AI application method for customizing industry fine-tuning large model and agent application development integration

Abstract

The invention discloses an AI application method integrating customized industry fine-tuning large models and agent application development, which comprises the following steps: S1, a dynamic knowledge graph construction module: generating an industry feature sensitive training data set through a knowledge density gradient sampling algorithm, performing data cleaning by adopting a hybrid verification mechanism of an expert rule engine and a weak supervision verification network; s2, a parameter constraint fine tuning system; s3, automatically generating an engine by the intelligent agent; s4, a cross-industry migration adaptation mechanism; and S5, dynamically feeding back and strengthening the system. A knowledge density gradient sampling algorithm is provided, a dynamic updating strategy based on kernel density estimation is combined, the problem of industry data long-tail distribution is effectively solved, the low-frequency key feature sampling probability is increased by 3.8-5.7 times, and the weekly updating rate of the knowledge graph exceeds 92%. Through a hybrid mechanism of an expert rule engine and a weak supervision verification network, high-precision cleaning (the accuracy rate is 94.1%) of multi-modal data is realized and is improved by 17.8% compared with a traditional single verification method.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and specifically to an AI application method that integrates customized industry fine-tuning large models with intelligent agent application development. Background Art

[0002] Domain-Specific Large Models are AI models that are deeply optimized for specific industry needs based on the architecture of general large models (such as GPT and BERT). Their core features include:

[0003] Domain Focus: Through training with industry-specific data (such as medical records, financial reports, legal documents, etc.), we have a deep understanding of industry terminology, business processes and knowledge systems.

[0004] Traditional AI industry applications have the following problems:

[0005] Static knowledge limitations: Traditional knowledge graphs are not updated quickly and cannot adapt to dynamic changes in the industry;

[0006] Long-tail distribution bias: low-frequency features (such as rare cases and special working conditions) are not learned enough, and the sampling coverage is less than 40%;

[0007] Catastrophic forgetting: fine-tuning all parameters leads to loss of general knowledge (retention rate < 60%);

[0008] Inefficient cross-industry migration: The cost of reusing industry-specific models is high, and accuracy drops by >30%;

[0009] Complex agent development: low efficiency in converting natural language instructions into executable code (manual configuration takes > 45 minutes per task);

[0010] System iteration lag: Traditional model updates require downtime for maintenance, resulting in service interruptions of >6 hours. Summary of the Invention

[0011] The purpose of the present invention is to provide an AI application method that integrates a customized industry fine-tuning large model with intelligent agent application development to solve the problems raised in the above background technology.

[0012] To achieve the above objectives, the present invention provides the following technical solution: an AI application method integrating a customized industry fine-tuning large model with intelligent agent application development, comprising the following steps:

[0013] S1. Dynamic knowledge graph construction module: Generates industry-specific sensitive training datasets through a knowledge density gradient sampling algorithm, while also using a hybrid verification mechanism combining an expert rule engine and a weakly supervised verification network for data cleaning.

[0014] S2, parameter constraint fine-tuning system: dynamically adjusts the fine-tuning strength based on a three-dimensional weight constraint matrix, where the three dimensions include domain similarity, parameter sensitivity, and knowledge importance;

[0015] S3, Agent Automatic Generation Engine: This engine uses a behavior tree metaprogramming framework to implement dynamic task orchestration and compiles natural language instructions into executable code through an intent-action converter.

[0016] S4, cross-industry migration and adaptation mechanism: This includes a domain feature distillation network and a transfer learning bridge to support cross-industry knowledge transfer and skill reuse;

[0017] S5. Dynamic feedback reinforcement system: Continuous system optimization is achieved through incremental reinforcement learning strategies and online hot update technology.

[0018] The knowledge density gradient sampling algorithm includes:

[0019] Generate industry knowledge distribution heat map based on kernel density estimation;

[0020] Apply the long-tail compensation function:

[0021]

[0022] Where d is the density of knowledge nodes, D max is the maximum node density in the industry knowledge graph, and λ is the industry sensitivity coefficient;

[0023] Adaptive Monte Carlo sampling is used to increase the sampling probability of low-frequency key features by 2.3-5.7 times.

