A digital employee cooperative scheduling method and system based on post responsibility driving

Abstract

The application discloses a kind of digital employee collaborative scheduling method and system based on post responsibility driving, belong to artificial intelligence and intelligent scheduling field, including formalizing the post structure of organization into post responsibility portrait, each post portrait contains responsibility boundary, knowledge range, rule set, executable action, accessible data range, collaborative upstream and downstream post and so on Multidimensional attribute;With post portrait as only anchor point, build digital employee instance;With the collaborative relationship between post portrait as the basis, generate scheduling chart;With scheduling chart as runtime skeleton, complete event distribution, task flow, decision fusion and audit trace. The digital employee collaborative scheduling method and system based on post responsibility driving greatly improve the collaborative efficiency of multiple digital employees and the accuracy of decision-making, realize the structured sedimentation of business experience, full-process compliance audit, and can be widely applied to multi-post collaborative scenarios such as industrial production, enterprise operation and urban infrastructure management.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and intelligent scheduling technology, and in particular to a digital employee collaborative scheduling method and system driven by job responsibilities. Background Technology

[0002] In industrial production and enterprise operations, business processes rely on the collaborative work of multiple positions, each with clearly defined work scopes, execution procedures, and approval authority. In the traditional model, information transfer and task flow between positions primarily depend on verbal coordination and paper records, which generally suffers from drawbacks such as delayed response times, easy information loss, difficulty in accumulating business experience, and low efficiency in cross-position collaboration.

[0003] With the development of technologies such as large language models and retrieval-enhanced generation, AI agents are gradually being applied to various business scenarios. Currently, the mainstream technical solutions are mainly divided into three categories, but all of them have obvious shortcomings: The first type is the single-agent all-around solution, represented by a general dialogue assistant, which integrates all business knowledge into a single model to complete tasks for multiple roles. This solution lacks clear boundaries of job responsibilities, making it prone to unauthorized operations; business knowledge is not layered, resulting in low knowledge retrieval recall and the illusion of cross-role content; and multi-task progress relies solely on repeated manual interaction, failing to achieve automatic collaboration.

[0004] The second category is workflow automation solutions based on process engines, primarily using traditional BPM platforms. These solutions construct processes using predefined program nodes, lacking job-specific semantics; the triggering conditions for workflows rely on hard-coded rule scripts, making them unable to respond to natural language events; and nodes only transmit structured field data, failing to carry job context and decision-making basis.

[0005] The third category is multi-agent collaboration framework solutions, typically including open-source frameworks such as AutoGen and CrewAI. These frameworks define agent roles only through prompts and do not build a strongly constrained responsibility data model; collaborative scheduling uses a general interaction mechanism that cannot be matched with industry organizational structures and job systems; and they lack permission isolation and decision traceability mechanisms, making it difficult to meet the security and compliance requirements of core businesses. Summary of the Invention

[0006] The purpose of this invention is to solve the problems of existing digital employees, such as lack of job responsibility boundaries, lack of permission isolation, weak cross-job automatic collaboration capabilities, untraceable decision-making, and easy generation of content illusion.

