability status, refund status, subscription status, recently launched status, account login status, and device compatibility status. The system identifies the original fields in the data returned by each game platform through field mapping rules and writes the identification results into the field parsing results.

[0043] During field parsing, the system first calls the corresponding field mapping template based on the platform source identifier, and then parses the original field name, field value type, and field value range. Game platform A records cumulative playtime in minutes, game platform B records cumulative playtime in hours, and game platform C only records the number of launches in the last 14 days. The system identifies the usage status fields in the unified model corresponding to the above fields respectively, and sets conversion flags for fields with different data granularities. For terminal device configuration data, the system maps processor model, graphics processor model, video memory capacity, RAM capacity, operating system version, and display parameters to device configuration parameters, forming a unified device profile field that matches the game's running configuration requirements.

[0044] Subsequently, the system performs format conversion and unit standardization on the field parsing results according to a unified data standard. For game identifiers, the system combines the platform game number, standardized game name, release time, and version information into candidate game identifiers; for rights status, the system converts the original statuses such as permanent ownership, subscription availability, shared availability, and limited-time trial into a unified rights status; for transaction records, the system converts different currencies, discount standards, refund flags, and gift pack acquisition records into a unified transaction field; for usage status, the system standardizes playtime, last launch time, achievement completion rate, and activity period into a comparable format; and for device configuration parameters, the system converts video memory, RAM, storage space, and display resolution into a unified unit.

[0045] In the credibility tagging process, the system generates a data credibility tag for each data entry based on the data source, collection method, and field completeness. Data obtained directly through open interfaces and with complete fields is tagged as high credibility; data obtained by parsing user-uploaded billing files is tagged as medium credibility; and data obtained by parsing client-cached or incomplete pages is tagged as credibility pending verification. If the transaction time is missing in the same order record on the same platform, or if the rights status of the same game is inconsistent across different data sources, the system adds a field missing verification tag or a definition conflict verification tag, and reduces the weight of this data in subsequent entity matching and asset value analysis.

[0046] After standardization, the system generates evidence fingerprints based on the standardized field content, platform source identifier, and collection time identifier. Evidence fingerprints can be generated by combining user account summary, game platform number, candidate game identifier, transaction time, rights status, and data summary, and are then bound to the corresponding data records. For example, if a target user purchases the standard edition of an open-world game on game platform A and obtains the same enhanced edition through a subscription on game platform B, the system generates two separate evidence fingerprints, retaining the differences in platform source, acquisition method, and collection time to avoid losing the asset formation process during subsequent merging. Through this embodiment, the system can convert multi-source heterogeneous game asset data into standardized game asset data with unified field meanings, consistent data definitions, distinguishable credibility, and traceable evidence sources, improving the accuracy of data processing in subsequent entity mapping, asset profile construction, and risk identification.

[0047] In some embodiments, based on a game entity matching model, semantic matching, rule verification, and rights coverage relationship identification are performed on game entities and rights entities in standardized game asset data to generate entity mapping results, including: Game entity description information and equity entity description information are extracted from standardized game asset data to generate a candidate entity set; Based on the game entity matching model, semantic vector encoding and similarity calculation are performed on the candidate entity set to obtain the candidate entity matching relationship; According to the preset entity verification rules, the consistency of platform identifier, version attribute and release attribute in the candidate entity matching relationship is verified. Based on the verified candidate entity matching relationships, the inclusion, substitution, and overlap relationships between the rights entities are identified, and entity mapping results are generated.

[0048] Specifically, after obtaining standardized game asset data, the system extracts game entity description information and rights entity description information from each data record. Game entity description information includes standardized game name, publisher, release date, game type, supported platforms, language / region, version name, and content tags. Rights entity description information includes normalized fields such as basic game rights, upgraded version rights, additional content rights, subscription-available rights, shared-available rights, and bundle split rights. Using candidate game identifiers as the primary index, the system aggregates data records from different game platforms that have similar names, similar release attributes, or related rights structures into a candidate entity set, retaining the platform origin, evidence fingerprint, and credibility marker for each candidate entity.

[0049] After the candidate entity set is generated, the system calls the game entity matching model for matching analysis. The game entity matching model includes a text semantic encoding layer, a structural feature encoding layer, and a fusion judgment layer. The text semantic encoding layer performs semantic vector encoding on the standardized game name, summary, version name, and content tags. The structural feature encoding layer performs feature encoding on the release time, publisher, supported platforms, achievement structure, and rights level. The fusion judgment layer generates candidate entity matching relationships based on semantic similarity, structural similarity, and data credibility. Taking the example of a target user owning the standard edition of an open-world game on platform A and the annual edition of the same name on platform B, the system first obtains a high semantic similarity based on the game name and content tags, and then determines that the two belong to different rights forms of the same unified game entity based on the publisher, release time, and achievement structure.

[0050] To prevent games with similar names but different actual games from being mistakenly merged, the system performs consistency checks on candidate entity matching relationships according to preset entity verification rules. These rules jointly assess platform identifiers, version attributes, and distribution attributes. When two candidate entities have similar names but significant differences in their publisher, release year, or game platform category, the system marks the matching relationship as pending review. When one candidate entity corresponds to a console platform remaster and another to a PC platform enhanced version, and both have the same publisher, the same series identifier, and similar content structure, the system identifies them as different versions of the same unified game entity. For data with missing fields or low credibility, the system reduces the verification weight and prioritizes data records with complete interface sources and stable evidence fingerprints as the verification basis.

