A method and system for constructing an evolvable knowledge infrastructure

Abstract

This invention provides a method and system for constructing an evolvable knowledge infrastructure, comprising: Step S1, designing a mandatory unified data format and constructing a plug-in hot-swappable architecture to access multiple heterogeneous data sources; Step S2, constructing a three-layer cascaded cleaning pipeline to extract knowledge from the original accessed materials; Step S3, based on the unified data format, normalizing the cleaned text after knowledge extraction into structured format data that can be directly consumed by intelligent agents through a job-specific knowledge unit extraction mechanism; Step S4, performing semantic-aware segmentation on the structured format data, dividing it into semantically independent and context-complete knowledge units, and constructing a hybrid retrieval index; Step S5, performing multi-dimensional quality scoring on the processed data and establishing a full-link data lineage tracing system. This invention can systematically transform multi-source heterogeneous data into structured, traceable, and verifiable job-specific knowledge units and capability assets that can be directly consumed by intelligent agents.

Description

Technical Field

[0001] This invention relates to input data processing technology solutions applied in the field of artificial intelligence, particularly to a method for constructing an evolvable knowledge infrastructure, and further to a system employing the method for constructing an evolvable knowledge infrastructure. Background Technology

[0002] As artificial intelligence technology centered on Large Language Models (LLM) moves from research to industrialization, AI Agents are becoming a key vehicle for enterprise digital transformation. The global AI Agent market reached $76.3 billion in 2025 and is projected to exceed $1.8 trillion by 2033 (Grand View Research), with a compound annual growth rate of nearly 50%. Financial institutions are deploying intelligent research assistants, law firms are introducing compliance review agents, medical institutions are building clinical decision support systems, and e-commerce platforms are operating intelligent customer service. AI Agents are penetrating the core business processes of various industries at an unprecedented pace. However, behind the exponential improvement in model capabilities and the continuous rise in industry investment, a structural contradiction that has been severely underestimated by the entire industry is becoming increasingly prominent: the upper limit of an Agent's capabilities does not depend on the parameter scale or inference ability of the underlying model, but rather on the quality, structure, and searchability of the data it can acquire. Gartner's research shows that over 60% of enterprise AI projects fail due to data quality issues.

[0003] The root of this contradiction lies in a fundamental, often overlooked gap: the knowledge infrastructure built by humanity over millennia—including books, libraries, knowledge bases, work systems, and collaborative platforms—was primarily designed for human understanding and reading, not for agent-based decision-making and reasoning. Truly valuable business information within an enterprise is not neatly stored in a single knowledge base, but rather scattered across various collaborative media such as chat logs, work order workflows, meeting minutes, emails, CRM follow-up records, SOP documents, screen recordings, and historical deliverables. When an AI agent attempts to enter a real business process, it faces not a data environment prepared for decision-making, but a pile of raw materials temporarily generated for human collaboration. These raw materials include chat logs and meeting minutes riddled with contextual omissions and colloquial expressions; work orders and approval flows with fragmented structures and hidden exceptions; emails and screen recordings present experience as actions rather than rules; and historical deliverables and rework records show results but lack explicit judgment criteria and boundary definitions. Therefore, it is clear that the fundamental difference between this knowledge designed for humans and the decision inputs needed by agents is not a difference in degree, but an essential difference at the level of knowledge foundation.

[0004] From a data engineering practice perspective, the severity of this predicament far exceeds general perception. For an enterprise to build a usable AI capability foundation for a specific role, it typically requires stages such as enterprise data source access and adaptation (1-3 weeks), conversation / recording / document cleaning (1-2 weeks), case deduplication and sensitive information processing (3-5 days), job knowledge unit schema refinement (1-2 weeks), and benchmark design and acceptance (3-5 days), totaling 4 to 8 weeks of full-load work for professional data engineers. In the rapidly changing AI competition of 2026, such a lengthy data preparation cycle means continuously missing market opportunities. More seriously, this process also exposed four major structural defects in the enterprise's AI knowledge supply system: 1. Fragmented data sources: IM, CRM, work orders, and emails operate independently, with multiple teams within the same company maintaining independent data parsing scripts that are not interconnected; 2. Chaotic and disordered formats: redundant document layouts, PDF OCR recognition errors, abnormal Emoji encoding, and inconsistent JSON structures cause data cleaning to occupy 60%-70% of engineers' work time; 3. Lack of auditable quality: AI systems output confident but incorrect answers, and the lack of data traceability makes it impossible to locate the root cause of the problem, forcing engineers to start over; 4. Continuous loss of data value: high-quality processed materials are discarded after immediate use, forcing the next similar task to be re-integrated, re-cleaned, and re-verified.

[0005] The core contradiction in the current industry can be summarized as follows: enterprises incur high costs on the model consumption side, including GPU procurement, API calls, and application development, but have not simultaneously built a job capability supply layer to provide agents with judgment criteria. This is not a technical problem that individual companies need to solve, but a systemic blind spot in the entire industry due to its inertia of emphasizing models and neglecting data. Therefore, the market urgently needs a technical solution that can systematically transform multi-source heterogeneous data scattered in organizational collaboration processes into structured, traceable, and verifiable job knowledge units and capability assets that agents can directly consume. This is precisely the core technical problem that this invention aims to solve. Summary of the Invention

[0006] The technical problem this invention aims to solve is to provide a method for constructing an evolvable knowledge infrastructure. This method automatically transforms multi-source, heterogeneous data scattered throughout organizational collaboration processes into structured, traceable, and verifiable job-specific knowledge units and capability assets that can be directly consumed by intelligent agents. This satisfies the agents' requirements for input data quality, structure, and searchability. Furthermore, this invention provides a system employing this method for constructing an evolvable knowledge infrastructure.

[0007] To address this, the present invention provides a method for constructing an evolvable knowledge infrastructure, comprising the following steps:

[0008] Step S1: Design a mandatory unified data format as a general knowledge container, preset the core field structure of the unified data format, and build a plug-in hot-swappable architecture based on the unified data format to access multiple heterogeneous data sources, so as to realize progressive data enhancement and full-link traceability during the data flow process.

[0009] Step S2: Construct a three-layer cascaded cleaning pipeline, which includes heuristic rule cleaning, large language model semantic cleaning, and code repair and consistency verification. Knowledge is extracted from the original access material through the three-layer cascaded cleaning pipeline.

[0010] Step S3: Based on the unified data format, the clean text after knowledge extraction is normalized into structured format data that can be directly consumed by the intelligent agent through the schema-guided job knowledge unit extraction mechanism; Schema refers to the predefined data structure specification used to guide, constrain and verify the structured extraction results;

[0011] Step S4: Semantically aware segmentation of structured data is performed, dividing it into semantically independent and context-complete knowledge units, and a hybrid retrieval index containing relational databases, vector databases and knowledge graphs is constructed.

[0012] Step S5: Perform multi-dimensional quality scoring on the processed data, and establish a full-link data lineage tracing system based on the unified data format carrying the complete processing trajectory.

[0013] A further improvement of the present invention is that, in step S1, the core field structure of the unified data format includes: a unique identifier, original content, cleaned content, document type enumeration, processing state machine, structured metadata container, text chunk list, document-level vector embedding, parent document reference, and processing history chain.

[0014] The hot-swappable plugin architecture supports multiple heterogeneous data source connectors, data processors, and report format generators. The new data source plugin fills the captured content into the core field structure of the unified data format, and fills in the cleaned content during the knowledge extraction process to generate a list of text blocks. Vector embedding is added to each text block, and corresponding records are added to the processing history chain in real time.

[0015] The original access materials from various heterogeneous data sources include chat logs, customer follow-up data, work order flow data, emails, meeting minutes, process documents, screen recordings, and historical deliverables.

[0016] A further improvement of this invention is that, in step S2, heuristic rule cleaning is the first-level cleaning pipeline, employing a dual degradation strategy of the web page content extraction tool Trafilatura and the HTML parsing library BeautifulSoup to remove noisy elements from the HTML. These noisy elements include navigation bars, advertisements, and script tags. The large language model semantic cleaning is the second-level cleaning pipeline, extracting core arguments, eliminating colloquial redundancy, and parsing implicit judgments and contextual omissions in the dialogue chain through the semantic understanding of the large language model. The encoding repair and consistency verification is the third-level cleaning pipeline, used to perform format unification, automatic language detection, and automatic identification and desensitization of personal sensitive information. During the knowledge extraction process, quality control checkpoints are preset between the three-level cleaning pipelines, including preset text length and character encoding format after extraction.

[0017] A further improvement of the present invention is that, in step S2, the knowledge extraction process incorporates heuristic quality screening standards based on open-source large-scale corpus cleaning projects, including word-level checks, document-level checks, line-level checks, and N-gram duplicate detection, and performs adaptation processing for a preset character set.

[0018] A further improvement of the present invention is that, in step S3, the structured format data corresponds to the target structure of the job knowledge unit, and the target structure of each job knowledge unit includes scenario, judgment signal, decision rule, exception condition, failure case, output template, tool calling method and result acceptance standard;

[0019] During the normalization process, a multi-level schema registry mechanism is used to transform unstructured text into job knowledge units in JSON format using a large language model, thereby achieving semantic compression. Furthermore, a multi-level verification and repair mechanism is used to ensure the quality of structured extraction. This multi-level verification and repair mechanism includes schema verification of the base class of basic data, confidence scoring, and multi-model cross-validation.

[0020] A further improvement of the present invention is that, in step S4, during the semantic-aware segmentation process, the segmentation granularity of the segmentation strategy includes any one of fixed size, sliding window, sentence, and paragraph; after each text block is processed by vector embedding, it is indexed to a relational database, a vector database, and a knowledge graph for storage, and exposed to the agent; when the agent queries, it supports using vector semantic similarity, structured metadata condition filtering, and knowledge graph relationships simultaneously for reasoning to obtain the corresponding context.

[0021] A further improvement of the present invention is that, in step S5, the evaluation dimensions of the multi-dimensional quality score include completeness, accuracy, timeliness, relevance, uniqueness and traceability. If any dimension fails to meet the standard, the corresponding data will be fed back to the upstream processing node for supplementation or correction.

[0022] By processing the historical chain, a full-link data lineage tracking system is established. Each general knowledge container carries a complete processing trajectory, recording the participation of data connectors, cleaning steps, the version of the large language model, the version of the prompt words, the quality score of each stage, the processing timestamp, and the token consumption, forming a complete and auditable data lineage chain. When an agent makes an incorrect decision based on a knowledge unit, it can be corrected by locating along the processing trajectory.

