Game entrusting task management method and device and medium

Abstract

The invention discloses a game entrusting task management method and device and a medium, and relates to the technical field of computers.The method comprises the steps that data structural processing is conducted on a game entrusting task to obtain game task structural data; based on the historical performance data of the service providers, performing dynamic capability analysis on the service providers to determine capability portraits of the current service providers; according to the game task structure data and the capability portrait, task allocation data corresponding to the game entrusting task is obtained through task adaptation degree analysis and current service load analysis of a service provider; performing user interaction security configuration on the task allocation data to determine session context data of user security communication; and performing real-time risk assessment on the session context data to obtain a user security risk assessment report. According to the method, the technical problems that in the prior art, the safety of user information in the game entrusting task is insufficient, and the distribution efficiency of the game entrusting task is low are solved.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, device and medium for managing game commission tasks. Background Technology

[0002] In current gaming services, there exists a user demand scenario where some gamers, due to time constraints or insufficient skills, need to outsource certain in-game tasks to third-party service providers. These services typically involve assisting with game progress advancement, virtual achievement acquisition, and other similar tasks. The current market solution involves task publishers posting task requests through e-commerce platforms or dedicated applications, and service providers receiving and fulfilling these requests through the corresponding platforms.

[0003] In existing technologies, after a client publishes a game commission task on a corresponding e-commerce platform, a service intermediary typically accepts the task on that platform and distributes it to service providers located on different e-commerce platforms. This process presents several problems: First, service intermediaries in the market are all promoted online, requiring clients to upload relevant information. Due to a lack of unified oversight, client account passwords and other identity information are at risk of being leaked by the service intermediary, leading to serious after-sales issues. Second, service providers and clients do not directly transact, making direct communication inconvenient. Because service intermediaries lack secure dedicated communication channels, communication records between service providers and clients are difficult to trace. Furthermore, the current open registration method for service providers often lacks effective identity verification and service qualification review, resulting in a large number of unqualified service providers. This poses serious problems such as client information leakage and online fraud. Third, service intermediaries lack dynamic adjustment and management of game commission task distribution, leading to low efficiency in task allocation when game commission task data surges. Summary of the Invention

[0004] This application provides a method, device, and medium for managing game commissioned tasks, which solves the technical problems of insufficient user information security and low efficiency in allocating game commissioned tasks in the prior art.

[0005] In a first aspect, embodiments of this application provide a method for managing game-related tasks, characterized in that the method includes: acquiring a user's game-related tasks and performing data structuring processing on the game-related tasks to obtain game task structure data; acquiring historical performance data of service providers and performing dynamic capability analysis on service providers based on the historical performance data to determine the current capability profile of service providers; obtaining task allocation data corresponding to the game-related tasks based on the game task structure data and capability profile, through task adaptability analysis and current service load analysis of service providers; configuring user interaction security on the task allocation data to determine session context data for secure user communication; and performing real-time risk assessment on the session context data to obtain a user security risk assessment report.

[0006] In one implementation of this application, the game delegation task is subjected to data structuring processing to obtain game task structure data. Specifically, this includes: obtaining task feature vectors based on the game delegation task through task description parsing using the BERT model; obtaining user privacy information of the game delegation task and desensitizing the user privacy information to determine user mask data; and integrating the task feature vector and user mask data to obtain game task structure data.

[0007] In one implementation of this application, a dynamic capability analysis of the service provider is performed based on its historical performance data to determine the current capability profile of the service provider. Specifically, this includes: assessing the service provider's skills and capabilities based on its historical performance data to determine a service skill vector; calculating the cosine similarity between the service skill vector and the task feature vector, and determining the service provider's skill matching score based on the cosine similarity through specialized game domain matching identified by K-means clustering; obtaining the service provider's historical game task completion data and assessing its service quality to obtain a service quality score; wherein the service quality assessment includes: task completion rate calculation and user satisfaction score; assessing the service provider's reputation to determine its reputation score; and determining the capability profile by calculating a weighted average based on the skill matching score, service quality score, and reputation score.

[0008] In one implementation of this application, based on game task structure data and capability profiles, task allocation data corresponding to game-delegated tasks is obtained through task suitability analysis and current service load analysis of service providers. Specifically, this includes: scoring the capability profiles for game task suitability to determine service provider scores; filtering service providers with scores greater than a preset matching threshold to obtain a set of candidate service providers; performing real-time service load data analysis on the candidate service provider set to determine the real-time service status of the candidate service providers; based on the real-time service status, obtaining the currently loadable tasks of the candidate service providers through historical task load analysis; and matching the game task structure data corresponding to the currently loadable tasks with the candidate service providers to determine task allocation data.

