Industry scenario-oriented large model empowerment private knowledge base construction and integration method

Abstract

The application relates to the technical field of large model industry application, in particular to a large model empowerment private knowledge base construction and integration method for industry scenarios, which comprises the following steps: multi-source heterogeneous data structured preprocessing, industry large model entity relationship extraction and knowledge graph construction, unified semantic space vector mapping, bidirectional knowledge alignment, privacy isolation knowledge integration interface layer construction, and collaborative reasoning scheduling whole process design; a large model fine-tuning, full life cycle incremental updating and hierarchical permission control mechanism are matched, high-precision construction of an industry private knowledge base and deep safety integration of a large model are realized, large model reasoning illusion is greatly reduced, response efficiency and business matching degree of a scenario reasoning are improved, and the method has good industry expansibility and engineering landing practicability.

Description

Technical Field

[0001] This invention relates to the field of large-scale model industry application technology, specifically to a method for building and integrating private knowledge bases with large-scale models for industry scenarios. Background Technology

[0002] With the rapid iteration and industrialization of large language model technology, it has demonstrated excellent performance in general natural language understanding, content generation, and logical reasoning scenarios. However, in deep applications within vertical industries, general-purpose large models generally suffer from core pain points such as insufficient industry-specific knowledge reserves, poor adaptability to business scenarios, susceptibility to illusions in reasoning results, and difficulty in ensuring the security of private business data. Therefore, deeply integrating large models with industry-specific private knowledge bases, leveraging private industry knowledge to enhance the professional capabilities of large models, and simultaneously relying on the semantic understanding capabilities of large models to achieve efficient retrieval and application of private knowledge, has become the core technological direction for the current industrial application of large models, and related technical solutions are gradually being made public.

[0003] Among existing related technologies, the invention patent with publication number CN117972046A, entitled "A Method for Integrating Private Knowledge Bases Based on Large Models," mainly includes the following core steps: First, determine the main purpose and usage scenarios of the private knowledge base, and build a private knowledge base system including a private database, a Transformer model, and a natural language processing interface; second, collect various types of data and store them in the private database, perform preprocessing such as cleaning and denoising on the data to obtain text data, classify the text data, and store it in different storage modules; then, use vector database technology to vectorize the text data, build an index, and store it in the vector database; next, train and optimize the Transformer model using the collected text data, and deploy the trained model to the knowledge base system; finally, build a natural language processing interface to convert the user's natural language query into a vector query, complete the matching retrieval in the vector database, and return the result to the user. This solution, through the combination of the Transformer model and the vector database, realizes basic retrieval and natural language interaction of the private knowledge base, which can, to a certain extent, make up for the problem of insufficient professional knowledge reserves of general large models and improve the query efficiency of private knowledge.

[0004] However, the technical solutions described in the aforementioned comparative documents still have significant technical shortcomings in in-depth applications targeting vertical industry scenarios. Firstly, these solutions only process and transform text-format data, failing to cover the diverse and heterogeneous private business data commonly found in industry scenarios, such as structured tables, images, audio, and video. They lack corresponding multimodal semantic extraction and standardized knowledge transformation capabilities, making it impossible to construct knowledge from all private business data within the industry, resulting in insufficient coverage and completeness of the knowledge base. Secondly, these solutions do not construct dedicated knowledge graphs based on the pre-defined business ontology rules of the industry scenario, failing to accurately extract industry business entities, attributes, and business relationships. Furthermore, they do not perform industry scenario weight adaptation and tagging management for knowledge units, resulting in low business relevance and scenario matching of knowledge units. This directly leads to insufficient business targeting of search results, failing to meet the refined business needs of industry scenarios. Third, the proposed solution fails to consider the consistency and alignment between the semantics of private knowledge and the semantic space of the large model base. The vectorized representation of private knowledge and the semantics of the large model base inherently exhibit distributional discrepancies, making semantic misunderstandings and illusions highly likely when the large model combines private knowledge for reasoning. Furthermore, the solution lacks privacy isolation and tiered access control mechanisms for the bidirectional integration process between the large model and the private knowledge base, posing a risk of direct leakage of sensitive industry private data. The bidirectional call process also lacks end-to-end refined control and compliance auditing capabilities. Fourth, the solution only implements the serial execution of vector retrieval and model reasoning, failing to construct a collaborative reasoning scheduling mechanism between the large model and the private knowledge base. This prevents optimal allocation of reasoning tasks, making it difficult to guarantee the response efficiency and accuracy of scenario-based reasoning. Additionally, the solution lacks a full lifecycle incremental update process for the private knowledge base, making it unable to adapt to the dynamic iteration of industry business data and rules. Consequently, the knowledge base suffers from poor timeliness, scalability, and practicality for engineering implementation.

[0005] Given the numerous shortcomings of existing technologies, there is an urgent need to develop a method for building and integrating private knowledge bases using large models tailored to industry scenarios. This method would address the technical problems of poor adaptability to multi-source heterogeneous data in industries, low matching degree of knowledge bases to industry scenarios, insufficient semantic alignment between large models and private knowledge, weak privacy and security control capabilities, low inference scheduling efficiency, and insufficient dynamic iteration capabilities of knowledge bases. The goal would be to achieve standardized and highly accurate construction of industry private knowledge bases, secure and deep integration with large model bases, and efficient and stable execution of scenario-based collaborative reasoning. Summary of the Invention

