Brain-hand cooperation and result intelligent enhanced ai digital employee system and construction method

Abstract

The present application relates to the field of artificial intelligence, in particular to an AI digital employee system with brain-hand cooperation and result intelligent enhancement and a construction method. The system comprises a natural language interaction module, an agent definition and management module, a process automation component library, a large language model engine, a collaborative scheduling engine and a result intelligent enhancement pipeline. The method receives a user natural language instruction, plans a task by the large language model engine, executes the task by the process automation component, and after the execution is completed, the result intelligent enhancement pipeline is started to intelligently process the execution result by the collaborative scheduling engine, and finally an enhanced result with business insight is output. The present application solves the problem that the existing automation system can only mechanically execute and lacks deep analysis and value refinement of the result, and realizes the transformation from an automation tool to an intelligent employee.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to an AI-powered digital employee system and its construction method that enhances brain-hand collaboration and result intelligence. Background Technology

[0002] Currently, there are two main types of technologies for enterprise automation: Robotic Process Automation (RPA), which excels at executing repetitive tasks based on fixed rules but lacks the cognitive ability to understand and cope with unstructured scenarios; and Large Language Models (LLM), which possesses excellent natural language understanding and generation capabilities but lacks the ability to interact with the physical world or business systems to execute specific operations. In existing technologies, these two are often loosely combined, for example, using LLM as the trigger command entry point for RPA processes. They have not yet formed a deeply collaborative, continuously learning, and decision-optimization-enabled closed-loop intelligent agent that integrates thinking, decision-making, execution, and reinforcement.

[0003] Patent searches revealed that existing technologies rely on a central scheduler to parse instructions and invoke a toolchain for execution. Their core functionality is limited to task analysis and planning, lacking the ability to deeply analyze and recreate value from the execution results. Existing solutions fail to address the crucial issue of enabling automated systems not only to execute tasks but also, like experienced employees, to analyze and gain insights into the work outcomes and generate high-value decision-making recommendations. Summary of the Invention

[0004] Therefore, the technical problem to be solved by the present invention is to overcome the lack of intelligent analysis and value enhancement capabilities of the execution results in the existing automated systems.

[0005] To address the aforementioned technical problems, this invention provides an AI-powered digital employee system that enhances brain-hand collaboration and result intelligence, comprising:

[0006] The natural language interaction module is used to receive intelligent agent creation instructions or task instructions input by the user in natural language. The agent definition and management module is used to generate an agent framework with business role attributes in response to the creation command; The process automation component library contains multiple pre-built executable process components that encapsulate automated operations. A large language model engine is used to understand user intent and perform task planning; A collaborative scheduling engine connects and schedules the large language model engine and the process automation component library; The result is intelligent enhancement of the pipeline; The collaborative scheduling engine is configured to: send task instructions to the large language model engine for intent parsing and step decomposition; call and schedule the corresponding process automation components to execute according to the decomposed steps; and after the process automation components have completed execution, start the result intelligent enhancement pipeline to intelligently process the execution results and generate enhanced output.

[0007] Preferably, the result intelligent enhancement pipeline includes: Task type recognizer; An enhancement strategy selector is used to dynamically select an enhancement mode based on the task instruction context and agent role attributes. The enhancement mode includes at least one of formatting, trend analysis, anomaly attribution, and comprehensive report generation.

[0008] Preferably, the system further includes: Knowledge base module; The intelligent enhancement pipeline is configured to: actively retrieve and inject relevant business background knowledge, historical data patterns, or analysis models from the knowledge base module into the context window of the large language model engine, based on the selected enhancement mode.

[0009] Preferably, the result intelligent enhancement pipeline includes a result enhancement processing chain run by the large language model engine, the result enhancement processing chain including at least a semantic understanding submodule for deep understanding of data semantics, a data insight submodule for mining data patterns, and a narrative generation submodule for organizing output content.

[0010] Preferably, the system further includes: A visual configuration interface allows users to associate and bind the process automation components, analysis models, and intelligent agent roles by dragging and dropping.

