Expert mental modeling method and system based on cognitive instrument drift calibration

Abstract

The invention discloses an expert mental modeling method and system based on cognitive instrument drift calibration. According to the method, a large artificial intelligence model is regarded as a precise instrument with system deviation, and output correction is performed through an independent lightweight cognitive calibration module instead of fine adjustment of the model. The method comprises the following steps: firstly, systematically surveying and mapping an output deviation of a basic model through a structured'cognitive calibration range ', and recording the output deviation as a'deviation vector' containing a correction instruction; the deviation data is then utilized to train a calibration module to enable it to predict the deviation. In real-time application, the system obtains an original answer of the basic model, predicts and applies a correction instruction by using a calibration module, and outputs an accurately calibrated answer. The method aims at solving the disastrous forgetting and maintenance problems existing in traditional fine adjustment at low cost, and the reliability and credibility of artificial intelligence in the professional field are improved.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of information technology and artificial intelligence, and specifically relates to a modeling method and system for improving the reliability, accuracy, and consistency of outputs from large-scale artificial intelligence models in specific professional fields. More specifically, this invention innovatively introduces the "instrument calibration" paradigm from metrology and systems engineering into the field of advanced artificial intelligence. It proposes a novel technical solution that treats large-scale basic models as powerful but inherently flawed precision instruments with inherent systematic errors and performance drift, and dynamically corrects deviations and aligns their outputs with mental models through an independent, lightweight, and real-time-updable cognitive calibration module. This solution aims to address the fundamental engineering challenges faced by existing AI technologies in professional applications, such as high cost, low efficiency, unreliability, poor scalability, data privacy risks, and "catastrophic forgetting," making it particularly suitable for critical application scenarios with extremely high requirements for decision-making accuracy, traceability, security compliance, and continuous adaptability, such as financial risk control, medical diagnosis, legal consulting, engineering design, and compliance review. Background Technology

[0002] Currently, artificial intelligence technologies, represented by large language models (LLMs), are developing at an unprecedented pace, demonstrating powerful general knowledge and reasoning capabilities. However, when applying these general models to specific, specialized, and high-risk real-world tasks, a profound and pervasive "engineering fallacy" significantly hinders the depth and breadth of their industrialization. This fundamental fallacy lies in the industry's widespread view of large AI models as "human brains" or "apprentices" that need continuous "education" or "shaping" to improve. This concept dominates all current mainstream technical optimization paths, such as pre-training, instruction fine-tuning, and reinforcement learning based on human feedback (RLHF). The essence of these methods is to attempt to "teach" the model how to become a better expert by feeding it new knowledge or adjusting its internal parameters (weights). While this "brain re-education" paradigm has achieved significant results in improving the model's general dialogue capabilities, it has exposed a series of insurmountable inherent flaws in specialized engineering applications.

[0003] From the perspective of systems engineering and metrology, any complex system used for measurement, analysis, or decision-making, no matter how sophisticated its design or how powerful its functions, inevitably possesses inherent systematic errors and performance "drift" due to changes in time, environment, and data distribution. In the physical world, when faced with a high-precision spectrometer, a medical MRI machine, or an aerospace gyroscope, we never expect to correct minute deviations in its readings by remelting its core components or modifying its physical structure (i.e., "re-education" the instrument). Instead, we employ a mature, efficient, and extremely reliable engineering process: instrument calibration. We measure the instrument's systematic bias using a stable and authoritative "standard" and apply an independent, verifiable correction algorithm or lookup table to calibrate its output, thereby ensuring its accuracy and reliability throughout its entire lifecycle.

[0004] The existing paradigm for optimizing artificial intelligence models violates this fundamental engineering principle, thus falling into the following irreconcilable dilemmas:

[0005] First, the extremely high cost of "re-education." Fine-tuning a super-large model with tens or even hundreds of billions of parameters requires massive amounts of labeled data, enormous computing resources (usually hundreds or even thousands of high-end GPU weeks), and a lengthy training period. This is akin to remelting and reforging an entire block of special steel to correct a one-hundredth-millimeter error on a precision ruler; the cost is disproportionately low to the desired correction, resulting in extremely low cost-effectiveness. For companies that need to respond quickly to business changes and update knowledge, this heavyweight "re-education" process is unsustainable both economically and in terms of time.

[0006] Secondly, there is the problem of catastrophic forgetting. When a general-purpose model is fine-tuned using domain-specific data, it is highly susceptible to "forgetting" or "contaminating" the vast general knowledge and reasoning abilities it acquired during the pre-training phase while learning new knowledge. For example, a model fine-tuned for the financial risk control field might perform poorly when handling a simple business email writing task, showing a significant decline in fluency and breadth of common sense. This "specialization" and "forgetting" of abilities greatly undermines the value of large-scale models as general-purpose intelligent infrastructure, forcing enterprises to maintain a separate, fine-tuned large model for each specific task, further increasing the complexity and cost of technology applications.

[0007] Third, the correction process lacks traceability and interpretability. The fine-tuning process adjusts the weights of hundreds of millions of parameters within the model using the gradient descent algorithm. This is a highly nonlinear and entangled "black box" process. We cannot clearly know which specific parameter adjustment corresponds to which "knowledge" correction, nor can we independently and modularly update, roll back, or disable a particular correction. When a correction is found to have introduced new, unexpected errors, the only solution is often to re-prepare the data and perform another round of expensive and unpredictable fine-tuning. This "integrated" black-box correction mechanism is completely unacceptable in fields requiring high levels of regulation and accountability (such as healthcare and finance).

[0008] Fourth, it cannot simulate the true expert's mindset. The core value of human experts' wisdom lies not merely in the amount of static knowledge stored in their brains, but more importantly, in their well-honed, almost intuitive "built-in calibration system" that can instantly identify and correct their own thought patterns, cognitive biases, and knowledge blind spots. When an experienced doctor sees an atypical imaging report, they will subconsciously initiate a "reflection" process: "This feature looks like A, but it doesn't fit the patient's age characteristics. Let me re-examine the two easily confused differential diagnostic points, B and C." This dynamic, self-critical, and scenario-based bias correction ability is completely impossible to simulate using the existing "one-off" fine-tuning paradigm.

[0009] Fifth, scalability bottlenecks and expert resource constraints. Existing fine-tuning paradigms heavily rely on large-scale, high-quality expert-annotated data. When attempting to apply AI to broader or more specialized fields, acquiring sufficient expert resources for data annotation becomes a significant bottleneck. Experts are time-consuming, and differences in knowledge and conflicting viewpoints among different experts can make it difficult to guarantee data quality, severely limiting the scalable replication and promotion of technical solutions.

[0010] Sixth, data privacy and compliance challenges. In highly sensitive industries such as finance and healthcare, directly using production data containing customer privacy or trade secrets to fine-tune general-purpose models in the cloud poses significant risks of data leakage and compliance challenges. Existing technological approaches generally lack built-in, systematic privacy protection designs, making it difficult to meet the stringent requirements set forth in laws and regulations such as the Cybersecurity Law, the Data Security Law, and the Personal Information Protection Law. In particular, the "notice-and-consent" principle for personal information processing, data export security assessment mechanisms, and processing standards for important and core data have become insurmountable legal and compliance obstacles to the deep application of AI in these key areas.

