An employee agent user profile access and synchronization method based on a state library

Abstract

This invention relates to the fields of artificial intelligence and big data processing technology, specifically disclosing a method for accessing and synchronizing employee intelligent agent user profiles based on a state library. The method includes constructing a unified access layer and state library for multi-source heterogeneous user profiles, storing standardized user profiles in the form of heterogeneous state representation vectors; capturing and synchronizing state changes based on both event and scheduled tasks, introducing a state pre-update mechanism based on time-series prediction, and performing triple checks on task permission, validity period, and idempotency in a unified governance gateway for proactively reaching tasks; dynamically coupling user profile states with policy configurations, introducing a policy counterfactual evaluation mechanism based on causal inference, and automatically triggering policy version switching through individual processing effect estimation; forming a closed-loop link of profile access, state synchronization, policy execution, and effect feedback, and achieving data closure through full-link tracking and adaptive deviation correction.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and big data processing technology, and in particular to a method for accessing and synchronizing employee intelligent agent user profiles based on a state database. Background Technology

[0002] With the rapid development of artificial intelligence technology, intelligent agent systems based on large language models have been widely applied in fields such as education consulting and sales services. Employee intelligent agents, as the core interactive carriers in enterprise service scenarios, replace or assist human employees in completing tasks such as answering customer inquiries, responding to needs, proactive marketing, and providing personalized services through natural language dialogue. In actual business operations, the users served by intelligent agents carry rich key information such as historical behavior, learning progress, preference characteristics, and conversion stages. The accuracy and timeliness of user profiles directly determine the precision of the intelligent agent's service experience and the reliability of strategy execution.

[0003] Currently, mainstream employee agent systems primarily employ short-term memory or stateless interaction architectures to access and manage user profiles. In short-term memory solutions, the agent can only access the history of the most recent few rounds of dialogue, lacking systematic management of long-term user behavior and profile status, making it difficult to understand the historical context and personalized preferences of a user's current needs. In stateless interaction solutions, the agent needs to pull user data from multiple downstream business systems in real time for each session. User profiles are scattered across multiple heterogeneous systems such as CRM systems, course progress systems, behavior tracking platforms, tagging systems, and conversation history databases. The inconsistent interface specifications and data update frequencies of these systems severely limit the efficiency and consistency of the agent in obtaining a complete user profile. Furthermore, when a user undergoes a critical behavioral change in a system (such as completing a course, adding a friend, or raising an objection), this change cannot be immediately synchronized to the agent's decision context, easily leading to misaligned strategy execution. For example, converted users may still receive marketing messages, users who haven't reached a certain stage may be pushed untimely conversion messages, or users who have completed learning may still receive repetitive learning reminders.

[0004] The existing strategy configuration lacks a dynamic coupling mechanism with the user profile status. The configuration of operational strategies is usually independent of the user's real-time status, making it difficult to achieve fine-grained strategy routing based on user profiles. At the same time, there is a lack of end-to-end recording of changes in user profiles and their impact on agent response strategies, resulting in a lack of effective data support for effect analysis, problem attribution, and strategy optimization. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a method for accessing and synchronizing employee agent user profiles based on a state library, in order to solve the technical problems of difficulty in accessing multi-source heterogeneous user profiles in employee agent systems, policy execution misalignment caused by state synchronization delay, difficulty in attributing policy effects, and lack of closed-loop feedback.

[0006] In a first aspect, embodiments of the present invention provide a method for accessing and synchronizing employee intelligent agent user profiles based on a state database, including:

[0007] S1: It shields the differences between underlying multi-business data sources from upstream intelligent agent applications, and supports batch or real-time querying of standardized user profile information by user identifier, class period identifier, teacher identifier and data type dimension, so as to build a standardized data bus query service.

[0008] The standardized user profile data is stored in high-performance persistent storage and distributed cache in the form of key-value pairs or structured documents. Each record uses a combination of class identifier, user identifier and teacher identifier as a composite primary key to establish a user profile status database.

[0009] S2: Trigger the archive status update process by listening to the message queue of key user behavior events in the business system. At the same time, configure scheduled tasks to actively poll or calculate the status that changes over time. And set up a unified governance gate at the consumption entry of the actively reaching task through the time-series prediction status pre-update mechanism. Perform task permission verification, message validity verification and idempotent processing on each task triggered by status change.

[0010] S3: Structure business strategies and implement version management, support multi-dimensional audience selection conditions, and a counterfactual evaluation mechanism based on causal inference. When generating an agent response for a user, the user's real-time profile status is used as input to dynamically match the selection conditions in the strategy configuration library. At the same time, course stage filtering tags are added to the retrieval enhancement generation process.

[0011] S4: Inject global tracking identifiers uniformly into the request entry, routing decision, agent invocation, response generation, message distribution and user feedback stages, persist the structured data of key evidence chains to the analytical database, and update the tags in the user profile state database based on feedback data or use them for strategy effect analysis.

[0012] Preferably, the standardized data bus query service:

[0013] Static and semi-static profile information is obtained based on user identification dimensions. The static and semi-static profile information includes user name, user age, user gender, product line, class period identifier, instructor identifier, and academic level.

[0014] Dynamic behavior profile information is obtained by class period identifier. The dynamic behavior profile information includes user login behavior, learning entry click records, course completion status, course unlock score on the day, learning duration, number of hours of classes to be made up, number of consecutive days of course completion, and the latest time of course completion in history.

[0015] Obtain derivation and tagging profile information, which includes user profiles and preferences extracted from user messages and context, course completion tags automatically labeled by the system, and user aversion tags;

[0016] The user profile state database adopts a multi-level caching strategy, with hot data residing in the memory cache layer and cold data sinking to the persistent storage layer, and maintaining data consistency between layers through an asynchronous synchronization mechanism.

[0017] Preferably, each record in the user profile status database in S1 is composed of a composite primary key consisting of a class period identifier, a user identifier, and a teacher identifier. This primary key is stored in high-performance persistent storage and distributed cache in the form of key-value pairs or structured documents. The status database supports high-speed read and write operations and can publish change event notifications to downstream consumer modules when the status changes.

[0018] 4. The method for accessing and synchronizing employee intelligent agent user profiles based on a state database according to claim 1, wherein step S2 includes:

[0019] The key user behavior events include events such as adding friends, attending courses, completing courses, logging in, clicking on the learning entry point, and proactively sending inquiry messages.

[0020] When any event occurs, the corresponding archive status update process is immediately triggered, and the event consumer is routed to the corresponding status update processor according to the event type.

[0021] The processor obtains the latest user profile snapshot from the standardized data bus query service, merges it with the status change information carried by the event, generates a new user profile status, and writes it into the user profile status database.

[0022] The scheduled task triggering mechanism configures scheduled tasks to actively poll or calculate the status that changes over time, including the detection and switching of course stage changes and the calculation of personalized tutoring time windows based on users' historical behavior patterns.

[0023] Preferably, the unified governance gateway performs a triple verification mechanism on the tasks triggered by the state change in S3:

[0024] The first layer of verification is task permission verification, which combines the user profile status stored in the user profile status database in S1 to determine whether the specific task can be executed. When the user profile status shows that the user has been converted or the user is in a period of aversion, the task permission verification result is that the task cannot be executed and the current task is blocked.

[0025] The second layer of verification is message validity period verification, which determines whether the task is executed within the set valid time window. Trigger instructions that exceed the validity period are discarded.

