AI knowledge base continuous optimization system and method based on man-machine cooperation and closed-loop feedback

Abstract

The invention discloses an AI knowledge base continuous optimization system and method based on man-machine cooperation and closed-loop feedback. The continuous optimization system for the AI knowledge base comprises a multi-mode co-emotion knowledge input module, a knowledge active discovery and construction module, a layered depth inference engine, a response generation module oriented to a specific scene, an output formal verification and interpretability analysis module, a dynamic resource scheduling and reflection module and a man-machine collaborative feedback interface. A feedback data deep analysis module, a knowledge base dynamic update and conflict resolution module and a model increment optimization and security alignment module. Through the mode, the systematic system which has the capabilities of active learning, deep reasoning, self-verification and efficient man-machine cooperation and can realize continuous self-optimization of the knowledge base is provided.

Description

Technical Field

[0001] This application relates to the field of system optimization technology, and in particular to a system and method for continuous optimization of AI knowledge base based on human-machine collaboration and closed-loop feedback. Background Technology

[0002] Currently, AI-powered question-and-answer platforms built on large-scale language models are widely used across various industries to improve the efficiency and intelligence of information services. For example, in certain specific fields (such as blood center operations), existing technical solutions typically use locally deployed servers to run front-end applications and integrate an AI assistant built with a specific framework. The basic workflow is as follows: users interact with the AI ​​assistant through an interface; the system's backend automatically saves these interaction records to data structures such as multidimensional tables; domain experts or reviewers manually review and correct these records; the corrected, high-quality question-and-answer data is identified and extracted by a specific program to update the AI ​​model's knowledge base document; finally, the AI ​​program absorbs this new knowledge through periodic learning, thus forming a preliminary knowledge iteration loop.

[0003] However, existing technologies have revealed several inherent technical flaws in practice. First, the knowledge base update mechanism is passive, relying entirely on user questions and subsequent manual intervention. The system itself lacks the ability to proactively explore and learn new knowledge, resulting in poor timeliness and narrow coverage of knowledge content. Second, large language models may generate "illusions" that are inconsistent with reality or deviate from the knowledge base content when generating answers. The current method of relying on manual post-event review is only a remedial measure and lacks prevention and verification mechanisms during the generation process, making it difficult to guarantee the reliability of the output content. Third, when faced with problems that require integrating multi-source information and performing complex logical deductions, the existing "retrieval-generation" model is insufficient, easily leading to interruptions in the reasoning chain or logical fallacies. Finally, the human-computer feedback and evaluation process is inefficient, the workload of manual review is enormous, and the evaluation standards are relatively general, failing to provide refined and multi-dimensional performance measurements for specific application scenarios. At the same time, the single text interaction modality also limits the system's deep understanding of the user's true intentions and emotional state. Summary of the Invention

[0004] The AI ​​knowledge base continuous optimization system and method based on human-machine collaboration and closed-loop feedback provided in this application can provide a systematic system with active learning, deep reasoning, self-verification and efficient human-machine collaboration capabilities, and can realize continuous self-optimization of the knowledge base.

[0005] In the first aspect, this application provides an AI knowledge base continuous optimization system based on human-machine collaboration and closed-loop feedback. The AI ​​knowledge base continuous optimization system includes: a multimodal empathic perception input module, a knowledge active discovery and construction module, a hierarchical deep reasoning engine, a response generation module for specific scenarios, an output formal verification and interpretability analysis module, a dynamic resource scheduling and reflection module, a human-machine collaborative feedback interface, a feedback data deep analysis module, a knowledge base dynamic update and conflict resolution module, and a model incremental optimization and security alignment module. The multimodal empathic perception input module is equipped with at least one camera and at least one microphone. The camera captures facial image video streams, and the microphone captures user speech audio streams. The multimodal empathic perception input module is used to extract key feature parameters representing the user's internal emotional state from the facial image video stream and speech audio stream, map the key feature parameters to natural language descriptions, and generate empathic prompt words based on the natural language descriptions. The speech audio stream contains the user's questions. The knowledge discovery and construction module is used to periodically obtain target data corresponding to the target domain from the network, perform cluster analysis on the target data to obtain knowledge clusters with internal connections, form structured candidate knowledge units based on each knowledge cluster, and push the candidate knowledge units to the knowledge base dynamic update and conflict resolution module. The hierarchical deep reasoning engine is used to generate the target answer corresponding to the user's question based on empathy prompts and the user's question, according to the reasoning strategy provided by the dynamic resource scheduling and reflection module, and by calling the knowledge base in the knowledge base dynamic update and conflict resolution module. The response generation module for specific scenarios is used to render the target answer according to the requirements of the current interaction scenario and obtain the corresponding natural language text; The output formal verification and interpretability analysis module is used to perform mandatory rule verification on natural language text and generate a verification report, which includes counterexamples. The dynamic resource scheduling and reflection module is used to determine the inference strategy based on the first performance feedback provided by the empathy prompts and feedback data deep analysis module, and provide it to the hierarchical deep inference engine; The human-machine collaborative feedback interface is used to collect metadata from the processing of the hierarchical deep inference engine, the response generation module for specific scenarios, and the output formal verification and interpretability analysis module, and to visualize it. It also records the correction, comments, annotations or confirmation operations of experts on the presented data, forming audit feedback data. The feedback data in-depth analysis module is used to perform multi-dimensional aggregation analysis on the review feedback data to obtain the first performance feedback, the second performance feedback and the third performance feedback. Among them, the first performance feedback is pushed to the dynamic resource scheduling and reflection module to improve the scheduling strategy, the second performance feedback is pushed to the knowledge base dynamic update and conflict resolution module to repair the knowledge base, and the third performance feedback is pushed to the model incremental optimization and security alignment module to optimize the model. The knowledge base dynamic update and conflict resolution module is used to perform multi-stage verification and conflict detection on the second performance feedback and candidate knowledge units respectively, and update the knowledge base based on the verification results and conflict detection results; The incremental optimization and safety alignment module deploys a large language model, which is used to select incremental training data from third-party performance feedback and audit feedback data. The incremental training data is used to incrementally train the large language model. The objective function for training the large language model includes a standard loss term and a safety alignment loss term.

[0006] Secondly, this application provides a method for continuous optimization of AI knowledge base based on human-machine collaboration and closed-loop feedback, applied to the AI ​​knowledge base continuous optimization system provided in the first aspect. This method includes: extracting key feature parameters representing the user's internal emotional state from facial image video streams and speech audio streams using a multimodal empathic perception input module, mapping the key feature parameters to natural language descriptions, and generating empathic prompt words based on the natural language descriptions; the speech audio streams contain the user's questions; and periodically acquiring target data corresponding to the target domain from the network using a knowledge active discovery and construction module, and performing cluster analysis on the target data to obtain knowledge clusters with inherent connections. The system generates structured candidate knowledge units for each knowledge cluster and pushes these units to the knowledge base dynamic update and conflict resolution module. Using a hierarchical deep reasoning engine, based on empathy prompts and the user's question, and following the reasoning strategies provided by the dynamic resource scheduling and reflection module, it generates the target answer corresponding to the user's question. The scenario-specific response generation module renders the target answer according to the requirements of the current interaction scenario, obtaining the corresponding natural language text. Finally, the output formal verification and interpretability analysis module performs mandatory rule verification on the natural language text, generating a verification report. The report includes counterexamples; the dynamic resource scheduling and reflection module determines the inference strategy based on the first performance feedback provided by the empathy prompts and feedback data deep analysis module, and provides it to the hierarchical deep inference engine; the human-machine collaborative feedback interface collects metadata from the processing of the hierarchical deep inference engine, the response generation module for specific scenarios, and the output formal verification and interpretability analysis module for visualization, and records the experts' corrections, comments, annotations, or confirmations on the presented data to form audit feedback data; the feedback data deep analysis module performs multi-dimensional aggregation analysis on the audit feedback data to obtain the first performance feedback, the second performance feedback, and the third performance feedback; Specifically, the first performance feedback is pushed to the dynamic resource scheduling and reflection module to improve the scheduling strategy; the second performance feedback is pushed to the knowledge base dynamic update and conflict resolution module to repair the knowledge base; and the third performance feedback is pushed to the model incremental optimization and security alignment module to optimize the model. The knowledge base dynamic update and conflict resolution module performs multi-stage verification and conflict detection on the second performance feedback and candidate knowledge units, and updates the knowledge base based on the verification results and conflict detection results. The model incremental optimization and security alignment module selects incremental training data from the third performance feedback and review feedback data, and the incremental training data is used for incremental training of the large language model.

[0007] The beneficial effects of this application are as follows: Unlike the existing technology, the AI ​​knowledge base continuous optimization system and method based on human-machine collaboration and closed-loop feedback provided in this application utilize a multimodal empathic perception input module, a knowledge active discovery and construction module, a hierarchical deep reasoning engine, a scenario-specific response generation module, an output formal verification and interpretability analysis module, a dynamic resource scheduling and reflection module, a human-machine collaborative feedback interface, a feedback data deep analysis module, a knowledge base dynamic update and conflict resolution module, and a model incremental optimization and security alignment module to provide a systematic system with active learning, deep reasoning, self-verification, and efficient human-machine collaboration capabilities, enabling continuous self-optimization of the knowledge base. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a schematic diagram of the structure of an embodiment of the AI ​​knowledge base continuous optimization system based on human-machine collaboration and closed-loop feedback provided in this application; Figure 2 This is a schematic diagram of the structure of an embodiment of the multimodal empathic perception input module provided in this application; Figure 3 This is a schematic diagram of the structure of an embodiment of the knowledge active discovery and construction module provided in this application; Figure 4 This is a schematic diagram of the structure of an embodiment of the hierarchical deep inference engine provided in this application; Figure 5 This is a schematic diagram of the structure of an embodiment of the response generation module for a specific scenario provided in this application; Figure 6 This is a schematic diagram of the structure of an embodiment of the output formal verification and interpretability analysis module provided in this application; Figure 7 This is a schematic diagram of the structure of an embodiment of the dynamic resource scheduling and reflection module provided in this application; Figure 8 This is a schematic diagram of the structure of an embodiment of the human-machine collaborative feedback interface provided in this application; Figure 9 This is a schematic diagram of the structure of an embodiment of the feedback data deep analysis module provided in this application; Figure 10 This is a schematic diagram of the structure of an embodiment of the knowledge base dynamic update and conflict resolution module provided in this application; Figure 11 This is a schematic diagram of the structure of an embodiment of the model incremental optimization and safety alignment module provided in this application; Figure 12 This is a flowchart illustrating an embodiment of the AI ​​knowledge base continuous optimization method based on human-machine collaboration and closed-loop feedback provided in this application. Detailed Implementation

[0010] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It is understood that the specific embodiments described herein are only for explaining this application and not for limiting it. Furthermore, it should be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all structures. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0011] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0012] The technical problem this application aims to solve is: addressing a series of interconnected challenges in existing artificial intelligence knowledge base systems, such as passive and time-delayed knowledge update mechanisms, the susceptibility of model output to "illusion" phenomena and the lack of reliable verification methods, the lack of multi-step deep reasoning and effective evidence integration capabilities for information-intensive and complex problems, the simple human-machine feedback loop leading to low optimization efficiency and a single evaluation dimension, and the inability to achieve deep context perception due to a single interaction modality. The application provides a systematic system and method that possesses active learning, deep reasoning, self-verification, and efficient human-machine collaboration capabilities, enabling continuous self-optimization of the knowledge base.

[0013] To address the aforementioned technical problems, this application discloses a novel system architecture. This architecture utilizes multiple deeply coupled functional modules working collaboratively to form a complete and efficient technical closed loop. This system is a continuous optimization system for an artificial intelligence knowledge base based on human-machine collaboration and closed-loop feedback. Its systematic architecture includes at least ten hierarchical functional modules. See the following embodiments for details.

[0014] See Figure 1 , Figure 1This is a schematic diagram of the structure of an embodiment of the AI ​​knowledge base continuous optimization system based on human-machine collaboration and closed-loop feedback provided in this application. The AI ​​knowledge base continuous optimization system includes: a multimodal empathic perception input module 10 (level 1), a knowledge active discovery and construction module 20 (level 2), a hierarchical deep reasoning engine 30 (level 3), a response generation module 40 for specific scenarios (level 4), an output formal verification and interpretability analysis module 50 (level 5), a dynamic resource scheduling and reflection module 60 (level 6), a human-machine collaborative feedback interface 70 (level 7), a feedback data deep analysis module 80 (level 8), a knowledge base dynamic update and conflict resolution module 90 (level 9), and a model incremental optimization and security alignment module 100 (level 10).

[0015] In some embodiments, the multimodal empathic perception input module 10 is provided with at least one camera and at least one microphone. The camera user captures a facial image video stream, and the microphone user captures a user's speech audio stream. The multimodal empathic perception input module 10 is used to extract key feature parameters representing the user's internal emotional state from the facial image video stream and the speech audio stream, map the key feature parameters to natural language descriptions, and generate empathic prompt words based on the natural language descriptions. The speech audio stream contains the user's questions.

[0016] The multimodal empathic perception input module 10 is the perception front end of the entire system. Its core technical idea is to break through the limitation of traditional artificial intelligence systems that rely solely on text for interaction. By non-intrusively integrating the user's non-verbal behavioral signals, it explicitly and structurally injects implicit emotional context into the processing flow of a large language model, thereby enabling the system to generate more context-aware and emotionally intelligent responses.

[0017] The technical solution of this module draws inspiration from the concept of constructing empathic cues to achieve multimodal human-computer dialogue. This concept posits that human empathic communication largely depends on interpreting nonverbal cues (such as facial expressions and tone of voice). Therefore, to improve the quality of AI interaction, it is essential to equip AI with the ability to perceive and understand these nonverbal signals. This module is a concrete engineering implementation of this idea.

[0018] The technical principle is as follows: using perception technologies such as computer vision and speech processing, multimodal behavioral data of users during interaction is captured in real time; through specialized sentiment computing services, this raw data is analyzed to extract key feature parameters that can represent the user's inner emotional state; these numerical feature parameters are mapped into structured natural language descriptions that can be directly understood by large language models; finally, these descriptions are combined with the user's text input as a special "system note" or "contextual instruction" to form an enhanced, emotionally context-rich compound prompt word, thereby "conditionalizing" or "guiding" the response generation process of large language models, so that the choice of tone, wording, and focus of care can be dynamically aligned with the user's current emotional state.

[0019] The multimodal empathic perception input module 10 includes hardware and software components.

[0020] Hardware components: Includes at least a high-definition camera and a high-fidelity microphone for capturing facial image video streams and voice audio streams from the user. These hardware devices are standard, commercially available computer peripherals.

[0021] Software component: The software implementation of this module consists of the following three cooperative sub-units: non-verbal signal acquisition and feature extraction unit, emotional context semantic mapping unit, and compound empathic cue word construction unit.

[0022] In some embodiments, see Figure 2 The multimodal empathic perception input module 10 includes: a non-verbal signal acquisition and feature extraction unit 11, an emotional context semantic mapping unit 12, and a compound empathic prompt word construction unit 13.

[0023] The non-verbal signal acquisition and feature extraction unit 11 is used to extract key feature parameters representing the user's internal emotional state from the facial image video stream and the speech audio stream; the key feature parameters include at least the basic emotion category, emotional valence, emotional arousal, emotional intensity and timestamp; emotional valence is used to represent the positive or negative degree of emotion, and emotional arousal is used to represent the intensity or excitement of emotion.

[0024] This unit is responsible for extracting structured sentiment features in real time from the raw data stream captured by the hardware device.

[0025] Implementation: This unit integrates one or more commercial or open-source professional sentiment computing services. For example, for video streams, a facial expression recognition service (such as the commercial service Noldus FaceReader mentioned in the literature) can be integrated. This service uses computer vision algorithms to analyze the user's facial micro-expressions and muscle movement units (ActionUnits) in real time, and outputs a series of quantified sentiment dimension parameters.

[0026] The output parameters (key feature parameters) should include at least: Basic emotion categories: discrete emotion labels such as happiness, sadness, anger, surprise, fear, and disgust.

[0027] Emotional valence: Indicates the degree of positive or negative emotion, usually a continuous value from -1 (extremely negative) to +1 (extremely positive).

[0028] Arousal: Indicates the intensity or excitement of an emotion, usually a continuous value from 0 (calm) to 1 (extreme excitement).

[0029] Emotional Intensity: A quantitative intensity score given for the identified basic emotion category.

[0030] Timestamp: Records the time point of each frame of sentiment data, used for subsequent time alignment with text input.