[0007] To achieve the above objectives, the present invention employs the following technology: a digital employee collaborative scheduling method and system based on job responsibilities, comprising the following steps: S1. Job Responsibility Profile Construction: Extract seven types of attributes for each position from industry standards, corporate systems, and practical experience, including the job description, typical work scenarios, scope of accessible knowledge base, executable rule set, triggerable workflow template, readable and writable data assets, upstream event sources, and downstream collaborative objects, to form a structured job responsibility profile RRP. S2. Digital Employee Instantiation: Digital employee instances are automatically derived based on the job responsibility profile RRP. Each digital employee instance is configured with system prompts, search routes, response format constraints, and permission policies that are bound to the job responsibility profile. Multiple digital employee instances of the same job share the same job responsibility profile RRP. S3. Collaborative Scheduling Graph Compilation: Scan the collaborative object fields in all job responsibility profiles (RRPs) and automatically compile to generate a directed job collaborative scheduling graph (RCG). The nodes of the job collaborative scheduling graph (RCG) correspond to the job positions, and the edges correspond to the collaborative relationships. Each edge is configured with natural language and structured dual-mode triggering conditions and data contracts. Collaborative relationships include four types of semantics: trigger, inform, escalate, and handoff. S4. Event Routing: Receive external events. The scheduler determines the job position based on the event characteristics and routes the event to the digital employee corresponding to the matching job position. The same event can be subscribed to by multiple jobs and processed from different perspectives. S5. Digital Employee Execution: The routed digital employee, based on the job responsibility profile RRP constraints, calls the bound knowledge base and rule base to perform retrieval enhancement generation, and outputs a response content with procedure references, rule numbers, confidence levels and suggested actions. S6. Collaborative Flow: Based on the edge semantics of the job collaborative scheduling graph RCG, the scheduler converts the current job output into the downstream job input according to the data contract and automatically dispatches orders; when the confidence of the current digital employee output is lower than the preset threshold, the event is escalated to the superior job through the escalate edge. S7. Human-Machine Collaborative Governance Closed Loop: High-risk actions require human confirmation, and the human confirmation behavior and corresponding context are written into the audit log; at the same time, human feedback is used as a sample feedback to dynamically optimize event routing weights.

[0008] As a further description of the above technical solution: In step S4, the event routing adopts a two-layer routing strategy, including a rule scoring fast path and an LLM fallback slow path; The rule-based scoring fast track performs multi-dimensional matching and scoring based on keywords, domain terms, and terminology in the job responsibility profile; The LLM fallback slow path is used for weakly correlated scenarios, relying on a large model to complete job selection, and the event routing module is configured with minute-level caching and concurrency control mechanisms.

[0009] As a further description of the above technical solution: In step S5, when the digital employee runs in an independent sandbox and there are multiple knowledge base scenarios, the retrieval results are weighted and fused according to the relevance of the knowledge base.

[0010] As a further description of the above technical solution: In step S7, the audit log is recorded in the format of "event-employee-reference-action-approval" five-tuple; Feedback flow is based on the success rate of each route path calculated using a rolling time window, and the event route weight is dynamically updated.

[0011] As a further description of the above technical solution: In small scenarios with 3 or fewer job positions, event routing only enables the rule scoring fast path; In large-scale scenarios with 10 or more job positions, both the rule-based scoring fast path and the LLM fallback slow path are enabled, and the timeout threshold for the LLM fallback slow path is set to 2.5 seconds.

[0012] As a further description of the above technical solution: the edge types of the job collaborative scheduling graph (RCG) can be trimmed or added according to the application scenario. In scheduling scenarios, the trigger and escalate edge types are retained, and in document archiving scenarios, the audit edge type is added. In data-sensitive scenarios, only the success rate of the backflow routing path is used as an indicator. In scenarios with strict compliance requirements, audit logs are stored in a hot and cold tier system.