[0009] In one implementation of this application, user interaction security configuration is performed on task allocation data to determine session context data for secure user communication. Specifically, this includes: constructing a WebSocket real-time communication channel between the user and the service provider based on the task allocation data, and shielding direct contact information between the user and the service provider; the user and the service provider communicate through the WebSocket real-time communication channel, and saving session context data between the user and the service.

[0010] In one implementation of this application, a real-time risk assessment is performed on the session context data to obtain a user security risk assessment report. Specifically, this includes: performing multimodal security analysis on the session context data to determine a first session security state; performing potential fraud detection on the session context data to determine a second session security state; and obtaining a user security risk assessment report based on the first and second session security states.

[0011] In one implementation of this application, potential fraud detection is performed on session context data to determine the second session security state. Specifically, this includes: identifying the intent of fraudulent fuzzy words in the session context data to obtain inducement fields; constructing a dynamic behavior baseline of the service provider and identifying abnormal behavior on the dynamic behavior baseline to obtain an abnormal service state; and determining the second session security state based on the frequency of inducement fields and the frequency of abnormal service states.

[0012] In one implementation of this application, after performing real-time risk assessment on the session context data to obtain a user security risk assessment report, the method further includes: updating the user security risk assessment report in real-time for game task execution to determine the user security status under the current game task; and obtaining the service provider's game task execution security assessment data through execution monitoring of the corresponding service provider based on the user security status.

[0013] Secondly, embodiments of this application also provide a game-related process management device for user security, characterized in that the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform a management method such as a game task assignment.

[0014] Thirdly, embodiments of this application also provide a non-volatile computer storage medium for managing game-related processes for user security, storing computer-executable instructions, characterized in that, when the computer-executable instructions are executed, a method for managing game-related tasks is implemented.

[0015] This application provides a method, device, and medium for managing game commission tasks. By allocating game commission tasks based on service provider capability profiles, configuring dedicated interaction channels between service providers and users, real-time monitoring of the session context between service providers and users, and user security risk assessment, it solves the technical problems of insufficient user information security and low efficiency in game commission task allocation in the prior art. It enables service intermediaries to reasonably allocate game commission tasks, improves the security of the client's account password and other identity information, and prevents the client from being exposed to information leakage and online fraud during the game commission task process. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A flowchart illustrating a method for managing game-related tasks, as provided in this application embodiment; Figure 2 This is a schematic diagram of the internal structure of a game task management device provided in an embodiment of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] This application provides a method, device, and medium for managing game commission tasks. By allocating game commission tasks based on service provider capability profiles, configuring dedicated interaction channels between service providers and users, real-time monitoring of the session context between service providers and users, and user security risk assessment, it solves the technical problems of insufficient user information security and low efficiency in game commission task allocation in the prior art. It enables service intermediaries to reasonably allocate game commission tasks, improves the security of the client's account password and other identity information, and prevents the client from being exposed to information leakage and online fraud during the game commission task process.

[0019] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0020] Figure 1 This is a flowchart illustrating a method for managing game-related tasks, as provided in an embodiment of this application. Figure 1 As shown in the figure, the method for managing game commission tasks provided in this application embodiment specifically includes the following steps: Step 101: Obtain the user's game task assignment and perform data structuring on the game task assignment to obtain game task structure data.

[0021] For example, game commission service intermediaries are all promoted online. Due to the lack of unified supervision, the client's account password and other identity information are at risk of being leaked by the service intermediaries, resulting in a high after-sales rate of up to 40% and serious after-sales problems. In order to facilitate the subsequent allocation of game commission tasks and ensure the security of user privacy information, this application performs data structuring processing on game commission tasks to give them quantifiable adaptation parameters and improve the security of user privacy information.

[0022] Specifically, the game task assignments are processed to obtain game task structure data, including: obtaining task feature vectors by parsing the task description using the BERT model based on the game task assignments; obtaining user privacy information of the game task assignments and desensitizing the user privacy information to determine user mask data; and integrating the task feature vectors and user mask data to obtain game task structure data.

[0023] In one embodiment, when desensitizing a user's mobile phone number, a partial mask is used. The first 3 and last 4 digits of the 11-digit mobile phone number are retained, and the middle 4 digits are replaced with asterisks to generate a secure mobile phone number in the format of 1XX****5678. The user's game account is completely masked, and the query condition is set to send a query link with explicit text to the user. The query can only be performed after the user agrees.

