A governance permission determination method, apparatus, device, medium, and program product

Abstract

This application discloses a method, apparatus, device, storage medium, and program product for determining governance permissions. The method includes: acquiring multi-dimensional behavioral data of each user in a target platform at a first moment; comprehensively scoring the multi-dimensional behavioral data of each user at the first moment to obtain a governance level score for each user at the first moment; and matching the governance level score of each user at the first moment with a level-permission mapping model to obtain the content governance permissions of each user in the target platform at the first moment. The level-permission mapping model includes a mapping relationship between one or more governance levels and one or more content governance permissions.

Description

Technical Field

[0001] This application relates to the field of information security and access control technology, and in particular to a method, apparatus, device, storage medium and program product for determining access control. Background Technology

[0002] In digital platforms and virtual communities, users are increasingly engaging in content creation, interaction, and governance in diverse ways. To improve user experience and platform management efficiency, many platforms have introduced user ranking systems, categorizing users based on different dimensions of behavior to reflect their contributions or activity levels. Related technologies typically use single-dimensional indicators such as the number of posts, game points, or spending records to classify users into ranks, which are then used for feature unlocking or reputation display. However, these methods lack a systematic connection to actual platform governance permissions, making it difficult to promptly reflect changes in user behavior in governance settings. Summary of the Invention

[0003] To address the aforementioned technical problems, embodiments of this application provide a method, apparatus, device, storage medium, and program product for determining governance permissions.

[0004] The governance authority determination method provided in this application includes: Obtain multi-dimensional behavioral data of each user on the target platform at the first moment; A comprehensive score is calculated on the multi-dimensional behavioral data of each user at the first moment to obtain the governance level score of each user at the first moment. Based on the hierarchical permission mapping model, the governance level score of each user at the first moment is matched to obtain the content governance permissions of each user in the target platform at the first moment; wherein, the hierarchical permission mapping model includes a mapping relationship between one or more governance levels and one or more content governance permissions.

[0005] The governance authority determination device provided in this application embodiment includes: The acquisition unit is used to acquire multi-dimensional behavioral data of each user on the target platform at the first moment. The scoring unit is used to comprehensively score the multi-dimensional behavioral data of each user at the first moment to obtain the governance level score of each user at the first moment. The matching unit is used to match the governance level score of each user at the first moment based on the level permission mapping model to obtain the content governance permissions of each user in the target platform at the first moment; wherein, the level permission mapping model includes a mapping relationship between one or more governance levels and one or more content governance permissions.

[0006] The processing device provided in this application includes a processor and a memory, wherein the memory is used to store computer programs, and the processor is used to call and run the computer programs stored in the memory to execute any of the above-described governance permission determination methods.

[0007] In the technical solution of this application embodiment, multi-dimensional behavioral data of each user in the target platform at a first moment is obtained; the multi-dimensional behavioral data of each user at the first moment is comprehensively scored to obtain the governance level score of each user at the first moment; the governance level score of each user at the first moment is matched based on the level permission mapping model to obtain the content governance permissions of each user in the target platform at the first moment; wherein, the level permission mapping model includes a mapping relationship between one or more governance levels and one or more content governance permissions. Thus, by obtaining the multi-dimensional behavioral data of each user in the target platform at a first moment and performing a comprehensive score to obtain the governance level of each user at the first moment, and then combining it with the level permission mapping model to achieve dynamic allocation of governance permissions. On the one hand, it can transform the user's actual behavior into a quantifiable governance level and link it to specific governance permissions, achieving effective allocation of governance permissions; on the other hand, by establishing a clear level permission mapping mechanism, users can obtain actual power to participate in governance based on their own behavior, improving user participation and governance effectiveness. Attached Figure Description

[0008] Figure 1 This is a flowchart illustrating a governance authority determination method provided in an embodiment of this application; Figure 2 This is a flowchart illustrating a dynamic content governance method based on virtual identity level and scene trust level provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a governance authority determination device provided in an embodiment of this application; Figure 4 This is a schematic diagram of the processing device provided in the embodiments of this application. Detailed Implementation

[0009] The technical solutions of the embodiments of this application will now be described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0010] To facilitate understanding of the technical solutions of the embodiments of this application, the relevant technologies of the embodiments of this application are described below. The following relevant technologies are optional solutions and can be combined with the technical solutions of the embodiments of this application in any way, and they all fall within the protection scope of the embodiments of this application.

[0011] In related technologies, traditional content communities, mobile apps, and some games already have user level systems, for example: (1) Game platforms use experience points or combat power to classify users into levels, which are mainly used for function unlocking and matching strategies.

[0012] (2) Content community platforms use the number of posts or the frequency of interaction as the rating criteria, but the rating only affects the user's reputation or the display of the identifier.

[0013] (3) A minority of voting mechanisms participate in content scoring through consumption points or virtual currency, but are not substantially linked to identity governance rights.

[0014] However, the above solution still has the following drawbacks: (1) User level is only used as a display indicator and is not embedded in the platform governance logic. The level cannot be linked to governance.

[0015] (2) The lack of a clear level-permission mapping mechanism means that users cannot exert governance influence on platform content, which is not conducive to the establishment of a self-operating metaverse ecosystem.

[0016] (3) Lack of dynamic feedback mechanism, rigid level promotion and demotion mechanism, unable to adjust level or governance authority in a timely manner based on behavior.

[0017] (4) The governance complexity varies greatly in different virtual scenarios, and the system cannot accurately adapt to its governance strategy.

[0018] (5) During long-term operation, the problem of inflation distortion will occur, and all users will have similar qualifications, and the incentive system will fail.

[0019] Based on this, this application aims to address the issues of virtual community and metaverse platform users being unable to participate in platform governance and the disconnect between levels and permissions. Accordingly, it proposes a method for determining governance permissions, including a user-participatory content governance mechanism based on virtual identity levels. Through dynamic points, scenario-adaptive trust mechanisms, self-learning scoring models, and group endorsement graphs, it achieves user-governed participatory governance permission allocation, improves user participation and governance effectiveness, and helps to create a self-sustaining metaverse ecosystem.

[0020] To facilitate understanding of the technical solutions of the embodiments of this application, the technical solutions of this application are described in detail below through specific embodiments. The above-mentioned related technologies are optional solutions and can be arbitrarily combined with the technical solutions of the embodiments of this application, all of which fall within the protection scope of the embodiments of this application. The embodiments of this application include at least some of the following contents.

[0021] This application proposes a method for determining governance authority. Figure 1 This is a flowchart illustrating a governance authority determination method provided in an embodiment of this application, as shown below. Figure 1 As shown, the method includes the following steps: Step 101: Obtain multi-dimensional behavioral data for each user on the target platform at the first moment.

[0022] Here, the target platform can be a metaverse content operation platform, a virtual community, an online game, or other system environment that supports user participation in content governance. Multi-dimensional behavioral data refers to various behavioral records generated by users when they engage in governance-related activities on the target platform, such as task completion status, interaction feedback, appeal processing, violation marking, voting, and rule proposals.

