Agent-driven industry expert capability replication system and method

Abstract

The application provides an agent-driven industry expert capability replication system and method. It includes a data acquisition module for collecting text materials, interactive data, decision data, behavior trajectories and oral interview data corresponding to industry experts; a behavior analysis module for extracting task types, decision step chains, attention features, decision styles and thought links; a knowledge extraction module for extracting factual knowledge, rule constraints, strategy experience and risk information; a capability map construction module for constructing an expert capability map; an agent generation module for generating an industry expert agent; a calling module for calling the industry expert agent to perform reasoning processing and output processing results and explanation links; and a self-evolution module for updating the expert capability map and the industry expert agent based on task feedback. The application can improve expert capability reuse efficiency, enhance task processing automation level and enhance decision output explainability.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to an agent-driven system and method for replicating the capabilities of industry experts. Background Technology

[0002] In business scenarios such as industry decision-making, professional consulting, risk assessment, planning optimization, and process execution, enterprises typically rely on a small number of experienced industry experts to handle complex tasks. These experts not only possess domain knowledge but also have comprehensive abilities to filter information, focus on key features, make trade-off judgments, select strategies, and interpret results based on experience. Therefore, how to digitally accumulate and reuse the capabilities of industry experts has become an important technical direction in the construction of intelligent systems.

[0003] In existing technologies, common solutions mainly include knowledge graphs, expert systems, and large-model-based question-answering systems. Knowledge graphs are primarily used to organize domain entities, relationships, and factual knowledge; expert systems mainly achieve reasoning and judgment under specific conditions through pre-set rules; and large-model-based question-answering systems mainly rely on large-scale corpus training to achieve knowledge-based question answering and content generation. While these technologies can provide knowledge retrieval, rule-based judgment, or intelligent question-answering capabilities to some extent, their processing objects are mostly limited to knowledge points, rule items, or text semantics.

[0004] However, existing technologies still have the following obvious shortcomings: First, it is difficult to express in an integrated way the decision-making steps, attention characteristics, preference styles and implicit reasoning links of industry experts in actual tasks; second, it is difficult to further encapsulate expert knowledge, rules, strategies and decision-making capabilities into executable industry expert intelligent agents; third, existing systems can usually only output results and lack the explanation links corresponding to the expert processing process; fourth, it is difficult to continuously update the expert capability expression structure and intelligent agent execution mechanism based on task feedback, thus making it difficult to achieve large-scale replication and long-term evolution of industry expert capabilities. Summary of the Invention

[0005] In view of this, embodiments of this application provide an agent-driven industry expert capability replication system and method to solve the problems of existing technologies, such as the difficulty in structurally replicating expert capabilities, the difficulty in agents performing interpretations, and the difficulty in continuously evolving capability models.

[0006] The first aspect of this application provides an agent-driven industry expert capability replication system, comprising: a data acquisition module for collecting textual materials, interaction data, decision data, behavioral trajectories, and oral interview data corresponding to industry experts, and performing desensitization, annotation, and structuring processing; a behavior analysis module for extracting task type, decision step chain, attention features, decision style, and thought process based on the structuring processing results; a knowledge extraction module for extracting factual knowledge, rule constraints, strategy experience, and risk information based on the decision step chain, attention features, decision style, and thought process; and a capability graph construction module. This module is used to construct an expert capability graph, which includes a concept knowledge graph, a rule and strategy graph, and a decision-making capability graph, based on factual knowledge, rule constraints, strategic experience, and risk information. The agent generation module generates an industry expert agent based on the expert capability graph, consisting of a task planning agent, a strategy reasoning agent, a knowledge retrieval agent, an explanation generation agent, and an output synthesis agent. The invocation module responds to task requests, invokes the industry expert agent to perform reasoning processing, and outputs the processing results and explanation chain. The self-evolution module updates the expert capability graph and the industry expert agent based on task feedback.

[0007] The second aspect of this application provides an agent-driven method for replicating the capabilities of industry experts based on the system of the first aspect. The method includes: collecting textual data, interaction data, decision data, behavioral trajectories, and oral interview data corresponding to industry experts, and performing desensitization, annotation, and structuring processing on the collected data; extracting task type, decision-making step chain, focus features, decision-making style, and thought process based on the structuring processing results; extracting factual knowledge, rule constraints, strategic experience, and risk information based on the decision-making step chain, focus features, decision-making style, and thought process; constructing an expert capability graph including a concept knowledge graph, a rule-strategy graph, and a decision-making capability graph based on the factual knowledge, rule constraints, strategic experience, and risk information; generating an industry expert agent composed of task planning, strategy reasoning, knowledge retrieval, explanation generation, and output synthesis based on the expert capability graph; responding to task requests, invoking the industry expert agent to perform reasoning processing, generating processing results and explanation processes; and updating the expert capability graph and the industry expert agent based on task feedback.

[0008] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects: The system employs several modules: a data acquisition module to collect textual data, interaction data, decision-making data, behavioral trajectories, and oral interview data from industry experts, followed by anonymization, labeling, and structuring; a behavior analysis module to extract task types, decision-making step chains, attention features, decision-making styles, and thought processes based on the structuring results; a knowledge extraction module to extract factual knowledge, rule constraints, strategic experience, and risk information based on the decision-making step chains, attention features, decision-making styles, and thought processes; a capability graph construction module to construct an expert capability graph, including a concept knowledge graph, a rule-strategy graph, and a decision-making capability graph, based on factual knowledge, rule constraints, strategic experience, and risk information; an agent generation module to generate an industry expert agent based on the expert capability graph, composed of a task planning agent, a strategy reasoning agent, a knowledge retrieval agent, an explanation generation agent, and an output synthesis agent; a calling module to respond to task requests, calling the industry expert agent to perform reasoning processing and output processing results and explanation processes; and a self-evolution module to update the expert capability graph and the industry expert agent based on task feedback. This application can improve the efficiency of expert capability reuse, enhance the level of task processing automation, and improve the interpretability of decision output. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a schematic diagram of the structural framework of the agent-driven industry expert capability replication system provided in the embodiments of this application; Figure 2 This is a flowchart illustrating the agent-driven method for replicating industry expert capabilities provided in this application embodiment. Detailed Implementation

[0011] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0012] In existing technologies, the digital applications targeting industry expert capabilities typically employ knowledge graphs, expert systems, and large-model-based question-answering systems. Knowledge graphs focus on organizing and associating domain entities, relationships, and factual knowledge; expert systems focus on achieving conditional judgments and conclusion outputs through pre-set rules; and large-model-based question-answering systems focus on providing knowledge-based question-answering or auxiliary analysis capabilities through semantic understanding and text generation. While these technologies can support knowledge retrieval, rule-based reasoning, or content generation to some extent, their processing objects are mostly limited to knowledge points, rule items, or semantic text levels, and they are still insufficient to fully encompass the decision-making steps, focus characteristics, preference styles, implicit thought processes, and task execution methods exhibited by industry experts in actual business operations.

[0013] Based on this, existing technologies have at least the following problems: On the one hand, it is difficult to extract and model industry expert capabilities in a structured manner from multi-source data, making it difficult for expert knowledge, rules, strategies, and decision-making processes to form a unified capability expression structure; on the other hand, existing solutions are unable to further transform the above capability expressions into industry expert intelligent agents that can directly participate in business processing, thus making it difficult to achieve automatic invocation and execution of expert capabilities in scenarios such as problem solving, decision suggestion, solution review, and task execution; in addition, existing systems usually focus on result output and lack an explanation link corresponding to the expert processing process, and it is also difficult to continuously update the capability model and execution mechanism based on task feedback, thus making it difficult to achieve large-scale replication and long-term evolution of industry expert capabilities.

[0014] To address the aforementioned technical issues, this application provides an agent-driven industry expert capability replication system. The system first collects textual materials, interaction data, decision-making data, behavioral trajectories, and oral interview data corresponding to industry experts, and performs anonymization, annotation, and structuring processing on the collected data. Then, based on the structuring processing results, it extracts task type, decision-making step chain, focus features, decision-making style, and thought process. Next, based on the decision-making step chain, focus features, decision-making style, and thought process, it extracts factual knowledge, rule constraints, strategic experience, and risk information, and constructs an expert capability graph including a concept knowledge graph, a rule-strategy graph, and a decision-making capability graph. On this basis, it further generates an industry expert agent based on the expert capability graph, composed of a task planning agent, a strategy reasoning agent, a knowledge retrieval agent, an explanation generation agent, and an output synthesis agent. Upon receiving a task request, the industry expert agent is invoked to perform reasoning processing and output the processing results and explanation process. Finally, based on task feedback, the expert capability graph and the industry expert agent are updated to form a continuously evolving capability replication mechanism.

[0015] Through the above technical solutions, this application can improve the efficiency of expert capability reuse, enhance the level of task processing automation, and strengthen the interpretability of decision output. At the same time, this application can further transform industry expert capabilities from static knowledge representations into executable, reasonable, and updatable intelligent agent capability structures, thereby enhancing the stability and continuous evolution capability of expert capabilities in complex business scenarios.

[0016] The specific framework and functions of the agent-driven industry expert capability replication system provided in this application embodiment will be described in detail below with reference to the accompanying drawings and specific embodiments. Figure 1 This is a schematic diagram of the structural framework of the agent-driven industry expert capability replication system provided in the embodiments of this application, as shown below. Figure 1 As shown, the system may specifically include: The data acquisition module 101 is used to collect textual materials, interaction data, decision data, behavioral trajectories and oral interview data corresponding to industry experts, and to perform anonymization, annotation and structuring processing. The behavior analysis module 102 is used to extract task type, decision-making step chain, attention features, decision-making style and thinking chain based on the structured processing results; The knowledge extraction module 103 is used to extract factual knowledge, rule constraints, strategy experience and risk information based on the decision-making step chain, attention features, decision-making style and thinking chain. The capability graph construction module 104 is used to construct an expert capability graph that includes a concept knowledge graph, a rule strategy graph, and a decision-making capability graph based on factual knowledge, rule constraints, strategy experience, and risk information. The intelligent agent generation module 105 is used to generate an industry expert intelligent agent based on the expert capability graph, which is composed of a task planning intelligent agent, a strategy reasoning intelligent agent, a knowledge retrieval intelligent agent, an explanation generation intelligent agent, and an output synthesis intelligent agent. Module 106 is invoked to respond to task requests, invoke industry expert intelligent agents to perform inference processing and output processing results and explanation links; The self-evolution module 107 is used to update the expert capability graph and industry expert agents based on task feedback.

