An agent-based notification management method, apparatus, device, and medium

Abstract

The application discloses an agent-based notification management method and device, computer equipment and a storage medium, the notification management method comprises the following steps: obtaining the working state data of a bound object based on a preset agent; identifying the state of the bound object through the agent according to the working state data to obtain a state identification result; generating a notification message matched with a target object through the agent according to the state identification result, the notification message being used for prompting the target object to perform a target task; and sending the notification message to the target object through the agent. The method realizes real-time sensing and intelligent analysis of the working state of the bound object through the agent, ensures that the generation and pushing of the notification message have timeliness and pertinence, and thus improves the task response efficiency.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and enterprise information management technology, and in particular to a notification management method, device, equipment and medium based on intelligent agents. Background Technology

[0002] Existing enterprise notification and task assignment systems, such as email, instant messaging tools, and workflow engine notifications, are essentially passive or reactive systems. Their notification patterns are mainly divided into two categories: one is "system status notifications" triggered by specific events, such as "Your leave application has been approved"; the other is "interpersonal notifications," such as "User A @ed User B in the chat."

[0003] These systems generally lack the ability to continuously perceive and understand the real-time work context of individual employees (such as current workload and project participation status) and the overall operational health of the enterprise. Therefore, they cannot proactively and forward-lookingly provide information or task-driven guidance that is strongly relevant to individual performance and corporate goals when employees most need guidance or reminders. This results in a large number of management decisions relying on manual follow-up, leading to low efficiency, and employees are prone to missing key work milestones due to information lag or gaps. Summary of the Invention

[0004] This invention provides a notification management method, apparatus, device, and medium based on intelligent agents to solve the problems of delayed information or task reminders and low management efficiency in existing enterprise notification systems.

[0005] A notification management method based on an intelligent agent includes the following steps: acquiring the working status data of a bound object based on a preset intelligent agent; performing state recognition on the bound object through the intelligent agent according to the working status data to obtain a state recognition result; generating a notification message matching a target object through the intelligent agent according to the state recognition result, the notification message being used to prompt the target object to perform a target task; and sending the notification message to the target object through the intelligent agent.

[0006] A notification management device based on an intelligent agent includes: a data acquisition module for acquiring work status data of a bound object based on a preset intelligent agent; a status recognition module for performing status recognition on the bound object through the intelligent agent based on the work status data, and obtaining a status recognition result; a notification generation module for generating a notification message matching a target object through the intelligent agent based on the status recognition result, the notification message being used to prompt the target object to perform a target task; and a notification sending module for sending the notification message to the target object through the intelligent agent.

[0007] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the above-described agent-based notification management method.

[0008] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described agent-based notification management method.

[0009] In the aforementioned technical solution of the intelligent agent-based notification management method, device, computer equipment, and storage medium, the notification management method includes the following steps: acquiring the working status data of the bound object based on a preset intelligent agent; performing status recognition on the bound object through the intelligent agent based on the working status data to obtain the status recognition result; generating a notification message matching the target object through the intelligent agent based on the status recognition result, the notification message being used to prompt the target object to perform the target task; and sending the notification message to the target object through the intelligent agent. This method achieves real-time perception and intelligent analysis of the working status of the bound object through the intelligent agent, ensuring that the generation and push of notification messages are timely and targeted, thereby improving task response efficiency. Attached Figure Description

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

[0011] Figure 1 This is a flowchart of a notification management method based on an intelligent agent according to an embodiment of the present invention; Figure 2 This is a specific flowchart of step S1 in the notification management method based on intelligent agents in one embodiment of the present invention; Figure 3 This is a specific flowchart of step S2 in the notification management method based on intelligent agents in one embodiment of the present invention; Figure 4 This is a schematic diagram of a notification management device based on an intelligent agent according to an embodiment of the present invention; Figure 5 This is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] In one embodiment, such as Figure 1 As shown, an agent-based notification management method is provided, including the following steps: Step S1: Based on the preset intelligent agent, obtain the working status data of the bound object.

[0014] It should be noted that an intelligent agent is a software entity with autonomous perception and decision-making capabilities. It can collect multi-dimensional state information of bound objects in real time through preset interfaces and dynamically adjust the data collection frequency and range according to the context to ensure the timeliness and completeness of the acquired data. Furthermore, it can proactively interact with target objects based on different trigger scenarios, providing a reliable basis for subsequent task allocation and resource scheduling. Bound objects include employees and / or enterprise information systems within the enterprise. Corresponding work status data includes dynamic information such as employee attendance, task progress, online status, and system operation behavior, and / or the service operation status, load level, and interaction logs of the enterprise information system.