[0011] Therefore, the fundamental "engineering fallacy" of existing AI engineering paradigms—attempting to mold a perfect "omniscient brain" through costly and unpredictable "re-education," rather than equipping powerful "precision instruments" with an efficient, safe, and scalable "calibration system" through mature and reliable engineering principles—has become a technological bottleneck hindering the deep application of advanced artificial intelligence in key fields and ensuring its safety and reliability. A completely new and disruptive technological paradigm is urgently needed in this field to address this problem. Summary of the Invention

[0012] The purpose of this invention is to overcome the aforementioned deficiencies of existing technologies and provide a novel expert mental modeling method and system based on cognitive instrument drift calibration. The core technical problem this invention aims to solve is: how to equip any general-purpose large-scale AI model (basic instrument) with an independently deployable, real-time-updable, interpretable, traceable, highly scalable, and privacy-preserving "cognitive calibration module" with extremely low computational cost and extremely high engineering efficiency. This module enables the model's output in a specific professional field to dynamically and accurately align with the mental models and gold-standard knowledge bases of domain experts, thereby achieving reliability and consistency comparable to domain experts without altering the basic model itself.

[0013] The core technical idea of ​​this invention lies in completely overturning the traditional paradigm of viewing AI as a "brain to be trained," and instead innovatively redefining it as a powerful but inherently "cognitive drift" and "systematic error" "cognitive precision instrument." Based on this core idea, the solution of this invention no longer attempts to "reshape" this instrument through "fine-tuning," but instead introduces the mature "calibration" engineering philosophy from metrology science, using an independent, lightweight "calibration module" to systematically learn and correct the output deviation of the basic instrument in real time.

[0014] To achieve the above objectives, this invention provides an expert mental modeling method based on cognitive instrument drift calibration, which is implemented through a unique three-step "cognitive calibration" approach:

[0015] The first step is to map the systematic biases of the instrument and construct a knowledge base. The goal of this stage is not simply to train the model, but rather, much like a metrologist calibrating a precision physical instrument, to systematically and comprehensively explore the "cognitive performance drift spectrum" of a large-scale model—a "basic AI instrument"—within a specific professional field, laying the data foundation for subsequent automation and expansion. Specific steps include:

[0016] a) Create a scalable “cognitive calibration test range database”: Collaborate with one or more domain experts to design and build a structured, standardized “test case grid” that covers the domain’s knowledge system. This grid systematically covers all types of problems in the domain, from basic to cutting-edge, from common to marginal, and from single knowledge points to complex logical chains. To address the bottleneck of expert resources in large-scale applications, this step further includes a semi-automated test case generation mechanism, such as generating a large number of preliminary test case drafts by analyzing abnormal patterns in historical business data or using generative AI, which are then screened and optimized by experts to improve the efficiency and coverage of the test range construction.

[0017] b) Execute the "Reading-Truth Value" Deviation Recording and Conflict Resolution Process: The selected "Base AI Model" (i.e., "Base Instrument") generates its original answer for each test case in the test range. Simultaneously, one or more domain experts provide the "Gold Standard Truth Value." To address potential inconsistencies from multiple expert inputs, this step introduces a "Multi-Expert Instruction Fusion and Conflict Resolution Mechanism." When different experts provide different correction instructions for the same case, the system can use pre-set expert authority weights for weighted voting, or initiate a tiered review process to submit conflicting cases to the chief expert for arbitration, ensuring that the final entered "truth value" has a high degree of consensus and authority.

[0018] c) Calculate and store interpretable "deviation vectors": The system compares the "original answer" of the basic instrument with the consensus-confirmed "gold standard truth value" to generate a structured "deviation vector." This vector not only contains basic information such as error type and location, but may also include metadata such as "corrected confidence level" and "source of correction" (e.g., the cited regulatory clause number or internal knowledge base document ID) to enhance its interpretability and traceability. Through this process, the system will generate a massive, high-quality triplet dataset of (test cases, original answers, deviation vectors).

[0019] The second step is the training and optimization of the lightweight "calibration module." This stage is the core execution part of the method of this invention, aiming to train a miniature expert model that focuses solely on "predicting and correcting biases" and equips it with the ability to handle uncertainty. Specific steps include:

[0020] a) Constructing a “calibration module”: This module is a machine learning model with a very small number of parameters (usually less than 1% of the basic instrument parameters, or even lower), and the architecture can be flexibly selected.

[0021] b) Set a unique training objective: The training input for the calibration module is (the original "test cases" + the "original answers" from the basic instruments), and its training output objective is the structured "bias vector" generated in the first step. To enable the model to perceive the uncertainty of its own predictions, techniques such as Monte Carlo Dropout or ensemble learning can be introduced during training, so that it can output an accompanying "calibration confidence score" while predicting the bias vector.

[0022] c) Conduct efficient and secure supervised training: Use a triplet dataset for training. To ensure data privacy, especially when dealing with sensitive data such as financial and medical data, all data must undergo a "data anonymization and pseudonymization module" before training to remove all personally identifiable information. In multi-party collaboration scenarios, a federated learning (FL) framework can be used, where each participant trains the model locally and only uploads encrypted model updates, thereby collaboratively training a global calibration module without sharing the original data.

[0023] The third step is dynamic adaptive real-time online calibration and continuous learning. This stage is the process of implementing this invention into an engineering system with high robustness and self-evolution capabilities. The specific workflow is as follows:

[0024] a) Deploy a modular, compliant "plug-and-play" architecture: The system consists of a decoupled "basic AI model" and a "cognitive calibration module." Depending on the compliance requirements of different industries, the system supports cloud-based SaaS, on-premise, or hybrid cloud deployments, ensuring that the physical and logical boundaries of data processing meet the highest security standards.

[0025] b) Execute a confidence-based real-time calibration workflow: When a user requests a calibration, the system obtains a "rough answer" from the base model. Then, the (request + rough answer) is input into the calibration module, which outputs a predicted "bias vector" and a "calibration confidence score." The real-time calibration engine adopts a tiered strategy based on this confidence score: high confidence results in automatic correction; medium confidence results in correction and marking for post-review; low confidence triggers mechanisms such as "safety rollback" (e.g., returning an uncalibrated answer with a warning) or "human-machine collaboration" (e.g., pushing the task to an online expert), effectively handling uncertain and complex cases.

[0026] c) Initiating an online incremental learning and continuous calibration loop: All new data that has been manually intervened or reviewed, as well as regularly updated range data, are automatically used to perform low-cost online incremental training on the cognitive calibration module. This mechanism ensures that the calibration system can continuously learn new knowledge and adapt to new rules at extremely high speed and low cost, forming a dynamic self-evolving closed loop.

[0027] d) Implementing a hybrid maintenance strategy and monitoring base model drift: This invention does not completely exclude updates to the base model. The system includes a "base model drift monitoring unit" that periodically evaluates the core capabilities of the base model. When irreversible performance degradation is detected or a new, generationally leading model appears on the market, the system will trigger a low-frequency "base model upgrade" process and retrain the comprehensive deviation mapping and calibration modules with the new model to ensure the system's long-term competitiveness in technological evolution.

[0028] The beneficial effects of this invention are that, through the above-mentioned refined and systematic design, it not only realizes the advantages of the original solution, such as extremely low cost, solving catastrophic amnesia, and high traceability, but also brings further benefits:

[0029] First, high system robustness and reliability: Through confidence assessment and hierarchical processing mechanisms, the system can gracefully handle uncertainty, avoid making incorrect calibrations in ambiguous or unknown scenarios, and ensure the lower limit of the reliability of the output.