[0023] In this embodiment, the system collects multi-dimensional behavioral data—such as the number of adopted suggestions, the number of incorrect reports, the task completion rate, and the frequency of violation decisions—from the target platform in real time at the current moment (the first moment). This data is then stored in a structured format. The first moment is a specific point in time used to define the scope of behavioral data collection for that period, facilitating subsequent real-time scoring calculations. For example, if user A completes 5 governance tasks, submits 2 adopted suggestions, reports 3 violations, and also has 1 incorrectly judged report at a given moment, the system will record this series of behavioral data generated by user A at that moment and use these records for subsequent scoring calculations.

[0024] The methods for collecting multi-dimensional behavioral data may include, but are not limited to: system log analysis, user operation interface monitoring, third-party service call records, and user behavior tracking. To ensure the authenticity and integrity of the data, the system should have anti-tampering mechanisms, such as blockchain-based data storage or digital signature verification.

[0025] Step 102: Analyze the multi-dimensional behavioral data of each user at the first moment to obtain the governance level score of each user at the first moment.

[0026] In this embodiment, the multi-dimensional behavioral data of each user at the first moment is divided into positive governance behaviors and negative governance behaviors, and different scoring rules are assigned to positive and negative governance behaviors. For example, a corresponding score is added for each positive governance behavior (such as effective suggestions and successful reports), and a corresponding score is deducted for each negative governance behavior (such as misjudgment and violation). At the same time, the scoring weights of each positive governance behavior and each negative governance behavior are combined to comprehensively score the multi-dimensional behavioral data of each user at the first moment, so as to obtain the governance level score of each user at the first moment, reflecting the governance ability and contribution of each user at the first moment.

[0027] Positive governance behavior refers to users' positive contributions to content governance on the platform, including the adoption of valid user suggestions, user participation in governance voting, and the successful processing of user appeals. Negative governance behavior refers to user actions that may affect the quality of platform governance, including erroneous user reports, incorrect user governance decisions, and user-submitted governance opinions that violate regulations.

[0028] In some embodiments, step 102 includes: Step 1021: Based on the multi-dimensional behavioral data of each user at the first moment, determine the number of first governance actions. The number of first governance actions refers to the number of times one or more governance actions occurred for each user at the first moment. For example, how many valid suggestions did the user submit, how many votes did they participate in, and how many governance tasks did they complete?

[0029] In this embodiment of the application, based on the multi-dimensional behavioral data of each user at the first moment, the number of times one or more governance behaviors performed by each user at the first moment can be counted, thereby initially determining the participation and contribution level of each user in the governance task.

[0030] Step 1022: Based on the total number of governance actions and the contribution reduction adjustment coefficient corresponding to the first moment, determine one or more contribution reduction factors corresponding to the first moment.

[0031] Here, the total number of governance actions refers to the cumulative total number of governance actions performed by each user up to the first moment within the preset statistical period. The contribution diminishing returns adjustment coefficient is a dynamically adjusted parameter used to control the marginal benefit of points for each user when repeatedly performing the same type of governance action. As the frequency of a user's governance actions increases, the point increase from each user's actions will gradually decrease, thus preventing the points system from becoming unbalanced due to inflation. The contribution diminishing returns factor is used to calculate the decay rate of points gained from a single positive governance action at a given moment.

[0032] In this embodiment, one or more contribution reduction factors can be calculated based on the total number of governance actions performed by each user within a statistical period and the contribution reduction adjustment coefficient at the first moment. Each contribution reduction factor corresponds to a positive governance action at the first moment. For example, when a positive governance action (such as a suggestion to adopt) has been performed extensively in the current period, the marginal contribution value of each action will decrease, that is, the contribution reduction adjustment coefficient at the corresponding moment will gradually decrease.

[0033] Step 1023: Based on one or more scoring weights and one or more penalty weights corresponding to the second time point, and the comprehensive governance behavior feedback indicators corresponding to the second time point, determine one or more scoring weights and one or more penalty weights corresponding to the first time point; wherein, the second time point is before the first time point.

[0034] Here, the rating weight and penalty weight are used to measure the importance of positive and negative governance behaviors in the rating model, respectively. The rating weight and penalty weight are adjusted in real time based on the user's governance behavior. For example, when the system detects an increase in the user's governance error rate, the penalty weight may be increased to suppress erroneous behavior; while when the system's governance quality is high, the rating weight may be increased to encourage more high-quality governance behaviors.

[0035] In this embodiment, one or more scoring weights and one or more penalty weights corresponding to the second moment can be determined based on the scoring weight of each positive governance behavior at the second moment before the first moment and the penalty weight of each negative governance behavior at the second moment before the first moment. At the same time, based on the governance behavior performance at the second moment, such as governance review success rate, user satisfaction, dispute frequency, etc., a comprehensive governance behavior feedback index corresponding to the second moment is generated to guide the update direction of the scoring weights and penalty weights corresponding to the first moment. Thus, based on the one or more scoring weights and one or more penalty weights corresponding to the second moment, as well as the comprehensive governance behavior feedback index corresponding to the second moment, the scoring weights of each positive governance behavior at the first moment and the penalty weights of each negative governance behavior at the first moment are adjusted, that is, one or more scoring weights and one or more penalty weights corresponding to the first moment are determined.

[0036] Step 1024: Based on the number of first governance actions, one or more contribution reduction factors corresponding to the first moment, and one or more scoring weights and one or more penalty weights corresponding to the first moment, determine the governance level score corresponding to each user at the first moment.

[0037] In this embodiment, by comprehensively considering the number of times each user performs one or more governance actions at the first moment, one or more contribution reduction factors corresponding to the first moment, and one or more scoring weights and one or more penalty weights corresponding to the first moment, the value embodied by the user through governance actions can be accurately reflected. Thus, the quantity and quality of each user's actions (adjusted by contribution reduction factors), as well as the importance of the governance actions (reflected by scoring weights and penalty weights), can be comprehensively considered to obtain a quantitative result of each user's overall governance performance at the first moment, that is, the governance level score corresponding to each user at the first moment. This more reasonably reflects the actual performance of each user in governance activities and achieves accurate evaluation of the governance level score.

[0038] In some embodiments, step 1021 includes: Step 10211: Based on the multi-dimensional behavioral data of each user at the first moment, determine one or more positive governance behaviors and one or more negative governance behaviors of each user at the first moment.

[0039] In this embodiment of the application, by dividing the multi-dimensional behavioral data of each user at the first moment into positive governance behaviors and negative governance behaviors, it is possible to more comprehensively evaluate the contribution of each user's governance behaviors at the first moment to the platform ecosystem, thereby avoiding the bias caused by using a single-dimensional scoring method.

[0040] Step 10212: Based on one or more positive governance actions that each user performs at the first moment, determine the number of times each positive governance action occurs at the first moment.

[0041] Step 10213: Based on one or more negative governance actions that each user takes at the first moment, determine the number of times each negative governance action occurs at the first moment.

[0042] In this embodiment of the application, based on one or more positive governance behaviors performed by each user at a first moment, the number of times each positive governance behavior performed by each user at the first moment can be counted to determine its count or intensity value. For example, if user B completes 5 valid suggestion submissions and participates in 3 governance votes at a certain moment, the system will count these 5 and 3 as different positive governance behavior counts respectively.