[0024] The method for constructing the three-dimensional weight constraint matrix is:

[0025] Domain similarity dimension: Calculate the KL divergence of industry feature distribution through comparative learning;

[0026] Parameter sensitivity dimension: evaluates parameter importance based on gradient magnitude;

[0027] Knowledge importance dimension: An improved PageRank algorithm is used to sort graph nodes.

[0028] The implementation of the behavior tree metaprogramming framework includes:

[0029] Develop DAG task flow description language;

[0030] Build a dynamic node priority scheduler;

[0031] A conflict resolution algorithm for multi-agent collaboration.

[0032] The domain feature distillation network includes:

[0033] Knowledge compression layer: uses the self-attention mechanism to extract the core model features of the industry;

[0034] Adversarial Distillation Module: Domain-invariant learning achieved through gradient reversal layers;

[0035] Industry adapters: Dynamically adjust the feature vector mapping space.

[0036] The dynamic feedback reinforcement system specifically includes:

[0037] Dual-channel monitoring network: real-time collection of model inference results and agent execution trajectories;

[0038] Experience replay pool: uses a priority sampling strategy to store interaction data;

[0039] Online distillation mechanism: achieving interruption-free knowledge transfer through teacher-student network architecture.

[0040] Industry compliance sandbox system:

[0041] Dynamic rule engine: integrated with 200+ industry standard detection rule base;

[0042] Ethical Constraint Visual Programming Interface: supports drag-and-drop configuration of ethical boundaries;

[0043] Compliance stress testing module based on adversarial samples.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] This paper proposes a knowledge density gradient sampling algorithm, combined with a dynamic update strategy based on kernel density estimation, to effectively address the long-tail distribution problem of industry data. The sampling probability of low-frequency key features is increased by 3.8-5.7 times, and the weekly update rate of the knowledge graph exceeds 92%. Through a hybrid mechanism of an expert rule engine and a weakly supervised verification network, high-precision cleaning of multimodal data (94.1% accuracy) is achieved, a 17.8% improvement over traditional single verification methods.

[0046] This invention uses a dynamic fine-tuning technique based on a three-dimensional weight constraint matrix (domain similarity / parameter sensitivity / knowledge importance) to precisely infuse domain knowledge while retaining over 85% of general knowledge. Experiments have shown that in medical imaging diagnostics, the accuracy of rare disease recognition has increased from 51% to 89%, while retaining 95.2% of basic anatomical features, overcoming the catastrophic forgetting problem associated with traditional full-parameter fine-tuning.

[0047] This invention, through the collaborative design of a behavior tree metaprogramming framework and an intent-to-action converter, supports one-click compilation of natural language instructions into executable code, reducing task configuration time from 45 minutes to 7 minutes, and achieving a code generation accuracy of 92.7%. This technology significantly lowers the barrier to entry for AI application development and enables dynamic orchestration of complex business logic (such as medical diagnosis and industrial quality inspection processes).

[0048] The present invention combines a domain feature distillation network with a transfer learning bridge architecture to effectively extract core industry pattern features and support zero-shot cross-industry knowledge transfer. Actual data shows that when a medical model is transferred to the manufacturing sector, the equipment failure prediction accuracy is retained at 88%, training efficiency is increased by 3.2 times, and retraining costs are reduced by 70%.

[0049] This invention is based on incremental reinforcement learning strategy and online hot update technology to achieve seamless iteration of model parameters and dynamic expansion of knowledge base, ensuring zero service interruption. In photovoltaic operation and maintenance scenarios, the response speed of new fault modes is accelerated by 2.3 times, and the model update delay is compressed from 48 hours to real-time effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0052] See also Figure 1 The present invention provides a technical solution: an AI application method that integrates a customized industry fine-tuning large model with intelligent agent application development, comprising the following steps:

[0053] S1. Dynamic knowledge graph construction module: Generates industry-specific sensitive training datasets through a knowledge density gradient sampling algorithm, while also using a hybrid verification mechanism combining an expert rule engine and a weakly supervised verification network for data cleaning.

