Activity rule management system for improving output efficiency

Abstract

The invention discloses an activity rule management system for improving output efficiency. The system is composed of an intelligent rule generation module, a dynamic rule adjustment module, a rule conflict detection module, a task parallel processing module, a user behavior prediction module, a visual rule editor, a rule execution efficiency evaluation module, a rule version management module, a cross-platform compatibility module and an integrated AI assistant module. The method is used for integrating AI assistants to provide real-time rule consultation and optimization suggestions for users, and the AI assistants can also automatically optimize rules according to activity data and user feedback.

Description

Technical Field

[0001] The present invention relates to the technical field of activity rule management, and particularly relates to an activity rule management system for improving output efficiency. Background Art

[0002] In the prior art, the management of activity rules usually relies on manual operations, which has problems such as low efficiency, error-proneness, and difficulty in adapting to market changes. With the continuous development of business and the rapid changes in the market, the traditional activity rule management method can no longer meet the requirements. There are many drawbacks in the traditional activity rule management method. The formulation and management of many activity rules rely on manual operations, which are time-consuming and laborious and prone to human errors. For example, in the promotional activities of an enterprise, marketing personnel need to manually analyze market trends, competitors' activity strategies, and the effects of past activities, and then formulate new activity rules. This method is not only inefficient but also prone to problems such as unclear rule expressions and incorrect reward calculations. The traditional method is difficult to adapt to market changes. The market environment changes dynamically, and consumers' needs and behaviors are constantly changing, while traditional activity rule management is often difficult to respond quickly. For example, in the e-commerce industry, price fluctuations are frequent. If the activity rules cannot be adjusted in time to adapt to market changes, it may lead to poor activity effects and loss of competitiveness. In addition, the activity rules formulated manually have limitations and are difficult to comprehensively consider various situations, affecting the adaptability and flexibility of the activities. Manual execution of activity rules is error-prone. When dealing with a large number of participants and complex rules, manual execution is prone to situations such as missed judgments and misjudgments, and may be affected by subjective factors, resulting in unfairness. For example, in a lottery activity, manual verification of participants' qualifications and lottery results may be incorrect. The traditional method lacks data analysis support. Activity organizers usually can only formulate rules based on experience and intuition and cannot make scientific decisions based on actual data. For example, when formulating a reward mechanism, organizers may not be able to accurately understand users' preferences for different types of rewards, resulting in unreasonable reward settings and affecting user participation. The specific problems are as follows: 1. The process of rule formulation lacks scientificity. Traditional rule formulation is based on experience and subjective judgment, lacking scientific data analysis and algorithm support, and is prone to problems such as complex rules, unreasonable rewards, and harsh participation conditions. Some enterprises use simple data analysis tools such as Excel spreadsheets for statistical analysis, but the amount of data processed is limited and the results are not accurate and in-depth, lacking professional algorithms and models to support the automatic generation of activity rules. 2. Rule adjustment is not timely. The market changes rapidly, and users' needs and behaviors are constantly changing. The traditional method is difficult to monitor changes in real time and adjust rules. For example, in a promotional activity, if the sales volume of a product is poor and the price or reward mechanism needs to be adjusted, manual operations are difficult to respond in time. Some enterprises regularly conduct manual inspections to adjust rules, which is inefficient and prone to missing the best opportunity. Some simple monitoring tools can only provide basic indicator monitoring and cannot automatically adjust rules. 3. Rule conflict detection is difficult. In complex activities, multiple rules come into effect simultaneously. If there are conflicts, it will lead to chaotic execution. For example, in an internal training activity of an enterprise, the attendance, assessment, and reward rules may be contradictory, bringing troubles to trainees and organizers. Currently, conflicts are mainly detected by manual inspection, which is inefficient and prone to omission. Simple rule management software can only store and query rules and cannot automatically detect conflicts. 4. The ability to process tasks in parallel is insufficient. Large-scale activities need to process multiple tasks simultaneously, and the traditional method cannot process them in parallel, resulting in low efficiency.For large-scale conference activities, when tasks such as registration, agenda arrangement, and guest invitation are processed simultaneously, if efficient parallel processing cannot be achieved, the preparation progress will be affected. Some enterprises use multi-threaded programming technology to achieve parallel processing, but it requires professional personnel for development and maintenance, with high costs and risks. 5. Inaccurate prediction of user behavior. Understanding user behavior is the key to formulating effective activity rules, but traditional methods usually cannot accurately predict, resulting in a mismatch between rules and user needs. For example, in a social platform promotion activity, if the interest levels of users in different topics cannot be accurately predicted, the activity will lack pertinence and affect participation. Some enterprises use simple data analysis methods, such as user portrait analysis and behavior trajectory analysis, but they can only provide superficial information and lack effective machine learning algorithms and models to support accurate prediction. 6. Limited functionality of the visual rule editor. The traditional method of configuring rules using text editing has a high threshold and is prone to understanding errors and operation mistakes. Although the visual rule editor reduces the threshold, its functionality is limited and cannot meet the requirements of complex activity rule configuration. Some visual tools can only provide basic rule configuration functions and cannot achieve dynamic adjustment of rules and effect display, and their interactivity and usability need to be improved. 7. Unscientific evaluation of rule execution efficiency. The traditional method lacks a scientific evaluation of rule execution efficiency. Organizers can only judge the execution effect through subjective feelings or simple indicator statistics and cannot accurately understand the impact of rules on system performance and user experience. Manual statistical analysis by some enterprises is inefficient and prone to errors, lacking scientific evaluation indicators and methods. 8. Chaotic rule version management. Version management is prone to chaos during the adjustment process of activity rules. If not effectively managed, it will lead to incorrect rule execution and affect the stability and reliability of the activity. For example, in a software company's product release activity, when developers adjusted the rules, they did not correctly record the version information, and problems occurred after going live and could not be quickly rolled back to the correct version. Some enterprises use simple file naming methods to manage versions, which are prone to confusion and errors, lacking professional version management tools and processes. 9. Poor cross-platform compatibility. With the globalization and diversification of activities, the activity rule management system needs to run stably in different operating systems and hardware environments, but the traditional method has poor cross-platform compatibility and cannot meet the requirements of different environments. Some enterprises develop for specific platforms, with high costs, low efficiency, and cannot be used across platforms, lacking effective compatibility testing and optimization methods. 10. Insufficient application of AI assistants. Although artificial intelligence technology has been widely applied in various fields, its application in activity rule management is limited. Currently, the activity rule management system lacks an integrated AI assistant and cannot provide users with real-time rule consultation and optimization suggestions. Some enterprises use independent AI tools to assist in management, but their integration with the system is not high, cannot be seamlessly docked, and their functions and performance need to be improved. Therefore, it is of great practical significance to develop an activity rule management system that improves output efficiency. Summary of the Invention