[0024] The task description is semantically parsed by fine-tuning the BERT model to identify key elements such as game type, task difficulty, and time requirements, and transformed into a 128-dimensional task feature vector. Finally, the de-identified privacy information, task feature vector, and basic task information are integrated to construct a standardized task data structure containing complete metadata.

[0025] Furthermore, the fine-tuned BERT model first preprocesses the descriptive text, removing irrelevant symbols and segmenting long sentences. The model captures the semantic context of the text through a 12-layer Transformer encoder, specifically targeting game-specific terms such as "ranking up," "dungeon," and "ranked play." The model then employs a multi-task learning architecture to simultaneously identify game types, such as MOBA and FPS. For task difficulty, it optimizes word vector representations by analyzing key elements such as speedrun, high difficulty, and time requirements.

[0026] Finally, the 768-dimensional output vector corresponding to the label is reduced to 128 dimensions through a fully connected layer to obtain the task feature vector of the game commission task, which is used to effectively distinguish different game categories and task complexity, and to characterize the features of the game commission task.

[0027] Step 102: Obtain historical performance data of the service provider, and perform dynamic capability analysis on the service provider based on the historical performance data to determine the current capability profile of the service provider.

[0028] For example, the service provider is the actual completion end of the game commission task. In order to fully meet the needs of users to complete the game commission task more efficiently, this application conducts dynamic analysis of the service provider's capabilities to determine the current capability profile of the service provider, providing a data foundation for matching the service provider with the game commission task.

[0029] Specifically, based on the service provider's historical performance data, a dynamic capability analysis is performed to determine the current capability profile of the service provider. This includes: assessing the service provider's skills and capabilities based on historical performance data to determine service skill vectors; calculating the cosine similarity between the service skill vectors and task feature vectors, and determining the service provider's skill matching score based on the cosine similarity through specialized game domain matching identified by K-means clustering; obtaining the service provider's historical game task completion data and assessing the service quality of this data to obtain the service provider's service quality score; the service quality assessment includes: task completion rate calculation and user satisfaction score; assessing the service provider's reputation to determine the service provider's reputation score; and determining the capability profile by calculating a weighted average based on the skill matching score, service quality score, and reputation score.

[0030] In one embodiment, firstly, historical task data of the service provider is collected, including task type, game difficulty, completion status, and user feedback. Key features, such as game type preference, task success rate, average completion time, and skill tags, are extracted from the historical data. Natural Language Processing (NLP) is then used to vectorize the skill tags, generating an initial feature vector. Principal Component Analysis (PCA) is performed on the initial feature vector to reduce its dimensionality to 128 dimensions, thus determining the service skill vector.

[0031] Then, the cosine similarity between the service skill vector and the task feature vector is calculated, with similarity values ​​ranging from [-1, 1]. This application uses the similarity value mapped to [0, 1] as the basic matching degree. K-means clustering is used to cluster the skill vectors of all service providers to identify the specialized game domains. If the cluster to which the service provider belongs is consistent with the cluster to which the task feature vector belongs, the basic matching degree is added to determine the skill matching degree score of the service provider.

[0032] Finally, historical game task completion data of service providers is obtained, and service quality is evaluated based on this data. The ratio of the number of game tasks completed by the service provider within the most recent 30 days to the total number of game tasks accepted is calculated; this completion rate is directly used as a sub-item of service quality, with a weight of 0.6. Star ratings and text reviews are extracted from user reviews. The star rating is the average, and the text reviews use a sentiment analysis model to calculate a sentiment score (0-1), with a weight of 0.4. If a service provider has a completion rate of 95%, a user satisfaction rating of 4.5 stars (corresponding to 0.9), and a sentiment score of 0.85, then its service quality score is 0.924.

[0033] The final score of the capability profile is dynamically adjusted based on the platform's strategy and weighting, with a focus on skill matching. The capability profile is updated daily to reflect the latest performance of the service provider.

[0034] Step 103: Based on the game task structure data and capability profile, obtain the task allocation data corresponding to the game commissioned tasks through task adaptability analysis of service providers and current service load analysis.

[0035] For example, after determining the game task structure data and capability profile, this application achieves intelligent matching between game task assignments and service providers by analyzing the task adaptability of service providers and the current service load, thereby improving the allocation efficiency and adaptability of game task assignments.