[0054] S2, parameter constraint fine-tuning system: dynamically adjusts the fine-tuning strength based on a three-dimensional weight constraint matrix, where the three dimensions include domain similarity, parameter sensitivity, and knowledge importance;

[0055] Dimensional definition of the three-dimensional weight constraint matrix: Domain similarity dimension: Use contrastive learning to calculate the KL divergence (Kullback-Leibler Divergence) of the industry feature distribution:

[0056]

[0057] Among them, P is the source domain distribution, Q is the target domain distribution;

[0058] Parameter sensitivity dimension: Evaluate parameter importance based on gradient magnitude:

[0059]

[0060] L is the loss function, θ i is the i-th parameter, N is the number of samples in the training batch, and is used for the average calculation of the gradient amplitude;

[0061] Knowledge importance dimension: The improved PageRank algorithm is used to sort the knowledge graph nodes. The core formula is:

[0062]

[0063] Among them, d is the damping coefficient, which represents the probability of the user continuing to jump randomly, In(v i ) indicates pointing to v i The node set, PR(v i ) and PR(v j ) represent the nodes v i and v j The PageRank value of a page reflects its importance in the knowledge graph;

[0064] Dynamic adjustment strategy: Automatically adjust the fine-tuning intensity based on the three-dimensional weight matrix:

[0065] High domain similarity-enhanced fine-tuning;

[0066] High parameter sensitivity - limiting the amplitude of parameter updates;

[0067] High knowledge importance - prioritize preserving core features.

[0068] S3, Agent Automatic Generation Engine: This engine uses a behavior tree metaprogramming framework to implement dynamic task orchestration and compiles natural language instructions into executable code through an intent-action converter.

[0069] S4, cross-industry migration and adaptation mechanism: This includes a domain feature distillation network and a transfer learning bridge to support cross-industry knowledge transfer and skill reuse;

[0070] S5. Dynamic feedback reinforcement system: Continuous system optimization is achieved through incremental reinforcement learning strategies and online hot update technology.

[0071] The knowledge density gradient sampling algorithm includes:

[0072] Use Kernel Density Estimation (KDE) to model the node density in the industry knowledge graph and generate a heat map to visualize the knowledge distribution;

[0073] Apply the long-tail compensation function:

[0074] In order to increase the sampling probability of low-frequency key features, a compensation function is introduced to adjust the weight:

[0075]

[0076] Where d is the density of knowledge nodes, D max is the maximum node density in the industry knowledge graph, and λ is the industry sensitivity coefficient;

[0077] Adaptive Monte Carlo sampling is used to increase the sampling probability of low-frequency key features by 2.3-5.7 times.

[0078] Hybrid authentication mechanism:

[0079] Expert rule engine: pre-defined industry expert rule base (such as logical constraints, semantic associations) to filter out noisy data.

[0080] Weakly supervised verification network: Combined with semi-supervised learning models (such as label propagation algorithms), secondary corrections are made to the rule engine results.

[0081] The implementation of the behavior tree metaprogramming framework includes:

[0082] Developed a DAG task flow description language to define task dependencies based on a directed acyclic graph (DAG), supporting both parallel and serial execution logic;

[0083] Build a dynamic node priority scheduler to evaluate the resource requirements and priorities of task nodes in real time and dynamically adjust the execution order;

[0084] Implement a conflict resolution algorithm for multi-agent collaboration, and solve resource competition problems through game theory models (such as Nash equilibrium) in multi-agent collaboration scenarios.

[0085] Intent-Action Converter: uses the Transformer architecture to parse natural language instructions and generate an abstract syntax tree (AST);

[0086] Mapping the AST to executable code (like Python or a domain-specific language)

[0087] The domain feature distillation network includes:

[0088] Knowledge compression layer: Use the self-attention mechanism to extract the core pattern features of the industry. The formula is:

[0089]

[0090] Q, K, V (query, key, value matrix), the matrix generated by linear transformation of input features, used to calculate attention weights, d k (key vector dimension), function: scaling factor to prevent the dot product value from being too large and causing gradient instability.

[0091] Adversarial Distillation Module: This module implements domain-invariant learning through a gradient reversal layer, eliminates inter-domain differences, and improves feature generalization.

[0092] Industry adapter: Dynamically adjust the feature vector mapping space to adapt to the characteristics of the target industry.

[0093] Transfer learning bridge: Designing a hybrid architecture with shared parameter layers and domain-specific layers;

[0094] The maximum mean difference (MMD) loss is used to minimize the distribution difference between domains, which is an indicator to measure the difference between the distributions of two domains. The smaller the value, the closer the distribution.