[0017] In some embodiments, the data acquisition module is used for: Acquire textual materials, interaction data, decision data, behavioral patterns, and oral interview data from industry experts, and aggregate and organize them according to data source, task scenario, and time relationship to generate a multi-source expert dataset; Based on preset sensitive information identification rules and semantic replacement rules, a desensitization mapping process is performed on a multi-source expert dataset to generate a desensitized dataset. Based on task semantics, decision semantics, and behavioral semantics, segmented indexing, feature annotation, and association alignment are performed on the de-identified dataset to generate an annotated dataset corresponding to task type, decision node, behavioral event, and semantic fragment. Vector representation processing and structured reorganization are performed on the labeled dataset to generate structured processing results that are associated with the expert task context, decision process context, and behavioral process context.

[0018] Specifically, the data acquisition module does not merely summarize the raw data generated by industry experts. Instead, it focuses on the data granularity required for subsequent expert behavior analysis, knowledge extraction, and expert capability mapping. It uniformly collects, de-identifies, semantically annotates, and structurally reorganizes expert data from multiple sources, in different formats, and across different time spans to form structured processing results that characterize the expert's task processing, decision-making, and behavioral execution processes. Based on the aforementioned overall system solution, the collected data includes not only the knowledge content explicitly expressed by experts but also behavioral and process data reflecting their processing habits, judgment criteria, and experience transfer methods. This provides an input foundation for subsequent extraction of task types, decision-making step chains, focus features, decision-making styles, and thought processes.

[0019] In some implementations, the data acquisition module can access document management systems, business processing systems, instant messaging systems, terminal operation log systems, and interview recording transcription systems. Textual data may include expert-written reports, research articles, training materials, standard interpretation documents, and historical review comments; interactive data may include expert Q&A records, annotation records, and consultation response records in business groups; decision data may include input materials for historical tasks, analysis process records, and final output results; behavioral trajectories may include expert click sequences, page dwell order, search term input records, field viewing order, and modification action records in the business system; oral interview data may include the judgment criteria, exclusion logic, risk identification methods, and rules of thumb explained by experts around typical business cases. To support subsequent cross-source association, this embodiment aggregates and organizes the above data according to data source identifiers, task scenario identifiers, and timestamp information, organizing data belonging to the same business matter, the same analysis round, or the same expert processing link into a unified multi-source expert dataset.

[0020] In the desensitization mapping process, sensitive information identification rules and semantic replacement rules can be pre-configured. Sensitive information identification rules can be used to identify names, customer names, project numbers, specific addresses, contact information, monetary values, or other protected fields. Semantic replacement rules can be used to map identified sensitive content to alternative identifiers that maintain business semantics but do not expose the true identity; for example, replacing a specific customer name with a customer entity identifier, a specific project number with a task number, and a specific personnel name with a role identifier. Through this process, the impact of sensitive information on subsequent training, graph construction, and agent generation processes can be reduced while preserving the expert judgment context, business relationships, and decision-making basis.

[0021] During the annotation and processing phase, the data acquisition module performs segmented indexing, feature annotation, and correlation alignment on the de-identified data based on task semantics, decision semantics, and behavioral semantics. Task semantics is mainly used to identify the task objective, task category, and processing object to which the current data segment belongs; decision semantics is mainly used to identify condition judgments, risk warnings, rule application, strategy selection, and result conclusions; behavioral semantics is mainly used to identify actions such as viewing, retrieving, comparing, excluding, confirming, and correcting.

[0022] For example, in a risk assessment scenario within a specific industry, experts review the application materials submitted by a company. The text documents record the review guidelines, chat logs show the experts' reminders of key risk points, historical task input / output pairs reflect the handling conclusions of similar cases, and system operation logs show that experts prioritize viewing financial fields, related-party transaction fields, and abnormal change records. The data acquisition module can label "reviewing application materials" as a task-type related segment, "first verifying the main entity information, then checking abnormal changes, and finally comparing risk rules" as a decision-node related segment, and operations such as "viewing financial fields" and "retrieving historical penalty records" as behavioral event related segments. It also aligns semantically consistent or temporally connected segments from different sources to form a labeled dataset.

[0023] In the vector representation and structured reorganization stages, the data acquisition module can generate vector representations for semantic fragments, decision node descriptions, behavioral event descriptions, and interview transcripts, respectively, to support subsequent clustering of similar fragments, cross-case alignment, and context-related retrieval. Simultaneously, based on the aforementioned annotation results, the original discrete data can be reorganized into expert task context, decision process context, and behavioral process context. The expert task context represents the expert's input conditions, target objects, and output requirements in a certain type of task; the decision process context represents the expert's step sequence, judgment conditions, and basis for judgment; and the behavioral process context represents the expert's operation path, information viewing order, and processing habits within the system. Through these processes, the original expert data, originally scattered across multiple carriers, is transformed into structured processing results that can be directly accessed by the behavior analysis module.

[0024] For example, for data from a senior risk control expert across multiple due diligence tasks, this embodiment can uniformly incorporate their historical due diligence reports, project review annotations, consultation Q&A, system search records, and interview descriptions into a multi-source expert dataset; anonymize and map the company names, customer identities, and transaction amounts involved; further annotate the task semantics and decision semantic fragments corresponding to "entity verification," "transaction chain review," "anomaly identification," and "risk rating," and annotate content such as "prioritize checking counterparty concentration" and "conduct secondary searches on abnormal association paths" as behavioral semantic fragments; finally, a structured processing result is formed that is associated with the context of the due diligence task, the context of the risk judgment process, and the context of the review behavior, allowing subsequent modules to extract the expert's decision-making step chain, focus features, and thought process.

[0025] Through the above implementation methods, this embodiment can uniformly collect and structure the text, interaction, decision-making, behavior, and oral data of industry experts, enabling data from different sources and at different granularities to form related, searchable, and computable processing results. This improves the efficiency of converting raw expert data into subsequent capability analysis data and provides a consistent data foundation for expert behavior analysis, knowledge extraction, and expert capability graph construction.

[0026] In some embodiments, the behavior parsing module is used for: Based on the structured processing results, the task objectives, input elements, output elements and processing scenarios corresponding to each semantic segment are identified, and the task type is determined. Based on the task type, the operation events, judgment events and result events in the structured processing results are subjected to temporal correlation and causal merging processing to generate a decision step chain corresponding to the task type; Based on the decision-making step chain, the target concern object, constraint concern object, risk concern object and trade-off concern object corresponding to each step are extracted to generate concern features; Based on the decision-making step chain, the set of features of concern, and historical output results, the risk preference parameters, robustness preference parameters, and trade-off preference parameters corresponding to each task type are identified, and decision-making style representation results are generated. Based on the decision-making step chain, the set of features of concern, and the representation results of decision-making styles, a chain-like structure is constructed between input information selection, feature extraction, trade-off judgment, preference application, and result output to generate a thought process chain.

[0027] Specifically, the behavior analysis module, based on the structured processing results output by the aforementioned data acquisition module, analyzes the task identification methods, step-by-step progression methods, focus areas, style preferences, and implicit thinking processes exhibited by industry experts in actual business processing. This transforms the expert processing experience, originally scattered across textual materials, interaction records, historical decision results, behavioral trajectories, and oral interviews, into behavioral structure results that can be used for subsequent knowledge extraction and expert capability mapping. Unlike methods that merely record expert conclusions, this embodiment emphasizes explicitly reconstructing the processes of "how experts handle tasks, how they filter information, how they form judgments, and how they impose personal preferences," in order to subsequently generate intelligent agents with industry expert styles.

[0028] In this embodiment, the term "task type" refers to the processing category corresponding to different business matters faced by experts, such as consultation and answering, solution review, risk identification, decision-making advice, or process handling. The task objective characterizes the business goal that the task aims to achieve. Input elements characterize the materials, indicators, events, or contextual information relied upon by the expert when making judgments. Output elements characterize the expert's final conclusion, recommendation, conclusion level, or handling plan. The processing scenario characterizes the industry environment, business stage, and constraints in which the task occurs. The behavior analysis module can first perform semantic recognition processing on each semantic fragment in the structured processing result, merging text descriptions, operation events, judgment statements, and result expressions within the same task chain to determine the task type to which the current fragment belongs.

[0029] After identifying the task type, the behavior analysis module further performs temporal correlation and causal merging processing on the operational events, judgment events, and result events in the structured processing results to form a decision-making step chain. Operational events can include behaviors such as retrieval, viewing, comparison, filtering, verification, exclusion, confirmation, and correction; judgment events can include rule application, anomaly identification, boundary judgment, risk assessment, and path selection; result events can include outputting risk levels, providing handling suggestions, forming review opinions, or triggering subsequent processes. By associating these events according to their sequence of occurrence and causal relationships, a relatively stable sequence of steps for experts under specific task types can be obtained, such as "receiving input materials—filtering key fields—comparing rule constraints—identifying abnormal signs—comprehensive weighing—outputting conclusions."

[0030] Furthermore, after the decision-making process chain is formed, the behavior analysis module extracts key features from each step. These key features are not ordinary keywords, but rather the objects and factors that experts continuously focus on when handling tasks. They can include at least target objects of focus, constraint objects of focus, risk objects of focus, and trade-off objects of focus. Target objects of focus reflect the core business objects that experts initially identify; constraint objects of focus reflect the rule boundaries, limiting conditions, and preconditions that experts focus on verifying during the judgment process; risk objects of focus reflect abnormal signs, potential risk triggers, and uncertainties that experts are particularly sensitive to; and trade-off objects of focus reflect the key considerations experts make when choosing between multiple options, indicators, or risks.

[0031] Furthermore, the behavior analysis module can combine decision-making step chains, focus features, and historical output results to summarize the stable preferences exhibited by experts when handling similar tasks, thereby generating decision-making style representation results. In this embodiment, decision-making style includes at least risk preference parameters, robustness preference parameters, and trade-off preference parameters. For example, some experts tend to be more rigorous in risk identification tasks, with higher risk preference parameters; some experts pay more attention to the consistency of rule boundaries, with higher robustness preference parameters; and some experts tend to retain key evidence chains when efficiency and integrity conflict, with their trade-off preference parameters showing evidence priority. By statistically summarizing the output conclusions of multiple historical tasks and their corresponding step chains, the behavior analysis module can establish a mapping relationship between different task types and corresponding decision-making styles.