[0051] After successful verification, the system further identifies the inclusion, substitution, and overlap relationships between the benefit entities. Inclusion relationships indicate that the annual edition covers the standard edition and some additional content; substitution relationships indicate that the enhanced edition can replace the basic benefits of the old version; and overlap relationships indicate that users obtain the same game benefits through both permanent purchase and subscription services. Taking a target user who has purchased the standard edition and obtained the complete collection edition through a subscription as an example, the system maps the standard edition benefits, subscription benefits, and collection edition benefits to the same unified game entity and marks benefit statuses such as overlapping basic benefits, incomplete coverage of additional content, and temporary availability of subscription benefits. Finally, the system combines the unified game entity, unified benefit entity, platform source relationship, and benefit coverage relationship to generate the entity mapping result.

[0052] Through this embodiment, the system can perform semantic fusion, rule verification, and hierarchical rights identification for game entities and rights entities even when data standards are inconsistent across platforms. This reduces errors such as erroneous merging of identical names, omissions in version merging, and duplicate identification of rights, thereby improving the accuracy of constructing unique game asset profiles for users and the stability of subsequent asset risk analysis.

[0053] In some embodiments, based on the entity mapping results, asset records, transaction evidence, rights status, usage status, and device compatibility status from different game platforms are associated with the user asset graph to construct a unique user game asset profile, including: The node structure of the user asset graph is established with the target user as the root node of the graph and the unified game entity and unified equity entity in the entity mapping result as asset nodes. Link asset records and transaction evidence from different gaming platforms to corresponding asset nodes to generate asset source relationships; Based on the status of rights and usage, establish the rights coverage relationship and usage status relationship between asset nodes; The device compatibility status is associated with the corresponding asset node to generate a unique game asset profile for each user.

[0054] Specifically, after generating the entity mapping results, the system constructs a user asset graph with the target user as the root node. The root node is used to associate the target user's platform accounts, authorized sessions, and data collection batches across different game platforms; the unified game entity represents the same game object determined through cross-platform matching; and the unified rights entity represents the different rights obtained by the target user for the unified game entity. Based on the entity mapping results, the system sets the unified game entity and the unified rights entity as asset nodes, and writes the entity identifier, rights type, platform source set, evidence fingerprint set, and credibility marker into the asset nodes, enabling each asset node to trace its corresponding original platform record.

[0055] When establishing the node structure, the system first creates game asset nodes based on a unified game entity, and then creates equity asset nodes under the game asset nodes based on a unified equity entity. For cases where the same game has records on multiple game platforms, the system does not directly overwrite the existing data. Instead, it uses the asset records from different platforms as source data and attaches them to the same game asset node. For example, if a target user purchases the standard version of a game on game platform A and obtains the complete collection version of the game through a subscription service on game platform B, the system identifies this game as a unified game entity and creates a standard version equity node and a subscription collection equity node under this unified game entity, thus preserving the difference between permanent purchase equity and temporary subscription equity.

[0056] The system further links asset records and transaction evidence from different game platforms to corresponding asset nodes, generating asset source associations. Asset records include game library records, rights acquisition records, additional content records, and subscription availability records. Transaction evidence includes order records, gift pack splitting records, refund records, and rights change records. The system links each piece of transaction evidence to the corresponding rights asset node according to the platform source identifier, collection time identifier, and evidence fingerprint, and establishes an evidence chain index for data from different sources. If there is a time difference between order data returned by a game platform and game library data, the system associates them as transaction formation evidence and asset existence evidence, respectively. Instead of directly deleting conflicting data during the graph construction stage, conflict verification markers are added for subsequent analysis.

[0057] After establishing asset source relationships, the system establishes equity coverage and usage status relationships between asset nodes based on equity status and usage status. Equity coverage relationships describe the coverage hierarchy among Standard Edition, Upgrade Edition, Additional Content, Collection Equities, Subscription Equities, and Shared Equities. Usage status relationships describe the association between game asset nodes and cumulative playtime, most recent launch time, active period, achievement completion status, and inactivity time. For example, if a target user already owns the Standard Edition and two Additional Content items of a game, and simultaneously obtains the Full Collection Edition through a subscription, the system establishes an coverage relationship between the Full Collection Edition equity node and the Standard Edition equity node and Additional Content equity nodes, marking the subscription equity as available for a limited time and the permanent purchase equity as available indefinitely.

[0058] Simultaneously, the system associates device compatibility status with the corresponding asset node. The system generates device compatibility status based on terminal device configuration data and game runtime configuration requirements, and then attaches this status to the game asset node or equity asset node. For games with different version configuration requirements, the system matches the runtime configuration requirements for each version with the target user's terminal device configuration. For example, if the standard version meets the recommended runtime requirements on the target user's device, while the enhanced version only meets the minimum runtime requirements due to insufficient video memory, the system associates the two compatibility results with the corresponding equity asset nodes separately, preventing the compatibility status of different equity versions under the same game entity from being mixed up.

[0059] After completing the above processing, the system creates a unique game asset profile for each user. This profile is based on the user's asset map, which unifies and links cross-platform asset records, transaction evidence, rights status, usage status, and device compatibility status, and retains the source of each piece of analytical data through an evidence chain index.

[0060] It's important to clarify that asset source association refers to the connection between asset nodes and their original data sources within the user asset graph. Asset source association characterizes which game platform a given unified game entity or unified rights entity's asset records originate from, how they were acquired, which acquisition batch they correspond to, and which transaction evidence or fingerprints they are associated with. The purpose of asset source association is to maintain traceability between asset nodes and their original platform records. For example, if a target user purchases a standard version of a game on game platform A and obtains a bundled version of the same game through a subscription service on game platform B, the system will link the asset records from both platforms to different rights asset nodes under the same unified game entity through asset source association. Asset source association does not indicate whether rights overlap or contain each other; it only characterizes the source, evidence, and attribution path of asset data.

[0061] Rights coverage relationships refer to the associations within a user asset graph that represent the inclusion, substitution, or overlap of content scope, availability period, or acquisition method between different rights asset nodes. The data object for rights coverage relationships is the scope of rights between unified rights entities, not the source from the game platform. This relationship is used to determine whether a user's already acquired rights cover all or part of the content of another right. For example, the complete collection version rights cover basic game rights and some additional content rights; subscription rights and permanent purchase rights may overlap for a certain period; and enhanced version rights can substitute for older version rights in terms of content scope. Rights coverage relationships are used for subsequent identification of duplicate rights, determination of missing rights, and normalization calculation of asset value.