[0006] The purpose of this invention is to provide a method for building and integrating private knowledge bases with large models for industry scenarios, in order to solve the problems mentioned in the background art, such as poor adaptability of private knowledge bases to industry scenarios, insufficient processing capabilities for multi-source heterogeneous data, low semantic alignment between large models and private knowledge, weak privacy and security control, and insufficient reasoning efficiency.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A method for building and integrating private knowledge bases using large-scale models tailored to industry scenarios includes the following steps: S1: Collect multi-source heterogeneous private business data corresponding to the target industry scenario, perform structured preprocessing such as cleaning, deduplication, and entity normalization on the private business data, and split it into several standardized knowledge units; S2: Based on the preset business ontology rules of the target industry scenario, the entity, attribute and business relationship are extracted through the large model after industry fine-tuning, the industry-specific knowledge graph is constructed, and a set of knowledge nodes with scenario tags is generated; S3: Based on the semantic encoding layer of the large model, perform a unified vector space mapping on the standardized knowledge units and knowledge graph nodes to generate a knowledge embedding vector set with consistent dimensions; S4: Perform bidirectional knowledge alignment between the large model base and the private knowledge base, complete the consistency calibration between the semantic space of the private knowledge and the semantic space of the large model base, and generate the aligned private knowledge base; S5: Construct a knowledge integration interface layer with a privacy isolation mechanism, and establish a bidirectional calling channel between the large model inference engine and the aligned private knowledge base through the interface layer; S6: Receive the reasoning request for the target industry scenario, and based on the business tag of the reasoning request, perform collaborative reasoning scheduling between the large model and the private knowledge base, and output the corresponding scenario-based reasoning result.

[0008] Preferably, in step S2, when generating the set of knowledge nodes with scene tags, the industry scene weight is calculated for each knowledge node, and the calculation formula is as follows: ; In the formula, For the first The industry scenario weight of each knowledge node This represents the normalized word frequency of the node in the target industry scenario corpus. This represents the degree of relevance between the node and the core business entities in the industry. A pre-defined score is assigned to the business importance of this node. The preset weight allocation coefficients are used, and they satisfy... Based on the scene weights, knowledge nodes are sorted and filtered to generate a set of knowledge nodes with scene tags.

[0009] Preferably, in step S3, when performing the unified vector space mapping, an industry-scenario contrastive learning strategy is used to fine-tune the semantic encoding layer of the large model, and the loss function for fine-tuning is: ; In the formula, For the embedding vector of anchor knowledge unit, This is the embedding vector of the positive sample knowledge unit. This is the embedding vector of the negative sample knowledge unit. The cosine similarity function is used. For temperature coefficient, The number of negative samples is used to generate a knowledge embedding vector set with consistent dimensions based on the fine-tuned semantic encoding layer.

[0010] Preferably, in step S4, the specific steps for performing bidirectional knowledge alignment are as follows: S41: Based on the semantic space of the large model base, perform global distribution calibration on the embedding vector of private knowledge to reduce the distribution deviation between private knowledge and the semantics of the large model base. S42: Calculate the scene adaptation similarity between the private knowledge unit embedding vector and the corresponding semantic embedding vector of the large model base. The calculation formula is as follows: ; In the formula, For the embedding vector of private knowledge units, This is the embedding vector corresponding to the semantics of the large model base. It is the sigmoid activation function. This refers to the frequency deviation of the occurrence of this knowledge unit in the target industry scenario; S43: For private knowledge units with similarity below a preset threshold, perform semantic adaptation fine-tuning on the large model base to complete bidirectional knowledge alignment.

[0011] Preferably, in step S5, the knowledge integration interface layer with privacy isolation mechanism uses a differential privacy mechanism to perform noise addition processing on the knowledge embedding vector. The noise addition calculation formula is as follows: ; In the formula, The knowledge embedding vector after adding noise. Embed vectors for the original knowledge. With a mean of 0 and a variance of Gaussian distribution, For the global sensitivity of knowledge embedding vectors, For the preset privacy budget, The default fault tolerance probability is set; the interface layer completes bidirectional data interaction between the large model and the private knowledge base based on the noisy knowledge embedding vector.

[0012] Preferably, in step S6, when performing collaborative inference scheduling, an optimization model is constructed with the goal of minimizing the total inference latency. The model expression is: ; ; In the formula, For total inference delay, The number of reasoning subtasks. For the first The large computational load of the model corresponding to each inference subtask The computational power allocation coefficients for the large model inference engine corresponding to this subtask. This represents the computational cost of retrieving the private knowledge base corresponding to this subtask. The retrieval computational power allocation coefficient for this subtask corresponding to the private knowledge base. The decision variables are used for task scheduling; collaborative reasoning scheduling is performed based on the solution results of the optimization model.

[0013] As a preferred option, it also includes a full lifecycle incremental update step for the private knowledge base, specifically including: S71: Collect new private business data of the target industry scenario in real time according to the preset incremental collection cycle. The new private business data includes new business documents, business system transaction data, new business Q&A records, and updated industry rule files. S72: Perform structured preprocessing on the newly collected private business data in the same way as step S1, split it into new standardized knowledge units, and perform deduplication verification on the new standardized knowledge units to remove knowledge units that are duplicated with the existing private knowledge base. S73: Through the semantic encoding layer fine-tuned in step S3, perform unified vector space mapping on the newly added standardized knowledge units after verification to generate new knowledge embedding vectors; S74: Calculate the semantic overlap between the newly added knowledge embedding vector and the existing knowledge embedding vector in the private knowledge base. When the semantic overlap is lower than the preset overlap threshold, mark the corresponding newly added knowledge unit as a knowledge unit to be added to the database. S75: Perform bidirectional knowledge alignment in step S4 for the knowledge unit to be added to the database, and synchronously update the knowledge nodes, attributes and relationships of the industry-specific knowledge graph to complete the writing of the knowledge unit to be added to the database. S76: Perform version snapshot retention on the updated content of the private knowledge base, generate the version number and change log of the corresponding updated version, and complete the entire incremental update process.

[0014] Preferably, in step S1, the multi-source heterogeneous private business data includes industry business data in text format, structured table format, image format, audio format, and video format, and its structured preprocessing process specifically includes the following steps: S11: For private business data in text format, perform sentence segmentation, word segmentation, stop word removal, technical terminology normalization, and business entity alignment processing to split and generate standardized knowledge units that meet the preset length threshold. S12: For private business data in structured table format, first extract the table's row and column headers, cell content, and table note information. Then, perform table semantic parsing through the large model to convert the cell content associated with rows and columns into structured semantic triples, and then split them to generate standardized knowledge units. S13: For private business data in image format, first perform optical character recognition to extract the text content in the image, then use the multimodal coding layer of the large model to extract industry-specific entity features, process features, and structural features in the image, and fuse the text content and feature information to convert it into textual semantic content, and decompose it to generate standardized knowledge units. S14: For private business data in audio and video formats, first perform speech-to-text processing to generate initial text content, and then perform professional terminology correction, tone word removal, and invalid segment filtering through the industry-fine-tuned large model to split the corrected text content into standardized knowledge units. S15: Perform global deduplication and entity normalization verification on standardized knowledge units generated from all data formats to complete the entire structured preprocessing process.