[0043] Simultaneously, based on one or more negative governance actions performed by each user at the first moment, the system can count the number of times each negative governance action was performed by each user at that moment to determine its count or intensity value. For example, if user C made two invalid reports and one violation at the first moment, the system will count these two and one violations as different negative governance actions. The statistics on negative governance actions help establish a punishment mechanism to prevent malicious behavior from undermining platform governance.

[0044] Step 10214: Determine the number of times the first governance action occurs based on the number of times each positive governance action occurs at the first moment and the number of times each negative governance action occurs at the first moment.

[0045] In this embodiment of the application, by the number of times each positive governance behavior occurs at the first moment and the number of times each negative governance behavior occurs at the first moment, the total number of times one or more positive governance behaviors occur at the first moment and the total number of times one or more negative governance behaviors occur at the first moment can be obtained. Thus, the total number of governance behaviors executed by each user at the first moment (the number of first governance behaviors) can be obtained, which accurately measures the actual performance of each user at different moments in the governance process.

[0046] In some embodiments, step 1022 includes: Step 10221: For each positive governance behavior in one or more positive governance behaviors within the statistical period, determine the total number of each positive governance behavior based on the total number of governance behaviors.

[0047] In this embodiment, the total number of governance actions refers to the cumulative total number of all governance actions performed by each user up to the first moment within a preset statistical period (such as a week or a month). By classifying the total number of governance actions, the cumulative total number of all positive governance actions performed by each user up to the first moment within the statistical period and the cumulative total number of all negative governance actions can be obtained, and the total number of times each positive governance action occurs up to the first moment within the statistical period (the total number of each positive governance action) can be further determined.

[0048] Step 10222: Based on the contribution reduction adjustment coefficient corresponding to the second time step and the contribution reduction self-learning feedback index corresponding to the second time step, determine the contribution reduction adjustment coefficient corresponding to the first time step.

[0049] Here, the diminishing contribution adjustment coefficient is a time-varying parameter used to control the degree of marginal contribution reduction of governance behaviors. It reflects the system's judgment on the importance of a certain type of governance behavior at the current point in time. The diminishing contribution adjustment coefficient is usually updated through self-learning based on the effects of historical governance behaviors. The diminishing contribution self-learning feedback index is a comprehensive evaluation index, usually composed of factors such as the rejection rate after adoption, user satisfaction feedback, the growth rate of recent contribution behaviors, and changes in the quality score of governance suggestions.

[0050] In this embodiment, the contribution reduction adjustment coefficient corresponding to the second time step before the first time step and the contribution reduction self-learning feedback index, combined with the learning adjustment rate used to control the magnitude of self-learning changes, can be dynamically updated and determined in real time. There is a causal relationship between the contribution reduction adjustment coefficient and the contribution reduction self-learning feedback index: the contribution reduction adjustment coefficient is the dynamic output result under the influence of the contribution reduction self-learning feedback index. When the contribution reduction self-learning feedback index changes significantly, the system will adjust the contribution reduction adjustment coefficient, thereby affecting the subsequent integral calculation logic.

[0051] Step 10223: Based on the total number of positive governance actions and the contribution reduction adjustment coefficient corresponding to the first moment, determine the contribution reduction factor corresponding to each positive governance action at the first moment.

[0052] Step 10224: Based on the contribution reduction factor corresponding to each positive governance behavior at the first moment, determine one or more contribution reduction factors corresponding to the first moment.

[0053] In this embodiment, the contribution diminishing factor for each positive governance behavior at the first moment is determined by two factors: the total number of occurrences of each positive governance behavior and the contribution diminishing adjustment coefficient at the first moment. By applying the contribution diminishing factor to each positive governance behavior, the actual contribution value of the positive governance behavior at the current moment can be accurately calculated. This mechanism can prevent users from obtaining unreasonable points accumulation through a large number of repeated low-quality positive governance behaviors, thereby maintaining the fairness of the points system and the effectiveness of incentives, and thus promoting the occurrence of high-quality governance behaviors.

[0054] After obtaining the contribution reduction factor corresponding to each positive governance behavior at the first moment, it can be aggregated and processed. That is, the contribution reduction factor corresponding to each positive governance behavior at the first moment is integrated to form one or more contribution reduction factors corresponding to the first moment. Each contribution reduction factor corresponds to a positive governance behavior at the first moment, thus realizing a comprehensive consideration of multiple positive governance behaviors.

[0055] In some embodiments, step 1023 includes: Step 10231: Based on one or more scoring weights and one or more penalty weights corresponding to the second time step, determine the scoring weights of one or more positive governance behaviors and the penalty weights of one or more negative governance behaviors corresponding to the second time step.

[0056] In this embodiment of the application, by classifying one or more scoring weights and one or more penalty weights corresponding to the second time, the scoring weights of one or more positive governance behaviors and the penalty weights of one or more negative governance behaviors corresponding to the second time can be obtained. Each positive governance behavior corresponds to a scoring weight at the second time, and each negative governance behavior corresponds to a penalty weight at the second time.

[0057] Step 10232: Based on the scoring weight of each positive governance behavior at the second time point and the comprehensive governance behavior feedback index at the second time point, determine the scoring weight of each positive governance behavior at the first time point.

[0058] Here, the comprehensive governance behavior feedback indicators are a set of evaluation parameters dynamically generated by the system based on recent governance data, used to assess the overall quality of the current governance environment. These indicators may include the appeal rejection rate, user satisfaction, and the number of dispute arbitrations, reflecting the operational health of the governance system.

[0059] In this embodiment, by using the scoring weight of each positive governance behavior at the second time step and the comprehensive governance behavior feedback index at the second time step, combined with a learning rate used to control the speed of weight adjustment, the scoring weight of each positive governance behavior at the first time step can be dynamically adjusted and determined in real time. For example, during a period of governance chaos, if the comprehensive governance behavior feedback index at the second time step indicates improved governance efficiency, the system may increase the scoring weight of behaviors that effectively improve governance efficiency at the first time step and decrease the scoring weight of inefficient behaviors, thereby guiding users to adopt governance behaviors that are more conducive to the development of the platform.

[0060] Step 10233: Based on the penalty weight corresponding to each negative governance behavior at the second time and the comprehensive governance behavior feedback index corresponding to the second time, determine the penalty weight corresponding to each negative governance behavior at the first time.

[0061] Here, the dynamic adjustment mechanism for penalty weights is similar to that for scoring weights, but the goal of this mechanism is to strengthen the constraint on negative governance behaviors. The system will decide whether to increase the penalty for a certain type of negative governance behavior based on the sensitivity of the current governance environment.

[0062] In this embodiment, the penalty weight for each negative governance behavior at the second time step and the comprehensive governance behavior feedback index at the second time step are used, along with a learning rate used to control the speed of weight adjustment. This allows for dynamic adjustment and real-time determination of the penalty weight for each negative governance behavior at the first time step. For example, if the comprehensive governance behavior feedback index at the second time step indicates a large number of false reports, the system may increase the penalty weight for false reports at the first time step to create a deterrent effect.

[0063] Step 10234: Based on the scoring weight of each positive governance behavior at the first moment and the penalty weight of each negative governance behavior at the first moment, determine one or more scoring weights and one or more penalty weights corresponding to the first moment.