[0013] A job-responsibility-driven digital employee collaborative scheduling system, used to implement the job-responsibility-driven digital employee collaborative scheduling method, includes: The job profile modeling module is used to complete the input, version management, release, and templated batch initialization of job responsibility profiles (RRPs), and to set up a job profile repository. The digital employee generation module, connected to the job profile modeling module, is used to automatically generate digital employee instances based on the job responsibility profile RRP. It has a built-in conversational generation sub-module that completes the synchronous configuration of the profile and the digital employee through multiple rounds of natural language interaction. The scheduling diagram compilation module is connected to the job profile modeling module and the digital employee generation module, respectively. It is used to scan the job responsibility profile (RRP) and automatically compile and generate the job collaborative scheduling diagram (RCG). It has scheduling diagram visualization and conflict detection functions, and is equipped with scheduling diagram storage. The event routing module, connected to the scheduling graph compilation module, includes a rule scoring fast path and an LLM fallback slow path, used to receive external events and complete job assignment determination and event routing; The digital employee runtime module is connected to the event routing module and carries each digital employee instance. It is used to complete the retrieval routing, response generation and action suggestion output according to the job responsibility profile constraints. The collaborative workflow module is connected to the digital employee runtime module and the scheduling graph compilation module, respectively. It is used to complete cross-digital employee task assignment, data contract conversion, task upgrade and downgrade, and multi-employee response fusion based on the job collaborative scheduling graph (RCG). The permissions and auditing module is connected to the digital employee runtime module and the collaborative workflow module, respectively. It is used to achieve row-level data isolation based on the data asset scope of the job responsibility profile RRP and record the full-process audit log in the five-tuple format. The feedback loop module is connected to the permissions and auditing module and the event routing module respectively. It is used to count the success rate of routing paths and dynamically adjust the event routing weight under the rolling time window. External event sources, which are connected to the event routing module, include business systems, client terminals, and timers, and are used to push various external events to the system.

[0014] As a further description of the above technical solution: the permissions and auditing module meets the security and compliance requirements of generative artificial intelligence services and critical infrastructure, and implements a dual verification mechanism of AI suggestions plus human confirmation for all high-risk actions.

[0015] As a further description of the above technical solution: the system's underlying storage layer uniformly stores job responsibility profiles, job collaboration scheduling diagrams, and audit log data.

[0016] As a further description of the above technical solution: Each digital employee in the digital employee runtime module is independently deployed in a sandbox environment, and weighted fusion processing is performed on the search results in multi-knowledge base scenarios.

[0017] As a further description of the above technical solution:

[0018] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. Significantly improve collaboration efficiency: Based on job responsibility profile-driven event routing, in the trial operation scenario, the average response time from alarm generation to the formation of handling suggestions is shortened from about 15 minutes to 2-5 minutes, with an efficiency improvement of about 87%.

[0019] 2. Significantly improve decision-making accuracy: Job-level RAG retrieval routing, combined with response format constraints, enables an invalid alarm filtering rate of 60%-85%, and reduces the cross-job illusion occurrence rate by more than 70% compared to single-agent all-around solutions.

[0020] 3. Significant effect of experience accumulation: Every reference, rule and action of human-computer interaction is structured and accumulated into the job profile according to the five-tuple, and the independent job-taking period of new employees is shortened from the original 6-9 months to 2-3 months, a reduction of about 67%.

[0021] 4. Key operational indicators can be quantified and optimized: Through a job-level suggestion mechanism based on a rule base, key operational indicators in the trial operation scenario can be optimized by 3%-10%.

[0022] 5. Safety, compliance and traceability: All high-risk actions are subject to dual checks of "AI suggestions + human confirmation" and the entire process is audited and recorded, meeting the requirements of the "Interim Measures for the Administration of Generative Artificial Intelligence Services" and the security requirements of critical infrastructure.

[0023] 6. High scalability: Job profiles and digital employees are decoupled. When changing industries, only one RRP template needs to be replaced to adapt to different industry scenarios, without the need to redevelop the underlying scheduling logic. Attached Figure Description

[0024] Figure 1 This diagram illustrates the overall architecture of a job-responsibility-driven digital employee collaborative scheduling system according to an embodiment of the present invention. Figure 2 A flowchart of a job-responsibility-driven digital employee collaborative scheduling method according to an embodiment of the present invention is shown; Figure 3 This diagram illustrates a seven-dimensional structure of a Job Responsibility Profile (RRP) provided according to an embodiment of the present invention. Figure 4 An example diagram of a job collaborative scheduling diagram (RCG) provided according to an embodiment of the present invention is shown; Figure 5 A two-layer decision-making flowchart of an event routing module provided according to an embodiment of the present invention is shown; Figure 6 A timing diagram of a collaborative workflow module provided according to an embodiment of the present invention is shown; Figure 7 A schematic diagram illustrating the statistics and weight update of the feedback loop module provided according to an embodiment of the present invention is shown.