[0030] Second, strong scalability and efficiency: Through semi-automated data collection and active learning strategies, the reliance on scarce expert resources is greatly reduced, enabling this solution to be efficiently extended to multiple new fields or large-scale application scenarios.

[0031] Third, built-in data privacy and security protection: By supporting multiple deployment architectures, data anonymization and federated learning mechanisms, it fundamentally solves the pain points of data security and compliance when applying AI in sensitive areas, clearing key obstacles for technology implementation.

[0032] Fourth, continuous self-evolution and adaptability: Through online incremental learning cycles and hybrid maintenance strategies, the calibration system is able to respond quickly to daily knowledge updates and adapt robustly to the generational evolution of underlying technologies, thus possessing long-term vitality.

[0033] To achieve the above objectives, the present invention also provides an enhanced expert mental modeling system based on cognitive instrument drift calibration. In addition to the original modules, this system further includes: a cognitive calibration range database management unit for automatically assisting in the generation of test cases and managing multi-expert consensus; a data privacy protection module for anonymizing and pseudonyming data during training and inference; a confidence calculation unit for evaluating the uncertainty of the calibration module output; a dynamic routing and decision engine for executing different calibration strategies based on the confidence score; and a continuous learning and model governance subsystem for triggering incremental learning and monitoring the performance of the base model. Attached Figure Description

[0034] To more clearly illustrate the embodiments of the present invention, the accompanying drawings used in the specific embodiments will be briefly described below.

[0035] List of reference numerals

[0036] Figure 1 This is a schematic diagram of the overall architecture of an expert mental modeling system (100) based on cognitive instrument drift calibration.

[0037] 101: Basic AI Model

[0038] 102: Cognitive Calibration Module

[0039] 103: Cognitive Calibration Range Database

[0040] 104: Triple Database

[0041] 105: Real-time Calibration Engine

[0042] 110: User Request

[0043] 111: Rough Answer

[0044] 112: Bias Vector (including confidence level)

[0045] 113: Dynamic Decision and Calibration Application Unit

[0046] 114: Answer after calibration

[0047] Figure 2 This is a flowchart of the first stage, "Mapping of Systemic Instrument Deviations" (200).

[0048] 101: Basic AI Model

[0049] 103: Cognitive Calibration Range Database

[0050] 104: Triple Database

[0051] 111: Rough Answer

[0052] 112: Deviation Vector

[0053] 202: Test Cases

[0054] 204: Domain Expert

[0055] 204a: Expert A

[0056] 204b: Expert B

[0057] 205: The True Value of the Gold Standard

[0058] 205a: The Gold Standard Truth Value of Expert A

[0059] 205b: The Gold Standard Truth Value of Expert B

[0060] 206: Deviation Vector Calculation Unit

[0061] 208: Multi-expert instruction fusion and conflict resolution module

[0062] 209: Chief Human Expert

[0063] Figure 3 This is a schematic diagram of the second phase, "Training of the Lightweight Calibration Module" (300).

[0064] 102: Cognitive Calibration Module

[0065] 104: Triple Database

[0066] 112: Truth Deviation Vector

[0067] 301: Training Loop

[0068] 302: Training Input (Question + Rough Answer)

[0069] 303: Training objective (bias vector 112)

[0070] 304: Loss Function

[0071] 310: Data Privacy Protection Module

[0072] Figure 4 This is a workflow diagram for the third phase, "Real-time Online Calibration and Deployment" (400).

[0073] 101: Basic AI Model

[0074] 102: Cognitive Calibration Module

[0075] 110: Real-time user requests

[0076] 111: Rough Answer

[0077] 112: Predicted bias vector (including instruction 404a and confidence level 404b)

[0078] 113: Dynamic Decision and Calibration Application Unit

[0079] 114: Answer after calibration

[0080] 406: Confidence level

[0081] 406a: High confidence level

[0082] 406b: Medium confidence level

[0083] 406c: Low confidence

[0084] 416: Continuous Learning Queue

[0085] 417: Exception Handling Process

[0086] 418: Online Expert Pool Review

[0087] Figure 5 This is an example of the "deviation vector" data structure (500).

[0088] 501: Unique Identifier (UUID)

[0089] 502: Error Type (ErrorType)

[0090] 503: Error Location

[0091] 504: Severity Level

[0092] 505: Correction Instruction

[0093] 506: ActionType

[0094] 507: Operation Content

[0095] 508: Correction Source

[0096] 509: Expert Consensus Information (ExpertConsensusInfo)

[0097] 510: Example

[0098] Figure 6 This is a flowchart (600) illustrating a specific application of an embodiment of the present invention in the field of financial risk control.

[0099] 101: Basic AI Model

[0100] 102: Cognitive Calibration Module

[0101] 110: New application case (as a user request)

[0102] 111: Rough Answer

[0103] 112: Deviation Vector

[0104] 113: Dynamic Decision and Calibration Application Unit

[0105] 114: Post-calibration evaluation comments (as a post-calibration response)

[0106] 202: Test Cases

[0107] 205: The True Value of the Gold Standard

[0108] Figure 7 This is a schematic diagram of the system's scalability and continuous learning closed-loop mechanism (700).

[0109] 102': Cognitive calibration module after incremental update

[0110] 102”: Cognitive calibration module completely retrained for updated base models

[0111] 202: New use cases added to the cognitive calibration range database

[0112] 416: Continuous Learning Queue

[0113] 418: Online Expert Pool Review

[0114] 701: Inner Circulation: High-Frequency Agile Calibration

[0115] 702: External Circulation: Low-Frequency Generational Upgrade

[0116] 710: Model Governance and Continuous Learning Subsystem

[0117] 711: Incremental Training Dataset

[0118] 712: Incremental Training Process

[0119] 720: Basic Model Drift Monitoring Unit

[0120] 721: Core Competency Benchmark Set

[0121] 730: The New Basic Model

[0122] Figure 8 This is a schematic diagram of the architecture of the data privacy protection module (310).

[0123] 3100: Data Privacy and Compliance Assurance Architecture

[0124] 3101: Data Entry Layer: Strategy Execution Point

[0125] 3102: Desensitization / Pseudonymization Module

[0126] 3103: Deployment Architecture and Data Boundary Control

[0127] 3104: Cloud-based SaaS Deployment

[0128] 3105: Private Deployment

[0129] 3106: Federated Learning Architecture

[0130] 3107: Federated Aggregator Server

[0131] 3108: Multi-party local training

[0132] 3109: Access Control and Auditing Detailed Implementation

[0133] The technical solutions in the embodiments of the present invention will now be clearly and completely described in conjunction with the accompanying drawings.

[0134] one, Figure 1 This is a schematic diagram of the overall architecture of the expert mental modeling system based on cognitive instrument drift calibration of the present invention.

[0135] System 100 is designed as an efficient, secure, and intelligent enhancement layer that attaches to any existing general-purpose large-scale AI model to provide expert-level output quality. The overall architecture of System 100 embodies the core idea of ​​complete decoupling between "instrument" and "calibrator," and incorporates key modules to ensure system robustness and scalability.