[0015] In this embodiment, the intelligent agent can be called a TTC robot. The TTC robot achieves seamless integration with various business systems within the enterprise through a distributed deployment architecture, acquires employee work data and system operation data of the enterprise information system, and achieves high-frequency collection and low-latency transmission through a dynamic perception mechanism.

[0016] Specifically, such as Figure 2 As shown, step S1 includes the following sub-steps: Step S11: Obtain employee work data through the business system interface.

[0017] It should be noted that the business system interface serves as the data interaction channel between the intelligent agent and the enterprise business system. It supports multiple protocol adaptations and format conversions, and can extract employee work data in real time, including attendance records, task completion progress, and system operation logs. The enterprise business system encompasses human resource management systems, project management platforms, and daily office collaboration tools. The intelligent agent establishes a secure connection with these systems through the business system interface, using API polling or event-driven mechanisms to periodically retrieve or receive employee work data in real time, ensuring the timeliness and accuracy of information updates. Employee work data includes real-time workload and project participation data.

[0018] In this embodiment, the intelligent agent establishes a secure connection with systems such as the project management system, calendar, and task list through a standardized interface protocol. With authorization, it automatically synchronizes data such as employees' real-time workload and project participation. Meanwhile, to ensure data privacy and system security, all transmission processes employ end-to-end encryption and access control is implemented based on the principle of least privilege.

[0019] Step S12: Collect system operation data of the enterprise information system through the information system interface.

[0020] It's important to note that the information system interface is a dedicated data channel between the intelligent agent and the enterprise information system. It's used to acquire key metrics such as the system service's operational status, resource utilization, and interaction logs in real time. This interface supports integration with mainstream monitoring tools, enabling data retrieval and push via SNMP, RESTful APIs, or message queues, ensuring comprehensive awareness of the enterprise's IT infrastructure. The enterprise information system is the core carrier supporting business operations, encompassing components such as databases, application services, network architecture, and cloud platforms. System operational data includes the enterprise information system's resource utilization, response latency, and fault logs.

[0021] In this embodiment, the intelligent agent interfaces with mainstream monitoring platforms such as Zabbix and Prometheus through the information system interface to collect system operation data such as resource utilization, response latency and fault logs of the enterprise information system in real time. The collection frequency can be dynamically adjusted according to the system load to ensure that the data accuracy is not less than once per minute during high load periods.

[0022] Step S2: Based on the working status data, the intelligent agent performs status recognition on the bound object and obtains the status recognition result.

[0023] It should be noted that the status identification results include the employee's work status and / or the system operation status of the enterprise information system.

[0024] In this embodiment, the intelligent agent analyzes the collected work status data in real time through a preset multi-dimensional analysis model. Combining historical behavior patterns and contextual scenarios, it determines whether the enterprise information system is in an abnormal operating state and whether the employees' work status is overloaded or idle. Furthermore, the intelligent analysis model performs multi-dimensional correlation analysis on the collected work status data to identify the potential impact relationship between system malfunctions and employees' work status.

[0025] like Figure 3 As shown, specifically, step S2 includes: Step S21: Based on the system operation data, assess the operational health of the enterprise information system and obtain the system operation status.

[0026] In this embodiment, system operation data includes resource utilization, response latency, and failure frequency. A weighted scoring mechanism is used to quantitatively analyze resource utilization, response latency, and failure frequency to assess the operational health of the enterprise information system, ultimately outputting a health score of 0-100. Based on the health score, the system operation status is divided into two states: normal operation and abnormal operation. When the health score is below the health threshold, it is determined to be in an abnormal operation state, and the process proceeds directly to step S3. When the health score is above the health threshold, it is determined to be in a normal operation state, and the system operation status continues to be monitored and the health score is updated periodically.

[0027] It should be noted that the health threshold can be dynamically adjusted based on the company's historical operation and maintenance data. The initial value is set at 80 points, and can be adaptively adjusted in combination with seasonal business fluctuations and the impact factors of major events.

[0028] Furthermore, under normal system operation, the intelligent agent will identify the employee's work status, i.e., proceed to step S22.