[0025] Legend: 100. Job Profile Modeling Module; 101. Job Profile Repository; 200. Digital Employee Generation Module; 201. Dialogue-based Generation Submodule; 300. Scheduling Graph Compilation Module; 301. Scheduling Graph Storage; 400. Event Routing Module; 401. Rule Scoring Fast Path; 402. LLM Last-Choice Slow Path; 500. Digital Employee Runtime Module; 600. Collaborative Flow Module; 700. Permissions and Auditing Module; 800. Feedback Module; 900. External Event Source. Detailed Implementation

[0026] 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 embodiments of the present invention, and not all embodiments. 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.

[0027] Reference Figures 1-5 This embodiment provides a job responsibility-driven digital employee collaborative scheduling system. The system is used to execute a job responsibility-driven digital employee collaborative scheduling method. The system includes a job profile modeling module 100, a digital employee generation module 200, a scheduling graph compilation module 300, an event routing module 400, a digital employee runtime module 500, a collaborative flow module 600, an access control and auditing module 700, a feedback loop module 800, and an external event source 900. The job profile modeling module 100 is configured with a job profile storage repository 101, the digital employee generation module 200 has a built-in dialogic generation submodule 201, the scheduling graph compilation module 300 is configured with a scheduling graph storage 301, and the event routing module 400 includes a rule scoring fast path 401 and an LLM fallback slow path 402.

[0028] A digital employee collaborative scheduling method driven by job responsibilities includes the following steps: S1. Construct a job description: Based on industry standards, enterprise management systems, and front-line practical experience in industrial production sites, a structured job responsibility profile (RRP) was constructed for six job categories: front-line duty, on-site operations, expert diagnosis, data archiving, operational analysis, and team management. Seven attributes were extracted for each category. Taking the front-line duty position as an example, its job responsibility profile attributes are as follows: Duty declaration: 24 / 7 monitoring, initial alarm judgment, and key parameter recording; Typical work scenarios: abnormal vibration of key equipment, flow deviation from baseline, and abnormal load alarms; Knowledge scope: duty procedure library, alarm handling manual, and equipment operation procedures; Rule set: alarm classification rules and key parameter threshold rules; Workflow templates: shift handover, emergency notification, and alarm dispatch; Data assets: real-time data (read-only) and operation logs (read / write); Collaboration objects: upstream: data acquisition system and IoT devices; downstream: expert diagnosis and team management; horizontal collaboration object: on-site operations. All job responsibility profiles are entered into the job profile modeling module, version management and release are completed, and they are stored in the job profile repository.

[0029] S2, Instantiation of Digital Employees: The digital employee generation module reads the Responsibility Profiles (RRPs) of each job in the job profile repository and automatically generates digital employee instances for the corresponding jobs. Each digital employee instance is configured with four sets of configuration items: system prompts, search routes, response format constraints, and permission policies. The system prompts are automatically generated by the corresponding job profile. For front-line duty positions, the search routes are configured using a combination of BM25 search and semantic search. The response format is uniformly set to a three-part structure of inquiry-reference-suggestion, and corresponding access permissions are configured based on the scope of data assets. For multi-shift operation requirements, multiple digital employee instances are generated for the same job, and all instances share the same job profile RRP. During the configuration process, the dialogic generation submodule completes the synchronous configuration of the job profile and digital employee instances through multiple rounds of natural language interaction.