[0032] Building upon this foundation, the behavior analysis module combines the decision-making step chain, the characteristics of interest, and the representation results of decision-making styles to construct a chain-like structure connecting input information selection, feature extraction, weighted judgment, preference application, and output, thereby generating a thought process chain. This thought process chain represents how experts do not directly move from input to output. Instead, they first select key information, then identify features with decision-making value, weigh them according to rules, experience, and risk, and finally apply personal style preferences, ultimately forming the output result. In this way, the implicit judgment logic of experts can be transformed from previously invisible experiential activities into a representable and callable chain structure.

[0033] For example, in a risk assessment scenario within a specific industry, the aforementioned data acquisition module has already obtained a due diligence report from a senior risk control expert, project review annotations, group Q&A records, system operation logs, and interview transcripts, and generated structured processing results. The behavior analysis module first identifies the current task as a risk identification and assessment task type from semantic fragments such as "project due diligence," "risk review," and "opinion issuance." It then extracts input elements such as corporate entity information, transaction records, related party information, and penalty records from the input materials, and output elements such as risk level, supplementary verification suggestions, and approval status from historical review opinions. Finally, it extracts the processing scenario from business timelines and review requirements.

[0034] Subsequently, based on the order of actions in the operation log ("first check the entity change record, then search for abnormal transaction paths, and then verify historical penalty information"), and the judgment expressions in the annotation records ("abnormal association needs further verification" and "insufficient entity consistency should increase the risk level"), the module performs temporal correlation and causal merging of operation events, judgment events, and result events to obtain the decision step chain corresponding to this type of task.

[0035] Building upon this decision-making process chain, the behavioral analysis module further identifies the experts' focus areas at different stages. For example, in the entity verification stage, the target focus areas are the enterprise's entity information and control relationships; in the rule comparison stage, the constraint focus areas are the reporting rules, disclosure requirements, and historical penalty thresholds; in the anomaly identification stage, the risk focus areas are abnormal related-party transactions, abnormal funding paths, and inconsistencies in documentation; and in the final rating stage, the focus areas are the severity of the risk, the sufficiency of evidence, and the time-bound requirements of the business.

[0036] Furthermore, considering the expert's track record in various historical cases where they generally adopt a rigorous review approach, prioritize the preservation of risk evidence, and tend to conduct supplementary investigations before drawing conclusions in marginal cases, the module generates risk preference parameters, robustness preference parameters, and trade-off preference parameters for this task type. Finally, the module constructs the expert's thought process: after receiving project materials, they first select relevant information on the subject, transaction, and penalties; then, they extract characteristics of abnormal changes, abnormal transactions, and rule conflicts; subsequently, they weigh the rule boundaries against the risk level, apply a preference for rigorous review and prioritizing evidence, and finally output a risk rating and review recommendations.

[0037] Through the above implementation methods, this embodiment can uniformly analyze the task identification logic, step-by-step progression logic, key focus logic, and style preference logic reflected by industry experts in multi-source data, forming a decision-making step chain, focus features, decision-making style representation results, and thought process chain. This improves the explicitness and computability of expert behavior processes and provides a consistent and complete behavioral analysis foundation for subsequent knowledge extraction, expert capability graph construction, and industry expert agent generation.

[0038] In some embodiments, the knowledge extraction module is used for: Based on the decision-making step chain and thinking link, the fact expression unit corresponding to the task object, business element, state attribute and relationship is identified, and entity normalization and relationship alignment are performed on the fact expression unit to generate fact knowledge; Based on the decision-making step chain, focus features, and thought process, constraint expression units corresponding to condition judgments, boundary restrictions, dependencies, and triggering relationships are extracted, and condition items and result items are mapped to generate rule constraints. Based on the decision-making step chain, decision-making style, and thinking path, the strategy expression units corresponding to the step selection method, the order of trade-offs, the disposal path, and the preference application method are extracted, and scenario classification and pattern merging are performed to generate strategy experience. Based on the focus on characteristics, decision-making style and thinking process, risk expression units corresponding to abnormal signs, risk types, risk triggering conditions and avoidance paths are extracted, and risk level association and disposal association processing are performed to generate risk information; Factual knowledge, rule constraints, strategic experience, and risk information are hierarchically linked and organized to generate a knowledge set for constructing an expert capability graph.

[0039] Specifically, the knowledge extraction module, based on the decision-making step chain, focus characteristics, decision-making style, and thought process chain output by the aforementioned behavior analysis module, further transforms the factual cognition, rule grasp, strategic experience, and risk judgment demonstrated by industry experts in actual business processing into hierarchically organized and associatively invoked knowledge units, thereby providing direct input for subsequent expert capability graph construction. Unlike methods that only extract entities and relationships from text, the knowledge extraction in this embodiment is not an isolated extraction from static corpora, but rather combines the step sequence, focus, style preferences, and chain-like thinking paths in the expert's processing process to perform evidence-based hierarchical refinement and organization of multiple types of knowledge content.

[0040] In this embodiment, factual knowledge is mainly used to characterize the task objects, business elements, status attributes, and their interrelationships identified by experts in task processing. For example, in an industry risk assessment scenario, the task object can be the applicant company, related parties, transaction entities, or review matters; the business elements can be application materials, transaction records, penalty records, control relationships, and historical change information; the status attributes can be normal, abnormal, pending verification, high-risk, etc.; and the relationships can be control relationships, transaction relationships, dependency relationships, and temporal relationships.

[0041] The knowledge extraction module, based on decision-making process chains and thought processes, identifies corresponding factual expression units from textual materials, annotations, Q&A records, and interview transcripts. It then performs entity unification and relationship alignment on these factual expression units. For example, it unifies "project company," "applicant entity," and "applicant enterprise" into the same task object entity, and aligns "abnormal transactions," "abnormal funding paths," and "suspicious transaction chains" into similar business relationships, thereby generating factual knowledge that can be uniformly referenced.

[0042] The extraction of rule constraints focuses on identifying the conditions, boundary restrictions, dependencies, and triggering relationships that experts rely on during the decision-making process. The knowledge extraction module can combine the judgment events reflected in the decision-making step chain, the constraint objects reflected in the focus features, and the judgment nodes in the thought process to extract constraint expression units with the feature of "under what conditions can a certain conclusion be reached", and perform mapping processing between condition items and result items.

[0043] For example, in the aforementioned risk control experts' handling of project due diligence tasks, if statements such as "the risk level should be increased when the subject information is inconsistent and there is an abnormal transaction path" and "the project should not be directly approved before the historical penalties are eliminated" appear repeatedly in the interview content and historical review opinions, the knowledge extraction module can identify "inconsistent subject information," "abnormal transaction path," and "historical penalties not eliminated" as condition items, and "increase the risk level" and "cannot be directly approved" as result items, and establish a mapping relationship between condition items and result items to generate rule constraints.

[0044] In some examples, the extraction of strategic experience primarily focuses on the experiential approaches experts develop when handling complex tasks. The knowledge extraction module extracts strategy expression units corresponding to step selection methods, trade-off sequences, handling paths, and preference application methods based on decision-making step chains, decision-making styles, and thought processes. Step selection methods can be understood as where experts prioritize starting; trade-off sequences can be understood as what factors experts compare first and last among multiple factors; handling paths can be understood as choosing which handling route under what circumstances; and preference application methods reflect the expert's style bias in areas such as robust review, efficiency priority, or evidence priority. The knowledge extraction module can perform scenario classification and pattern merging processing on the above strategy expression units, grouping content with similar processing logic into strategic experience. For example, "first verify the consistency of the subject, then review the transaction path, and finally compare the rule boundaries" can be grouped into a pre-verification strategy, and "supplementary verification of marginal cases before drawing conclusions" can be grouped into a robust review strategy.

[0045] In some examples, risk information extraction is primarily used to refine the anomaly identification and risk management experience accumulated by experts in long-term business operations. The knowledge extraction module, based on focus characteristics, decision-making styles, and thought processes, extracts risk expression units corresponding to anomaly symptoms, risk types, risk triggering conditions, and avoidance paths, and performs risk level association and management association processing. For example, in a due diligence task for a company, experts might point out in their annotations that abnormal concentration of related-party transactions, frequent changes in control relationships, and inconsistencies in the accuracy of materials are anomaly symptoms, and corresponding management conclusions such as "high risk," "needs supplementary due diligence," and "temporarily suspended" are given in the historical output results. The knowledge extraction module can establish associations between these anomaly symptoms and the corresponding risk types, risk levels, and management paths to form risk information, enabling the subsequent system not only to know what constitutes a risk, but also the corresponding management methods and priorities.

[0046] Furthermore, after extracting factual knowledge, rule constraints, strategic experience, and risk information, the knowledge extraction module performs hierarchical association processing on the above content to generate a knowledge set for constructing an expert capability graph. Factual knowledge can be categorized into the fact layer, representing the objective objects and relationships identified by the expert; rule constraints can be categorized into the rule layer, representing the conditional logic followed by the expert in making judgments; strategic experience and risk information can be further associated with the rule layer and the decision layer, representing the expert's experiential processing methods and risk response logic in specific scenarios. In this way, the subsequent capability graph construction module can integrate static facts, rule logic, and dynamic experience into the expert capability expression system under a unified structure.

[0047] For example, in a due diligence example for a project handled by a senior risk control expert, the knowledge extraction module can extract factual knowledge such as the applicant entity, related transaction counterparties, and historical penalties from its historical due diligence reports, review comments, Q&A records, and interview materials. It can also extract rule constraints that prevent direct approval when entity information is abnormal and penalties have not been rectified, extract strategic experience that prioritizes verifying entity consistency, then identifies abnormal transactions, and finally conducts a comprehensive rating, and extract risk information that corresponds to a higher risk level and requires supplementary verification due to frequent changes in control relationships. Subsequently, this content is hierarchically linked and organized into a knowledge set for graph construction, which can then be used to build a conceptual knowledge graph, a rule and strategy graph, and a decision-making capability graph.

[0048] Through the above implementation methods, this embodiment can combine the expert's decision-making process chain, focus characteristics, decision-making style and thinking chain to extract and organize factual knowledge, rule constraints, strategic experience and risk information in a hierarchical manner, so that the expert's knowledge content, judgment basis, experience pattern and risk cognition in business processing can be uniformly expressed, thereby improving the consistency between the knowledge extraction results and the expert's actual processing process, and providing a clear and hierarchical knowledge foundation for the construction of expert capability map.