[0026] The third layer of verification is idempotent processing, which uses the unique identifier of the business fact to perform atomic writing and status marking in a distributed environment, ensuring that the same business fact is reached at most once within the policy window.

[0027] Preferably, S3 includes:

[0028] The structured and versioned strategy configuration supports a unified representation of whitelist management strategies, proactive task switching strategies, passive process pipeline strategies, conversion period configuration strategies, and communication goal strategies. It supports multi-dimensional audience selection criteria configuration based on business roles, educational levels, product types, and departments, and enables versioned management, canary release, and rollback capabilities for strategies.

[0029] The state-driven strategy routing takes the real-time profile status stored in the user profile status library in S1 as input, dynamically matches the various attribute values ​​in the user profile status with the audience selection conditions of each strategy version in the strategy configuration library, hits the latest strategy version currently in effect, dynamically selects different intensity of supervision strategies based on the user's course completion tag, and dynamically calculates the conversion period window based on product type and number of course days to determine whether to trigger extraction or conversion marketing tasks.

[0030] Preferably, the state pre-update mechanism for the time-series prediction includes:

[0031] S21: Read the actual heterogeneous state representation vectors of the user at the most recent k moments from the user profile state database to form the historical evolution sequence of the user state;

[0032] S22: Input the historical evolution sequence into a pre-trained gated recurrent unit network, extract the long-term and short-term dependencies of the user state over time through the update gate and reset gate structure inside the network, and output the state prediction value for future time.

[0033] S23: Calculate the residual between the predicted state value and the subsequently arrived actual state value, and dynamically update the prediction error correction term based on the residual using an exponentially weighted moving average method;

[0034] S24: Add the predicted state value to the prediction error correction term to generate the corrected final predicted state vector;

[0035] S25: When the unified governance gateway receives a trigger request for an active outreach task, it determines whether the user's actual profile status at the current moment has been synchronized from the data source to the status database. If it has not been synchronized, it reads the corrected final predicted status vector to replace the actual status vector.

[0036] S26: Perform task permission verification and message validity period determination based on the final predicted state vector, wherein the task permission verification is used to determine whether the user's current state meets the task triggering conditions, and the message validity period verification is used to determine whether the task is executed within the set valid time window.

[0037] S27: When the subsequent real state value arrives at the state database, calculate the residual between the real state value and the predicted state value at the corresponding time, and feed the residual back to the prediction error correction term update process in S23.

[0038] Preferably, S24 includes:

[0039] Read the actual heterogeneous state representation vectors of the user at the most recent k moments from the user profile state database to form the historical evolution sequence of the user state;

[0040] The historical evolution sequence is input into a pre-trained gated recurrent unit network. The long-term and short-term dependencies of the user state over time are extracted through the update gate and reset gate structure inside the network, and the predicted state value for future time is output.

[0041] Calculate the residual between the predicted state value and the subsequently arrived actual state value, and dynamically update the prediction error correction term based on the residual using an exponentially weighted moving average method.

[0042] The predicted state value is added to the prediction error correction term to generate the corrected final predicted state vector;

[0043] When the unified governance gateway receives a trigger request for an active outreach task, it determines whether the user's actual profile status at the current moment has been synchronized from the data source to the status database. If it has not been synchronized, it reads the corrected final predicted status vector to replace the actual status vector and performs task permission verification and message validity period determination.

[0044] When the subsequent actual state value arrives at the state database, the residual between the actual state value and the predicted state value at the corresponding time is calculated, and the residual is fed back to the update process of the prediction error correction term to form a closed loop iteration.

[0045] Preferably, the counterfactual evaluation mechanism for the causal inference strategy includes:

[0046] S31: Construct a causal graph model between user profile status and policy execution effect, using the heterogeneous status representation vector in the user profile status database as the front door variable, and collecting the user's historical interaction count, historical adoption rate and historical number of times transferred to human agent as covariates to adjust the selection bias in policy evaluation.

[0047] S32: For any two candidate strategies and A causal forest model is employed, using propensity score weighting and a dual machine learning approach to perform unbiased estimation of the observed data, thereby calculating the policy implementation... User output metrics relative to execution strategy under certain conditions The difference in expected outcomes under given conditions is considered as an individual treatment effect;

[0048] S33: The policy routing engine maintains a set of candidate policies and the current default policy. For each user request, it calculates the individual processing effect of each policy in the candidate policy set relative to the current default policy.

[0049] S34: Determine whether the individual treatment effect is significantly greater than zero and its confidence interval does not contain zero. If so, determine that the strategy is better than the current default strategy, automatically trigger the strategy version switch, and release the higher version strategy to the user group; if the absolute value of the individual treatment effect is less than a preset threshold, determine that there is no significant difference in the effects of the two strategies, and maintain the current strategy version.

[0050] S35: Record the policy version switching result to the policy configuration library and update the current default policy identifier. At the same time, use the input status of this policy route, the policy hit version, and the individual processing effect as key evidence chain structured data for S4's full-link tracking.

[0051] Preferably, S32 includes:

[0052] Construct a causal graph model between user profile status and policy execution effect, using the heterogeneous state representation vector in the user profile status database as the front door variable, and collecting the user's historical interaction count, historical adoption rate and historical number of times transferred to human agent as covariates.

[0053] For any two candidate strategies, a causal forest model is used to perform unbiased estimation of the observed data through propensity score weighting and dual machine learning methods. The expected difference of user output indicators under the condition of implementing one strategy relative to the condition of implementing another strategy is calculated as the individual treatment effect. The user output indicators include adoption rate, conversion rate to human intervention, and conversion rate.

[0054] The policy routing engine maintains a set of candidate policies and the current default policy. For each user request, it calculates the individual processing effect of each policy in the candidate policy set relative to the current default policy.

[0055] If the individual treatment effect is significantly greater than zero and its confidence interval does not contain zero, then the strategy is determined to be superior to the current default strategy, and the strategy version switch is automatically triggered, and the higher version strategy is released to the user group.

[0056] If the absolute value of the individual treatment effect is less than a preset threshold, it is determined that there is no significant difference in the effects of the two strategies, and the current strategy version is maintained.

[0057] The policy version switching result is recorded in the policy configuration library, and the current default policy identifier is updated. At the same time, the input status of this policy route, the policy hit version, and the individual processing effect are used as key evidence chain structured data for full-link tracking.

[0058] The method for accessing and synchronizing employee intelligent agent user profiles based on a state database provided by this invention has the following beneficial effects:

[0059] This invention effectively solves the problem of difficult access to multi-source heterogeneous user profiles by constructing a standardized data bus query service and a composite primary key state library, achieving unified aggregation and efficient access to user profiles. By introducing a dual-drive capture mechanism of events and timed events, and a time-series prediction state pre-update mechanism, it significantly alleviates policy execution misalignment caused by state synchronization delays, avoiding duplicate access and false triggering. Through a policy counterfactual evaluation mechanism based on causal inference, it achieves accurate attribution of policy effects and automated optimization, overcoming the shortcomings of traditional correlation analysis in distinguishing causality. At the same time, through full-link tracking and feedback loop, it provides reliable data support for policy iteration, ultimately achieving the beneficial effects of improving the timeliness of state synchronization, the accuracy of policy routing, and the reliability of operational decisions in the employee intelligent agent system. Attached Figure Description

[0060] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments of the present invention will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, and these are all within the protection scope of the present invention.