[0064] In this embodiment, after obtaining the score weight corresponding to each positive governance behavior at the first moment and the penalty weight corresponding to each negative governance behavior at the first moment, they are aggregated to obtain one or more score weights and one or more penalty weights corresponding to the first moment. The combination of score weights and penalty weights not only affects the user's governance level but also determines the scope of governance activities the user can participate in. For example, a user with a high score weight and a low penalty weight may be granted higher governance privileges by the system, allowing such users to participate in high-level governance decisions; conversely, when a user has a high penalty weight, the user's governance privileges may be restricted or temporarily frozen.

[0065] In some embodiments, step 1024 includes: Step 10241: Determine the first score based on the number of times each positive governance behavior occurs at the first moment, the contribution reduction factor corresponding to each positive governance behavior at the first moment, and the scoring weight corresponding to each positive governance behavior at the first moment.

[0066] In this embodiment of the application, the system can multiply the number of times each positive governance behavior occurs at the first moment, the contribution reduction factor corresponding to each positive governance behavior at the first moment, and the scoring weight corresponding to each positive governance behavior at the first moment to obtain the score of each positive governance behavior at the first moment, which represents the positive contribution value of each positive governance behavior to each user at the first moment. Furthermore, the scores of each positive governance behavior at the first moment are summed up to obtain the first score.

[0067] Step 10242: Determine the second score based on the number of times each negative governance behavior occurs at the first moment and the penalty weight corresponding to each negative governance behavior at the first moment.

[0068] In this embodiment of the application, the system can multiply the number of times each negative governance behavior occurs at the first moment by the penalty weight corresponding to each negative governance behavior at the first moment to obtain the score of each negative governance behavior at the first moment, which represents the negative impact value of each negative governance behavior on each user at the first moment. The system can further summarize and add up the scores of each negative governance behavior at the first moment to obtain a second score.

[0069] Step 10243: Based on the first score and the second score, determine the governance level score corresponding to each user at the first moment.

[0070] In this embodiment, the governance level score is a comprehensive evaluation of each user's governance performance at the first moment, reflecting each user's governance capability and reputation level on the target platform at that moment. The governance level score is jointly determined by a first score (positive contribution) and a second score (negative penalty) to reflect each user's net governance value.

[0071] Step 103: Based on the hierarchical permission mapping model, match the governance level score of each user at the first moment to obtain the content governance permissions of each user in the target platform at the first moment.

[0072] The hierarchical permission mapping model includes a mapping relationship between one or more governance levels and one or more content governance permissions.

[0073] In this embodiment, the hierarchical permission mapping model is a mechanism that maps governance levels to specific content governance permissions. Each governance level corresponds to a set of content governance permissions, and the content governance permissions gradually expand as the governance level increases. The system can determine the governance level of each user at the first moment based on the governance level score, query the hierarchical permission mapping model, match the governance level of each user at the first moment with each governance level in the hierarchical permission mapping model, and use the set of content governance permissions corresponding to the matched governance level as the content governance permissions of each user at the first moment.

[0074] For example, a hierarchy-based access control model can include the following mapping rules: (1) Governance Level 1 corresponds to: allowing users only to submit reports and make suggestions; (2) Governance Level 2 corresponds to: the platform allows users to participate in voting and allows users to view governance proposals; (3) Governance Level 3 corresponds to: allowing users to initiate governance proposals and participate in the review process; (4) Governance level 4 corresponds to: allowing users to serve as members of the governance committee and chair governance meetings.

[0075] In practice, when a user's governance level changes, such as when the user's governance level at the third moment (a moment after the first moment) is higher than the user's governance level at the first moment, the system will reassess and adjust the user's content governance permissions at the third moment based on the new governance level. This allows for a rapid response to changes in the user's governance level, enabling dynamic updates to content governance permissions and enhancing the user's predictability and controllability of the governance results.

[0076] In some embodiments, the above method further includes: Step S11: For each scenario in the target platform, identify one or more governance tasks included in each scenario.

[0077] Here, a scenario refers to different functional areas or interactive environments within the target platform, with different scenarios having different governance needs and complexities. For example, a metaverse project may include multiple scenarios such as a content publishing area, a user interaction area, and a transaction management area, each with different types and levels of governance tasks.

[0078] Governance tasks refer to the specific operations or responsibilities that users undertake in content governance within a specific virtual scenario, such as content review, reporting violations, rule suggestions, and voting decisions.

[0079] In this embodiment, one or more governance tasks can be assigned to each scenario based on the characteristics or needs of different scenarios involved in the target platform. Different scenarios may involve different types of governance tasks. For example, in social scenarios, governance tasks may include content review and sensitive word filtering; in transaction scenarios, governance tasks may include order anomaly detection and dispute arbitration.

[0080] Step S12: For each governance task included in each scenario, based on the multi-dimensional behavioral data of each user in each governance task at the first moment, determine the execution quality index corresponding to each governance task at the first moment.

[0081] Here, execution quality metrics refer to quantitative values ​​calculated based on user behavior data in governance tasks, reflecting the user's governance effectiveness. These metrics measure the quality of a user's execution of a specific governance task at a particular point in time, such as task completion rate, number of incorrect rulings, and user appeal rejection rate. Higher execution quality metrics indicate stronger governance capabilities and a greater sense of responsibility demonstrated by the user in a given governance task.

[0082] In this embodiment of the application, for each governance task in each scenario, the various behavioral data of each user at the first moment of the governance task can be collected and analyzed in real time. For example, it can record whether the user completes the governance task on time, whether high-quality governance suggestions are submitted, and whether they are recognized or rejected by other users. Then, according to the preset scoring model, the various behavioral data of each user at the first moment of the governance task are converted into specific execution quality indicators (quantitative values), and the summation and average of these indicators are obtained to obtain the execution quality indicator (quantitative value) of each governance task at the first moment.

[0083] Step S13: Perform a risk assessment on each governance task at the first moment to obtain the complexity coefficient of each governance task at the first moment.

[0084] In this embodiment, the overall risk level of each governance task can be assessed at the first moment based on factors such as the historical execution status of each task, user feedback data, and dispute arbitration results, thus obtaining the complexity coefficient (complexity level) of each governance task at that moment. High-risk governance tasks typically involve high levels of controversy, complex judgment criteria, or a large scope of impact, such as those related to user privacy protection, sensitive content review, or significant rule changes. For example, for governance tasks that have recently received a large number of user complaints or appeals, the system will automatically increase the complexity coefficient of the task to remind users to be more cautious and meticulous when executing such tasks.

[0085] Step S14: Based on the execution quality index and complexity coefficient of each governance task at the first moment, a comprehensive score is obtained to get the trust score of each user at the first moment in each scenario.

[0086] In this embodiment, for each governance task included in each scenario, the execution quality index and complexity coefficient of each governance task at the first moment in that scenario can be combined to comprehensively score each user's governance performance at the first moment in each governance task, thereby obtaining a trust score for each user at the first moment in that scenario. This trust score is used to measure each user's credibility and governance capabilities in that scenario. The trust score can be used to determine whether a user can obtain higher governance privileges, participate in higher-level governance tasks, or require further supervision and review.