[0011] Preferably, the result intelligent enhancement pipeline supports iterative feedback, whereby user evaluations of the enhancement outputs are recorded and used to optimize the enhancement strategy selection for subsequent tasks of the same agent.

[0012] Preferably, the components in the process automation component library include robotic process automation components and application programming interface (API) calling components.

[0013] This invention also provides an AI-powered digital employee construction method that enhances brain-hand collaboration and outcome intelligence, applied to the system described above. The method includes: Receives user-inputted instructions for creating an intelligent agent in natural language; In response to the creation instruction, an intelligent agent framework with business role attributes is generated; Receive task instructions from the user in natural language; The task instruction is sent to the large language model engine for intent parsing and step decomposition. Based on the decomposed steps, call and schedule the corresponding process automation components in the process automation component library to execute the tasks; After the process automation component completes its execution, a result intelligent enhancement pipeline is initiated to intelligently process the execution results; and Output enhanced results after intelligent processing.

[0014] Preferably, the steps of the intelligent enhancement pipeline for startup results include: Identify the task type of the task instruction; Based on the task type and the business role attributes of the intelligent agent, a target enhancement mode is selected from a variety of preset enhancement modes; Based on the target enhancement mode, the execution result is subjected to context-aware deep analysis and content reconstruction.

[0015] Preferably, the step of performing in-depth analysis and content reconstruction of the execution result based on the target enhancement mode includes: Based on the target enhancement mode, retrieve relevant business background knowledge or analysis models from the knowledge base; The execution results are combined with the retrieved business background knowledge or analysis model and input into the large language model engine for processing; The present invention generates enhanced reports containing at least one of data insights, attribution analysis, or decision recommendations. Compared with existing technologies, the above-described technical solution has the following advantages: The AI-powered digital employee system and its construction method, which features brain-hand collaboration and result intelligence enhancement, achieves a qualitative leap in automated execution, transforming output data into output insights by introducing a result intelligence enhancement pipeline. This endows the intelligent agent with the experience and judgment of a junior employee. Through a dynamic enhancement strategy selection mechanism, the system can flexibly handle tasks of varying value densities, optimizing the allocation of computing resources. Encapsulating business knowledge within the enhancement pipeline reduces reliance on the accuracy of user instructions, improving system usability and the reliability of output results. Supporting iterative feedback and continuous learning allows the intelligent agent to continuously optimize its analysis strategies based on user feedback, adapting to business changes. Attached Figure Description

[0016] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein: Figure 1 This is a schematic diagram of the structure of an AI-powered digital employee system that enhances brain-hand collaboration and result intelligence, provided by the present invention. Figure 2This is a flowchart of an AI-powered digital employee construction method that enhances brain-hand collaboration and result intelligence, provided by the present invention. Detailed Implementation

[0017] The core of this invention is to provide an AI-powered digital employee system and its construction method that enhances brain-hand collaboration and result intelligence, effectively solving the problem that existing automated systems lack in-depth analysis and value extraction of execution results.

[0018] To enable those skilled in the art to better understand the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely 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.

[0019] Please refer to Figure 1. Figure 1 The schematic diagram of the structure of the AI-powered digital employee system with brain-hand collaboration and result intelligence enhancement provided by the present invention is as follows: The natural language interaction module 100 is used to receive intelligent agent creation instructions or task instructions input by the user in natural language. The agent definition and management module 200 is used to generate an agent framework with business role attributes in response to the creation command; The process automation component library contains 300 pre-built executable process components that encapsulate automated operations. Large Language Model Engine 400 is used to understand user intent and perform task planning; The collaborative scheduling engine 500 connects and schedules the large language model engine and the process automation component library; The result is a smart-enhanced pipeline 600; The collaborative scheduling engine is configured to: send task instructions to the large language model engine for intent parsing and step decomposition; call and schedule the corresponding process automation components to execute according to the decomposed steps; and after the process automation components have completed execution, start the result intelligent enhancement pipeline to intelligently process the execution results and generate enhanced output.