[0023] A further improvement of the present invention is that it also includes step S6, which is used to construct a capability verification system and generate verification standards while manufacturing knowledge; the evaluation elements of each test case in the capability verification system include: a description of a real scenario, an input material package, a key signal that must be hit, a standard decision result, an acceptable exception path, an example of an incorrect decision, and a manual scoring rule; the comparison modes supported by the capability verification system include: a before-and-after comparison mode, a hierarchical comparison mode, a red team testing mode, and an online shadow evaluation mode.

[0024] A further improvement of the present invention is that it also includes step S7, for embedding a production-level security compliance system, comprising the following sub-steps:

[0025] Step S701: Automatically identify and desensitize personal information using a personal identifiable information desensitization component;

[0026] Step S702: Implement sandbox isolation based on the plugin isolation mechanism of the Docker containerization platform;

[0027] Step S703: Record a full operation audit log including every data access, processing and output operation. Support multi-dimensional query by time range, operation type and user identity to meet audit requirements;

[0028] Step S704: A preset configurable data retention strategy is implemented, which supports configuring different data lifecycle management rules and automatically performs the marking, archiving, and secure deletion of expired data.

[0029] Step S705: Implement tenant-level data isolation using the tenant identifier PipelineContext.tenant_id;

[0030] Step S706: Support data subject consent management, data export, and data deletion request processing through a separate compliant API endpoint.

[0031] This invention also provides a system for constructing an evolvable knowledge infrastructure, employing the method for constructing an evolvable knowledge infrastructure as described above, and comprising:

[0032] The knowledge discovery layer is used to design a mandatory unified data format as a general knowledge container. It presets the core field structure of the unified data format and builds a plug-in hot-swappable architecture based on the unified data format to access multiple heterogeneous data sources, realizing progressive data enhancement and full-link traceability during data flow.

[0033] The knowledge extraction layer is used to construct a three-layer cascaded cleaning pipeline, which includes heuristic rule cleaning, large language model semantic cleaning, and code repair and consistency verification. Knowledge is extracted from the original access material through the three-layer cascaded cleaning pipeline.

[0034] The knowledge structuring layer, based on the unified data format, normalizes the clean text after knowledge extraction into structured format data that can be directly consumed by the intelligent agent through the schema-guided job knowledge unit extraction mechanism.

[0035] The semantic indexing layer is used to perform semantically aware segmentation of structured data into semantically independent and context-complete knowledge units, and to build a hybrid retrieval index that includes relational databases, vector databases and knowledge graphs.

[0036] The trust verification layer is used to perform multi-dimensional quality scoring on the processed data and establish a full-link data lineage tracing system based on the unified data format carrying the complete processing trajectory.

[0037] Compared with existing technologies, the beneficial effects of this invention are as follows: First, a mandatory unified data format is designed as a universal knowledge container, and a plug-in hot-swappable architecture is built based on this unified data format to access multiple heterogeneous data sources. This enables progressive data enhancement and end-to-end traceability during data flow, solving the problem of unified access and standardization of multi-source heterogeneous data. Second, a three-layer cascaded cleaning pipeline is constructed to extract knowledge from the original access materials, achieving multi-level intelligent cleaning and semantic compression. Third, the cleaned text after knowledge extraction is normalized into structured format data that can be directly consumed by the intelligent agent. Semantic-aware segmentation is performed on the structured format data, dividing it into semantically independent and context-complete knowledge units. A hybrid retrieval index, including relational databases, vector databases, and knowledge graphs, is constructed, thereby realizing the structured extraction and semantic indexing of job-specific knowledge units. Finally, multi-dimensional quality scoring is performed on the processed data, and a full-link data lineage tracing system is established based on the unified data format carrying a complete processing trajectory. Therefore, this invention can automatically transform multi-source heterogeneous data scattered in the organizational collaboration process into structured, traceable, and verifiable job knowledge units and capability assets that can be directly consumed by intelligent agents through an overall technical solution. This can better meet the practical application needs of intelligent agents for the quality, structure, and searchability of input data. Attached Figure Description

[0038] Figure 1 This is a technical architecture diagram of one embodiment of the present invention;

[0039] Figure 2 This is a schematic diagram of the workflow of one embodiment of the present invention;

[0040] Figure 3 This is an architecture diagram of a unified data format according to an embodiment of the present invention. Detailed Implementation

[0041] In the description of this invention, the term "several" means one or more; the term "multiple" means two or more. The terms "first," "second," etc., should be understood as being used only to distinguish between identical or similar technical features, and should not be construed as implying / indicating the relative importance of the technical features, the number of technical features, or the sequential relationship between the technical features.

[0042] Before detailing the preferred embodiments of the present invention, the relevant technologies will be described first.

[0043] One technical solution related to this invention is a single-point data extraction tool represented by Firecrawl, Apify, Crawl4AI, etc., which is also the most popular data acquisition solution on the market at present.

[0044] These single-point data extraction tools obtain the HTML source code of the target webpage through HTTP requests or headless browser rendering, extract target fields from the DOM tree using CSS selectors, XPath expressions, or regular expression matching rules, and output the extraction results in standard formats such as JSON and CSV. However, their output remains at the raw data level and does not involve knowledge structuring.

[0045] This single-point data extraction tool has the following drawbacks: First, it only covers the initial acquisition stage of the data processing chain, lacking the ability to completely transform raw data into structured knowledge units. The performance bottleneck of AI systems has shifted from model architecture to data engineering, and systematic data development is key to improving efficiency. However, the signal-to-noise ratio of the content crawled by the tool is still low (30%-45% for news web pages, 15%-25% for social media), and semantic noise (contextual omission, colloquial expressions, implicit judgments, multilingual mixing, etc.) is completely unprocessed. If directly used as agent input, a significant amount of computing power will be consumed in information processing and noise filtering.

[0046] Second, these single-point data extraction tools lack a unified data contract and a full-chain traceability mechanism. Each tool outputs data in a different format: Firecrawl returns Markdown text plus a metadata dictionary, Apify outputs a JSON structure, and Crawl4AI generates a custom Result object. When enterprises need to simultaneously access and process multiple heterogeneous data sources, adaptation and conversion code must be written for each data source and downstream consumer. Theoretically, N×M adapters are needed between N data sources and M consumers, resulting in exponentially increasing system complexity. More importantly, the crawled data does not carry processing history, making it impossible to trace cleaning steps, extraction models, and quality scores. When the agent generates erroneous output, it's impossible to pinpoint the specific problem node in the data chain, necessitating a complete overhaul.

[0047] Third, these single-point data extraction tools lack the ability to transform raw data into job competency assets. The output of these tools remains at the level of raw or preliminarily cleaned data, while agents require high-density knowledge units containing elements such as scenarios, judgment signals, and decision-making rules. There is a compression ratio of 7:1 to 33:1 between raw data and job knowledge units; single-point tools completely fail to cover this crucial semantic compression and knowledge creation process, merely remaining at the level of extracting single-point data.

[0048] Another technical solution related to this invention is the browser automation platform represented by Browser Use, Playwright MCP, Stagehand, etc., which represents another data acquisition and interaction technology route.

[0049] The core idea behind this type of browser automation platform is to deeply integrate a large language model with the browser automation engine, enabling the AI ​​agent to operate the browser interface like a human user, including navigating to a specified URL, clicking buttons and links, filling out forms, scrolling pages, taking screenshots, and extracting visible text. However, this type of browser automation platform suffers from the following fundamental technical limitations.

[0050] First, the essence of such browser automation platforms is a "data transfer tool" rather than a "knowledge creation system." Browser automation solves the interactive problem of how to retrieve data from protected or dynamically rendered pages, but the retrieved data is still raw HTML text, screenshots, or form field values, without any knowledge extraction or structuring processing.

[0051] Second, such browser automation platforms heavily rely on real-time inference from the LLM (Language Model) to drive each operation, resulting in high execution costs and insufficient stability. Each page interaction requires calling a large language model for visual understanding and operational decisions; a 10-step webpage operation process can consume tens of thousands of tokens of LLM call overhead. Furthermore, due to the non-deterministic nature of LLM output, the same page state may produce different operational decisions in different runs, leading to low repeatability of the automation process. When the target website's page layout changes, the entire automation process may suddenly fail, lacking automatic adaptation and degradation recovery mechanisms.

[0052] Third, such browser automation platforms completely lack data traceability and quality governance capabilities. Browser operations only record temporary logs, failing to create a structured processing history and thus unable to support data lineage tracing and quality auditing. In case of errors, it is impossible to trace back the complete acquisition path. Furthermore, they lack enterprise-level governance capabilities such as PII anonymization, compliance auditing, and multi-tenant isolation, making it difficult to meet the production deployment requirements of industries such as finance, healthcare, and legal.

[0053] Another technical solution related to this application is the Agent development framework represented by LangChain, LlamaIndex, CrewAI, etc., which provides a componentized toolbox for building AI Agent applications.

[0054] The core value of this type of agent development framework lies in abstracting common development patterns such as LLM invocation, prompt management, tool registration, memory management, and agent orchestration into standardized components. However, current agent development frameworks suffer from the following fundamental technical limitations.

[0055] First, these agent development frameworks provide component-based toolkits rather than end-to-end technical solutions, lacking a complete processing chain from raw data to high-quality knowledge assets. Some solutions only perform basic text extraction or employ simple segmentation strategies, lacking intelligent chunking based on semantic understanding. The process from raw enterprise data to high-quality knowledge units requires a complete workflow including multi-source access, cleaning, quality gating, structured extraction, semantic indexing, and quality verification. These frameworks do not provide these processes, requiring developers to build them themselves, a workload equivalent to building a data engineering system from scratch.

[0056] Secondly, the lack of a unified data contract in such agent development frameworks leads to extremely high integration costs when multiple components collaborate. Taking LangChain as an example, different components output different formats, including Document objects, Document lists, embedding vectors, and metadata dictionaries. Developers need to manually maintain the metadata transmission mechanism, which is prone to errors. Without a mandatory unified data contract, the amount of code for format adaptation in a complex pipeline with more than 40 plugins may exceed the amount of code for business logic, creating serious technical debt.

[0057] Third, such agent development frameworks lack the high-level capability to transform knowledge into job competency assets. RAG frameworks output retrieved text blocks rather than validated, structured job judgment units, failing to provide dimensions such as scenarios, judgment signals, and decision rules. When faced with complex decisions, agents still need to extract judgment criteria from the text themselves, wasting a significant amount of computing power on "understanding the original text" rather than "executing the judgment."