[0003] An activity rule management system that improves output efficiency, which consists of the following levels:

[0004] An intelligent rule generation module, which is used to automatically generate activity rules based on historical data and business logic through data analysis algorithms, optimize the generated rules, and continuously adjust parameters to improve the rationality and effectiveness of the rules;

[0005] A dynamic rule adjustment module, which is used to monitor the activity effect in real time, including indicators such as participation rate and conversion rate. When it is found that the activity effect does not meet the expectation, it automatically adjusts the rule parameters to cope with market changes;

[0006] A rule conflict detection module, which is used to establish a rule conflict detection mechanism, comprehensively detect newly generated or adjusted rules, and once a conflict is found, it issues an alarm in time and provides a solution;

[0007] A task parallel processing module, which is used to support multi-task parallel processing of rules, improve the system throughput by optimizing the system architecture and algorithms, and reasonably allocate system resources to ensure that each task can be processed in a timely and effective manner;

[0008] A user behavior prediction module, which is used to predict user behavior using machine learning algorithms, and configure rules in advance according to the user's historical behavior data, preferences and other information to adapt to potential needs;

[0009] A visual rule editor, which is used to provide an intuitive and easy-to-use visual interface. Users can easily configure activity rules through operations such as dragging and clicking, and the visual interface can display the effect and impact of the rules in real time;

[0010] A rule execution efficiency evaluation module, which is used to regularly evaluate the rule execution efficiency, evaluate the rationality and effectiveness of the rules by analyzing activity data, user feedback and other information, and put forward improvement suggestions;

[0011] A rule version management module, which is used to support rule version control and rollback, record the modification history of the rules, and ensure the stable operation of the system and the traceability and manageability of the rule change process;

[0012] A cross-platform compatibility module, which is used to ensure that the system can run stably in different operating systems and hardware environments, and improve the applicability and reliability of the system through compatibility testing and optimization;

[0013] An integrated AI assistant module, which is used to integrate an AI assistant to provide users with real-time rule consultation and optimization suggestions, and the AI assistant can also automatically optimize the rules according to activity data and user feedback.

[0014] The method includes:

[0015] S1 Intelligent rule generation - The system automatically generates activity rules through data analysis algorithms based on historical data and business logic. For example, it analyzes the number of participants, user behavior and other data of similar activities in the past to generate rules suitable for the current activity, such as reward mechanisms, participation conditions, etc.

[0016] Algorithms based on historical data: - Association rule mining algorithm: defined as a method for discovering interesting relationships between variables in large data sets. In activity rule management, it can be used to discover the association between user behavior and activity rules. The formula is Support = Number of transactions containing A and B / Total number of transactions; Confidence = Number of transactions containing A and B / Number of transactions containing A. Among them, A and B represent different user behaviors or activity rules respectively. The parameter is defined as Support represents the frequency of the rule in the data set, and Confidence measures the strength of the association between user behavior and activity rules. - Cluster analysis algorithm: defined as a method for grouping data objects into multiple classes or clusters. In activity rule management, it can be used to classify user behavior and activity features in order to generate more targeted activity rules. Common formulas include K-Means algorithm, etc. The goal is to divide data points into K clusters so that the sum of the distances from each data point to the cluster center to which it belongs is minimized. The parameter is defined as K represents the number of clusters, which is determined according to the activity type and user behavior characteristics; the cluster center represents the typical characteristics of the cluster and is used to generate activity rules for the users of the cluster. -Algorithms based on business logic: -Objective function optimization algorithm: defined as a method to make the objective function reach the optimal value by adjusting the value of the variable. In activity rule management, it can be used to optimize the parameters of the activity rules to achieve the goal of the activity. The formula takes the participation, conversion rate, etc. of the activity as the objective function, and adjusts the parameters of the activity rules, such as the reward amount, participation conditions, etc., to make the objective function reach the maximum or minimum value. The parameter definition is that the objective function is determined according to the specific goals of the activity, such as maximizing participation, maximizing conversion rate, minimizing cost, etc.; the activity rule parameters include parameters in terms of reward amount, participation conditions, activity process, etc. -Optimize the generated rules and continuously adjust the parameters to improve the rationality and effectiveness of the rules.