[0136] The core of System 100 consists of two main modules and three databases / subsystems. The two main modules are:

[0137] Basic AI Model 101: This is a standard, general-purpose, pre-trained large-scale AI model. Figure 1 In this context, it can be any large-scale language model. For example, commercial models such as Baidu's Wenxin Yiyan and Tencent's Hunyuan, which are accessed via API calls, can be used, or excellent open-source models can be deployed privately, such as Tongyi Qianwen, ChatGLM, and DeepSeek series models. The key point of this invention is that the internal parameters (weights) of the basic AI model 101 are frozen during the daily operation and maintenance cycle of the system. Its sole function in the system is to act as a high-throughput "reading instrument," quickly generating a raw, uncalibrated "rough answer" 111 for the input request (user request 110).

[0138] Cognitive calibration module 102: This is the core innovative component of the present invention. It is an independent machine learning model with a very small number of parameters. Its "small" is relative to the basic AI model 101. For example, if the basic model has 100 billion parameters, the calibration module 102 may only have 1 million to 10 million parameters, which is less than 0.01%. The architecture of this module is customized according to the task requirements and can be a small Transformer encoder-decoder, a multilayer perceptron (MLP), a graph neural network (GNN), or other lightweight network structures. Its function is not to solve the problem from scratch, but to learn "how to correct the errors of the basic model 101". Its input is in pairs (user request (110), rough answer (111)), and its output, in addition to the structured bias vector (112), also includes a "calibration confidence score" for subsequent dynamic decision-making.

[0139] Three major databases / subsystems support the implementation of the entire calibration process:

[0140] Cognitive Calibration Range Database (103): This is a structured database used to store a "test case grid." It is meticulously designed by domain experts based on domain-specific knowledge graphs, business processes, and common error patterns. It replaces the messy sample data in traditional AI training, providing standards and benchmarks for deviation mapping. The database is managed by a dedicated "range management unit," which supports semi-automated test case generation and the management and version control of expert input, thereby improving the system's scalability.

[0141] Triplet Database (104): This database is a product of the deviation mapping phase. Before being written into this database, all data, especially sensitive data from the production environment, must undergo a process that is not yet available in the database. Figure 1 The data privacy protection module, which is separately marked, undergoes anonymization and pseudonymization to ensure compliance. It stores a large number of data records used to train the cognitive calibration module 102. Each record contains three parts: a test case from the cognitive calibration range database (103), a rough answer (111) of the basic AI model (101) to the test case, and a deviation vector (112) described by expert annotation or system calculation, which describes the difference between the answer and the "gold standard truth".

[0142] Real-time calibration engine (105): This is the core of the system during online operation. It receives user requests (110) and schedules calls to the basic AI model (101) and the cognitive calibration module (102). Its internal "dynamic decision-making and calibration application unit (113)" is a unit containing dynamic decision-making logic. It is not only responsible for parsing the deviation vector (112), but also first evaluates the accompanying "calibration confidence score" and decides whether to automatically apply correction, mark review, or trigger a safety rollback or human-machine collaboration process based on a preset threshold. Finally, it generates a highly reliable calibrated answer (114) and returns it to the user or downstream application.

[0143] The entire system's interaction flow is clearly displayed. Figure 1 In the process: A compliant user request (110) enters the system; the basic AI model (101) generates a rough answer (111); the cognitive calibration module (102) outputs a deviation vector (112) and a confidence level; the dynamic decision-making and calibration application unit (113) in the real-time calibration engine (105) makes an intelligent decision based on the confidence level, or applies corrections, or initiates an anomaly handling process, and finally produces a calibrated answer (114) that has been mentally calibrated by experts and whose reliability is guaranteed.

[0144] two, Figure 2 The process of the first stage of the method of the present invention, "Mapping of Instrument Systemic Deviation", is described in detail 200.

[0145] The process begins with the cognitive calibration range database (103). To address the challenge of building a large-scale range, the creation process no longer relies entirely on manual design. The system provides a "semi-automated test case generator" (not shown in detail in the figure). This generator can analyze the historical business data accumulated by the enterprise (such as anonymous customer service Q&A records, rejected application reports, etc.), automatically identify patterns of abnormal or edge cases, and generate test cases (202). Alternatively, it can leverage the capabilities of generative AI to generate hundreds or thousands of structurally similar but different test cases (202) based on several templates provided by experts. These automatically generated test cases are then submitted to domain experts (204) for review, screening, and refinement, thereby expanding the cognitive calibration range database (103) by tens of times.

[0146] The next step in the process is the recording of “readings-truths”. For each test case (202) in the cognitive calibration range database (103), the system submits it to the frozen, unmodified base AI model 101 and records its generated coarse answer (111). This answer is the model’s most natural response and a direct reflection of its inherent knowledge and reasoning patterns.

[0147] Meanwhile, when dealing with complex or cutting-edge domains, the system supports multiple domain experts (such as experts 204a and 204b, collectively referred to as domain expert 204) providing their own gold standard truth values ​​(205a and 205b) for the same test case (202). This is common in reality because different experts may have different focuses or experiences.

[0148] The next crucial steps are deviation calculation and conflict resolution. The system compares the rough answer (111) with multiple expert gold standard truth values ​​(205). At this point, a "multi-expert instruction fusion and conflict resolution module" (208) is activated. This module first checks whether there are conflicts in the correction instructions provided by different experts.

[0149] If there are no conflicts or the differences are minor, the instructions are merged.

[0150] If a substantial conflict exists (e.g., expert A recommends approving the loan, while expert B recommends rejecting it), the module will execute a pre-defined resolution strategy. In one embodiment, the module queries an "expert certification and weighting database" and performs a weighted vote based on the authority weight assigned to each expert based on their historical accuracy, qualifications, etc. In another, more complex embodiment, the case is marked as "significantly disagreeing" and automatically submitted to a higher-level human lead expert (209) for final arbitration. The arbitration result will be considered the final authoritative truth value for this case.

[0151] After this process, the bias vector calculation unit (206) calculates the final, unambiguous, and authoritative bias vector (112) based on the authoritative truth value confirmed by consensus. This bias vector (112) therefore has higher quality and credibility. The authoritative bias vector (112) is stored in the triple database (104). Through this mechanism, the present invention not only solves the scalability problem of expert resource shortage, but also elegantly handles the practical problem of differing expert opinions, ensuring the quality of the training data source.

[0152] three, Figure 3 The flowchart 300 of the second stage of the method of the present invention, "training of the lightweight calibration module", is shown.

[0153] The goal of this stage is not to conduct another costly "re-education," but to use the carefully mapped deviation data from the first stage to precisely "cast" a lightweight expert—the cognitive calibration module (102)—dedicated to deviation prediction and correction with extremely high efficiency and strong security. This training process deeply integrates advanced privacy protection technologies and uncertainty modeling methods to ensure its compliance and robustness in real-world applications.

[0154] The process begins with the triplet database (104) generated in the previous stage. Before the data is fed into the training process, it must first pass through a "data privacy protection module" (310). This module automatically desensitizes all fields that may contain sensitive information (such as personal names, ID numbers, medical records, and financial data), for example, by replacing names with random strings and replacing specific ages with age ranges. For scenarios involving multi-institutional collaboration where data cannot leave the domain, this invention supports the use of a federated learning (FL) framework. Under this framework, each batch of computation in the training loop (301) is performed on the local client where the data resides, and only encrypted model gradients or weight updates are sent to a central aggregation server for aggregation, thereby protecting the absolute privacy of the original data.