[0015] Preferably, in step S2, the fine-tuning construction process of the large model after industry fine-tuning specifically includes the following steps: S21: Construct an industry-specific fine-tuning training dataset, collect labeled business corpora, standardized business rule documents, historical business question-and-answer pairs, and industry-specific entity and relation labeled data for the target industry scenario, perform format standardization, noise data removal, positive and negative sample balancing on the training dataset, and divide it into training set, validation set and test set; S22: Perform pre-trained base adaptation, load the general large model pre-trained base, fix all parameters of the backbone transformer network of the pre-trained base, and only unlock the trainable parameters of the semantic encoding layer, entity relationship extraction head, and scene classification head. S23: Perform phased supervised fine-tuning. The first phase is based on industry-annotated entity and relationship data, and fine-tunes the entity and relationship extraction head until the extraction accuracy reaches the preset accuracy threshold. The second phase is based on industry business corpus and business rule documents, and performs comparative learning fine-tuning on the semantic encoding layer to complete industry semantic space adaptation. The third phase is based on business question-and-answer pairs data, and fine-tunes the scenario classification head to complete business scenario label adaptation. S24: Perform fine-tuning effect verification. Based on the test set, perform indicator verification on the fine-tuned large model for entity extraction accuracy, relation extraction recall, and scene classification accuracy. When all indicators reach the preset threshold, the model verification is completed. S25: Execute parameter fixation on the large model that has passed the verification to generate a large model with industry fine-tuning, which is used for the extraction of entities, attributes and business relationships in step S2.

[0016] Preferably, in step S5, the construction and operation of the knowledge integration interface layer with privacy isolation mechanism specifically includes the following steps: S51: Construct the basic communication framework of the interface layer, establish a communication channel that supports both RESTful and gRPC protocols, adapt the communication protocols of the large model inference engine and the private knowledge base respectively, and complete the construction of the bidirectional data transmission link. S52: Construct a privacy isolation module at the interface layer. Based on a preset privacy budget and fault tolerance probability, a differential privacy mechanism is used to add noise to the knowledge embedding vector output by the private knowledge base. At the same time, sensitive information is desensitized on the request content input by the large model inference engine to block the direct exposure of private raw data to the outside world. S53: Construct a hierarchical permission control module for the interface layer, pre-set a multi-level user permission system, configure corresponding private knowledge base access scope, knowledge granularity permissions, and large model reasoning function call permissions for different permission levels, and establish a mapping relationship between user identity and permission level; S54: Execute access authentication request. When receiving an inference request, first perform identity verification on the request initiator. If the verification is successful, match the corresponding user's permission level, generate an access token with the corresponding permission, and configure the knowledge base access scope and large model call permission for this request based on the access token. S55: Performs end-to-end control of bidirectional calls, performs permission verification on every bidirectional call operation between the large model inference engine and the private knowledge base based on access tokens, intercepts call requests that exceed the permission scope, and performs end-to-end data forwarding and protocol conversion on legitimate call operations; S56: Construct a compliance audit module for the interface layer, which performs full-process logging of all bidirectional call operations. The logged content includes the identity of the request initiator, the request time, the call content, the access scope, and a summary of the reasoning result. At the same time, it generates a compliance audit report according to a preset period, thus completing the full-process control of the interface layer.

[0017] Compared with the prior art, the beneficial effects of the present invention are: (1) This invention achieves efficient transformation of industry-specific business data into a standardized and structured knowledge base through a full-process design of multi-source heterogeneous data standardization processing, industry-specific knowledge graph construction, and unified semantic space mapping. This invention completes the accurate extraction of entities and business relationships through a phased fine-tuning of the industry-specific large model, achieves the screening and optimization of knowledge nodes by combining industry scenario weights, and completes the unified vectorized representation of knowledge units through an industry comparative learning strategy. This solves the technical problems of fragmented industry-specific knowledge, inconsistent multi-source data formats, poor adaptability of industry business ontology, and inconsistent semantic representation. The constructed private knowledge base can accurately match the business rules and scenario requirements of the target industry, providing a standardized and highly accurate knowledge foundation for the deep integration and collaborative application of the large model and private knowledge.

[0018] (2) This invention completes the consistency calibration of the semantic space of private knowledge and the semantic space of the large model base through a two-way knowledge alignment mechanism. Combined with scene adaptation similarity calculation, it realizes targeted semantic adaptation of low-matching knowledge units, eliminates the semantic distribution deviation between private knowledge and the large model base, and significantly reduces the hallucination rate in the process of reasoning by combining the large model with private knowledge. At the same time, through the design of the knowledge integration interface layer with differential privacy isolation mechanism, this invention realizes the security protection of private sensitive data under the premise of ensuring the availability of knowledge semantics. Combined with hierarchical permission control and full-link compliance audit mechanism, it completes the fine-grained control of the two-way calling process between the large model and the private knowledge base. It solves the technical problems of insufficient semantic adaptability, high privacy and security risks, weak calling control capabilities, and poor cross-system compatibility in the integration process of the large model and the private knowledge base, and realizes the secure, stable, and deep integration of the large model base and the private knowledge base.