[0087] In practice, the system dynamically adjusts each user's governance permissions at any given moment based on their trust score. For example, in a virtual scenario, users with high trust scores at a given moment may be granted more governance powers, such as being allowed to participate in the formulation of key rules or serve as heads of governance teams. Conversely, users with low trust scores at a given moment may have their governance permissions restricted or even have their governance qualifications suspended to ensure the fairness and effectiveness of the entire governance process.

[0088] In some embodiments, the above method further includes: Step S21: Based on the comparison between the governance level score and the level threshold corresponding to each user at the first moment, determine one or more high-level users.

[0089] Here, the governance level score is a comprehensive score dynamically calculated based on the user's behavioral data in the Metaverse platform. This score is used to reflect the user's governance capabilities and credibility at a specific point in time.

[0090] In this embodiment, a scoring baseline can be set as a level threshold, which is used to distinguish user groups with different governance permission levels. By comparing the governance level score of each user at the first moment with the level threshold, one or more target users whose governance level score at the first moment is greater than the level threshold can be identified, and these one or more target users can be identified as high-level users. High-level users can obtain higher-level governance permissions, including participating in key rule proposals and reviewing votes.

[0091] Step S22: Determine one or more endorsers based on one or more high-level users, and determine one or more endorsed objects based on one or more endorsers.

[0092] Here, the endorsers are users with significant influence and governance experience selected from one or more high-level users. These users can recommend other users to become governance participants on the platform. The endorsed users are new users recommended by these endorsers, and these new users may also gain governance privileges after gaining a certain level of trust.

[0093] In this embodiment, a governance system based on a user-interaction trust chain can be constructed by determining one or more endorsers based on one or more high-level users, and further determining one or more endorsed entities based on these endorsers. This system aims to encourage high-quality users to lead more users to participate in governance and to achieve joint responsibility for governance actions through endorsement relationships. For example, in a virtual community, a high-level user A recommends user B as a new content moderator and incorporates them into the governance system. If user B frequently makes misjudgments during the review process, user A's risk factor will increase, negatively impacting user A's future governance eligibility.

[0094] Step S23: Conduct a comprehensive evaluation of the multi-dimensional behavioral data of each endorsed entity in the target platform at the first moment to obtain the governance failure severity coefficient of each endorsed entity at the first moment.

[0095] In this embodiment, by comprehensively evaluating the multi-dimensional behavioral data of each endorsed entity on the target platform at the first moment, a governance failure severity coefficient for each endorsed entity at the first moment is obtained. This allows for a comprehensive assessment of whether each endorsed entity has committed any significant errors or misconduct during the governance process. The purpose of this step is to continuously monitor and evaluate newly joined administrators, ensuring that their behavior complies with the platform's governance standards. By quantifying the degree of governance failure, the system can promptly identify potential problems and take corresponding measures, such as restricting the governance permissions of newly joined administrators or strengthening supervision of them.

[0096] Step S24: Conduct a comprehensive assessment of the governance failure severity coefficient and risk factor corresponding to each endorsed entity at the first moment to obtain the cumulative risk coefficient of endorsement liability corresponding to each endorsed entity at the first moment.

[0097] Here, the risk factor is an indicator calculated based on the endorser's historical behavior and governance performance, used to measure the potential risks associated with the endorser recommending others to participate in governance.

[0098] In this embodiment, based on the risk factor corresponding to each endorser and the severity coefficient of governance errors corresponding to each endorsed entity at the first moment, the overall liability risk level of each endorser can be comprehensively assessed, and the cumulative risk coefficient of endorsement liability corresponding to each endorser at the first moment can be obtained. If an endorser repeatedly recommends an endorsed entity with serious errors, the endorser's own risk factor will increase, which will raise the cumulative risk coefficient of endorsement liability corresponding to the endorser at the current moment, thereby affecting the endorser's current endorsement qualification and governance authority. The purpose of this step is to establish a responsibility-sharing mechanism, making endorsers responsible for the behavior of the entities they recommend. This mechanism can effectively suppress malicious or irresponsible endorsement behavior, avoid systemic risks caused by individual high-risk endorsers, and ultimately improve the stability and reliability of the entire platform governance.

[0099] In some embodiments, for each endorsement object, if the cumulative risk coefficient of the endorsement liability corresponding to the endorsement object at the first moment is greater than the risk threshold, the governance level score corresponding to the endorsement object at the first moment can be adjusted, or the recommendation permission of the endorsement object at the first moment can be suspended, or the endorsement limit of the endorsement object at the first moment can be reduced, thereby further optimizing the platform governance structure.

[0100] In the technical solution of this application embodiment, multi-dimensional behavioral data of each user in the target platform at a first moment is obtained; the multi-dimensional behavioral data of each user at the first moment is comprehensively scored to obtain the governance level score of each user at the first moment; the governance level score of each user at the first moment is matched based on the level permission mapping model to obtain the content governance permissions of each user in the target platform at the first moment; wherein, the level permission mapping model includes a mapping relationship between one or more governance levels and one or more content governance permissions. Thus, by obtaining the multi-dimensional behavioral data of each user in the target platform at a first moment and performing a comprehensive score to obtain the governance level of each user at the first moment, and then combining it with the level permission mapping model to achieve dynamic allocation of governance permissions. On the one hand, it can transform the user's actual behavior into a quantifiable governance level and link it to specific governance permissions, achieving effective allocation of governance permissions; on the other hand, by establishing a clear level permission mapping mechanism, users can obtain actual power to participate in governance based on their own behavior, improving user participation and governance effectiveness.

[0101] This application also proposes a dynamic content governance method based on virtual identity level and scene trust level. Figure 2 This is a flowchart illustrating a dynamic content governance method based on virtual identity level and scene trust level provided in an embodiment of this application, as shown below. Figure 2 As shown, the process includes the following steps: Step 201: Establish a behavior data collection module. The system collects user behavior data on the Metaverse platform, such as task completion rate, number of interactions, and evaluation records.

[0102] This module collects user behavior data on the Metaverse platform, including real-time collection of multi-dimensional behavioral data such as user task completion, interactive feedback, governance participation, social activity, and cross-scenario governance behavior, providing basic data support for subsequent points and trust assessment.

[0103] Step 202: Establish a dynamic rating module to calculate user levels based on user behavior data in the Metaverse platform and grant them corresponding content governance permissions.

[0104] (1) Real-time dynamic scoring User at any time The corresponding governance level score can be calculated using the following formula: (1) in: Indicates the user at a certain time. The governance level score; Indicates the first Positive governance behavior at all times The count or intensity value of occurrences, such as the number of times effective suggestions were adopted, the number of times interactive feedback was contributed, and the number of times governance tasks were completed on time; Indicates the first Positive governance behavior at all times The corresponding scoring weight is dynamically and adaptively adjusted according to the platform's governance status, indicating the importance that the system currently considers to be the positive governance behavior in the overall governance scoring system; Indicates the first Positive governance behavior at all times The corresponding contribution diminishing factor controls the marginal score of a single behavior to decrease dynamically with ecological evolution, preventing the accumulation and expansion of the score. Indicates the first Negative governance behaviors at all times The frequency or intensity of occurrence, such as the number of erroneous reports, the number of incorrect governance decisions, and the number of governance decisions that violate regulations; Indicates the first Negative governance behaviors at all times The corresponding penalty weights are adjusted in real time by the system based on the overall sensitivity to governance errors, with negative behaviors receiving increased penalties during peak governance risks.