[0062] Usage status relationships refer to the associations in a user asset graph that connect asset nodes with actual user behavior. Usage status relationships characterize the usage status of a game asset after acquisition, including launch, play, achievement completion, recent use, and inactivity duration. The data objects for usage status relationships are asset nodes and usage behavior records; they do not represent the asset acquisition process or account security status. For example, if an asset node is associated with cumulative playtime, recent launch time, and achievement completion percentage, this usage behavior data is linked to that asset node using usage status relationships. Usage status relationships are used to generate play efficiency representations, cost-effectiveness representations, and asset usage deviation results.

[0063] Device compatibility status refers to the status data generated after matching the target user's terminal device configuration data with the corresponding operational configuration requirements of the asset node. It characterizes the degree of compatibility between the current terminal device and the corresponding game asset. Device compatibility status can be determined based on the matching results between the processor, graphics processor, memory, video memory, storage space, operating system version, driver version, display resolution, and target operating conditions, and can be corrected by incorporating historical startup failures, crashes, or user feedback. The data object of device compatibility status is the matching result between the asset node and the terminal device's operating environment; it is not equivalent to account status, nor does it indicate whether the user owns the game asset. For example, if the standard version of the game meets the recommended operating conditions, while the enhanced version only meets the minimum operating conditions due to insufficient video memory, the system will generate different device compatibility statuses for the two asset nodes.

[0064] Through the above limitations, the asset source relationship is used to clarify the data source and evidence path of asset nodes; the rights coverage relationship is used to clarify the content coverage boundaries between rights nodes; the usage status relationship is used to clarify the correspondence between asset nodes and actual usage behavior; and the device adaptation status is used to clarify the matching result between asset nodes and the operational capabilities of terminal devices. The four relationships differ in their data objects, generation basis, and uses, ensuring clear boundaries for relationship types and status types in the user asset graph and avoiding ambiguity in the scope of protection.

[0065] This embodiment can transform game assets scattered across different game platforms into structured, traceable, and dynamically updatable user asset profiles, improve the accuracy of cross-platform asset aggregation, enhance the consistency of rights coverage and usage status identification, and provide a stable data foundation for subsequent asset value analysis, abnormal consumption identification, and configuration adaptation prompts.

[0066] In some embodiments, based on a user's unique game asset profile, asset value representation, consumption structure representation, gameplay efficiency representation, cost-effectiveness representation, and device compatibility representation are generated, including: Extract transaction evidence, rights status, usage status, and device compatibility status corresponding to the asset nodes from the user's unique game asset profile; Based on transaction evidence and equity status, the asset acquisition cost, current available cost, and equity availability status are normalized and calculated to generate an asset value representation. Based on transaction evidence and usage status, construct baselines for the target user's consumption structure and gameplay usage; Based on the consumption structure baseline, the play usage baseline, and the asset value representation, a consumption structure representation, a play efficiency representation, and a cost-effectiveness representation are generated. Adaptation analysis is performed based on the terminal device configuration data and the corresponding operational configuration requirements of the asset nodes to generate device adaptation characteristics.

[0067] Specifically, after the user's unique game asset profile is constructed, the system traverses each unified game entity and unified rights entity according to the asset node index, and extracts transaction evidence, rights status, usage status, and device compatibility status from each asset node. Transaction evidence includes purchase orders, refund records, gift pack splitting records, subscription acquisition records, and rights change records. Rights status includes normalized states such as permanent ownership, limited-time availability, shared availability, additional content availability, and rights expiration. Usage status includes cumulative playtime, recent launch frequency, most recent launch time, achievement completion rate, and long-term inactivity time. Device compatibility status includes the matching level between the terminal device configuration and the game's running configuration requirements. The system aligns the above data according to asset nodes to form a node status dataset for asset analysis.

[0068] When generating asset value representations, the system determines asset acquisition costs based on transaction evidence and normalizes different acquisition methods by combining the status of rights. For game assets purchased individually, the system calculates the asset acquisition cost based on the actual payment amount, discount information, and refund status. For game assets acquired through bundles, the system breaks down the cost based on the reference prices of each game within the bundle during the purchase period and the user's existing rights. For game assets acquired through subscriptions, the system generates subscription-allocated costs based on the subscription period, actual usage time, and the available period of rights. The currently obtainable cost is calculated based on the current store reference price, version substitution relationships, additional content coverage relationships, and the status of available rights. If the target user already owns the standard version and the current main sales are for the complete collection version, the system identifies the coverage of standard version rights and additional content rights separately, generating obtainable costs that match the current scope of rights. The system further forms an asset value representation based on the asset acquisition cost, the currently obtainable cost, and the status of available rights.

[0069] When generating consumption structure and play efficiency representations, the system constructs a consumption structure baseline for target users based on transaction evidence and a play usage baseline based on usage status. The consumption structure baseline reflects the distribution of target users' consumption across different time periods, game platforms, game types, and benefit forms. The play usage baseline reflects the frequency of launches, play duration, and completion progress of target users across different game types. For example, if a target user makes concentrated purchases of multiple open-world games within the past six months, the system incorporates the purchase amount, frequency, discount percentage, and subsequent launch status of these games into the consumption structure baseline. It then forms a play usage baseline based on cumulative playtime, launches in the past 30 days, and achievement completion rate to determine whether this type of asset is in a high-use, low-use, or dormant state.