[0020] Based on the above embodiments, this embodiment provides a detailed description of the natural language interaction module 100: In some embodiments, the natural language interaction module 100 serves as a unified entry point for user interaction with the system, supporting multiple input methods such as text and voice. Its core function is to receive user instructions expressed in natural language, which are divided into two categories: one is the intelligent agent creation instruction used to create and define AI employees (e.g., I need a financial reimbursement assistant); the other is the specific task instruction issued to the created intelligent agent (e.g., the reimbursement assistant, process last month's travel expenses).

[0021] In other embodiments, module 100 also integrates an intent pre-identification and clarification mechanism. When user instructions are ambiguous or incomplete (e.g., the user only says to analyze the data), module 100 can proactively call the large language model engine 400 for a brief interaction to guide the user to clarify the specific analysis target, data range, and time period, thereby generating more accurate and executable instructions to be passed to downstream modules.

[0022] In one specific embodiment, the module 100 is designed to be embedded into an enterprise's existing office system or communication tools. For example, within the approval workflow interface of a financial system or a chat window, users can directly converse without needing to switch to a separate platform, achieving a seamless experience of "conversation equals operation." This directly addresses pain points such as rigid tools, high interaction barriers, and delayed response to demands.

[0023] Specifically, after receiving an instruction, module 100 will attach user context information (such as the user's department, role and permissions) and session context information (such as the history of the current conversation) to it, forming a structured instruction request, which will be submitted to agent definition and management module 200 or collaborative scheduling engine 500.

[0024] It should be noted that the design of this module greatly lowers the barrier to entry, enabling business personnel who are not familiar with SQL or programming to directly drive complex automated processes, which aligns with the goal of creating an intelligent data governance assistant and empowering all employees to unleash their value.

[0025] Based on the above embodiments, this embodiment provides a detailed description of the agent definition and management module 200: In some embodiments, the agent definition and management module 200 is the system's human resources department. It receives creation instructions from module 100, and its core task is to generate and maintain an agent entity with a lifecycle. Module 200 parses key elements (such as domain, function, and expected capabilities) in the instructions and matches or combines them from a basic template library to generate an initial agent framework.

[0026] In other embodiments, module 200 provides a visual, low-code configuration interface (which can be used as a standalone function or integrated into module 100). Users can select specific RPA processes and API interfaces from the process automation component library 300, and business domain knowledge packages from the knowledge base, and bind them to the intelligent agent framework, not only through verbal descriptions but also through graphical drag-and-drop. This essentially builds the intelligent agent's capabilities—a knowledge graph—ensuring that it has the foundation to perform specific tasks and utilize specific knowledge from the moment it joins the workforce.

[0027] In one specific embodiment, the agents managed by module 200 possess role attributes, which are crucial in determining their behavioral logic. For example, an agent defined as a financial compliance auditor would have built-in role attributes of high sensitivity to financial regulations, risk aversion, and strict reporting format requirements; while a sales trend analyst agent would focus more on data correlation, trend prediction, and innovative visualization. These role attributes will serve as important inputs for decision-making by downstream modules (especially the collaborative scheduling engine 500 and the results intelligence enhancement pipeline 600).

[0028] Specifically, module 200 maintains a unique configuration profile for each agent, recording its bound components, knowledge base, role attributes, historical task records, and user feedback ratings. This enables agents to be reused, optimized, and quickly cloned for large-scale deployment, achieving the goals of standardized products and large-scale replication.

[0029] It should be noted that this module realizes the transformation from a general AI tool to a dedicated business partner, which is the foundation for the concept of AI-powered digital employees. Its dynamic configuration and role-based management are important features that distinguish it from traditional fixed scripts or general chatbots.

[0030] Based on the above embodiments, this embodiment provides a detailed description of the process automation component library 300: In some embodiments, the process automation component library 300 is a system skill toolbox. It is not a simple list, but a reusable asset library categorized and standardized according to function, application system, and operation object. Each component encapsulates a piece of independently executable automation operation logic.