[0029] Step S22: Based on employee work data, assess the employee's workload status to obtain the employee's work status.

[0030] In this embodiment, employee work data includes real-time workload and project participation. The employee's workload status is assessed based on their real-time workload and project participation, and a workload score of 0-100 is generated by combining task urgency and role responsibilities. Based on the workload score, the employee's work status is categorized into three states: idle, overloaded, and normal. When the workload score is below a first threshold, it is determined to be an idle state, and a risk of idle human resources should be indicated. When the workload score is above a second threshold, it is determined to be an overloaded state, and a resource allocation warning should be triggered. When the workload score is between the first and second thresholds, it is determined to be a normal work state, and the current resource allocation should be maintained. The business process node where the employee is located can be identified, and the task progress status of the corresponding business process node can be obtained, thus proceeding to step S23.

[0031] It should be noted that the first load threshold is generally set at 30 points, and the second load threshold is generally set at 75 points. The threshold range can be configured differently according to the characteristics of the department and the job functions.

[0032] Furthermore, for periods of low workload, the system can identify any currently active projects or pending tasks for the corresponding employee, and then generate a notification message with relevant suggestions in step S3. For periods of overload, the agent will identify the backlog of associated high-priority tasks and analyze the workload distribution of other employees in the same department to determine whether the pressure can be alleviated through internal resource collaboration. When cross-project resource conflicts or continuous overload of an individual employee are detected, an early warning mechanism is automatically triggered, and a notification message containing dynamic job reassignment suggestions, task reallocation plans, and potential risk warnings is generated in step S3 and then pushed to the company's management staff.

[0033] Step S23: Based on employee work data and system operation data, identify the business process node where the employee is located and obtain the task progress status of the corresponding business process node.

[0034] It should be noted that a business process node is a phased work unit defined in a business process to achieve a specific goal. The progress status of a task is determined by comprehensively considering the completion rate, delay status, and related dependencies of the tasks within the node.

[0035] In this embodiment, an event matching triggering mechanism and a status threshold triggering mechanism based on work status data and system operation data are used to identify the business process node where the employee is located and obtain the task progress status of the corresponding business process node.

[0036] Specifically, the event matching trigger mechanism obtains the corresponding business process node by using the process node event logs in the system's runtime data when a preset business event (such as task assignment or role change) is detected. For example, if employee A has been confirmed to have joined the project collaboration environment through work status data, the current business process node is identified as the project startup node when a member addition event is detected from the system runtime data.

[0037] Furthermore, a status threshold triggering mechanism automatically triggers the status identification task for the corresponding business process node, initiating real-time data collection and analysis to obtain the task progress status of that business process node. For the status threshold triggering mechanism, by comparing the task completion progress in the work status data with the process node time limits in the system operation data, when the task completion progress is lower than a preset threshold and the deadline of the process node is approaching, a progress warning identification process is automatically triggered. This involves comparing real-time collected task completion data with the planned baseline to calculate the overall node completion rate, and combining this with the difference between the actual and expected times of key milestones to assess the degree of progress deviation. Simultaneously, the matching of resource consumption rate and budget allocation is monitored to identify whether there are resource overruns or idle resources, thereby comprehensively determining the task progress status of that business process node. For example, when the task completion progress of a node is less than 40% and there are less than 2 working days left until the deadline, it is determined to be in a delayed state, the node will be marked as a high-risk node, and a resource intervention assessment will be initiated.

[0038] Furthermore, if the business process node is a performance appraisal node, it will automatically associate data such as the corresponding employee's task completion quality, collaboration contribution, and key indicator achievement rate within the performance appraisal period, and obtain the task progress status of the corresponding employee at the end of the performance appraisal period and the completion of the performance evaluation.

[0039] In some other embodiments, steps S21 and S22 can be executed in parallel, achieving collaborative analysis of system operation status and employee work status through multi-source data fusion technology. Steps S21, S22, and S23 can also be executed in parallel or individually according to actual business needs, achieving flexible process orchestration and adapting to dynamic needs in different business scenarios.

[0040] This application constructs a unified system health and employee workload assessment framework to achieve cross-dimensional data correlation analysis, effectively identifying the coupling risks between system anomalies and manpower imbalances. Combined with real-time monitoring and trend prediction, it supports subsequent automatic triggering of resource scheduling, task reallocation, or early warning notifications, improving overall operational efficiency and organizational responsiveness.