[0070] When generating cost-effectiveness metrics, the system jointly calculates asset value metrics, consumption structure baselines, and gameplay usage baselines. For games that have been purchased and played long-term, the system generates a higher cost-effectiveness metric based on the playtime corresponding to the unit acquisition cost, achievement completion rate, and recent activity level. For games that have been purchased but not launched for a long time or only launched once, the system generates a lower cost-effectiveness metric by combining acquisition cost and inactivity time. For games acquired through subscription, the system does not directly evaluate them based on the store price, but rather performs an amortization analysis based on the actual launch frequency and benefit duration within the subscription period to avoid confusing temporary and permanent benefits. For additional content, the system determines the corresponding cost-effectiveness metric based on the basic game usage status and the coverage scope of the additional content.

[0071] When generating device compatibility representations, the system performs compatibility analysis on the terminal device configuration data and the corresponding runtime configuration requirements of the asset node. The terminal device configuration data includes the processor, graphics processor, video memory, RAM, storage space, operating system version, display resolution, and driver version. The runtime configuration requirements include minimum configuration, recommended configuration, target resolution, and target frame rate reference conditions. The system analyzes the data at three levels: hardware capability matching, system environment matching, and display parameter matching, and adjusts the compatibility level based on historical startup failure records, crash records, and user feedback. For example, if a target user's graphics card meets the minimum requirements for a game but is below the recommended requirements, and their monitor resolution is high, the system marks the asset node as medium compatibility and writes this compatibility status into the device compatibility representation.

[0072] It should be noted that asset acquisition cost refers to the historical cost data actually incurred by a target user in acquiring a certain game asset or game right. The data object is the right acquisition behavior that has already occurred. Asset acquisition cost can be determined based on the actual payment amount, discount amount, refund status, bundle splitting results, subscription allocation results, redemption acquisition status, or gift acquisition status in the transaction record. For individually purchased game assets, the asset acquisition cost can be the actual payment amount in the corresponding order; for multiple game rights acquired through bundles or collections, the system splits the total payment amount based on the reference price of each right in the bundle, the rights already owned, and the coverage of the rights; for limited-time rights acquired through subscription services, the system allocates the cost based on the subscription fee, subscription period, actual usable period, and actual usage. Asset acquisition cost reflects the historical investment incurred by the target user when acquiring the corresponding right, and is not equivalent to the current market price of the game asset, nor does it represent the value of a financial asset.

[0073] Current obtainable cost refers to the cost data required for a target user to reacquire the same or similar range of rights corresponding to the current asset node during analysis. The data object is the currently obtainable combination of game rights. Current obtainable cost can be determined based on the game platform's current displayed price, current discount status, version substitution relationships, coverage of additional content, subscription availability, and bundle package rights. For example, if a target user already owns the standard edition of a game and some additional content, while the current game platform mainly sells the full bundle version, the system calculates the current acquisition reference cost matching the target user's existing range of rights based on the rights coverage relationships between the standard edition, additional content, and the bundle version. Current obtainable cost is used for normalized comparison with asset acquisition costs to assess changes in asset costs, the risk of duplicate acquisition, or purchase timing deviations; it is not used to determine tradable prices or investment returns.

[0074] The availability status of rights refers to the status data of whether a target user's game rights corresponding to a certain asset node can be accessed, activated, or continued to be used at the current analysis point in time. The data object is the validity and accessibility of the game rights. The availability status of rights can be determined comprehensively based on asset status records, account status records, subscription period, refund results, sharing authorization, platform access restrictions, content removal status, server availability status, and device compatibility status. The availability status of rights can include long-term availability, limited-term availability, shared availability, temporarily unavailable, expired, and pending verification. For example, if a user has permanently purchased a basic game and their account access is normal, the system can mark it as long-term availability; if a user obtains a game through a subscription and it is accessible within the subscription period, the system can mark it as limited-term availability; if the user has received a refund, the subscription has expired, or the sharing authorization has been canceled, the system can mark it as expired or temporarily unavailable. The availability status of rights is used to correct asset value representation and risk identification results; it is not equivalent to account security status, nor does it indicate whether the user has ever obtained the corresponding rights.

[0075] Through this embodiment, the system can transform transaction evidence, rights status, usage status, and device configuration into a calculable analytical representation based on the user's unique game asset profile. This improves the accuracy of asset value calculation, the consistency of consumption structure identification, the precision of play efficiency evaluation, and the reliability of device compatibility analysis. It also provides a structured analytical basis for subsequent abnormal consumption identification, duplicate rights judgment, and configuration incompatibility prompts.

[0076] In some embodiments, abnormal consumption status, duplicate rights status, misconfiguration status, and account risk status are identified based on various characteristics and account status data, generating professional analysis results for unified management and analysis of cross-platform game assets, including: Input the asset value representation, consumption structure representation, play efficiency representation, cost-effectiveness representation, and device compatibility representation into the state recognition model to generate asset usage deviation results and purchase behavior deviation results; Based on the equity coverage relationship in the entity mapping results, the equity overlap verification is performed on the asset usage deviation results to determine the duplicate equity status; Based on the matching relationship between device adaptation characteristics and the corresponding operational configuration requirements of asset nodes, the configuration mismatch status is determined. Joint risk identification is performed based on account status data and deviations in purchasing behavior to determine abnormal consumption status and account risk status. Based on the preset analysis template, evidence is linked and prompts are generated for abnormal consumption status, duplicate rights status, mismatched configuration status, and account risk status, resulting in professional analysis results.

[0077] Specifically, after generating various analytical representations, the system aligns asset value representations, consumption structure representations, play efficiency representations, cost-effectiveness representations, and device compatibility representations according to asset nodes in the user asset graph, forming state recognition input data. State recognition input data includes the acquisition cost corresponding to each asset node, current acquireable cost, equity availability status, purchase time distribution, recent consumption intensity, cumulative playtime, most recent launch interval, achievement completion rate, unit cost usage, and device compatibility level. The system inputs the state recognition input data into the state recognition model, which calculates the asset usage deviation degree and the purchase behavior deviation degree, respectively. The asset usage deviation degree characterizes the deviation of post-acquisition usage behavior from the target user's historical usage baseline, while the purchase behavior deviation degree characterizes the deviation of current period purchase frequency, purchase amount, and purchase type from the target user's historical purchase baseline.