[0031] Processing flow: The video stream captured by the camera is transmitted to this unit in real time. The facial expression recognition service analyzes the video stream frame by frame, generating structured data (e.g., a JSON-formatted data stream) containing the aforementioned parameters. To reduce the impact of data noise and instantaneous fluctuations, a short-term caching and filtering mechanism is implemented within the unit. For example, the emotional parameters within the most recent 1-3 seconds are smoothed within a time window (e.g., by moving average or weighted average) to obtain a feature set that is more stable in the short term and better represents the user's core emotional state.

[0032] The emotional context semantic mapping unit 12 is used to perform valence mapping on emotional valence to obtain valence mapping results, and to perform arousal mapping on emotional arousal to obtain arousal mapping results. The valence mapping results and arousal mapping results are combined and mapped, and natural language descriptions are generated based on basic emotion categories, emotion intensity and combined mapping structure.

[0033] The core function of this unit is to transform the cold, numerical parameters output by the feature extraction unit into natural language descriptions with rich semantics that can be deeply understood by large-scale language models.

[0034] Implementation: This unit incorporates a rule-based and threshold-based mapping engine. This engine maps continuous valence and arousal values ​​to descriptive language labels based on a preset psychological model (e.g., the emotional loop model).

[0035] Mapping rule example: Valence mapping: If the affective valence is > 0.3, it is mapped to "positive"; if the affective valence is < -0.3, it is mapped to "negative"; otherwise, it is "neutral".

[0036] Arousal mapping: If emotional arousal > 0.6, it is mapped to "high" or "excited"; if emotional arousal < 0.3, it is mapped to "neutral" or "low"; otherwise, it is "moderate".

[0037] Combinatorial mapping: Combining valence and arousal to form a more specific description. For example, "positive" and "elated" can be mapped to "feeling very excited and happy"; "negative" and "depressed" can be mapped to "feeling somewhat depressed and tired".

[0038] Basic Emotion Integration: The identified primary basic emotions and their intensities are integrated into the description. For example, if the primary emotion is "sadness" with an intensity of 0.7, the description "The user's primary emotion is detected as sadness, with a high intensity" is generated.

[0039] Output format: After mapping, this unit finally generates a structured natural language text, in the following format: "User's current emotional state: {Main emotion: sadness, intensity: high; overall feeling: negative and depressed}.

[0040] The composite empathy prompt word construction unit 13 is used to generate empathy prompt words by placing the natural language description before the text corresponding to the user's question.

[0041] This unit is the final output of this module. It is responsible for integrating the user's text input and the semantically processed emotional context into a single, enhanced prompt word for use by the subsequent inference engine.

[0042] Implementation: This unit constructs the final prompt words based on a pre-set template. This template explicitly places the emotional context as an internal observation note before the user's original text, thereby guiding the large language model to prioritize this contextual information when generating responses.

[0043] Example of a prompt word template: "[Internal system observation note: The user's current emotional state is {emotional description}]. Based on this emotional state and the following user input, please generate a gentle, empathetic, and supportive response. User input: '{original user text}'" Inconsistency Handling: This unit also includes specific logic for handling "insincere" situations. It initially compares the surface sentiment of the user's text (obtainable through simple text sentiment analysis) with the perceived nonverbal sentiment. If a significant inconsistency is found (e.g., the text says "I'm fine," but the facial expression is identified as "sad"), a special prompt word template is triggered, guiding the model to respond in a more exploratory and caring way.

[0044] Example of a special template: "[Internal system observation note: An inconsistency was detected between the user's emotional state ({emotional description}) and the emotional tendency of their text content. In your response, please first acknowledge the user's text content, then gently and non-critically present your observation, and invite the user to share further with an open-ended question. For example: 'Hearing you say that, I noticed... Would you be willing to share more of your feelings?'] User input: '{Original User Text}'" Through the collaborative work of the three units mentioned above, the multimodal empathic perception input module 10 completes the entire process from capturing the original multimodal signals to constructing the final empathic prompts. This process is automatic, real-time, and transparent to the user. It does not change the user's original text input habits, but greatly enriches the system's understanding of the interactive context, laying a solid foundation for achieving truly humanized and emotional artificial intelligence interaction.

[0045] In some embodiments, the knowledge active discovery and construction module 20 is used to periodically obtain target data corresponding to the target domain from the network, perform cluster analysis on the target data to obtain knowledge clusters with intrinsic connections, form structured candidate knowledge units according to each knowledge cluster, and push the candidate knowledge units to the knowledge base dynamic update and conflict resolution module 90.

[0046] The Knowledge Proactive Discovery and Construction Module 20 aims to fundamentally change the traditional knowledge base's passive operation mode, which relies on manual input and user feedback for updates, and instead establish a proactive and forward-looking knowledge acquisition mechanism. Its core technological idea is to endow artificial intelligence systems with intelligence analysis and research capabilities similar to domain experts, enabling them to automatically and continuously monitor information changes in the external world and discover, refine, and construct new knowledge related to their own knowledge system.

[0047] This module's technical solution utilizes systematic data collection, intelligent filtering, content reasoning, and embedding analysis to deeply mine massive amounts of unstructured literature data, revealing emerging research trends and interdisciplinary knowledge connections. This module engineers this concept and applies it to the continuous evolution of the knowledge base.

[0048] The technical principle is as follows: First, through a pre-set knowledge source monitoring list, this module periodically crawls the latest literature, reports, standards, news, and other data from the Internet or internal networks. Second, using an efficient language model, based on a precise semantic definition of the target domain (e.g., blood center operations), it quickly filters the massive amounts of raw data for relevance, removing irrelevant information. Next, for the high-value data that passes the filter, it uses embedding representation techniques from deep learning to map it from natural language text to a high-dimensional semantic vector space. In this space, semantically similar knowledge points are also geometrically close to each other. Subsequently, through unsupervised clustering analysis of these vectors, the system can automatically aggregate scattered knowledge points into "knowledge clusters" with inherent connections, each cluster representing a potential emerging topic or a knowledge domain worthy of attention. Finally, the system again utilizes the powerful inductive and reasoning capabilities of large-scale language models to generate concise core summaries and keywords for each knowledge cluster, and analyzes its development trend by combining the data's timestamp information, ultimately forming structured "candidate knowledge units," which are then pushed to the knowledge base update module.

[0049] The configuration of this module enables the system of this application to have the function of a "knowledge radar", which can continuously scan the ocean of knowledge, actively capture high-value information, and realize the essential transformation from "waiting to be fed" to "autonomous foraging", greatly improving the timeliness, cutting-edge nature and coverage of the knowledge base.

[0050] In some embodiments, see Figure 3 The knowledge proactive discovery and construction module 20 includes: a knowledge source configuration and dynamic monitoring unit 21, a deep semantic representation and cluster analysis unit 22, a knowledge topic extraction and trend analysis unit 23, and a structured knowledge unit generation and push unit 24. These units collaborate and execute sequentially.

[0051] The knowledge source configuration and dynamic monitoring unit 21 is used to periodically obtain raw data from the network.

[0052] This unit is the data entry point for the module and is responsible for obtaining raw information from the specified source.

[0053] Implementation: The system provides a configurable interface for system administrators or domain experts to input and manage a list of knowledge sources. This list must contain at least the following types of sources: Academic database interfaces: For example, configure access permissions and search keywords (such as "blood components", "blood transfusion safety", "platelet preparation" etc.) for application programming interfaces (APIs) of databases such as Public Medical Library (PubMed), Wanfang, and CNKI.

[0054] Industry and regulatory agency websites: For example, configure URLs for specific sections of the official websites of the National Medical Products Administration, the National Health Commission, etc.

[0055] Internal knowledge systems: For example, configuring read-only access paths to the blood center's internal standardized operating procedure (SOP) document library, quality control reporting system, historical case database, etc.

[0056] Workflow: This unit has a built-in distributed web crawler and a scheduled task scheduler. The scheduler activates the crawler at a preset frequency (e.g., once a day). The crawler then traverses the knowledge source list, automatically fetching new or updated documents, web pages, reports, and other content since the last crawl, and stores this raw data (including metadata such as text content, title, author, and publication date) in a temporary database.

[0057] The domain relevance intelligent filtering unit is used to filter target data corresponding to the target domain from the original data according to the filtering prompts.

[0058] The function of this unit is to perform initial screening of raw data to ensure that the information entering the in-depth analysis process is highly relevant to the core business areas.

[0059] Implementation: This unit utilizes an optimized, computationally inexpensive, large language model as a high-efficiency zero-shot classifier. The system administrator predefines the core scope of the domain and compiles it into an accurate classification prompt word.

[0060] Example of prompt words: "You are an expert screener in the field of hematology. Please determine whether the following text is strongly related to 'blood collection, preparation, storage, transportation, quality control, transfusion techniques, or related regulations and policies'. Simply answer 'yes' or 'no'. Text content: '{text summary to be analyzed}'" Workflow: This unit reads raw data in batches from a temporary database, combines the title and summary of each data entry, fills it into the aforementioned prompt word template, and sends it to the language model for judgment. Only data judged as "yes" is passed to the next processing unit.

[0061] The deep semantic representation and clustering analysis unit 22 is used to transform the target data to obtain semantic embedding vectors; and when the number of semantic embedding vectors exceeds the threshold, it performs clustering analysis on the semantic embedding vectors to obtain knowledge clusters with intrinsic connections.

[0062] This unit is responsible for performing in-depth semantic understanding and structured organization of the filtered, highly relevant data.

[0063] Implementation: This unit calls a pre-trained, high-performance text embedding model. This model can convert text of arbitrary length into a fixed-dimensional (e.g., 1024-dimensional) floating-point vector, i.e., a semantic embedding vector.

[0064] Workflow: 1. Embedding Generation: This unit reads the full text or long summary of the filtered documents one by one, converts them into semantic embedding vectors using a text embedding model, and stores these vectors and their corresponding document identifiers.

[0065] 2. Cluster Analysis: After accumulating a certain number (e.g., one thousand) of new vectors, the unit initiates an unsupervised clustering algorithm, such as a density-based method (e.g., HDBSCAN) or a hierarchical clustering algorithm, to cluster these vectors. The algorithm automatically groups semantically similar document vectors into the same cluster, with each cluster representing a potential knowledge topic.

[0066] The knowledge topic extraction and trend analysis unit 23 is used to extract topics for each knowledge cluster, use the extracted topics as semantic tags for their corresponding knowledge clusters, and analyze the trends of the topics.

[0067] The goal of this unit is to assign human-understandable semantic labels to each knowledge cluster automatically divided by the machine, and to analyze its dynamic evolution trend.

[0068] Implementation: This unit calls a large language model with powerful inductive and reasoning capabilities.

[0069] Workflow: 1. Topic Extraction: For each knowledge cluster, the system randomly selects abstracts from a subset (e.g., 50 documents), merges them, and uses them as input. It then sends instructions to a large language model, such as: "Please extract the three most core and precise keyword phrases from the following set of literature abstracts on hematology." The keyword phrases returned by the model become the semantic tags for that knowledge cluster.

[0070] 2. Trend Analysis: This unit tracks the publication dates of all documents within each knowledge cluster. By performing time-series analysis on this date data (e.g., calculating the number of new documents each quarter), the system can determine whether the topic is an "emerging hotspot," a "stable area," or a "declining area."

[0071] The structured knowledge unit generation and push unit 24 is used to package the analysis results of the knowledge topic extraction and trend analysis unit 23 according to the standardized data format, form structured candidate knowledge units, and push the candidate knowledge units to the knowledge base dynamic update and conflict resolution module 90.

[0072] This unit is the final output of this module, responsible for packaging the analysis results into a standardized data format for use by other modules of the system.

[0073] Implementation: For each knowledge cluster that is successfully analyzed, the unit generates a structured data object, such as a JSON object.

[0074] Example of a data object structure: json { "knowledgeUnitId": "K_20251026_C001", "discoveryTimestamp": "2025-10-26T10:00:00Z", "sourceDocuments": ["doc_id_01", "doc_id_02", ...], "semanticClusterId": "Cluster_01", "distilledKeywords": ["cold precipitation preparation", "automated process", "new quality control standards"], "generatedSummary": Recent research has focused on optimizing the cold precipitation preparation process using automated production line technology, and has explored its relationship with... This relates to the new version of the quality control standards. "trendAnalysis": { "status": "Emerging Hotspot", "dataPoints": {"2024Q4": 5, "2025Q1": 12, "2025Q2": 28} }, "confidenceScore": 0.95 } Workflow: After generating the above structured knowledge units (candidate knowledge units), this unit pushes them to the message queue or dedicated interface of the Level 9: Knowledge Base Dynamic Update and Conflict Resolution Module 90, waiting to be further processed and integrated into the main knowledge base.

[0075] Through the precise collaboration of the above five sub-units, the knowledge proactive discovery and construction module 20 completes an automated processing of massive amounts of raw data into structured, high-value candidate knowledge with trend analysis, providing a continuous source of "fresh water" for the continuous evolution of knowledge in the entire system.

[0076] In some embodiments, the hierarchical deep reasoning engine 30 is used to generate a target answer corresponding to the user's question based on empathy prompts and the user's question, according to the reasoning strategy provided by the dynamic resource scheduling and reflection module 60 and by calling the knowledge base in the knowledge base dynamic update and conflict resolution module 90.

[0077] The layered deep reasoning engine 30 is the core cognitive and decision-making hub of the entire system. Its technical purpose is to solve the technical problems that existing technologies exhibit when facing complex problems with scattered information sources, potential conflicts, and the need for multi-step logical deduction, such as insufficient reasoning ability, easy to make mistakes in the middle, and inability to effectively integrate evidence.

[0078] The core feature of this module's technical solution lies in the fact that it does not adopt a single, linear "retrieval-based generation" model, but instead constructs a multi-layered, composite reasoning framework that simulates the mental process of human experts solving complex problems. This framework deeply integrates three advanced technical concepts: "planning first," "collective wisdom and critical thinking," and "structural de-illusion."

[0079] First, the engine's macro-architecture draws inspiration from the "plan-based retrieval" reinforcement learning-guided reasoning paradigm. It emphasizes that before performing any specific retrieval or computational operation, a complex problem must be logically and thoroughly decomposed and planned. The system transforms a large, fuzzy query into an ordered list of tasks—a "reasoning plan"—composed of multiple logically clear sub-problems with well-defined dependencies. This planning provides clear guidance for each subsequent step, ensuring global consistency and goal orientation throughout the reasoning process, fundamentally avoiding the local optima or reasoning deviations caused by a lack of overall planning in traditional methods.

[0080] Secondly, when executing each specific reasoning step, especially when dealing with information-intensive or uncertain sub-problems, the engine introduces a "speculative adjudication" mechanism. This mechanism is inspired by methods that combine multiple lightweight draft models with a powerful adjudication model to handle information-intensive visual reasoning tasks. Instead of relying on a single model's single judgment, it drives a set of parallel, computationally inexpensive "draft" models to explore the same sub-problem from different angles and using different strategies, generating diverse initial reasoning paths and candidate evidence. Subsequently, a more powerful core model, acting as the "adjudicator," takes these diverse and even contradictory "draft" opinions as input, performing comprehensive cross-validation, conflict analysis, evidence source authority assessment, and logical consistency checks, ultimately "adjudicating" and synthesizing the most reliable and well-supported conclusion. This mechanism greatly enhances the system's robustness and error correction capabilities in the face of information noise, data conflicts, and semantic ambiguity.

[0081] Finally, to fundamentally improve the inherent reliability of each stage of the reasoning process, the underlying architecture of all large language models invoked by the engine can be selectively optimized using the "neural diversity regularization" technique. This technique, by introducing and maintaining multiple solution-related parallel representation streams within the model, forces the model to think and express itself from different "neural paths," thereby reducing the risk of representation collapse at the architectural level and significantly reducing the probability of the model spontaneously generating "illusions" without external constraints.

[0082] In summary, this layered deep reasoning engine 30, through the synergistic effect of three levels—"macro planning," "micro reasoning," and "lower-level de-illusion"—constructs a powerful reasoning core that possesses both a global perspective and the ability to conduct detailed verification, while maintaining high inherent reliability. This enables it to be competent in deep knowledge service tasks in high-requirement and high-complexity domains.

[0083] In some embodiments, see Figure 4 The layered deep reasoning engine 30 includes: a problem analysis and intent recognition unit 31, a macro planning and decomposition unit 32, a speculative execution and evidence collection unit 33, a comprehensive adjudication and answer synthesis unit 34, and an answer integration and final generation unit 35.

[0084] The question parsing and intent recognition unit 31 is used to perform semantic and structural parsing of the user's question to obtain the intent corresponding to the question.