[0019] (3) This invention constructs a collaborative reasoning scheduling optimization model with the goal of minimizing total inference latency, achieving optimal task scheduling for large-scale model reasoning and private knowledge base retrieval, effectively reducing the total latency of scenario-based reasoning, and improving the response efficiency and scenario matching accuracy of reasoning requests. Simultaneously, this invention designs a full lifecycle incremental update process for the private knowledge base, enabling real-time processing of new business data, knowledge verification, and database updates, while simultaneously completing dynamic iteration of knowledge graph and semantic alignment, ensuring real-time matching of knowledge base content with industry business rules and business data, and solving the problem of insufficient timeliness of private knowledge. The phased supervised fine-tuning strategy of freezing the backbone network adopted in this invention, while retaining the general capabilities of large-scale model pre-training, achieves precise improvement in industry-specific task processing capabilities, avoiding catastrophic forgetting during the fine-tuning process. The overall technical solution forms a closed-loop process from knowledge construction and integration adaptation to reasoning application and dynamic iteration, possessing good industry scenario scalability and engineering practicality. Attached Figure Description

[0020] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are explained in detail together with the embodiments of the invention, but do not constitute a limitation thereof.

[0021] Figure 1 Main flowchart for building and integrating a private knowledge base to enable the large-scale model of this invention for industry scenarios; Figure 2 A flowchart illustrating the construction of a knowledge integration interface layer with a privacy isolation mechanism for this invention; Figure 3 This is a diagram showing the construction module for fine-tuning the large model after industry-specific fine-tuning of this invention; Figure 4 This is a flowchart illustrating the incremental update process of the private knowledge base of this invention throughout its entire lifecycle. Detailed Implementation

[0022] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0023] like Figures 1-3 As shown, this invention provides a method for constructing and integrating private knowledge bases with large-scale models for industry scenarios. The overall process includes multi-source heterogeneous data collection and structured preprocessing, industry-specific knowledge graph construction, unified vector space mapping, bidirectional knowledge alignment, establishment of a knowledge integration interface layer with privacy isolation, collaborative reasoning scheduling, and supporting full lifecycle incremental updates, multimodal data processing, phased large-scale model fine-tuning, and hierarchical permission control mechanisms. The implementation method is described in detail below with specific steps.

[0024] S1 collects multi-source heterogeneous private business data corresponding to the target industry scenario, performs structured preprocessing such as cleaning, deduplication, and entity normalization on the private business data, and splits it into several standardized knowledge units.

[0025] Multi-source heterogeneous private business data includes industry business data in text, structured table, image, audio, and video formats. The structured preprocessing process is as follows: S11 performs sentence segmentation, word segmentation, stop word removal, professional terminology normalization, and business entity alignment processing on private business data in text format, splitting it into standardized knowledge units that meet preset length thresholds.

[0026] S12 targets private business data in structured table format. First, it extracts the table header and column header cell content and annotation information. Then, it performs table semantic parsing through a large model to convert the cell content associated with rows and columns into structured semantic triples, and then splits them to generate standardized knowledge units.

[0027] For private business data in image format, S13 first performs optical character recognition to extract the text content in the image, and then extracts industry-specific entity features, process features, and structural features in the image through the multimodal coding layer of a large model. The text content and feature information are then fused and converted into textual semantic content, and then split to generate standardized knowledge units.

[0028] S14 first performs speech-to-text processing to generate initial text content for private business data in audio and video formats. Then, it uses a large model with industry fine-tuning to perform professional terminology correction, removal of interjections, and filtering of invalid segments. The corrected text content is then broken down into standardized knowledge units.

[0029] S15 performs global deduplication and entity normalization verification on standardized knowledge units generated from all data formats, completing the entire structured preprocessing process.

[0030] S2 uses preset business ontology rules based on the target industry scenario to extract entity attributes and business relationships through a large model with industry fine-tuning, constructs an industry-specific knowledge graph, and generates a set of knowledge nodes with scenario tags.

[0031] When generating a set of knowledge nodes with scene tags, the industry scene weight is calculated for each knowledge node. The calculation formula is as follows: ; In the formula, For the first The industry scenario weight of each knowledge node This represents the normalized word frequency of the node in the target industry scenario corpus. This represents the degree of relevance between the node and the core business entities in the industry. A pre-defined score is assigned to the business importance of this node. The preset weight allocation coefficients are used, and they satisfy... Knowledge nodes are sorted and filtered based on scene weights to generate a set of knowledge nodes with scene tags.

[0032] The fine-tuning process for the large model after industry adjustments is as follows: S21 Constructs an industry-specific fine-tuning training dataset, collects labeled business corpus of target industry scenarios, standardized business rule documents, historical business Q&A, and industry-specific entity and relationship labeled data, performs format standardization, noise data removal, positive and negative sample balancing on the training dataset, and divides it into training set, validation set, and test set.

[0033] S22 performs pre-trained base adaptation, loads a general large model pre-trained base, fixes all parameters of the backbone transformer network of the pre-trained base, and only unlocks the trainable parameters of the semantic encoding layer entity relationship extraction head and scene classification head.

[0034] S23 executes phased supervised fine-tuning. The first phase, based on industry-annotated entity and relationship data, fine-tunes the entity-relation extraction head until the extraction accuracy reaches a preset accuracy threshold. The second phase, based on industry business corpora and business rule documents, performs contrastive learning fine-tuning on the semantic encoding layer to achieve industry semantic space adaptation. The third phase, based on business question-and-answer pairs, fine-tunes the scenario classification head to achieve business scenario label adaptation.

[0035] S24 performs fine-tuning effect verification. Based on the test set, the fine-tuned large model is tested for indicators such as entity extraction accuracy, relationship extraction recall, and scene classification accuracy. When all indicators reach the preset threshold, the model verification is completed.

[0036] S25 executes parameter fixing on the large model that has passed the verification, and generates a large model with industry fine-tuning, which is used for entity attribute and business relationship extraction.

[0037] S3, based on a large model semantic encoding layer, performs a unified vector space mapping on standardized knowledge units and knowledge graph nodes to generate a knowledge embedding vector set with consistent dimensions.

[0038] When performing unified vector space mapping, an industry-specific contrastive learning strategy is used to fine-tune the semantic encoding layer of the large model. The loss function for fine-tuning is: ; In the formula, For the embedding vector of anchor knowledge unit, This is the embedding vector of the positive sample knowledge unit. This is the embedding vector of the negative sample knowledge unit. The cosine similarity function is used. For temperature coefficient, The number of negative samples. A knowledge embedding vector set with consistent dimensions is generated based on the fine-tuned semantic encoding layer.