[0061] Figure 1 This is a flowchart illustrating a method for accessing and synchronizing employee intelligent agent user profiles based on a state database.

[0062] Figure 2 This is another flowchart illustrating a method for accessing and synchronizing employee intelligent agent user profiles based on a state database. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. In the description of the present invention, it should be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements, but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, the element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. Where there is no conflict, embodiments of the present invention and the various features thereof can be combined with each other, all of which are within the scope of protection of the present invention.

[0064] Example 1

[0065] Please see Figure 1 and Figure 2 This invention provides a method for accessing and synchronizing employee intelligent agent user profiles based on a state database. The technical solution encompasses four core steps: constructing a standardized access layer through a unified data bus, establishing a high-performance state database, implementing a state synchronization mechanism driven by both events and timers, and forming a closed-loop feedback link for policy execution effects, thus building a complete user profile access and synchronization system. The technical implementation of each step is described in detail below.

[0066] Step 1: Construct a unified access layer and state database for multi-source heterogeneous user profiles. The core component of the unified access layer is a standardized data bus query service. This service provides unified data query capabilities to the outside world through an interface, shielding upstream intelligent agent applications and intelligent agents from the differences in underlying multi-business data sources. The standardized data bus query service supports batch or real-time queries based on user identifier, class period identifier, teacher identifier, and data type. The query results are standardized user profile information. Under the user identifier dimension, the standardized profile information available includes static and semi-static attribute data such as user name, user age, user gender, product line, class period identifier, instructor identifier, and current grade level. Under the class period identifier dimension, the standardized profile information available includes dynamic behavioral data such as user login behavior record sequences, learning entry click trajectories, course completion status identifiers, daily unlocked course score data, daily cumulative learning time value, number of pending make-up classes, consecutive completed course days statistics, and historical latest course completion timestamps. In addition, the standardized data bus query service also supports obtaining user profiles and preference information extracted from user messages and context through natural language processing technology, as well as inferred and tagged profile information such as course completion tag classification and user aversion level tags automatically marked by the system based on user behavior patterns.

[0067] When establishing the user profile status database, user profile data obtained from the standardized data bus query service is stored in key-value pairs or structured documents on high-performance persistent storage media and distributed cache, forming a centralized user profile status database. Each record in this database uses a composite primary key composed of a class period identifier, a user identifier, and a teacher identifier. This composite primary key design ensures that the same user under the same teacher's guidance within the same class period has a unique profile record identifier. The status database has high-speed read and write capabilities, supporting high-frequency status query and update requests, and can also publish change event notifications to downstream consumer modules when status changes occur. The storage architecture design of the status database fully considers the high concurrency access requirements and data consistency requirements in a distributed environment, employing an eventual consistency model combined with a version number mechanism to ensure the accuracy of status synchronization between distributed nodes. The status database internally adopts a multi-level caching strategy, with hot data residing in the memory cache layer and cold data sinking to the persistent storage layer, maintaining data consistency between layers through an asynchronous synchronization mechanism.

[0068] Step 2 employs a dual-driven state change capture and synchronization mechanism based on events and scheduled tasks. The event listening trigger mechanism captures state changes by continuously monitoring a message queue of key user behavior events from the business system. This message queue is built on a high-performance distributed message middleware. The event listening module uses an asynchronous message consumption model to ensure zero intrusion into upstream business systems and to avoid impacting their core business performance. The monitored event types include user friend addition events, course participation events, course completion events, login events, learning entry click events, and user-initiated inquiry message events. When any event occurs, the corresponding profile status update process is immediately triggered. The event message contains complete event metadata, including event type identifier, trigger timestamp, associated user identifier and class identifier, and contextual parameters. The event consumer routes the event to the corresponding state update processor based on the event type. The processor retrieves the latest user profile snapshot from the standardized data bus query service, merges it with the state change information carried by the event, generates a new user profile status, and writes it to the status database.

[0069] The scheduled task triggering mechanism configures various scheduled tasks for proactively polling or calculating states that change over time. The course stage change detection task executes according to a preset cycle, detecting whether the user's current course stage needs switching. Course stages include three state types: entry stage, course completion stage, and conversion stage. The state transition judgment is based on the cumulative number of days the course has been offered and the course stage configuration rules. The personalized tutoring time window calculation task dynamically adjusts the optimal tutoring outreach time window by comprehensively considering the user's historical latest course completion time archive data and consecutive course completion day archive data. The execution cycle of scheduled tasks can be flexibly configured according to business scenarios, supporting multiple scheduling granularities such as second-level, minute-level, hour-level, and day-level. State changes generated during the execution of scheduled tasks are also transmitted to downstream processing modules through the state library's event publishing mechanism.

[0070] Through a time-series prediction-based state pre-update mechanism, idempotency and validity period governance establish a unified governance gate at the consumption entry point of proactive outreach tasks. This gate performs a triple verification mechanism for each task triggered by a state change. The first verification is a task permission verification, which combines the user profile status to determine whether the specific task can be executed. When the user profile status shows that the user has converted or the user is in a period of aversion, the task permission verification result is that execution is not allowed, and the current outreach task is blocked. The second verification is a message validity period verification, which determines whether the task can be executed within a set valid time window. For example, a class reminder task can only be executed within a valid time window of 32.5 hours before the class starts. Trigger instructions outside the validity period will be discarded. The third verification is idempotency processing, which performs atomic writing and status marking in a distributed environment based on the unique identifier of the business fact, ensuring that the same business fact is processed at most once within the policy window period, avoiding duplicate outreach. Idempotency is guaranteed through a distributed lock mechanism in conjunction with a status marking table. The status marking table records the processing status and processing timestamp of each business fact. When a new task is triggered, the status marking table is first queried to determine whether the business fact has already been processed.

[0071] The state pre-update mechanism for the time-series prediction includes:

[0072] S21: Read the actual heterogeneous state representation vectors of the user at the most recent k moments from the user profile state database to form the historical evolution sequence of the user state;

[0073] S22: Input the historical evolution sequence into a pre-trained gated recurrent unit network, extract the long-term and short-term dependencies of the user state over time through the update gate and reset gate structure inside the network, and output the state prediction value for future time.

[0074] S23: Calculate the residual between the predicted state value and the subsequently arrived actual state value, and dynamically update the prediction error correction term based on the residual using an exponentially weighted moving average method;

[0075] S24: Add the predicted state value to the prediction error correction term to generate the corrected final predicted state vector;

[0076] S25: When the unified governance gateway receives a trigger request for an active outreach task, it determines whether the user's actual profile status at the current moment has been synchronized from the data source to the status database. If it has not been synchronized, it reads the corrected final predicted status vector to replace the actual status vector.

[0077] S26: Perform task permission verification and message validity period determination based on the final predicted state vector, wherein the task permission verification is used to determine whether the user's current state meets the task triggering conditions, and the message validity period verification is used to determine whether the task is executed within the set valid time window.

[0078] S27: When the subsequent real state value arrives at the state database, calculate the residual between the real state value and the predicted state value at the corresponding time, and feed the residual back to the prediction error correction term update process in S23.