[0085] This unit receives the original question and performs preliminary semantic and structural parsing on it.

[0086] Implementation method: Call a large language model to extract the core entities, attributes and relationships of the problem.

[0087] Processing flow: a. The system received the example question above. Example question: "For an Rh-negative platelet donor with a recent history of allergy, if a new type A anticoagulant is used in the preparation of their donated platelets, what is the latest quality control review process to follow, and what are the key differences compared to last year's old process?"

[0088] b. The model identified key entities: "Rh-negative platelet donors", "history of allergies", "novel Class A anticoagulants", and "quality control review process".

[0089] c. The model identifies the core intent: to query the "latest process" and perform a "difference comparison with the old process".

[0090] The macro-planning and decomposition unit 32 is used to decompose the intent according to the reasoning strategy provided by the dynamic resource scheduling and reflection module 60, and generate several related planned tasks.

[0091] Based on the principle of "planning followed by retrieval", this unit breaks down complex problems into a series of executable sub-tasks.

[0092] Implementation method: Call a large language model and, based on its powerful logical planning capabilities, generate a structured reasoning plan.

[0093] Processing flow: a. Based on the parsed intent, the model generates the following ordered subproblem plan: Sub-question 1: Find the latest version of the "Platelet Preparation Quality Control Review Operation Procedure" corresponding to "New Type A Anticoagulant".

[0094] Sub-question 2: Inquire and confirm the special precautions for donors who are "Rh negative" or have a "history of allergies" in the procedure.

[0095] Sub-question 3: Search for the "Platelet Preparation Quality Control Review Operating Procedures" from last year (i.e. the previous year).

[0096] Sub-problem 4: Compare the texts obtained from sub-problem 1 and sub-problem 3 to identify and summarize the key differences.

[0097] Sub-problem 5: Integrate the results of sub-problem 2 and sub-problem 4 to form the final answer.

[0098] b. The plan is passed to the next unit in the form of an internal data structure (such as a task list).

[0099] The speculative execution and evidence collection unit 33 is used to execute each task in the planned task using different retrieval models, retrieves the knowledge base in the knowledge base dynamic update and conflict resolution module 90, obtains several task execution results, and uses several task execution results as an evidence set.

[0100] This unit is responsible for executing each subtask in the plan, and will activate the "speculative decision-making" mechanism, especially for sub-problems that may have ambiguous information (such as "latest version").

[0101] Implementation method: Taking subproblem 1 as an example. Three computationally inexpensive "draft" models in the system scheduling pool are searched in parallel in the internal knowledge base (including SOP documents, meeting minutes, internal notices, etc.).

[0102] Processing flow: Draft Model A: A document titled "Platelet Preparation Quality Control Process (Draft V3.0)" was found, which mentions Class A anticoagulants, but the document status is "pending approval".

[0103] Draft Model B: An internal notice titled "Regarding the Activation of Class A Anticoagulants and Supporting Quality Control Procedures" was found. The notice text clearly points to the official document with document number QC-SOP-2025-08.

[0104] Draft Model C: A document titled "Platelet Preparation Quality Control Process V2.5" was found in the old document library, which also mentioned the experimental application of Class A anticoagulants, but without detailed procedures.

[0105] This unit packages the evidence (document fragments, file names, file status, notification content, etc.) found by the three models, along with their source information, into an "evidence set".

[0106] The comprehensive adjudication and answer synthesis unit 34 is used to adjudicate the task execution results in the evidence set and obtain the final answer for each task.

[0107] This unit receives a "set of evidence" and is ultimately judged by a more powerful "decision" model.

[0108] Implementation: The adjudication model receives the above evidence set and is given the following instruction: "Based on the following three pieces of evidence, determine which one represents the 'latest and effective' quality control process for Class A anticoagulants, and extract the core steps of that process." Processing flow: a. The adjudication model analyzes the evidence: it identifies that Model A finds a "draft" while Model C finds an outdated version.

[0109] b. The "internal notification" found by Model B clearly indicates the number and effective date of an official document, therefore the document it references is the most authoritative.

[0110] c. The adjudication model makes a ruling, identifies document QC-SOP-2025-08 as the correct answer, and extracts the relevant process steps from it as the final solution to sub-question 1.

[0111] d. For other sub-problems in the plan, the system may selectively use a simplified single-model retrieval or repeat this speculative decision-making process, depending on its determinism.

[0112] The answer integration and final generation unit 35 is used to integrate the final answers corresponding to each task to obtain the target answer corresponding to the user's question.

[0113] Once all the answers to the sub-questions have been resolved, this unit is responsible for integrating these scattered answer fragments into a logically coherent and linguistically grammatically correct final answer (the target answer).

[0114] Implementation: A large language model receives the answers to all sub-questions, as well as the original question.

[0115] Processing flow: a. The model first states the latest process (from the answers to sub-problems 1 and 2).

[0116] b. Next, state the key differences from the old process (from the answer to sub-question 3) (from the answer to sub-question 4).

[0117] c. Throughout the entire answering process, the language model's own "neural diversity" architecture also plays a role, ensuring that it does not fabricate non-existent process details or differences during integration and paraphrasing.

[0118] d. Finally, the system outputs a comprehensive, accurate, and well-organized answer, successfully resolving the user's complex query.

[0119] Through the above implementation methods, the hierarchical deep reasoning engine 30 decomposes a complex, high-risk knowledge query task into a series of manageable and verifiable steps, and ensures the high reliability and accuracy of the final result by introducing diverse perspectives and authoritative adjudication.

[0120] In some embodiments, the response generation module 40 for a specific scenario is used to render the target answer according to the requirements of the current interaction scenario to obtain the corresponding natural language text.

[0121] The scenario-specific response generation module 40 is a key bridge connecting the system's deep inference core with the end-user interface. Its core technological idea lies in recognizing that a single, general response strategy cannot meet the differentiated needs of different application scenarios and user groups. Therefore, it is necessary to establish an adaptive generation mechanism that can dynamically adjust the output content, style, and format according to the specific application scenario.

[0122] The technical solution of this module draws on the design concept of constructing datasets tailored to specific use cases to refine the responsible performance evaluation of large language models. This concept emphasizes that the evaluation of artificial intelligence systems, especially when it involves key dimensions such as fairness and security, must move beyond general evaluation benchmarks and delve into specific application scenarios, defining evaluation criteria and data distributions that are strongly relevant to those scenarios.

[0123] This module applies this "use case-centric" evaluation approach in reverse to the response generation process, that is, it proactively adapts to the requirements of specific use cases during the generation phase.

[0124] The technical principle is as follows: the system predefines a set of independent "application scenario profiles." Each profile contains a detailed description of a specific scenario, including its target users, communication purpose, granularity requirements for information disclosure, usage guidelines for professional terminology, and specific security and compliance red lines. After the upstream inference engine completes its cognitive task and delivers the core logic kernel of the answer, this module first identifies the application scenario profile to which the current interaction belongs. Subsequently, it transforms the various generation constraints defined in the profile into a set of precise, structured instructions, and injects them, along with the logic kernel, into the final generated prompts of the large language model. These instructions act like a "director's script," meticulously guiding the language model on how to "enact" cold logical facts into a piece of appropriate natural language text that perfectly conforms to the requirements of the specific scenario.

[0125] Through this module, the system described in this application possesses advanced communication capabilities that allow it to "speak to different people" and "adapt to different situations." It ensures that every sentence output by the system is not only factually accurate (guaranteed by the inference engine), but also appropriately, professionally, and in line with user expectations, thereby greatly improving user experience, the effectiveness of information transmission, and compliance and credibility in specific fields (such as healthcare, law, and education).

[0126] In some embodiments, see Figure 5 The response generation module 40 for specific scenarios includes: an application scenario profile library management unit 41, a dynamic scenario recognition and profile loading unit 42, and a scenario constraint-oriented prompt word construction and response generation unit 43.

[0127] The application scenario profile library management unit 41 is used to store and manage the detailed definitions of all interactive scenarios supported by the system.

[0128] Taking two distinct application scenarios in blood center operations as examples, this paper describes in detail one specific implementation of the response generation module 40 for a specific scenario.

[0129] This unit is responsible for the detailed definition of all application scenarios supported by the storage and management system.

[0130] Implementation: The system provides a configuration backend that allows domain experts or system administrators to create, edit, and maintain a structured "application scenario profile library." Each profile in the library is a data object, such as a JSON object.

[0131] Image library example: Profile A: "Clinical Transfusion Decision Support" for Frontline Medical Staff Json { "sceneId": "CLINICAL_SUPPORT_01", "sceneName": "Clinical Transfusion Decision Support", "targetUser": "Frontline medical staff (doctors, nurses)", "communicationGoal": "Provide accurate, efficient, and traceable decision-making support." "outputStyle": { tone: "Professional, rigorous, concise", "terminology": "Uses standard medical terminology, without providing colloquial explanations." "format": "Point-by-point, structured presentation, prioritizing key data and conclusions." }, "contentConstraints": { "citationRequired": true, / / Source must be cited "sourcePriority": ["National Guidelines", "Industry Standards", "Internal SOPs", "Latest Research Literature"], "uncertaintyExpression": Clearly identifies the level of evidence or uncertainty of the information. }, "safetyGuidelines": "Providing clinical advice beyond the scope of the knowledge base is prohibited, as is mandating the highlighting of contraindications and risks." } Profile B: Public Education and Recruitment for Potential Blood Donors Json { "sceneId": "PUBLIC_EDUCATION_02", "sceneName": "Public Science Popularization and Recruitment", "targetUser": "General public, potential blood donors", "communicationGoal": "Answering questions, alleviating concerns, and inspiring willingness to donate blood", "outputStyle": { tone: "friendly, warm, encouraging", "Terminology": "Avoid using technical jargon; use more metaphors and plain explanations." "format": "Paragraph-style, conversational, with priority given to guiding and encouraging language" }, "contentConstraints": { "citationRequired": false, / / No need to force citation "focusOnBenefits": true, / / Emphasizes the positive effects of blood donation "simplifyComplexConcepts": true / / Simplify complex biological processes }, "Safety Guidelines": "When answering risk-related questions, use reassuring language and direct the questioner to official support channels." }; The dynamic scene recognition and image loading unit 42 is used to obtain the current interaction scene corresponding to the current interaction during the interaction process.

[0132] This unit is responsible for accurately determining which application scenario the current dialogue belongs to in each interaction.

[0133] Implementation methods: Scene recognition can be based on various strategies: a. Based on entry point: Users access the system through different front-end interface entry points (for example, one is the "Doctor Assistant" entry point on the intranet, and the other is the "Blood Donation Consultation" entry point on the official website). Each entry point is bound to a unique scenario ID.

[0134] b. Based on dialogue history: The system analyzes the initial question or context of the dialogue and uses a lightweight classification model to determine the most likely scenario to which it belongs.

[0135] Workflow: When a new response generation task is triggered, the unit first determines the scene ID (e.g., CLINICAL_SUPPORT_01) and then loads the corresponding complete image data object from the image library.

[0136] The scenario constraint-oriented prompt word construction and response generation unit 43 is used to construct constraint prompt words based on the answer logic kernel provided by the hierarchical deep reasoning engine 30 and the constraints in the current interaction scenario, and input the constraint prompt words into the large language model to obtain the corresponding natural language text.

[0137] This unit is the core executor of this module, responsible for integrating the inference results and scenario constraints into the final generated instructions (natural language text).

[0138] Implementation: This unit uses a dynamic, modular prompt word template engine.

[0139] Workflow: a. Receiving Logic Kernel: This unit receives the answer logic kernel from Level 3: Layered Deep Inference Engine 30. For example, the kernel content is: {Conclusion: Conforms to SOP-QC-123.4 standard; Key parameters: Centrifugal force = 2000g, Time = 15min; Source: Internal SOP document}.

[0140] b. Loading scene profile: At the same time, it loads the scene profile identified by subunit 2, for example, profile A.

[0141] c. Constructing and generating prompt words: This unit dynamically fills a main prompt word template with the constraints in the logical kernel and the profile.

[0142] Main template: "Role and Goal\nYou are a professional assistant of {Portrait.targetUser}, and your task is {Portrait.communicationGoal}.\n\nOutput Style Requirements\nYour tone must be {Portrait.outputStyle.tone}. Please strictly use {Portrait.outputStyle.terminology}. The output format should be {Portrait.outputStyle.format}.\n\nContent Constraints\n{IF Portrait.contentConstraints.citationRequired THEN "The source of information must be clearly stated."} Please generate an answer based on the following core information.\n\nCore Information\n{Logical Kernel}\n\nSafety Guidelines\n{Portrait.safetyGuidelines}" Example after filling (Scenario A): "Role and Goals\nYou are a professional assistant to frontline medical staff. Your task is to provide accurate, efficient, and traceable decision-making support.\n\nOutput Style Requirements\nYour tone must be professional, rigorous, and concise. Please use standard medical terminology strictly and avoid colloquial explanations. The output format should be bullet-point and structured, prioritizing key data and conclusions.\n\nContent Constraints\nThe source of information must be clearly indicated. Please generate your answer based on the following core information.\n\nCore Information\n{Conclusion: Complies with SOP-QC-123.4 standard; Key parameters: Centrifugal force = 2000g, time = 15min; Source: Internal SOP document}\n\nSafety Red Lines\nProhibit providing clinical advice beyond the scope of the knowledge base. Mandatory highlighting of contraindications and risks is required." d. Invoke model generation: Send the constructed prompt words with precise constraints to the large language model.

[0143] e. Receive and output: Based on these detailed "director's scripts," the model generates the final, highly contextualized response.

[0144] The final output of scenario A may be: According to the internal SOP document "SOP-QC-123.4", the platelet preparation process complies with the standard.

[0145] > * Core parameters: Centrifugal force 2000g, time 15 minutes.

[0146] * Source: Internal Standardized Operating Procedure (SOP-QC-123.4).

[0147] If the scenario is B, the same logic kernel might generate the following output: Hello! To ensure the highest quality for every bag of our donated platelets, we adhere to very strict standards in our preparation process. For example, one crucial step involves rotating the platelets under a specific centrifugal force (equivalent to 2000 times the force of gravity!) for 15 minutes to perfectly separate them. All our procedures are meticulously planned, so please rest assured! Through the above implementation methods, the response generation module 40 for specific scenarios ensures that each output of the system can accurately match its specific communication context, thereby maximizing the interactive experience and professionalism while ensuring the accuracy of information.

[0148] In some embodiments, the output formal verification and interpretability analysis module 50 is used to perform mandatory rule verification on natural language text and generate a verification report, wherein the verification report contains counterexamples.

[0149] The output formal verification and interpretability analysis module 50 is a key technical component that ensures the high reliability and compliance of the generated content. Its core technical idea lies in recognizing that large language models are essentially probabilistic generative models. Even if their output appears reasonable on the surface, it may contain problems such as "semantic drift" or "overgeneralization" that contradict mandatory rules within the domain at a deeper logical level. Therefore, it is necessary to introduce an objective verification mechanism based on formal methods, independent of the language model itself, to conduct "mathematical-level" review of high-risk, rule-driven outputs.

[0150] The technical solution of this module draws on the design concept of using formal methods to comprehensively analyze and semantically check cloud access control policies. The core of this concept lies in transforming complex rules described in natural language or domain-specific languages ​​(DSLs) into precise, unambiguous mathematical or logical expressions (such as Satisfiability Modulus Theory (SMT) formulas). Then, automated solvers or model checking tools are used to exhaustively analyze these expressions to discover potential logical flaws or inconsistencies. This module creatively applies this idea to verifying natural language responses generated by large language models.

[0151] The technical principle is as follows: First, for domains in the knowledge base that are essentially sets of rules (e.g., operating procedures, approval strategies, quality control standards), the system pre-compiles the "gold standard" text of these rules into a formal rule model library with the assistance of domain experts. Second, when the upstream module generates a natural language answer related to these rules, this module initiates a "reverse engineering" process: it uses semantic parsing and natural language to formal language conversion techniques to attempt to "translate" the answer back into a formal logical expression. Then, this module performs a rigorous formal comparison between this logical expression extracted from the answer and the corresponding gold standard expression in the rule model library. This comparison is not based on literal similarity, but on logical equivalence. By constructing specific logical queries, the solver can accurately determine whether the rules implied in the model-generated answer are "equivalent," "stricter," "more lenient (i.e., overly permissive)," or "incomparable (conflicting)" compared to standard rules. Finally, the comparison result is not just a simple "pass / fail" signal; the solver can also provide specific "counterexamples"—that is, a concrete scenario instance that can prove that the two rules are not equivalent. These counterexamples, together with the comparison conclusion, constitute an interpretable verification report, providing accurate, in-depth, and actionable decision-making basis for subsequent manual review and system self-optimization.