[0058] Measured by quantitative metrics, the context window utilization rate in the traditional RAG model is only 0.4 to 0.6, meaning that 40%-60% of the context tokens are occupied by irrelevant information. Even though some solutions enhance global reasoning capabilities by building entity knowledge graphs and community summaries, the related technical solutions still focus on retrieval enhancement rather than end-to-end knowledge creation oriented towards job-specific capabilities. This gap directly affects the agent's reasoning quality, response latency, and operational costs.

[0059] To address the shortcomings of the aforementioned technologies, this invention aims to solve the following core technical problems: how to provide a method and system for constructing an evolvable knowledge infrastructure, that is, how to build an end-to-end knowledge manufacturing pipeline system that can automatically transform raw collaborative materials scattered across various heterogeneous data sources (also known as multi-source heterogeneous data) within an enterprise, including but not limited to chat logs (IM chat logs), customer follow-up data (CRM customer follow-up), work order flow data, email correspondence, meeting minutes, process documents (SOP documents), screen recordings, and historical deliverables, into high-quality job knowledge units and capability assets that can be directly consumed by the AI ​​Agent, and achieve full-link data traceability, multi-dimensional quality gating, and production-level security compliance throughout the entire transformation process.

[0060] So, the technical problems that this invention needs to solve include: (1) the unified access and standardization of multi-source heterogeneous data, how to design a unified data format, also known as a general data contract, so that the original access materials from multiple heterogeneous data sources can flow seamlessly and be progressively enhanced in a unified data structure, and achieve zero-coupling interoperability of more than 40 processing plugins; the original access materials from multiple heterogeneous data sources include, but are not limited to, the original content of more than 16 different types of data sources, such as HTML web pages, PDF documents, Word files, Markdown text, JSON data, images, audio, video, etc. (2) the multi-level intelligent cleaning and semantic compression problem, how to construct a three-layer cleaning pipeline that includes heuristic rule filtering, LLM semantic cleaning and code repair and consistency verification, combined with RedPajama's 14 quality screening standards (including CJK adaptation), to increase the signal-to-noise ratio of the original data from 30%-45% to more than 85%, and achieve a token compression rate of 55%-70% and a structured compression ratio of 7:1 to 13:1. (3) The structured extraction and semantic indexing of job knowledge units: How to accurately extract eight-dimensional job judgment elements from natural language text through a schema-guided knowledge unit extraction mechanism, including scenarios, judgment signals, decision rules, exception conditions, failure cases, output templates, tool calling methods, and result acceptance standards, and build an efficient knowledge index layer through semantic-aware segmentation and hybrid retrieval. (4) The end-to-end trust verification and security compliance: How to maintain complete data lineage tracing (processing_history) throughout the entire knowledge creation process, achieve automated quality scoring through ValidationAgent, and integrate production-grade security compliance mechanisms such as PII anonymization, sandbox isolation, audit logs, and multi-tenant isolation. Specific technical architecture diagrams are as follows: Figure 1 As shown.

[0061] The preferred embodiments of the present invention will now be described in further detail with reference to the accompanying drawings.

[0062] like Figures 1 to 3 As shown, this invention provides a method for constructing an evolvable knowledge infrastructure, comprising the following steps:

[0063] Step S1: Design a mandatory unified data format as a general knowledge container, preset the core field structure of the unified data format, and build a plug-in hot-swappable architecture based on the unified data format to access multiple heterogeneous data sources, so as to realize progressive data enhancement and full-link traceability during the data flow process.

[0064] Step S2: Construct a three-layer cascaded cleaning pipeline, which includes heuristic rule cleaning, large language model semantic cleaning, and code repair and consistency verification. Knowledge is extracted from the original access material through the three-layer cascaded cleaning pipeline.

[0065] Step S3: Based on the unified data format, the clean text after knowledge extraction is normalized into structured format data that can be directly consumed by the intelligent agent through the schema-guided job knowledge unit extraction mechanism; where, schema does not refer to simple field templates, but to predefined data structure specifications used to guide, constrain and verify the structured extraction results.

[0066] Step S4: Semantically aware segmentation of structured data is performed, dividing it into semantically independent and context-complete knowledge units, and a hybrid retrieval index containing relational databases, vector databases and knowledge graphs is constructed.

[0067] Step S5: Perform multi-dimensional quality scoring on the processed data, and establish a full-link data lineage tracing system based on the unified data format carrying the complete processing trajectory.

[0068] Below, we will first explain several important concepts in this embodiment.

[0069] In this embodiment, the knowledge unit, the job knowledge unit, and the knowledge capsule have different technical meanings and hierarchical relationships.

[0070] Among them, a knowledge unit refers to the smallest knowledge object that can be indexed, retrieved, combined, and invoked after being cleaned, extracted in a structured manner, and semantically segmented. It can be a structured knowledge carrier such as fact fragments, rule fragments, relation fragments, or judgment fragments.

[0071] A job-specific knowledge unit is a concrete form of a knowledge unit within a job competency scenario. It is a type of knowledge unit (i.e., a subordinate concept of knowledge unit), specifically referring to a structured knowledge object constructed for a particular job task, decision-making process, or business judgment scenario. Preferably, the job-specific knowledge unit includes at least some or all of the following fields: scenario, judgment signal, decision rule, exception condition, failure case, output template, tool invocation method, and result acceptance criteria, used to provide the agent with directly consumable job judgment criteria. In this embodiment, the processed and output knowledge units all refer to job-specific knowledge units.

[0072] A knowledge capsule is a high-density knowledge encapsulation formed by extracting one or more knowledge units from multiple source documents, especially multiple job-related knowledge units, and then performing cross-document alignment, deduplication and merging, conflict resolution, boundary completion, and semantic compression. The knowledge capsule is not equivalent to a single knowledge unit or a single job-related knowledge unit, but rather a further fusion of multiple knowledge units, used to provide the agent with a more complete, stable, and information-density comprehensive knowledge representation with less contextual overhead.

[0073] Therefore, in this embodiment, the original access material can be structured and extracted to form multiple job knowledge units, and these multiple job knowledge units can be further integrated to form a knowledge capsule; the knowledge capsule has a higher level of information aggregation and stronger reusability than a single job knowledge unit.

[0074] In step S3 of this embodiment, the predefined data structure specification (Schema) includes a set of fields, hierarchical relationships, data types, required constraints, value ranges, and validation rules, used to stably convert unstructured text into job knowledge units in a unified format. This predefined data structure specification can be specifically represented as an eight-dimensional structure of job knowledge units. The eight-dimensional structure includes scenarios, judgment signals, decision rules, exception conditions, failure cases, output templates, tool invocation methods, and result acceptance criteria, for example:

[0075] {

[0076] "scenario": "Customer complained about a refund but there was no order record".

[0077] "signals": [

[0078] "Customer provided payment screenshots",

[0079] "No corresponding order in CRM"

[0080] Historical work orders show that there have been system delays.

[0081] ],

[0082] "decision_rules": [

[0083] "Verify payment transaction number first",

[0084] If payment is successful but no order is generated, proceed to the order completion process.

[0085] ],

[0086] "exceptions": [

[0087] "Refunds will not be issued if the screenshot information is incomplete."

[0088] ],

[0089] "failure_cases": [

[0090] "Refunds issued without verification of transaction history resulted in duplicate compensation."

[0091] ],

[0092] "output_templates": [

[0093] "Dear customer, your payment record has been verified. We are now processing your order replacement / refund."

[0094] ],

[0095] "tool_use": [

[0096] "Calling the CRM query interface",

[0097] Call the payment transaction verification interface

[0098] ],

[0099] "acceptance_criteria": [

[0100] "Payment verification completed"

[0101] "Generate processing conclusion",

[0102] "Work order status update complete" ]

[0104] }

[0105] Step S1 in this embodiment is used to implement a unified data contract and a plug-in hot-swappable architecture.

[0106] like Figure 3As shown, the infrastructure of this embodiment innovatively designs a mandatory unified data format called EKIFIS-Document, also known as a unified data contract or universal data contract, as the sole carrier for data exchange between all Agents and plugins in the entire knowledge creation pipeline. In this embodiment, EKIFIS-Document is designed as a universal knowledge container capable of carrying any type of content, tracking the entire lifecycle, and supporting progressive enhancement. The core field structure of the unified data format includes: unique identifier (id), original content (content), cleaned content (clean_content), document type enumeration (doc_type), processing state machine (status), structured metadata container (meta), text chunk list (chunks), document-level vector embeddings (embeddings), parent document reference (parent_id), and processing history chain (processing_history). Among them, the document type enumeration (doc_type) supports eight types: HTML, PDF, TEXT, MARKDOWN, IMAGE, AUDIO, VIDEO, and JSON. The processing state machine includes five states: PENDING, PROCESSING, COMPLETED, FAILED, and SKIPPED. The structured metadata container contains URL, title, author, language, tags, and custom field extensions. Each text chunk in the list of chunks carries an independent vector embedding and position index. The parent document reference (parent_id) is used for tracing. The processing history chain (processing_history) records the complete processing trajectory.

[0107] The unified data format described in this embodiment employs three key technologies. The first layer is a mandatory contract for universal format implementation: the system can support over 40 plugins, including 16 data source connectors, 19 data processors, and 4 report format generators, all operating on the same data structure. The output of any plugin can be seamlessly consumed by any other plugin without any format conversion code. A new data source plugin only needs to populate the captured content into the core field structure of EKIFIS-Document, and its output can immediately be consumed by all existing processors and extractors. This zero-coupling design allows the system to continuously add plugins without modifying the core code, achieving true hot-swappable plugins.

[0108] The second layer is a cumulative design of progressive enhancement: EKIFIS-Document is not populated all at once, but is progressively enhanced as the data traverses the pipeline: initially, the data source plugin only populates the raw content and structured metadata container; then, the cleansing plugin populates the cleaned content; the chunking plugin generates a list of text chunks; and the embedding plugin adds vector embeddings to each chunk. Each processing step adds a record to the processing history. The same data object exhibits different levels of maturity at different stages, but always maintains a consistent format.

[0109] The third layer is a compliance design that mandates source tracing: the parent document reference (parent_id) records which document the current document is derived from, and the processing history chain (processing_history) records the complete processing chain, such as recording the original source of the data through the data source meta.source_plugin.