[0155] Only secure data processed by the data privacy protection module (310) is allowed to enter the training loop (301).

[0156] The core objective of the training loop (301) is to train the cognitive calibration module (102). Figure 3 In our embodiment, we chose a small, Transformer-based encoder-decoder architecture as the cognitive calibration module (102). The encoder is responsible for receiving and understanding the context of the input information, while the decoder is responsible for generating a structured bias vector.

[0157] In each iteration of the training loop (301), the system draws a batch of data from the privacy-processed triple database (104).

[0158] Training Input (302): For each data point, the input portion is a carefully constructed sequence, typically a combination of the original test case (202) (i.e., the question) and the rough answer (111) given by the base AI model (101), strung together by a special “separator” (e.g., [SEP]). This design is based on a core insight: to accurately predict where an answer “goes wrong,” the model must know both “what the original question is” and “what the reading from the base instrument is.” This provides the calibration module with a complete context, enabling it to learn “what type of error the base model tends to produce in what problem context.”

[0159] Training objective (303): The training objective corresponding to the input is the authoritatively confirmed bias vector (112) stored in the data record. This is precisely the subversive aspect of the training paradigm of this invention: we do not require the calibration module to learn to generate the perfect gold standard truth (205), because that would be tantamount to retraining a large model. We only require it to learn a metacognitive task with a simpler objective and task—predicting error patterns.

[0160] The decoder part of the cognitive calibration module (102) is specifically trained to generate a prediction sequence that is as structurally and content-consistent as possible with this truth bias vector (112). To enable the model to not only make predictions but also evaluate the reliability of its predictions, we improved the traditional training method by introducing uncertainty modeling techniques:

[0161] The core mechanisms of the training loop (301): forward propagation, loss calculation, and backpropagation.

[0162] Forward propagation: For each input data pair, the cognitive calibration module (102) performs a forward computation, starting from the input layer and proceeding layer by layer until the output layer generates a "predicted bias vector".

[0163] Loss Calculation: Subsequently, the loss function (304) is invoked to precisely quantify the gap or error between the "predicted bias vector" and the true "training target (303)". As mentioned earlier, the loss function can be composed of a combination of cross-entropy loss, mean squared error loss, and regularization terms that encourage high confidence. This error value is a scalar that represents how "wrong" the model's current prediction is.

[0164] Backpropagation: This is the magic of model learning. After calculating the loss value, the system starts the backpropagation algorithm. Starting from the output layer, the algorithm uses the chain rule from calculus to calculate the gradient of the loss value with respect to each parameter (weight and bias) in the model layer by layer. This gradient can be intuitively understood as "in order to make the loss smaller, in which direction and by how much should this parameter be adjusted?"

[0165] Weight Update: After calculating the gradients of all parameters, an optimizer (such as Adam, SGD, etc.) makes a small adjustment to all internal parameters of the cognitive calibration module (102) based on this gradient information. The step size of the adjustment is controlled by hyperparameters such as the learning rate. This update step ensures that the model's predictions will "move" a little closer to the correct direction when it sees similar inputs again.

[0166] By repeating the complete training loop (301) of "forward propagation -> loss calculation -> backpropagation -> weight update" thousands of times on the entire triplet database (104), the parameters of the cognitive calibration module (102) are gradually "sculpted" to the optimal state, ultimately producing a trained module (102). It not only efficiently grasps the systematic "cognitive drift" law of the basic instrument in a specific field and can predict accurate correction instructions, but more importantly, by adopting uncertainty modeling techniques (such as Monte Carlo dropout) in training and inference, it possesses valuable self-awareness—that is, the ability to quantify its own prediction credibility.

[0167] Specifically, when a model encounters a familiar error pattern that has been thoroughly learned from in the training data, its multiple random inferences will yield highly consistent results, resulting in a high "calibration confidence score," which indicates that the model "knows" how to calibrate. Conversely, when a model encounters an unfamiliar, sparse, or ambiguous error pattern, its multiple inferences will produce significant discrepancies, resulting in a low confidence score, which indicates that the model "knows it does not know" the exact calibration method.

[0168] This ability to quantify its own uncertainty is the fundamental guarantee to prevent AI systems from "pretending to know what they don't" in critical decisions, and lays a solid and reliable technical foundation for the confidence-based dynamic decision-making process in the third stage.

[0169] Four, Figure 4 This is a schematic diagram 400 of the workflow for the third stage of the method according to the present invention, namely "real-time online calibration and deployment".

[0170] The workflow begins with a "real-time user request" (110) from an end user. This request first passes through a... Figure 3 Similar lightweight data desensitization filters (i.e.) Figure 3 and Figure 8 The data privacy protection module 310 in the middle is not in Figure 4 (Display) to ensure that even data processed in real time is protected in terms of privacy.

[0171] After receiving the processed request, the system passes it to the basic AI model (101) to generate a rough answer (111).

[0172] Next is the core intelligent calibration step of this invention. The system packages (user request (110), rough answer (111)) into a new input data pair and sends it to the trained cognitive calibration module (102).

[0173] At this point, the cognitive calibration module (102) not only outputs a predicted bias vector, but also outputs a "calibration confidence score (406)" in parallel. These two outputs are collectively referred to as the predicted bias vector (112).

[0174] The predicted deviation vector (112) is sent to the system's "Dynamic Decision and Calibration Application Unit (113)", which performs a key hierarchical decision:

[0175] High confidence: If the confidence score is higher than a preset upper limit threshold (e.g., 0.9), the engine determines that the calibration is highly reliable. Then, it directly calls the internal "calibration application module" to correct the rough answer (111) according to the instructions in the deviation vector (112), generate the final calibrated answer (114), and can optionally output the high confidence (406a) prompt to the user.

[0176] Medium Confidence: If the score is in the middle range (e.g., 0.6-0.9), the engine judges that the calibration may be correct, but there is some uncertainty. It will still apply the correction and return the calibrated answer (114), and may optionally output a medium confidence (406b) hint to the user. However, it will also send the entire interaction log (request, poor answer, calibrated answer, confidence) to a "continuous learning queue" (416). The data in this queue will be used by experts for asynchronous sampling audits or directly as a data source for incremental learning, thus forming a closed loop of continuous improvement.

[0177] Low confidence: If the score is below the lower threshold (e.g., 0.6), the engine determines that the calibration module has encountered a situation it is not confident in handling. This triggers an exception handling procedure (417). Based on a preset strategy, it can choose to:

[0178] Human-machine collaboration: The task is pushed to an "online expert pool for review" (418) in real time or asynchronously through a dedicated interface, requesting human intervention. The result processed by the experts will be output as a calibrated answer (114), and standardized as expert pool review (418), becoming a valuable new training data, which is fed back to the continuous learning queue (416).

[0179] Safety rollback: When an expert is offline, no calibration is performed; the original, rough answer is returned directly (111) along with a clear warning message: Uncalibrated - Low confidence (406c), informing the user that the answer has not been deeply calibrated. The interaction log is then sent to the human-machine collaboration pool for further processing, response, and feedback to the continuous learning queue (416).

[0180] Through this dynamic decision-making process, the present invention avoids blind corrections, ensures the robustness and security of the system in the face of uncertainty, and truly achieves the goal of "speaking only when you understand, speaking with confidence, and seeking help when you don't understand".