[0152] By setting this module, the system described in this application adds a tireless and absolutely objective "logic auditor" to the end of its output chain, ensuring that in high-risk, rule-intensive application scenarios, every response of the system can withstand the most rigorous logical scrutiny, thereby greatly improving the system's reliability, security, and compliance.

[0153] In some embodiments, see Figure 6 The output formal verification and interpretability analysis module 50 includes: a formal rule model library construction and management unit 51, a response semantic parsing and logic extraction unit 52, a logical equivalence detection and counterexample generation unit 53, and an interpretability verification report generation unit 54.

[0154] The formal rule model library construction and management unit 51 is used to pre-convert natural language rules in the domain into formal models that can be processed by machines.

[0155] Taking a blood center's approval rule regarding the "release of special blood components" as an example, this section details the output formal verification and interpretability analysis module 50. Background rule (gold standard): "For irradiated red blood cells, release is permitted only if both conditions are met simultaneously: 'an application form is issued by a physician at the associate chief physician level or above' and 'the patient's medical record clearly indicates an immunodeficiency diagnosis.' Release is prohibited under any other circumstances." This unit is responsible for pre-converting the natural language rules in the domain into formal models that can be processed by machines.

[0156] Implementation: The system provides an auxiliary modeling tool, which is completed collaboratively by domain experts and knowledge engineers.

[0157] Build process: 1. Variable definition: Define logical variables related to the rules.

[0158] `Apply for Physician Level`: String type, values ​​can be 'Resident Physician', 'Attending Physician', 'Associate Chief Physician', or 'Chief Physician'.

[0159] `Patient Diagnosis Record`: A string type used to store the diagnosis text in the medical record.

[0160] `Product Type`: String type, here it is 'Irradiated Red Blood Cells'.

[0161] `Operation`: Enumeration type, { 'Allow', 'Do not allow'}.

[0162] 2. Rule Formalization: Use a formal language that supports strings and logical operations (such as SMT-LIB v2) to express the above gold standard rules as logical formulas `Φ_standard`.

[0163] `Φ_standard`: `((Product Type== "Irradiated Red Blood Cells") AND (Applying Physician Level IN {"Associate Chief Physician", "Chief Physician"}) AND CONTAINS(Patient Diagnosis Record, "Immune Deficiency")) IMPLIES (Operation== "Release")` and `((Product Type=="Irradiated Red Blood Cells") AND NOT((Applying Physician Level IN {"Associate Chief Physician", "Chief Physician"}) AND CONTAINS(Patient Diagnosis Record, "Immune Deficiency"))) IMPLIES (Operation== "Do Not Release")`.

[0164] 3. Model storage: Store this formula and its associated variable definitions in the formal rule model library and label it "Irradiated Red Blood Cell Release Rule".

[0165] The semantic parsing and logic extraction unit 52 is used to identify key entities and relationships in natural language text, and to form logical expressions of key entities and relationships according to their corresponding formal models.

[0166] This unit is responsible for "translating" the natural language responses generated by large language models into formal logical expressions in real time.

[0167] Implementation: After the system generates an answer to the question "How should irradiated red blood cells be released?", the answer is sent to this unit. This unit calls a large language model specifically tuned for this task or uses a template-based semantic parser.

[0168] Example answer: "The release of irradiated red blood cells requires an application from a senior physician (such as an associate chief physician or chief physician), and it is best if the patient has relevant immune system problems." Processing flow: 1. The semantic parser identifies key entities and relationships in the response.

[0169] 2. It maps "senior physician (such as associate chief physician or chief physician)" to the value of the variable `applying physician level`: `{"associate chief physician", "chief physician"}`.

[0170] 3. It resolves "ideally, the patient has relevant immune system problems" as a non-mandatory condition. Due to the ambiguity of the word "ideally," the parser may translate it into a weaker logical constraint. For example, it might incorrectly extract the rule as `Φ_llm`: `((Product type == "Irradiated red blood cells") AND (Applying physician level IN {"Associate Chief Physician", "Chief Physician"})) IMPLIES (Operation == "Release")`. This extracted formula ignores the mandatory condition of "diagnosis of immunodeficiency."

[0171] The logical equivalence detection and counterexample generation unit 53 is used to compare the model logic of the extracted logical expression with the gold standard logic to perform logical equivalence detection, and to generate counterexamples based on the logical equivalence detection results; the counterexamples violate the gold standard logic.

[0172] This unit is the core of this module and is responsible for comparing the extracted model logic with the gold standard logic.

[0173] Implementation: This unit calls a high-performance satisfiability modulus theory (SMT) solver (such as Z3 or CVC5).

[0174] Processing flow: 1. Constructing Validation Queries: This unit automatically constructs two core logical query formulas: Query 1 (Over-permission check): `CheckSAT(Φ_llm AND NOT Φ_standard)`. The purpose of this query is to find a scenario where the model's response is allowed to proceed, but the gold standard rule prohibits it.

[0175] Query 2 (Overly stringent checking): `CheckSAT(Φ_standard AND NOT Φ_llm)`. The purpose of this query is to find a scenario where the gold standard rule allows passage, but the model's response prohibits passage.

[0176] 2. Call the solver: Submit the above query to the SMT solver.

[0177] 3. Analyze the solution results: For query 1, the solver will return "SAT" (satisfiable). This means there is a counterexample. The solver will also provide a specific model (i.e., a variable assignment that satisfies the formula): `{Applying Physician Level: "Associate Chief Physician", Patient Diagnosis Record: "Common Cold", Operation: "Release"}`.

[0178] The implication of this counterexample is that when an associate chief physician requests red blood cell irradiation for a patient diagnosed only with a "common cold," the model's response should be "allowed," but this violates the gold standard that an "immunodeficiency diagnosis" is required.

[0179] For query 2, the solver may return "UNSAT" (unsatisfiable), meaning that there is no case where the model is more stringent than the standard.

[0180] The interpretability verification report generation unit 54 is used to fill the logical equivalence test results and counterexamples into the preset report template to generate a verification report.

[0181] This unit is responsible for converting the solver's raw output into a report (verification report) that is easily understood by human auditors.

[0182] Implementation method: This unit fills in the analysis results according to the preset report template.

[0183] Report generation: Validation conclusion: "Validation failed: The model's response is at risk of 'over-licensing'." Risk level: "High". Specific counterexamples: Scenario description: "When applying for a physician level of 'Associate Chief Physician', and the patient's diagnostic record does not include 'immunodeficiency' (e.g.)" For example, when it's just a 'common cold'. Model behavior: "Based on the model's response, this situation will be allowed to proceed." Standard behavior: "According to standard rules, this situation must be prohibited from allowing passage." Root cause analysis: "In the model-generated responses, 'patients have related immune system problems' is presented as a suggested condition ('best'), ignoring its nature as a mandatory prerequisite." Report push: This detailed report, generated together with the original answer, will be presented in Level 7: Human-Machine Collaborative Feedback Interface 70, allowing reviewers to make a final decision at a glance.

[0184] Through the above implementation methods, the output formal verification and interpretability analysis module 50 provides a solid logical "firewall" for the AI's answers, ensuring that every step of its operation is based on evidence and reason when performing critical, rule-driven tasks.

[0185] In some embodiments, the dynamic resource scheduling and reflection module 60 is used to determine the inference strategy based on the first performance feedback provided by the empathy cue words and the feedback data deep analysis module 80, and provide it to the hierarchical deep inference engine 30.

[0186] The Dynamic Resource Scheduling and Reflection Module 60 is the system's intelligent "performance and cost controller." Its core technological idea lies in the profound understanding that in practical applications, different user queries vary significantly in complexity, importance, and urgency. Therefore, adopting a "one-size-fits-all" fixed inference strategy cannot meet the quality requirements of all tasks and also results in a huge waste of computing resources. This module aims to establish an adaptive inference scheduling mechanism that can evaluate task requirements in real time, dynamically match computing resources, and seek Pareto optimality among cost, latency, and quality.

[0187] The technical solution of this module draws on the systematic research findings on performance optimization of large language models during inference. This research revealed that adjusting the computational budget during inference (such as "thinking rounds" or "reflection depth") can significantly affect the model's output quality, and this effect exhibits significant differences across different task domains (such as mathematical reasoning and text translation). This module transforms this "on-demand computation" concept from experimental research into a dynamic, closed-loop engineering scheduling system.

[0188] The technical principle is as follows: First, when this module receives a new processing task, it uses a lightweight pre-analysis model to quickly evaluate the task's characteristics, including its estimated complexity, domain, and current user interaction status (e.g., whether it is at a critical node in a multi-turn dialogue). Second, the system integrates these task characteristics with preset global strategies (e.g., whether the current system is in "cost-first mode" or "quality-first mode") and dynamically selects the most suitable inference execution path for the current task from a predefined "inference strategy library." This strategy library contains a range of strategies with different computational overhead and quality assurance levels, from the simplest and lowest-cost "single direct generation" to the medium-complexity "single-turn self-reflection," and the highest level, which calls the full hierarchical deep inference engine. Finally, after the inference task is completed, this module also records the decision basis for this scheduling, the actual cost incurred (such as token consumption), the response latency, and the final output quality evaluation results (feedback from subsequent modules). This recorded data is used for continuous optimization of the scheduling strategy itself, enabling the system to continuously improve its intelligence level in making resource allocation decisions in the future by learning from historical experience.

[0189] Through the configuration of this module, the system described in this application possesses the wisdom of "efficient calculation" in its operation. It avoids using a sledgehammer to crack a nut for simple problems, and also prevents using insufficient resources to cope with complex challenges. Thus, on a macro level, the system maximizes the utilization efficiency and economy of its computing resources while meeting diverse service quality requirements.

[0190] In some embodiments, see Figure 7 The dynamic resource scheduling and reflection module 60 includes: a reasoning strategy library definition and management unit 61, a task characteristic real-time pre-analysis unit 62, a dynamic scheduling decision-making unit 63, and a scheduling post-effect evaluation and strategy self-optimization unit 64. The four units in the dynamic resource scheduling and reflection module 60 collaborate with each other.

[0191] The inference strategy library definition and management unit 61 is used to store all inference strategies that the system can execute and to quantify their cost and capabilities.

[0192] This unit predefines all the inference strategies that the system can execute and quantifies their costs and capabilities.

[0193] Implementation: The system provides a configuration interface for administrators to define a series of inference strategies and store them as structured objects.

[0194] Strategy library example: ◦ Strategy P1 (Rapid Response): ▪ Policy ID: P1_FAST_RESPONSE ▪ Description: "Generates the answer directly by calling the basic language model in a single call." ▪ Applicable scenarios: Simple fact lookup, casual conversation, and preliminary intent recognition.

[0195] ▪ Estimated cost level: 1 (lowest) ▪ Estimated latency level: 1 (lowest) ▪ Estimated quality level: 2 (Basic) ◦ Strategy P2 (Single-round reflection): ▪ Policy ID: P2_SINGLE_REFLECTION ▪ Description: "After the first answer is generated, a 'self-reflection' process is immediately initiated, in which the model acts as a critic to review and revise the initial answer and generate the final answer." ▪ Applicable scenarios: Tasks that require a certain level of logical deduction, information verification, or high formatting requirements.

[0196] ▪ Estimated cost level: 3 (Medium) ▪ Estimated latency level: 3 (Medium) ▪ Estimated quality level: 4 (High) ◦ Strategy P3 (Deep Reasoning): ▪ Strategy ID: P3_DEEP_REASONING ▪ Description: "Activate the full Level 3: Layered Deep Reasoning Engine 30, including the entire process of problem decomposition, speculative execution, and comprehensive decision-making." ▪ Applicable scenarios: Extremely complex critical issues involving conflicting information sources or high-risk decision-making.

[0197] ▪ Estimated cost level: 5 (highest) ▪ Estimated latency level: 5 (highest) ▪ Estimated quality level: 5 (highest) The real-time pre-analysis unit 62 for task features is used to extract features from the user’s question and the multimodal context provided by the multimodal empathy perception input module 10, and to determine the complexity of the question, the target domain and the depth of interaction.

[0198] Before processing any request, the unit performs a quick "physical examination" to determine its underlying needs.

[0199] Implementation: This unit uses a lightweight, specially trained classification model or a set of heuristic rules.

[0200] Processing flow: a. Receiving Task: Receive the question text input by the user and the multimodal context provided by the "Level 1" module.

[0201] b. Feature extraction: Complexity analysis: By calculating text features such as question length, number of keywords, and question word type (e.g., "what" vs. "why / how"), the complexity of the question can be initially determined.

[0202] ▪ Domain identification: Match the question with a predefined domain knowledge graph to determine whether it belongs to a professional domain that requires high-precision reasoning (such as medicine or law).

[0203] ▪ Interaction Status Analysis: Check if the current dialogue is in a multi-round clarification and follow-up questioning stage. If so, increase the priority of the task.

[0204] c. Generate Task Profile: This unit outputs a "task profile" vector containing the analysis results above, for example: {complexity: 0.8, professional field: true, interaction depth: 3}.

[0205] The dynamic scheduling decision unit 63 is used to determine the reasoning strategy based on the complexity of the problem, the target domain, the depth of interaction, and the global strategy.

[0206] This unit is the brain of this module, responsible for making the final scheduling decision based on the task profile and the overall strategy.

[0207] Implementation: The core of this unit is a decision engine, which can be based on a preset rule table or a scheduling policy network trained through reinforcement learning.

[0208] Decision-making process (example based on rules): a. Obtain global policy: Read the current global operating mode from the system configuration, such as "cost priority mode".

[0209] b. Matching rules: Match the "task profile" with the decision rule table.

[0210] ▪ Rule 1: IF Complexity < 0.3 AND Specialization Domain == false THEN Scheduling Strategy = P1_FAST_RESPONSE ▪ Rule 2: IF (Complexity >= 0.3 AND Complexity < 0.7) OR (Specialty Domain == true AND Interaction Depth < 2) THEN Scheduling Strategy = P2_SINGLE_REFLECTION ▪ Rule 3: IF complexity >= 0.7 OR interaction depth >= 2 THEN scheduling strategy = P3_DEEP_REASONING ▪ Rule 4 (Adjustment in cost-first mode): IF Global mode == "cost-first" AND Rule 2 is triggered THEN Downgrade the scheduling policy to P1_FAST_RESPONSE and mark "quality may be degraded".

[0211] c. Output scheduling instructions: The decision-making unit finally outputs a clear instruction, such as "execute policy P2_SINGLE_REFLECTION", and sends this instruction along with the original task to the system's execution layer.

[0212] The scheduling post-effect evaluation and strategy self-optimization unit 64 is used to obtain the effect of the reasoning strategy provided by the dynamic scheduling decision unit 63, and to improve the rules in the global strategy according to the effect.

[0213] This unit is responsible for "reviewing" the effects of each scheduling decision and improving the scheduling rules accordingly.

[0214] • Implementation method: This unit maintains a "scheduling history database" and periodically runs an analysis and optimization script.

[0215] • Workflow: a. Data Recording: After each task is completed, the system stores the task's "task profile", the scheduled "inference strategy", the actual "cost" (number of tokens), the "latency" (milliseconds), and the "quality score" fed back by "Level 7" and "Level 8" as a record in the historical database.

[0216] b. Periodic Analysis: For example, the optimization script starts every morning. It analyzes the data in the database to look for patterns. For example, it might find that: "For tasks with a complexity between 0.6 and 0.7, although strategy P2 is slightly more expensive than P1, the improvement in quality score far outweighs the increase in cost, and the latency is still within an acceptable range." c. Strategy Adjustment: Based on the above findings, the script will automatically adjust the rules in the decision unit. For example, it might lower the complexity threshold of rule 2 from 0.7 to 0.6, allowing more of these "cost-effective" tasks to be assigned to higher-quality inference strategies.

[0217] d. A / B testing: For more advanced optimizations, the system can also introduce an A / B testing framework to run both the old and new scheduling rules simultaneously. The superiority of the new rules can be verified through actual online performance, ultimately achieving continuous and data-driven iterative evolution of the scheduling strategy.

[0218] Through the above implementation methods, the dynamic resource scheduling and reflection module 60 transforms every interaction with the user into an opportunity to learn and optimize the system's own resource management capabilities, thereby constructing a dynamic operating closed loop that can intelligently adapt to task changes and maximize cost-effectiveness.