[0110] At the plugin architecture level, this embodiment employs a model-independent LLMOuter multi-model routing mechanism. Through a unified abstraction layer, it supports seamless switching between various large language models such as Claude, DeepSeek, Qwen, and Ollam, and includes built-in automatic degradation strategies and cost tracking. When the primary model times out or returns an error, the system automatically switches to a backup model, ensuring uninterrupted pipeline execution. Simultaneously, the model name, prompt word version, token consumption, and response latency of each LLM call are recorded in the processing history, providing a complete basis for subsequent cost optimization and quality auditing. The report generation system adopts a multi-agent collaborative architecture: planning agent → writing agent → polishing agent, naturally supporting HTML, Markdown, PDF, and PPT output formats. This hot-swappable, model-independent architecture allows the entire system to flexibly adapt to different enterprise technology stacks and model preferences, while maintaining the consistency and auditability of the core knowledge generation pipeline.

[0111] Therefore, in step S1 of this embodiment, the hot-swappable plugin architecture supports multiple heterogeneous data source connectors, data processors, and report format generators. New data source plugins fill the captured content into the core field structure of the unified data format (EKIFIS-Document), and during knowledge extraction, fill in cleaned content (clean_content), generating a list of text chunks. Vector embeddings, such as document-level embeddings, are added to each text chunk, and corresponding records are appended to the processing history in real time. The original access materials from various heterogeneous data sources include, but are not limited to, chat logs, customer follow-up data, work order flow data, emails, meeting minutes, process documents, screen recordings, and historical deliverables.

[0112] The plug-in hot-swappable architecture described in this embodiment achieves compatibility with various heterogeneous data source connectors, data processors, and report format generators through a unified data format, type normalization, metadata extension container, progressive enhancement, and processing history chain. More specifically, the plug-in hot-swappable architecture does not merely employ conventional plug-in call relationships, but rather establishes a unified data contract compatibility mechanism based on the unified data format. Different heterogeneous data source connectors first map the original access materials to the core field structure of the unified data format and complete format normalization based on document type enumeration. The extended attributes unique to different data sources (heterogeneous data sources) are written into the structured metadata container. Each data processor progressively enhances the same data object without changing the unified data contract, sequentially filling in the cleaned content, text block list, and vector embedding. Each report format generator generates corresponding output results based on the standard fields and extended metadata in the unified data format, thereby achieving zero-coupling interoperability and hot-swappable extension between different plug-ins.

[0113] Another inventive aspect of this embodiment lies in constructing a complete five-layer knowledge manufacturing pipeline, realizing the systematic transformation from various heterogeneous data sources to job competency assets. These five layers include a knowledge discovery layer, a knowledge extraction layer, a knowledge structuring layer, a semantic index layer, and a trust verification layer. It should be noted that the five-layer knowledge manufacturing pipeline implemented in this embodiment is not a simple stacking of functions, but rather a progressive knowledge density enhancement link with strict quality gating. Data undergoes five fundamentally different information density leaps within this pipeline, each leap transforming data from a lower-value form to a higher-value form, ultimately producing high-density job knowledge units that the AI ​​Agent can directly consume.

[0114] The first layer is the Discovery Layer, implemented through step S1, responsible for the intelligent access and archiving of multi-source enterprise data. Unlike traditional file import methods, this embodiment's Discovery Layer simulates the process of business experts tracing back the sources of their experience, with the system automatically determining which chat logs, work order records, emails, documents, screen recordings, and historical cases need to be accessed. This layer achieves concurrent multi-source data collection through 16 heterogeneous data source connectors (covering search engines, academic databases, code repositories, news aggregations, social media, enterprise IM, CRM, work order systems, email systems, cloud storage, etc.), supporting four access methods: batch import, scheduled synchronization, event triggering, and Webhook push. A data slicing mechanism is also introduced, finely grouping accessed data by department, position, process, and project dimensions to avoid noise pollution caused by full-database import, and ensuring data access security through permission mapping and minimum access domain control. The quality gates of this layer include HTTP response status code verification and content non-empty verification to ensure that the raw data entering the pipeline is at least usable.

[0115] In this embodiment, step S2 is used to implement the knowledge extraction layer (Distill Layer), which extracts human collaborative material into clean, machine-processable text. Preferably, step S2 constructs a three-layer cascaded cleaning pipeline.

[0116] Specifically, in the three-layer cascaded cleaning pipeline constructed in step S2 of this embodiment, heuristic rule cleaning is the first-level cleaning pipeline. It employs a dual degradation strategy using the web content extraction tool Trafilatura and the HTML parsing library BeautifulSoup to remove noisy elements from the HTML. These noisy elements include navigation bars, advertisements, and script tags. It utilizes the PyPDF main strategy in conjunction with EasyOCR to degrade text extraction from image-based PDFs, automatically detecting and repairing mojibake garbled characters caused by the mixing of UTF-8, GBK, and Latin-1 encodings. Testing has verified that this level of processing increases the information content ratio from approximately 50% to over 85% of the original content, and reduces the average number of tokens by 55%-70% compared to the original HTML. The Large Language Model Semantic Cleaning (LLM Semantic Cleaning) is the second-level cleaning pipeline. It extracts core arguments, eliminates colloquial redundancy, and parses implicit judgments and contextual omissions in dialogue chains through semantic understanding of the large language model; it also handles multilingual mixing and industry terminology standardization. Encoding repair and consistency verification form the third-level cleaning pipeline, used to perform format unification, automatic language detection, and automatic identification and de-identification of sensitive personal information. Format unification includes processing such as normalizing line breaks, whitespace characters, and punctuation marks; automatic identification and de-identification of sensitive personal information supports various sensitive field types such as email addresses, ID card numbers, and mobile phone numbers. Preferably, during the knowledge extraction process, quality control checkpoints are preset between the three-level cleaning pipelines, including preset text length and character encoding format after extraction. For example, the extracted text length must be at least 300 characters and should not contain a large number of residual HTML tags (i.e., HTML tags are removed), and the character encoding format must be uniformly UTF-8.

[0117] In this embodiment, the construction process of the three-layer cascaded cleaning pipeline is as follows: First, the unified data format in step S1 is used as the unified input and output carrier between each level of cleaning nodes, so that the heuristic rule cleaning node, the large language model semantic cleaning node, and the encoding repair and consistency verification node are all processed based on the same data object and the processing results are written back; Second, according to the document type of the original access material, the first-level parsing and denoising strategy is assembled for different types of materials, and the text extraction, noise stripping, layout correction, and preliminary normalization are performed on HTML web pages, PDF documents, emails, chat logs, meeting minutes, and process documents respectively; Then, a preset quality gate is set between the first level and the second level to judge the extracted text length, tag residual ratio, garbled character ratio, repetition rate, and OCR confidence. The process involves several steps. First, only text meeting preset conditions (including preset quality gating thresholds) is sent to the second-level large language model semantic cleaning node. Then, using preset prompt word templates and industry context constraints, the large language model performs core argument extraction, speech redundancy removal, context omission completion, terminology standardization, and implicit rule explicitness. Next, semantic consistency gating is implemented between the second and third levels to detect key information retention rate, compression ratio, and semantic deviation. Finally, the third level performs encoding repair, character normalization, language detection, sensitive information desensitization, and metadata consistency verification, outputting standardized clean text for subsequent structured extraction. The processing history records the processing actions, quality scores, anomalies, and backflow records for each level. This three-layer cascaded cleaning pipeline is not simply a series of cleaning tools; rather, it forms a scalable cascaded processing architecture through a unified data contract, layered node assembly, inter-layer quality gating, and anomaly backflow mechanisms, thus ensuring compatibility with different data sources, parsing strategies, and model capabilities.

[0118] In step S2 of this embodiment, the knowledge extraction process incorporates heuristic quality screening standards based on open-source large-scale corpus cleaning projects, including word-level checks, document-level checks, line-level checks, and N-gram duplicate detection, and performs adaptation processing for preset character sets. For example, 14 heuristic quality screening standards validated by the RedPajama large-scale corpus cleaning project perform batch quality filtering of text from four dimensions: word-level, document-level, line-level, and N-gram duplicate detection. The system checks the average word length (range 1.0-50.0), the unique word ratio (not less than 5%), the unigram information entropy (not less than 1.0 bit), and the ratio of alphabetic words at the word level; at the document level, it checks the total number of words (range 25-200,000) and the percentage of alphabetic characters; at the line level, it checks the percentage of lines with punctuation at the end of sentences (not less than 25%), the percentage of lines with only numbers (not more than 60%), and the percentage of lines with all uppercase letters (not more than 80%); and at the N-gram duplicate detection dimension, it checks the percentage of Top 2-gram characters (not more than 40%) and Top 3-gram characters (not more than 36%) to identify a large amount of templated content and SEO-generated spam pages on the Internet.

[0119] It is particularly worth noting that this embodiment performs adaptation processing for a preset character set, which includes the CJK (Chinese, Japanese, and Korean) character set, and has been specially adapted to it. Since Chinese text does not use spaces for word segmentation, the calculation method for word length and word count is completely different from that of English. The system's QualityFilter component has been customized to ensure that Chinese content is not misjudged as low-quality content due to differences in word segmentation.

[0120] More specifically, this embodiment sets up a QualityFilter component as a heuristic quality screening module in the knowledge extraction process, used to perform character set recognition, statistical feature calculation, quality scoring, gating judgment, and backflow control on the cleaned text. The QualityFilter component is connected to a unified data format, using the original content, cleaned content, structured metadata container, and processing history as input and output carriers, and includes a character set recognition unit, a text segmentation unit, an index calculation unit, a threshold judgment unit, and a backflow control unit.

[0121] For the preset character set, this embodiment specifically adapts to the CJK (Chinese, Japanese, and Korean) character set. First, the text to be detected undergoes full-width / half-width character unification, line break unification, whitespace folding, control character deletion, HTML residual tag stripping, punctuation standardization, and garbled character repair, resulting in a standardized text sequence X={x_1,x_2,...,x_N}, where N is the total number of sequences. Then, based on the Unicode segment, the number of CJK characters N_cjk, the number of Latin characters N_lat, and the number of numeric characters N_num are statistically analyzed, and the proportion of CJK characters R_cjk is calculated using the formula R_cjk=N_cjk / max(1, N_cjk+N_lat+N_num).

[0122] When the proportion of CJK characters R_cjk is greater than or equal to the first preset threshold τ_cjk, it is determined to be CJK-dominated text; otherwise, it is determined to be Latin text or mixed text based on character distribution. For CJK-dominated text, instead of using space-based English word segmentation, dictionary matching, statistical word segmentation, sub-word segmentation, or character sliding window methods are used to generate word segment sequences T={t_1,t_2,...,t_M}, where M is the total number of word segment sequences. These word segment sequences replace space words as the object of quality statistics. The first preset threshold τ_cjk refers to the pre-set character proportion threshold, which can be set and adjusted according to actual conditions and needs.