[0041] Step S3: Based on the state recognition result, generate a notification message matching the target object through the intelligent agent.

[0042] It should be noted that the notification message is generated based on the task progress status, risk level, and business priority in the status recognition results, and is personalized by combining the target object's role attributes and information preferences. It is used to prompt the target object to perform the target task or to take timely risk response measures.

[0043] In this embodiment, when the system operating status in the status identification result is an abnormal operating state, a notification message containing a sequence of system operating abnormalities and error information is generated, allowing direct entry into step S4. At this point, the target audience is the technical personnel responsible for system operation and maintenance, and the target task is to troubleshoot and repair system anomalies. For example, when a surge in "NullPointerException" errors is detected, a notification message indicating a system operating abnormality is generated, along with detailed error stack information, facilitating rapid location and repair of the root cause by technical personnel.

[0044] Furthermore, when the system operating status in the status recognition result is in a normal operating state, a notification message guiding the employee to perform the target task is generated based on the employee's work status.

[0045] Specifically, when an employee's work status is in a free period, a notification message is generated reminding the employee to take on a new task. In this case, the target audience is ordinary employees in a free period, and the target task is to take on a new task. For example, the notification message might read, "We have detected that you currently have no project tasks. We suggest you communicate with your supervisor as soon as possible to create a sub-project or join an existing project team according to the monthly work schedule." Furthermore, the notification messages generated by the intelligent agent can further explain the underlying rationale and management logic behind the corresponding suggestions. For example, an additional explanation such as, "To avoid affecting the performance evaluation results for this month!" can transform a simple status notification into a driving management action.

[0046] Secondly, when an employee's work status is overloaded, a notification message suggesting task reassignment or postponement is generated. In this case, the target audience is special employees with organizational management rank, and the target task is approving resource allocation requests. The notification message includes the urgency of the pending matter, the scope of associated risks, and recommended decision-making solutions to assist managers in making optimal judgments in complex situations.

[0047] Furthermore, when the employee's work status in the status recognition result is "normal," a notification message containing the current task execution progress and pending tasks is generated based on the task progress status. At this point, the target audience is the designated employee who needs to execute the corresponding task at the relevant business process node, and the target task is to complete the pending tasks within that node. The notification message can intelligently recommend best practice paths based on preset business rules and historical processing patterns, and highlight key considerations and compliance requirements for the current stage, ensuring operational standardization and timeliness.

[0048] At the project initiation stage, the generated notification message is a highly structured "task reminder." This reminder not only includes basic facts but also integrates "instructions" (what to do next), "task timeline" (time requirements), and "friendly reminders" (such as "Please note that this project evaluation will affect your monthly performance"), ensuring that the information is complete and actionable.

[0049] Furthermore, during project execution, the intelligent agent automatically triggers an early warning mechanism based on real-time monitoring of key node delay risks, generating a notification message with a risk level indicator, and pushing it to relevant responsible persons and their superiors via step S4. The notification message not only clearly identifies deviations and potential cascading effects but also recommends response strategies based on historical data, improving response efficiency and decision-making accuracy, and ensuring the overall project progress is controllable and traceable. When the status identification result indicates resource shortages or bottleneck warnings, the intelligent agent will automatically activate a cross-departmental collaboration mechanism, pushing a notification message containing priority reallocation suggestions and alternative resource matching solutions to relevant personnel via step S4, ensuring efficient resource flow and seamless task coordination.

[0050] Furthermore, at performance appraisal milestones, upon the end of the performance appraisal cycle and the completion of the performance evaluation, a notification message containing the corresponding employee's performance results and subsequent automated business processes can be generated. This message can then be pushed to the employee's personal workbench and the HR management system via step S4, simultaneously triggering subsequent processes such as salary adjustments, promotion reviews, or training plans. For example, if an employee's performance rating is B in this appraisal cycle, the generated notification message would be: "Your final performance rating for month X is B. Your appraisal results have been automatically pushed to the Beisen system for salary calculation," thus achieving end-to-end closed-loop management from performance appraisal to salary calculation.

[0051] It should be noted that the above content lists the application logic under typical scenarios. In actual operation, the intelligent agent can also adjust the generation rules of notification messages according to the organization's dynamic strategy. Moreover, the notification messages generated by the intelligent agent are highly structured and contextualized, not only informing the facts but also integrating multi-dimensional information such as operation guidance, time period, and risk consequences, greatly improving the operability and value density of the messages.