[0036] Specifically, based on game task structure data and capability profiles, task allocation data corresponding to game-related tasks is obtained through task suitability analysis and current service load analysis of service providers. This includes: scoring the capability profiles for game task suitability to determine the service provider's score; filtering service providers with scores greater than a preset matching threshold to obtain a set of candidate service providers; performing real-time service load data analysis on the candidate service provider set to determine the real-time service status of the candidate service providers; based on the real-time service status, obtaining the currently loadable tasks of the candidate service providers through historical task load analysis; and matching the game task structure data corresponding to the currently loadable tasks with the candidate service providers to determine the task allocation data.

[0037] In one embodiment, firstly, a multi-dimensional weighted scoring method is used to analyze the game task structure data and ability profile. The weights can be dynamically adjusted according to the task type. For example, the service quality weight can be increased for urgent tasks, and the skill matching weight can be increased for high-difficulty tasks.

[0038] Taking MOBA games as an example, if service provider A has a skill matching degree of 0.9 (weight set to 0.5), a service quality of 0.85 (weight set to 0.3), and a reputation of 0.95 (weight set to 0.2), then the suitability score is 0.895.

[0039] Then, matching thresholds are set according to task complexity: 0.7 for simple tasks, 0.8 for medium tasks, and 0.9 for complex tasks. All candidates with a fit score greater than the threshold are retrieved from the service provider database and sorted in descending order of score to obtain a set of candidate service providers. Data such as the current number of tasks, task progress, device performance, and online status of the candidate service providers are obtained in real time.

[0040] The service status of the above service providers is divided into four levels: idle (0 tasks in progress), light load (1-2 tasks), medium load (3-4 tasks), and heavy load (≥5 tasks). If service provider A currently has 2 tasks in progress, and it is expected to complete 1 task within 1 hour, and the equipment performance is good, it is assessed as a light load status.

[0041] Finally, by analyzing the historical average number of tasks processed simultaneously, the relationship between task completion quality and load for the aforementioned service providers, the number of additional tasks that the service providers can currently accept is calculated. If historical data shows that service provider A can process a maximum of 4 tasks simultaneously while ensuring quality, currently has 2 tasks, and reserves 1 buffer slot, then the number of tasks that can be handled is 1.

[0042] A priority matching algorithm is used to prioritize allocation to service providers with high adaptability and light load. When an emergency task occurs, the load limit of the service provider is appropriately relaxed to obtain structured allocation data containing information such as task ID, service provider ID, expected start time, and expected completion time.

[0043] For example, a high-difficulty dungeon task is assigned to service provider A with a suitability of 0.92 and currently under light load, generating the following assignment data: {Task ID: "T2024001", Service Provider ID: "SP0088", Assignment Time: "2024-01-15 14:30:00", Expected Completion Time: "20xx-0x-16 10:00:00"}.

[0044] Step 104: Configure user interaction security for task allocation data to determine session context data for secure user communication.

[0045] For example, in the prior art, service providers and clients do not transact directly and it is inconvenient for them to communicate directly. Due to the lack of secure dedicated communication channels for service intermediaries, communication records between service providers and clients are difficult to trace. This application enables service providers and clients to communicate directly through a monitorable dedicated communication channel by configuring user interaction security for task allocation data, thereby improving the information security of clients' users.

[0046] Specifically, user interaction security configuration is performed on task allocation data to determine the session context data for secure user communication. This includes: building a WebSocket real-time communication channel between the user and the service provider based on the task allocation data, and shielding direct contact information between the user and the service provider; the user and the service provider communicate through the WebSocket real-time communication channel, and saving the session context data between the user and the service.

[0047] In one embodiment, task allocation data is published through e-commerce platform A. This process only requires the user's anonymized mobile phone number, and the task allocation data is received through e-commerce platform B under the same regulatory system. At this time, the service provider in e-commerce platform B cannot see any user-related information, and the service provider's identity information is transparent as it is verified and signed by e-commerce platform B, facilitating the traceability of after-sales issues.

[0048] After the task is assigned, the service provider receives the corresponding game task assignment. E-commerce platform B sends specified information to the user's mobile phone via SMS verification. The user can open a private chat channel with the service provider through the SMS link. This channel is built on WebSocket real-time communication and saves the chat content between the service provider and the user in real time.

[0049] Step 105: Perform a real-time risk assessment on the session context data to obtain a user security risk assessment report.

[0050] For example, in the prior art, service providers have problems such as leaking client information and online fraud, which seriously threaten users' information security and virtual property security. This application prevents clients from being leaked information and defrauded online during the game commission task by conducting real-time risk assessment of session context data and monitoring sensitive words and potential fraudulent behaviors in the session context data.