[0123] Based on the word segment sequence, the average word segment length L_avg and the unique word segment ratio R_unique are calculated using the formulas L_avg = (1 / M)×Σ|t_i| and R_unique = |Unique(T)| / max(1, M), respectively. i represents the sequence number.

[0124] Simultaneously, the character-level univariate information entropy H_1 is calculated for the character set V using the formula H_1 = -Σp(c) × log2p(c) (c∈V), where p(c) = f(c) / Σf(c), p(c) refers to the probability of character c appearing, and f(c) refers to the frequency of character c appearing. The character-level univariate information entropy H_1 is used to characterize the richness of text information; when the character-level univariate information entropy H_1 is too low, it indicates that the text may have templated repetition or noise accumulation.

[0125] Furthermore, the effective character density R_eff is calculated using the formula R_eff = (N_cjk+N_lat+N_num) / max(1, N), and the proportion of lines ending with punctuation R_end and the proportion of lines containing only numbers R_num are calculated based on the total number of lines L. The calculation formulas are as follows: R_end=L_end / max(1, L) and R_num=L_num / max(1, L); where L_end is the number of lines ending with a period, question mark, exclamation mark, semicolon or corresponding Western punctuation, and L_num is the number of lines containing only numbers.

[0126] For CJK text, since there is no all-caps feature for English, the proportion of all-caps lines is not used as a hard indicator. Instead, the proportion of anomalous symbols R_sym is increased by the formula R_sym=N_sym / max(1, N).

[0127] For duplicate detection, 2-gram and 3-gram duplicate indices are calculated for the standardized valid character stream Y={y_1,y_2,...,y_K}, using the following formulas: R_2=max_g(2×f_2(g)) / max(1, K) and R_3=max_h(3×f_3(h)) / max(1, K). When the 2-gram duplicate index R_2 or the 3-gram duplicate index R_3 exceeds the second preset threshold, the text is determined to contain templated duplicates, OCR duplicate scans, or splicing noise. The second preset threshold refers to the pre-set duplicate index threshold, which can be set and adjusted according to actual conditions and needs.

[0128] Finally, the QualityFilter component maps each indicator (including the proportion of CJK characters R_cjk, character-level unary information entropy H_1, effective character density R_eff, and 2-gram and 3-gram repetition indicators) to a normalized sub-score s_i, and calculates the overall quality score Q using the formula Q = Σ(w_i × s_i), where Σw_i = 1, and w_i refers to the weight coefficient of the i-th indicator. When the overall quality score Q is higher than the third preset threshold θ and all hard thresholds meet the requirements, the text is deemed to have passed the quality gate; otherwise, the corresponding text object is marked as low-quality content and returned to the upstream processing node or manual review node. Through the above adaptation process, it can be ensured that CJK text will not be misjudged as low-quality content due to missing spaces, missing capitalization features, or word segmentation differences. The third preset threshold θ refers to the pre-set overall quality score threshold, which can also be set and adjusted according to actual conditions and needs.

[0129] The comprehensive quality score Q is a predefined evaluation index in this embodiment used to quantify the overall quality level of text. Its design logic is as follows: The overall quality level of text is a comprehensive representation of multi-dimensional features, such as the previously mentioned word-level, document-level, line-level, and N-gram duplication detection dimensions. A single index cannot fully reflect the usability of the text; therefore, it is necessary to integrate multiple independent quality indices into a unified quantitative score. This embodiment adopts a weighted linear combination form Q=Σ(w_i×s_i) for the following reasons: First, by using each dimension index (information entropy, effective character density, duplication rate, etc.) to measure different independent aspects of text quality, the linear combination ensures that the contribution of each dimension can be independently controlled and does not interfere with each other; Second, by normalizing the sub-scores s_i, the original indices with different dimensions and value ranges are mapped to a unified interval, making different types of indices comparable; Third, the weight coefficient w_i allows for adjustment of the relative importance of each dimension according to specific application scenarios, and the constraint Σw_i=1 ensures that the scoring result is always within an interpretable normalization range.

[0130] Step S3 in this embodiment is used to extract the knowledge structuring layer and job knowledge units.

[0131] The third layer is the Structure Layer, a key technological step in transforming human paragraphs into composable knowledge units. This layer uses the EKIFIS-Document unified data format to normalize the extracted clean text into a structured format that the agent can directly consume. This includes document type annotation, metadata extraction, entity relation extraction, and key fact extraction. Metadata extraction includes data source, timestamp, author, and language. The core innovation of step S3 and its Structure Layer lies in the introduction of a schema-guided knowledge unit extraction mechanism. Utilizing a large language model and a predefined multi-level schema registry, it transforms unstructured natural language text into JSON-formatted knowledge units.

[0132] Unlike traditional general entity-relation extraction, the job knowledge unit extraction in this embodiment is designed for the job cognition layer. In step S3 of this embodiment, the structured data corresponds to the target structure of the job knowledge unit. The target structure of each job knowledge unit includes eight standard dimensions: scenario, signals, decision rules, exceptions, failure cases, output templates, tool use, and acceptance criteria.

[0133] From the perspective of information density, this structuring process achieves significant semantic compression: a 3,000-word industry analysis report (approximately 2,000 tokens), after structured extraction, outputs JSON containing approximately 150 to 300 tokens (covering 10-15 entities, 8-12 indicators, and 5-8 relationships), with a compression ratio of approximately 7:1 to 13:1. This means that the agent can accommodate 7 to 13 times more document information within the same context window. When the structured results of multi-source documents are further integrated into a knowledge capsule, the end-to-end compression ratio can reach 12:1 to 33:1, meaning the total number of tokens in the knowledge capsule is only 3% to 8% of the total number of tokens in all source documents, but it retains more than 90% of the key information points in the original documents. These key information points can be further evaluated through manual sampling, with the main discarded elements being repetitive expressions, background information, and rhetorical text.

[0134] Preferably, during the normalization process, a multi-level schema registry mechanism is used to transform unstructured text into job knowledge units in JSON format using a large language model, thereby achieving semantic compression; and a multi-level verification and repair mechanism is used to ensure the quality of structured extraction, which includes schema verification of the base class of basic data, confidence scoring, and multi-model cross-validation.

[0135] This embodiment preferably introduces a multi-level schema registry mechanism, allowing different industries and positions to use different extraction templates. It supports confidence scoring, conflict detection, rule merging, and boundary condition identification, and automatically generates an evidence map, establishing an auditable mapping relationship between each position's knowledge unit and the source text fragment.

[0136] Preferably, this embodiment constructs a multi-layered verification and repair mechanism to ensure the quality of structured extraction. First, the extraction results undergo type and structure verification through strict schema verification based on the Pydantic BaseModel, a base class for basic data verification. For simple errors that can be automatically corrected, such as safe conversion between numeric strings and numeric types, the system automatically corrects and records the repair log. For errors that cannot be automatically corrected, a detailed set of validation_errors verification error information is generated for manual review. Second, the ConfidenceScorer confidence scoring component comprehensively considers a weighted combination of the null value ratio (the proportion of key fields being empty), source reliability, and extraction completeness, outputting a confidence score between 0.0 and 1.0. Results below the system's set threshold are automatically marked as pending review (needs_review) and enter the manual review queue. Finally, multi-model cross-validation is used, such as using different LLMs or Prompts to independently extract from the same information source and comparing the consistency of the results, thereby evaluating the accuracy of the extraction results. When the discrepancy between the results of the two models exceeds 20%, manual review is triggered. This embodiment employs a closed-loop quality assurance design combining machine automation and manual review, enabling the system to continuously improve in accuracy with each iteration. Each manual correction provides a sample of the "correct answer," which is used to optimize the Prompt design for the next round of extraction, forming a positive feedback loop. The Prompt refers to a prompt word or instruction, i.e., the text input into the large language model.

[0137] Step S4 in this embodiment is used to implement the semantic indexing layer and the hybrid retrieval mechanism.

[0138] The fourth layer is the Semantic Index Layer, which is responsible for transforming structured knowledge units into a foundation for efficient retrieval by the agent. This embodiment innovatively designs a semantic-aware chunking strategy at this layer. Unlike traditional text segmentation based on a fixed number of characters, this embodiment comprehensively utilizes paragraph boundaries, overlapping windows, and metadata embedding to segment the document into semantically independent and context-complete knowledge units.

[0139] Preferably, in step S4 of this embodiment, during the semantic-aware segmentation process, the segmentation granularity of the segmentation strategy includes any one of fixed size, sliding window, sentence, and paragraph; allowing different downstream tasks to choose the most suitable segmentation granularity. Each text block, after vector embedding processing, is indexed and stored in three complementary persistent relational databases, vector databases, and knowledge graphs, and exposed to the agent. When querying, the agent supports simultaneous reasoning using vector semantic similarity, structured metadata conditional filtering, and knowledge graph relationships to obtain the corresponding context. Vector embedding processing supports sentence-transformers, OpenAI embeddings, and Gemma models, and provides TF-IDF and hash vectors as degradation schemes with zero external dependencies. Relational databases include SQLite or PostgreSQL, used to store document metadata and structured extraction results, supporting precise conditional queries. Vector databases include ChromaDB or Milvus, used to store the vector embeddings of text blocks, supporting semantic similarity retrieval. Knowledge graphs include Neo4j, used to store entities and relationships, supporting graph traversal and path reasoning.

[0140] All three storage methods are uniformly exposed to the Agent through the HybridRetriever hybrid retrieval engine designed in this embodiment. A single query by the Agent can simultaneously utilize vector semantic similarity, structured metadata condition filtering (time range, source type, confidence threshold, etc.), and knowledge graph relational reasoning to obtain the most complete and relevant context. Tests have shown that this hybrid retrieval mechanism improves the Agent's information retrieval efficiency by tens of times compared to traditional single-vector retrieval.

[0141] Measured by quantitative metrics, in the traditional RAG mode, directly retrieving text blocks consumes 2500 to 4000 tokens for the Top-5 search results, with approximately 40%-60% of the content highly relevant to the query, resulting in a context window utilization of approximately 0.4-0.6. In the knowledge capsule mode of this embodiment, the same amount of information may only require 300 to 600 tokens for structured representation, and almost 100% is relevant to the query—the context window utilization is close to 1.0. This means that with the same token budget, the agent in this embodiment can reference 5 to 10 times more knowledge sources, supporting more comprehensive and in-depth reasoning. In actual testing on Claude 3.5 Sonnet, using the knowledge capsules of this embodiment as context (average 400 tokens) compared to using raw text blocks (average 3000 tokens) reduces the first token latency by approximately 35%-50% and the complete reasoning time by approximately 25%-40%.