[0052] In other embodiments, the judgment steps for the different scenarios described above can be deployed independently or combined, and can be flexibly configured according to the organization's actual business processes. The above method can be extended to more areas, such as compliance checks. When the agent "detects that a contract an employee is about to sign lacks a legal approval process," it can proactively issue a risk warning: "The contract you submitted lacks legal approval and may pose a compliance risk. It is recommended that you submit it for review immediately." Step S4: Send the notification message to the target object through the intelligent agent.

[0053] In this embodiment, after generating a notification message for the corresponding scenario, the intelligent agent will automatically push it to the relevant responsible person's workbench, instant messaging tool, or email system according to the preset permission policy and distribution rules, ensuring that the information reaches the relevant personnel in a timely and effective manner.

[0054] Furthermore, in addition to pushing notification messages, when the intelligent agent detects a critical state (such as an employee having "no projects"), it can proactively pop up an interactive dialog box or start a guided workflow to directly guide the user to complete subsequent operations (such as "initiating communication with superiors with one click" or "directly creating project proposals").

[0055] Furthermore, the presentation format of notification messages is not limited to text; it can integrate multiple modalities such as voice reminders and digital human interaction to adapt to different work scenarios and user preferences. For example, when the intelligent agent recognizes that an employee has been in a "no project" state for two consecutive weeks, in addition to pushing in-system messages, it can also trigger a voice reminder: "You are not currently involved in any projects. Do you need assistance in matching new tasks?" It also supports one-click feedback of intentions or scheduling resource coordination meetings, improving response efficiency and user experience.

[0056] Furthermore, the agent can learn from users' historical behavior and feedback on notifications to personalize the timing, frequency, and wording of notifications, thereby improving user experience and notification effectiveness. For example, for employees who prefer to receive work reminders in the morning, the agent can automatically optimize the notification push time to before 9:00 AM and present the content in a concise, itemized format; while for employees who are accustomed to evening reviews, a more detailed version with a contextual summary can be pushed after 6:00 PM. Simultaneously, if the agent identifies that an employee frequently ignores "training to-do" messages, it will gradually escalate the reminder method, progressing from in-app messages to pop-up notifications and even digital human voice calls, ensuring that no critical information is missed. This mechanism not only improves organizational collaboration efficiency but also enhances the interaction and stickiness between individuals and the agent. Through a continuously iterative feedback loop, the agent gradually builds an understanding of individual work patterns, evolving from "passive response" to "proactive prediction." For example, before identifying an employee's impending project lull, the intelligent agent can send a personalized suggestion 48 hours in advance: "Based on your skill profile, the 'Intelligent Office Platform Upgrade' project is about to launch; we recommend you register to participate," along with a one-click registration link. This intelligent collaborative mechanism, which deeply integrates business processes and personal development paths, is redefining the boundaries of human-machine collaboration, making organizational resource allocation more efficient and allowing individual value to be fully realized.

[0057] In summary, the aforementioned agent-based notification management method possesses the ability to continuously and proactively monitor the individual work status of employees and the operational status of enterprise information systems. Through multi-dimensional data perception and intelligent reasoning, it achieves real-time insights into employee and system status, and generates intelligent notification messages strongly correlated with enterprise management logic. These messages then send intelligent suggestions, reminders, and alerts to relevant personnel at appropriate times, prompting them to take timely action to mitigate potential risks or seize development opportunities, thereby enhancing the organization's overall agility and resilience. The proposed agent provides proactive guidance for employee work and immediate response to risks in enterprise information systems, covering multiple distinct areas such as human resources (performance, projects), business processes (to-do lists), and IT operations (system anomalies). It offers users a unified and coherent proactive interactive experience, significantly improving organizational collaboration efficiency, risk control capabilities, and management sophistication.

[0058] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0059] In one embodiment, an agent-based notification management device is provided, which corresponds one-to-one with the agent-based notification management method described in the above embodiments. For example... Figure 4 As shown, the agent-based notification management device includes a data acquisition module 101, a status recognition module 102, a notification generation module 103, and a notification sending module 104. Detailed descriptions of each functional module are as follows: The data acquisition module 101 is used to acquire the working status data of the bound object based on the preset intelligent agent.

[0060] The status recognition module 102 is used to identify the status of the bound object through the intelligent agent based on the working status data, and obtain the status recognition result.