[0031] In other embodiments, the components in the library are mainly divided into two categories: Robotic Process Automation (RPA) components and Application Programming Interface (API) components. RPA components encapsulate operation sequences for specific software graphical user interfaces (such as logging into an ERP system, clicking a button, entering data, and retrieving web forms); API components encapsulate standardized interface calls to internal or third-party services (such as calling an invoice verification API, calling a DeepSeek model API, and querying a database). All components are described and managed through a unified metadata interface, including input / output parameter formats, execution preconditions, and exception handling methods.

[0032] In one specific embodiment, an RPA component named "Export Q3 Sales Details from SAP System" has metadata defining that it requires SAP account permissions as a prerequisite, and upon execution, it outputs a structured list of sales data. When scheduling it, the Coordinating Scheduling Engine 500 treats it like calling a function, requiring only an account token and time parameters to obtain the result. This encapsulation transforms complex, volatile system operations into stable, programmable building blocks.

[0033] Specifically, this component library supports dynamic expansion. Developers or business experts can encapsulate new automation scripts or service interfaces into new components according to a unified specification and register them in the library. This allows the system's utilitarian capabilities to continuously expand as business grows, solving the problems of data silos and rigid tools.

[0034] It should be noted that the Process Automation Component Library 300 is the cornerstone of achieving operational capabilities. Its standardization and callability are prerequisites for the Collaborative Scheduling Engine 500 to flexibly and accurately orchestrate complex task flows, and also the technical guarantee for achieving the promise of cost reduction and efficiency improvement.

[0035] Based on the above embodiments, this embodiment provides a detailed description of the large language model engine 400: In some embodiments, the large language model engine 400 is the brain and intelligence center of the system. It does not directly deploy a raw, basic large model, but is a comprehensive engine that includes a model service layer, a prompt word engineering layer, and a context management layer.

[0036] In other embodiments, engine 400 can interface with and schedule different underlying large models (such as DeepSeek, Tongyi Qianwen, etc.), intelligently selecting or combining them based on the task's computational requirements, cost, and requirements for the generated result format. Its core functions include: 1) Deep semantic understanding: parsing natural language instructions from module 100 to accurately extract user intent, entities, and constraints; 2) Task planning and decomposition: transforming complex user intent into an ordered, executable sequence of steps, clarifying which process automation component should execute each step, and the data dependencies between steps; 3) Result processing and generation: being invoked in the result intelligent enhancement pipeline 600 to analyze, reason, summarize, and format structured data.

[0037] In one specific embodiment, when receiving an instruction to compare and analyze the power generation efficiency of photovoltaic power plants in East China and South China during the first half of the year, identify anomalies, and analyze possible causes, Engine 400 will perform the following tasks: First, it understands that this is a multi-regional comparative analysis task, with the core entities being photovoltaic power plants and power generation efficiency, and the constraints being the first half of the year and the East and South China regions. Next, it plans the task steps: First, it calls two components that obtain power plant operation data from the new energy data platform to acquire data from the two regions respectively; second, it calls a component that calculates key indicators of power generation efficiency to process the raw data; third, it waits for the components to complete their execution and then enters the enhanced analysis phase.

[0038] Specifically, Engine 400 incorporates prompt word templates and a mind chain guidance mechanism optimized for enterprise scenarios. When the execution result is enhanced, it automatically assembles a complete prompt word based on the task type, including role setting (you are a senior energy data analyst), task requirements, output format specifications, and injected background knowledge, thereby guiding the large model to generate professional, reliable, and formatted business insights.

[0039] It should be noted that the thinking ability of the Large Language Model Engine 400, especially its ability to combine business roles and knowledge for task planning and in-depth analysis, is the core of achieving brain-hand collaboration and intelligence enhancement, and is the key to improving the upper limit of the value of the automated system in this invention.

[0040] Based on the above embodiments, this embodiment provides a detailed description of the collaborative scheduling engine 500: In some embodiments, the collaborative scheduling engine 500 is the central nervous system and command center of the system, responsible for establishing an efficient and reliable collaborative link between the large language model engine 400 (brain) and the process automation component library 300 (hand). Its scheduling logic goes far beyond simple sequential invocation.