[0039] S4 performs bidirectional knowledge alignment between the large model base and the private knowledge base, completes consistency calibration between the semantic space of the private knowledge and the semantic space of the large model base, and generates the aligned private knowledge base.

[0040] The specific steps for performing two-way knowledge alignment are as follows: S41 uses the semantic space of a large model base to perform global distribution calibration on the embedding vectors of private knowledge, thereby reducing the distribution deviation between private knowledge and the semantics of the large model base.

[0041] S42 calculates the scene adaptation similarity between the private knowledge unit embedding vector and the corresponding semantic embedding vector of the large model base. The calculation formula is as follows: ; In the formula, For the embedding vector of private knowledge units, This is the embedding vector corresponding to the semantics of the large model base. It is the sigmoid activation function. This refers to the frequency deviation of the occurrence of this knowledge unit in the target industry scenario.

[0042] S43 performs semantic adaptation fine-tuning on the large model base for private knowledge units with similarity below a preset threshold, completing bidirectional knowledge alignment.

[0043] S5 constructs a knowledge integration interface layer with a privacy isolation mechanism, and establishes a bidirectional calling channel between the large model inference engine and the aligned private knowledge base through the interface layer.

[0044] The knowledge integration interface layer with privacy isolation mechanism is constructed and operated as follows: S51 constructs the basic communication framework of the interface layer, establishes a communication channel supporting both RESTful and gRPC protocols, adapts to the communication protocols of the large model inference engine and the private knowledge base respectively, and completes the construction of a two-way data transmission link.

[0045] S52 constructs a privacy isolation module at the interface layer. Based on a preset privacy budget and fault tolerance probability, it employs a differential privacy mechanism to add noise to the knowledge embedding vector. The noise addition calculation formula is as follows: ; In the formula, The noisy knowledge embedding vector, Embed vectors for the original knowledge. The mean is 0 and the variance is Gaussian distribution, For the global sensitivity of knowledge embedding vectors, For the preset privacy budget, This is a preset fault tolerance probability. The interface layer completes bidirectional data interaction between the large model and the private knowledge base based on the noisy knowledge embedding vector.

[0046] S53 constructs a hierarchical permission control module for the interface layer, pre-sets a multi-level user permission system, configures corresponding private knowledge base access scope, knowledge granularity permissions, and large model reasoning function call permissions for different permission levels, and establishes a mapping relationship between user identity and permission level.

[0047] S54 executes access authentication requests. When receiving inference requests, it first performs identity verification on the request initiator. If the verification is successful, it matches the corresponding user's permission level, generates an access token with the corresponding permission, and configures the knowledge base access scope and large model call permissions for this request based on the access token.

[0048] S55 performs end-to-end control of bidirectional calls, performing permission verification on every bidirectional call operation between the large model inference engine and the private knowledge base based on access tokens, blocking call requests that exceed the permission scope, and performing end-to-end data forwarding and protocol conversion on legitimate call operations.

[0049] S56 constructs a compliance audit module for the interface layer, which performs full-process logging of all bidirectional call operations. The logged content includes the identity of the request initiator, the request time, the call content, the access scope, the reasoning result summary, and generates a compliance audit report according to a preset period, thus completing the full-process control of the interface layer.

[0050] S6 receives reasoning requests from the target industry scenario, performs collaborative reasoning scheduling between the large model and the private knowledge base based on the business tags of the reasoning request, and outputs the corresponding scenario-based reasoning results.

[0051] When performing collaborative inference scheduling, an optimization model is constructed with the goal of minimizing the total inference latency. The model expression is as follows: ; ; In the formula, For total inference delay, The number of reasoning subtasks. For the first The large computational load of the model corresponding to each inference subtask The computational power allocation coefficients for the large model inference engine corresponding to this subtask. This represents the computational cost of retrieving the private knowledge base corresponding to this subtask. The retrieval computational power allocation coefficient for this subtask corresponding to the private knowledge base. These are the decision variables for task scheduling. Collaborative reasoning scheduling is performed based on the solution results of the optimization model.

[0052] This method also includes a full lifecycle incremental update step for the private knowledge base, specifically including: S71 collects new private business data of the target industry scenario in real time according to the preset incremental collection cycle. The new private business data includes new business documents, business system transaction data, new business Q&A records, and updated industry rule files.

[0053] S72 performs the same structured preprocessing as S1 on the newly collected private business data, splits it into new standardized knowledge units, and performs deduplication verification on the new standardized knowledge units to remove knowledge units that are duplicated with the existing private knowledge base.

[0054] S73 uses the fine-tuned semantic encoding layer in S3 to perform a unified vector space mapping on the newly added standardized knowledge units after verification, and generates new knowledge embedding vectors.

[0055] S74 calculates the semantic overlap between the newly added knowledge embedding vector and the existing knowledge embedding vector in the private knowledge base. When the semantic overlap is lower than the preset overlap threshold, the corresponding newly added knowledge unit is marked as a knowledge unit to be added to the database.

[0056] S75 performs bidirectional knowledge alignment with S4 on the knowledge units to be added to the database, synchronously updating the knowledge node attributes and relationships of the industry-specific knowledge graph, and completing the writing of the knowledge units to be added to the database.

[0057] S76 performs version snapshot retention on the updated content of the private knowledge base, generates the version number and change log of the corresponding updated version, and completes the entire incremental update process.

[0058] Example 1: Construction and Integration of a Private Knowledge Base for Bank Credit Approval Scenarios The target industry scenario for this embodiment is bank credit approval. The multi-source heterogeneous private business data collected includes credit approval policy documents in text format, customer credit reports in text format, approval history records in table format, post-loan inspection reports in image format, and credit approval training audio format.