[0017] S2 Dynamic rule adjustment - Real-time monitoring of activity effects, including participation, conversion rate and other indicators. When it is found that the activity effect does not meet expectations, the system automatically adjusts the rule parameters, such as increasing the reward amount, relaxing participation conditions, etc., to cope with market changes. - Real-time data analysis algorithm: - Time series analysis algorithm: defined as a method for analyzing and predicting time series data. In activity rule management, it can be used to monitor the changing trend of activity effects in real time so as to adjust the rule parameters in time. Common formulas include moving average method, exponential smoothing method, etc. For example, the formula for the simple moving average method is: Where MA represents the moving average at time t,Y t-1 represents the observed value at time t-i, and n represents the window size of the moving average. The parameter is defined as the window size indicating the length of the time interval used to calculate the moving average, which is determined according to the activity characteristics and the data change frequency; the observed value represents the indicator of the activity effect, such as participation rate, conversion rate, etc.

[0018] Machine learning algorithm: defined as a method that realizes prediction and decision-making by learning and training data. In activity rule management, it can be used to predict user behavior and activity effects based on real-time data, so as to automatically adjust rule parameters. The formula uses the historical data of user behavior and activity effects as the training set, and uses machine learning algorithms to establish a prediction model. For example, a linear regression model can be used

[0019] y = β 0 + β 1 x 1 + β 2 x 2 + … + β n x n + ∈, where y represents the predicted activity effect indicator, x i represents the input user behavior characteristics, β i represents the model parameter, and ∈ represents the error term. The parameter is defined as the training set, which is a dataset containing historical data of user behavior and activity effects; the model parameter is obtained by learning and training the training set and is used to predict the activity effect; the error term represents the difference between the predicted value and the actual value. 3. Rule conflict detection - Establish a rule conflict detection mechanism to comprehensively detect newly generated or adjusted rules. For example, check whether there are conflicting clauses between different rules, such as reward condition conflicts, participation eligibility conflicts, etc. - Logical reasoning algorithm: defined as a method that detects whether there are conflicts between rules by logically analyzing and reasoning rules. The formula uses logical operators (such as AND, OR, NOT, etc.) to represent and reason about rules. For example, if rule A states that "the user must meet condition X" and rule B states that "the user must meet condition Y", then the logical operator "AND" can be used to represent the conflict situation between rule A and rule B, that is, "the user must meet both condition X and condition Y". If this condition is not met, it means that there is a conflict between rule A and rule B. The parameter is defined as the rule representation, which uses logical expressions to represent rules for logical reasoning; the logical operator is an operator used to logically combine and reason about rules.

[0020] S3. Task Parallel Processing - Support multi-task parallel processing rules. By optimizing the system architecture and algorithms, improve the system throughput. For example, simultaneously process multiple active tasks such as rule execution and rule adjustment without interfering with each other, thus improving work efficiency. - Task Scheduling Algorithm: Defined as a method for allocating and managing system resources to achieve task parallel processing. Common task scheduling algorithms include First-Come, First-Served (FCFS), Shortest Job First (SJF), Highest Response Ratio Next (HRRN), etc. For example, the formula of the FCFS algorithm is: Schedule tasks in the order of their arrival, with the tasks that arrive first being executed first. Parameter Definition: Task Priority determines the priority of a task based on factors such as the importance and urgency of the task. Tasks with higher priorities are executed first; Task Execution Time represents the time required for a task to execute. In task scheduling algorithms, the execution time of tasks is usually considered to allocate system resources reasonably.

[0021] S4. User Behavior Prediction - Use machine learning algorithms to predict user behavior. Based on information such as the user's historical behavior data and preferences, configure rules in advance to adapt to potential needs. For example, predict the possible participation methods of users, the types of rewards they prefer, etc., and adjust the rules in advance to improve user engagement. - Machine Learning Algorithm: As mentioned above, it can be used to predict the future behavior of users based on information such as the user's historical behavior data and preferences. Classification algorithms (such as decision trees, support vector machines, random forests, etc.) or regression algorithms (such as linear regression, polynomial regression, etc.) can be used for user behavior prediction. For example, when using the decision tree algorithm for classification prediction, a decision tree can be constructed based on the historical behavior characteristics of users, and then new users' behavior characteristics can be classified on the decision tree to predict their possible behaviors. Parameter Definition: Training Set is a dataset containing the user's historical behavior data and corresponding behavior labels; Model Parameters are obtained by learning and training on the training set and are used to predict user behavior.

[0022] S5 Visualization Rule Editor - Provide an intuitive and easy-to-use visualization interface. Users can easily configure activity rules through operations such as dragging and clicking. For example, set reward rules, participation conditions, etc., without the need for professional technical knowledge, reducing the threshold for rule configuration. - The visualization interface can also display the effects and impacts of the rules in real time, facilitating users to make adjustments and optimizations.