[0078] The state recognition model can include a baseline encoding unit, a node deviation calculation unit, and a confidence output unit. The baseline encoding unit generates game type preferences, typical purchase cycles, typical launch cycles, and typical device compatibility preferences based on the target user's historical asset profile. The node deviation calculation unit compares the current asset node state with the aforementioned baselines, identifying deviation characteristics such as long-term inactivity, high acquisition cost with low usage, concentrated purchases in short cycles, and purchases with low compatibility levels. The confidence output unit generates a confidence score for each deviation characteristic based on data credibility markers, evidence fingerprint integrity, and field verification results. For example, if a target user purchases four large games consecutively within seven days, and three of them are weakly correlated with historical preferences, have not been launched after purchase, and have device compatibility levels below recommended operating conditions, the system will generate purchase behavior deviation results and asset usage deviation results with higher confidence.

[0079] After determining the deviation in asset usage, the system performs rights overlap verification based on the rights coverage relationships in the entity mapping results. The system traverses the unified game entity, unified rights entity, and rights coverage edges in the user asset graph to determine whether the deviating asset overlaps with existing permanent rights, subscription rights, shared rights, or gift pack rights. If the target user already owns the standard version of a game and obtains the complete collection version through a subscription service, and then separately purchases additional content already covered in the collection version, the system marks the corresponding asset node as a duplicate rights status and associates the platform source, acquisition method, and evidence fingerprint of the duplicate rights with the risk evidence chain. For cases where temporary subscription rights and permanent purchase rights coexist, the system further distinguishes between temporary overlap and long-term overlap to avoid simply judging it as a completely duplicate asset.

[0080] For configuration incompatibility issues, the system determines the issue based on the matching relationship between device compatibility characteristics and the corresponding runtime configuration requirements of the asset node. The system performs layered matching between the target user's terminal device configuration and the game's minimum configuration, recommended configuration, target resolution conditions, and driver environment requirements, and corrects these issues by incorporating historical records of startup failures, crashes, and reduced graphics parameters. If a game has insufficient video memory, an incompatible operating system version, or less storage space than required for installation at the recommended configuration level, the system marks the corresponding asset node as incompatible and records the reason for incompatibility, the affected rights version, and the matching failure field. For cases where the standard and enhanced versions of the same game have different configuration requirements, the system generates separate compatibility conclusions to avoid confusion regarding the runtime conditions of different versions.

[0081] During the joint risk identification phase, the system performs correlation analysis between account status data and deviations in purchasing behavior. Account status data includes changes in login device, login region, number of authorized devices, security verification status, and abnormal transaction time. If the system detects that a target user account makes high-amount purchases after abnormal login, makes consecutive purchases within a short period, or makes purchases again after a refund, and the confidence level of the deviation in purchasing behavior is high, the corresponding asset node is marked as an abnormal consumption status and an account risk status. If the purchase behavior occurs on a user's frequently used device, in a frequently used region, and the corresponding game has been on their wishlist for a long time, the system lowers the abnormal consumption level and retains context correction records.

[0082] Finally, the system generates professional analysis results according to a preset analysis template. The preset analysis template includes an asset anomaly summary, duplicate rights explanation, configuration compatibility prompts, account risk warnings, and evidence association information. The system associates abnormal consumption status, duplicate rights status, configuration mismatch status, and account risk status with asset nodes, platform origin, transaction evidence, usage records, and device matching results, generating prompts for unified management and analysis of cross-platform game assets. This embodiment transforms multi-dimensional analysis characteristics into traceable risk identification results, improving the accuracy of abnormal consumption identification, the consistency of duplicate rights judgment, the reliability of configuration mismatch prompts, and the efficiency of account risk detection.

[0083] In some embodiments, asset value representation, consumption structure representation, play efficiency representation, cost-effectiveness representation, and device compatibility representation are input into the state recognition model to generate asset usage deviation results and purchase behavior deviation results, including: Based on the asset nodes in the user's unique game asset profile, the nodes of asset value representation, consumption structure representation, play efficiency representation, cost-effectiveness representation and device adaptation representation are aligned to generate a state recognition input vector. Based on the state recognition model, the deviation of the state recognition input vector from the target user's historical usage baseline and historical purchase baseline is calculated to generate usage deviation features and purchase deviation features. Based on the confidence levels of the usage deviation feature and the purchase deviation feature, the asset usage deviation outcome and the purchase behavior deviation outcome are determined respectively.

[0084] Specifically, after generating asset value representations, consumption structure representations, play efficiency representations, cost-effectiveness representations, and device compatibility representations, the system first aligns the data according to the asset nodes in the user's unique game asset file. Each asset node corresponds to a unified game entity or a unified rights entity. The system uses the asset node identifier as the primary key to read fields such as the corresponding asset acquisition cost, current acquireable cost, rights availability status, purchase time, purchase source, cumulative playtime, recent launch frequency, recent launch interval, achievement completion rate, unit cost usage, and device compatibility level. Fields for the same asset node in different representations are merged into a node status record. For asset nodes where some platforms do not return complete fields, the system determines the completion method based on data confidence markers and field verification markers. Data with low missing values ​​is completed using statistical values ​​of similar assets or historical period averages, while data with high missing values ​​retains a null value marker and reduces subsequent confidence.