[0105] (2) Self-learning update of positive behavior rating weights The system adjusts its governance health indicators in real time based on overall governance accuracy, review rejection rate, and frequency of dispute arbitration. Corresponding scoring weights and The corresponding penalty weight. Positive governance behavior at all times Corresponding rating weights It can be dynamically updated using the following formula: (2) in: Indicates the first Positive governance behavior at all times The corresponding scoring weights; This represents the learning rate, used to control the speed of weight adjustment; Indicates at time The system integrates dynamic feedback indicators such as the success rate of administrative reconsideration, user satisfaction, and dispute frequency; it can calculate and integrate dynamic feedback, such as recent changes in the rejection rate of reconsideration, changes in group satisfaction scores, changes in the total number of arbitration cases, and changes in the proportion of corrective actions taken through reconsideration; the system... It can observe governance quality trends and automatically increase or decrease the contribution weight of each governance behavior.

[0106] Similarly, the first Negative governance behaviors at all times Corresponding penalty weight It can be dynamically updated using the following formula: (3) in, Indicates the first Negative governance behaviors at all times The corresponding penalty weight.

[0107] (3) Diminishing contribution model Through dynamic reduction logic, as the frequency of governance activities increases, the system automatically suppresses inflated points, maintains the effectiveness of point incentives, and prevents the point system from becoming unbalanced and inflated. Positive governance behavior at all times Corresponding contribution reduction factor It can be dynamically updated using the following formula: (4) (5) in: Indicates the number of times within the current statistical period Positive governance behavior at all times The total cumulative occurrence; Indicates at time The corresponding contribution diminishing adjustment coefficient, Indicates at time The corresponding contribution diminishing adjustment coefficient is used to control the degree of diminishing marginal returns, and is automatically learned and adjusted by the system. Indicates at time The corresponding contribution diminishing self-learning feedback indicator takes into account the proportion of adoptions that are revoked after review, the growth rate of recent contribution behavior, and changes in the quality score of governance suggestions. This represents the learning adjustment rate, used to control... Self-learning variation range; In addition, it is also set up This serves as the minimum marginal contribution protection value, ensuring the long-term effectiveness of the points-based incentive system.

[0108] For example, consider the act of adopting a suggestion: = (6) (7) in, The current statistical period ends at [time]. Total adoption volume across all platforms to date; It can be driven by governance outcome data such as the rejection rate of post-adoption appeals and user satisfaction feedback.

[0109] Step 203: After obtaining the corresponding content governance permissions, users can perform governance operations, including but not limited to content submission, organization and management voting, editorial suggestions, appeal handling, violation marking, rule proposals, and review voting.

[0110] In this process, all user governance behaviors are recorded by the system and incorporated into the behavior data collection module in step 201, and used by the dynamic scoring module in step 202 for dynamic adjustment.

[0111] Step 204: Synchronously establish a scenario trust complexity adaptation module. User levels and governance permissions in different scenarios are related but inconsistent to ensure consistent user experience and adapt to multiple scenarios of the metaverse product.

[0112] Independent trust models are established for diverse virtual scenarios within the metaverse. Different scenarios automatically adjust scoring criteria based on the complexity of actual governance tasks, improving the accuracy of governance task adaptation and preventing the misuse or mismatch of single governance experiences across scenarios. Users in the scenario... The next moment Corresponding trust score It can be calculated using the following formula: (8) in: Indicates in the scene Next, users perform governance tasks. At any moment Corresponding performance indicators, such as effective governance task completion rate, number of erroneous rulings, and number of user appeal rejections; Indicates in the scene Next, governance tasks At any moment The corresponding complexity coefficient is automatically increased in tasks with high controversy and high risk of error.

[0113] Step 205: Simultaneously establish an endorsement trust graph risk accumulation module to achieve long-term constraints and adapt to the self-circulation of metaverse products.

[0114] An endorsement-based governance development mechanism is introduced, supporting high-level users to endorse and recommend new governance leaders. However, a risk control logic is in place: endorsement errors will accumulate the high-level user's own risk coefficient. Long-term malicious endorsement behavior will automatically lower the high-level user's governance qualification, for example, by lowering the high-level user's level, or maintaining their level but canceling their corresponding content governance permissions, to prevent collusion and manipulation within the governance group. A high-level user at any time The corresponding cumulative risk value of endorsement liability It can be calculated using the following formula: (9) in: This represents a set of endorsement objects, where each endorsement object is a high-level user; Indicates the first The risk factors corresponding to each endorsed entity are used to distinguish the potential governance risk level of the endorsed entity; Indicates the first The endorsed entity at any given moment The corresponding severity of governance errors, such as the number of governance errors, the degree of serious violations, and the number of failed appeals.

[0115] Step 206: Establish a governance behavior feedback and full-chain traceability module to conduct on-chain evidence storage and user behavior analysis to ensure that the governance process is open, fair and traceable.

[0116] Each governance action (reporting, appealing, voting, proposal) is recorded in its entirety, including the execution process, voting dynamics, arbitration results, and final handling. All governance actions are recorded in a chain to form a complete governance history traceability chain, supporting accountability review, governance reputation assessment, and risk-controlled accountability.

[0117] Step 207: Establish a user-transparent and explainable mechanism module to increase user trust in using the product.

[0118] The system discloses the current points weight range, dynamic rules for diminishing contribution, and permission upgrade logic window to users. Users can view the impact of their current governance activity on their points growth rate in real time, ensuring the fairness and predictability of the governance incentive system and enhancing their enthusiasm and trust in governance participation.

[0119] Take a governance scenario of a metaverse project as an example: User A has completed 5 tasks (+25 points) and had suggestions adopted 2 times in the past 7 days (each initial addition of 10 points, after adjustment by the diminishing factor, only 6 points are added each time, for a total of +12 points) and provided positive feedback 10 times (+10 points), for a total of 47 positive points. During the same period, User A made 2 reports of errors (each deducting 10 points, for a total of -20 points) and made 1 disputed error (-15 points), resulting in a total negative deduction of 35 points; The real-time rating is 47-35=12 points, and the current governance authority remains unchanged.

[0120] The system detected a rapid increase in the overall adoption rate in this scenario recently, and automatically increased the adoption contribution diminishing returns coefficient to suppress points inflation and ensure balanced development of governance incentives.

[0121] Based on the above, the technical solution provided in this application has the following beneficial effects: (1) Provides a self-learning adjustment mechanism for the dynamic integral scoring system, with bidirectional adaptive adjustment between integral contribution weight and marginal score, which can prevent the integral system from becoming unbalanced; (2) Provide a scenario trust complexity adaptation mechanism to accurately match the complexity of governance tasks in diverse virtual spaces and avoid governance mismatch and abuse; (3) It provides a risk accumulation mechanism for the endorsement trust graph, which can control the evolution of the governance endorsement network and strengthen the self-stabilization ability of the governance group; (4) Provide a full-chain evidence storage and review mechanism for governance behavior, which can form a closed loop of governance history and support highly credible review and accountability tracing; (7) Provide an explainable mechanism for user governance, and provide a transparent window for the points logic to ensure the enthusiasm for governance and the perception of fairness.