[0142] The HybridRetriever engine described in this embodiment is a unified retrieval abstraction component designed at the semantic indexing layer. It encapsulates three heterogeneous storage backends—relational databases, vector databases, and knowledge graphs—into a single retrieval interface for intelligent agents. The hybrid retrieval engine receives natural language query requests from the intelligent agent, decomposes these requests into sub-queries adapted to each of the three storage backends, and executes parallel three-way retrieval: vector semantic similarity retrieval, structured metadata condition filtering, and knowledge graph relational reasoning. It then performs unified score fusion and reordering on the candidate result sets returned by the three retrievals, finally returning a sorted comprehensive retrieval result list to the intelligent agent, taking into account semantic relevance, structured condition matching degree, and entity relational association. Through this hybrid retrieval engine, a single query by the intelligent agent can simultaneously utilize the advantages of each of the three storage backends: vector databases provide semantic-level fuzzy matching capabilities, relational databases provide precise filtering capabilities for structured fields, and knowledge graphs provide the ability to infer and discover multi-hop relationships between entities. These three complement each other and work synergistically to obtain the most complete and relevant contextual information. The implementation process of the HybridRetriever engine preferably includes steps S4a to S4e.

[0143] Step S4a is used to perform query preprocessing and subquery generation.

[0144] The hybrid retrieval engine receives retrieval requests from an agent, containing natural language query text and optional filtering conditions. These optional filtering conditions include one or more combinations of time range, source type, confidence threshold, document type, and domain tags. The hybrid retrieval engine parses the retrieval request, generating three parallel subqueries: the first is a vector semantic subquery, which transforms the natural language query text into query vectors using a vector embedding model; the second is a structured conditional subquery, which transforms the filtering conditions into structured query statements for relational databases; and the third is a graph relational subquery, which identifies the entity names or entity types involved in the natural language query text and generates a graph traversal query for knowledge graphs.

[0145] Step S4b is used to perform vector semantic similarity retrieval.

[0146] The query vector generated in step S4a is sent to the vector database. Semantic matching retrieval is performed by calculating the cosine similarity between the query vector and the knowledge unit vector embedding. The formula for calculating the cosine similarity is S_vector(q,d) = (q·d) / (‖q‖×‖d‖). Where q is the query vector, d is the vector embedding of the candidate knowledge unit, q·d is the inner product of the two vectors, and ‖q‖ and ‖d‖ are the L2 norms of the two vectors, respectively. The vector database returns the top K1 candidate knowledge units in descending order of cosine similarity, forming the first candidate result set R_vector={(d_i, S_vector_i) |i=1, 2, ..., K1}, where K1 is the preset upper limit for the number of vector retrieval results.

[0147] Step S4c is used to implement conditional filtering of structured metadata.

[0148] The structured query statement generated in step S4a is sent to a relational database to perform precise conditional queries on the structured metadata container fields in the unified data format. These precise conditional queries support interval filtering based on time range, enumeration matching based on source type, threshold filtering based on confidence score, and set inclusion judgment based on custom tags. The query result is a set of knowledge units that satisfy all filtering conditions. For each candidate knowledge unit in the set, a metadata matching score S_meta is calculated using the formula: S_meta(d)=(1 / N)×Σ(j=1, N)w_j×m_j(d). Where N is the total number of filtering conditions, w_j is the weight of the j-th filtering condition, and Σw_j=N is satisfied. m_j(d) is the matching function of candidate knowledge unit d on the j-th filtering condition. When the condition is fully satisfied, m_j(d)=1, and when it is not satisfied, m_j(d)=0. For continuous conditions, normalized distance is used for calculation: m_j(d)=1-|v_actual-v_target| / v_range, where v_actual is the actual value, v_target is the target value, and v_range is the range of values ​​for the condition. The relational database returns the first K2 candidate knowledge units to form the second candidate result R_meta={(d_i, S_meta_i)|i=1, 2, ...,K2}.

[0149] The metadata matching score S_meta is a predefined evaluation index in this embodiment used to quantify the degree of matching between candidate knowledge units and structured filtering conditions. Its design logic is as follows: the results of structured condition filtering are not simple binary judgments, but rather exhibit differences in degree. For example, newer documents within a time range match older documents more closely. Therefore, a continuous scoring mechanism is needed to quantify this degree of difference. This embodiment uses a multi-condition weighted average form S_meta(d) = (1 / N) × Σw_j × m_j(d) for the following reasons: First, different filtering conditions have different importance to the retrieval results, and the weight w_j reflects the relative importance of each condition; second, the matching function m_j(d) assigns 0 / 1 values ​​to discrete conditions and normalized distance values ​​to continuous conditions, thereby unifying exact matching and fuzzy matching under the same scoring framework; third, normalization is performed by dividing by the total number of conditions N to ensure that regardless of the number of filtering conditions, the final score always falls within a comparable range of 0 to 1.

[0150] Step S4d is used to implement knowledge graph relation reasoning retrieval.

[0151] The graph traversal query generated in step S4a is sent to the knowledge graph. Starting with the entities identified in the query, a graph traversal of limited depth is performed along the relationship edges between entities. The graph traversal supports relationship types including competition, supply, cooperation, causality, and regulation. The maximum depth of the graph traversal is a preset upper limit H for the number of hops, with a default value of H = 3. For each candidate knowledge unit discovered through graph traversal, a graph relation score S_graph is calculated based on the path characteristics between it and the query origin entity. The calculation formula is: S_graph(d)=max(p∈P(d))[(1 / |p|)×Σ(e∈p)r_strength(e)×λ^(hop(e)-1)]; where P(d) is the set of all paths from the query origin entity to the entity associated with the candidate knowledge unit d, |p| is the number of relation edges in path p, e is each relation edge in path p, r_strength(e) is the strength value of the relation edge and its value ranges from 0 to 1, hop(e) is the hop number of the relation edge in the path, and λ is the path decay factor and 0 <λ≤ 1, with a default value of λ= 0.7, which is used to assign lower weights to relations that are farther away from the query origin. When there are multiple paths, the path with the highest score is taken as the graph relation score of the candidate knowledge unit. The knowledge graph returns the first K3 candidate knowledge units, forming the third candidate result set R_graph={(d_i, S_graph_i)|i=1, 2, ..., K3}.

[0152] Step S4e is used for multi-path result fusion and reordering.

[0153] The three candidate result sets returned in steps S4b, S4c, and S4d are merged and deduplicated. A comprehensive fusion score is calculated for each candidate knowledge unit in the merged candidate knowledge unit set R_union = R_vector∪R_meta∪R_graph. The formula for calculating the comprehensive fusion score is: S_hybrid(d) = α × S_norm_vector(d) + β × S_norm_meta(d) + γ × S_norm_graph(d), where α, β, and γ are the pre-set fusion weights for vector semantic retrieval, structured metadata filtering, and knowledge graph relation reasoning, respectively, and satisfy α + β + γ = 1; S_norm_vector(d), S_norm_meta(d), and S_norm_graph(d) are the normalized scores of the three retrieval paths, mapped to a range of 0 to 1. When a candidate knowledge unit only appears in the result set of some retrieval paths, its normalized score on paths where it does not appear is set to 0.

[0154] The comprehensive fusion score S_hybrid is a predefined fusion evaluation index in this embodiment used to uniformly sort the three candidate result sets (including the first candidate result set, the second candidate result set, and the third candidate result set, also known as the results of the three heterogeneous retrieval paths). Its design logic is as follows: the three retrieval paths capture different levels of information matching respectively. Vector semantic retrieval captures semantic approximate associations, structured metadata filtering captures precise attribute constraints, and knowledge graph relation reasoning captures deep logical associations between entities. These three are orthogonal to each other and complement each other. The reason for adopting the weighted linear fusion form S_hybrid(d)=α×S_norm_vector(d)+β×S_norm_meta(d)+γ×S_norm_graph(d) in this embodiment is as follows: First, linear fusion allows candidate results that only appear in some retrieval paths to still receive reasonable scores, and the normalized score of paths that do not appear is set to 0, which does not affect the contribution of other paths, thus avoiding the defect of a single missing path causing the overall score to drop to zero under the product fusion method; Second, the constraint α+β+γ=1 ensures that the value range of the fusion score is always in the range of 0 to 1, which makes it easy to set a uniform quality threshold; Third, the adaptive adjustment mechanism of the weights enables the system to dynamically allocate the relative importance of the three retrieval paths according to the query features, rather than using fixed weights to ignore query differences.

[0155] The fusion weights α, β, and γ can be adaptively adjusted according to the query type: when the query is dominated by semantic fuzzy matching, the vector semantic retrieval weight α is increased; when the query contains explicit structured filtering conditions, the metadata filtering weight β is increased; when the query involves reasoning about relationships between entities, the knowledge graph weight γ is increased. The default weight configuration is α=0.5, β=0.25, and γ=0.25.

[0156] The merged candidate knowledge units are reordered from highest to lowest according to the comprehensive fusion score S_hybrid, and the top K results are taken as the final retrieval output and returned to the agent, where K is a preset upper limit for the number of final returns. Each returned retrieval result carries its comprehensive fusion score, sub-scores, and the identifier of the hit retrieval path, so that the agent can judge the credibility and matching dimension of the retrieval result in subsequent reasoning.

[0157] Step S5 in this embodiment is used to implement the trust verification layer and full-link data traceability.

[0158] The fifth layer is the Trust Layer, a key technological guarantee for ensuring the credibility of the knowledge creation pipeline output in this embodiment. This layer comprises two core subsystems: the ValidationAgent quality assessment system and the processing_history end-to-end data lineage tracing system.

[0159] The ValidationAgent quality assessment system is a predefined automated data quality assessment subsystem in this embodiment, which includes the following modules and processing flow: (1) a multi-dimensional quality scoring module, used to calculate independent scores for each knowledge unit in six dimensions: completeness score, accuracy score, timeliness score, relevance score, uniqueness score, and traceability score; (2) a quality gating judgment module, used to perform threshold judgment on the scores of each dimension, and trigger backflow processing when any dimension score is lower than the corresponding preset threshold; (3) a backflow control module (also known as a backflow routing module), which routes the data to the corresponding upstream processing node for supplementation or correction according to the specific dimension that does not meet the standard, including backflow to the knowledge structure layer for supplementary extraction when the completeness does not meet the standard, backflow to the multi-model cross-validation link when the accuracy does not meet the standard, and marking the data as expired data to be updated when the timeliness does not meet the standard; (4) an audit queue module, which marks the results with confidence scores lower than the preset threshold as pending manual review and enters the audit queue. The above four sub-modules are executed in the order of dimension scoring, gating judgment, backflow routing, or audit queue.