[0049] In some embodiments, the capability mapping construction module is used for: Based on factual knowledge, a conceptual knowledge graph is constructed, consisting of task object nodes, business element nodes, state attribute nodes, and relation edges. Based on rule constraints and policy experience, a rule-policy graph consisting of condition nodes, rule nodes, policy nodes and constraint edges is constructed, and a semantic mapping relationship between the rule-policy graph and the concept knowledge graph is established. Based on the decision-making step chain, decision-making style, thinking link and risk information, a decision-making capability graph is constructed, consisting of decision-making step nodes, migration edges, preference constraint edges and risk association edges, and node weights and migration weights are configured for each decision-making step node and migration edge. Based on concept knowledge graphs, rule strategy graphs, and decision capability graphs, we perform identifier alignment, inter-layer link establishment, and path coupling processing on cross-graph related nodes to generate expert capability graphs.

[0050] Specifically, the capability graph construction module is used to construct an expert capability graph that can uniformly represent the knowledge structure, rule structure, and decision-making structure of industry experts, based on the factual knowledge, rule constraints, strategic experience, and risk information output by the aforementioned knowledge extraction module, as well as the decision-making step chain, decision-making style, and thought process chain output by the behavior analysis module. This module does not merely establish connections between static knowledge, but incorporates the object identification methods, rule application methods, path selection methods, and risk handling methods of experts in actual business processing into the graph structure, enabling the subsequently generated industry expert agents to perform task planning, knowledge retrieval, strategy reasoning, and interpretation generation based on the graph.

[0051] In this embodiment, the capability graph construction module first constructs a conceptual knowledge graph based on factual knowledge. The conceptual knowledge graph primarily serves to represent the task objects, business elements, state attributes, and their relationships identified by industry experts during task processing. For example, in the project due diligence scenario corresponding to the aforementioned risk control expert, task object nodes may include the applicant, related parties, counterparties, and review items; business element nodes may include application materials, transaction records, penalty records, control relationships, and change records; and state attribute nodes may include states such as consistent, abnormal, pending verification, and high risk. Based on the entities and relationships that have been normalized and aligned in the factual knowledge, the capability graph construction module establishes relationship edges between task object nodes, business element nodes, and state attribute nodes, thereby forming a conceptual knowledge graph used to represent the semantic structure of business objects.

[0052] When constructing the rule-policy graph, the capability graph construction module further builds a rule-policy graph composed of condition nodes, rule nodes, policy nodes, and constraint edges based on rule constraints and policy experience. Among them, condition nodes are used to represent the preconditions for triggering judgments, rule nodes are used to represent the judgment rules adopted by experts under specific conditions, and policy nodes are used to represent the empirical handling paths adopted by experts in complex or boundary scenarios.

[0053] For example, in the aforementioned project due diligence example, inconsistent subject information, abnormal transaction paths, and unresolved historical penalties can be constructed as condition nodes, while raising the risk level and prohibiting direct passage can be constructed as rule nodes, and supplementary verification before forming a conclusion and prioritizing the preservation of key evidence chains can be constructed as strategy nodes. The interaction relationship between condition nodes, rule nodes, and strategy nodes can be established through constraint edges.

[0054] Meanwhile, in order to ensure that the rule strategy graph corresponds to the business object structure, the capability graph construction module also establishes a semantic mapping relationship between the rule strategy graph and the concept knowledge graph. For example, the "inconsistent subject information" condition node is mapped to the declaration subject node and status attribute node in the concept knowledge graph, and the "abnormal transaction path" is mapped to the counterparty node, transaction flow node and abnormal status node.

[0055] Furthermore, the capability graph construction module constructs a decision-making capability graph based on the decision-making step chain, decision-making style, thought process, and risk information. This graph is mainly used to represent the expert's step-by-step logic, path-transfer logic, style application logic, and risk response logic in task processing. Specifically, the module can construct processing stages such as "subject verification," "anomaly identification," "rule comparison," "comprehensive rating," and "output recommendations" as decision-making step nodes, construct the sequential transfer relationships between steps as migration edges, apply the corresponding robustness preference, risk preference, and trade-off preference in the decision-making style to the relevant step nodes or migration edges to form preference constraint edges, and establish risk association edges between the abnormal signs, risk levels, and handling paths in the risk information and the corresponding decision-making steps. To represent the expert's emphasis on different steps and different paths, the capability graph construction module can also configure node weights for each decision-making step node and migration weights for migration edges, reflecting that certain steps are more often prioritized and certain migration paths are more frequently adopted in similar tasks.

[0056] Furthermore, after the three sub-graphs are constructed, the capability graph construction module performs cross-graph association processing on the concept knowledge graph, rule strategy graph, and decision capability graph. This processing includes at least identifier alignment, inter-layer link establishment, and path coupling. Identifier alignment is used to uniformly identify nodes representing the same business object, the same risk event, or the same decision-making stage in different graphs; inter-layer link establishment is used to establish explicit associations between concept layer nodes and rule layer nodes, and between rule layer nodes and decision layer nodes; path coupling is used to connect the semantic path of the business object, the rule application path, and the decision migration path, thereby forming a complete link from task object identification to rule judgment and then to decision output.

[0057] For example, in the aforementioned project due diligence example corresponding to the risk control expert, the conceptual knowledge graph includes nodes such as the reporting entity, abnormal transaction path, and historical penalties; the rule strategy graph includes the rule node "cannot pass directly when the entity is abnormal and the penalty has not been eliminated" and the strategy node "supplementary verification before rating"; and the decision capability graph includes step nodes such as entity verification, anomaly identification, rule comparison, and risk rating. The capability graph construction module can align the conceptual layer state node "abnormal reporting entity" with the rule layer condition node, link the "cannot pass directly" rule node with the risk rating step node in the decision layer, and couple the "supplementary verification before rating" strategy node with the decision migration path, enabling the system to complete object identification, rule invocation, and decision path selection along a unified link, ultimately generating the expert capability graph.

[0058] Through the above implementation methods, this embodiment can construct the business object knowledge, rule strategy knowledge, and decision-making ability knowledge of industry experts into a multi-layered graph structure that is mutually coupled, so that the knowledge expression, rule expression, and decision expression of experts form a unified relationship, thereby improving the completeness and structural consistency of expert capability modeling, and providing a searchable, reasonable, and interpretable graph foundation for the subsequent generation of industry expert intelligent agents.

[0059] In some embodiments, the agent generation module is used for: Based on the expert capability graph, task path templates, rule strategy paths, decision transfer paths, and interpretation association paths corresponding to task types are extracted to generate intelligent agent collaborative configuration results. Based on the agent collaborative configuration results and task path templates, a task planning agent is constructed to determine the task decomposition order, step dependencies and execution phase boundaries. Based on rule-based policy paths, decision transfer paths, and decision styles, a policy reasoning agent is constructed for performing condition determination, path selection, preference constraint imposition, and risk trade-off processing. Based on the graph nodes, inter-layer links, and semantic mapping relationships in the expert capability graph, a knowledge retrieval agent is constructed to perform knowledge localization, association expansion, and evidence aggregation processing. Based on the interpretation of the associated path, the decision transfer path, and the evidence aggregation results, an interpretation generation agent is constructed to generate the basis for the steps, the basis for the judgment, and the description of the path. Based on the collaborative invocation relationship between the task planning agent, the strategy reasoning agent, the knowledge retrieval agent, and the interpretation and generation agent, an output synthesis agent is constructed for integrating execution results and organizing output, so as to generate an industry expert agent.

[0060] Specifically, the agent generation module, based on the expert capability graph formed by the aforementioned capability graph construction module, further transforms the task object semantics, rule strategy semantics, decision transfer semantics, and interpretation association semantics carried in the graph into an executable multi-agent collaborative structure. This allows industry expert capabilities to move beyond a graph-based representation and participate in specific task processing through agent collaboration. Unlike implementations that directly generate results based on a single large model, in this embodiment, the functional agents are not isolated but are constructed by dividing tasks around different path structures and inter-layer relationships within the expert capability graph, forming a unified industry expert agent through collaborative configuration.

[0061] In this embodiment, the agent generation module first extracts task path templates, rule-policy paths, decision-transfer paths, and interpretation-related paths corresponding to the task type based on the expert capability graph, and generates agent collaborative configuration results. The task path template represents the standard processing stages, stage order, and stage boundaries under a specific task type; the rule-policy path represents the combination of rule nodes and policy nodes to be invoked under different conditions; the decision-transfer path represents the migration order, branching method, and preference constraint method of experts among multiple processing steps; and the interpretation-related path represents the correspondence between the result output and the step basis, judgment basis, and risk basis. By extracting and arranging the above paths, different functional agents can subsequently operate collaboratively around the same task chain when invoked.

[0062] In the construction of the task planning agent, the agent generation module, based on the agent's collaborative configuration results and task path templates, forms a planning structure to determine the task decomposition order, step dependencies, and execution phase boundaries. Taking the aforementioned industry risk review scenario as an example, under the project due diligence task type, the task path template can include stages such as "receiving data—subject verification—anomaly identification—rule comparison—risk rating—conclusion output." Based on this, the task planning agent can break down the external input task into multiple sub-steps and clarify the prerequisite dependencies and subsequent connections of each sub-step. For example, the risk rating stage is not entered before subject verification is completed, and the rule comparison stage is triggered only after anomaly identification is completed, thereby ensuring that the subsequent reasoning process conforms to the expert's actual processing order.

[0063] The strategy reasoning agent is constructed based on rule-based strategy paths, decision transition paths, and decision-making styles. This agent is primarily responsible for determining execution conditions, selecting paths, imposing preference constraints, and handling risk trade-offs. Taking a risk control expert's due diligence task as an example, when knowledge retrieval results indicate inconsistencies in the applicant's information, anomalies in the transaction path, and unresolved historical penalties, the strategy reasoning agent can invoke the rule node corresponding to "cannot pass directly" according to the rule-based strategy path. Based on robustness and evidence-first preferences reflected in its decision-making style, it prioritizes the transition path of "supplementary verification before rating," rather than directly outputting a low-risk conclusion. Thus, the strategy reasoning agent not only executes rules but also reflects the expert's path-choice approach in boundary scenarios.