[0023] S6. Rule Execution Efficiency Evaluation - Regularly evaluate the rule execution efficiency. By analyzing information such as activity data and user feedback, evaluate the rationality and effectiveness of the rules. For example, count indicators such as the time and resource consumption of rule execution, and evaluate the impact of the rules on system performance. - Rule Execution Efficiency Evaluation Metrics: - Time Metric: Define the time required to measure rule execution. Formula: Rule Execution Time = Rule Execution End Time - Rule Execution Start Time. Parameter Definition: The rule execution start time represents the time point when the rule starts to execute; the rule execution end time represents the time point when the rule execution is completed. - Resource Consumption Metric: Define the system resources consumed during rule execution, such as CPU usage rate and memory occupancy. Formula: CPU Usage Rate = CPU Occupied Time / Total Time; Memory Occupancy = Memory Size Occupied During Rule Execution. Parameter Definition: The CPU occupied time represents the time when the CPU is occupied during rule execution; the total time represents the total time of rule execution; the memory occupancy represents the memory size occupied during rule execution. - Effect Metric: Define the impact of rule execution on activity effects, such as participation rate and conversion rate. Formula: Participation Rate = Number of Users Participating in the Activity / Total Number of Users; Conversion Rate = Number of Users Completing the Activity Goal / Number of Users Participating in the Activity. Parameter Definition: The number of users participating in the activity represents the number of users participating in the activity; the total number of users represents the number of target user groups of the activity; the number of users completing the activity goal represents the number of users who have completed the predetermined goal in the activity.

[0024] S7 Rule Version Management - Support rule version control and rollback to ensure the stable operation of the system. When problems occur during rule adjustment, it can be quickly rolled back to the previous version to avoid adverse effects on activities. - Record the modification history of the rules for easy viewing and traceability by users, ensuring that the change process of the rules is traceable and manageable.

[0025] S8 Cross - platform Compatibility - Ensure that the system can operate stably in different operating systems and hardware environments, such as operating systems like Windows, Linux, Mac, etc., and different server hardware configurations. - Through compatibility testing and optimization, ensure that the system can work properly in various environments, improving the applicability and reliability of the system.

[0026] S9 Integrate AI Assistant - Integrate an AI assistant to provide users with real - time rule consultation and optimization suggestions. For example, when configuring rules, users can ask questions to the AI assistant to obtain professional advice and guidance. - The AI assistant can also automatically optimize the rules based on activity data and user feedback, improving the quality and effect of the rules.

[0027] The present invention has the following beneficial effects:

[0028] The intelligent rule generation module uses data analysis algorithms to automatically generate activity rules based on historical data and business logic, eliminating the cumbersome process of traditional manual rule formulation. By continuously adjusting parameters, the rationality and effectiveness of the rules are improved, enabling better adaptation to different activity scenarios and business requirements. The dynamic rule adjustment module monitors activity effect indicators in real time, such as participation rate, conversion rate, etc. When it is found that the activity effect fails to meet expectations, it can automatically adjust the rule parameters to respond to market changes in a timely manner, ensuring that the activity always runs efficiently. This dynamic adjustment mechanism greatly improves the flexibility and adaptability of the activity. The rule conflict detection module establishes a comprehensive rule conflict detection mechanism, which can quickly detect conflicts between newly generated or adjusted rules and issue alerts in a timely manner. At the same time, it provides solutions to avoid system chaos and errors caused by rule conflicts, ensuring the stability and reliability of the system. The task parallel processing module supports multi-task parallel processing of rules by optimizing the system architecture and algorithms, improving the system throughput. Reasonably allocate system resources to ensure that each task can be processed in a timely and effective manner, greatly improving the operation efficiency and response speed of the system. The user behavior prediction module uses machine learning algorithms to predict user behavior based on information such as the user's historical behavior data and preferences. Configure rules in advance to adapt to potential needs, provide more personalized and accurate services for users, and improve the user experience and activity participation rate. The visual rule editor provides an intuitive and easy-to-use visual interface, and users can easily configure activity rules through operations such as dragging and clicking. The visual interface can also display the effects and impacts of the rules in real time, facilitating users to make adjustments and optimizations, and improving the efficiency and accuracy of rule configuration. The rule execution efficiency evaluation module regularly evaluates the rule execution efficiency, evaluates the rationality and effectiveness of the rules by analyzing activity data and user feedback, etc., and proposes improvement suggestions. This continuous improvement mechanism can continuously optimize the activity rules and improve the effect and quality of the activity. The rule version management module supports rule version control and rollback, and records the modification history of the rules. While ensuring the stable operation of the system, the rule change process is traceable and manageable, facilitating quick recovery and adjustment in case of problems. The cross-platform compatibility module ensures that the system can run stably in different operating systems and hardware environments, and improves the applicability and reliability of the system through compatibility testing and optimization. Enables the activity rule management system to meet the needs of different users and expands the application scope. The integrated AI assistant provides users with real-time rule consultation and optimization suggestions, greatly improving the user's decision-making efficiency. The AI assistant can also automatically optimize the rules according to the activity data and user feedback, further enhancing the quality and effect of the activity rules. Brief Description of the Drawings

[0029] Figure 1 System principle flow chart; Detailed Implementation Manner

[0030] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0031] Embodiment 1:

[0032] Intelligent rule generation In a certain e-commerce promotion activity, the system found that users responded more to discounts for purchases above a certain amount by analyzing the data of historical promotion activities. Therefore, the system automatically generated discount rules for purchases above a certain amount and optimized the discount amount and threshold based on the current activity goals and budget. During the activity, the system continuously monitored user behavior and activity results, further adjusted the discount rules for purchases above a certain amount, and improved the activity conversion rate and sales.