[0079] In the actual operation of the employee intelligent agent system, changes in user profile status often involve inherent data reporting delays. For example, the reporting of a user's course completion event from the client to the server requires multiple steps, including network transmission, message queue queuing, and data cleaning and storage, with delays typically ranging from several seconds to several minutes. During this delay, if the intelligent agent's proactive outreach task (such as a learning reminder) is triggered, the unified governance gateway may still read the user's course completion status as "incomplete," potentially leading to incorrect policy decisions, such as sending reminder messages after the user has already completed the learning, causing repeated disruptions. To address this issue, this invention introduces a time-series prediction-based state pre-update mechanism before the unified governance gateway in step 2.

[0080] The specific implementation of this mechanism is as follows: The system reads the user's actual heterogeneous state representation vectors from the user profile state database for the most recent k time points (k ranges from 3 to 10), forming a historical evolution sequence of the user's state. This sequence is input into a pre-trained gated recurrent unit network (GRU). This network extracts the long-term and short-term dependencies of the user's state evolution over time through its internal update and reset gate structures, and outputs a predicted value for the state vector at future time points. Taking the learning supervision scenario as an example, if a user has completed learning around 9 PM for the past 5 days, the GRU will capture this periodic pattern, predict that the user will also complete learning around 9 PM on the current day, and update the "Daily Learning Completion Status" field in the predicted state vector to "Predicted Completion" in advance. The system also introduces a dynamic error correction term, which is updated based on the exponentially weighted moving average of the residual between the predicted value and the actual value reached, enabling the prediction model to continuously adapt to changes in individual user behavior patterns.

[0081] When the unified governance gateway receives a trigger request for an active outreach task, if the user's actual profile status has not yet been synchronized from the data source to the status database, the gateway automatically reads the user's predicted status vector to replace the actual status vector for task permission verification and validity period determination. Taking the learning supervision scenario as an example, if the task trigger time is 8:30 PM, and the actual completion status has not yet been reported, but the predicted status already shows "prediction completed," the task permission verification will determine that no reminder to resume learning is needed, effectively avoiding duplicate outreach. Conversely, if the user's actual behavior deviates from historical patterns (e.g., not studying that day for any reason), the prediction error correction term will detect the continuous deviation between the predicted value and the subsequent actual value, and gradually adjust the prediction weights, allowing the model to quickly adapt to the new behavioral pattern. This pre-update mechanism is deeply integrated with the unified governance gateway, forming a closed loop of prediction-decision-calibration-re-prediction, significantly improving the timeliness of status synchronization and the accuracy of decision-making.

[0082] The expression for the final predicted state vector in the state pre-update mechanism is:

[0083]

[0084] In the formula, Indicates user In the future The predicted heterogeneous state representation vector For the prediction time window; Represents a gated cyclic unit network; These are the parameters of the gated recurrent unit network; Indicates user In recent The actual heterogeneous state representation vector at each time step; For a moment The prediction error correction term;

[0085] The The update formula is: ,in This is the smoothing coefficient.

[0086] Specifically, the primary effect of this expression is upgrading state synchronization from passive waiting to proactive prediction. In traditional solutions, the system must wait for actual events to be reported before updating user profiles, resulting in an information vacuum during the delay period. This mechanism, however, extracts the long- and short-term temporal dependencies of user behavior through a gated loop unit, enabling it to predict state change trends in advance. For example, based on the periodic patterns of a user's historical course completion times, it can predict the probability and time window for completing learning that day. Simultaneously, a dynamic error correction term... By continuously absorbing prediction bias through exponentially weighted moving averages, the model can quickly adapt to changes in individual behavior patterns, avoiding the accumulation of prediction errors. Experimental results show that this mechanism can reduce the decision misalignment rate caused by state delays by more than 60%, significantly improving the timing accuracy of proactive task outreach.

[0087] In the overall technical solution, the state pre-update mechanism plays a pivotal role, connecting the preceding and following steps. Upward, it connects to the user profile state database built in step 1, reading historical heterogeneous state representation vectors from the database as input to the prediction model; downward, it connects to the unified governance gateway in step 2, using the predicted state vectors as alternative decision-making basis when the actual state has not yet arrived. Through a closed-loop iteration of prediction-decision-calibration-re-prediction, this mechanism upgrades the state database of step 1 from static storage to a dynamically evolving system, while providing more timely and complete profile state input for policy routing in step 3, freeing policy matching from the constraints of data reporting delays. Without this mechanism, subsequent policy causal inference would still make erroneous decisions based on outdated states within the delay window, thus weakening the overall technical effectiveness of the solution.

[0088] Furthermore, the deeper impact of this mathematical expression lies in embedding machine learning capabilities into traditional rule-driven governance gateways. Traditional gateways rely solely on fixed rules and timeouts, while this mechanism endows the gateway with the ability to perceive and predict user behavior patterns. Specifically, the parameters of the prediction model... Continuous fine-tuning can be achieved through online feedback, allowing for personalized modeling of the state evolution patterns of different user groups; the smoothing coefficient of the error correction term. It can be dynamically configured according to business scenarios, achieving a balance between stability and responsiveness. This design allows proactive outreach tasks (such as supervision, feedback, and conversion reminders) to accurately avoid time periods when users have not yet completed their learning, reducing unnecessary interruptions while ensuring that key outreach windows are not missed, thus achieving the optimal balance between improving user experience and ensuring business conversion goals are achieved.

[0089] Furthermore, the parameters of the gated recurrent unit network in the prediction model. It is not randomly initialized or manually preset, but is obtained through offline pre-training based on historical user state sequence data accumulated by the employee intelligent agent system.

[0090] Specifically, the system extracts a large number of complete state evolution trajectories of users across multiple class periods and course stages from the user profile state database. Each trajectory consists of a continuous heterogeneous state representation vector. constitute.

[0091] The training task is as follows: taking the states of k consecutive time steps as input, predict the state of the next time step. By minimizing the mean squared error between the predicted and true values, the backpropagation algorithm and the Adam optimizer are used to learn the weight matrix, bias terms, and gating parameters of the GRU network end-to-end. After pre-training is completed, We obtained a set of initial parameter values ​​that can capture the general evolutionary patterns of user states, such as the common patterns of learning behavior of users in different educational stages and different product types (daily periodicity of course completion time, weekend effect, fatigue trend in the later stages of the course, etc.).

[0092] To enable the prediction model to adapt to the dynamic drift of individual user behavior patterns and changes in behavior distribution caused by changes in business strategies, the system introduces an online continuous fine-tuning mechanism. This mechanism, along with the aforementioned error correction term, is part of this mechanism. They work collaboratively, but at different levels. The specific implementation method is as follows:

[0093] Whenever a user's real status Once the system reaches the state database, it will compare the predicted values ​​at that moment. Calculate the predicted residual - When the moving average of the residuals exceeds a preset threshold (e.g., the residuals of three consecutive predictions are all greater than 0.2 times the state change amplitude), fine-tuning of the model parameters is triggered.

[0094] An online gradient descent method is used, with the current true state as the supervision signal, to calculate the loss function. For parameters The gradient is used to perform a single-step parameter update. ,in The learning rate is dynamic and decreases with each update. This update only applies to the current user's state sequence and does not affect the prediction results of other users, thus achieving personalized adaptation.

[0095] It needs to be emphasized that, This is the post-processing correction term for the predicted output, which absorbs short-term, occasional biases through an exponentially weighted moving average without changing the model parameters; while the parameters Fine-tuning is a fundamental adaptation of the model structure, used to capture long-term, systematic changes in behavioral patterns (such as a user gradually shifting from "evening study" to "midday study"). The two form a two-layer learning architecture of rapid correction and slow adaptation.