[0030] S3, Compilation Co-scheduling Diagram: The scheduling graph compilation module iterates through the collaborative object fields in all job responsibility profiles, automatically compiles and generates a directed job collaboration scheduling graph (RCG), and stores the scheduling graph in the scheduling graph storage. In this embodiment, the job collaboration scheduling graph uses six job types as nodes, with four types of collaborative relationship edges between nodes: trigger edges, inform edges, escalate edges, and handoff edges. Each edge is configured with natural language + structured dual-mode trigger conditions and a data contract. Specific collaborative relationship configurations are as follows: a trigger edge is set between the front-end duty node and the expert diagnosis node, triggered when the alarm level is ≥ yellow; a handoff edge is set between the expert diagnosis node and the data archiving node, triggered when the work order is closed; an escalate edge is set between all job nodes and the team management node, triggered when the digital employee output confidence level is <0.6 or a major risk occurs; inform edges are configured between each job as needed. After the scheduling graph is compiled, the module performs conflict detection and provides visualization functionality.

[0031] S4, Event Routing and Distribution: External event sources include business systems, users, and timers. In this embodiment, the data acquisition system acts as the external event source, pushing device alarm events. After receiving an external event, the event routing module selects a routing strategy based on the scenario scale: in this embodiment, there are 6 job positions, and both a rule-based scoring fast path and an LLM fallback slow path are enabled. The rule-based scoring fast path performs multi-dimensional matching and scoring based on keywords, domain terms, and a glossary of job responsibility profiles. In weakly related scenarios, the LLM fallback slow path completes job selection. The module is configured with minute-level caching and a concurrency control mechanism. When the data acquisition system pushes a yellow vibration alarm event for critical device A, the rule-based scoring fast path completes multi-dimensional matching, determines the event's attribution to the front-line duty position, and completes event routing. The routing time is less than 50ms. The same event can be subscribed to by multiple job positions, each handling the event from its own perspective.

[0032] S5, Digital Employee Execution and Handling: The event is routed to the corresponding digital employee on duty at the front desk, which runs within an independent sandbox. The digital employee's runtime module, based on the constraints of the job responsibility profile, calls the bound knowledge base and rule base to perform enhanced retrieval and generation. In multi-knowledge base scenarios, the retrieval results are weighted and fused according to the relevance of the knowledge bases. In this embodiment, the front desk digital employee retrieves the duty procedure library and alarm handling manual, obtaining a total of four relevant procedure contents. After fusion reasoning, a response is output, including a procedure reference, rule number, a confidence level of 0.82, and corresponding action suggestions.

[0033] S6. Cross-functional collaborative workflow: The collaborative workflow module reads the job collaborative scheduling graph (RCG) stored in the scheduling graph storage and executes the collaborative workflow based on edge semantics, triggering conditions, and data contracts. In this embodiment, the output result of the front-line duty post triggers the trigger edge. The scheduler converts the data format according to the preset data contract, automatically dispatches work orders to the expert diagnosis digital employee, and simultaneously pushes an event briefing to the team management post via the inform notification edge. Because the confidence level of this output (0.82) is higher than the threshold of 0.6, the escalate edge is not triggered. After the expert diagnosis post completes the processing, the handoff edge is triggered, transferring the completed work order to the data archiving post.

[0034] S7, Human-Machine Collaboration, Auditing and Feedback: For high-risk operations in the process, the system activates a human-machine co-governance mode, requiring manual confirmation. The permissions and audit module implements row-level data isolation based on the data asset scope of the job responsibility profile, and records AI suggestions, manual confirmation actions, and the entire process context as audit logs according to the five-tuple format of event-employee-reference-action-approval. A total of 17 audit records were generated throughout this alarm handling process. The feedback loop module reads the audit logs, calculates the success rate of each routing path within a rolling time window, and dynamically adjusts the routing weight of the event routing module based on the statistical results. In this embodiment, business content loopback is not enabled; only the loopback path success rate indicator is used. Based on this embodiment, the method flow is adaptively replaced for different application scenarios, specifically in three replacement implementation methods: Event routing strategy replacement: For small application scenarios with ≤3 job positions, the event routing module only enables the rule scoring fast path and disables the LLM fallback slow path; for large and complex scenarios with ≥10 job positions, both the rule scoring fast path and the LLM fallback slow path are enabled, and the timeout threshold of the LLM fallback slow path is fixed at 2.5 seconds.