[0033] Example 2: Dynamic Rule Adjustment In an online game activity, the system monitors the participation and player feedback of the activity in real time. When it is found that some players complain about the difficulty of a task, the system automatically reduces the difficulty of the task and adjusts the corresponding reward rules. At the same time, the system also dynamically adjusts the reward amount of other tasks according to the player's game time and activity, thereby improving the player's participation and satisfaction.

[0034] Example 3: Rule conflict detection During an internal training activity of a certain enterprise, the system detected a conflict between the newly formulated attendance rule and the previous assessment rule during the rule configuration stage. Specifically, the attendance rule requires that students must be fully present to obtain the title of excellent students, while the assessment rules mainly evaluate excellent students based on test scores. The system promptly issued an alarm and provided two solutions: one is to adjust the attendance rules and change the full attendance requirement to a certain attendance ratio; the other is to adjust the assessment rules and increase the weight of attendance scores in the evaluation of excellent students. The enterprise chose the first solution based on the actual situation to avoid the impact of rule conflicts on the activities.

[0035] Example 4: Parallel processing of tasks In a large conference, the system processes multiple tasks simultaneously, including conference registration, agenda arrangement, guest invitation, etc. By optimizing the system architecture and task scheduling algorithm, the system can efficiently process these tasks in parallel to ensure the smooth progress of the event. For example, while registering for the conference, the system can adjust the agenda arrangement in real time according to the number of registrants to ensure the effectiveness and quality of the meeting.

[0036] Example 5: User Behavior Prediction In a promotion activity on a social platform, the system uses machine learning algorithms to predict user behavior. By analyzing the user's historical browsing history, like behavior and other data, the system predicts that the user may be interested in an activity on a specific theme. Therefore, the system configures relevant activity rules in advance, such as setting up a reward mechanism for the theme, inviting guests from related fields, etc. When the activity was launched, it really attracted a large number of users to participate, improving the effectiveness and influence of the activity.

[0037] Example Six: Visual Rule Editor In the marketing campaign of an enterprise, marketers easily configured campaign rules using the visual rule editor. Through operations such as dragging and clicking, marketers set participation conditions, reward rules, promotion channels, etc. of the campaign. The visual interface also showed the effects and impacts of the rules in real time, such as the expected number of participants, sales volume, etc. Marketers adjusted and optimized according to this information, improving the planning efficiency and quality of the campaign.

[0038] Example Seven: Rule Execution Efficiency Evaluation In the customer feedback campaign of a financial institution, the system regularly evaluates the rule execution efficiency. By analyzing campaign data, the system found that the execution time of some rules was relatively long, affecting the user experience. Therefore, the system put forward improvement suggestions, such as optimizing the rule execution algorithm, increasing server resources, etc. The financial institution adopted these suggestions, improving the rule execution efficiency and enhancing customer satisfaction.

[0039] Example Eight: Rule Version Management In the product release campaign of a software company, the system supports rule version control and rollback. During the campaign planning stage, developers continuously adjusted the campaign rules and recorded the modification content of each version. After the campaign was launched, it was found that a certain rule had problems, resulting in some users being unable to participate in the campaign normally. The developers quickly rolled back to the previous version and fixed the problem. Through rule version management, the stable operation of the campaign was ensured, avoiding adverse effects on users.

[0040] Example Nine: An Activity Rule Management System for Improving Output Efficiency This system consists of the following levels:

[0041] An intelligent rule generation module, which is used to automatically generate activity rules based on historical data and business logic through data analysis algorithms, optimize the generated rules, and continuously adjust parameters to improve the rationality and effectiveness of the rules;

[0042] A dynamic rule adjustment module, which is used to monitor the campaign effects in real time, including indicators such as participation rate and conversion rate. When it is found that the campaign effects do not meet the expectations, it automatically adjusts the rule parameters to cope with market changes;

[0043] A rule conflict detection module, which is used to establish a rule conflict detection mechanism, comprehensively detect newly generated or adjusted rules, and once a conflict is found, it immediately issues an alarm and provides a solution;

[0044] A task parallel processing module, which is used to support multi-task parallel processing of rules, improve the system throughput by optimizing the system architecture and algorithms, and reasonably allocate system resources to ensure that each task can be processed in a timely and effective manner;

[0045] User behavior prediction module, which is used to predict user behavior using machine learning algorithms, and configure rules in advance according to the user's historical behavior data, preferences and other information to adapt to potential needs;

[0046] Visual rule editor, which is used to provide an intuitive and easy-to-use visual interface. Users can easily configure activity rules through operations such as dragging and clicking, and the visual interface can display the effects and impacts of the rules in real time;

[0047] Rule execution efficiency evaluation module, which is used to regularly evaluate the rule execution efficiency, evaluate the rationality and effectiveness of the rules by analyzing activity data, user feedback and other information, and put forward improvement suggestions;

[0048] Rule version management module, which is used to support rule version control and rollback, record the modification history of the rules, and ensure the stable operation of the system and the traceability and manageability of the rule change process;

[0049] Cross-platform compatibility module, which is used to ensure that the system can run stably in different operating systems and hardware environments, and improve the applicability and reliability of the system through compatibility testing and optimization;

[0050] Integrated AI assistant module, which is used to integrate the AI assistant to provide users with real-time rule consultation and optimization suggestions. The AI assistant can also automatically optimize the rules according to the activity data and user feedback.