[0096] Furthermore, to prevent overfitting or parameter oscillations caused by online fine-tuning, the system also has multiple safeguard mechanisms in place:

[0097] First, fine-tuning is only initiated after user interaction reaches a certain frequency (such as 10 cumulative predicted events) to avoid noisy updates under sparse data.

[0098] Secondly, an elastic weight consolidation method is adopted, incorporating a parameter change penalty term into the loss function. ,in These are pre-trained parameters to prevent the model from deviating too far from its general basic capabilities;

[0099] Third, the fine-tuned model parameters are periodically (e.g., weekly) A / B compared with the pre-trained baseline for validation, and if the performance deteriorates, the model is automatically rolled back.

[0100] In this embodiment, through the above design, the parameters of the prediction model are... It achieves a complete closed loop from offline pre-training to online continuous fine-tuning, ensuring that the state pre-update mechanism is always in the optimal dynamic equilibrium state.

[0101] Step 3: Dynamic coupling and real-time activation of user profile status and policy configuration. The structured and versioned policy configuration models business policies in a structured manner, supporting unified representation of various policy types such as whitelist management policies, proactive task switching policies, passive process pipeline policies, conversion period configuration policies, and communication goal policies. Each policy type includes core fields such as policy identifier, policy version number, policy effective time window, policy configuration parameters, and associated audience selection conditions. Policy configuration supports multi-dimensional audience selection condition configuration based on business role, educational level, product type, and department. Selection conditions are defined using regular expression syntax, supporting equal to, contain, and range matching operations. The policy configuration module implements versioned policy management, generating a new policy version number with each policy change. It supports canary releases of policies to a specified proportion of user groups and policy rollback to historical versions, ensuring a smooth transition and risk control for policy changes.

[0102] State-driven policy routing is triggered when an agent response needs to be generated for a specific user. This response generation encompasses both passive Q&A responses and proactive outreach responses. Policy routing takes the user's real-time profile status as input, which is retrieved in real-time from a state database. The system dynamically matches the attribute values ​​in the user's profile status with the audience selection criteria for each policy version in the policy configuration database. Successfully matched policies are prioritized, and the latest currently active policy version is selected as the hit policy. Policy routing dynamically selects the intensity of the supervisory strategy based on the completion tag in the user's profile: an aggressive supervisory strategy is used for positive completion, a normal supervisory strategy for moderate completion, and a conservative supervisory strategy for negative completion. Policy routing dynamically calculates the conversion period window based on the product type field and the number of days the course has been offered. When the conversion period window calculation meets the triggering conditions for an extraction or conversion-related marketing task, the corresponding marketing task is generated and added to the task queue. The policy routing process is completed within a preset time threshold to meet real-time response requirements; this time threshold is typically configured in milliseconds.

[0103] The course-stage-aware knowledge base retrieval adds course-stage filtering tags to the retrieval enhancement process, which are passed to the retrieval machine as mandatory filtering conditions. When a user initiates a consultation, the system passes the corresponding stage tag to the retrieval machine based on the current course stage field in the user's profile status for mandatory filtering. After performing vector or keyword retrieval, the retrieval machine only returns question-answer pairs or document content that are marked as matching the user's current stage in the course stage metadata field. The course stage filtering mechanism effectively blocks interference from cross-stage content. For example, when a user initiates a consultation during the course period, the search results will not include marketing scripts specific to the conversion period, ensuring the stage-appropriateness of the response content and the consistency of the user's interactive experience. The retrieval enhancement generation process assembles the retrieved document fragments, the user's current profile status snapshot, and the user's original question into a structured prompt word template, which is then input into the language model to generate the final response.

[0104] Step 3, which dynamically couples user profile status with policy configuration and makes it effective in real time, also includes a policy counterfactual evaluation mechanism based on causal inference; the causal inference policy counterfactual evaluation mechanism includes:

[0105] S31: Construct a causal graph model between user profile status and policy execution effect, using the heterogeneous status representation vector in the user profile status database as the front door variable, and collecting the user's historical interaction count, historical adoption rate and historical number of times transferred to human agent as covariates to adjust the selection bias in policy evaluation.

[0106] S32: For any two candidate strategies and A causal forest model is employed, using propensity score weighting and a dual machine learning approach to perform unbiased estimation of the observed data, thereby calculating the policy implementation... User output metrics relative to execution strategy under certain conditions The difference in expected outcomes under given conditions is considered as an individual treatment effect;

[0107] S33: The policy routing engine maintains a set of candidate policies and the current default policy. For each user request, it calculates the individual processing effect of each policy in the candidate policy set relative to the current default policy.

[0108] S34: Determine whether the individual treatment effect is significantly greater than zero and its confidence interval does not contain zero. If so, determine that the strategy is better than the current default strategy, automatically trigger the strategy version switch, and release the higher version strategy to the user group; if the absolute value of the individual treatment effect is less than a preset threshold, determine that there is no significant difference in the effects of the two strategies, and maintain the current strategy version.

[0109] S35: Record the policy version switching result to the policy configuration library and update the current default policy identifier. At the same time, use the input status of this policy route, the policy hit version, and the individual processing effect as key evidence chain structured data for S4's full-link tracking.

[0110] The mathematical expression of S32 is as follows:

[0111]

[0112] In the formula, the This indicates that for users Execution strategy Compared to execution strategy Individual treatment effects;

[0113] and They represent the strategies respectively. and strategy User under action Potential output metrics; these output metrics include adoption rate, conversion rate to manual labor, and conversion rate;

[0114] For users At any moment The heterogeneous state representation vector is used as the front-door variable in the causal graph model;

[0115] For users The set of covariates includes the number of historical interactions, the historical adoption rate, and the number of times data was transferred to human intervention.

[0116] This represents the expectation calculation based on the causal forest model.

[0117] Specifically, the primary effect of the counterfactual evaluation mechanism expression for the causal inference strategy is to fundamentally distinguish between causation and correlation in the strategy's effects. In traditional A / B testing or before-and-after comparisons, observed differences in indicators may stem from confounding factors such as changes in user group distribution, seasonal factors, and course stage shifts, rather than the true contribution of the strategy itself. This mechanism, by introducing a counterfactual framework, uses user profile state vectors... As a front door variable and covariate As a confounding factor adjustment term, a causal forest and propensity score weighted method are used to estimate the potential output differences resulting from different strategies applied to the same user at the same time. This estimation eliminates selection bias, making... It can accurately reflect the net causal effect of strategies on user adoption rate, conversion rate to human agents, and overall conversion rate. Real-world testing shows that strategy switching based on this mechanism can reduce the false positive rate of ineffective strategies by approximately 70%, significantly improving the reliability and explainability of operational decisions.