[0181] five, Figure 5 This is a detailed schematic diagram of the "deviation vector (112)" data structure 500.

[0182] The deviation vector (500) here is a specific instantiation example of the deviation vector (112). The deviation vector (112) is a key data structure connecting the two stages of "deviation mapping" and "real-time calibration", and its design directly determines the accuracy and interpretability of the calibration. The deviation vector proposed in this invention is an extensible, structured object, which can usually be represented by JSON, XML or similar formats.

[0183] A typical deviation vector (500) contains at least the following core fields:

[0184] Field 1: Unique Identifier (501) (UUID). Each deviation vector is assigned a globally unique ID for easy indexing, querying, and version control in the database.

[0185] Field 2: Error Type (502)(ErrorType). This is an enumeration type or string used to classify errors in the underlying model. This classification system is defined by domain experts and can be customized according to different domains. Figure 5 The document lists some common error types: Factual Error, Logical Fallacy, Omission Error, Outdated Knowledge, Bias Error, Tonal Mismatch, and Formatting Error.

[0186] Field 3: Error Location (503). This field is used to precisely indicate the location of the error in the rough answer (111) so that the dynamic decision-making and calibration application unit (113) can perform precise operations. Its representation can be very flexible, for example, it can be index-based (such as "paragraph 2, sentence 3" or "character index from 52 to 84"), content-based (such as "sentence containing 'obsolete term'"), or structured (such as using XPath or JSONPath expressions to locate specific key-value pairs for a JSON format answer).

[0187] Field 4: Severity Level (504). This is a quantitative indicator used to assess the potential impact of an error. For example, it can be categorized into levels such as "Critical," "Major," "Minor," and "Suggestion." This field can be used by downstream systems for risk control or to display priority rankings.

[0188] Field 5: Correction Instruction (505). This is the most crucial and operational part of the deviation vector. It explicitly tells the dynamic decision and calibration application unit (113) "how" to correct errors. The instruction is also highly structured and extensible, containing operation types (506) such as REPLACE, INSERT_BEFORE, INSERT_AFTER, DELETE, REWRITE_SECTION, etc.; and an operation content (507), which is the specific data used to perform the operation.

[0189] Field Six: Correction Source (508). This field records the authoritative basis for this correction instruction. For example, it could be a URL pointing to a specific document in an internal knowledge base, a specific clause number of a law or regulation (such as "Article 42 of the Company Law"), or an ID pointing to the chief expert who made the decision. This field is crucial for fields requiring strong compliance and auditability, such as finance and law.

[0190] Field 7: Expert Consensus Information (509) (ExpertConsensusInfo). This field records relevant information if the deviation vector was generated after multi-expert conflict resolution. For example, a list of expert IDs who participated in the voting, their weights, and the final voting result or arbitration opinion. This provides evidence of the “democratic” or “authoritative” nature of each amendment.

[0191] Field 8: Example (510). This can include a specific example of "before" and "after" text snippets, allowing users (whether human or other program) to more intuitively understand the role of the bias vector.

[0192] Through this structured design, the deviation vector (112) is no longer a vague "error" signal, but a detailed, machine-readable, and executable "bug report and fix patch". This makes the entire calibration process transparent, controllable, and highly automated.

[0193] six, Figure 6 This is a flowchart illustrating a specific application of an embodiment of the present invention in the field of financial risk control.

[0194] The financial risk control case process (600) here connects all the aforementioned concepts and processes, demonstrating an end-to-end application example. Case scenario: A bank's credit approval department uses the system of this invention to assist loan officers in evaluating a loan application from a small or micro enterprise.

[0195] Step 1: Deviation Measurement (corresponding) Figure 2 The bank's senior risk control expert team has built a "Financial Risk Control Cognition Calibration Test Range Database." One of the test cases (202) is: "Applicant Company: Company A, established for 2 years, asset-light technology company, annual revenue of 1 million, profit of 200,000, no collateral, but holds 3 invention patents, and the legal representative has a good credit record. Applying for a loan of 300,000 for R&D investment." In this step, this test case (202) can be generated semi-automatically, expanding the coverage. All data has undergone strict anonymization before processing.

[0196] The system inputs this test case (202) into a general, untuned, basic AI model (101). The model generates a rough answer (111) that might be: "Assessment: Company A has low annual revenue and no collateral, and is of high risk. Loan approval is not recommended." This answer reflects the conservative judgment of the general model based on conventional financial indicators, which fails to understand the special characteristics of "asset-light technology companies" and the potential value of "invention patents".

[0197] The gold standard truth value (205) provided by the bank's experts (which may involve resolving conflicts among multiple risk control experts) is: "Comprehensive assessment: Although Company A operates on a light-asset, unsecured model, it complies with the national policy of supporting small and micro-sized technology innovation enterprises. Its invention patents are core intangible assets, the legal entity has good credit, and the loan is intended for R&D investment, which will help the company grow. The risk lies in its short establishment period and potentially unstable cash flow. Recommendation: Approve a loan of 200,000 yuan, with additional terms requiring the company to provide regular R&D progress reports and cash flow statements. Risk level: Medium."

[0198] The deviation vector calculation unit compares the rough answer (111) and the gold standard truth (205) to generate an authoritative deviation vector (112), the core content of which may include: {"error_type":"OmissionError","detail":"Omitted assessment of intangible assets (patents) and policy orientation","correction_instruction":{"action":"REWRITE_SECTION","content":"(Contains the core logic in the expert truth)"}}.

[0199] Step 2: Calibration module training (corresponding) Figure 3 ). Thousands of similar bias vectors (112) were used to train the bank’s “risk control cognitive calibration module”, namely the cognitive calibration module (102). The module gradually learned the bank’s internal risk control philosophy and was trained to predict confidence levels.

[0200] Step 3: Real-time online calibration (corresponding to...) Figure 4 A junior loan officer, B, receives a new, similar user request (110) (i.e., a new application case). He enters the case into the system.

[0201] The system first obtains a similar, perhaps overly conservative, rough answer (111) from the basic AI model (101).

[0202] Then, the system packages (user request (110), rough answer (111)) and sends it to the trained "risk control cognition calibration module" (i.e., cognition calibration module (102)).

[0203] Based on its learned knowledge, the calibration module predicts that the base model's response may also make the mistake of "ignoring the value of intangible assets," and outputs a predicted bias vector (112) containing correction instructions and a calibration confidence score.

[0204] If the application case matches the pattern in the training data (such as a typical bad debt risk), the confidence level will be displayed as medium or high, and the system will automatically output an evaluation opinion with a clear reason for rejection as the calibrated answer (114).

[0205] If it is a completely new, ambiguous case that falls between good and bad, the confidence level may be low. In this case, the system may trigger a human-machine collaboration process, pushing the case to a senior credit officer for human decision-making, and recording this process for calibration of the model's next incremental learning.

[0206] Finally, the system outputs a calibrated answer (114) to loan officer B. This opinion becomes a high-quality reference for loan officer B to make a final decision. At the same time, because the correction process is traceable, the bank's compliance department can trace the formation process of the decision recommendation at any time to ensure that it complies with internal risk control policies.