[0041] In other embodiments, the workflow of Engine 500 comprises three core stages: 1) Task orchestration stage: receiving and parsing the planning results (step sequence diagram) from the large language model engine 400, performing resource checks and dependency resolution; 2) Execution monitoring stage: calling process automation components sequentially or in parallel according to the plan, and monitoring the execution status of each component in real time, capturing execution logs and output results. It has fault tolerance mechanisms such as error retries and timeout handling; 3) Enhancement triggering and bridging stage: after all necessary execution steps are completed, Engine 500 does not directly return the original data, but actively judges and initiates the value enhancement process. This is the key point of the present invention.

[0042] In one specific embodiment, the engine 500 embeds a task type identifier. After the monitoring phase is completed, it combines the initial instructions of the task, the types of components executed, and the characteristics of the output data to determine whether the task is a simple query, in-depth analysis, or risk diagnosis, etc. For example, if the task instructions contain keywords such as analysis, attribution, and insight, and the executed components include data acquisition and calculation components, then it is identified as a deep analysis type.

[0043] Specifically, based on the task type identification result, Engine 500 will work in conjunction with the result intelligent enhancement pipeline 600, sending it an enhancement request package containing the task context (original instructions, task type), execution context (which components were used and how long it took), and data context (the output results of the components), thereby triggering intelligent processing. This targeted triggering mechanism ensures that the enhancement actions are targeted and optimizes the use of computing resources.

[0044] It should be noted that the intelligent scheduling of the Collaborative Scheduling Engine 500, especially its context-based enhanced triggering decision logic, is the technological hub for realizing a complete business closed loop of thinking-decision-execution-enhancement. It connects the discrete activities of the brain and hands into an organic, value-added intelligent agent behavior.

[0045] Based on the above embodiments, this embodiment provides a detailed description of the intelligent enhancement pipeline 600: In some embodiments, the result intelligent enhancement pipeline includes: Task type recognizer; An enhancement strategy selector is used to dynamically select an enhancement mode based on the task instruction context and agent role attributes. The enhancement mode includes at least one of formatting, trend analysis, anomaly attribution, and comprehensive report generation.

[0046] In one specific embodiment, the task type identifier, serving as the starting point of the pipeline, is responsible for accurately classifying the upcoming augmentation tasks. Its identification is not based on a single factor, but rather on a neural network or rule model that comprehensively judges multi-dimensional contextual information. Judgment factors include: Instruction semantics: analyzing keywords in the original user instructions (such as analysis, prediction, diagnosis, and reporting); Agent role attributes: identifying whether the agent is a compliance auditor or a market analyst, as different roles have different perspectives and depth requirements for interpreting the same data; Executed component characteristics: determining whether the upstream process automation components are data extraction, computation, or operation-related. For example, if complex statistical computation components are executed, the augmentation task is more likely to lead to trend analysis; Output data structure: initially analyzing the scale, type (time series data, tabular data, text data), and characteristics (whether there are a large number of null values ​​or outliers) of the original result data. Through comprehensive judgment, the identifier classifies the task into types such as descriptive statistics, diagnostic attribution, predictive analysis, or prescriptive recommendations, providing accurate input for subsequent strategy selection.

[0047] In one specific embodiment, the enhancement strategy selector receives the task type from the recognizer and dynamically matches and activates one or more enhancement strategy execution units from a pre-defined enhancement strategy library. These strategies are predefined, configurable enhancement recipes, such as: Formatting strategy unit: suitable for simple queries, mainly performing data cleaning, unit standardization, table beautification, and outputting standard reports. Trend analysis strategy unit: suitable for time-series data, automatically calling built-in or retrieved time-series analysis models (such as moving average, seasonal decomposition) to identify long-term trends, periodic fluctuations, and inflection points. Anomaly attribution strategy unit: suitable for problem diagnosis, its core being a root cause analysis engine. It first locates data anomalies using statistical methods (such as isolation forest, 3-Sigma principle), then guides the large language model engine, combined with injected business rule knowledge, to conduct multiple rounds of reasoning and questioning along the logical chain of phenomenon -> related factors -> root cause, ultimately locating the most likely business cause. The comprehensive report generation strategy unit is suitable for scenarios requiring in-depth summarization. It defines the report's structural template (e.g., summary-background-methodology-findings-recommendations), narrative style (e.g., rigorous auditing style or insightful business analysis style), and chart combination scheme. The strategy selector can initiate a strategy chain based on task complexity. For example, for a task analyzing the reasons for a sales decline, it might sequentially initiate trend analysis (confirming the decline) -> anomaly attribution (identifying declining products and regions) -> comprehensive report generation (writing the analysis report).