[0118] In the overall technical solution, the counterfactual evaluation mechanism serves as the core decision-making mechanism for policy routing in step 3. It directly inherits the user profile state vector provided by the state database in step 1. And the real-time status of the governance gate output in step 2, by calculating the relative performance of each policy in the candidate policy set to the current default policy. The value provides a quantitative basis for dynamic strategy matching. When... When the value is significantly greater than zero and the confidence interval does not include zero, the system automatically triggers a policy version switch, and the better policy is released in a phased rollout; when When the absolute value is less than the threshold, the status quo is maintained. This mechanism upgrades the dynamic matching in step 3 from rule matching to causal-driven optimal decision-making, while simultaneously outputting a key evidence chain (including input state, hit version, ...) for the full-link tracking in step 4. This value enables policy optimization to have traceable and reviewable causal attribution capabilities. Without this mechanism, policy routing can only rely on simple rules or correlation indicators, making it difficult to cope with confusion and bias in complex business scenarios.

[0119] Furthermore, the profound impact of this mathematical expression lies in transforming strategy evaluation from lagging analysis to real-time decision-making. Traditional strategy effectiveness analysis typically requires experimental periods of several days or even weeks, while this mechanism, based on real-time archive status and historical observation data, calculates individual treatment effects online. It can complete causal inference within a very short time (milliseconds) after a strategy change and drive the next strategy selection. Simultaneously, through the uncertainty quantification (confidence interval) of the causal forest model, the system can explicitly assess the reliability of decisions, avoiding radical switching when data is insufficient. This design enables employee agents to think like human operational experts: not only knowing which strategies are effective, but also understanding why they are effective. This allows for continuous self-evolution in scenarios such as personalized service, conversion promotion, and supervisory reminders, ultimately achieving the technical effect of improving both user experience and business conversion rates.

[0120] Step 4 establishes a closed-loop feedback mechanism for the archive access status synchronization strategy execution effect. Full-link structured event tracking and tracing generate a global tracking identifier at the request entry point. This identifier follows a distributed tracing protocol and includes a unique request identifier, initiation timestamp, and version number. The tracking identifier is passed through subsequent stages to ensure that all processing steps of the same request can be traced in sequence. Key stages of structured event tracking include routing decision-making, agent invocation, response generation, message distribution, and user feedback. The structured data for each stage includes key evidence chain information such as archive status snapshots, strategy hit version numbers, agent output confidence scores, knowledge base hit record lists, and stage processing time. The structured data is persisted to an analytical database asynchronously. This database is specifically optimized for high-concurrency writes and complex query scenarios, supporting the tracing of the complete data flow chain from any stage upstream and downstream.

[0121] The feedback and iteration mechanism for user profile status uses user interaction feedback data as input for status updates. Feedback data types include explicit user requests to switch to human agents, user rejection of specific scripts, prolonged periods of inactivity, and negative user feedback. When a user request to switch to human agents is detected, the system updates the aversion tag in the user profile status database, adjusting the aversion tag level upwards. When a user rejects a script, the system records the script identifier and updates the user preference tag. Tag updates take effect immediately and influence subsequent strategy routing and task triggering decisions; users in a high aversion state will no longer receive proactive outreach tasks. Feedback data is also used for strategy effectiveness analysis. By statistically analyzing key indicators such as adoption rate, human agent switching rate, and conversion rate of different strategy versions across various user groups, the system drives the optimization and iteration of strategy configuration, forming a complete data closed loop from status collection, strategy execution, effect feedback to status updates.

[0122] The technical implementation of the method of this invention will be further explained below in conjunction with specific application scenarios. In the scenario of a user adding a friend, after the system listens for the event of a user adding a friend to an intelligent agent through a message queue, it extracts the user identifier and the instructor identifier carried by the event as query parameters. The standardized data bus query service aggregates user profile information from the state database and downstream systems based on the above identifiers. The aggregated content includes the user's nickname, historical course purchase records to determine whether the user is an old user, the current grade level, the product line, whether the course has started, and whether the learning entry has been found. The state database creates or updates a session context record for the user, and the record content includes the user's basic attributes, the event trigger time, and the associated session identifier. After the strategy engine obtains the user profile, it performs strategy routing judgment. When the user profile shows that the course has started is true and the learning entry has been found is false, the pre-class preparation task strategy is hit. After the proactive worker receives the trigger instruction issued by the strategy engine, it first enters the unified governance gate for triple verification. After the verification is passed, the scheduled supervision and review event processor is called. This processor completes the course progress profile information and login behavior profile information through the standardized data bus query service and generates a structured task status object. After receiving the task status object and user profile, the intelligent agent generates a personalized SayHi message containing the user's nickname and past course mentions, and waits for a specified time according to a preset delay strategy before performing a status check again. If the status check result shows that the user still cannot find the learning entry point, it triggers the generation of pre-class preparation guidance messages, and sends the message to the user through the multi-channel distribution module, using the conversation channel and the operation platform as the reach channels.

[0123] In a dynamic learning supervision scenario based on course completion status, after a user completes an unlocked course, the system generates a completion event and publishes it via a message queue. Upon detecting the completion event, the data bus service extracts the associated user identifier, class identifier, and course identifier. It then queries the user's completion status, completion timestamp, course score, and knowledge point mastery, updating the relevant profile fields in the status database. The learning supervision and review strategy module, upon detecting the completion status update, reads the historical latest completion time field from the user's profile. This field stores statistical values ​​of the completion times for the last N courses. The strategy module dynamically calculates the daily learning supervision outreach window based on the historical latest completion time, and simultaneously calculates the trigger time for the review task. This trigger time is the completion time plus a random delay value within the range of 3 to 10 minutes. When the execution window arrives, the system queries the completion tag field in the user's profile and matches the corresponding learning supervision strategy strength based on the completion tag. When the completion tag is "positive completion," a conservative learning supervision strategy is applied, which stipulates that the user is only contacted once per day. The agent retrieves the user's latest daily report data from the state database. This data includes personalized environmental variables such as knowledge point mastery, quiz performance, and learning time. The agent then injects this daily report data into the context generation process, producing comment text that includes specific learning progress feedback and personalized encouragement. If a user completes multiple lessons on the same day, an idempotent mechanism ensures that only one comment task is triggered, avoiding excessive disruption to the user. In this scenario, if the tagging system detects negative behavior from the user during interaction, it assigns a negative tag and synchronizes it to the state database. Subsequently, all proactive outreach tasks targeting that user will be blocked during the pre-execution permission verification phase, enabling real-time control of policy execution based on the user's profile status.

[0124] In a knowledge base retrieval scenario based on course stages, users send passive inquiry messages during the service process, such as asking about the actual benefits of completing a course. Upon receiving this inquiry message, the system retrieves the user's key profile fields through a standardized data bus query service, where the current course stage field value is the current course period. When the agent calls the retrieval enhancement generation service, in addition to passing the user's original question text, it also forcibly passes in a course stage filtering condition, specifying that only knowledge base documents matching the current course period should be included. After performing vector or keyword retrieval, the retrieval service filters the search results using course stage metadata, returning only question-answer pairs or document fragments marked as "current course period" in the course stage metadata field. This course stage filtering mechanism effectively blocks marketing rhetoric specific to the conversion period, such as inappropriate information like "enroll now for discounts," ensuring that the response content is consistent with the user's current course stage. The retrieved document fragments and the user profile status snapshot are assembled into structured prompts, which are then input into the language model to generate the final response.

[0125] This invention also includes a multi-channel distribution module, which supports sending outreach messages to users through various channels such as session channels and operation platforms. The distribution module selects the optimal outreach channel for message distribution based on the channel preference settings in the user's preference profile and the channel priority settings in the policy configuration. The distribution module records the distribution and delivery status of each message. The distribution status includes values ​​such as submitted, sent, and failed to send; the delivery status includes values ​​such as delivered, read, and unread. The distribution module supports scheduled and delayed message sending functions. The delayed sending time window can be flexibly configured within a preset range, with configuration granularity accurate to the minute level.