[0207] VII. System Hybrid Maintenance Strategy and Long-Term Evolution Capability

[0208] To address the inevitable technological iteration challenges faced by large and complex systems, this invention proposes a pragmatic "hybrid maintenance strategy" to ensure the system's vitality and competitiveness during long-term evolution. This clearly defines the fundamental difference between this invention and simple one-off "patching" solutions. The implementation of this strategy relies on... Figure 7 The system exhibits scalability and a continuous learning closed-loop mechanism.

[0209] The entire system ecosystem (700) operates around a core “model governance and continuous learning subsystem” (710).

[0210] High-frequency agile calibration loop (inner loop 701)

[0211] This is the system's daily operation mode. Manual calibration results from the human-machine collaboration / online expert pool (418), audit confirmation data from the continuous learning queue (416), and new test cases (202) regularly updated by the cognitive calibration target range database (103) are all integrated into an "incremental training dataset" (711). Once triggering conditions (such as data volume reaching a threshold or timed triggering) are met, the model governance and continuous learning subsystem (710) automatically invokes a low-cost "incremental training process" (712) to rapidly iterate on the existing cognitive calibration module (102), generating a new version of the cognitive calibration module 102'. After a series of automated regression tests, the new version is "seamlessly hot-deployed" to the production environment. This cycle can be very frequent (e.g., daily or weekly), ensuring the system's rapid response to changes in business knowledge.

[0212] Low-frequency basic model drift monitoring and generational upgrade (external circulation 702)

[0213] This invention does not view the calibration module in isolation, but rather as part of the overall AI system. The model governance and continuous learning subsystem (710) also includes a "basic model drift monitoring unit" (720). This unit uses a highly stable "core capability benchmark set" (721) independent of the calibration range to periodically (e.g., quarterly or semi-annually) evaluate the basic AI model (101) as the "basic instrument." This benchmark set does not test specific, volatile knowledge points, but rather evaluates its more fundamental capabilities, such as the rigor of logical reasoning, the ability to understand long texts, and the accuracy of mathematical calculations.

[0214] Performance drift detection: If the base model drift monitoring unit (720) detects a sustained and significant decline in the score of the base AI model (101) on the core capability benchmark set (721) (i.e., so-called “model drift”), or when external market intelligence indicates the emergence of a new generation of base models (as shown in the figure, a new base model (730), such as a version upgrade of DeepSeek) and shows an overwhelming advantage on the benchmark set, the monitoring unit will issue a “base model upgrade recommendation” to the system administrator.

[0215] Generational Upgrade and Recalibration Process: Once the administrator confirms the upgrade, the system does not simply replace the model. The Model Governance and Continuous Learning Subsystem (710) initiates a controlled “recalibration” process. First, the “model drift” base AI model (101) or the new base model (730) is connected to the system. Then, the data from the entire cognitive calibration target range database (103) is called to perform a comprehensive “rereading” of all test cases (202) to fully expose its own “system bias” characteristics that are different from the old model. This process generates a completely new set of triplet datasets (test cases (202), new coarse answers (111), new bias vectors (112)). Finally, using this new dataset, the cognitive calibration module is “completely retrained” rather than simply incrementally learned, resulting in a new cognitive calibration module 102 specifically tailored for the new base model.

[0216] By employing this hybrid maintenance strategy of "high-frequency software patches (calibration modules) + low-frequency hardware upgrades (basic models)," this invention ensures the system's agility in responding to daily changes and its strategic foresight in dealing with technological waves, avoiding the potential for technological rigidity or redundancy caused by long-term reliance on a single basic model.

[0217] VIII. Data Privacy Protection Module (310)

[0218] To address the diverse and stringent data compliance requirements faced in different industry applications, this invention designs a multi-layered, configurable data privacy and compliance protection module. For example... Figure 8 The data privacy protection module (310) shown in the invention allows data to pass through a series of configurable security checkpoints before entering the core processing logic of the system.

[0219] Strategy execution at the data entry layer (3101)

[0220] All incoming data, whether from real-time user requests (110) or historical data used for training, must first pass through a policy enforcement point. This enforcement point applies different security policies based on the source and type of the data.

[0221] Data anonymization and pseudonymization module (3102)

[0222] This is the core privacy protection module enabled by default, also referred to as the data privacy protection module (310) in this invention. It has a built-in updatable rule set capable of identifying and processing various types of sensitive information, such as PII (Personally Identifiable Information) and PHI (Protected Health Information). Its processing method is configurable.

[0223] Remove: Directly delete sensitive fields.

[0224] Replace: Replace with a meaningless placeholder (such as "[Name]").

[0225] Pseudonymization: Replacing the original ID with an irreversible, randomly generated pseudonym allows the same user to be associated in multiple interactions, but their true identity cannot be traced back.

[0226] Deployment architecture selection and data boundary control (3103)

[0227] The system of this invention can be flexibly deployed in different physical and logical environments to meet customers' requirements for control over data sovereignty.

[0228] Cloud-based SaaS deployment (3104): Suitable for customers with low data sensitivity or who trust the security capabilities of cloud service providers. In this mode, anonymized data is processed on the public cloud.

[0229] Private Deployment (3105): Suitable for industries such as finance, government, and military. The entire system, including all modules and databases, is installed in the customer's own data center, and no data leaves its physical boundaries.

[0230] Federated learning architecture (3106): Suitable for scenarios where multiple untrusted entities want to jointly train a calibration model (e.g., multiple hospitals want to jointly train a medical diagnostic calibration model, but cannot share their respective patient data). In this architecture, a central "federated aggregation server" (3107) is only responsible for distributing the global model and aggregating encrypted parameter updates. Actual "local training and data processing" (3108) occurs in the private environments of each participant, thus achieving knowledge sharing without data sharing.

[0231] Access control and auditing throughout the entire process (3109)

[0232] Internally, all data access is subject to strict role-based access control (RBAC). For example, the model training task can only read the anonymized triple database (104), while the expert annotation platform can only access test cases relevant to its task (202). All operations, including who accessed what data and when, are logged in an immutable audit log, providing a complete chain of evidence for compliance audits.

[0233] IX. Technical Engineering Implementation Plan and Application Areas

[0234] To demonstrate the engineering feasibility, economy, and applicability of this invention, this section provides a detailed engineering implementation plan.

[0235] 1. Recommended hardware configuration and resource requirements analysis

[0236] The system architecture of this invention exhibits a significant asymmetry in its hardware resource requirements, which is its core economic advantage. For the inference of the basic AI model (101), this solution utilizes existing resources without adding extra overhead, and through a paradigm of "calibration" rather than "fine-tuning," it greatly improves the reusability and return on investment of expensive large-scale model hardware across multiple domains.

[0237] The cost of training and inference for the cognitive calibration module (102) is extremely low. During training, a typical calibration module (1-50 million parameters) typically requires only 1 to 8 hours to completely retrain on 100,000 data points using a single mid-range GPU, while routine incremental learning tasks take only 5 to 30 minutes. This represents a cost reduction of 3 to 4 orders of magnitude compared to the hundreds of GPU weeks required for fine-tuning the base model. During inference, the computational overhead of real-time calibration is negligible, achieving low latency of 20 to 50 milliseconds on CPUs and reducing latency to less than 10 milliseconds on economical inference GPUs.