[0051] Specifically, real-time risk assessment is performed on session context data to obtain a user security risk assessment report, including: performing multimodal security analysis on session context data to determine the first session security status; detecting potential fraudulent behavior on session context data to determine the second session security status; and obtaining a user security risk assessment report based on the first and second session security statuses.

[0052] Furthermore, potential fraudulent behavior detection is performed on the session context data to determine the second session security status. Specifically, this includes: identifying the intent of fraudulent fuzzy words in the session context data to obtain inducement fields; constructing a dynamic behavior baseline of the service provider and identifying abnormal behavior on the dynamic behavior baseline to obtain abnormal service status; and determining the second session security status based on the frequency of inducement fields and the frequency of abnormal service status.

[0053] Furthermore, after performing real-time risk assessment on the session context data to obtain a user security risk assessment report, the method also includes: updating the user security risk assessment report in real-time based on the execution of the game task to determine the user's security status under the current game task; and obtaining the service provider's game task execution security assessment data through the execution monitoring of the corresponding service provider based on the user's security status.

[0054] In one embodiment, multimodal security analysis uses a fine-tuned BERT model to perform semantic understanding of conversational text, detect the risk of sensitive information leakage, such as account passwords and payment information, and identify illegal content, including prohibited transactions and inappropriate remarks, through a rule engine and deep learning model.

[0055] In the conversation context data, the service provider repeatedly mentioned that the platform's transaction fees were high and that private transactions were cheaper. Text analysis identified the intention to induce offline transactions, and behavioral analysis found an abnormal topic guidance pattern. After comprehensive analysis, the first conversation security status was determined to be medium risk with a confidence level of 0.75.

[0056] Furthermore, real-time monitoring of keyword density and topic shifts in conversation context data can identify abnormal dialogue patterns and allow for timely manual intervention.

[0057] Detecting potential fraud requires building a domain-specific fuzzy vocabulary, as follows: For price-inducing tactics: more favorable prices, discounts, private deals, etc.; For methods with ambiguity: special channels, internal, effects, etc.; For those who circumvent regulations: communicating outside the platform or through other means, etc.

[0058] The true intent of the above words in the context can be identified by using a BiLSTM model with an attention mechanism, so as to obtain the frequency and intensity index of the occurrence of the persuasive field.

[0059] Finally, a personalized behavioral baseline is established for each service provider, including but not limited to: common vocabulary and expression style, topic switching patterns, and negotiation habits. The behavioral baseline is dynamically updated using a sliding time window of the most recent 50 conversations, and anomalies are detected through the Isolation Forest algorithm to determine the degree of deviation of the current conversation from the baseline.

[0060] By weighted fusion processing of the deviation of the current session between the service provider and the user from the baseline (second session security status) and the first session security status obtained earlier, a corresponding traceable risk assessment report is obtained.

[0061] In one embodiment, a user communicates with a service provider about a game commission task. The first session security status analysis shows that the service provider asked the user to add them on WeChat twice to discuss the details. The first security status assessment is: medium risk, score 0.68.

[0062] The second session security status analysis revealed five leading fields in the current session between the user and the service provider, with a frequency of 0.25; the communication style was significantly inconsistent with the historical baseline, with an anomaly level of 0.8. The second security status assessment result was high risk, with a score of 0.83.

[0063] At this point, the overall risk score is calculated to be 0.79, the risk level is set to warning, manual monitoring is initiated, and relevant information of the manual monitoring process is saved.

[0064] The above are embodiments of the method proposed in this application. Based on the same inventive concept, embodiments of this application also provide a management device for game commission tasks, the structure of which is as follows: Figure 2 As shown.

[0065] Figure 2 This is a schematic diagram of the internal structure of a game task management device provided in an embodiment of this application. Figure 2 As shown, the device includes: At least one processor 201; And a memory 202 that is communicatively connected to at least one processor; The memory 202 stores instructions that can be executed by at least one processor, which are executed by at least one processor 201 to enable at least one processor 201 to perform a management method such as a game task assignment.

[0066] Some embodiments of this application provide corresponding to Figure 1 A non-volatile computer storage medium for managing game-assigned tasks, storing computer-executable instructions, such as a method for managing game-assigned tasks.

[0067] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for IoT devices and media are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0068] The systems, media, and methods provided in this application are one-to-one correspondences. Therefore, the systems and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the systems and media will not be repeated here.