[0085] After node alignment is completed, the system converts the node state records into a state recognition input vector. This state recognition input vector includes asset value, consumption structure, play efficiency, cost-effectiveness, and device compatibility components. The asset value component represents the normalized difference between the asset acquisition cost and the currently available cost; the consumption structure component represents the position of the current asset's consumption cycle, consumption type, and platform source relative to the target user's long-term consumption distribution; the play efficiency component represents the post-purchase activation status, continuous playtime, and achievement completion status; the cost-effectiveness component represents the actual usage level corresponding to the unit acquisition cost; and the device compatibility component represents the degree of matching between the terminal device configuration and the asset node's operational configuration requirements. The system concatenates these components according to a preset dimension order and writes them into the asset node identifier, time window identifier, and credibility weight to form a state recognition input vector that can be input into the state recognition model.

[0086] The state recognition model calculates deviations based on the target user's historical usage and purchase baselines. The historical usage baseline comprises the target user's historical game asset activation cycle, cumulative play growth trend, achievement completion pace, dormancy duration distribution, and usage habits across different game types. The historical purchase baseline comprises purchase frequency, purchase amount range, purchase platform distribution, discount sensitivity, type of benefits acquired, and post-purchase activation / conversion cycle. The system compares the current state recognition input vector with both the historical usage and purchase baselines using distance calculation, distribution offset calculation, and temporal change analysis to generate usage deviation features and purchase deviation features. Usage deviation features reflect states such as low activation, low completion, long-term dormancy, or high cost with low usage after asset acquisition. Purchase deviation features reflect purchase states such as short-term concentrated purchases, purchases exceeding the normal amount range, low-fitness purchases, or purchases deviating from historical preferences.

[0087] For example, a target user historically typically purchases 1 to 2 strategy or role-playing games per month, launches them at least once within 7 days of purchase, and shows a steady increase in cumulative playtime over the past 3 months. In the current period, the system detected that the target user purchased 4 high-configuration action games within 5 days, 3 of which were on devices with compatibility levels below the recommended running conditions. These games were not launched within 14 days of purchase, and the acquisition cost was significantly higher than the historical purchase baseline. The system compares the state recognition input vector of the corresponding asset node with the historical baseline, generating high usage deviation features and high purchase deviation features. A higher confidence level is determined based on factors such as the completeness of interface-collected data, stable transaction evidence fingerprints, and complete device configuration fields. If a game remains on the wishlist for an extended period and the purchase price is below the historical attention range, the system lowers the confidence level of the corresponding purchase deviation feature to avoid misidentifying normal purchase behavior as abnormal behavior.

[0088] After identifying the deviation characteristics, the system generates asset usage deviation results and purchase behavior deviation results based on the confidence levels of the usage deviation characteristics and purchase deviation characteristics, respectively. The asset usage deviation results include the asset node, deviation type, deviation degree, evidence field, and confidence level; the purchase behavior deviation results include the purchase cycle, consumption deviation, preference deviation, fit deviation, and confidence level. Through this embodiment, the system can convert multi-dimensional asset analysis representations into node-level state recognition inputs and combine them with individual historical baselines to complete deviation judgments, improving the accuracy and stability of asset usage anomaly identification and purchase behavior anomaly identification, providing a reliable basis for subsequent duplicate rights verification, configuration mismatch judgment, and account risk identification.

[0089] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0090] Figure 2This is a schematic diagram of the cross-platform game asset unified management and analysis device provided in this application embodiment. Figure 2 As shown, the device includes: The acquisition module 201 is used to obtain the target user's game asset association data and terminal device configuration data from multiple game platforms in response to user authorization; Standardization module 202 is used to perform field parsing, definition conversion, credibility marking and evidence fingerprint generation on the acquired data according to the preset cross-platform data model to obtain standardized game asset data; The identification module 203 is used to perform semantic matching, rule verification, and identification of rights coverage relationships for game entities and rights entities in standardized game asset data based on the game entity matching model, and generate entity mapping results. The association module 204 is used to associate asset records, transaction evidence, rights status, usage status and device compatibility status in different game platforms with the user asset graph based on the entity mapping results, and to build a unique game asset profile for each user. The representation module 205 is used to generate asset value representation, consumption structure representation, gameplay efficiency representation, cost-effectiveness representation and device compatibility representation based on the user's unique game asset profile. The generation module 206 is used to identify abnormal consumption status, duplicate rights status, mismatched configuration status and account risk status based on various characteristics and account status data, and generate professional analysis results for unified management and analysis of cross-platform game assets.

[0091] In some embodiments, Figure 2 The acquisition module 201 receives platform binding requests from target users for multiple game platforms, establishes corresponding authorization sessions based on the platform binding requests, and configures platform adapters for each authorization session; it identifies the data openness capabilities and data field definitions of the corresponding game platforms through the platform adapters, and generates a data collection strategy that matches the game platforms; it acquires the target user's asset status records, transaction behavior records, usage behavior records, and account status records in the corresponding game platforms according to the data collection strategy, and acquires the device configuration records of the target user's corresponding terminal devices; it associates the acquired data with the platform source identifier, authorization time identifier, and collection method identifier to generate game asset association data and terminal device configuration data to be standardized.

[0092] In some embodiments, Figure 2The standardization module 202, based on a preset cross-platform data model, identifies platform fields, business fields, and status fields in game asset-related data and terminal device configuration data, and generates field parsing results. Following a unified data standard, it performs format conversion and unit normalization on game identifiers, rights status, transaction records, usage status, and device configuration parameters in the field parsing results. It generates data credibility markers based on data source, collection method, and field completeness, and adds verification markers to data with missing fields or conflicting standards. Based on the standardized field content, platform source identifier, and collection time identifier, it generates evidence fingerprints, associates these fingerprints with corresponding data records, and obtains standardized game asset data.

[0093] In some embodiments, Figure 2 The identification module 203 extracts game entity description information and equity entity description information from standardized game asset data to generate a candidate entity set; based on the game entity matching model, it performs semantic vector encoding and similarity calculation on the candidate entity set to obtain the candidate entity matching relationship; according to the preset entity verification rules, it performs consistency verification on the platform identifier, version attribute and release attribute in the candidate entity matching relationship; based on the verified candidate entity matching relationship, it identifies the inclusion relationship, substitution relationship and overlap relationship between equity entities to generate entity mapping results.