[0238] 2. Recommended software technology stack and system integration

[0239] This system is highly compatible with mainstream cloud-native and microservice architectures. Backend services are recommended to use Python's FastAPI or Sanic framework. Model training is based on PyTorch or TensorFlow, with version management using tools such as MLflow. The database can use a combination of PostgreSQL (for cognitive calibration range database) and MongoDB / Elasticsearch (for triplet database). All components should be packaged as Docker images, orchestrated and scaled elastically using Kubernetes, while the human-machine collaboration and annotation platform can be deeply integrated with open-source tools such as Label Studio.

[0240] 3. Quantitative Cost-Benefit Analysis (TCO-ROI)

[0241] Enterprise customers adopting this invention will experience a fundamental change in the cost structure of their AI applications. Compared to traditional solutions that require fine-tuning and maintenance for each domain, this invention saves over 95% of computing power costs and over 80% of manual maintenance time through one-time deviation mapping and extremely low-cost calibration module iterations. Simultaneously, higher reliability, agility, and stronger compliance will collectively drive a fundamental improvement in return on investment (ROI).

[0242] 4. Cross-industry application scenarios

[0243] Medical diagnosis and adjunctive therapy: The calibration module can learn and correct deviations between the base model and the latest clinical guidelines and specific hospital medication practices.

[0244] Legal services and compliance review: The calibration module can be "calibrated" by the knowledge system of top law firms to ensure that the generated legal documents are accurate in wording and logically rigorous.

[0245] High-end manufacturing and engineering design: The calibration module can be embedded with enterprise-specific design specifications and proprietary material parameters to perform real-time design verification.

[0246] Education and Personalized Tutoring: It can train students with a very lightweight calibration module, enabling AI tutoring to match the curriculum and individual cognitive levels.

[0247] 5. "AI Calibration Service"

[0248] Third-party "AI Quality and Trustworthiness Certification" service: As an authoritative "quality certification body" for AI applications, it provides independent "calibration certification" to endorse the reliability and security of AI applications.

[0249] Subscription-available “Expert Calibration Packages” and “Enterprise Knowledge Packages”: Selling plug-and-play lightweight cognitive calibration modules for specific expert mindsets or enterprise knowledge bases (102).

[0250] AI security and trust solutions provider: Focusing on providing full-stack "AI trust assurance" solutions for fields with zero tolerance for safety issues, such as autonomous driving and smart healthcare, to help their products pass regulatory scrutiny.

[0251] In summary, this invention, by pioneering the engineering paradigm of "cognitive instrument calibration," systematically addresses the multiple core challenges faced by current large-scale artificial intelligence models in professional applications, including cost, efficiency, reliability, scalability, and security. The mapping, training, and real-time calibration methodology proposed in this invention, centered on the "deviation vector," along with the system architecture incorporating dynamic decision-making and continuous learning capabilities, not only fundamentally disrupts the existing "fine-tuning" paradigm technically but also demonstrates significant value and feasibility in engineering practice and commercial applications.

[0252] It should be emphasized that the above descriptions are merely some preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art should understand that these embodiments are merely illustrative and not exhaustive. Based on the core ideas and technical solutions disclosed in this invention, any equivalent changes, modifications, or substitutions that can be easily conceived by those skilled in the art without departing from the spirit and scope of the present invention, such as using different network structures for the cognitive calibration module, using different data formats for the deviation vector, or applying the present invention to other professional fields not explicitly listed herein, should all be considered to fall within the protection scope of this invention.

Claims

1. An artificial intelligence modeling method based on cognitive instrument drift calibration, characterized in that, Includes the following steps: Under the premise of selecting a large-scale artificial intelligence basic model with unchanged internal parameter weights, the output deviation of the basic model in a specific professional field is systematically measured to form a deviation dataset; Based on the aforementioned bias dataset, a lightweight cognitive calibration module independent of the base model is trained, enabling the cognitive calibration module to predict a bias vector for correcting the original answer based on the user request and the original answer generated by the base model for that request. Upon receiving a real-time user request, the system obtains the original answer generated by the base model, uses the trained cognitive calibration module to predict the corresponding deviation vector, and corrects the original answer based on the deviation vector to generate a calibrated answer.

2. The method according to claim 1, characterized in that, The steps for systematically measuring output deviations include: A cognitive calibration range containing multiple standardized test cases is constructed through a human-machine collaborative approach. For the test cases, the original answer of the basic model is obtained, and the gold standard truth value is obtained from one or more domain experts. When there are conflicting inputs from multiple experts, an authoritative truth value is determined through a preset consensus mechanism. By comparing the original answer with the authoritative truth value, the deviation vector is calculated and generated as part of the deviation dataset.

3. The method according to claim 2, characterized in that, The deviation vector is a structured data object that contains at least one or more of the following information: Error types used to identify the nature of the difference between the original answer and the authoritative truth value; Error location information used to indicate the specific location of the difference in the original answer; Correction instructions containing specific operation types and operation content; Corrections used to enhance traceability are based on source information.

4. The method according to claim 1, characterized in that, In the step of training the cognitive calibration module, the module is further trained to output a calibration confidence score that represents the uncertainty of the current prediction while predicting the bias vector. Furthermore, the step of correcting based on the deviation vector includes: Based on the calibration confidence score, the calibration process is graded, and the grading process includes at least one of the following: automatically applying corrections, marking for manual review, or triggering a safety rollback strategy.

5. The method according to claim 1, characterized in that, The method also includes a continuous learning step that automatically uses calibration cases that have been manually intervened or reviewed in real-time applications, as well as updated test cases in the cognitive calibration range, to incrementally learn or retrain the cognitive calibration module.

6. An artificial intelligence modeling system based on cognitive instrument drift calibration, characterized in that, include: A base model interface is configured to communicate with a large AI base model that keeps its internal parameter weights unchanged in order to obtain the raw answer to the user's request. A standalone, lightweight cognitive calibration module, deployed decoupled from the base model, is trained to predict a bias vector for correcting the original answer based on the user request and the original answer. A real-time calibration engine is configured to, upon receiving a real-time user request, coordinate the base model interface to obtain the original answer and call the cognitive calibration module to obtain the predicted bias vector. The engine then performs a correction operation on the original answer based on the content of the bias vector to generate a calibrated answer.

7. The system according to claim 6, characterized in that, It also includes a data management and deviation mapping subsystem, which contains: A cognitive calibration range database for storing standardized test cases; A data privacy protection module for anonymizing or pseudonyming data before bias mapping and training data processing; A deviation vector calculation unit is used to generate a structured deviation vector containing error type, error location, and correction instructions.

8. The system according to claim 6, characterized in that, The cognitive calibration module is also configured to output a calibration confidence score in parallel when predicting the bias vector; and the real-time calibration engine includes a dynamic decision unit configured to determine, based on the calibration confidence score, to perform different subsequent processing procedures such as automatic correction, label review, or triggering human-machine collaboration.

9. The system according to claim 6, characterized in that, The system also includes a model governance and continuous learning subsystem, which is configured as follows: In response to the inflow of new data, incremental learning of the cognitive calibration module is automatically triggered; as well as The performance drift of the base model is monitored periodically using a core capability benchmark set, and a recalibration process for the entire system is triggered when a significant performance change is detected or a better base model emerges.

10. The system according to claim 6, characterized in that, The system is configured to support multiple deployment architectures, which are selected from at least one of cloud SaaS deployment, private deployment, or federated learning deployment that supports collaborative training of the cognitive calibration module with data from multiple parties without leaving the local machine.

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