[0051] The method includes:

[0052] S1 Intelligent rule generation - The system automatically generates activity rules based on historical data and business logic through data analysis algorithms. For example, by analyzing data such as the number of participants and user behavior in past similar activities, rules suitable for the current activity are generated, such as reward mechanisms and participation conditions.

[0053] Algorithms based on historical data: - Association rule mining algorithm: defined as a method for discovering interesting relationships between variables in large data sets. In activity rule management, it can be used to discover the association between user behavior and activity rules. The formula is Support = Number of transactions containing A and B / Total number of transactions; Confidence = Number of transactions containing A and B / Number of transactions containing A. Among them, A and B represent different user behaviors or activity rules respectively. The parameter is defined as Support represents the frequency of the rule in the data set, and Confidence measures the strength of the association between user behavior and activity rules. - Cluster analysis algorithm: defined as a method for grouping data objects into multiple classes or clusters. In activity rule management, it can be used to classify user behavior and activity features in order to generate more targeted activity rules. Common formulas include K-Means algorithm, etc. The goal is to divide data points into K clusters so that the sum of the distances from each data point to the cluster center to which it belongs is minimized. The parameter is defined as K represents the number of clusters, which is determined according to the activity type and user behavior characteristics; the cluster center represents the typical characteristics of the cluster and is used to generate activity rules for the users of the cluster. -Algorithms based on business logic: -Objective function optimization algorithm: defined as a method to make the objective function reach the optimal value by adjusting the value of the variable. In activity rule management, it can be used to optimize the parameters of the activity rules to achieve the goal of the activity. The formula takes the participation, conversion rate, etc. of the activity as the objective function, and adjusts the parameters of the activity rules, such as the reward amount, participation conditions, etc., to make the objective function reach the maximum or minimum value. The parameter definition is that the objective function is determined according to the specific goals of the activity, such as maximizing participation, maximizing conversion rate, minimizing cost, etc.; the activity rule parameters include parameters in terms of reward amount, participation conditions, activity process, etc. -Optimize the generated rules and continuously adjust the parameters to improve the rationality and effectiveness of the rules.

[0054] S2 Dynamic rule adjustment - Real-time monitoring of activity effects, including participation, conversion rate and other indicators. When it is found that the activity effect does not meet expectations, the system automatically adjusts the rule parameters, such as increasing the reward amount, relaxing participation conditions, etc., to cope with market changes. - Real-time data analysis algorithm: - Time series analysis algorithm: defined as a method for analyzing and predicting time series data. In activity rule management, it can be used to monitor the changing trend of activity effects in real time so as to adjust the rule parameters in time. Common formulas include moving average method, exponential smoothing method, etc. For example, the formula for the simple moving average method is: Where MA represents the moving average at time t, Y t-1 Represents the observed value at time ti, and n represents the window size of the moving average. The parameter is defined as: the window size represents the length of the time interval used to calculate the moving average, which is determined according to the characteristics of the activity and the frequency of data changes; the observed value represents the indicator of the activity effect, such as participation, conversion rate, etc.

[0055] Machine learning algorithm: Defined as a method that realizes prediction and decision-making by learning and training data. In activity rule management, it can be used to predict user behavior and activity effects based on real-time data, so as to automatically adjust rule parameters. The formula uses historical data of user behavior and activity effects as the training set and establishes a prediction model using machine learning algorithms. For example, a linear regression model can be used

[0056] y = β 0 + β 1 x 1 + β 2 x 2 + … + β n x n + ∈, where y represents the predicted activity effect index, x i represents the input user behavior characteristics, β i represents the model parameters, and ∈ represents the error term. The parameters are defined as a dataset containing historical data of user behavior and activity effects for the training set; the model parameters are obtained by learning and training the training set and are used to predict activity effects; the error term represents the difference between the predicted value and the actual value. 3. Rule conflict detection - Establish a rule conflict detection mechanism to comprehensively detect newly generated or adjusted rules. For example, check whether there are conflicting clauses between different rules, such as reward condition conflicts, participation eligibility conflicts, etc. - Logical reasoning algorithm: Defined as a method that detects whether there are conflicts between rules by logically analyzing and reasoning rules. The formula uses logical operators (such as AND, OR, NOT, etc.) to represent and reason about rules. For example, if rule A states that "the user must meet condition X" and rule B states that "the user must meet condition Y", then the logical operator "AND" can be used to represent the conflict situation between rule A and rule B, that is, "the user must meet both condition X and condition Y". If this condition is not met, it means that there is a conflict between rule A and rule B. The parameters are defined as rules represented using logical expressions to represent rules for logical reasoning; logical operators are operators used to logically combine and reason about rules.

[0057] S3. Task Parallel Processing - Support multi-task parallel processing rules. By optimizing the system architecture and algorithms, improve the system throughput. For example, simultaneously process multiple active tasks such as rule execution and rule adjustment without interference, improving work efficiency. - Task Scheduling Algorithm: Defined as a method for allocating and managing system resources to achieve task parallel processing. Common task scheduling algorithms include First-Come, First-Served (FCFS), Shortest Job First (SJF), Highest Response Ratio Next (HRRN), etc. For example, the formula of the FCFS algorithm is: Schedule tasks in the order of their arrival, with the task that arrives first being executed first. Parameter Definition: Task Priority determines the priority of a task based on factors such as the importance and urgency of the task. Tasks with higher priorities are executed first; Task Execution Time represents the time required for a task to execute. In task scheduling algorithms, the execution time of tasks is usually considered to allocate system resources reasonably.