[0048] In some embodiments, the result intelligent enhancement pipeline includes a result enhancement processing chain run by the large language model engine, the result enhancement processing chain including at least a semantic understanding submodule for deep understanding of data semantics, a data insight submodule for mining data patterns, and a narrative generation submodule for organizing output content.

[0049] In one specific embodiment, the result enhancement processing chain is the concrete executor of the strategy, a series of thought steps executed by the Large Language Model Engine 400 under the guidance of a specific strategy. As a logical pipeline, it typically includes: The semantic understanding submodule interprets, rather than reads, raw data. For example, it maps the database field name rev_q3 to the business term "third-quarter revenue," understanding the severity of a 15% year-over-year decline within an industry context.

[0050] The Data Insights submodule: Building upon semantic understanding, it performs in-depth analysis as required by the strategy. This may involve calling external analysis models (such as calling a prediction model via an API) or guiding large language models to perform complex reasoning such as comparative analysis, correlation analysis, and pattern recognition.

[0051] The narrative generation submodule organizes the analysis results into a narrative that conforms to human cognitive logic. Based on the agent's role (e.g., conclusions first for senior leaders, detailed processes for technical personnel) and output format requirements, it weaves data, insights, chart references, and other content into a logically coherent, focused, and highly readable final output. This addresses the pain point of traditional BI tools that offer charts but lack a narrative.

[0052] In some embodiments, the result-based intelligent enhancement pipeline supports iterative feedback, where user evaluations of the enhancement outputs are recorded and used to optimize the selection of enhancement strategies for subsequent tasks of the same agent.

[0053] In one specific embodiment, the core intelligence of Pipeline 600 lies in its ability to proactively retrieve knowledge on demand. The system includes a knowledge base module that stores structured business rules, historical cases, domain terminology, analytical model metadata, and best practice documents. When the enhanced strategy selector selects a strategy (such as anomaly attribution), Pipeline 600 initiates a targeted query to the knowledge base. For example, for a financial statement anomaly task, query conditions might include: Knowledge Domain = Financial Audit, Anomaly Type = Related Party Transactions, Relevant System = Accounting Standard No. 36. The knowledge base returns relevant clauses, conclusions of handling similar past cases, and risk rating models. Subsequently, Pipeline 600 injects this retrieved high-value background knowledge as part of system prompts into the current context window of the large language model engine 400. This ensures that the large model's analysis is no longer based on general knowledge but on precise and authoritative business context, thereby greatly reducing illusions and improving the professionalism and credibility of the output results. This directly corresponds to the goal in the project materials of providing the model with precise business context and reducing illusions.

[0054] Based on the above embodiments, the system further includes: Knowledge base module; The intelligent enhancement pipeline is configured to: actively retrieve and inject relevant business background knowledge, historical data patterns, or analysis models from the knowledge base module into the context window of the large language model engine, based on the selected enhancement mode.

[0055] In one specific embodiment, pipeline 600 possesses continuous learning capabilities. The system employs a closed-loop feedback mechanism: after each enhancement output, users can provide feedback via interface buttons (e.g., "helpful," "inaccurate") or natural language evaluations (e.g., "This analysis didn't consider factor XX"). These feedback signals are recorded, analyzed, and correlated with the characteristics of the task (task type, strategy used, injected knowledge items). For example, if an anomaly attribution strategy is repeatedly rated as inaccurate by users in a certain type of contract compliance review task, the system will reduce the priority weight of that strategy in such tasks or trigger an alarm to prompt the administrator to check whether the strategy logic or related rules in the knowledge base are outdated. In the long run, this mechanism enables each agent to become increasingly accurate within its specific business domain, achieving personalized strategy optimization and demonstrating growth potential from tool to employee.