[0094] In some embodiments, Figure 2 The association module 204 uses the target user as the root node of the graph and the unified game entity and unified rights entity in the entity mapping result as asset nodes to establish the node structure of the user asset graph; it associates asset records and transaction evidence in different game platforms with the corresponding asset nodes to generate asset source association relationships; it establishes rights coverage relationships and usage status relationships between asset nodes according to rights status and usage status; and it associates device adaptation status with the corresponding asset nodes to generate a unique game asset file for the user.

[0095] In some embodiments, Figure 2 The representation module 205 extracts transaction evidence, rights status, usage status, and device compatibility status corresponding to the asset node from the user's unique game asset file; based on the transaction evidence and rights status, it performs normalized calculations on the asset acquisition cost, current acquireable cost, and rights availability status to generate an asset value representation; based on the transaction evidence and usage status, it constructs the target user's consumption structure baseline and gameplay usage baseline; based on the consumption structure baseline, gameplay usage baseline, and asset value representation, it generates a consumption structure representation, gameplay efficiency representation, and cost-effectiveness representation; based on the terminal device configuration data and the corresponding operational configuration requirements of the asset node, it performs adaptation analysis to generate a device compatibility representation.

[0096] In some embodiments, Figure 2The generation module 206 inputs asset value representation, consumption structure representation, play efficiency representation, cost-effectiveness representation, and device compatibility representation into the status recognition model to generate asset usage deviation results and purchase behavior deviation results. Based on the rights coverage relationship in the entity mapping results, it performs rights overlap verification on the asset usage deviation results to determine the duplicate rights status. Based on the matching relationship between the device compatibility representation and the corresponding operation configuration requirements of the asset node, it determines the configuration mismatch status. Based on the account status data and purchase behavior deviation results, it performs joint risk identification to determine the abnormal consumption status and account risk status. According to the preset analysis template, it performs evidence association and generates prompt content for the abnormal consumption status, duplicate rights status, configuration mismatch status, and account risk status to obtain professional analysis results.

[0097] In some embodiments, Figure 2 The generation module 206 aligns the asset value representation, consumption structure representation, gameplay efficiency representation, cost-effectiveness representation, and device compatibility representation with the asset nodes in the user's unique game asset file to generate a state recognition input vector. Based on the state recognition model, the deviation of the state recognition input vector from the target user's historical usage baseline and historical purchase baseline is calculated to generate usage deviation features and purchase deviation features. Based on the confidence levels of the usage deviation features and purchase deviation features, the asset usage deviation results and purchase behavior deviation results are determined respectively.

[0098] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0099] Figure 3 This is a schematic diagram of the electronic device 3 provided in an embodiment of this application. Figure 3 As shown, the electronic device 3 of this embodiment includes: a processor 301, a memory 302, and a computer program 303 stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program 303, it implements the steps in the various method embodiments described above. Alternatively, when the processor 301 executes the computer program 303, it implements the functions of each module / unit in the various device embodiments described above.

[0100] Electronic device 3 can be a desktop computer, laptop, handheld computer, cloud server, or other electronic device. Electronic device 3 may include, but is not limited to, processor 301 and memory 302. Those skilled in the art will understand that... Figure 3 This is merely an example of electronic device 3 and does not constitute a limitation on electronic device 3. It may include more or fewer components than shown, or different components.

[0101] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0102] The memory 302 can be an internal storage unit of the electronic device 3, such as a hard disk or memory of the electronic device 3. The memory 302 can also be an external storage device of the electronic device 3, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 3. The memory 302 can also include both internal and external storage units of the electronic device 3. The memory 302 is used to store computer programs and other programs and data required by the electronic device.

[0103] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device 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 embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0104] If integrated modules / units are implemented as software functional units and sold or used as independent products, they can be stored in a readable storage medium (e.g., a computer-readable storage medium). Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program may include computer program code, which may be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable storage medium may include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0105] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A cross-platform game asset unified management and analysis method, characterized in that, include: In response to user authorization, the system obtains game asset association data and terminal device configuration data of the target user from multiple game platforms. According to the preset cross-platform data model, the acquired data is parsed, converted in terms of definition, marked with credibility and generated with evidence fingerprints to obtain standardized game asset data. Based on the game entity matching model, semantic matching, rule verification, and rights coverage relationship identification are performed on the game entities and rights entities in the standardized game asset data to generate entity mapping results. Based on the entity mapping results, asset records, transaction evidence, rights status, usage status and device compatibility status in different game platforms are associated with the user asset graph to construct a unique game asset profile for each user. Based on the user's unique game asset profile, generate asset value representation, consumption structure representation, gameplay efficiency representation, cost-effectiveness representation, and device compatibility representation; Based on various characteristics and account status data, abnormal consumption status, duplicate rights status, mismatched configuration status, and account risk status are identified, generating professional analysis results for unified management and analysis of game assets across platforms.

2. The method according to claim 1, characterized in that, The process of obtaining the target user's game asset association data and terminal device configuration data from multiple game platforms in response to user authorization includes: Receive platform binding requests from target users for multiple game platforms, establish corresponding authorization sessions based on the platform binding requests, and configure platform adapters for each authorization session; The platform adapter identifies the data access capabilities and data field definitions of the corresponding game platform, and generates a data collection strategy that matches the game platform. According to the data collection strategy, the target user's asset status record, transaction behavior record, usage behavior record and account status record in the corresponding game platform are obtained, and the device configuration record of the target user's corresponding terminal device is obtained. The acquired data is associated with the platform source identifier, authorization time identifier, and collection method identifier to generate game asset association data and terminal device configuration data to be standardized.