[0064] The knowledge retrieval agent is constructed based on graph nodes, inter-layer links, and semantic mapping relationships in the expert capability graph, and is used to perform knowledge localization, association expansion, and evidence aggregation processing. Specifically, the agent can locate the corresponding task object nodes, business element nodes, and state attribute nodes in the conceptual knowledge graph according to the current task step, and then expand them to the condition nodes, rule nodes, and strategy nodes in the rule and strategy graph through inter-layer links. It is further associated with the corresponding step nodes and risk-related edges in the decision-making capability graph, thereby aggregating and forming an evidence set that matches the current task stage. For example, in the anomaly identification stage, the knowledge retrieval agent can retrieve evidence such as abnormal transaction paths, unremoved penalty records, and frequent changes in the subject's control relationship, and organize it as input for subsequent strategy reasoning and interpretation generation.

[0065] The explanation-generating agent is constructed based on explanation-related paths, decision-transfer paths, and evidence aggregation results. This agent does not simply restate conclusions, but rather generates step-by-step explanations, judgment criteria, and path descriptions corresponding to the task execution process, based on the established step-by-step explanation relationships, rule-based relationships, and risk-related relationships in the expert capability graph. For example, if a project is assessed as high-risk and requires supplementary verification, the explanation-generating agent can explain that this conclusion originates from the subject information verification results, abnormal transaction identification results, and historical penalty status, and further explain why the "supplementary verification followed by re-evaluation" processing path is adopted under the current evidence conditions, thus ensuring that the explanation content is consistent with the expert's actual judgment chain.

[0066] Building upon the above, the agent generation module further constructs an output synthesis agent based on the collaborative invocation relationships among the task planning agent, strategy reasoning agent, knowledge retrieval agent, and interpretation generation agent. This output synthesis agent integrates execution results and organizes output. It receives task execution sequences, reasoning conclusions, evidence sets, and interpretations generated by each preceding agent and arranges them uniformly according to a preset output structure, forming a final result suitable for consultation responses, review opinions, risk warnings, or decision-making suggestions. In this way, the industry expert agent can be presented as a cohesive carrier of expert capabilities that is executable, reasonable, and interpretable.

[0067] Through the above implementation methods, this embodiment can further transform the task paths, rule strategies, decision transfers, and interpretation associations in the expert capability graph into a collaborative structure of multifunctional intelligent agents, enabling task planning, knowledge retrieval, strategy reasoning, interpretation generation, and result output to form a closed-loop calling relationship. This improves the degree of transformation of expert capabilities into intelligent agent execution capabilities and provides a unified intelligent agent foundation for the automatic processing of subsequent task requests and expert interpretation output.

[0068] In some embodiments, the calling module is used to: Receive task requests and parse the task objectives, input elements, constraints, and output requirements corresponding to the task requests to generate task context; Based on the task context and expert capability graph, the target task path, target rule strategy path and target decision migration path corresponding to the task request are determined, and an intelligent agent invocation scheme is generated. Based on the agent invocation scheme, the task planning agent is invoked to decompose and orchestrate the execution steps of the task context, and generate a task execution sequence. According to the task execution sequence, the knowledge retrieval agent and the strategy reasoning agent are invoked to perform graph retrieval, evidence assembly, condition determination, path selection and preference constraint processing, and generate intermediate reasoning results; Based on the intermediate inference results, the interpretation generation agent is invoked to generate the interpretation link corresponding to the task execution sequence, and the output synthesis agent is invoked to encapsulate the intermediate inference results and interpretation links, and generate and output the processing results and interpretation links.

[0069] Specifically, the invocation module, based on the already constructed industry expert agent, receives external task requests for actual business scenarios and drives the task planning agent, knowledge retrieval agent, strategy reasoning agent, interpretation generation agent, and output synthesis agent to execute collaboratively. This transforms the expert capabilities formed through data collection, behavior analysis, knowledge extraction, capability graph construction, and agent generation into processing results and interpretation chains for specific tasks. Unlike implementations that merely use the graph as a query base or a large model as an answering tool, the invocation module in this embodiment emphasizes dynamically matching task paths, rule-strategy paths, and decision transition paths based on the task context, enabling the industry expert agent to complete task execution in the manner that experts handle similar business scenarios.

[0070] In this embodiment, the calling module first receives a task request and parses the task objective, input elements, constraints, and output requirements corresponding to the task request to generate a task context. The task request can originate from a business system, external interface, process engine, or expert-assisted terminal. In the aforementioned industry risk review example, the task request could be a due diligence application for a project or a risk review request for a company. The task objective could be to generate a risk rating and review opinion; input elements could include application materials, transaction records, entity information, related relationships, and historical penalty records; constraints could include review time limits, applicable rule scope, and verification depth requirements; and output requirements could include risk level, supplementary verification suggestions, and explanations. By parsing the above content, the calling module forms a task context corresponding to the current task, providing a unified input for subsequent path selection and agent scheduling.

[0071] Furthermore, after generating the task context, the invocation module further determines the target task path, target rule-policy path, and target decision-transfer path corresponding to the task request based on the task context and expert capability graph, and generates an agent invocation scheme. The target task path corresponds to the standard processing flow under this task type, the target rule-policy path corresponds to the rule nodes and policy nodes that need to be prioritized under the current input conditions, and the target decision-transfer path corresponds to the step transfer method and style constraint method that match the current scenario in the expert capability graph.

[0072] For example, if a company has inconsistent entity information, abnormal transaction chain, and penalty records that have not been cleared, the calling module can determine the target task path that should be adopted from the expert capability graph: "entity verification - anomaly identification - rule comparison - risk escalation - supplementary verification suggestion". At the same time, it matches the target rule strategy path of "cannot pass directly" and "prioritize supplementary verification", and further matches the target decision migration path with robustness preference and evidence priority preference, thereby forming an intelligent agent calling scheme for the current task.

[0073] Furthermore, after obtaining the agent invocation plan, the invocation module calls the task planning agent to decompose and orchestrate the task context execution steps, generating a task execution sequence. The task planning agent does not simply execute all steps in a fixed order, but rather adapts and orchestrates the task steps based on the input completeness, constraints, and output requirements of the current task context. For example, in scenarios with incomplete input materials, the task planning agent can first insert a "material completeness verification" sub-step before proceeding to the main body verification and anomaly identification steps; in scenarios with tight deadlines but significant risk characteristics, high-risk item screening steps can be prioritized. The resulting task execution sequence more closely resembles the processing methods used by experts in real-world business scenarios.

[0074] According to the task execution sequence, the calling module further calls the knowledge retrieval agent and the strategy reasoning agent to perform graph retrieval, evidence assembly, condition determination, path selection, and preference constraint processing, generating intermediate reasoning results. Specifically, the knowledge retrieval agent can retrieve task objects, business elements, and state attributes from the concept knowledge graph around the current step, and then extend them to the condition nodes, rule nodes, and strategy nodes in the rule and strategy graph through inter-layer links, and associate them with the step nodes and risk association edges in the decision-making capability graph to complete the evidence assembly.

[0075] Subsequently, the strategy reasoning agent performs condition determination and path selection based on the assembled evidence set. For example, in the project due diligence example, the knowledge retrieval agent can retrieve evidence such as inconsistent subject information, abnormal transaction paths, and unresolved historical penalties. The strategy reasoning agent then determines to trigger risk escalation rules based on this evidence and prioritizes the path of "supplementary verification before rating" according to the robustness preference parameter, generating the corresponding intermediate reasoning results.

[0076] Furthermore, after obtaining the intermediate inference results, the calling module invokes the interpretation generation agent to generate an interpretation link corresponding to the task execution sequence. The interpretation link does not simply state the final conclusion, but rather describes the evidence content invoked at each step, the applicable rule nodes, the triggered strategy nodes, and the migration path adopted, focusing on the key steps in the task execution sequence. For example, it might explain that the consistency of the reporting entity is verified first, then abnormal transaction paths are identified, followed by triggering risk escalation rules based on historical penalty status, and supplementary verification suggestions are provided based on an evidence-first processing style. Afterwards, the calling module invokes the output synthesis agent to encapsulate the intermediate inference results and interpretation links, generating and outputting the processing results and interpretation links. The output format can be review opinions, risk warnings, decision suggestions, consultation responses, or structured results triggering subsequent business processes.

[0077] For example, in a specific implementation example, the business system submits a corporate due diligence request to the calling module. After parsing, the calling module obtains the task objective as completing a project risk rating. The input elements include entity information, transaction records, and penalty records. The constraints are that a written opinion must be output within a preset time limit, and the output requirements are to provide a risk level and suggestions for supplementary verification. Subsequently, the calling module determines the calling scheme based on the expert capability graph, calls the task planning agent to generate a task execution sequence of "entity verification - abnormal transaction identification - rule comparison - comprehensive rating", then calls the knowledge retrieval agent and the strategy reasoning agent to generate an intermediate reasoning result of "high risk and requiring supplementary verification", and finally calls the interpretation generation agent to generate an interpretation link of "risk level increased due to inconsistent entity information, abnormal transaction path, and unresolved penalty records", and encapsulates it into a complete review opinion through the output synthesis agent and outputs it to the business system.

[0078] Through the above implementation methods, this embodiment can dynamically schedule the various functional agents in the industry expert agent based on the task context, so that the task request can be matched with the task path, rule strategy path and decision migration path in the expert capability graph, thereby improving the adaptability of the industry expert agent to the actual business task, the completeness of the result output and the consistency of the interpretation link.

[0079] In some embodiments, the self-evolution module is used for: Obtain task feedback data corresponding to the task request. The task feedback data includes processing result feedback, explanation link feedback, execution result feedback, and expert correction feedback. Based on task feedback data, intermediate inference results, and interpretation links, determine result bias, path bias, strategy bias, and style bias, and generate bias analysis results. Based on the deviation analysis results, weight updates, association strength updates, and path corrections are performed on the target graph nodes, target migration edges, target rule strategy paths, and inter-layer relationships in the expert capability graph. Based on the deviation analysis results and task feedback data, adaptive updates are performed on the task path template, rule strategy path template, explanation generation template and style parameters corresponding to the industry expert intelligent agent. Based on the updated expert capability graph and the updated task path template, rule and strategy path template, interpretation and generation template and style parameters, the task planning agent, strategy reasoning agent, interpretation and generation agent and output synthesis agent are reconfigured to generate the updated industry expert agent.