[0056] Based on the above embodiments, the system further includes: A visual configuration interface allows users to associate and bind the process automation components, analysis models, and intelligent agent roles by dragging and dropping.

[0057] In one specific embodiment, the system's flexibility and ease of use are greatly enhanced through a visual configuration interface. This interface is not only used for assembling processes but also serves as an intuitive tool for configuring enhancement pipelines. Administrators or business experts can use drag-and-drop to: bind domain knowledge: associating specific knowledge base folders or tags with a particular agent role to ensure that its enhancement analysis always obtains the correct knowledge background; associate analysis models: directly binding a dedicated sales forecasting machine learning model to the sales forecasting assistant, which is automatically invoked when a trend analysis strategy is selected; and customize enhancement strategies: for high-frequency scenarios, users can combine basic strategy units and set parameters (such as anomaly thresholds and report templates) to save a customized one-click enhancement solution for team reuse. This design completely transfers the configuration power of enhancement capabilities from developers to business experts, realizing the project's concept of everyone being a digital enabler and empowering frontline employees.

[0058] Please refer to Figure 2 , Figure 2 This invention provides a flowchart of an AI-powered digital employee construction method that enhances brain-hand collaboration and outcome intelligence; specifically, it includes: S101: Receives the intelligent agent creation instruction input by the user in natural language; executed by the natural language interaction module.

[0059] S102: In response to the creation instruction, generate an intelligent agent framework with business role attributes; the intelligent agent definition and management module is executed.

[0060] S103: Receives task instructions input by the user in natural language; executed by the natural language interaction module.

[0061] S104: The task instruction is sent to the large language model engine for intent parsing and step decomposition; the large language model engine executes the instruction upon receipt.

[0062] S105: Based on the decomposition steps, call and schedule the corresponding process automation components in the process automation component library to execute the task; the collaborative scheduling engine schedules the specific components in the process automation component library to complete the task based on the decomposition steps.

[0063] S106: After the process automation component completes its execution, the result intelligent enhancement pipeline is initiated to intelligently process the execution results. This is the core control step of the collaborative scheduling engine that triggers the result intelligent enhancement pipeline to work after the component's execution is complete. Sub-steps such as identifying task types, selecting enhancement modes, and performing deep analysis and content reconstruction are all implemented within the result intelligent enhancement pipeline through its task type recognizer, enhancement strategy selector, and interaction and collaboration with the large language model engine and knowledge base module.

[0064] S107: Output the enhanced result after intelligent processing. After the intelligent enhancement pipeline has completed the processing, the final result will be presented through the system output interface.

[0065] In some embodiments, the steps of the startup result intelligent enhancement pipeline include: Identify the task type of the task instruction; Based on the task type and the business role attributes of the intelligent agent, a target enhancement mode is selected from a variety of preset enhancement modes; Based on the target enhancement mode, the execution result is subjected to context-aware deep analysis and content reconstruction.

[0066] In some embodiments, the step of performing in-depth analysis and content reconstruction of the execution result according to the target enhancement mode includes: Based on the target enhancement mode, retrieve relevant business background knowledge or analysis models from the knowledge base; The execution results are combined with the retrieved business background knowledge or analysis model and input into the large language model engine for processing; Generate enhanced reports that include at least one of the following: data insights, attribution analysis, or decision recommendations.

[0067] This method embodies the dynamic collaboration process of the modules in the system. Its specific implementation details and beneficial effects have been explained in detail in the aforementioned system embodiments, and will not be repeated here.