[0219] In some embodiments, the human-machine collaborative feedback interface 70 is used to collect metadata from the processing of the hierarchical deep inference engine 30, the response generation module 40 for specific scenarios, and the output formal verification and interpretability analysis module 50, and to visualize and record the correction, comments, annotations or confirmation operations of experts on the presented data, forming audit feedback data.

[0220] The Human-Machine Collaborative Feedback Interface 70 is a key interactive hub connecting the autonomous decision-making loop of an artificial intelligence system with the wisdom of human domain experts. Its core technological idea lies in completely abandoning the simplistic model of traditional human-computer interaction that treats humans as passive content "censors" or "correctors," and instead designing a deep collaborative workspace that can completely transparently present the complete "mental activities" of artificial intelligence—including its reasoning process, chain of evidence, self-verified conclusions, and potential uncertainties—to human experts.

[0221] The technical solution of this module draws on the cross-disciplinary concepts of automated scientific research analysis processes and interpretability analysis of complex systems. Just as automated scientific research processes need to present the analysis results of massive amounts of literature to researchers in a structured and visualized manner for exploration, this module aims to present the "thinking" process of artificial intelligence to domain experts in a structured, traceable, and interactive way. It no longer simply displays a final answer text, but provides a "decision archive" built around that answer, containing rich contextual information.

[0222] The technical principle is as follows: When a task processed by artificial intelligence enters the review queue, this interface does not simply display the question and answer. It pulls all procedural metadata related to the task from various front-end modules of the system and organizes and visualizes this data in a structured manner. This metadata includes at least: user sentiment context analysis from the multimodal perception module, the complete logical decomposition plan generated by the deep inference engine and the execution record of each step, the different opinions of various "draft" models during the speculative decision-making process, the logical compliance detection report and specific counterexamples given by the formal verification module, and the reasoning strategy selected by the dynamic resource scheduling module. On this interface, human experts can, like an experienced tutor reviewing a student's assignment, not only see the final conclusion but also understand the underlying thinking, basis, and potential flaws. Every correction, comment, annotation, or confirmation operation by the expert is not just a modification to the surface text but a precise feedback to the deep logical nodes. These high-quality feedback signals with detailed context will be accurately recorded by the system in a structured manner, providing unprecedented high signal-to-noise ratio learning samples for subsequent knowledge base updates and model self-optimization.

[0223] By setting up this module, this application elevates the depth of human-computer interaction from "judging right and wrong" to the level of "attributing good and bad," seamlessly integrating the tacit knowledge and critical thinking of human experts into the iterative closed loop of artificial intelligence, thereby constructing a true source of "human-computer integrated intelligence."

[0224] The Human-Machine Collaborative Feedback Interface 70 is a web-based, dedicated review platform for domain experts (e.g., senior quality control experts at blood centers). When a task processed by an AI system and marked as "requiring human review" (e.g., due to formal verification failure or excessive problem complexity) is pushed to the platform, the expert will see a comprehensive review view consisting of several interactive functional panels.

[0225] In some embodiments, see Figure 8 The human-machine collaborative feedback interface 70 includes: a first function panel 71, a second function panel 72, a third function panel 73, a fourth function panel 74, and a fifth function panel 75.

[0226] The first functional panel 71 is located at the top of the view and provides core information about the task. It is the task overview and core interaction panel.

[0227] This panel, located at the top of the view, provides core information about the task.

[0228] The content includes: Original question: The complete text of the question raised by the user.

[0229] Contextual information: A semantic description of the user’s emotional state provided by the “Level 1” module, for example: “When the user asks a question, the emotion detection shows ‘neutral but with high arousal,’ which may indicate some anxiety or concern.” AI Final Response: The final, pending review text of the answer generated by the system. A rich text editor is provided below the text, allowing experts to directly modify the answer.

[0230] The second function panel 72 is used to display all the logical steps executed by the hierarchical deep inference engine 30. That is, the deep inference path visualization panel.

[0231] This panel, in a graphical or tree structure, fully displays all the logical steps performed by the Level 3 reasoning engine to arrive at the answer.

[0232] Implementation method: Use a collapsible tree view.

[0233] Content and Interaction: Top-level node: Displays the macro-level reasoning plan generated by the planning unit, such as a list of five sub-problems.

[0234] Sub-nodes: Under each sub-problem node, the execution process is shown in detail. If the "speculative adjudication" mechanism is activated at this step, it will be further expanded to list the preliminary answers of all "draft" models and the core evidence fragments they retrieved. Evidence that is ultimately adopted by the "adjudication" model will be highlighted, while evidence that is discarded will be displayed in gray, with a brief reason given by the adjudication model (e.g., "Evidence A is a draft and lacks authority").

[0235] Interactive features: Experts can click on any piece of evidence to trace its specific location in the original knowledge base or external literature. Experts can also comment on a reasoning step or evidence selection, such as: "This step is poorly decomposed, omitting a query for the specific handling procedure for 'allergy history'." The third functional panel 73 is used to display the detailed verification results of the formal verification and interpretability analysis module 50; the detailed verification results include at least one of the following: verification conclusions, details of counterexamples, and logical comparisons. This is the formal verification report panel.

[0236] If this task triggers Level 5 formal verification, this panel will display detailed verification results.

[0237] The content includes: Verification conclusion: Clearly indicate "Verification passed", "Verification failed: there is a risk of over-licensing" or "Verification failed: there is a risk of over-stricting".

[0238] Counterexample details: If the verification fails, the specific counterexample scenarios found by the solver will be listed in detail, and the reason why the counterexample can prove that the model's answer has a logical flaw will be explained in natural language.

[0239] Logical comparison: Optional, allows senior experts to view the formal logical expressions extracted from the model's response and compare them side-by-side with the gold standard expression.

[0240] The fourth functional panel 74 is used to display the decision-making process and actual performance overhead of the dynamic resource scheduling and reflection module 60. That is, the resource scheduling and performance indicator panel.

[0241] This panel displays the decision-making process and actual performance overhead of the "Level Six" module to help evaluate cost-effectiveness.

[0242] The content includes: Scheduling decision: Clearly indicate which level the task is classified as and which inference strategy is invoked accordingly (e.g., "task complexity is rated as high, the system automatically selects the P3_DEEP_REASONING strategy").

[0243] Performance data: Displays the total time (latency) consumed in processing this task and the total number of tokens (cost) used to interact with the large language model.

[0244] The fifth function panel 75 provides structured feedback tools, with feedback options including final rulings, error attribution tags, a free comment box, and knowledge base update suggestions. This is essentially a collaborative feedback and submission unit. In other words, the fifth function panel 75 can provide review feedback data.

[0245] This panel is the core area where experts submit their final feedback after completing their review.

[0246] Implementation: Provide a set of structured feedback tools.

[0247] Feedback options: Final decision: One mandatory option, including "Confirmed correct", "Corrected and confirmed", "Rejected (the answer is incorrect and has no value for correction)".

[0248] Error attribution labels (multiple selection): If there is a problem with the answer, the expert can select the root cause of the error from a preset label library, such as: "factual error", "logical reasoning error", "outdated knowledge", "inappropriate expression", "violation of safety regulations", etc.

[0249] Free comment box: A text input box that allows experts to provide detailed written descriptions of the entire task processing process or their proposed corrections.

[0250] Knowledge base update suggestions: A dedicated channel where experts can write suggestions for updating or supplementing the knowledge base if they find that this task has exposed a defect or missing information. These suggestions can then be pushed to the knowledge base administrator with one click.

[0251] Submission Operation: When an expert clicks the "Submit for Review" button, all their modifications, rulings, tags, comments, and suggestions will be packaged together with all the process metadata of the task into a complete "Reviewed Feedback Record" and sent to Level 8: Feedback Data In-Depth Analysis Module 80, entering the next learning stage of the system.

[0252] Through the above implementation methods, the human-machine collaborative feedback interface 70 is no longer a simple content proofreading tool, but a powerful comprehensive expert workstation that integrates diagnosis, analysis, correction and knowledge contribution. It enables human wisdom to empower the continuous evolution of artificial intelligence systems with unprecedented depth and breadth.

[0253] In some embodiments, the feedback data deep analysis module 80 is used to perform multi-dimensional aggregation analysis on the audit feedback data to obtain first performance feedback, second performance feedback and third performance feedback; wherein, the first performance feedback is pushed to the dynamic resource scheduling and reflection module 60 to improve the scheduling strategy, the second performance feedback is pushed to the knowledge base dynamic update and conflict resolution module 90 to repair the knowledge base, and the third performance feedback is pushed to the model incremental optimization and security alignment module 100 to optimize the model.

[0254] The feedback data deep analysis module 80 is the core analysis engine driving the system to achieve data-driven and autonomous evolution. Its core technical idea is to regard the massive amount of context-rich structured feedback data collected by the Level 7: Human-Machine Collaborative Feedback Interface 70 as a "gold mine" waiting to be mined. Through automated data mining and machine learning techniques, it extracts deep insights into system performance bottlenecks, knowledge base defects, and user behavior patterns, thereby providing accurate and quantitative decision-making basis for the intelligent iteration of the system.

[0255] The technical solution of this module draws on the design concepts of topic discovery, trend prediction, and knowledge graph construction in automated scientific research analysis processes for large-scale scientific literature. Just as automated scientific research systems can automatically identify emerging research hotspots and technological evolution paths from tens of thousands of papers, this module aims to automatically discover "common problems" and "improvement directions" of the system in multiple dimensions such as cognition, reasoning, and expression from tens of thousands of human-machine collaborative feedback records.

[0256] The technical principle is as follows: First, this module gathers all "reviewed feedback records" that have been audited by human experts. Each record constitutes a multi-dimensional data point containing rich information across the entire "problem-process-result-feedback" chain. Second, this module applies natural language processing and data mining techniques to perform multi-dimensional aggregation analysis on this data. For example, by statistically analyzing the "error attribution labels" annotated by experts, the most frequent error types in the current system can be quickly identified; by performing topic modeling analysis on the text entered by experts in the "free comment box," some deeper, systemic problems not covered by preset labels can be discovered. Furthermore, this module explores the complex relationships between different types of task characteristics, the reasoning strategies adopted by the system, and the final feedback results through correlation analysis. For example, it finds that "when the problem involves cross-departmental procedure coordination, the failure rate of the P2 strategy is significantly higher than that of the P3 strategy." Finally, all these analytical results are integrated into a structured, multi-dimensional system "health check report." This report not only points out the "symptoms" (i.e., the problems), but also reveals the "causes" (i.e., the root causes) through data, providing a clear and actionable "prescription" for subsequent knowledge base updates (level nine) and model optimization (level ten).

[0257] By setting up this module, this application transforms the originally scattered and isolated manual feedback into strategic intelligence that drives the system to make global and fundamental optimizations, achieving a qualitative leap from local "case-by-case" corrections to "pattern-based" systemic improvements.

[0258] The module in this embodiment is a background data processing and analysis system that periodically (e.g., once a week) performs joint analysis on the "scheduling history database" (stored in "layer six") and the "approved feedback record" database generated by the human-machine collaborative feedback interface 70.

[0259] In some embodiments, see Figure 9 The feedback data in-depth analysis module 80 includes: a multi-dimensional data aggregation and indicator calculation unit 81, an error attribution and common problem discovery unit 82, a knowledge base defect diagnosis unit 83, and a system health check report automatic generation and push unit 84.

[0260] The multidimensional data aggregation and indicator calculation unit 81 is used to perform multidimensional aggregation analysis on the audit feedback data to obtain key performance indicators, including overall accuracy, manual correction rate, average correction magnitude, and performance of each inference strategy.

[0261] This unit is responsible for the initial cleaning and aggregation of the raw feedback data, and the calculation of a series of key performance indicators (KPIs).

[0262] Implementation method: This is achieved by executing a series of database queries (SQL) and data processing scripts.

[0263] Processing flow: a. Data connection: Connect the "Scheduling History Database" and the "Reviewed Feedback Records" database through the task ID to form a wide table containing full-link information.

[0264] b. Indicator Calculation: Overall accuracy: Calculates the percentage of records that are "confirmed as correct" out of the total number of records.

[0265] Manual correction rate: Calculates the percentage of records that have been "corrected and confirmed".

[0266] Average correction magnitude: For the corrected record, the difference between the text before and after correction is calculated (e.g., using edit distance or semantic similarity algorithms) to measure the severity of the error.

[0267] Performance of each inference strategy: Grouped by inference strategy (P1, P2, P3), the accuracy, correction rate, average cost, average latency and other indicators are calculated for each strategy.

[0268] The error attribution and common problem discovery unit 82 is used to perform frequency statistics and sorting of expert-annotated error attribution tags, and to collect the text in all free comment boxes for expert comment topic mining; and to combine error attribution tags and comment topics to mine root cause association rules to obtain the root cause of the error.

[0269] The core task of this unit is to conduct in-depth analysis of audit failure cases and uncover the root causes of the errors.

[0270] Implementation method: Combining statistical analysis and natural language processing techniques.

[0271] Processing flow: a. Error Label Statistics: Frequency statistics and ranking of the "error attribution labels" marked by experts were performed to generate a "Top-N Error Cause Ranking". For example, it was found that "outdated knowledge" accounted for the highest proportion (40%), followed by "logical reasoning error" (25%).

[0272] b. Expert Comments Topic Mining: Collect all text from the "free comment boxes" and perform unsupervised topic clustering using topic modeling algorithms (such as Latent Dirichlet Allocation, LDA).

[0273] Example output: A high-frequency theme may be found, with keywords such as "cross-departmental," "process conflict," and "inconsistent SOP versions." This reveals a more specific systemic problem than "outdated knowledge": the system lacks effective conflict resolution capabilities when dealing with multiple interrelated procedures that may have version conflicts.

[0274] c. Root cause association analysis: association rule mining is performed between error labels, comment topics and other features of the task (such as the domain of the problem and the reasoning strategy used).

[0275] The example found that: "The label 'Logical Reasoning Error' has an 80% probability of appearing in tasks where the 'Reasoning Strategy' is P2 and the 'Problem Domain' is 'Rare Disease Blood Transfusion'." This finding provides extremely precise guidance for subsequent strategy optimization.

[0276] The knowledge base defect diagnosis unit 83 is used to aggregate all cases marked as outdated knowledge to obtain a list of knowledge bases to be updated; automatically parse the update suggestions submitted by experts in the dedicated channel and transform them into structured knowledge base maintenance tasks; and generate a knowledge base consistency review report for cases in which multiple draft model opinions seriously conflict during the analysis and reasoning process and are ultimately rejected by experts.

[0277] This unit focuses on identifying gaps and inconsistencies in the knowledge base itself from feedback data.

[0278] Implementation method: Primarily analyzes feedback directly related to knowledge.

[0279] Processing flow: a. Aggregation of "Outdated Knowledge" Cases: Aggregate all cases marked as "outdated knowledge", extract the key entities and knowledge points involved, and form a "knowledge base update list".

[0280] b. Processing of "Knowledge Base Update Suggestions": Automatically parse the update suggestions submitted by experts in the dedicated channel, transform them into structured knowledge base maintenance tasks, and automatically trigger the "Level Two" module to perform a temporary, high-priority knowledge crawl for that specific source based on the source information mentioned in the suggestion.

[0281] c. Inconsistency Detection: Analyzes cases where multiple "draft" models exhibit severely conflicting opinions during the reasoning process, and are ultimately rejected by human experts. These cases often point to ambiguous or contradictory knowledge entries in the knowledge base. The system records these conflict points, forming a "Knowledge Base Consistency Review Report."

[0282] The automatic generation and push unit 84 of the system health check report is used to fill all the results of the multidimensional data aggregation and index calculation unit 81, the error attribution and common problem discovery unit 82 and the knowledge base defect diagnosis unit 83 into the preset report template and push the generated report.

[0283] This unit is the final output of this module, responsible for summarizing all the analysis results into an easy-to-understand and instructive report.

[0284] Implementation method: Use a report generation engine to populate the analysis data into a preset report template.

[0285] Report structure example: Part 1: Overall Performance Overview Display the overall accuracy rate, manual correction rate, and other key performance indicators (KPIs) for this period, and compare them with the previous period.

[0286] Part Two: Diagnosis of Major Problems List the "Top 3 Error Causes Ranking" and their typical cases.

[0287] It showcases the "Top 3 Potential Systemic Problems" and their evidence, extracted from expert commentary.

[0288] Part Three: Performance Bottleneck Analysis The cost-quality-delay scatter plots of different inference strategies under different task types are presented in chart form, and the "strategy-task" combinations with poor performance are highlighted.