[0080] Specifically, the self-evolution module is used to continuously update the expert capability graph and the industry expert agent based on multi-dimensional feedback information after the industry expert agent completes the task and outputs the processing results and explanation chain. This allows the industry expert agent generated by the system to gradually approach the actual processing method of the target industry expert during long-term operation. Unlike schemes that only operate based on static knowledge bases or fixed parameter models, the self-evolution module in this embodiment does not simply record historical feedback. Instead, it establishes an adaptive iterative mechanism from task feedback to graph correction, template updates, and intelligent agent weight configuration, encompassing multiple levels such as task results, inference paths, strategy application, and expression style.

[0081] In this embodiment, the self-evolution module first acquires task feedback data corresponding to the task request. Task feedback data includes at least processing result feedback, explanation link feedback, execution result feedback, and expert correction feedback. Processing result feedback can be used to characterize whether the task output conclusion is consistent with expectations, such as whether the risk rating is accurate or the review conclusion is reasonable. Explanation link feedback can be used to characterize whether the explanation content fully covers key evidence and whether it conforms to the expert's explanatory habits. Execution result feedback can be used to characterize the actual performance of the processing result in subsequent business processes, such as whether supplementary verification confirms the original judgment and whether subsequent business execution is smooth. Expert correction feedback can directly reflect the revision opinions of experts in the target industry regarding the system's output results, reasoning paths, or expression methods. In the aforementioned project due diligence example, if the system outputs a review opinion of "high risk and requiring supplementary verification," and the expert further adds that "the authenticity of the change in control relationship should be verified first, and the penalty items should be listed separately," then this supplementary content can be recorded as expert correction feedback.

[0082] Furthermore, after obtaining task feedback data, the self-evolutionary module further combines the intermediate inference results and interpretation links formed during task processing to determine outcome bias, path bias, strategy bias, and style bias, generating bias analysis results. Outcome bias characterizes the difference between the final processing conclusion and the expert's revised conclusion; path bias characterizes the difference between the task execution sequence and the expert's actual processing order; strategy bias characterizes the difference between rule invocation or strategy selection and the expert's usual practices; and style bias characterizes the difference between the interpretation, conclusion expression, or risk wording and the target expert's style. For example, in a due diligence task for a company, although the system correctly identified the abnormal transaction path, it did not prioritize considering changes in the entity's control relationship when generating intermediate inference results, resulting in an inconsistency between the inference order and the expert's actual habits. This can be identified as path bias. If the system adopted a direct rating path while the expert believes that supplementary verification should be conducted first, this can be identified as strategy bias.

[0083] Based on the deviation analysis results, the self-evolution module performs weight updates, correlation strength updates, and path corrections on the target graph nodes, target migration edges, target rule-strategy paths, and inter-layer relationships in the expert capability graph. Specifically, if a certain type of evidence is emphasized by experts in multiple task feedbacks, the weights of the corresponding concept knowledge graph nodes and rule-strategy graph nodes can be increased; if a migration path is repeatedly corrected by experts to other paths, the weights of the original migration edges can be reduced and the migration weights of the corrected paths can be increased; if the triggering relationship between a condition node and a strategy node has been proven to be more stable in practice, the inter-layer correlation strength between the two can be increased. Taking the aforementioned risk control scenario as an example, if experts repeatedly correct the system output, emphasizing that abnormal control relationships should be checked before abnormal transaction amounts, the self-evolution module can increase the weights of the nodes corresponding to the control relationships and their related migration paths.

[0084] Meanwhile, the self-evolution module also adaptively updates the task path template, rule-strategy path template, explanation generation template, and style parameters corresponding to the industry expert agent based on the deviation analysis results and task feedback data. Updating the task path template can correct the step breakdown order and stage boundaries; updating the rule-strategy path template can correct the rule triggering conditions and strategy invocation order; updating the explanation generation template can supplement the explanation structure, reasoning organization, and evidence citation methods; and updating the style parameters can adjust the conclusion wording, risk expression strength, and level of detail in the explanation. For example, if a target expert prefers an expression style of "giving the conclusion first, then explaining the basis item by item," the self-evolution module can adjust the corresponding explanation generation template and output style parameters to reflect this expression structure.

[0085] Furthermore, after updating the expert capability graph and templates, the self-evolution module further reconfigures the task planning agent, strategy reasoning agent, explanation generation agent, and output synthesis agent based on the updated expert capability graph, updated task path templates, rule-strategy path templates, explanation generation templates, and style parameters, generating an updated industry expert agent. This reconfiguration is not limited to replacing parameters; it can also include rebinding task path templates and strategy path templates, reorganizing explanation-related paths, adjusting inference constraint parameters, and updating output organization rules. Thus, when receiving similar task requests subsequently, the industry expert agent can execute the task according to the revised processing logic and expression.

[0086] For example, in a corporate risk review task, the system's initial output was "Medium risk, supplementary transaction explanation recommended." However, expert feedback suggested that the case should first examine the anomaly in control relationships and upgraded the risk level to "High risk, supplementary proof of control relationships and penalty rectification materials recommended." The self-evolution module can identify result bias, path bias, and style bias accordingly, and increase the weight of nodes, rule nodes, and migration paths related to "anomalies in control relationships." Simultaneously, the explanation generation template is revised to an output structure that "first explains the core anomaly, then explains the rating basis, and finally provides handling suggestions." After reconfiguration, the updated industry expert agent can prioritize control relationship verification in subsequent similar tasks and output review opinions that are closer to the target expert's style.

[0087] Through the above implementation methods, this embodiment can update the expert capability graph and industry expert agent in a coordinated manner based on processing result feedback, interpretation link feedback, execution result feedback and expert correction feedback. This allows the node relationships, rule paths and decision migrations in the graph to be continuously corrected with actual feedback. At the same time, the task planning, strategy reasoning, interpretation generation and output organization processes gradually become closer to the actual processing methods of the target industry experts, thereby improving the continuous evolution capability and long-term application stability of the industry expert agent.

[0088] In some embodiments, the self-evolution module is further configured to: Based on task feedback data, processing results, and expert reference decision results, we construct result difference samples, path difference samples, and explanation difference samples. Based on the result difference samples, path difference samples, and interpretation difference samples, determine the parameter correction direction and reward constraint signal corresponding to the industry expert intelligent agent; Based on the parameter correction direction and reward constraint signal, low-rank adaptation fine-tuning and reinforcement constraint update are performed on the output synthetic agent and the interpretation generated agent. Based on the fine-tuned output synthesis agent and interpretation generation agent, the output style parameters and interpretation generation templates corresponding to the task type are updated to ensure that the updated industry expert agent remains relevant to the decision expression style of the target industry expert.

[0089] Specifically, in addition to updating the expert capability map, task path template, and rule strategy path template, the self-evolution module further performs parameter-level adaptive optimization on the output synthesis agent and explanation generation agent in the industry expert agent, so that the system can not only gradually approach the target industry expert in terms of judgment conclusions during long-term operation, but also gradually approach the real expression habits of the target industry expert in terms of result expression, reasoning organization, and explanation style.

[0090] This implementation focuses on addressing the issue that while the system can generate relatively reasonable processing results based on expert capability maps, discrepancies may still exist between the output tone, the order of data arrangement, the granularity of risk warnings, and the organizational method of explanation and analysis, compared to those of experts in the target industry. Therefore, this embodiment constructs discrepancy samples and introduces parameter correction directions and reward constraint signals to perform lightweight fine-tuning and constraint updates on the output-related agents.

[0091] In this embodiment, the self-evolution module first constructs result difference samples, path difference samples, and explanation difference samples based on task feedback data, processing results, and expert reference decision results. The result difference samples characterize the differences between the system's output conclusions and the expert reference decision results in terms of conclusion level, content, or recommended actions. The path difference samples characterize the differences between the order of reasons, evidence citations, or judgment chains presented by the system during the explanation process and the corresponding order of explanation in the expert reference decision results. The explanation difference samples characterize the differences between the system's explanation text and the expert reference statements in terms of wording style, level of detail, emphasis, and conclusion organization.

[0092] For example, in the aforementioned enterprise due diligence and risk assessment example, the system may output "It is recommended to conduct supplementary verification before rating", while the expert's corresponding reference decision result is expressed as "Based on the abnormal control relationship of the main entity and the failure to eliminate historical penalties, it is recommended to supplement the control relationship proof materials first, and then conduct a comprehensive rating". This difference is not only reflected in the different granularity of the interpretation content, but also in the fact that experts tend to point out the core abnormality first, and then give disposal suggestions.

[0093] Furthermore, after constructing the difference samples, the self-evolution module further determines the parameter correction direction and reward constraint signal corresponding to the industry expert agent based on the result difference samples, path difference samples, and interpretation difference samples. The parameter correction direction is used to indicate which expression mode and organizational mode the output synthesis agent and interpretation generation agent should adjust to in subsequent updates, such as increasing the tendency to prioritize core evidence, increasing the tendency to output risk conclusions first, and increasing the tendency to explicitly cite rule basis. The reward constraint signal is used to give positive constraints to outputs that conform to the expert's expression habits and negative constraints to outputs that deviate from the expert's expression habits. For example, when the interpretation generated by the system can be unfolded in the manner of "core anomaly - rule basis - handling suggestion" and the wording used is consistent with the expert's usual expression, a higher reward can be given; when the system's interpretation is correct in conclusion but the order is disordered or does not highlight the expert's key points, a lower reward is given.

[0094] Furthermore, the self-evolution module performs low-rank adaptation fine-tuning and reinforcement constraint updates on the output synthetic agent and the interpretation generator agent based on parameter correction direction and reward constraint signals. Low-rank adaptation fine-tuning is mainly used to lightweight adjust local parameters in the output-related model without retraining the underlying model as a whole, making it more suitable for learning the expression style of industry experts in specific task types. Reinforcement constraint updates are mainly used to introduce expert-style constraint signals during the generation process, so that the model prioritizes retaining expression patterns that conform to expert habits when outputting candidate results. For example, in risk assessment tasks, the interpretation generator agent can prioritize the expression structure of "conclusion first, then evidence, then suggestion"; in consultation and response tasks, the output synthetic agent can prioritize the organization of "problem summary - rule application - processing suggestion". Thus, the system output no longer only pursues semantic correctness, but further pursues the expression style to be consistent with the target expert.