[0059] The S1 structured preprocessing is performed on text-based credit approval policy documents. This involves sentence segmentation, word segmentation, stop word removal, professional terminology normalization, and business entity alignment, resulting in standardized knowledge units of 50-200 characters each. For table-based approval history records, row and column headers, cell content, and table notes are extracted, parsed into semantic triples using a large model, and then split into standardized knowledge units. For image-based post-loan inspection reports, optical character recognition (OCR) is used to extract text, followed by multimodal coding to extract entity and process features. These features are then fused and split into standardized knowledge units. For audio-based training content, speech-to-text conversion is performed, followed by industry-adjusted large model error correction and filtering, and then split into standardized knowledge units. Global deduplication and entity normalization validation are performed on all knowledge units to complete the preprocessing.

[0060] The S2 algorithm constructs a knowledge graph, pre-setting business ontology rules including credit approval process rules and customer risk rating rules. Entities such as customer name, debt-to-asset ratio, and overdue records are extracted from a finely tuned industry-specific model. Attributes include debt-to-asset ratio values ​​and overdue days. Business relationships include customer loan applications and corresponding loan approval processes. When generating a set of knowledge nodes with scenario tags, the industry scenario weight of each knowledge node is calculated, pre-setting... , , A certain knowledge point is the debt-to-equity ratio. , , Substitute into the formula: ; Based on weighted sorting and filtering, a set of knowledge nodes with scene tags is generated.

[0061] The process of building a large-scale model after industry fine-tuning involves collecting credit approval annotation corpus, approval rule documents, historical question-and-answer pairs, and entity relationship annotation data, and dividing them into training, validation, and test sets. A general large-scale model base is loaded, core parameters are fixed, and the semantic encoding layer entity relationship extraction head and scenario classification head are unlocked. Fine-tuning is performed in stages: the first stage uses entity relationship data to fine-tune the extraction head; the second stage uses business corpus for comparative learning to fine-tune the semantic encoding layer; and the third stage uses question-and-answer pairs to fine-tune the scenario classification head. Once the validation metrics are met, the parameters are fixed, generating the industry-fine-tuned large-scale model.

[0062] S3 vector mapping is performed, and a contrastive learning strategy is used to fine-tune the semantic encoding layer. In the loss function calculation, [the following is taken]: Embed vectors for knowledge units related to debt-to-equity ratio. Embed vectors for knowledge units related to debt-to-income ratio. , , Two negative samples and The similarity scores are 0.2 and 0.3 respectively. Substituting these into the loss function simplifies the calculation: ; Generate a knowledge embedding vector set with consistent dimensions based on the fine-tuned semantic encoding layer.

[0063] Perform S4 bidirectional knowledge alignment, first globally distributed calibration, then calculate scene adaptation similarity, for a specific private knowledge unit. Corresponding to the large model base The cosine similarity is 0.7. , Substitute into the formula: ; If the preset threshold is 0.5, then semantic adaptation fine-tuning is performed on the knowledge unit to complete the alignment.

[0064] The S5 interface layer is built, establishing a communication channel between RESTful and gRPC. In the privacy isolation module, preset... , , Calculate the variance of the noise addition: ; Embedding vectors of original knowledge Noise addition The tiered access control module has three preset access levels: ordinary loan officers can only access basic customer information, approval supervisors can access all risk information, and system administrators have full access. Upon requesting access, identity is verified, an access token is generated, and the entire process is monitored, with logs maintained and audit reports generated.

[0065] Execute S6 collaborative inference scheduling, receive credit approval inference requests, and label the business as customer risk rating. Number of inference subtasks. Subtask 1 involves generating an initial risk assessment for the large model. Subtask 2 involves retrieving historical similar cases from the knowledge base. , The optimization model was solved to obtain... , Minimize total latency. The execution schedule outputs the risk rating results.

[0066] Example 2: Construction and Integration of a Private Knowledge Base for Automobile Manufacturing Quality Inspection Scenarios The target industry scenario in this embodiment is automobile manufacturing quality inspection. The multi-source heterogeneous private business data collected includes quality inspection standard document text format, inspection record table format, defective part image format, and inspection operation video format.

[0067] S1 preprocessing is performed: text format documents are split into standardized knowledge units; table format detection records are parsed into semantic triples and then split; image format defect components are extracted and split after text and features are extracted; video format operation tutorials are converted into text, corrected, and then split; global deduplication and normalization are performed.

[0068] The S2 algorithm constructs a knowledge graph, pre-setting business ontology rules including inspection process rules and defect classification rules. It extracts entities such as bolt torque, body clearance, and defect type; attributes such as torque value and clearance size; and business relationships such as inspection procedures corresponding to defect types. It calculates the scenario weights of knowledge nodes, pre-setting... , , A certain knowledge point is the vehicle body clearance. Substitute: ; Sort and filter to generate a set of labeled nodes.

[0069] The industry fine-tuning of the large model construction involves collecting quality inspection and annotation corpus, standard documents, historical question-and-answer pairs, entity relationship data, fine-tuning in stages, and solidifying after verification.

[0070] Perform S3 vector mapping, compare and learn the loss function calculation, and take... Embed vectors for vehicle body gaps. Embed a vector for the door assembly gap. , , The negative sample similarity is 0.15 and 0.25, and the simplified calculation loss is about 0.008.

[0071] Performing S4 knowledge alignment, the cosine similarity of a certain private knowledge unit is 0.75. , , A threshold of 0.5 allows for fine-tuning of alignment.

[0072] Execute the S5 interface layer and perform noise calculation. The variance is approximately 29.35, and noise has been added. Hierarchical access control is implemented: quality inspectors access basic testing standards, quality engineers access defect analysis data, and administrators have full access. Access is controlled after authentication, and logs are maintained.

[0073] Perform S6 collaborative reasoning; the reasoning request is defect cause analysis; subtask. Subtask 1: Major Model Analysis Subtask 2 Knowledge Base Retrieval Solving for the given information, we can obtain the following results: With a delay of 2, output the analysis results.

[0074] Example 3: Construction and Integration of a Private Knowledge Base for Hospital Clinical Auxiliary Diagnosis Scenarios The target industry scenario for this embodiment is hospital clinical auxiliary diagnosis. The multi-source heterogeneous private business data collected includes clinical diagnosis and treatment guidelines in text format, electronic medical records in table format, medical image reports in image format, and expert consultation audio format.