3. The method according to claim 1, characterized in that, The process involves parsing fields, converting definitions, marking credibility, and generating evidence fingerprints on the acquired data according to a preset cross-platform data model to obtain standardized game asset data, including: Based on the preset cross-platform data model, the platform field, business field, and status field in the game asset association data and the terminal device configuration data are identified, and field parsing results are generated. In accordance with a unified data standard, the game identifier, rights status, transaction records, usage status, and device configuration parameters in the parsing results of the aforementioned fields are converted in format and their units are normalized. Data credibility tags are generated based on data source, collection method, and field completeness, and verification tags are added to data with missing fields or conflicting definitions. Evidence fingerprints are generated based on standardized field content, platform source identifiers, and collection time identifiers. The evidence fingerprints are then associated with the corresponding data records to obtain standardized game asset data.

4. The method according to claim 1, characterized in that, The game entity matching model performs semantic matching, rule verification, and rights coverage relationship identification on game entities and rights entities in the standardized game asset data to generate entity mapping results, including: Game entity description information and equity entity description information are extracted from the standardized game asset data to generate a candidate entity set; Based on the game entity matching model, semantic vector encoding and similarity calculation are performed on the candidate entity set to obtain candidate entity matching relationships; According to the preset entity verification rules, the platform identifier, version attribute and release attribute in the candidate entity matching relationship are verified for consistency. Based on the verified candidate entity matching relationships, the inclusion, substitution, and overlap relationships between the rights entities are identified, and the entity mapping results are generated.

5. The method according to claim 1, characterized in that, Based on the entity mapping results, the asset records, transaction evidence, rights status, usage status, and device compatibility status from different game platforms are associated with the user asset graph to construct a unique user game asset profile, including: Using the target user as the root node of the graph and the unified game entity and unified equity entity in the entity mapping result as asset nodes, a node structure for the user asset graph is established. Link asset records and transaction evidence from different gaming platforms to corresponding asset nodes to generate asset source relationships; Based on the equity status and the usage status, establish the equity coverage relationship and usage status relationship between the asset nodes; The device adaptation status is associated with the corresponding asset node to generate a unique game asset profile for the user.

6. The method according to claim 1, characterized in that, The generation of asset value representation, consumption structure representation, gameplay efficiency representation, cost-effectiveness representation, and device compatibility representation based on the user's unique game asset profile includes: Extract transaction evidence, rights status, usage status, and device compatibility status corresponding to the asset nodes from the user's unique game asset file; Based on the transaction evidence and the equity status, the asset acquisition cost, current available cost, and equity availability status are normalized and calculated to generate an asset value representation. Based on the transaction evidence and the usage status, construct the target user's consumption structure baseline and game usage baseline; Based on the consumption structure baseline, the gaming usage baseline, and the asset value representation, a consumption structure representation, a gaming efficiency representation, and a cost-effectiveness representation are generated. Based on the terminal device configuration data and the corresponding operational configuration requirements of the asset nodes, an adaptation analysis is performed to generate a device adaptation characterization.

7. The method according to claim 1, characterized in that, The process involves identifying abnormal consumption status, duplicate rights status, mismatched configuration status, and account risk status based on various characteristics and account status data, generating professional analysis results for unified management and analysis of cross-platform game assets, including: Input the asset value representation, consumption structure representation, play efficiency representation, cost-effectiveness representation, and device compatibility representation into the state recognition model to generate asset usage deviation results and purchase behavior deviation results. Based on the equity coverage relationship in the entity mapping result, the asset is used to perform equity overlap verification using the deviation result to determine the duplicate equity status; Based on the matching relationship between the device adaptation characteristics and the corresponding operational configuration requirements of the asset nodes, the configuration mismatch status is determined. Based on the account status data and the deviation results of the purchase behavior, joint risk identification is performed to determine the abnormal consumption status and the account risk status. According to the preset analysis template, evidence is associated and prompts are generated for the abnormal consumption status, duplicate rights status, mismatched configuration status and account risk status, and the professional analysis results are obtained.

8. The method according to claim 7, characterized in that, The process of inputting the asset value representation, consumption structure representation, play efficiency representation, cost-effectiveness representation, and device compatibility representation into the state recognition model to generate asset usage deviation results and purchase behavior deviation results includes: Based on the asset nodes in the user's unique game asset profile, the asset value representation, consumption structure representation, gameplay efficiency representation, cost-effectiveness representation, and device compatibility representation are aligned with nodes to generate a state recognition input vector. Based on the state recognition model, the deviation of the state recognition input vector from the target user's historical usage baseline and historical purchase baseline is calculated to generate usage deviation features and purchase deviation features. Based on the confidence levels of the usage deviation features and the purchase deviation features, the asset usage deviation results and purchase behavior deviation results are determined respectively.

9. A cross-platform game asset unified management and analysis device, characterized in that, include: The acquisition module is used to obtain the target user's game asset association data and terminal device configuration data from multiple game platforms in response to user authorization; The standardization module is used to perform field parsing, definition conversion, credibility marking, and evidence fingerprint generation on the acquired data according to a preset cross-platform data model, so as to obtain standardized game asset data. The identification module is used to perform semantic matching, rule verification, and identification of rights coverage relationships on game entities and rights entities in the standardized game asset data based on the game entity matching model, and generate entity mapping results. The association module is used to associate asset records, transaction evidence, rights status, usage status and device compatibility status in different game platforms with the user asset graph based on the entity mapping results, and to construct a unique game asset profile for each user. The representation module is used to generate asset value representation, consumption structure representation, gameplay efficiency representation, cost-effectiveness representation and device compatibility representation based on the user's unique game asset profile. The generation module is used to identify abnormal consumption status, duplicate rights status, mismatched configuration status, and account risk status based on various characteristics and account status data, and generate professional analysis results for unified management and analysis of game assets across platforms.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 8.

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