[0061] The notification generation module 103 is used to generate a notification message matching the target object through the intelligent agent based on the state recognition result. The notification message is used to prompt the target object to perform the target task.

[0062] The notification sending module 104 is used to send notification messages to the target object through the intelligent agent.

[0063] For specific limitations regarding the agent-based notification management device, please refer to the limitations of the agent-based notification management method above, which will not be repeated here. Each module in the aforementioned agent-based notification management device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0064] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an agent-based notification management method.

[0065] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the agent-based notification management method described in the above embodiments, for example... Figure 1 S1-S4, as shown, will not be described again here to avoid repetition. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in this embodiment of the agent-based notification management device, for example... Figure 4 The functions of the data acquisition module 101, status recognition module 102, notification generation module 103, and notification sending module 104 shown are not described again here to avoid duplication.

[0066] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When executed by a processor, the computer program implements the agent-based notification management method described above, for example... Figure 1 S1-S4, as shown, will not be described again here to avoid repetition. Alternatively, when the computer program is executed by the processor, it implements the functions of each module / unit in this embodiment of the agent-based notification management device, for example... Figure 4The functions of the data acquisition module 101, status recognition module 102, notification generation module 103, and notification sending module 104 shown are not described again here to avoid repetition. The computer-readable storage medium can be non-volatile or volatile.

[0067] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0068] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0069] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. 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 of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A notification management method based on intelligent agents, characterized in that, Including the following steps: Based on a pre-defined intelligent agent, obtain the working status data of the bound object; Based on the working status data, the intelligent agent performs status recognition on the bound object to obtain the status recognition result; Based on the state recognition result, the intelligent agent generates a notification message matching the target object, which is used to prompt the target object to perform the target task. The notification message is sent to the target object through the intelligent agent.

2. The notification management method as described in claim 1, characterized in that, The bound object includes employees, the work status data includes the employee's work data, and the status identification result includes the employee's work status. The step of identifying the state of the bound object through the intelligent agent based on the working state data and obtaining the state identification result includes: Based on the employee's work data, the employee's workload status is assessed to obtain the employee's work status.

3. The notification management method as described in claim 1, characterized in that, The bound object includes an enterprise information system, the working status data includes the system operation data of the enterprise information system, and the status identification result includes the system operation status of the enterprise information system; The step of identifying the state of the bound object through the intelligent agent based on the working state data and obtaining the state identification result includes: Based on the system operation data, the operational health of the enterprise information system is assessed to obtain the system's operational status.

4. The notification management method as described in claim 1, characterized in that, The binding objects include employees and enterprise information systems; the work status data includes the employee's work data and the enterprise information system's system operation data; and the status identification results include the task progress status of the corresponding business process node. The step of identifying the state of the bound object through the intelligent agent based on the working state data and obtaining the state identification result includes: Based on the employee's work data and the system's operation data, the business process node where the employee is located is identified, and the task progress status of the business process node is obtained.

5. The notification management method as described in claim 2, characterized in that, The step of generating a notification message matching the target object through the intelligent agent based on the state recognition result includes: Based on the employee's work status, generate a notification message that guides the employee to perform the target task; Specifically, when the employee's work status is in a work-free state, a notification message is generated to remind the employee to take on a new task; when the employee's work status is in a work-overload state, a notification message is generated suggesting task diversion or postponement.

6. The notification management method as described in claim 3, characterized in that, The step of generating a notification message matching the target object through the intelligent agent based on the state recognition result includes: When the system is in an abnormal operating state, a notification message containing a sequence of system operating abnormalities and error information is generated.

7. The notification management method as described in claim 4, characterized in that, The step of generating a notification message matching the target object through the intelligent agent based on the state recognition result includes: Based on the task progress status, generate the notification message containing the current node's task execution progress and pending items.

8. A notification management device based on intelligent agents, characterized in that, include: The data acquisition module is used to acquire the working status data of the bound object based on the preset intelligent agent; The status recognition module is used to perform status recognition on the bound object through the intelligent agent based on the working status data, and obtain the status recognition result; The notification generation module is used to generate a notification message matching the target object through the intelligent agent based on the state recognition result. The notification message is used to prompt the target object to perform the target task. The notification sending module is used to send the notification message to the target object through the intelligent agent.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the agent-based notification management method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the agent-based notification management method as described in any one of claims 1 to 7.

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