[0160] The processing_history full-link data lineage tracing system is a predefined subsystem in this embodiment for recording and tracing the complete data processing path. It includes the following modules and processing flow: (1) a trajectory recording module, which records the complete processing trajectory. After each processing node is completed, it automatically adds a processing record to the processing history chain field with a unified data format. The record includes the processing node identifier, execution timestamp, model name and version number used, prompt word version identifier, quality score of the node, and token consumption; (2) a lineage graph construction module (also known as a data lineage chain construction module), which, based on the processing history, records the processing history. The chain and parent document reference relationship automatically construct a directed acyclic graph from the original access material to the final knowledge unit. Each node in the graph represents a processing operation, and the edge represents the data flow direction. This constructs a lineage graph that includes the data lineage chain; (3) The source query module supports starting from any knowledge unit and traversing backward along the lineage graph to return the complete processing path and processing details of each link; (4) The repair and positioning module is used to locate and repair along the processing trajectory. When the agent makes an erroneous decision based on the knowledge unit, it automatically identifies the earliest processing node that introduced the deviation by comparing the erroneous output with the intermediate results of each node in the lineage graph, providing a positioning basis for accurate repair.

[0161] In step S5 of this embodiment, the ValidationAgent quality assessment system performs multi-dimensional quality scoring on each batch of processed data. The evaluation dimensions of the multi-dimensional quality scoring include completeness, accuracy, timeliness, relevance, uniqueness, and traceability. If any dimension fails to meet the standard, the corresponding data will be returned to the upstream processing node for supplementation or correction, instead of directly entering the downstream consumption stage. Completeness refers to the key field filling rate, which is quantified by filled_fields / total_fields; accuracy refers to the consistency rate of multi-model cross-validation; timeliness is preferably based on the time decay function f(t)∈[0,1]; relevance refers to the vector similarity and LLM score weighting; uniqueness is preferably based on the independent information increment after URL deduplication and MD5 content hash deduplication; traceability is represented by the number of complete traceability levels, which is preset to 1-5 levels by default.

[0162] The `processing_history` end-to-end data lineage tracing system is one of the unique capabilities that distinguishes this embodiment from all existing technologies. Each EKIFIS-Document carries a complete processing trajectory, precisely recording: which data connectors participated in data acquisition, which cleaning steps were performed, which LLM models and specific prompt word versions were used, the quality scores of each stage, processing timestamps, and token consumption. This means that any knowledge unit, decision reference, or downstream output can be traced back to its original source, forming a complete and auditable data lineage chain. When an agent makes an incorrect decision based on a certain knowledge unit, it can precisely locate the processing stage where the problem occurred by tracing the `processing_history`, whether the original data itself is incorrect, whether the cleaning process lost key information, or whether the structured extraction deviated from the original semantics, thus achieving precise repair rather than starting from scratch. This capability fundamentally solves the auditability problem of AI systems "confidently generating illusions."

[0163] Therefore, this embodiment establishes a full-link data lineage tracking system by processing the history chain. Each general knowledge container carries a complete processing trajectory, recording the participation of data connectors, cleaning steps, the version of the large language model, the version of the prompt words, the quality scores of each stage, the processing timestamp, and the token consumption, forming a complete and auditable data lineage chain. When the agent makes an incorrect decision based on the knowledge unit, it can be corrected by locating along the processing trajectory.

[0164] Preferably, the trust verification layer also integrates a human-in-the-Loop review mechanism. After automatic extraction, ConfidenceScorer scores the confidence level of each result. Results below a preset threshold are marked by HumanReviewQueue as requiring human review, with reasons such as confidence level below 0.6, missing multiple fields, or insufficient source reliability. Human reviewers see the entries in the queue, make corrections or confirmations, and the corrections are written back to the system, serving as feedback signals to optimize the prompt design for the next round of extraction. Each human correction provides a sample of correct answers. The accumulation of these samples is used to evaluate and improve extraction quality, making the system continuously more accurate with use, and the proportion of data requiring human review continuously decreasing. This closed-loop design of machine automation + artificial intelligence collaboration is also an important technical feature of this embodiment in terms of quality assurance.

[0165] Preferably, this embodiment further includes step S6, used to construct a Benchmark Foundry capability verification system, generating verification standards while creating knowledge; so that the value of knowledge units and job capability assets is no longer limited to the narrative level, but can be quantified and proven. The evaluation elements of each benchmark case in the capability verification system include: a real-world scenario description, input material package, key signals that must be hit, standard decision results, acceptable exception paths, examples of incorrect decisions, and manual scoring rules; the capability verification system supports the following comparison modes: before-and-after comparison mode, hierarchical comparison mode, red team testing mode, and online shadow evaluation mode. Specifically, the before-and-after comparison mode connects to the knowledge creation pipeline of this embodiment to verify whether the Agent's capabilities have improved before and after; the hierarchical comparison mode is used to implement layer-by-layer difference analysis from bare model → ordinary RAG retrieval enhancement → knowledge unit enhancement → knowledge unit + Skill complete capability package; the red team testing mode includes robustness testing of boundary scenarios, conflict scenarios, deception samples, and error-induced samples; online shadow evaluation refers to shadow evaluation, used to run evaluations in real business environments without directly taking over the production environment.

[0166] The Benchmark Foundry capability verification system is designed because without evaluation, the value of knowledge units can only remain a qualitative description. Traditional RAG systems typically lack a systematic evaluation mechanism, forcing developers to rely on fragmented manual testing to verify system effectiveness, which is neither comprehensive nor repeatable. This embodiment's Benchmark Foundry capability verification system achieves full automation and standardization of evaluation. After the knowledge creation pipeline completes the production of a batch of job-specific knowledge units, Benchmark Foundry automatically generates corresponding evaluation task sets, driving the Agent to execute the same tasks with and without knowledge unit support. It also automatically calculates multi-dimensional performance difference indicators, including retrieval hit rate (traditional RAG mode 25%-40% vs. knowledge capsule mode 60%-80%), improved complex task completion rate (+18% to +35%), reduced single-task token cost (40%-60%), and improved end-to-end response latency (30%-50%). This system enables enterprises to quantify the actual value brought by this embodiment using precise data.

[0167] Preferably, this embodiment further includes step S7, which is used to embed a production-level security compliance system, including the following sub-steps:

[0168] Step S701: Automatically identify and de-identify personal information using a personal identifiable information de-identification component (such as the PiiRedactorPlugin component); personal information includes various sensitive personal information such as email address, ID card number, mobile phone number, and bank card number, and supports configurable de-identification strategies, such as replacement, masking, or hashing.

[0169] Step S702: Sandbox isolation is achieved based on the plugin isolation mechanism of the Docker containerization platform; this can prevent malicious code from escaping, and each third-party plugin runs in an independent container environment, completely isolated from the main system process and data storage.

[0170] Step S703: Record a full operation audit log including every data access, processing and output operation. Supports multi-dimensional querying by time range, operation type and user identity to meet SOC 2 audit requirements.

[0171] Step S704: A preset configurable data retention strategy is implemented, which supports configuring different data lifecycle management rules and automatically performs the marking, archiving, and secure deletion of expired data.

[0172] Step S705: Tenant-level data isolation is achieved through the tenant identifier PipelineContext.tenant_id, ensuring that the data of different tenants are completely separated in the storage, processing and retrieval stages;

[0173] Step S706: Support data subject consent management, data export, and data deletion request processing through a separate compliance API endpoint ( / api / v1 / compliance / ).

[0174] The architecture deployed in this embodiment is based on Docker+Kubernetes+Celery asynchronous task queues, supporting horizontal scaling and production-grade fault tolerance.

[0175] This embodiment also provides a system for constructing an evolvable knowledge infrastructure, which employs the method for constructing an evolvable knowledge infrastructure as described above, and includes:

[0176] The knowledge discovery layer is used to design a mandatory unified data format as a general knowledge container. It presets the core field structure of the unified data format and builds a plug-in hot-swappable architecture based on the unified data format to access multiple heterogeneous data sources, realizing progressive data enhancement and full-link traceability during data flow.

[0177] The knowledge extraction layer is used to construct a three-layer cascaded cleaning pipeline, which includes heuristic rule cleaning, large language model semantic cleaning, and code repair and consistency verification. Knowledge is extracted from the original access material through the three-layer cascaded cleaning pipeline.

[0178] The knowledge structuring layer, based on the unified data format, normalizes the clean text after knowledge extraction into structured format data that can be directly consumed by the intelligent agent through the schema-guided job knowledge unit extraction mechanism.

[0179] The semantic indexing layer is used to perform semantically aware segmentation of structured data into semantically independent and context-complete knowledge units, and to build a hybrid retrieval index that includes relational databases, vector databases and knowledge graphs.

[0180] The trust verification layer is used to perform multi-dimensional quality scoring on the processed data and establish a full-link data lineage tracing system based on the unified data format carrying the complete processing trajectory.

[0181] In summary, compared with the prior art, this embodiment has the following advantages:

[0182] First, a mandatory unified data format is designed as a general knowledge container, and a plug-in hot-swappable architecture is built based on this unified data format to access multiple heterogeneous data sources. In the process of data flow, progressive data enhancement and full-link traceability are realized to solve the problem of unified access and standardization of multi-source heterogeneous data. The process of building a job competency base that traditionally requires 4 to 8 weeks of work by professional data engineers can be compressed into a standardized pipeline process that is automatically executed by the system.

[0183] Second, a three-layer cascaded cleaning pipeline is constructed to extract knowledge from the original access materials, achieving multi-level intelligent cleaning and semantic compression. After processing by the three-layer cascaded cleaning pipeline, the token compression rate of the original data reaches 55%-70%, meaning that the Agent only needs to consume one-third to one-half of the original tokens to fully obtain the same information content. After schema-guided structured extraction, the structured compression ratio reaches 7:1 to 13:1, meaning that the same amount of context window can accommodate 7-13 times the document information. After multi-source fusion knowledge capsule encapsulation, the end-to-end compression ratio reaches 12:1 to 33:1, while retaining more than 90% of the key information points of the original document. The context window utilization rate is improved from 0.4-0.6 in the traditional RAG mode to nearly 1.0. In terms of inference latency, the first token latency is reduced by 35%-50%, the complete inference time is reduced by 25%-40%, and the total latency of 5 rounds of iterative inference is reduced from about 45 seconds in the traditional mode to 20-28 seconds.