[0075] Perform S1 preprocessing, split text guidelines into units, parse table medical records into triples, extract text features from image reports, convert audio consultations to text for error correction, and perform global deduplication and normalization.

[0076] The S2 algorithm is used to construct a knowledge graph, with pre-defined ontology rules including diagnostic process rules and disease classification rules. Entities are extracted, such as symptom names, examination indicators, and disease names; attributes include indicator values ​​and symptom duration; and business relationships include symptom prompts, disease examinations, and confirmed diseases. Scenario weights are calculated. , , A certain knowledge node is troponin. Substitute: ; Sort and filter to generate a set of nodes.

[0077] The industry fine-tunes the large model construction, collects clinical annotation corpus, guideline documents, historical question-and-answer pairs entity relationship data, fine-tunes in stages, and solidifies after verification.

[0078] Perform S3 vector mapping and compare the learning loss calculation. This is the troponin embedding vector. This is the embedding vector for myocardial enzyme profile. , , The negative sample similarity is 0.18 and 0.22, with a loss of approximately 0.007.

[0079] Performing S4 knowledge alignment, the cosine similarity of a certain private knowledge unit is 0.72. , , A threshold of 0.5 allows for fine-tuning of alignment.

[0080] Execute the S5 interface layer and perform noise calculation. , The variance is approximately 19.57, and noise has been added. Hierarchical access control is implemented: resident physicians access basic diagnostic guidelines, attending physicians access detailed medical record data, and chief physicians have full access. Access is controlled after authentication, and logs are maintained.

[0081] Perform S6 collaborative reasoning, with the reasoning request being to assist in the diagnosis of the cause of chest pain; subtask. Subtask 1: Large Model Generation for Differential Diagnosis Subtask 2: Retrieve similar cases from the knowledge base. Solving for the given information, we can obtain the following results: With a delay of 2 seconds, output auxiliary diagnostic results.

[0082] This invention achieves efficient transformation of industry-specific business data into a standardized, structured knowledge base through a comprehensive design encompassing multi-source heterogeneous data standardization processing, industry-specific knowledge graph construction, and unified semantic space mapping. It precisely extracts entities and business relationships using a phased, fine-tuned industry-wide model, filters and optimizes knowledge nodes by incorporating industry scenario weights, and achieves unified vectorized representation of knowledge units through an industry-specific comparative learning strategy. This solves the technical problems of fragmented industry-specific knowledge, inconsistent multi-source data formats, poor adaptability to industry business ontology, and inconsistent semantic representation. The constructed private knowledge base can accurately match the business rules and scenario requirements of the target industry, providing a standardized and highly accurate knowledge foundation for the deep integration and collaborative application of the large model and private knowledge.

[0083] This invention achieves consistency calibration between the semantic space of private knowledge and the semantic space of the large model base through a bidirectional knowledge alignment mechanism. Combined with scene-adaptive similarity calculation, it enables targeted semantic adaptation of low-matching knowledge units, eliminating semantic distribution deviations between private knowledge and the large model base, and significantly reducing the incidence of illusions during reasoning processes involving the large model and private knowledge. Simultaneously, through a knowledge integration interface layer design with a differential privacy isolation mechanism, this invention ensures the security protection of private sensitive data while guaranteeing the availability of knowledge semantics. Combined with hierarchical permission control and a full-link compliance audit mechanism, it achieves refined control over the bidirectional call process between the large model and the private knowledge base. This solves the technical problems of insufficient semantic adaptability, high privacy and security risks, weak call control capabilities, and poor cross-system compatibility during the integration of the large model and the private knowledge base, achieving secure, stable, and deep integration between the large model base and the private knowledge base.

[0084] This invention constructs a collaborative reasoning scheduling optimization model with the goal of minimizing total inference latency. It achieves optimal task scheduling for large-scale model inference and private knowledge base retrieval, effectively reducing the total latency of scenario-based inference and improving the response efficiency and scenario matching accuracy of inference requests. Simultaneously, this invention designs a full lifecycle incremental update process for the private knowledge base, enabling real-time processing, knowledge verification, and database updates of newly added business data. It also synchronously completes dynamic iteration of knowledge graph and semantic alignment, ensuring real-time matching of knowledge base content with industry business rules and data, and solving the problem of insufficient timeliness of private knowledge. The phased supervised fine-tuning strategy of freezing the backbone network adopted in this invention achieves precise improvement in industry-specific task processing capabilities while retaining the general capabilities of large-scale model pre-training. It avoids catastrophic forgetting during the fine-tuning process. The overall technical solution forms a closed-loop process from knowledge construction and integration adaptation to inference application and dynamic iteration, possessing good industry scenario scalability and engineering practicality.

[0085] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for building and integrating private knowledge bases using large-scale models tailored to industry scenarios, characterized by: Includes the following steps: S1: Collect multi-source heterogeneous private business data corresponding to the target industry scenario, perform structured preprocessing such as cleaning, deduplication, and entity normalization on the private business data, and split it into several standardized knowledge units; S2: Based on the preset business ontology rules of the target industry scenario, the entity, attribute and business relationship are extracted through the large model after industry fine-tuning, the industry-specific knowledge graph is constructed, and a set of knowledge nodes with scenario tags is generated; S3: Based on the semantic encoding layer of the large model, perform a unified vector space mapping on the standardized knowledge units and knowledge graph nodes to generate a knowledge embedding vector set with consistent dimensions; S4: Perform bidirectional knowledge alignment between the large model base and the private knowledge base, complete the consistency calibration between the semantic space of the private knowledge and the semantic space of the large model base, and generate the aligned private knowledge base; S5: Construct a knowledge integration interface layer with a privacy isolation mechanism, and establish a bidirectional calling channel between the large model inference engine and the aligned private knowledge base through the interface layer; S6: Receive the reasoning request for the target industry scenario, and based on the business tag of the reasoning request, perform collaborative reasoning scheduling between the large model and the private knowledge base, and output the corresponding scenario-based reasoning result.