[0095] Furthermore, after fine-tuning and constraint updates, the self-evolution module updates the output style parameters and explanation generation templates corresponding to the task type based on the fine-tuned output synthesis agent and explanation generation agent. The output style parameters can be used to limit the strength of risk statements, the conciseness of conclusions, the density of evidence citations, and the suggested wording under different task types; the explanation generation template can be used to limit the paragraph order of the explanation content, the position of evidence insertion, the position of rule explanations, and the presentation of conclusions.

[0096] For example, for a risk control expert, in a high-risk case, the expression might first point out "the existence of a lack of consistency between the parties and abnormal transaction paths," then explain that "the risk level should be upgraded based on historical penalty status and current verification results," and finally propose "suggesting supplementary verification of control relationship supporting materials." After being updated in this embodiment, the interpretation generation template can be solidified into a template structure corresponding to this style, and the output style parameters can also be simultaneously adjusted to an expression mode that is biased towards prudence, prioritization of evidence, and clear recommendations.

[0097] For example, in a specific implementation example, the system initially outputs "Medium risk, supplementary explanation recommended" for a due diligence task, while the target expert's reference decision is "Given the abnormal control relationship and the unresolved penalties, it is recommended to classify it as high risk and supplement the control relationship supporting materials." Based on this, the self-evolutionary module constructs result difference samples, path difference samples, and interpretation difference samples, identifying problems such as low conclusion levels, lack of prior core evidence, and insufficiently specific recommendations, and determining corresponding parameter correction directions and reward constraint signals. Subsequently, low-rank adaptation fine-tuning and reinforcement constraint updates are performed on the output synthesis agent and interpretation generation agent, ensuring they prioritize the expert's preferred expression methods in subsequent similar tasks, ultimately updating the output style parameters and interpretation generation template.

[0098] Through the above implementation methods, this embodiment can perform lightweight fine-tuning and constraint updates on the output synthetic agent and the interpretation generator agent based on task feedback data, processing results and expert reference decision results. This enables the industry expert agent to further improve the consistency between the output expression and the target industry expert while ensuring the rationality of the processing results, thereby enhancing the professional fit of the agent's result expression and its long-term learning ability.

[0099] The above embodiments have described in detail the specific modules and functions of the intelligent agent-driven industry expert capability replication system of this application. The implementation process of the intelligent agent-driven industry expert capability replication method of this application will be described in detail below with reference to specific embodiments. Figure 2 This is a flowchart illustrating the agent-driven method for replicating industry expert capabilities provided in this application. Figure 2 As shown, the method may specifically include the following steps: S201 collects textual materials, interaction data, decision data, behavioral patterns, and oral interview data from industry experts, and performs anonymization, labeling, and structuring processing on the collected data. S202, based on the structured processing results, extracts task type, decision-making step chain, attention features, decision-making style and thinking path; S203 extracts factual knowledge, rule constraints, strategic experience, and risk information based on the decision-making process chain, focus characteristics, decision-making style, and thinking chain. S204, based on factual knowledge, rule constraints, strategic experience and risk information, constructs an expert capability graph that includes a conceptual knowledge graph, a rule strategy graph and a decision-making capability graph; S205, based on the expert capability graph, generates an industry expert intelligent agent composed of task planning, strategy reasoning, knowledge retrieval, interpretation generation and output synthesis collaboration. S206, responding to the task request, calls on an industry expert intelligent agent to perform inference processing, and generates processing results and explanation links; S207 updates the expert capability graph and industry expert intelligent agents based on task feedback.

[0100] Specifically, in step S201, textual materials, interaction data, decision-making data, behavioral trajectories, and oral interview data corresponding to industry experts are collected, and the collected data undergoes desensitization, annotation, and structuring processing. Specifically, this can involve accessing document management systems, business processing systems, instant messaging systems, operation log systems, and interview transcription systems to obtain reports, articles, training materials, Q&A records, historical task input / output pairs, system operation records, and interview content generated by industry experts in their long-term business activities. The multi-source data is then aggregated and arranged according to data source, task scenario, and temporal relationship to generate a multi-source expert dataset. Subsequently, desensitization mapping processing is performed on the multi-source expert dataset based on preset sensitive information identification rules and semantic replacement rules. Then, segmented indexing, element annotation, and association alignment processing are performed based on task semantics, decision semantics, and behavioral semantics. Finally, through vector representation and structured recombination, a structured processing result associated with the expert's task context, decision process context, and behavioral process context is formed. For example, in a project due diligence scenario, expert due diligence reports, review comments, group Q&A records, operation logs, and interview content can be uniformly collected and recombined into structured data revolving around the same due diligence task.

[0101] In step S202, based on the structured processing results, task type, decision-making step chain, focus features, decision-making style, and thought process are extracted. Specifically, this can involve first identifying the task objective, input elements, output elements, and processing scenario corresponding to each semantic fragment to determine the task type of the current fragment; then performing temporal correlation and causal merging processing on the operational events, judgment events, and result events in the structured processing results to form a decision-making step chain corresponding to the task type; next, extracting the target focus object, constraint focus object, risk focus object, and trade-off focus object from each step to form focus features; further combining historical output results to identify risk preference parameters, robustness preference parameters, and trade-off preference parameters to generate decision-making style representation results; finally, constructing a chain-like association structure between input information selection, feature extraction, trade-off judgment, preference application, and result output to generate a thought process. For example, for the due diligence task of a risk control expert, a decision-making step chain of "subject verification—anomaly identification—rule comparison—comprehensive rating" can be parsed out, as well as a decision-making style of "evidence priority, strict review".

[0102] In step S203, based on the decision-making step chain, focus features, decision-making style, and thought process, factual knowledge, rule constraints, strategic experience, and risk information are extracted. Specifically, this can involve: identifying factual expression units corresponding to task objects, business elements, state attributes, and relationships based on the decision-making step chain and thought process, forming factual knowledge after entity normalization and relationship alignment; extracting constraint expression units corresponding to condition judgments, boundary restrictions, dependencies, and triggering relationships based on the decision-making step chain, focus features, and thought process, and establishing mapping relationships between condition items and result items to form rule constraints; extracting strategy expression units corresponding to step selection methods, trade-off sequences, handling paths, and preference application methods based on the decision-making step chain, decision-making style, and thought process, forming strategic experience after scenario classification and pattern merging; and extracting risk expression units corresponding to abnormal symptoms, risk types, risk triggering conditions, and avoidance paths based on focus features, decision-making style, and thought process, and establishing risk level associations and handling associations to form risk information. This allows the expert's knowledge and experience to be uniformly organized into the knowledge set required for subsequent graph construction.

[0103] In step S204, an expert capability graph is constructed based on factual knowledge, rule constraints, strategic experience, and risk information, comprising a conceptual knowledge graph, a rule-strategy graph, and a decision-making capability graph. Specifically, this can involve: constructing a conceptual knowledge graph based on factual knowledge, consisting of task object nodes, business element nodes, state attribute nodes, and relational edges; constructing a rule-strategy graph based on rule constraints and strategic experience, consisting of condition nodes, rule nodes, strategy nodes, and constraint edges, and establishing a semantic mapping relationship between the rule-strategy graph and the conceptual knowledge graph; constructing a decision-making capability graph based on decision-making step chains, decision-making styles, thought processes, and risk information, consisting of decision-making step nodes, migration edges, preference constraint edges, and risk-related edges, and configuring weights for relevant nodes and migration edges; finally, performing identifier alignment, inter-layer link establishment, and path coupling processing on the three graphs to generate the expert capability graph. Taking the aforementioned due diligence scenario as an example, business objects such as the applicant entity, abnormal transaction paths, and historical penalty matters can be associated with rules and strategies such as not being allowed direct approval or requiring supplementary verification before re-rating, as well as decision-making paths such as "entity verification—anomaly identification—risk escalation."

[0104] In step S205, based on the expert capability graph, an industry expert agent is generated, consisting of task planning, strategy reasoning, knowledge retrieval, interpretation generation, and output synthesis. Specifically, this can be achieved by first extracting task path templates, rule-strategy paths, decision-transfer paths, and interpretation-related paths corresponding to the task type from the expert capability graph, generating agent collaborative configuration results; then, a task planning agent is constructed based on the task path templates to determine the task decomposition order, step dependencies, and execution phase boundaries; a strategy reasoning agent is constructed based on the rule-strategy paths, decision-transfer paths, and decision styles to perform condition judgment, path selection, preference constraint application, and risk trade-off processing; a knowledge retrieval agent is constructed based on graph nodes, inter-layer links, and semantic mapping relationships to perform knowledge localization, association expansion, and evidence aggregation; an interpretation generation agent is constructed based on the interpretation-related paths, decision-transfer paths, and evidence aggregation results to generate step basis, judgment basis, and path description; and finally, an output synthesis agent is constructed based on the collaborative invocation relationships of the aforementioned agents to form a complete industry expert agent.

[0105] In step S206, in response to the task request, an industry expert agent is invoked to perform inference processing, generating processing results and explanation links. Specifically, this can involve first receiving the task request and parsing the task objective, input elements, constraints, and output requirements to generate a task context; then, based on the task context and expert capability graph, determining the target task path, target rule strategy path, and target decision migration path to form an agent invocation scheme; next, invoking the task planning agent to generate a task execution sequence, and invoking the knowledge retrieval agent and strategy reasoning agent according to the task execution sequence to perform graph retrieval, evidence assembly, condition judgment, path selection, and preference constraint processing to obtain intermediate inference results; then, invoking the explanation generation agent to generate explanation links corresponding to the task execution sequence, and finally invoking the output synthesis agent to encapsulate the intermediate inference results and explanation links, generating and outputting the processing results and explanation links. For example, in an enterprise risk review task, the output can include the risk level, handling recommendations, and explanations of "why the risk level was raised, what rules were used, and what path was adopted."

[0106] In step S207, the expert capability graph and industry expert agent are updated based on task feedback. Specifically, this may involve acquiring task feedback data, including processing result feedback, interpretation link feedback, execution result feedback, and expert correction feedback; then, combining intermediate inference results and interpretation link identification result deviations, path deviations, strategy deviations, and style deviations to generate deviation analysis results; based on the deviation analysis results, performing weight updates, association strength updates, and path correction processing on target graph nodes, target migration edges, target rule-strategy paths, and inter-layer relationships in the expert capability graph; simultaneously, performing adaptive updates on the task path template, rule-strategy path template, interpretation generation template, and style parameters corresponding to the industry expert agent; in some embodiments, result difference samples, path difference samples, and interpretation difference samples can be further constructed based on task feedback data, processing results, and expert reference decision results, and parameter correction directions and reward constraint signals can be determined accordingly, performing low-rank adaptation fine-tuning and reinforcement constraint updates on the output synthetic agent and interpretation generation agent, and finally generating the updated industry expert agent.