[0184] Third, the clean text extracted from knowledge is normalized into structured data that can be directly consumed by the intelligent agent. This structured data is then semantically segmented into semantically independent and context-complete knowledge units. A hybrid retrieval index, incorporating relational databases, vector databases, and knowledge graphs, is constructed, thereby achieving structured extraction and semantic indexing of job-specific knowledge units. This embodiment achieves zero-coupling interoperability and true hot-swappable plugins for over 40 plugins. Adding any new data source connector or processor plugin only requires filling the data into the core field structure of EKIFIS-Document according to the standard interface; its output can then be seamlessly consumed by all existing downstream components without writing any format conversion or adaptation code. Simultaneously, the LLMRouter model-independent routing mechanism allows the system to flexibly switch underlying large language models (such as Claude, DeepSeek, Qwen, Ollam, etc.), with built-in automatic degradation and cost tracking, freeing enterprises from being tied to a single model provider and allowing them to freely choose the optimal model combination based on performance, cost, and compliance requirements.

[0185] Finally, a multi-dimensional quality score is performed on the processed data, and a full-link data lineage tracing system is established based on the unified data format carrying the complete processing trajectory. This fundamentally solves the auditability problem of the "illusion of confidence" generated by AI systems. Any knowledge unit can be traced back to its original source and its complete processing link. When the agent produces erroneous output, engineers can accurately locate the problem at which link in the data link and perform precise repairs. Combined with Benchmark Foundry's four comparative evaluation modes (before and after comparison, stratified comparison, red team testing, and shadow evaluation), enterprises can use quantitative indicators to demonstrate the actual improvement brought by the knowledge creation pipeline, rather than relying on qualitative descriptions.

[0186] Fifth, this embodiment provides comprehensive enterprise-level security and compliance assurance, including PII anonymization, sandbox isolation, audit logs, data retention policies, multi-tenant isolation, and GDPR-compliant APIs. This enables the system to meet production deployment requirements in industries with extremely high data security and compliance requirements, such as finance, healthcare, and legal affairs. This security and compliance system is deeply integrated with the knowledge creation pipeline, rather than being a supplementary external module, ensuring that every stage of the data lifecycle, from access to consumption, is secure and controllable. The entire system is based on a production-grade architecture with asynchronous task queues, supporting horizontal scaling and fault tolerance, and can meet the performance and stability requirements of enterprises in large-scale data processing scenarios.

[0187] Therefore, this embodiment can automatically transform multi-source heterogeneous data scattered in the organizational collaboration process into structured, traceable, and verifiable job knowledge units and capability assets that can be directly consumed by intelligent agents through an overall technical solution. This can better meet the practical application needs of intelligent agents for the quality, structuring degree, and searchability of input data.

[0188] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for constructing an evolvable knowledge infrastructure, characterized in that, Includes the following steps: Step S1: Design a mandatory unified data format as a general knowledge container, preset the core field structure of the unified data format, and build a plug-in hot-swappable architecture based on the unified data format to access multiple heterogeneous data sources, so as to realize progressive data enhancement and full-link traceability during the data flow process. Step S2: Construct a three-layer cascaded cleaning pipeline, which includes heuristic rule cleaning, large language model semantic cleaning, and code repair and consistency verification. Knowledge is extracted from the original access material through the three-layer cascaded cleaning pipeline. Step S3: Based on the unified data format, the clean text after knowledge extraction is normalized into structured format data that can be directly consumed by the intelligent agent through the schema-guided job knowledge unit extraction mechanism; Schema refers to the predefined data structure specification used to guide, constrain and verify the structured extraction results; Step S4: Semantically aware segmentation of structured data is performed, dividing it into semantically independent and context-complete knowledge units, and a hybrid retrieval index containing relational databases, vector databases and knowledge graphs is constructed. Step S5: Perform multi-dimensional quality scoring on the processed data, and establish a full-link data lineage tracing system based on the unified data format carrying the complete processing trajectory.

2. The method for constructing an evolvable knowledge infrastructure according to claim 1, characterized in that, In step S1, the core field structure of the unified data format includes: unique identifier, original content, cleaned content, document type enumeration, processing state machine, structured metadata container, text chunk list, document-level vector embedding, parent document reference, and processing history chain. The hot-swappable plugin architecture supports multiple heterogeneous data source connectors, data processors, and report format generators. The new data source plugin fills the captured content into the core field structure of the unified data format, and fills in the cleaned content during the knowledge extraction process to generate a list of text blocks. Vector embedding is added to each text block, and corresponding records are added to the processing history chain in real time. The original access materials from various heterogeneous data sources include chat logs, customer follow-up data, work order flow data, emails, meeting minutes, process documents, screen recordings, and historical deliverables.

3. The method for constructing an evolvable knowledge infrastructure according to claim 1, characterized in that, In step S2, heuristic rule cleaning serves as the first-level cleaning pipeline, employing a dual degradation strategy using the webpage content extraction tool Trafilatura and the HTML parsing library BeautifulSoup to remove noisy elements from the HTML. These noisy elements include navigation bars, advertisements, and script tags. The large language model semantic cleaning serves as the second-level cleaning pipeline, extracting core arguments, eliminating colloquial redundancy, and parsing implicit judgments and contextual omissions in dialogue chains through semantic understanding of the large language model. Encoding repair and consistency verification serve as the third-level cleaning pipeline, used to perform format unification, automatic language detection, and automatic identification and desensitization of sensitive personal information. During knowledge extraction, quality control checkpoints are preset between the three cleaning pipelines, including preset text length and character encoding format after extraction.

4. The method for constructing an evolvable knowledge infrastructure according to claim 3, characterized in that, In step S2, the knowledge extraction process incorporates heuristic quality screening standards based on open-source large-scale corpus cleaning projects, including word-level checks, document-level checks, line-level checks, and N-gram duplicate detection, and performs adaptation processing for preset character sets.

5. The method for constructing an evolvable knowledge infrastructure according to claim 1, characterized in that, In step S3, the structured format data corresponds to the target structure of the job knowledge unit. The target structure of each job knowledge unit includes scenario, judgment signal, decision rule, exception condition, failure case, output template, tool calling method and result acceptance standard. During the normalization process, a multi-level schema registry mechanism is used to transform unstructured text into job knowledge units in JSON format using a large language model, thereby achieving semantic compression. Furthermore, a multi-level verification and repair mechanism is used to ensure the quality of structured extraction. This multi-level verification and repair mechanism includes schema verification of the base class of basic data, confidence scoring, and multi-model cross-validation.

6. The method for constructing an evolvable knowledge infrastructure according to claim 1, characterized in that, In step S4, during the semantic-aware segmentation process, the segmentation granularity of the segmentation strategy includes any one of fixed size, sliding window, sentence, and paragraph. After vector embedding, each text block is indexed into a relational database, vector database, and knowledge graph for storage and exposed to the agent. When querying, the agent can simultaneously use vector semantic similarity, structured metadata condition filtering, and knowledge graph relationships for reasoning to obtain the corresponding context.

7. The method for constructing an evolvable knowledge infrastructure according to claim 1, characterized in that, In step S5, the evaluation dimensions of the multi-dimensional quality score include completeness, accuracy, timeliness, relevance, uniqueness and traceability. If any dimension fails to meet the standard, the corresponding data will be fed back to the upstream processing node for supplementation or correction. By processing the historical chain, a full-link data lineage tracking system is established. Each general knowledge container carries a complete processing trajectory, recording the participation of data connectors, cleaning steps, the version of the large language model, the version of the prompt words, the quality score of each stage, the processing timestamp, and the token consumption, forming a complete and auditable data lineage chain. When an agent makes an incorrect decision based on a knowledge unit, it can be corrected by locating along the processing trajectory.

8. The method for constructing an evolvable knowledge infrastructure according to any one of claims 1 to 7, characterized in that, It also includes step S6, which is used to build a capability verification system and generate verification standards while creating knowledge; The evaluation elements for each test case in the capability verification system include: a description of the real scenario, an input material package, a key signal that must be hit, a standard decision result, an acceptable exception path, an example of an incorrect decision, and manual scoring rules. The capability verification system supports the following comparison modes: before-and-after comparison mode, stratified comparison mode, red team testing mode, and online shadow evaluation mode.

9. The method for constructing an evolvable knowledge infrastructure according to any one of claims 1 to 7, characterized in that, It also includes step S7, which is used to embed a production-grade security compliance system, and includes the following sub-steps: Step S701: Automatically identify and desensitize personal information using a personal identifiable information desensitization component; Step S702: Implement sandbox isolation based on the plugin isolation mechanism of the Docker containerization platform; Step S703: Record a full operation audit log including every data access, processing and output operation. Support multi-dimensional query by time range, operation type and user identity to meet audit requirements; Step S704: A preset configurable data retention strategy is implemented, which supports configuring different data lifecycle management rules and automatically performs the marking, archiving, and secure deletion of expired data. Step S705: Implement tenant-level data isolation using the tenant identifier PipelineContext.tenant_id; Step S706: Support data subject consent management, data export, and data deletion request processing through a separate compliant API endpoint.

10. A system for constructing an evolvable knowledge infrastructure, characterized in that, The method for constructing an evolvable knowledge infrastructure as described in any one of claims 1 to 9 is adopted, and includes: The knowledge discovery layer is used to design a mandatory unified data format as a general knowledge container. It presets the core field structure of the unified data format and builds a plug-in hot-swappable architecture based on the unified data format to access multiple heterogeneous data sources, realizing progressive data enhancement and full-link traceability during data flow. The knowledge extraction layer is used to construct a three-layer cascaded cleaning pipeline, which includes heuristic rule cleaning, large language model semantic cleaning, and code repair and consistency verification. Knowledge is extracted from the original access material through the three-layer cascaded cleaning pipeline. The knowledge structuring layer, based on the unified data format, normalizes the clean text after knowledge extraction into structured format data that can be directly consumed by the intelligent agent through the schema-guided job knowledge unit extraction mechanism. The semantic indexing layer is used to perform semantically aware segmentation of structured data into semantically independent and context-complete knowledge units, and to build a hybrid retrieval index that includes relational databases, vector databases and knowledge graphs. The trust verification layer is used to perform multi-dimensional quality scoring on the processed data and establish a full-link data lineage tracing system based on the unified data format carrying the complete processing trajectory.

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