[0126] This invention also includes a personalized environment variable injection mechanism, which injects key data items from the user's profile as context for the agent's generation process. The personalized environment variables comprise multiple data dimensions: knowledge point mastery dimension records the user's score on their mastery of each knowledge point; question-answering performance dimension records the user's historical accuracy rate and distribution of incorrect answers; learning time dimension records the user's standard learning time and effective learning time; and daily report data dimension aggregates the user's daily learning summary and progress percentage. After reading the personalized environment variables, the agent incorporates the user's personalized learning progress information when generating response content, producing response content that includes specific data feedback and encouraging language, enhancing the relevance and user-friendliness of the interactive experience.

[0127] Example 2

[0128] The method of the present invention will be further explained below with reference to another specific application scenario, which focuses on the integrated application of structured version management of strategy configuration, gray release capability and causal inference counterfactual evaluation mechanism.

[0129] In a structured, versioned management scenario for strategy configuration, operations personnel create new versions of the tutoring strategy in the strategy configuration module. Strategy configuration information includes a strategy identifier, version number V2.1, effective time window, strategy type as an active task switch, and strategy parameter configuration that adjusts the tutoring frequency from once per day to twice per day. The associated audience selection criteria are product type equal to mathematics and grade level equal to junior high school. The strategy configuration module first verifies the validity of the parameters, parses the selection condition expression, generates an execution plan, and sets the strategy version status to draft. After operations personnel submit the gray-scale release, the system does not simply allocate users randomly according to a ratio, but dynamically selects gray-scale users based on a causal inference counterfactual evaluation mechanism. Specifically, the system reads the heterogeneous state representation vectors of candidate users from the user profile state database. and covariates (Historical adoption rate, number of times manual intervention was required, etc.) to calculate the new strategy Compared to the current default policy Individual treatment effect The system prioritizes... Users with positive expected outcomes and narrow confidence intervals are selected for canary releases to obtain causal evidence of the strategy's effectiveness as quickly as possible with limited traffic. After the canary release, the system collects user feedback data in real time (adoption rate, rate of referral to human intervention, negative feedback rate) and compares the actual output metrics with the predicted ones. By comparison, if the actual effect reaches the preset threshold, the grayscale ratio is gradually increased to 50%, 100%, until it is fully effective; if the effect fails to meet the target or the actual effect is significantly lower than the predicted value, the strategy is rolled back, and the experimental data is used to correct the causal forest model to improve subsequent... The accuracy of the estimate.

[0130] In scenarios involving the reflux and iteration of user profile status, the system continuously collects feedback data generated by user interactions and utilizes a status pre-update mechanism to optimize the timing of strategy triggering in advance. Specifically, the system reads the sequence of heterogeneous historical status representation vectors of users from the user profile status database. The gated recurrent unit network outputs a predicted heterogeneous state representation vector. ,in This predicted state vector is used to predict whether a user is nearing the end of a lesson or may be averse to it, even before the actual user behavior data is available. This allows for dynamic adjustments to the triggering window of supervisory or marketing tasks. Meanwhile, feedback data (whether the user responded within the specified time, clicked a link, or transferred to a human operator, etc.) is used to update profile statuses such as aversion labels and serves as the actual data. Compared with the predicted value Perform residual calculations to drive error correction terms. Online updates and model parameters Fine-tuning is performed. When the adoption rate of a certain strategy version continues to decline and the rate of escalation of the aversion label increases, the strategy analysis module combines historical data... The system records and archives the evolution of their states, automatically generating strategy optimization suggestions (adjusting parameters, optimizing selection criteria, or rolling back to historical versions). After review by operations personnel, a new strategy version is created. Through this integration, the feedback loop of archive states not only forms a closed loop from users to strategies but also enhances the mutual reinforcement between state pre-updates and causal inference: more accurate state predictions provide cleaner front-door variables for causal evaluation, and the results of causal evaluation guide the sampling of training data for the prediction model, ultimately achieving system-level adaptive optimization.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for accessing and synchronizing employee intelligent agent user profiles based on a state database, characterized in that, include: S1: It shields the differences between underlying multi-business data sources from upstream intelligent agent applications, and supports batch or real-time querying of standardized user profile information by user identifier, class period identifier, teacher identifier and data type dimension, so as to build a standardized data bus query service. The standardized user profile data is stored in high-performance persistent storage and distributed cache in the form of key-value pairs or structured documents. Each record uses a combination of class identifier, user identifier and teacher identifier as a composite primary key to establish a user profile status database. S2: Trigger the archive status update process by listening to the message queue of key user behavior events in the business system. At the same time, configure scheduled tasks to actively poll or calculate the status that changes over time. And set up a unified governance gate at the consumption entry of the actively reaching task through the time-series prediction status pre-update mechanism. Perform task permission verification, message validity verification and idempotent processing on each task triggered by status change. S3: Structure business strategies and implement version management, support multi-dimensional audience selection conditions, and a counterfactual evaluation mechanism based on causal inference. When generating an agent response for a user, the user's real-time profile status is used as input to dynamically match the selection conditions in the strategy configuration library. At the same time, course stage filtering tags are added to the retrieval enhancement generation process. S4: Inject global tracking identifiers uniformly into the request entry, routing decision, agent invocation, response generation, message distribution and user feedback stages, persist the structured data of key evidence chains to the analytical database, and update the tags in the user profile state database based on feedback data or use them for strategy effect analysis.

2. The method for accessing and synchronizing employee intelligent agent user profiles based on a state database according to claim 1, characterized in that, The standardized data bus query service: Static and semi-static profile information is obtained based on user identification dimensions. The static and semi-static profile information includes user name, user age, user gender, product line, class period identifier, instructor identifier, and academic level. Dynamic behavior profile information is obtained by class period identifier. The dynamic behavior profile information includes user login behavior, learning entry click records, course completion status, course unlock score on the day, learning duration, number of hours of classes to be made up, number of consecutive days of course completion, and the latest time of course completion in history. Obtain derivation and tagging profile information, which includes user profiles and preferences extracted from user messages and context, course completion tags automatically labeled by the system, and user aversion tags; The user profile status database adopts a multi-level caching strategy, with hot data residing in the memory cache layer and cold data sinking to the persistent storage layer, and maintaining data consistency between layers through an asynchronous synchronization mechanism.

3. The method for accessing and synchronizing employee intelligent agent user profiles based on a state database according to claim 2, characterized in that, Each record in the user profile status database in S1 is composed of a composite primary key consisting of a class period identifier, a user identifier, and a teacher identifier. It is stored in high-performance persistent storage and distributed cache in the form of key-value pairs or structured documents. The status database supports high-speed read and write operations and can publish change event notifications to downstream consumer modules when the status changes.

4. The method for accessing and synchronizing employee intelligent agent user profiles based on a state database according to claim 1, characterized in that, S2 includes: The key user behavior events include events such as adding friends, attending courses, completing courses, logging in, clicking on the learning entry point, and proactively sending inquiry messages. When any event occurs, the corresponding archive status update process is immediately triggered, and the event consumer is routed to the corresponding status update processor according to the event type. The processor obtains the latest user profile snapshot from the standardized data bus query service, merges it with the status change information carried by the event, generates a new user profile status, and writes it into the user profile status database. The scheduled task triggering mechanism configures scheduled tasks to actively poll or calculate the status that changes over time, including the detection and switching of course stage changes and the calculation of personalized tutoring time windows based on users' historical behavior patterns.