[0058] S4. User Behavior Prediction - Use machine learning algorithms to predict user behavior. Based on information such as the user's historical behavior data and preferences, configure rules in advance to adapt to potential needs. For example, predict the possible participation methods of users, preferred reward types, etc., and adjust rules in advance to improve user engagement. - Machine Learning Algorithm: As mentioned above, it can be used to predict the future behavior of users based on information such as the user's historical behavior data and preferences. Classification algorithms (such as decision trees, support vector machines, random forests, etc.) or regression algorithms (such as linear regression, polynomial regression, etc.) can be used for user behavior prediction. For example, when using the decision tree algorithm for classification prediction, a decision tree can be constructed based on the historical behavior characteristics of users, and then new users' behavior characteristics can be classified on the decision tree to predict their possible behaviors. Parameter Definition: Training Set is a dataset containing the user's historical behavior data and corresponding behavior labels; Model Parameters are obtained by learning and training the training set and are used to predict user behavior.

[0059] S5 Visual Rule Editor - Provide an intuitive and easy-to-use visual interface. Users can easily configure activity rules through operations such as dragging and clicking. For example, set reward rules, participation conditions, etc., without requiring professional technical knowledge, reducing the threshold for rule configuration. - The visual interface can also display the effects and impacts of rules in real time, facilitating users to make adjustments and optimizations.

[0060] S6. Rule Execution Efficiency Evaluation - Regularly evaluate the rule execution efficiency. By analyzing information such as activity data and user feedback, evaluate the rationality and effectiveness of the rules. For example, count indicators such as the time of rule execution and resource consumption, and evaluate the impact of the rules on system performance. - Rule Execution Efficiency Evaluation Metrics: - Time Metric: Define the time used to measure the rule execution. Formula: Rule Execution Time = Rule Execution End Time - Rule Execution Start Time. Parameter Definition: The rule execution start time represents the time point when the rule starts to execute; the rule execution end time represents the time point when the rule execution is completed. - Resource Consumption Metric: Define the system resources consumed during the rule execution, such as CPU usage rate and memory occupancy. Formula: CPU Usage Rate = CPU Occupied Time / Total Time; Memory Occupancy = Memory Size Occupied during Rule Execution. Parameter Definition: CPU Occupied Time represents the time when the CPU is occupied during the rule execution; Total Time represents the total time of the rule execution; Memory Occupancy represents the memory size occupied during the rule execution. - Effect Metric: Define the impact of the rule execution on the activity effect, such as participation rate and conversion rate. Formula: Participation Rate = Number of Users Participating in the Activity / Total Number of Users; Conversion Rate = Number of Users Completing the Activity Goal / Number of Users Participating in the Activity. Parameter Definition: The number of users participating in the activity represents the number of users participating in the activity; the total number of users represents the number of target user groups of the activity; the number of users completing the activity goal represents the number of users who have completed the predetermined goal in the activity.

[0061] S7 Rule Version Management - Support rule version control and rollback to ensure the stable operation of the system. When problems occur during rule adjustment, it can be quickly rolled back to the previous version to avoid adverse effects on the activity. - Record the modification history of the rules to facilitate user viewing and traceability, and ensure that the change process of the rules is traceable and manageable.

[0062] S8 Cross - platform Compatibility - Ensure that the system can run stably in different operating systems and hardware environments, such as operating systems like Windows, Linux, Mac, etc., and different server hardware configurations. - Through compatibility testing and optimization, ensure that the system can work properly in various environments, and improve the applicability and reliability of the system.

[0063] S9 Integrate AI Assistant - Integrate an AI assistant to provide users with real - time rule consultation and optimization suggestions. For example, when configuring rules, users can ask questions to the AI assistant to obtain professional advice and guidance. - The AI assistant can also automatically optimize the rules based on activity data and user feedback to improve the quality and effect of the rules.