[0035] Collaborative relationship edge type replacement: For scheduling scenarios such as power and water, the edge types of the job collaborative scheduling graph (RCG) are pruned, retaining only the trigger edge and escalate edge; for document management, document processing and other data archiving intensive scenarios, an audit edge type is added to the original four edge types.

[0036] Audit and feedback strategy replacement: For data-sensitive scenarios such as finance and government affairs, the feedback return module disables the function of returning business content and only counts and returns the success rate of routing paths; for critical infrastructure scenarios with strict compliance requirements, audit logs are stored in a cold and hot tiered manner, and the storage architecture meets the requirements of Level 3 network security protection.

[0037] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A digital employee collaborative scheduling method driven by job responsibilities, characterized in that, Includes the following steps: S1. Job Responsibility Profile Construction: Extract seven types of attributes for each position from industry standards, corporate systems, and practical experience, including the job description, typical work scenarios, scope of accessible knowledge base, executable rule set, triggerable workflow template, readable and writable data assets, upstream event sources, and downstream collaborative objects, to form a structured job responsibility profile RRP. S2. Digital Employee Instantiation: Digital employee instances are automatically derived based on the job responsibility profile RRP. Each digital employee instance is configured with system prompts, search routes, response format constraints, and permission policies that are bound to the job responsibility profile. Multiple digital employee instances of the same job share the same job responsibility profile RRP. S3. Collaborative Scheduling Graph Compilation: Scan the collaborative object fields in all job responsibility profiles (RRPs) and automatically compile to generate a directed job collaborative scheduling graph (RCG). The nodes of the job collaborative scheduling graph (RCG) correspond to the job positions, and the edges correspond to the collaborative relationships. Each edge is configured with natural language and structured dual-mode triggering conditions and data contracts. Collaborative relationships include four types of semantics: trigger, inform, escalate, and handoff. S4. Event Routing: Receive external events. The scheduler determines the job position based on the event characteristics and routes the event to the digital employee corresponding to the matching job position. The same event can be subscribed to by multiple jobs and processed from different perspectives. S5. Digital Employee Execution: The routed digital employee, based on the job responsibility profile RRP constraints, calls the bound knowledge base and rule base to perform retrieval enhancement generation, and outputs a response content with procedure references, rule numbers, confidence levels and suggested actions. S6. Collaborative Flow: Based on the edge semantics of the job collaborative scheduling graph RCG, the scheduler converts the current job output into the downstream job input according to the data contract and automatically dispatches orders; when the confidence of the current digital employee output is lower than the preset threshold, the event is escalated to the superior job through the escalate edge. S7. Human-Machine Collaborative Governance Closed Loop: High-risk actions require human confirmation, and the human confirmation behavior and corresponding context are written into the audit log; at the same time, human feedback is used as a sample feedback to dynamically optimize event routing weights.

2. The digital employee collaborative scheduling method based on job responsibilities as described in claim 1, characterized in that, In step S4, the event routing adopts a two-layer routing strategy, including a rule scoring fast path and an LLM fallback slow path; The rule-based scoring fast track performs multi-dimensional matching and scoring based on keywords, domain terms, and terminology in the job responsibility profile; The LLM fallback slow path is used for weakly correlated scenarios, relying on a large model to complete job selection, and the event routing module is configured with minute-level caching and concurrency control mechanisms.

3. The digital employee collaborative scheduling method based on job responsibilities as described in claim 1, characterized in that, In step S5, when the digital employee runs in an independent sandbox and there are multiple knowledge base scenarios, the retrieval results are weighted and fused according to the relevance of the knowledge base.