[0095] The dynamic feedback reinforcement system specifically includes:

[0096] Dual-channel monitoring network: collects model inference results (performance indicators) and agent execution trajectories (behavior logs) in real time;

[0097] Experience replay pool: uses a priority sampling strategy to store interaction data, and the sampling probability is positively correlated with the importance of the data;

[0098] Online Distillation Mechanism: Teacher-Student Network Architecture:

[0099] The teacher model generates knowledge distillation signals, and the student model updates parameters in real time to avoid service interruptions.

[0100] The loss function of the online distillation mechanism is

[0101] L distill =αL task +(1-α)L KD

[0102] L KD is the knowledge distillation loss, and α is the balance coefficient.

[0103] Industry compliance sandbox system:

[0104] Dynamic rule engine: integrated with 200+ industry standard detection rule base;

[0105] Ethical Constraint Visual Programming Interface: supports drag-and-drop configuration of ethical boundaries;

[0106] Compliance stress testing module based on adversarial samples.

[0107] Example 1: Dynamic Knowledge Graph Construction (Medical Industry)

[0108] Scenario: Build a medical knowledge graph to support the training of disease diagnosis models.

[0109] Data source: Public medical datasets (such as MIMIC-III), containing 500,000 patient records and 100,000 medical literature abstracts.

[0110] Knowledge density gradient sampling effect:

[0111] Apply the long-tail compensation function (λ=2.5,D max =0.8), the sampling probability of rare diseases (such as porphyria) increased from 0.3% to 1.4%, and the number of covered cases increased by 4.6 times.

[0112] After the weakly supervised verification network was corrected, the data noise rate was reduced from 12% to 3.5%.

[0113] As shown in the table below, the hybrid mechanism filters explicit noise through expert rules, and the weakly supervised network corrects implicit errors, with the overall performance significantly better than that of a single method.

[0114] method Noise rate Key feature retention rate Expert rules engine only 8.2％ 72％ Only weakly supervised networks 6.5％ 85％ Hybrid authentication mechanism 3.5％ 93％

[0115] Results: The F1 score of the trained diagnostic model in the rare disease identification task increased from 0.62 to 0.78.

[0116] Example 2: Parameter Constraint Fine-tuning System (Financial Risk Control Field)

[0117] Scenario: Fine-tuning a large pre-trained model (such as GPT-3) for credit risk assessment.

[0118] Three-dimensional weight constraint matrix adjustment:

[0119] Domain similarity: The KL divergence between the source domain (e-commerce user behavior) and the target domain (credit records) is 1.2, triggering medium-intensity fine-tuning.

[0120] Parameter sensitivity: Screen out the top 10% highly sensitive parameters (gradient amplitude > 0.05) and limit their update amplitude to 30%.

[0121] Knowledge importance: By improving PageRank (d=0.85), the weight of core features (such as "repayment history") is increased by 40%.

[0122] Results: The fine-tuned model improves the AUC from 0.81 to 0.89 in the credit default prediction task, and reduces the false positive rate by 18%.

[0123] Example 3: Intelligent Agent Automatic Generation Engine (Intelligent Manufacturing Scenario)

[0124] Scenario: Generate industrial robot control code to perform "material sorting-assembly-quality inspection" assembly line tasks.

[0125] Behavior tree metaprogramming framework implementation:

[0126] The DAG task flow description language defines 15 parallel nodes, and the dynamic scheduler reduces task completion time by 25%.

[0127] The conflict resolution algorithm (Nash equilibrium) resolves robot path conflicts, reducing the number of conflicts by 72%.

[0128] Intent-Action Converter Performance:

[0129] Natural language instructions (such as "sort red parts first") are compiled into executable code with 93% accuracy.

[0130] Example 4: Cross-industry Migration Adaptation (Finance → Retail)

[0131] Scenario: Migrating the financial customer segmentation model to the retail user profiling task.

[0132] Effect of domain feature distillation network:

[0133] The knowledge compression layer (self-attention mechanism) extracts core features and compresses the feature dimension from 1024 to 256, retaining 95% of the information.

[0134] The adversarial distillation module (MMD loss) reduces the domain distribution difference from 0.45 to 0.12.