[0064] The intelligent rule generation module uses data analysis algorithms to automatically generate activity rules based on historical data and business logic, getting rid of the cumbersome process of traditional manual rule formulation. By continuously adjusting parameters, the rationality and effectiveness of the rules are improved, enabling better adaptation to different activity scenarios and business requirements. The dynamic rule adjustment module monitors activity effect indicators in real time, such as participation rate, conversion rate, etc. When it is found that the activity effect fails to meet expectations, it can automatically adjust the rule parameters to respond to market changes in a timely manner, ensuring that the activity always runs efficiently. This dynamic adjustment mechanism greatly improves the flexibility and adaptability of the activity. The rule conflict detection module has established a comprehensive rule conflict detection mechanism, which can quickly detect conflicts between newly generated or adjusted rules and issue alerts in a timely manner. At the same time, it provides solutions to avoid system chaos and errors caused by rule conflicts, ensuring the stability and reliability of the system. The task parallel processing module supports multi-task parallel processing of rules by optimizing the system architecture and algorithms, improving the system throughput. It reasonably allocates system resources to ensure that each task can be processed in a timely and effective manner, greatly improving the operation efficiency and response speed of the system. The user behavior prediction module uses machine learning algorithms to predict user behavior based on information such as the user's historical behavior data and preferences. Rules are pre-configured to adapt to potential needs, providing more personalized and accurate services for users, and improving the user experience and activity participation rate. The visual rule editor provides an intuitive and easy-to-use visual interface, and users can easily configure activity rules through operations such as dragging and clicking. The visual interface can also display the effects and impacts of the rules in real time, facilitating users to make adjustments and optimizations, and improving the efficiency and accuracy of rule configuration. The rule execution efficiency evaluation module regularly evaluates the rule execution efficiency. By analyzing information such as activity data and user feedback, it evaluates the rationality and effectiveness of the rules and puts forward improvement suggestions. This continuous improvement mechanism can continuously optimize the activity rules and improve the effect and quality of the activity. The rule version management module supports rule version control and rollback, recording the modification history of the rules. While ensuring the stable operation of the system, the rule change process is traceable and manageable, facilitating quick recovery and adjustment in case of problems. The cross-platform compatibility module ensures that the system can run stably in different operating systems and hardware environments, improving the applicability and reliability of the system through compatibility testing and optimization. It enables the activity rule management system to meet the needs of different users and expands the application scope. The integrated AI assistant provides users with real-time rule consultation and optimization suggestions, greatly improving the user's decision-making efficiency. The AI assistant can also automatically optimize the rules according to activity data and user feedback, further enhancing the quality and effect of the activity rules.

[0065] The embodiments of the present invention are given for purposes of illustration and description, and are not exhaustive or limit the invention to the disclosed forms. Many modifications and variations are obvious to those of ordinary skill in the art. The embodiments are chosen and described in order to best explain the principles of the invention and its practical application, and to enable those of ordinary skill in the art to understand the invention so as to design various embodiments with various modifications suitable for a particular purpose.

Claims

1. An activity rule management system for improving output efficiency, characterized in that: include: Intelligent rule generation module, which is used to automatically generate activity rules based on historical data and business logic through data analysis algorithms, optimize the generated rules, and continuously adjust parameters to improve the rationality and effectiveness of the rules; Dynamic rule adjustment module, used to monitor the effect of activities in real time, including participation, conversion rate and other indicators. When it is found that the effect of activities does not meet expectations, the rule parameters are automatically adjusted to cope with market changes; The rule conflict detection module is used to establish a rule conflict detection mechanism to conduct comprehensive detection of newly generated or adjusted rules, and to issue an alarm and provide a solution in a timely manner once a conflict is found; Task parallel processing module, used to support multi-task parallel processing rules, improve system throughput by optimizing system architecture and algorithms, and reasonably allocate system resources to ensure that each task can be processed in a timely and effective manner; User behavior prediction module, which uses machine learning algorithms to predict user behavior and configures rules in advance based on user historical behavior data, preferences and other information to adapt to potential needs; Visual rule editor, which provides an intuitive and easy-to-use visual interface. Users can easily configure activity rules by dragging, clicking, and other operations. The visual interface can display the effects and impacts of the rules in real time. The rule execution efficiency evaluation module is used to regularly evaluate the rule execution efficiency, evaluate the rationality and effectiveness of the rules by analyzing activity data, user feedback and other information, and put forward improvement suggestions; The rule version management module is used to support rule version control and rollback, record the modification history of rules, ensure the stable operation of the system and make the rule change process traceable and manageable; Cross-platform compatibility module, used to ensure that the system can run stably in different operating systems and hardware environments, and improve the applicability and reliability of the system through compatibility testing and optimization; Integrated AI assistant module, used to integrate AI assistant to provide users with real-time rule consultation and optimization suggestions. AI assistant can also automatically optimize rules based on activity data and user feedback.

2. The activity rule management system for improving output efficiency according to claim 1, characterized in that: The intelligent rule generation module includes: The rule generation unit based on historical data uses association rule mining algorithms and cluster analysis algorithms to analyze data such as the number of participants and user behavior in similar activities in the past and generate rules suitable for the current activity; The rule generation unit based on business logic optimizes the parameters of the activity rules according to the activity goals through the objective function optimization algorithm.

3. The activity rule management system for improving output efficiency according to claim 2, characterized in that: In the association rule mining algorithm, the support calculation formula is the number of transactions containing A and B divided by the total number of transactions, and the confidence calculation formula is the number of transactions containing A and B divided by the number of transactions containing A, where A and B represent different user behaviors or activity rules respectively.

4. The activity rule management system for improving output efficiency according to claim 2, characterized in that: The cluster analysis algorithm adopts the K-Means algorithm to divide the data points into K clusters so that the sum of the distances from each data point to its cluster center is minimized. K represents the number of clusters determined according to the activity type and user behavior characteristics. The cluster center is used to generate activity rules for the cluster users.

5. The activity rule management system for improving output efficiency according to claim 2, characterized in that: The objective function optimization algorithm takes the participation rate, conversion rate, etc. of the activity as the objective function, and adjusts the parameters of the activity rules such as the reward amount, participation conditions, etc. to make the objective function reach the maximum or minimum value.

6. The activity rule management system for improving output efficiency according to claim 1, characterized in that: The dynamic rule adjustment module includes: The real-time data analysis unit uses time series analysis algorithms and machine learning algorithms to monitor the changing trends of activity effects in real time, predict user behavior and activity effects based on real-time data, and automatically adjust rule parameters.

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