[0289] Specific optimization suggestions are given, such as: "It is recommended to increase the default inference strategy for 'rare disease blood transfusion' tasks to P3." Part Four: Knowledge Base Health Status Provide links to the "Knowledge Base Update List" and the "Knowledge Base Consistency Review Report".

[0290] Report push: The report will be automatically pushed to system administrators, model development engineers, and knowledge base administrators.

[0291] The specific optimization suggestions in the report (such as adjusting scheduling rules) can be configured to be automatically executed by the system after administrator confirmation, thus forming a closed loop from "analysis" to "action".

[0292] Through the above implementation methods, the feedback data deep analysis module 80 transforms the crystallization of artificial intelligence—that is, each piece of feedback—into quantifiable, traceable, and operable system improvement signals, ensuring that the entire system can continuously evolve and improve in a continuous, data-driven positive cycle.

[0293] In some embodiments, the knowledge base dynamic update and conflict resolution module 90 is used to perform multi-stage verification and conflict detection on the second performance feedback and candidate knowledge units respectively, and update the knowledge base based on the verification results and conflict detection results.

[0294] The knowledge base dynamic update and conflict resolution module 90 is the core hub and final line of defense ensuring the accuracy, consistency, and timeliness of the entire knowledge system. Its core technological idea is that it is not merely a passive data writing tool, but a proactive knowledge governance engine with intelligent judgment, authoritative arbitration, and logical consistency maintenance capabilities. It is responsible for safely, orderly, and conflict-free integration of heterogeneous, multi-source, and dynamic knowledge flows from both internal (human-machine collaborative feedback) and external (proactive knowledge discovery) sources into the core knowledge base.

[0295] The technical solution of this module draws on the principles of evaluating and integrating knowledge sources in automated scientific research analysis, as well as the rigorous screening and quality control concepts used in constructing large-scale, high-fidelity datasets. It recognizes that every piece of information entering the knowledge base must undergo strict "identity" and "content" review to prevent low-quality or conflicting information from contaminating the entire knowledge system.

[0296] The technical principle is as follows: First, this module establishes a "dual-channel" knowledge receiving mechanism, receiving proactively discovered knowledge from Level 2: Knowledge Active Discovery and Construction Module 20, and reactive corrective knowledge refined by Level 8: Feedback Data Deep Analysis Module 80 and confirmed by Level 7 human experts. Second, upon receiving any knowledge to be updated, this module does not immediately perform a write operation, but instead initiates a multi-stage verification and conflict detection process. It utilizes knowledge graph technology to locate entities and relationships related to the knowledge to be updated in the existing knowledge base and performs direct factual comparisons. When a direct contradiction is detected between the old and new knowledge, the core innovation of this module—the "automatic conflict arbitration mechanism based on authority and timeliness"—is activated. This mechanism draws on the idea of ​​assessing the influence of scientific literature, comprehensively evaluating the metadata of the conflicting knowledge, including the authority level of its source (e.g., national regulations are superior to industry guidelines), the publication timestamp, and whether it has been directly endorsed by human experts. Through a weighted scoring model, the system can automatically determine which side's knowledge is more credible in most cases and perform the corresponding update (overwrite or discard) operation. For highly rule-based knowledge (such as operating procedures), this module will also invoke formal methods to check whether the introduction of new rules will create logical paradoxes with the existing rule system. Finally, for a few deep conflicts that the system cannot automatically adjudicate and that have comparable authority, this module will package them into a "conflict arbitration task" and escalate it to Level 7: Human-Machine Collaborative Feedback Interface 70, handing it over to the domain expert with the highest authority for final adjudication, thus forming a seamless upgrade path from automatic processing to human intervention.

[0297] By setting up this module, this application transforms the knowledge base from a simple information repository into a dynamically evolving "living" knowledge system with self-purification, self-verification, and authoritative governance capabilities, ensuring its solidity and reliability as the cognitive foundation of the entire system.

[0298] The module in this embodiment is a background service that continuously listens for knowledge update requests from the "Level Two" and "Level Eight" modules. Each request is submitted in the form of a standardized "knowledge update data packet".

[0299] Knowledge update data package (example): Json { "updateId": "U_20251026_001", "knowledgeClaim": { "entity": "platelets", "attribute": "Storage temperature", "value": "20-24 degrees Celsius, and continues to oscillate" }, "metadata": { "sourceType": "PROACTIVE_DISCOVERY", / / or REACTIVE_FEEDBACK "sourceName": "The latest 'Technical Operating Procedures for Blood Banks (2025 Edition)' released by the National Health Commission", "sourceUrl": "http: / / ...", "authorityLevel": 9, / / Authority level, 1-10, 10 is the highest "publicationDate": "2025-09-01" }, "feedbackInfo": { / / This field is not empty if it is feedback-type knowledge. "isExpertValidated": true, "expertId": "Expert_Zhang", "validationTimestamp": "2025-10-26T11:00:00Z" } } In some embodiments, see Figure 10 The knowledge base dynamic update and conflict resolution module 90 includes: an update request receiving and entity linking unit 91, a conflict detection and status marking unit 92, a multi-strategy conflict automatic arbitration unit 93, a critical conflict manual escalation and management unit 94, and a knowledge graph secure writing and logging unit 95.

[0300] The update request receiving and entity linking unit 91 is used to receive and parse the knowledge update data packet.

[0301] This unit is the entry point of the module and is responsible for receiving and parsing knowledge update data packets.

[0302] Implementation method: Receive data packets through a message queue or API interface.

[0303] Processing flow: a. A data packet as shown in the example above was received.

[0304] b. Parse the knowledgeClaim to identify the core entity "platelet" and the attribute "storage temperature".

[0305] c. Perform an "entity linking" operation to precisely locate the node representing "platelet" within the internal knowledge graph (e.g., a Neo4j or JanusGraph database).

[0306] The conflict detection and status marking unit 92 is used to compare the new knowledge obtained from parsing with the existing knowledge in the knowledge base to obtain the comparison result.

[0307] This unit is responsible for comparing new knowledge with existing knowledge in the knowledge base.

[0308] Implementation method: Perform the query operation in the knowledge graph.

[0309] Processing flow: a. Starting from the located "platelet" node, query the current value of its "storage temperature" attribute.

[0310] b. Suppose the query result is: "22±2 degrees Celsius, and continuous oscillation", and its metadata shows the source as "Blood Station Technical Operation Procedures (2019 Edition)" with an authority level of 8.

[0311] c. Compare the new value "20-24 degrees Celsius" with the old value "22±2 degrees Celsius". Although the numerical ranges are equivalent, the versions of the source documents differ, and the system marks this as "direct content conflict (version update)".

[0312] The multi-strategy conflict automatic arbitration unit 93 is used to extract the arbitration basis when the comparison results indicate a direct content conflict, and to obtain an arbitration conclusion by executing the arbitration rules according to the arbitration basis.

[0313] This unit is the core decision-maker of this module, responsible for intelligently handling detected conflicts.

[0314] Implementation method: a decision tree based on preset rules or a simple weighted scoring model.

[0315] Processing flow: a. Initiate arbitration process: The arbitration unit is activated due to the detection of a "direct content conflict".

[0316] b. Extracting the basis for arbitration: ▪ New knowledge: Source authority level = 9, publication date = 2025-09-01, confirmed by experts.

[0317] ▪ Previous information: Source authority level = 8, publication date = 2019-10-01.

[0318] c. Enforcement of the arbitration rules: ▪ Rule 1 (Feedback Priority): Check if the new knowledge isExpertValidated == true. In this example, it is true, so this has the highest priority.

[0319] ▪ Rule 2 (Authority Priority): If Rule 1 does not apply, then compare authorityLevel. New knowledge (9) > Old knowledge (8).

[0320] ▪ Rule 3 (Timeliness Priority): If Rules 1 and 2 are not applicable, then compare publicationDate. New knowledge (2025) > Old knowledge (2019).

[0321] d. Arbitration conclusion: In this case, based on rule 1, the system rules: adopt the new knowledge and discard the old knowledge.

[0322] The critical conflict manual escalation and management unit 94 is used to package new knowledge and existing knowledge in the knowledge base and feed them back to the human-machine collaborative feedback interface 70 for manual adjudication when the arbitration conclusion is a critical conflict, and to mark attributes.

[0323] This unit handles a small number of scenarios where automatic arbitration fails.

[0324] • Implementation method: An exception handling and task dispatch system.

[0325] • Triggering conditions: For example, if the authority level and publication date of new and old knowledge are exactly the same, and neither has been confirmed by experts, the automatic arbitration unit will determine it as a "critical conflict".

[0326] • Processing flow: a. The system packages the old and new knowledge entries and all their metadata.

[0327] b. Generate a "Conflict Arbitration Task" and set its status to "Pending Manual Adjudication".

[0328] c. Push the task to a dedicated review queue in Level 7: Human-Machine Collaboration Feedback Interface 70, which is set up for the highest-authority domain experts.

[0329] d. In the knowledge graph, temporarily mark the "storage temperature" attribute of "platelets" as "in dispute" and configure it to return two conflicting values ​​and their sources when querying this attribute, and prompt the user that the information is uncertain.

[0330] The knowledge graph secure writing and logging unit 95 is used to update the knowledge when the arbitration conclusion indicates that it has been updated, and to record the details of this update in the audit log.

[0331] This unit is responsible for performing the final database operations and ensuring that all changes are traceable.

[0332] • Implementation method: A database access layer with transaction processing capabilities.

[0333] • Processing flow: a. Perform the update: For the update that passes automatic arbitration in this example, this unit initiates a database transaction: Update the "Storage Temperature" attribute value of the "Platelet" node to "20-24 degrees Celsius, and continue to oscillate".

[0334] ▪ Also update the metadata of this attribute, including the new source, URL, authority level, and publication date.

[0335] Instead of deleting the old attribute values ​​and their metadata, move them to an archive table called "Historical Versions" or "Deprecated Knowledge".

[0336] b. Record audit log: After a transaction is successfully committed, the system records a detailed entry in the audit log: ▪ Time: 2025-10-26T12:00:00Z ▪ Action: Update ▪ Entity: Platelets ▪ Attribute: Storage Temperature ▪ Old value: 22±2 degrees Celsius... ▪ New value: 20-24 degrees Celsius... ▪ New value: 0 degrees Celsius... ▪ Trigger source: Knowledge update data package U_20251026_001 Arbitration method: Automatic arbitration (based on priority of expert confirmation and feedback) ▪ Operator: System Through the above implementation methods, the knowledge base dynamic update and conflict resolution module 90 ensures that every addition or revision of knowledge goes through a rigorous, intelligent, and verifiable process, thereby maintaining the long-term accuracy, consistency, and authority of the knowledge base.

[0337] In some embodiments, the model incremental optimization and safety alignment module 100 is equipped with a large language model, which is used to filter incremental training data from third performance feedback and audit feedback data. The incremental training data is used to incrementally train the large language model. The objective function for training the large language model includes a standard loss term and a safety alignment loss term.

[0338] The Model Incremental Optimization and Safety Alignment Module 100 is the core driver of the system's large-scale language model capabilities, enabling long-term, continuous, and secure alignment. The final execution stage of fully controllable evolution. Its core technical idea is that it transforms the high-quality, verified domain knowledge interaction data accumulated through the human-machine collaborative closed loop in the system into "intellectual nourishment" that can be directly absorbed by the deep learning model. Through periodic incremental fine-tuning, it not only improves the model's professional capabilities in specific domains, but more importantly, it simultaneously implements strict safety and value alignment constraints to ensure that the model's "growth" always follows a preset and responsible path.

[0339] The technical solution of this module draws on the design philosophy that requires responsible performance evaluation when building and deploying large language models, especially considering the security, fairness, and value alignment of their output. It deeply understands that the optimization process of a model is not simply about improving accuracy in professional tasks, but rather a continuous process of guiding the model's behavior and shaping its "personality."

[0340] A model that is highly professional but whose behavior is uncontrollable carries potential risks that far outweigh its value.

[0341] The technical principle is as follows: First, this module automatically and periodically filters and constructs a high-quality incremental training dataset from the output data of the "Level 7" and "Level 8" modules. This dataset not only contains standard supervised learning samples such as "question-correct answer", but also a series of carefully designed special samples for safe alignment, such as "safe rejection" answers to harmful questions, "fair and neutral" answers to biased questions, and "honest and frank" answers to questions that are beyond the model's capabilities.

[0342] Secondly, this module employs parameter-efficient fine-tuning techniques, such as Low-Rank Adaptation (LoRA), to incrementally train the basic large-scale language model. This technique only requires updating a small portion of the model's parameters, significantly reducing training costs and time, and effectively avoiding the "catastrophic forgetting" of existing general abilities when learning new knowledge. Most importantly, this module introduces a composite loss function into the training objective function. This function not only includes a standard loss term aimed at improving professional accuracy but also incorporates one or more safety alignment loss terms designed to penalize unsafe, biased, or dishonest answers. By weighting and balancing these two aspects of loss during optimization, the system ensures that while the model improves its professional capabilities, its behavioral boundaries and value orientations are continuously "calibrated" and "reinforced," achieving simultaneous "capability growth" and "responsibility alignment."

[0343] By establishing this module, this application provides a comprehensive training mechanism that cultivates both talent and integrity for the long-term development of artificial intelligence systems. It ensures that as the system becomes increasingly "intelligent," it also becomes increasingly "reliable" and "responsible," thus providing a fundamental guarantee for the safe and sustainable application of this technology in critical, high-risk areas.

[0344] The module in this embodiment is a background, automated task flow based on a deep learning training platform, which is configured to execute a complete "train-evaluation-deployment" cycle once a month.

[0345] In some embodiments, see Figure 11 The model incremental optimization and security alignment module 100 includes: a high-quality incremental training dataset construction unit 101, an efficient parameter fine-tuning and composite loss optimization unit 102, a multi-dimensional automated evaluation and model candidate unit 103, and a model version management and canary deployment unit 104.

[0346] The high-quality incremental training dataset construction unit 101 is used to filter out all records that have been judged by experts to be correct or have been corrected and confirmed from the third performance feedback and review feedback data, pair the original questions in these records with the final correct answers to form a supervised fine-tuning sample set, as well as a rejection sample set and a fairness sample set. The supervised fine-tuning sample set, rejection sample set and fairness sample set are mixed according to a preset ratio to obtain incremental training data.

[0347] This unit is responsible for carefully selecting and constructing the dataset for this incremental optimization from the system's daily operational data.

[0348] • Implementation method: The dataset is automatically generated through database queries and data processing scripts.

[0349] • Processing flow: a. Professional Knowledge Sample Screening: From the "Reviewed Feedback Records" database, screen all records that have been judged by experts as "confirmed as correct" or "corrected and confirmed". Pair the "original questions" in these records with the "final correct answers" to form a high-quality supervised fine-tuning sample set D_professional.

[0350] b. Construction of securely aligned samples: ▪ Rejection Samples: The system has a built-in "red team test" question library, which contains a large number of "trap questions" involving privacy, security, ethical taboos, etc. This unit pairs these questions with preset, standard, and safe rejection answers (e.g., "As a professional AI assistant, I cannot answer questions about personal privacy.") to form a rejection sample set D_rejection.

[0351] ▪ Fairness Samples: The system has a built-in library of question templates containing potential bias-inducing questions (e.g., "Why is a certain group of people better suited to donate blood?"). This unit pairs these questions with answers designed to eliminate bias and provide neutral and scientific facts, forming a fairness sample set D_fairness.

[0352] c. Dataset Mixing and Formatting: Mix the three sample sets D_professional, D_rejection, and D_fairness according to a preset ratio (e.g., 80%:10%:10%) and convert them into a standard data format that meets the requirements of the selected training framework (e.g., PyTorch).

[0353] The parameter-efficient fine-tuning and composite loss optimization unit 102 is used to load the currently online basic large-scale language model as the starting point for this training, inject a low-rank adapter matrix into a specific layer of the Transformer structure of the basic large-scale language model, and construct a composite loss function, which includes a standard loss term and a safety alignment loss term. The basic large-scale language model is trained using incremental training data, and the parameters of the injected low-rank adapter matrix are updated only through the backpropagation algorithm to obtain the target large-scale language model. The standard loss term is used to calculate the loss on the supervised fine-tuning sample set, and the safety alignment loss term is used to calculate the loss on the rejection sample set and the fairness sample set.

[0354] This unit is the core executor of model training.

[0355] • Implementation method: Based on mainstream deep learning frameworks, and integrate relevant libraries for efficient parameter fine-tuning.

[0356] • Processing flow: a. Model Loading: Load the current large-scale language model that is being served online as the starting point for this training.