[0135] Post-migration performance: The accuracy of the retail user purchase prediction model increased from 68% (without migration) to 82%.

[0136] Example 5: Dynamic Feedback Reinforcement System (E-commerce Recommendation Scenario)

[0137] Scenario: Real-time optimization of personalized recommendation strategies.

[0138] Incremental reinforcement learning strategy:

[0139] The dual-channel monitoring network collects 100,000 pieces of user interaction data per hour, and the priority sampling strategy increases the utilization rate of high-value data (click rate > 5%) by 50%.

[0140] Online hot update effect:

[0141] The teacher-student architecture (α=0.6) enables non-interruption updates, shortening the model iteration cycle from 24 hours to 2 hours.

[0142] The CTR (click-through rate) of the recommendation system increased from 2.1% to 3.8%.

[0143] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

Claims

1. An AI application method that integrates customized industry fine-tuning large models with intelligent agent application development, characterized by: The following steps are involved: S1. Dynamic knowledge graph construction module: Generates industry-specific sensitive training datasets through a knowledge density gradient sampling algorithm, while also using a hybrid verification mechanism combining an expert rule engine and a weakly supervised verification network for data cleaning. S2, parameter constraint fine-tuning system: dynamically adjusts the fine-tuning strength based on a three-dimensional weight constraint matrix, where the three dimensions include domain similarity, parameter sensitivity, and knowledge importance; S3, Agent Automatic Generation Engine: This engine uses a behavior tree metaprogramming framework to implement dynamic task orchestration and compiles natural language instructions into executable code through an intent-action converter. S4, cross-industry migration and adaptation mechanism: This includes a domain feature distillation network and a transfer learning bridge to support cross-industry knowledge transfer and skill reuse; S5. Dynamic feedback reinforcement system: Continuous system optimization is achieved through incremental reinforcement learning strategies and online hot update technology.

2. The AI application method for integrating a customized industry fine-tuning large model with intelligent agent application development according to claim 1, characterized in that: The knowledge density gradient sampling algorithm includes: Generate industry knowledge distribution heat map based on kernel density estimation; Apply the long-tail compensation function: Where d is the density of knowledge nodes, D max is the maximum node density in the industry knowledge graph, and λ is the industry sensitivity coefficient; Adaptive Monte Carlo sampling is used to increase the sampling probability of low-frequency key features by 2.3-5.7 times.

3. The AI application method for integrating customized industry fine-tuning large models with intelligent agent application development according to claim 1 is characterized by: The method for constructing the three-dimensional weight constraint matrix is: Domain similarity dimension: Calculate the KL divergence of industry feature distribution through comparative learning; Parameter sensitivity dimension: evaluates parameter importance based on gradient magnitude; Knowledge importance dimension: An improved PageRank algorithm is used to sort graph nodes.

4. The AI application method for integrating customized industry fine-tuning large models with intelligent agent application development according to claim 1 is characterized by: The implementation of the behavior tree metaprogramming framework includes: Develop DAG task flow description language; Build a dynamic node priority scheduler; A conflict resolution algorithm for multi-agent collaboration.

5. The AI application method for integrating customized industry fine-tuning large models with intelligent agent application development according to claim 1 is characterized by: The domain feature distillation network includes: Knowledge compression layer: uses the self-attention mechanism to extract the core model features of the industry; Adversarial Distillation Module: Domain-invariant learning achieved through gradient reversal layers; Industry adapters: Dynamically adjust the feature vector mapping space.

6. The AI application method for integrating customized industry fine-tuning large models with intelligent agent application development according to claim 1 is characterized by: The dynamic feedback reinforcement system specifically includes: Dual-channel monitoring network: real-time collection of model inference results and agent execution trajectories; Experience replay pool: uses a priority sampling strategy to store interaction data; Online distillation mechanism: achieving interruption-free knowledge transfer through teacher-student network architecture.

7. The AI application method for integrating customized industry fine-tuning large models with intelligent agent application development according to claim 1 is characterized by: Also includes industry compliance sandbox system: Dynamic rule engine: integrated with 200+ industry standard detection rule base; Ethical Constraint Visual Programming Interface: supports drag-and-drop configuration of ethical boundaries; Compliance stress testing module based on adversarial samples.

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