[0068] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. An AI-powered digital employee system featuring brain-hand collaboration and enhanced outcome intelligence, characterized in that: include: The natural language interaction module is used to receive intelligent agent creation instructions or task instructions input by the user in natural language. The agent definition and management module is used to generate an agent framework with business role attributes in response to the creation command. The process automation component library contains multiple pre-built executable process components that encapsulate automated operations. A large language model engine is used to understand user intent and perform task planning; A collaborative scheduling engine connects and schedules the large language model engine and the process automation component library; The result is intelligent enhancement of the pipeline; The collaborative scheduling engine is configured to: send task instructions to the large language model engine for intent parsing and step decomposition; call and schedule the corresponding process automation components to execute according to the decomposed steps; and after the process automation components have completed execution, start the result intelligent enhancement pipeline to intelligently process the execution results and generate enhanced output.

2. The AI-powered digital employee system with brain-hand collaboration and result intelligence enhancement as described in claim 1, characterized in that, The resulting intelligent enhancement pipeline includes: Task type recognizer; An enhancement strategy selector is used to dynamically select an enhancement mode based on the task instruction context and agent role attributes. The enhancement mode includes at least one of formatting, trend analysis, anomaly attribution, and comprehensive report generation.

3. The AI-powered digital employee system with brain-hand collaboration and result intelligence enhancement according to claim 2, characterized in that, The system also includes: Knowledge base module; The intelligent enhancement pipeline is configured to: actively retrieve and inject relevant business background knowledge, historical data patterns, or analysis models from the knowledge base module into the context window of the large language model engine, based on the selected enhancement mode.

4. The AI-powered digital employee system with brain-hand collaboration and result intelligence enhancement according to claim 1 or 2, characterized in that, The result intelligent enhancement pipeline includes a result enhancement processing chain run by the large language model engine. The result enhancement processing chain includes at least a semantic understanding submodule for deep understanding of data semantics, a data insight submodule for mining data patterns, and a narrative generation submodule for organizing output content.

5. The AI-powered digital employee system with brain-hand collaboration and result intelligence enhancement according to claim 1, characterized in that, The system also includes: A visual configuration interface allows users to associate and bind process automation components, analysis models, and intelligent agent roles by dragging and dropping.

6. The AI-powered digital employee system with brain-hand collaboration and result intelligence enhancement according to claim 1, characterized in that, The resulting intelligent enhancement pipeline supports iterative feedback, where user evaluations of the enhancement outputs are recorded and used to optimize the enhancement strategy selection for subsequent tasks of the same agent.

7. The AI-powered digital employee system with brain-hand collaboration and result intelligence enhancement according to claim 1, characterized in that, The components in the process automation component library include robotic process automation components and application programming interface (API) calling components.

8. A method for constructing AI-powered digital employees with brain-hand collaboration and result-oriented intelligence enhancement, characterized in that: Applied to the system as described in any one of claims 1-7, the method comprises: Receives user-inputted intelligent agent creation instructions in natural language; In response to the creation instruction, an intelligent agent framework with business role attributes is generated; Receive task instructions from the user in natural language; The task instruction is sent to the large language model engine for intent parsing and step decomposition. Based on the decomposed steps, call and schedule the corresponding process automation components in the process automation component library to execute the tasks; After the process automation component completes its execution, a result intelligent enhancement pipeline is initiated to intelligently process the execution results; and Output enhanced results after intelligent processing.

9. The AI-powered digital employee construction method based on brain-hand collaboration and result intelligence enhancement according to claim 8, characterized in that, The steps of the intelligent enhancement pipeline for startup results include: Identify the task type of the task instruction; Based on the task type and the business role attributes of the intelligent agent, a target enhancement mode is selected from a variety of preset enhancement modes; Based on the target enhancement mode, the execution result is subjected to context-aware deep analysis and content reconstruction.

10. The AI-powered digital employee construction method based on brain-hand collaboration and result intelligence enhancement according to claim 9, characterized in that, The steps of performing in-depth analysis and content reconstruction of the execution result based on the target enhancement mode include: Based on the target enhancement mode, retrieve relevant business background knowledge or analysis models from the knowledge base; The execution results are combined with the retrieved business background knowledge or analysis model and input into the large language model engine for processing; Generate enhanced reports that include at least one of the following: data insights, attribution analysis, or decision recommendations.

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