[0107] In summary, the method provided in this application, through steps such as data collection, behavior analysis, knowledge extraction, capability graph construction, agent generation, task invocation, and feedback updates, achieves a gradual transformation of industry expert capabilities from raw data form to structured capability form, and then to executable agent form. This method enables the integrated modeling of industry experts' knowledge, rules, strategies, decision-making paths, and expression styles, allowing the generated industry expert agent to output processing results and explanations corresponding to the target expert in actual task processing, while continuously updating and evolving under task feedback.

[0108] It should be understood that the sequence number of each step in the above method 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 this application.

[0109] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although the technical solutions of this application have 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 this application, and should all be included within the protection scope of this application.

Claims

1. An agent-driven system for replicating the capabilities of industry experts, characterized in that, include: The data acquisition module is used to collect textual materials, interaction data, decision data, behavioral patterns and oral interview data from industry experts, and to perform anonymization, annotation and structuring processing. The behavior analysis module is used to extract task type, decision step chain, attention features, decision style and thinking path based on the structured processing results. The knowledge extraction module is used to extract factual knowledge, rule constraints, strategy experience, and risk information based on the decision-making step chain, attention features, decision-making style, and thinking chain. The capability graph construction module is used to construct an expert capability graph that includes a concept knowledge graph, a rule and strategy graph, and a decision-making capability graph based on the factual knowledge, rule constraints, strategy experience, and risk information. The intelligent agent generation module is used to generate an industry expert intelligent agent based on the expert capability graph, which is composed of a task planning intelligent agent, a strategy reasoning intelligent agent, a knowledge retrieval intelligent agent, an explanation generation intelligent agent, and an output synthesis intelligent agent. The calling module is used to respond to task requests, call the industry expert intelligent agent to perform inference processing and output the processing results and explanation links; The self-evolution module is used to update the expert capability map and industry expert agents based on task feedback.

2. The system according to claim 1, characterized in that, The data acquisition module is used for: Acquire textual materials, interaction data, decision data, behavioral patterns, and oral interview data from industry experts, and aggregate and arrange them according to data source, task scenario, and time relationship to generate a multi-source expert dataset; Based on preset sensitive information identification rules and semantic replacement rules, the multi-source expert dataset is subjected to desensitization mapping processing to generate a desensitized dataset; Based on task semantics, decision semantics, and behavioral semantics, segmentation indexing, feature annotation, and association alignment are performed on the de-identified dataset to generate an annotated dataset corresponding to task type, decision node, behavioral event, and semantic fragment. Vector representation processing and structured reorganization processing are performed on the labeled dataset to generate structured processing results associated with the expert task context, decision process context, and behavioral process context.

3. The system according to claim 1, characterized in that, The behavior parsing module is used for: Based on the structured processing results, the task objectives, input elements, output elements, and processing scenarios corresponding to each semantic segment are identified, and the task type is determined. Based on the task type, the operation events, judgment events and result events in the structured processing results are subjected to temporal association and causal merging processing to generate a decision step chain corresponding to the task type; Based on the decision-making step chain, the target concern object, constraint concern object, risk concern object and trade-off concern object corresponding to each step are extracted to generate concern features; Based on the decision-making step chain, the set of features of concern, and historical output results, risk preference parameters, robustness preference parameters, and trade-off preference parameters corresponding to each task type are identified, and decision style representation results are generated. Based on the aforementioned decision-making step chain, the set of features of concern, and the representation results of decision-making styles, a chain-like structure is constructed between input information selection, feature extraction, trade-off judgment, preference application, and result output to generate a thought process chain.

4. The system according to claim 1, characterized in that, The knowledge extraction module is used for: Based on the decision-making step chain and thought chain, the fact expression units corresponding to the task object, business elements, state attributes and related relationships are identified, and entity normalization and relationship alignment are performed on the fact expression units to generate fact knowledge. Based on the decision-making step chain, attention features, and thought process, constraint expression units corresponding to condition judgments, boundary restrictions, dependencies, and triggering relationships are extracted, and condition item to result item mapping processing is performed to generate rule constraints. Based on the decision-making step chain, decision-making style, and thinking path, strategy expression units corresponding to the step selection method, trade-off order, disposal path, and preference application method are extracted, and scenario classification and pattern merging processing are performed to generate strategy experience. Based on the aforementioned attention features, decision-making styles, and thought processes, risk expression units corresponding to abnormal symptoms, risk types, risk triggering conditions, and avoidance paths are extracted, and risk level association and disposal association processing are performed to generate risk information. The factual knowledge, rule constraints, strategy experience, and risk information are hierarchically linked and organized to generate a knowledge set for constructing an expert capability graph.

5. The system according to claim 1, characterized in that, The capability mapping construction module is used for: Based on the aforementioned factual knowledge, a conceptual knowledge graph is constructed, consisting of task object nodes, business element nodes, state attribute nodes, and relation edges. Based on the rule constraints and policy experience, a rule-policy graph consisting of condition nodes, rule nodes, policy nodes and constraint edges is constructed, and a semantic mapping relationship between the rule-policy graph and the concept knowledge graph is established. Based on the decision-making step chain, decision-making style, thinking link and risk information, a decision-making capability graph is constructed, consisting of decision-making step nodes, migration edges, preference constraint edges and risk association edges, and node weights and migration weights are configured for each decision-making step node and migration edge. Based on the aforementioned concept knowledge graph, rule strategy graph, and decision capability graph, identifier alignment, inter-layer link establishment, and path coupling processing are performed on cross-graph related nodes to generate an expert capability graph.

6. The system according to claim 1, characterized in that, The agent generation module is used for: Based on the expert capability graph, task path templates, rule strategy paths, decision migration paths, and interpretation association paths corresponding to the task type are extracted to generate intelligent agent collaborative configuration results. Based on the agent collaborative configuration results and the task path template, a task planning agent is constructed to determine the task decomposition order, step dependencies and execution phase boundaries. Based on the aforementioned rule-based strategy path, decision transition path, and decision style, a strategy reasoning agent is constructed for performing condition determination, path selection, preference constraint application, and risk trade-off processing. Based on the graph nodes, inter-layer links, and semantic mapping relationships in the expert capability graph, a knowledge retrieval agent is constructed to perform knowledge localization, association expansion, and evidence aggregation processing. Based on the aforementioned explanation association path, decision transfer path, and evidence aggregation results, an explanation generation agent is constructed to generate step basis, judgment basis, and path description. Based on the collaborative invocation relationship between the task planning agent, strategy reasoning agent, knowledge retrieval agent, and interpretation generation agent, an output synthesis agent is constructed for integrating execution results and organizing output, so as to generate an industry expert agent.

7. The system according to claim 1, characterized in that, The calling module is used for: Receive a task request, and parse the task objective, input elements, constraints and output requirements corresponding to the task request to generate a task context; Based on the task context and the expert capability graph, the target task path, target rule strategy path and target decision migration path corresponding to the task request are determined, and an agent invocation scheme is generated. Based on the aforementioned agent invocation scheme, the task planning agent is invoked to decompose and orchestrate the execution steps of the task context, generating a task execution sequence. According to the task execution sequence, the knowledge retrieval agent and the policy reasoning agent are invoked to perform graph retrieval, evidence assembly, condition determination, path selection and preference constraint processing, and generate intermediate reasoning results; Based on the intermediate inference results, the interpretation generation agent is invoked to generate an interpretation link corresponding to the task execution sequence, and the output synthesis agent is invoked to encapsulate the intermediate inference results and the interpretation link, generating and outputting the processing results and the interpretation link.

8. The system according to claim 7, characterized in that, The self-evolution module is used for: Obtain task feedback data corresponding to the task request, including processing result feedback, explanation link feedback, execution result feedback, and expert correction feedback; Based on the task feedback data, the intermediate inference results, and the explanation chain, the result deviation, path deviation, strategy deviation, and style deviation are determined, and deviation analysis results are generated. Based on the deviation analysis results, weight updates, association strength updates, and path corrections are performed on the target graph nodes, target migration edges, target rule strategy paths, and inter-layer relationships in the expert capability graph. Based on the deviation analysis results and the task feedback data, adaptive updates are performed on the task path template, rule strategy path template, explanation generation template and style parameters corresponding to the industry expert agent. Based on the updated expert capability graph and the updated task path template, rule strategy path template, explanation generation template and style parameters, the task planning agent, strategy reasoning agent, explanation generation agent and output synthesis agent are reconfigured to generate the updated industry expert agent.

9. The system according to claim 8, characterized in that, The self-evolution module is also used for: Based on the task feedback data, the processing results, and the expert's corresponding reference decision results, a result difference sample, a path difference sample, and an explanation difference sample are constructed. Based on the result difference samples, path difference samples, and interpretation difference samples, determine the parameter correction direction and reward constraint signal corresponding to the industry expert agent; Based on the parameter correction direction and reward constraint signal, low-rank adaptation fine-tuning and reinforcement constraint update are performed on the output synthetic agent and the interpretation generator agent; Based on the fine-tuned output synthesis agent and interpretation generation agent, the output style parameters and interpretation generation template corresponding to the task type are updated so that the updated industry expert agent remains associated with the decision expression style of the target industry expert.

10. A method for replicating industry expert capabilities driven by an intelligent agent based on a system as described in any one of claims 1 to 9, characterized in that, include: Collect textual materials, interaction data, decision-making data, behavioral patterns, and oral interview data from industry experts, and perform anonymization, annotation, and structuring processing on the collected data; Based on the structured processing results, task type, decision-making step chain, attention features, decision-making style and thinking process are extracted; Based on the decision-making process chain, focus characteristics, decision-making style and thinking path, extract factual knowledge, rule constraints, strategic experience and risk information; Based on factual knowledge, rule constraints, strategic experience, and risk information, an expert capability graph is constructed, which includes a conceptual knowledge graph, a rule and strategy graph, and a decision-making capability graph. Based on the expert capability graph, an industry expert intelligent agent is generated, which is composed of task planning, strategy reasoning, knowledge retrieval, interpretation generation and output synthesis. In response to task requests, the system invokes industry expert AI agents to perform inference processing and generate processing results and explanation chains. Based on task feedback, the expert capability map and industry expert intelligent agents are updated.

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