4. The digital employee collaborative scheduling method and system based on job responsibilities as described in claim 1, characterized in that, In step S7, the audit log is recorded using the "event-employee-reference-action-approval" five-tuple format; Feedback flow is based on the success rate of each route path calculated using a rolling time window, and the event route weight is dynamically updated.

5. The digital employee collaborative scheduling method and system based on job responsibilities as described in claim 1, characterized in that, In small-scale scenarios with 3 or fewer job positions, event routing only enables the rule-based scoring fast path; In large-scale scenarios with 10 or more job positions, both the rule-based scoring fast path and the LLM fallback slow path are enabled, and the timeout threshold for the LLM fallback slow path is set to 2.5 seconds.

6. The digital employee collaborative scheduling method and system based on job responsibilities as described in claim 1, characterized in that, The edge types of the job collaboration scheduling graph (RCG) can be trimmed or added according to the application scenario. For scheduling scenarios, the trigger and escalate edge types are retained, and for document archiving scenarios, the audit edge type is added. In data-sensitive scenarios, only the success rate of the backflow routing path is used as an indicator. In scenarios with strict compliance requirements, audit logs are stored in a hot and cold tier system.

7. A digital employee collaborative scheduling system driven by job responsibilities, characterized in that, The method for implementing the job-responsibility-driven digital employee collaborative scheduling method according to any one of claims 1-6 includes: The job profile modeling module is used to complete the input, version management, release, and templated batch initialization of job responsibility profiles (RRPs), and to set up a job profile repository. The digital employee generation module, connected to the job profile modeling module, is used to automatically generate digital employee instances based on the job responsibility profile RRP. It has a built-in conversational generation sub-module that completes the synchronous configuration of the profile and the digital employee through multiple rounds of natural language interaction. The scheduling diagram compilation module is connected to the job profile modeling module and the digital employee generation module, respectively. It is used to scan the job responsibility profile (RRP) and automatically compile and generate the job collaborative scheduling diagram (RCG). It has scheduling diagram visualization and conflict detection functions, and is equipped with scheduling diagram storage. The event routing module, connected to the scheduling graph compilation module, includes a rule scoring fast path and an LLM fallback slow path, used to receive external events and complete job assignment determination and event routing; The digital employee runtime module is connected to the event routing module and carries each digital employee instance. It is used to complete the retrieval routing, response generation and action suggestion output according to the job responsibility profile constraints. The collaborative workflow module is connected to the digital employee runtime module and the scheduling graph compilation module, respectively. It is used to complete cross-digital employee task assignment, data contract conversion, task upgrade and downgrade, and multi-employee response fusion based on the job collaborative scheduling graph (RCG). The permissions and auditing module is connected to the digital employee runtime module and the collaborative workflow module, respectively. It is used to achieve row-level data isolation based on the data asset scope of the job responsibility profile RRP and record the full-process audit log in the five-tuple format. The feedback loop module is connected to the permissions and auditing module and the event routing module respectively. It is used to count the success rate of routing paths and dynamically adjust the event routing weight under the rolling time window. External event sources, which are connected to the event routing module, include business systems, client terminals, and timers, and are used to push various external events to the system.

8. A digital employee collaborative scheduling system based on job responsibilities as described in claim 7, characterized in that, The permissions and auditing module meets the security and compliance requirements for generative artificial intelligence services and critical infrastructure, and implements a dual verification mechanism of AI suggestions plus human confirmation for all high-risk actions.

9. A digital employee collaborative scheduling system based on job responsibilities as described in claim 7, characterized in that, The system's underlying storage layer uniformly stores job descriptions, job collaboration scheduling diagrams, and audit log data.

10. A digital employee collaborative scheduling system based on job responsibilities as described in claim 7, characterized in that, In the digital employee runtime module, each digital employee is deployed independently in a sandbox environment, and weighted fusion processing is performed on the search results in multi-knowledge base scenarios.

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