[0069] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0070] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0071] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0072] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0073] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0074] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0075] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0076] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0077] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A management method of a game commission task, characterized by, The method comprises: obtaining a game entrustment task of a user, and performing data structural processing on the game entrustment task to obtain game task structure data; obtaining service provider historical performance data, and performing capability dynamic analysis on the service provider based on the service provider historical performance data to determine a capability profile of a current service provider; based on the game task structure data and the capability profile, obtaining task allocation data corresponding to the game entrustment task by analyzing the task adaptation degree of the service provider and the current service load; performing user interaction security configuration on the task allocation data to determine session context data of user secure communication; performing real-time risk assessment on the session context data to obtain a user security risk assessment report.

2. The game commission task management method according to claim 1, wherein The game entrustment task is data structured to obtain game task structure data, specifically including: based on the game entrustment task, the task description analysis of the BERT model is performed to obtain a task feature vector; obtaining user privacy information of the game entrustment task, and performing desensitization processing on the user privacy information to determine user mask data; performing task data integration on the task feature vector and the user mask data to obtain the game task structure data.

3. The game commission task management method according to claim 2, wherein Based on the service provider historical performance data, the capability dynamic analysis is performed on the service provider to determine the capability profile of the current service provider, specifically including: performing skill capability evaluation on the service provider historical performance data to determine a service skill vector; calculate the cosine similarity of the service skill vector and the task feature vector, and based on the cosine similarity, determine the skill matching degree score of the service provider through the K-means clustering recognition of the specialized game field matching; obtaining historical game task completion data of the service provider, and performing service quality evaluation on the historical game task completion data to obtain the service quality score of the service provider; wherein the service quality evaluation includes: task completion rate calculation, user satisfaction score; performing reputation evaluation on the service provider to determine the reputation score of the service provider; determine the capability profile by weighted average calculation according to the skill matching degree score, the service quality score and the reputation score.

4. The game commission task management method of claim 1, wherein, Based on the game task structure data and the capability profile, the task allocation data corresponding to the game entrustment task is obtained by analyzing the task adaptation degree of the service provider and the current service load, specifically including: performing game task adaptation degree scoring on the capability profile to determine a service provider score value; screening service providers with service provider score values greater than a preset matching threshold to obtain a set of alternative service providers; performing real-time service load data analysis on the set of alternative service providers to determine the real-time service state of the alternative service providers; based on the real-time service state, the current loadable task of the alternative service provider is obtained by service provider historical task load analysis; The game task structure data corresponding to the current loadable task is matched with the alternative service provider to determine the task allocation data.

5. The method of claim 1, wherein the game client task is a game client task of a game client application. The task allocation data is configured for user interaction security to determine session context data of user security communication, specifically including: Based on the task allocation data, a WebSocket real-time communication channel between the user and the service provider is constructed, and direct contact information of both the user and the service provider is shielded; The user and the service provider communicate through the WebSocket real-time communication channel, and save the session context data between the user and the service.

6. The game commission task management method of claim 1, wherein, Real-time risk assessment is performed on the session context data to obtain a user security risk assessment report, specifically including: Multi-modal security analysis is performed on the session context data to determine a first session security state; Potential fraudulent behavior detection is performed on the session context data to determine a second session security state; Based on the first session security state and the second session security state, the user security risk assessment report is obtained.

7. The game commission task management method according to claim 6, wherein Potential fraudulent behavior detection is performed on the session context data to determine a second session security state, specifically including: Intention recognition of fraudulent ambiguous vocabulary is performed on the session context data to obtain an inducible field; A dynamic behavior baseline of the service provider is constructed, and abnormal behavior recognition is performed on the dynamic behavior baseline to obtain an abnormal service state; According to the frequency of the inducible field and the frequency of the abnormal service state, the second session security state is determined.

8. The game commission task management method according to claim 7, wherein After real-time risk assessment is performed on the session context data to obtain a user security risk assessment report, the method further includes: Real-time update of the user security risk assessment report for game task execution is performed to determine a user security state under a current game task; According to the user security state, game task execution security evaluation data of the service provider is obtained through execution monitoring of the corresponding service provider.

9. A game-related flow management device for user safety, characterized by, The device includes: At least one processor; and a memory connected in communication with the at least one processor; Wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform a game commissioned task management method according to any one of claims 1-8.

10. A non-transitory computer storage medium storing computer-executable instructions for game association process management for user security, the computer-executable instructions comprising: The computer executable instructions, when executed, implement a game commissioned task management method according to any one of claims 1-8. The computer executable instructions, when executed, implement a game commissioned task management method according to any one of claims 1-8.

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