[0122] This application also proposes a governance authority determination device. Figure 3This is a schematic diagram of the structure of a governance authority determination device provided in an embodiment of this application, as shown below. Figure 3 As shown, the device includes: The acquisition unit 301 is used to acquire multi-dimensional behavioral data of each user in the target platform at the first moment.

[0123] Scoring unit 302 is used to comprehensively score the multi-dimensional behavioral data of each user at the first moment to obtain the governance level score of each user at the first moment.

[0124] The matching unit 303 is used to match the governance level score of each user at the first moment based on the level permission mapping model to obtain the content governance permissions of each user in the target platform at the first moment; wherein, the level permission mapping model includes a mapping relationship between one or more governance levels and one or more content governance permissions.

[0125] In some embodiments, the scoring unit 302 is specifically used for: Based on the multi-dimensional behavioral data of each user at the first moment, the number of first governance actions is determined; wherein, the number of first governance actions is the number of times one or more governance actions of each user occur at the first moment. Based on the total number of governance actions and the contribution reduction adjustment coefficient corresponding to the first moment, determine one or more contribution reduction factors corresponding to the first moment. Based on one or more scoring weights and one or more penalty weights corresponding to the second time point, and the comprehensive governance behavior feedback indicators corresponding to the second time point, determine one or more scoring weights and one or more penalty weights corresponding to the first time point; wherein, the second time point is before the first time point; Based on the number of first governance actions, one or more contribution reduction factors corresponding to the first moment, and one or more scoring weights and one or more penalty weights corresponding to the first moment, determine the governance level score corresponding to each user at the first moment.

[0126] In some embodiments, the scoring unit 302 is further specifically used for: Based on the multi-dimensional behavioral data of each user at the first moment, determine one or more positive governance behaviors and one or more negative governance behaviors of each user at the first moment; Based on one or more positive governance actions performed by each user at the first moment, determine the number of times each positive governance action occurs at the first moment; Based on one or more negative governance actions performed by each user at the first moment, determine the number of times each negative governance action occurs at the first moment; The number of times the first governance action occurs is determined based on the number of times each positive governance action occurs at the first moment and the number of times each negative governance action occurs at the first moment.

[0127] In some embodiments, the scoring unit 302 is further specifically used for: For each positive governance behavior in one or more positive governance behaviors within a statistical period, the total number of each positive governance behavior is determined based on the total number of governance behaviors. Based on the contribution reduction adjustment coefficient corresponding to the second time step and the contribution reduction self-learning feedback index corresponding to the second time step, the contribution reduction adjustment coefficient corresponding to the first time step is determined. Based on the total number of each positive governance action and the contribution reduction adjustment coefficient corresponding to the first moment, the contribution reduction factor corresponding to each positive governance action at the first moment is determined; Based on the contribution diminishing factor corresponding to each positive governance behavior at the first moment, determine one or more contribution diminishing factors corresponding to the first moment.

[0128] In some embodiments, the scoring unit 302 is further specifically used for: Based on one or more scoring weights and one or more penalty weights corresponding to the second time step, determine the scoring weights of one or more positive governance behaviors and the penalty weights of one or more negative governance behaviors corresponding to the second time step; Based on the scoring weight of each positive governance behavior at the second time and the feedback index of the comprehensive governance behavior at the second time, the scoring weight of each positive governance behavior at the first time is determined. Based on the penalty weight corresponding to each negative governance behavior at the second time and the comprehensive governance behavior feedback index corresponding to the second time, the penalty weight corresponding to each negative governance behavior at the first time is determined. Based on the scoring weight of each positive governance behavior at the first moment and the penalty weight of each negative governance behavior at the first moment, determine one or more scoring weights and one or more penalty weights corresponding to the first moment.

[0129] In some embodiments, the scoring unit 302 is further specifically used for: The first score is determined based on the number of times each positive governance behavior occurs at the first moment, the contribution reduction factor corresponding to each positive governance behavior at the first moment, and the scoring weight corresponding to each positive governance behavior at the first moment. The second score is determined based on the number of times each negative governance behavior occurs at the first moment and the corresponding penalty weight of each negative governance behavior at the first moment; Based on the first and second scores, determine the governance level score for each user at the first moment.

[0130] In some embodiments, the device further includes: The determination unit is used to determine one or more governance tasks included in each scenario in the target platform; and for each governance task included in each scenario, based on the multi-dimensional behavioral data of each user in each governance task at the first moment, to determine the execution quality index corresponding to each governance task at the first moment.

[0131] The assessment unit is used to perform risk assessment on each governance task at the first moment and obtain the complexity coefficient of each governance task at the first moment.

[0132] In some embodiments, the scoring unit 302 is further configured to perform a comprehensive score based on the execution quality index corresponding to each governance task at the first moment and the complexity coefficient corresponding to each governance task at the first moment, so as to obtain the trust score of each user at the first moment in each scenario.

[0133] In some embodiments, the determining unit is further configured to determine one or more high-level users based on the comparison result between the governance level score corresponding to each user at the first moment and the level threshold; determine one or more endorsement objects based on the one or more high-level users; and determine one or more endorsed objects based on the one or more endorsement objects.

[0134] In some embodiments, the evaluation unit is further configured to comprehensively evaluate the multi-dimensional behavioral data of each endorsed object in the target platform at the first moment to obtain the governance failure severity coefficient of each endorsed object at the first moment; and to comprehensively evaluate the governance failure severity coefficient of each endorsed object at the first moment and the risk factor of each endorsed object to obtain the cumulative risk coefficient of endorsement liability of each endorsed object at the first moment.

[0135] Those skilled in the art should understand that Figure 3 The functions of each unit in the communication device shown can be understood by referring to the relevant description of the aforementioned method. Figure 3 The functions of each unit in the communication device shown can be implemented by a program running on a processor or by specific logic circuits.

[0136] Figure 4 This is a schematic diagram of the processing device provided in an embodiment of this application. The processing device may be a terminal device or a network device. Figure 4 The processing device shown includes a processor 401, which can call and run computer programs from memory to implement the methods in the embodiments of this application.

[0137] Optionally, such as Figure 4As shown, the processing device may further include a memory 402. The processor 401 can retrieve and run computer programs from the memory 402 to implement the methods described in this embodiment.

[0138] The memory 402 can be a separate device independent of the processor 401, or it can be integrated into the processor 401.

[0139] Optionally, such as Figure 4 As shown, the processing device may also include a transceiver 403, which the processor 401 can control to communicate with other devices. Specifically, it can send information or data to other devices or receive information or data sent by other devices.

[0140] The transceiver 403 may include a transmitter and a receiver. The transceiver 403 may further include an antenna, which may be one or more.

[0141] The processing device may specifically be the governance authority determination device in the embodiments of this application, and the processing device may implement the corresponding processes of the various methods implemented in the embodiments of this application. For the sake of brevity, it will not be described in detail here.

[0142] It should be understood that the processor in the embodiments of this application may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method embodiments can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor described above can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0143] It is understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DR RAM). It should be noted that the memory used in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0144] This application also provides a computer-readable storage medium for storing a computer program. This computer-readable storage medium can be applied to the processing device in this application embodiment, and the computer program causes the computer to execute the corresponding processes implemented by the various methods in this application embodiment; for brevity, further details are omitted here.