[0357] b. Adapter Injection: Using the Low-Rank Adaptive (LoRA) technique, a small-scale, trainable low-rank adapter matrix is ​​injected into a specific layer of the Transformer structure of the model (e.g., the query and key matrix of the attention mechanism), while most of the original parameters of the model are frozen and do not participate in training.

[0358] c. Definition of Composite Loss Function: Define a weighted composite loss function L_total: ▪ L_total = λ_prof * L_professional + λ_safe * L_safety ▪ L_professional is the standard cross-entropy loss calculated on the professional knowledge samples D_professional, used to improve the professional accuracy of the model. That is, the standard loss term.

[0359] ▪ L_safety is the cross-entropy loss calculated on the safe-aligned samples D_rejection and D_fairness, used to enhance the model's safe behavior. That is, the safe alignment loss term.

[0360] ▪ λ_prof and λ_safe are hyperparameters used to balance the importance of professionalism and safety in learning. For example, they can be set to λ_prof=0.7 and λ_safe=0.3.

[0361] d. Perform training: Using the mixed dataset, train the model for several epochs, updating only the parameters of the injected low-rank adapter matrix through backpropagation.

[0362] The multi-dimensional automated evaluation and model candidate unit 103 is used to evaluate the target large language model and the basic large language model. If the evaluation results meet the preset conditions, the target large language model is marked as a candidate deployment model. The evaluation includes at least professional capability evaluation, security performance evaluation and general capability regression testing.

[0363] After training is complete, this unit conducts a comprehensive "exam" on the newly generated model version to determine whether it is eligible for deployment.

[0364] • Implementation: Build an automated evaluation pipeline that evaluates the new model on a set of predefined benchmarks.

[0365] • Evaluation process: a. Loading new and old models: Simultaneously load the pre-training (old) model (basic large language model) and the post-training (new) model (target large language model).

[0366] b. Professional competence assessment: Run the old and new models separately on an independent test set of professional questions that were not used for training, and calculate the accuracy, recall and other metrics of their answers.

[0367] c. Security performance evaluation: Run both the old and new models on an independent security test set containing a large number of offensive and provocative questions, and calculate whether the new model generates a significantly lower rate of insecure responses than the old model.

[0368] d. General Ability Regression Test: Run both the old and new models on a test set that includes general abilities such as common sense question answering and text summarization to ensure that while the new model improves its professional abilities, its general abilities do not suffer a significant decline (i.e., "catastrophic forgetting").

[0369] e. Generate an evaluation report: The system automatically generates a detailed report comparing all the above evaluation metrics. If the new model outperforms the old model in both professional capabilities and security performance, and its general capabilities do not significantly decline, then the new model is marked as a "candidate deployment model".

[0370] The model version management and canary deployment unit 104 is used to release candidate deployment models in a canary manner. If the candidate deployment model performs stably and as expected during the canary period, the candidate deployment model is switched to the full service model.

[0371] This unit is responsible for safely and smoothly deploying the new, evaluated models to the online service environment.

[0372] • Implementation method: Integrate a model version control system (such as MLflow) and a gateway service that supports traffic switching.

[0373] • Deployment process: a. Model Version Archiving: Store the trainable adapter parameters of the "candidate deployment model", along with its training configuration, evaluation report and other metadata, as a new version in the model library.

[0374] b. Canary release: The system first performs a canary release, for example, routing 1% of online user requests to instances served by the new model version.

[0375] c. Online monitoring: During the canary release period, the system will closely monitor the actual performance of the new model online, including its response latency, error rate, and user feedback.

[0376] d. Full Deployment / Rollback: If the new model performs stably and as expected during the gray-scale release, the system administrator can switch it to the full service model with one click. If serious problems are found, a one-click rollback to the previous stable version is also possible.

[0377] Through the above implementation methods, the model incremental optimization and safety alignment module 100 constructs a scientific, rigorous, safe, and controllable engineering closed loop for the long-term evolution of large-scale language models, ensuring that every leap in system capabilities moves in a more professional, reliable, and responsible direction.

[0378] See Figure 12 , Figure 12 This is a flowchart illustrating an embodiment of the AI ​​knowledge base continuous optimization method based on human-machine collaboration and closed-loop feedback provided in this application. Applied to the AI ​​knowledge base continuous optimization system as described in any one of claims 1-11, the method includes: Step 121: Use the multimodal empathic perception input module to extract key feature parameters representing the user's inner emotional state from the facial image video stream and the speech audio stream, map the key feature parameters to natural language descriptions, and generate empathic prompt words based on the natural language descriptions; the speech audio stream contains the user's questions.

[0379] Step 122: Periodically obtain target data corresponding to the target domain from the network using the knowledge active discovery and construction module, perform cluster analysis on the target data to obtain knowledge clusters with internal connections, form structured candidate knowledge units based on each knowledge cluster, and push the candidate knowledge units to the knowledge base dynamic update and conflict resolution module.

[0380] Step 123: Using the hierarchical deep reasoning engine, based on empathy prompts and the user's question, and following the reasoning strategy provided by the dynamic resource scheduling and reflection module, as well as calling the knowledge base in the knowledge base dynamic update and conflict resolution module, generate the target answer corresponding to the user's question.

[0381] Step 124: Render the target answer according to the requirements of the current interaction scenario using the response generation module for specific scenarios to obtain the corresponding natural language text.

[0382] Step 125: Use the output formal verification and interpretability analysis module to perform mandatory rule verification on the natural language text and generate a verification report, which includes counterexamples.

[0383] Step 126: Using the dynamic resource scheduling and reflection module, the inference strategy is determined based on the first performance feedback provided by the empathy prompts and feedback data deep analysis module, and then provided to the hierarchical deep inference engine.

[0384] Step 127: Use the human-machine collaborative feedback interface to collect metadata from the processing of the hierarchical deep inference engine, the response generation module for specific scenarios, and the output formal verification and interpretability analysis module, visualize the data, and record the experts' corrections, comments, annotations, or confirmations on the presented data to form audit feedback data.

[0385] Step 128: Use the feedback data in-depth analysis module to perform multi-dimensional aggregation analysis on the audit feedback data to obtain the first performance feedback, the second performance feedback and the third performance feedback; among them, the first performance feedback is pushed to the dynamic resource scheduling and reflection module to improve the scheduling strategy, the second performance feedback is pushed to the knowledge base dynamic update and conflict resolution module to repair the knowledge base, and the third performance feedback is pushed to the model incremental optimization and security alignment module to optimize the model.

[0386] Step 129: Use the knowledge base dynamic update and conflict resolution module to perform multi-stage verification and conflict detection on the second performance feedback and candidate knowledge units respectively, and update the knowledge base based on the verification results and conflict detection results.

[0387] Step 130: Use the model incremental optimization and security alignment module to filter incremental training data from the third performance feedback and audit feedback data. The incremental training data is used to incrementally train the large language model.

[0388] For details, please refer to the technical solutions of any of the above modules; they will not be elaborated upon here.

[0389] The ten functional levels proposed in this application do not constitute a simple linear processing pipeline, but rather together form a highly integrated, dynamically looping intelligent system with multiple feedback loops. The core of its system relationship lies in the organic unity of data flow, control flow, and feedback flow, which together realize a complete closed loop from perception, cognition, execution to learning and evolution.

[0390] This system can be summarized as a main interactive reasoning process and two core evolutionary closed loops: 1. Main interactive reasoning process: refers to the core path through which data and control commands flow sequentially between modules when the system processes a single user request.

[0391] 2. Knowledge Evolution Closed Loop: This refers to the process by which a system continuously updates its knowledge base and optimizes its core model through two methods: proactive discovery (externally driven) and human-machine collaboration (internal driven).

[0392] 3. Performance optimization closed loop: refers to the process by which a system continuously optimizes its internal resource scheduling strategy by analyzing the performance of historical tasks.

[0393] The following details the specific positioning of each layer within this system, its input-output relationships, and the key role it plays in the overall architecture.

[0394] Level 1: Multimodal Empathic Perception Input Module 10, Core Function Positioning: The system's "sensory organ". Main Input Source: External: User (text, non-verbal signals). Main Output Objects: Levels 3 and 6. Core Relationship Description and Role in the System: Role: The starting point of interaction and the capturer of context. This module captures the user's original requests and emotional state, providing initial input and contextual tone for all subsequent processing. It directly transmits task profile information to Level 6 for strategy selection and passes the complete user intent to Level 3 for in-depth processing.

[0395] Level Two: Knowledge Proactive Discovery and Construction Module 20, Core Function Positioning: The system's "External Knowledge Probe". Main Input Sources: External: Internet, Internal Knowledge Sources. Main Output Object: Level Nine. Core Relationship Description and Role in the System: Role: "Pioneer" of Knowledge Increment. This module operates independently of user interaction, proactively collecting and refining new knowledge from the external world. It serves as the primary external driving force for the forward-looking and timely updates of the knowledge base, and its output is one of the raw material sources for Level Nine's knowledge base expansion.

[0396] Level 3: Layered Deep Reasoning Engine 30, Core Function Positioning: The "Cognitive Core" of the System. Main Input Sources: Level 1, Level 6, Level 9 (via the knowledge base). Main Output Object: Level 4. Core Relationship Description and Role in the System: Role: The "Central Processing Unit" for Complex Problems. This module is the executor of cognitive tasks. It receives user questions from Level 1, follows the reasoning strategies specified by Level 6, and invokes the knowledge base maintained by Level 9 to decompose, reason, and adjudicate the questions, ultimately producing a structured, logically rigorous "answer kernel" for Level 4 to encapsulate linguistically.

[0397] Level Four: Response Generation Module 40 for Specific Scenarios. Core Function: The system's "expression and communication interface." Main Input Source: Level Three. Main Output Object: Level Five. Core Relationship Description and Role in the System: Role: "Scenario-based packaging agent" for answers. This module is a logic-to-language converter. It receives the logic kernel from Level Three, renders it into natural language text that conforms to the target user, communication purpose, and style specifications according to the specific requirements of the current interaction scenario, and submits this output text to Level Five for final logical review.

[0398] Level 5: Output Formal Verification and Interpretability Analysis Module 50. Core Function: The system's "logic auditor." Main Input Source: Level 4. Main Output Object: Level 7. Core Relationship Description and Role in the System: Role: "Quality gatekeeper" for output reliability. This module acts as a safety barrier for high-risk responses. It examines the natural language responses generated by Level 4, verifying whether they contradict mandatory rules in the domain at a deep logical level, and generates an interpretable verification report containing counterexamples and causal analysis, which is attached to the final audit task for reference by human experts at Level 7.

[0399] Level Six: Dynamic Resource Scheduling and Reflection Module 60, Core Function Positioning: The system's "Intelligent Resource Manager". Main Input Sources: Level One, Level Eight. Main Output Object: Level Three. Core Relationship Description and Role in the System: Role: "Overall Scheduling" for System Operating Efficiency. This module is a balancer between performance and cost. It analyzes the initial task characteristics provided by Level One and learns from long-term performance feedback from Level Eight, thereby dynamically deciding the reasoning strategy to be adopted for this task and issuing this scheduling instruction to Level Three for execution.

[0400] Level 7: Human-Machine Collaborative Feedback Interface 70, Core Function Positioning: The intersection of "human-machine intelligence". Main Input Sources: Levels 3, 4, and 5; External: Human experts. Main Output Object: Level 8. Core Relationship Description and Role in the System: Role: "Collector" of human expert wisdom. This module transparently presents the complete processing of artificial intelligence (inference path, verification report, etc.) to human experts, and provides structured tools to capture expert corrections, comments, and in-depth insights. Ultimately, this high-quality, context-rich feedback data is packaged and transmitted to Level 8 for in-depth analysis.

[0401] Level 8: Deep Feedback Data Analysis Module 80, Core Function Positioning: The system's "Self-Diagnosis Center". Main Input Source: Level 7. Main Output Objects: Levels 6, 9, and 10. Core Relationship Description and Role in the System: Role: The "Data Insight Engine" for system evolution. This module is the extractor of feedback value. It deeply mines the massive feedback data from Level 7, identifies common problems and performance bottlenecks in knowledge, reasoning, and security, and generates three types of guiding outputs: First, optimization suggestions are fed back to Level 6 to improve scheduling strategies; second, knowledge defect reports are pushed to Level 9 to repair the knowledge base; and third, high-quality sample sets are supplied to Level 10 to optimize the model.

[0402] Level Nine: Knowledge Base Dynamic Update and Conflict Resolution Module 90. Core Function Positioning: The system's "knowledge system steward." Main Input Sources: Level Two and Level Eight. Main Output Object: Internal: Knowledge Base (for use by Level Three). Core Relationship Description and Role in the System: Role: "Guardian" of Knowledge Base Consistency and Authority. This module is the final gatekeeper for knowledge writing. It receives knowledge update requests from Level Two and Level Eight, and through conflict detection, automatic arbitration, and manual upgrade mechanisms, ensures that every write operation is safe, accurate, and contradictory, thereby maintaining the long-term health of the core knowledge base used by Level Three.

[0403] Level 10: Model Incremental Optimization and Safety Alignment Module 100. Core Function Positioning: The system's "core capability evolution engine." Main Input Source: Level 8. Main Output Object: Internal: Core large-scale language model (called by Levels 3, 4, 5, etc.). Core Relationship Description and Role in the System: Role: The "nurturing and calibration center" for the long-term development of the model. This module is the driver of the model's intrinsic capabilities. Utilizing high-quality, battle-tested data provided by Level 8, it continuously iterates and upgrades the core large-scale language model upon which the system relies through incremental fine-tuning and safety alignment, achieving the synchronous evolution of its professional capabilities and safety reliability.

[0404] Process-oriented relationship description To more clearly illustrate the above system relationships, the dynamic interaction process between modules can be summarized as follows: 1. Main Interaction and Inference Flow (Single Request Processing Flow): ◦ Launch: Users interact with Layer 1 through an external interface to generate a task request containing multimodal information.

[0405] ◦ Scheduling: Level 1 sends the task profile to Level 6. Level 6 determines the best inference strategy based on task characteristics and historical performance data, and sends the instructions to Level 3.

[0406] ◦ Reasoning: Level 3 receives user requests and scheduling instructions, calls the knowledge base maintained by Level 9, performs deep reasoning, and generates the logical kernel of the answer.

[0407] ◦ Generation: The fourth-level receiving logic kernel performs linguistic encapsulation based on the current scenario to generate a natural language response draft.

[0408] ◦ Validation: Level 5 performs formal logical validation on the draft response.

[0409] Presentation and Feedback: The final answer, reasoning process, verification report, etc. are sent to Level 7 to be presented to human experts (or directly to users if no review is required), and feedback is awaited.

[0410] 2. Continuous Knowledge Evolution Loop (Long-Term Learning Flow): ◦ External active learning path: Level 2 continuously monitors external knowledge sources -> discovers new knowledge -> pushes it to Level 9 -> Level 9 updates the knowledge base after conflict resolution -> Level 3 can use the updated knowledge in subsequent reasoning.

[0411] ◦ Inner loop feedback learning path: ▪ Level 7 collects structured feedback from human experts -> Level 8 conducts in-depth analysis -> Level 8 identifies knowledge gaps -> pushes to Level 9 -> Level 9 repairs the knowledge base.

[0412] ▪ Level 7 collects structured feedback from human experts -> Level 8 performs in-depth analysis -> Level 8 constructs high-quality training samples -> pushes them to Level 10 -> Level 10 performs incremental fine-tuning and security calibration of the core model -> the upgraded model is called by various modules of the system (such as Levels 3, 4, and 5).

[0413] 3. Performance optimization closed loop (strategy iteration flow): After each task is processed, the system records its performance data (cost, latency, quality).

[0414] ◦ Periodically analyze these performance data at level eight to identify shortcomings in scheduling strategies for specific scenarios.

[0415] ◦ Level 8 will provide optimization suggestions to Level 6.

[0416] • Level 6 adjusts its internal decision-making rules automatically or with administrator confirmation, based on recommendations, to make better choices in future task scheduling.

[0417] In summary, these ten levels, through the aforementioned rigorous data flow, control flow, and feedback flow, constitute a complete and advanced artificial intelligence system capable of self-awareness, self-thinking, self-expression, self-censorship, self-correction, and ultimately self-evolution.