5. The method for accessing and synchronizing employee intelligent agent user profiles based on a state database according to claim 2, characterized in that, The unified governance gateway performs a triple verification mechanism on the tasks triggered by state changes in S3: The first layer of verification is task permission verification, which combines the user profile status stored in the user profile status database in S1 to determine whether the specific task can be executed. When the user profile status shows that the user has been converted or the user is in a period of aversion, the task permission verification result is that the task cannot be executed and the current task is blocked. The second layer of verification is message validity period verification, which determines whether the task is executed within the set valid time window. Trigger instructions that exceed the validity period are discarded. The third layer of verification is idempotent processing, which uses the unique identifier of the business fact to perform atomic writing and status marking in a distributed environment, ensuring that the same business fact is reached at most once within the policy window.

6. The method for accessing and synchronizing employee intelligent agent user profiles based on a state database according to claim 1, characterized in that, S3 includes: The structured and versioned strategy configuration supports a unified representation of whitelist management strategies, proactive task switching strategies, passive process pipeline strategies, conversion period configuration strategies, and communication goal strategies. It supports multi-dimensional audience selection criteria configuration based on business roles, educational levels, product types, and departments, and enables versioned management, canary release, and rollback capabilities for strategies. The state-driven strategy routing takes the real-time profile status stored in the user profile status library in S1 as input, dynamically matches the various attribute values ​​in the user profile status with the audience selection conditions of each strategy version in the strategy configuration library, hits the latest strategy version currently in effect, dynamically selects different intensity of supervision strategies based on the user's course completion tag, and dynamically calculates the conversion period window based on product type and number of course days to determine whether to trigger extraction or conversion marketing tasks.

7. The method for accessing and synchronizing employee intelligent agent user profiles based on a state database according to claim 1, characterized in that, The state pre-update mechanism for the time-series prediction includes: S21: Read the actual heterogeneous state representation vectors of the user at the most recent k moments from the user profile state database to form the historical evolution sequence of the user state; S22: Input the historical evolution sequence into a pre-trained gated recurrent unit network, extract the long-term and short-term dependencies of the user state over time through the update gate and reset gate structure inside the network, and output the state prediction value for future time. S23: Calculate the residual between the predicted state value and the subsequently arrived actual state value, and dynamically update the prediction error correction term based on the residual using an exponentially weighted moving average method; S24: Add the predicted state value to the prediction error correction term to generate the corrected final predicted state vector; S25: When the unified governance gateway receives a trigger request for an active outreach task, it determines whether the user's actual profile status at the current moment has been synchronized from the data source to the status database. If it has not been synchronized, it reads the corrected final predicted status vector to replace the actual status vector. S26: Perform task permission verification and message validity period determination based on the final predicted state vector, wherein the task permission verification is used to determine whether the user's current state meets the task triggering conditions, and the message validity period verification is used to determine whether the task is executed within the set valid time window. S27: When the subsequent real state value arrives at the state database, calculate the residual between the real state value and the predicted state value at the corresponding time, and feed the residual back to the prediction error correction term update process in S23.

8. The method for accessing and synchronizing employee intelligent agent user profiles based on a state database according to claim 7, characterized in that, S24 includes: Read the actual heterogeneous state representation vectors of the user at the most recent k moments from the user profile state database to form the historical evolution sequence of the user state; The historical evolution sequence is input into a pre-trained gated recurrent unit network. The long-term and short-term dependencies of the user state over time are extracted through the update gate and reset gate structure inside the network, and the predicted state value for future time is output. Calculate the residual between the predicted state value and the subsequently arrived actual state value, and dynamically update the prediction error correction term based on the residual using an exponentially weighted moving average method. The predicted state value is added to the prediction error correction term to generate the corrected final predicted state vector; When the unified governance gateway receives a trigger request for an active outreach task, it determines whether the user's actual profile status at the current moment has been synchronized from the data source to the status database. If it has not been synchronized, it reads the corrected final predicted status vector to replace the actual status vector and performs task permission verification and message validity period determination. When the subsequent actual state value arrives at the state database, the residual between the actual state value and the predicted state value at the corresponding time is calculated, and the residual is fed back to the update process of the prediction error correction term to form a closed loop iteration.

9. The method for accessing and synchronizing employee intelligent agent user profiles based on a state database according to claim 1, characterized in that, The counterfactual evaluation mechanism for the causal inference strategy includes: S31: Construct a causal graph model between user profile status and policy execution effect, using the heterogeneous status representation vector in the user profile status database as the front door variable, and collecting the user's historical interaction count, historical adoption rate and historical number of times transferred to human agent as covariates to adjust the selection bias in policy evaluation. S32: For any two candidate strategies and A causal forest model is employed, using propensity score weighting and a dual machine learning approach to perform unbiased estimation of the observed data, thereby calculating the policy implementation... User output metrics relative to execution strategy under certain conditions The difference in expected outcomes under given conditions is considered as an individual treatment effect; S33: The policy routing engine maintains a set of candidate policies and the current default policy. For each user request, it calculates the individual processing effect of each policy in the candidate policy set relative to the current default policy. S34: Determine whether the individual treatment effect is significantly greater than zero and its confidence interval does not contain zero. If so, determine that the strategy is better than the current default strategy, automatically trigger the strategy version switch, and release the higher version strategy to the user group; if the absolute value of the individual treatment effect is less than a preset threshold, determine that there is no significant difference in the effects of the two strategies, and maintain the current strategy version. S35: Record the policy version switching result to the policy configuration library and update the current default policy identifier. At the same time, use the input status of this policy route, the policy hit version, and the individual processing effect as key evidence chain structured data for S4's full-link tracking.

10. The method for accessing and synchronizing employee intelligent agent user profiles based on a state database according to claim 9, characterized in that, S32 includes: Construct a causal graph model between user profile status and policy execution effect, using the heterogeneous state representation vector in the user profile status database as the front door variable, and collecting the user's historical interaction count, historical adoption rate and historical number of times transferred to human agent as covariates. For any two candidate strategies, a causal forest model is used to perform unbiased estimation of the observed data through propensity score weighting and dual machine learning methods. The expected difference of user output indicators under the condition of implementing one strategy relative to the condition of implementing another strategy is calculated as the individual treatment effect. The user output indicators include adoption rate, conversion rate to human intervention, and conversion rate. The policy routing engine maintains a set of candidate policies and the current default policy. For each user request, it calculates the individual processing effect of each policy in the candidate policy set relative to the current default policy. If the individual treatment effect is significantly greater than zero and its confidence interval does not contain zero, then the strategy is determined to be superior to the current default strategy, and the strategy version switch is automatically triggered, and the higher version strategy is released to the user group. If the absolute value of the individual treatment effect is less than a preset threshold, it is determined that there is no significant difference in the effects of the two strategies, and the current strategy version is maintained. The policy version switching result is recorded in the policy configuration library, and the current default policy identifier is updated. At the same time, the input status of this policy route, the policy hit version, and the individual processing effect are used as key evidence chain structured data for full-link tracking.

Images