[0145] This application also provides a computer program product, including computer program instructions. This computer program product can be applied to the processing device in this application embodiment, and the computer program instructions cause the computer to execute the corresponding processes implemented by the various methods in this application embodiment; for brevity, further details are omitted here.

[0146] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0147] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0148] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, one or more units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0149] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across one or more network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0150] In addition, the functional units in the various embodiments of this application 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.

[0151] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0152] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for determining governance authority, characterized in that, The method includes: Obtain multi-dimensional behavioral data of each user on the target platform at the first moment; A comprehensive score is calculated on the multi-dimensional behavioral data of each user at the first moment to obtain the governance level score of each user at the first moment. Based on the hierarchical permission mapping model, the governance level score of each user at the first moment is matched to obtain the content governance permissions of each user in the target platform at the first moment; wherein, the hierarchical permission mapping model includes a mapping relationship between one or more governance levels and one or more content governance permissions.

2. The method according to claim 1, characterized in that, The process of comprehensively scoring the multi-dimensional behavioral data of each user at the first moment to obtain the governance level score of each user at the first moment includes: Based on the multi-dimensional behavioral data of each user at the first moment, the number of first governance actions is determined; wherein, the number of first governance actions is the number of times one or more governance actions of each user occur at the first moment. Based on the total number of governance actions and the contribution reduction adjustment coefficient corresponding to the first moment, determine one or more contribution reduction factors corresponding to the first moment. Based on one or more scoring weights and one or more penalty weights corresponding to the second time point, and the comprehensive governance behavior feedback indicators corresponding to the second time point, one or more scoring weights and one or more penalty weights corresponding to the first time point are determined; wherein, the second time point is before the first time point; Based on the number of the first governance actions, one or more contribution reduction factors corresponding to the first time point, and one or more scoring weights and one or more penalty weights corresponding to the first time point, the governance level score corresponding to each user at the first time point is determined.

3. The method according to claim 2, characterized in that, The determination of the number of first governance actions based on the multi-dimensional behavioral data of each user at the first moment includes: Based on the multi-dimensional behavioral data of each user at the first moment, determine one or more positive governance behaviors and one or more negative governance behaviors of each user at the first moment; Based on one or more positive governance actions performed by each user at the first moment, determine the number of times each positive governance action occurs at the first moment; Based on one or more negative governance behaviors that each user engages in at the first moment, determine the number of times each negative governance behavior occurs at the first moment; The number of times the first governance action occurs is determined based on the number of times each positive governance action occurs at the first moment and the number of times each negative governance action occurs at the first moment.

4. The method according to claim 3, characterized in that, The method for determining one or more contribution reduction factors corresponding to the first moment based on the total number of governance actions and the contribution reduction adjustment coefficient corresponding to the first moment includes: For each positive governance behavior among one or more positive governance behaviors within a statistical period, the total number of each positive governance behavior is determined based on the total number of governance behaviors. Based on the contribution reduction adjustment coefficient corresponding to the second time step and the contribution reduction self-learning feedback index corresponding to the second time step, the contribution reduction adjustment coefficient corresponding to the first time step is determined. Based on the total number of each positive governance action and the contribution reduction adjustment coefficient corresponding to the first time point, the contribution reduction factor corresponding to each positive governance action at the first time point is determined; Based on the contribution reduction factor corresponding to each positive governance behavior at the first time point, one or more contribution reduction factors corresponding to the first time point are determined.

5. The method according to claim 4, characterized in that, The determination of one or more scoring weights and one or more penalty weights corresponding to the first time step based on one or more scoring weights and one or more penalty weights corresponding to the second time step, and the comprehensive governance behavior feedback indicators corresponding to the second time step, includes: Based on one or more scoring weights and one or more penalty weights corresponding to the second time step, determine the scoring weights of one or more positive governance behaviors and the penalty weights of one or more negative governance behaviors corresponding to the second time step; Based on the scoring weight of each positive governance behavior at the second time point and the comprehensive governance behavior feedback index at the second time point, the scoring weight of each positive governance behavior at the first time point is determined. Based on the penalty weight corresponding to each negative governance behavior at the second time point and the comprehensive governance behavior feedback index corresponding to the second time point, the penalty weight corresponding to each negative governance behavior at the first time point is determined. Based on the scoring weight corresponding to each positive governance behavior at the first moment and the penalty weight corresponding to each negative governance behavior at the first moment, one or more scoring weights and one or more penalty weights corresponding to the first moment are determined.

6. The method according to claim 5, characterized in that, The determination of the governance level score for each user at the first moment, based on the number of the first governance actions, one or more contribution reduction factors corresponding to the first moment, and one or more scoring weights and one or more penalty weights corresponding to the first moment, includes: The first score is determined based on the number of times each positive governance behavior occurs at the first moment, the contribution reduction factor corresponding to each positive governance behavior at the first moment, and the scoring weight corresponding to each positive governance behavior at the first moment. The second score is determined based on the number of times each negative governance behavior occurs at the first moment and the penalty weight corresponding to each negative governance behavior at the first moment; Based on the first score and the second score, the governance level score corresponding to each user at the first moment is determined.

7. The method according to any one of claims 1 to 6, characterized in that, The method further includes: For each scenario in the target platform, determine one or more governance tasks included in each scenario; For each governance task included in each scenario, the execution quality index corresponding to each governance task at the first moment is determined based on the multi-dimensional behavioral data of each user in each governance task at the first moment. A risk assessment is performed on each governance task at the first moment to obtain the complexity coefficient of each governance task at the first moment. Based on the execution quality index and complexity coefficient of each governance task at the first moment, a comprehensive score is obtained to determine the trust score of each user at the first moment in each scenario.

8. The method according to any one of claims 1 to 6, characterized in that, The method further includes: Based on the comparison between the governance level score and the level threshold corresponding to each user at the first moment, one or more high-level users are determined. One or more endorsers are determined based on the one or more high-level users, and one or more endorsed entities are determined based on the one or more endorsers. A comprehensive evaluation is performed on the multi-dimensional behavioral data of each endorsed entity in the target platform at the first moment to obtain the governance failure severity coefficient of each endorsed entity at the first moment. The severity coefficient of governance failure corresponding to each endorsed entity at the first moment and the risk factor corresponding to each endorsed entity are comprehensively evaluated to obtain the cumulative risk coefficient of endorsement liability corresponding to each endorsed entity at the first moment.

9. A device for determining governance authority, characterized in that, The device includes: The acquisition unit is used to acquire multi-dimensional behavioral data of each user on the target platform at the first moment. The scoring unit is used to comprehensively score the multi-dimensional behavioral data of each user at the first moment to obtain the governance level score of each user at the first moment. The matching unit is used to match the governance level score of each user at the first moment based on the level permission mapping model to obtain the content governance permissions of each user in the target platform at the first moment; wherein, the level permission mapping model includes a mapping relationship between one or more governance levels and one or more content governance permissions.

10. A processing apparatus, characterized in that, include: A processor and a memory for storing a computer program, the processor for calling and running the computer program stored in the memory to perform the method as described in any one of claims 1 to 8.

Images