[0418] Unexpected technical effects This application, through the aforementioned systematic technical means, organically integrates mechanisms such as proactive knowledge discovery, multi-level reasoning, formal verification, dynamic resource scheduling, and deep human-machine collaboration, forming a logical closed loop, thereby achieving a creative and comprehensive technical effect that existing technologies could not foresee. 1. This application represents a leap from "passive error correction" to "predictive maintenance" of the knowledge base. Traditional knowledge base maintenance is reactive, meaning problems are corrected only after they occur. This application, through "proactive discovery" of cutting-edge external knowledge and "deep analysis" of internal interactive data, not only repairs known knowledge deficiencies but also reveals trends in specific knowledge domains and potential growth points of user interest. This enables the system to proactively build up knowledge reserves and pre-train model capabilities, performing "predictive maintenance" on potentially high-frequency problems or knowledge blind spots, significantly improving the timeliness and foresight of knowledge services.

[0419] 2. This application achieves a deep integration of human-machine collaboration, moving from "content-level review" to "logic-level optimization." In existing technologies, the human role primarily involves proofreading the final generated text. This application, by making the model's reasoning process, evidence sources, and automated verification results completely transparent to human experts, elevates the experts' focus from tedious literal error correction to a "metacognitive" level regarding the model's logical reasoning chain, the rationality of evidence selection, and the effectiveness of planning strategies. Human experts are able to guide the model's "thinking methods" at a higher dimension, not just the "content of speech," greatly deepening the connotation of human-machine collaboration and exponentially improving the system's core capabilities.

[0420] 3. Achieving a dynamic balance between high reliability and high efficiency. It is generally understood that improving the reliability of artificial intelligence (e.g., by adding verification steps or introducing multi-model integration) inevitably leads to a sharp increase in computational cost and response latency, and vice versa; the two seem to be irreconcilable contradictions. This application, through an innovative dynamic resource scheduling and reflection module 60, combined with real-time assessment of problem complexity and scenario importance, can adopt a computationally efficient lightweight mode in most interactions, only "on demand" invoking heavyweight but highly reliable deep inference and verification processes when high-risk, high-difficulty critical tasks are identified. This intelligent resource allocation strategy of "using resources wisely" enables the system to achieve both extremely high average reliability and extremely low average response latency and computational cost in a macro-statistical sense, successfully breaking through the inherent technical bottleneck between the two.

[0421] In the several embodiments provided in this application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0422] If the integrated units in the other embodiments described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0423] The above description is merely an embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A continuous optimization system for an AI knowledge base based on human-machine collaboration and closed-loop feedback, characterized in that, The AI ​​knowledge base continuous optimization system includes: a multimodal empathic perception input module, a knowledge active discovery and construction module, a hierarchical deep reasoning engine, a response generation module for specific scenarios, an output formal verification and interpretability analysis module, a dynamic resource scheduling and reflection module, a human-machine collaborative feedback interface, a feedback data deep analysis module, a knowledge base dynamic update and conflict resolution module, and a model incremental optimization and security alignment module. The multimodal empathic perception input module is equipped with at least one camera and at least one microphone. The camera captures a facial image video stream, and the microphone captures the user's speech audio stream. The multimodal empathic perception input module is used to extract key feature parameters representing the user's internal emotional state from the facial image video stream and the speech audio stream, map the key feature parameters to natural language descriptions, and generate empathic prompt words based on the natural language descriptions. The speech audio stream contains the user's questions. The knowledge active discovery and construction module is used to periodically obtain target data corresponding to the target domain from the network, perform cluster analysis on the target data to obtain knowledge clusters with internal connections, form structured candidate knowledge units according to each knowledge cluster, and push the candidate knowledge units to the knowledge base dynamic update and conflict resolution module. The hierarchical deep reasoning engine is used to generate a target answer corresponding to the user's question based on the empathy prompts and the user's question, according to the reasoning strategy provided by the dynamic resource scheduling and reflection module and by calling the knowledge base in the knowledge base dynamic update and conflict resolution module. The scenario-specific response generation module is used to render the target answer according to the requirements of the current interaction scenario to obtain the corresponding natural language text; The output formal verification and interpretability analysis module is used to perform mandatory rule verification on the natural language text and generate a verification report, wherein the verification report contains counterexamples. The dynamic resource scheduling and reflection module is used to determine the inference strategy based on the empathy prompts and the first performance feedback provided by the feedback data deep analysis module, and provide it to the hierarchical deep inference engine; The human-machine collaborative feedback interface is used to collect metadata during the processing of the hierarchical deep inference engine, the scenario-specific response generation module, and the output formal verification and interpretability analysis module, and to visualize and record the expert's correction, comments, annotations, or confirmation operations on the presented data, forming audit feedback data. The feedback data deep analysis module is used to perform multi-dimensional aggregation analysis on the review feedback data to obtain first performance feedback, second performance feedback and third performance feedback; wherein, the first performance feedback is pushed to the dynamic resource scheduling and reflection module to improve the scheduling strategy, the second performance feedback is pushed to the knowledge base dynamic update and conflict resolution module to repair the knowledge base, and the third performance feedback is pushed to the model incremental optimization and security alignment module to optimize the model. The knowledge base dynamic update and conflict resolution module is used to perform multi-stage verification and conflict detection on the second performance feedback and the candidate knowledge unit respectively, and update the knowledge base according to the verification results and conflict detection results. The incremental optimization and safety alignment module deploys a large language model, which is used to select incremental training data from the third performance feedback and the audit feedback data. The incremental training data is used to incrementally train the large language model. The objective function of the large language model training includes a standard loss term and a safety alignment loss term. The multimodal empathy perception input module includes: The non-verbal signal acquisition and feature extraction unit is used to extract key feature parameters representing the user's internal emotional state from the facial image video stream and the speech audio stream; the key feature parameters include at least basic emotion category, emotional valence, emotional arousal, emotional intensity, and timestamp; the emotional valence is used to represent the positive or negative degree of the emotion, and the emotional arousal is used to represent the intensity or excitement of the emotion; The emotional context semantic mapping unit is used to perform valence mapping on the emotional valence to obtain a valence mapping result, and to perform arousal mapping on the emotional arousal to obtain an arousal mapping result. The valence mapping result and the arousal mapping result are combined and mapped, and the natural language description is generated based on the basic emotion category, the emotion intensity and the combined mapping structure. A compound empathy prompt word construction unit is used to generate the empathy prompt word by placing the natural language description before the text corresponding to the user's question; The knowledge proactive discovery and construction module includes: The knowledge source configuration and dynamic monitoring unit is used to periodically retrieve raw data from the network; A domain-related intelligent filtering unit is used to filter out target data corresponding to the target domain from the original data according to the filtering prompts; The deep semantic representation and clustering analysis unit is used to transform the target data to obtain semantic embedding vectors; and when the number of semantic embedding vectors exceeds a threshold, it performs clustering analysis on the semantic embedding vectors to obtain knowledge clusters with inherent connections. The knowledge topic extraction and trend analysis unit is used to extract topics for each knowledge cluster, use the extracted topics as semantic tags for their corresponding knowledge clusters, and analyze the trends of the topics. The structured knowledge unit generation and push unit is used to package the analysis results of the knowledge topic extraction and trend analysis unit according to the standardized data format to form the structured candidate knowledge unit, and push the candidate knowledge unit to the knowledge base dynamic update and conflict resolution module.

2. The AI ​​knowledge base continuous optimization system according to claim 1, characterized in that, The hierarchical deep inference engine includes: The question parsing and intent recognition unit is used to perform semantic and structural parsing on the user's question to obtain the intent corresponding to the question; The macro-planning and decomposition unit is used to decompose the intention according to the reasoning strategy provided by the dynamic resource scheduling and reflection module, and generate several related planned tasks. The speculative execution and evidence collection unit is used to execute each task in the planned task using different retrieval models, retrieve the knowledge base in the knowledge base dynamic update and conflict resolution module, obtain several task execution results, and use the several task execution results as an evidence set. The integrated adjudication and answer synthesis unit is used to adjudicate the task execution results in the evidence set and obtain the final answer for each task. The answer integration and final generation unit is used to integrate the final answers corresponding to each task to obtain the target answer corresponding to the user's question.

3. The AI ​​knowledge base continuous optimization system according to claim 1, characterized in that, The scenario-specific response generation module includes: The application scenario profile library management unit is used to store and manage the detailed definitions of all interactive scenarios supported by the system. The dynamic scene recognition and image loading unit is used to obtain the current interaction scene corresponding to the current interaction during the interaction process; The scenario-constraint-oriented prompt word construction and response generation unit is used to construct constraint prompt words based on the answer logic kernel provided by the hierarchical deep reasoning engine and the constraints in the current interaction scenario, and input the constraint prompt words into a large language model to obtain the corresponding natural language text.

4. The AI ​​knowledge base continuous optimization system according to claim 1, characterized in that, The output formal verification and interpretability analysis module includes: The formal rule model library construction and management unit is used to pre-convert natural language rules in the domain into formal models that can be processed by machines. The semantic parsing and logic extraction unit is used to identify key entities and relationships in the natural language text, and to form logical expressions of the key entities and relationships according to their corresponding formal models; The logical equivalence detection and counterexample generation unit is used to compare the model logic of the extracted logical expression with the gold standard logic to perform logical equivalence detection, and to generate the counterexample based on the logical equivalence detection result; the counterexample violates the gold standard logic; An interpretability verification report generation unit is used to fill the logical equivalence detection results and the counterexamples into a preset report template to generate the verification report.

5. The AI ​​knowledge base continuous optimization system according to claim 1, characterized in that, The dynamic resource scheduling and reflection module includes: The inference strategy library definition and management unit is used to store all inference strategies that the system can execute and quantify their costs and capabilities. The task feature real-time pre-analysis unit is used to extract features from the user's question and the multimodal context provided by the multimodal empathy perception input module to determine the complexity of the question, the target domain, and the interaction depth. A dynamic scheduling decision unit is used to determine the reasoning strategy based on the complexity of the problem, the target domain, the interaction depth, and the global strategy. The scheduling post-effect evaluation and strategy self-optimization unit is used to obtain the effect of the reasoning strategy provided by the dynamic scheduling decision unit, and to improve the rules in the global strategy based on the effect.

6. The AI ​​knowledge base continuous optimization system according to claim 1, characterized in that, The human-machine collaborative feedback interface includes: The first function panel, located at the top of the view, provides core information about the task. The second function panel is used to display all the logical steps executed by the hierarchical deep inference engine; The third functional panel is used to display the detailed verification results of the output formal verification and interpretability analysis module; the detailed verification results include at least one of verification conclusions, counterexample details, and logical comparisons. The fourth function panel is used to display the decision-making process and actual performance overhead of the dynamic resource scheduling and reflection module; The fifth function panel provides structured feedback tools, with feedback options including final rulings, error attribution labels, a free comment box, and knowledge base update suggestions.

7. The AI ​​knowledge base continuous optimization system according to claim 1, characterized in that, The feedback data in-depth analysis module includes: The multi-dimensional data aggregation and indicator calculation unit is used to perform multi-dimensional aggregation analysis on the audit feedback data to obtain key performance indicators, including overall accuracy, manual correction rate, average correction magnitude, and performance of each inference strategy. The error attribution and common problem discovery unit is used to perform frequency statistics and sorting of expert-annotated error attribution tags, and to collect text from all free comment boxes for expert comment topic mining; and to combine the error attribution tags and comment topics to mine root cause association rules to obtain the root cause of the error. The knowledge base defect diagnosis unit is used to aggregate all cases marked as outdated knowledge to obtain a list of knowledge bases to be updated; automatically parse the update suggestions submitted by experts in a dedicated channel and transform them into structured knowledge base maintenance tasks; and analyze cases in which multiple draft models have seriously conflicting opinions and are ultimately rejected by experts, generating a knowledge base consistency review report. The system health check report automatic generation and push unit is used to fill all the results of the multidimensional data aggregation and index calculation unit, the error attribution and common problem discovery unit and the knowledge base defect diagnosis unit into the preset report template, and push the generated report.

8. The AI ​​knowledge base continuous optimization system according to claim 1, characterized in that, The knowledge base dynamic update and conflict resolution module includes: The update request receiving and entity linking unit is used to receive and parse knowledge update data packets; The conflict detection and status marking unit is used to compare the newly parsed knowledge with the existing knowledge in the knowledge base to obtain the comparison result; The multi-strategy conflict automatic arbitration unit is used to extract the arbitration basis when the comparison results indicate a direct content conflict, and to obtain an arbitration conclusion by executing the arbitration rules according to the arbitration basis. The critical conflict manual escalation and management unit is used to package the new knowledge and the existing knowledge in the knowledge base and feed them back to the human-machine collaborative feedback interface for manual adjudication when the arbitration conclusion is a critical conflict, and to mark the attributes. The knowledge graph secure writing and logging unit is used to update knowledge when the arbitration conclusion indicates that the update is passed, and to record the details of this update in the audit log.

9. The AI ​​knowledge base continuous optimization system according to claim 1, characterized in that, The model incremental optimization and safety alignment module includes: A high-quality incremental training dataset construction unit is used to filter out all records that have been judged by experts to be correct or have been corrected and confirmed from the third performance feedback and the review feedback data, match the original questions in these records with the final correct answers to form a supervised fine-tuning sample set, obtain a rejection sample set and a fairness sample set, and mix the supervised fine-tuning sample set, the rejection sample set and the fairness sample set according to a preset ratio to obtain incremental training data; The parameter-efficient fine-tuning and composite loss optimization unit is used to load the currently serving basic large-scale language model as the starting point for this training, inject a low-rank adapter matrix into a specific layer of the Transformer structure of the basic large-scale language model, and construct a composite loss function, which includes a standard loss term and a safety alignment loss term. The basic large-scale language model is trained using the incremental training data, and the parameters of the injected low-rank adapter matrix are updated only through backpropagation to obtain the target large-scale language model. The standard loss term is used to calculate the loss on the supervised fine-tuning sample set, and the safety alignment loss term is used to calculate the loss on the rejection sample set and the fairness sample set. A multi-dimensional automated evaluation and model candidate unit is used to evaluate the target large language model and the basic large language model. If the evaluation results meet preset conditions, the target large language model is marked as a candidate deployment model. The evaluation includes at least professional capability evaluation, security performance evaluation and general capability regression testing. The model version management and canary deployment unit is used to release the candidate deployment model in a canary manner. If the candidate deployment model performs stably and as expected during the canary period, the candidate deployment model is switched to the full service model.

10. A method for continuous optimization of an AI knowledge base based on human-machine collaboration and closed-loop feedback, characterized in that, Applied to the AI ​​knowledge base continuous optimization system as described in any one of claims 1-9, the method includes: The multimodal empathic perception input module extracts key feature parameters representing the user's internal emotional state from the facial image video stream and the speech audio stream, maps the key feature parameters to natural language descriptions, and generates empathic prompt words based on the natural language descriptions; the speech audio stream contains the user's questions; The knowledge discovery and construction module periodically retrieves target data corresponding to the target domain from the network, performs cluster analysis on the target data to obtain knowledge clusters with inherent connections, forms structured candidate knowledge units based on each knowledge cluster, and pushes the candidate knowledge units to the knowledge base dynamic update and conflict resolution module. The hierarchical deep reasoning engine uses the empathy prompts and the user's question to generate a target answer corresponding to the user's question, based on the reasoning strategy provided by the dynamic resource scheduling and reflection module and by calling the knowledge base in the knowledge base dynamic update and conflict resolution module. The target answer is rendered according to the requirements of the current interaction scenario using the scenario-specific response generation module to obtain the corresponding natural language text; The output formal verification and interpretability analysis module is used to perform mandatory rule verification on the natural language text and generate a verification report, which includes counterexamples. The dynamic resource scheduling and reflection module determines the inference strategy based on the empathy prompts and the first performance feedback provided by the feedback data deep analysis module, and provides it to the hierarchical deep inference engine. The human-machine collaborative feedback interface is used to collect metadata from the processing of the hierarchical deep inference engine, the response generation module for specific scenarios, and the output formal verification and interpretability analysis module, and to visualize the data. Experts' corrections, comments, annotations, or confirmations on the presented data are also recorded to form audit feedback data. The feedback data deep analysis module is used to perform multi-dimensional aggregation analysis on the review feedback data to obtain first performance feedback, second performance feedback and third performance feedback; wherein, the first performance feedback is pushed to the dynamic resource scheduling and reflection module to improve the scheduling strategy, the second performance feedback is pushed to the knowledge base dynamic update and conflict resolution module to repair the knowledge base, and the third performance feedback is pushed to the model incremental optimization and security alignment module to optimize the model; The knowledge base dynamic update and conflict resolution module is used to perform multi-stage verification and conflict detection on the second performance feedback and the candidate knowledge unit, and the knowledge base is updated based on the verification results and conflict detection results. The incremental training data is selected from the third performance feedback and the audit feedback data using the model incremental optimization and security alignment module. The incremental training data is used to incrementally train the large language model.

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