2. The method for constructing and integrating private knowledge bases with large models for industry scenarios as described in claim 1, characterized in that, In step S2, when generating the set of knowledge nodes with scene tags, the industry scene weight is calculated for each knowledge node, and the calculation formula is as follows: ； In the formula, For the first The industry scenario weight of each knowledge node This represents the normalized word frequency of the node in the target industry scenario corpus. This represents the degree of relevance between the node and the core business entities in the industry. A pre-defined score is assigned to the business importance of this node. The preset weight allocation coefficients are used, and they satisfy... Based on the scene weights, knowledge nodes are sorted and filtered to generate a set of knowledge nodes with scene tags.

3. The method for constructing and integrating private knowledge bases with large models for industry scenarios as described in claim 1, characterized in that, In step S3, when performing the unified vector space mapping, an industry-specific contrastive learning strategy is used to fine-tune the semantic encoding layer of the large model. The loss function for this fine-tuning is: ； In the formula, For the embedding vector of anchor knowledge unit, This is the embedding vector of the positive sample knowledge unit. This is the embedding vector of the negative sample knowledge unit. The cosine similarity function is used. For temperature coefficient, The number of negative samples is used to generate a knowledge embedding vector set with consistent dimensions based on the fine-tuned semantic encoding layer.

4. The method for constructing and integrating private knowledge bases with large models for industry scenarios as described in claim 1, characterized in that, In step S4, the specific steps for performing bidirectional knowledge alignment are as follows: S41: Based on the semantic space of the large model base, perform global distribution calibration on the embedding vector of private knowledge to reduce the distribution deviation between private knowledge and the semantics of the large model base. S42: Calculate the scene adaptation similarity between the private knowledge unit embedding vector and the corresponding semantic embedding vector of the large model base. The calculation formula is as follows: ； In the formula, For the embedding vector of private knowledge units, This is the embedding vector corresponding to the semantics of the large model base. It is the sigmoid activation function. This refers to the frequency deviation of the occurrence of this knowledge unit in the target industry scenario; S43: For private knowledge units with similarity below a preset threshold, perform semantic adaptation fine-tuning on the large model base to complete bidirectional knowledge alignment.

5. The method for constructing and integrating private knowledge bases with large models for industry scenarios as described in claim 1, characterized in that, In step S5, the knowledge integration interface layer with privacy isolation mechanism uses a differential privacy mechanism to perform noise addition processing on the knowledge embedding vector. The noise addition calculation formula is as follows: ； In the formula, The knowledge embedding vector after adding noise. Embed vectors for the original knowledge. With a mean of 0 and a variance of Gaussian distribution, For the global sensitivity of knowledge embedding vectors, For the preset privacy budget, The default fault tolerance probability is set; the interface layer completes bidirectional data interaction between the large model and the private knowledge base based on the noisy knowledge embedding vector.

6. The method for constructing and integrating private knowledge bases with large models for industry scenarios as described in claim 1, characterized in that, In step S6, when performing collaborative inference scheduling, an optimization model is constructed with the goal of minimizing the total inference latency. The model expression is: ； ； In the formula, For total inference delay, The number of reasoning subtasks. For the first The large computational load of the model corresponding to each inference subtask This refers to the computational power allocation coefficients for the large model inference engine corresponding to this subtask. This represents the computational cost of retrieving the private knowledge base corresponding to this subtask. The retrieval computational power allocation coefficient for this subtask corresponding to the private knowledge base. For task scheduling decision variables; Cooperative reasoning scheduling is performed based on the solution results of the optimization model.

7. The method for constructing and integrating private knowledge bases with large models for industry scenarios as described in claim 1, characterized in that, It also includes the full lifecycle incremental update steps for the private knowledge base, specifically including: S71: Collect new private business data of the target industry scenario in real time according to the preset incremental collection cycle. The new private business data includes new business documents, business system transaction data, new business Q&A records, and updated industry rule files. S72: Perform structured preprocessing on the newly collected private business data in the same way as step S1, split it into new standardized knowledge units, and perform deduplication verification on the new standardized knowledge units to remove knowledge units that are duplicated with the existing private knowledge base. S73: Through the semantic encoding layer fine-tuned in step S3, perform unified vector space mapping on the newly added standardized knowledge units after verification to generate new knowledge embedding vectors; S74: Calculate the semantic overlap between the newly added knowledge embedding vector and the existing knowledge embedding vector in the private knowledge base. When the semantic overlap is lower than the preset overlap threshold, mark the corresponding newly added knowledge unit as a knowledge unit to be added to the database. S75: Perform bidirectional knowledge alignment in step S4 for the knowledge unit to be added to the database, and synchronously update the knowledge nodes, attributes and relationships of the industry-specific knowledge graph to complete the writing of the knowledge unit to be added to the database. S76: Perform version snapshot retention on the updated content of the private knowledge base, generate the version number and change log of the corresponding updated version, and complete the entire incremental update process.

8. The method for constructing and integrating private knowledge bases with large models for industry scenarios as described in claim 1, characterized in that, In step S1, the multi-source heterogeneous private business data includes industry business data in text format, structured table format, image format, audio format, and video format. Its structured preprocessing process specifically includes the following steps: S11: For private business data in text format, perform sentence segmentation, word segmentation, stop word removal, technical terminology normalization, and business entity alignment processing to split and generate standardized knowledge units that meet the preset length threshold. S12: For private business data in structured table format, first extract the table's row and column headers, cell content, and table note information. Then, perform table semantic parsing through the large model to convert the cell content associated with rows and columns into structured semantic triples, and then split them to generate standardized knowledge units. S13: For private business data in image format, first perform optical character recognition to extract the text content in the image, then use the multimodal coding layer of the large model to extract industry-specific entity features, process features, and structural features in the image, and fuse the text content and feature information to convert it into textual semantic content, and decompose it to generate standardized knowledge units. S14: For private business data in audio and video formats, first perform speech-to-text processing to generate initial text content, and then perform professional terminology correction, tone word removal, and invalid segment filtering through the industry-fine-tuned large model to split the corrected text content into standardized knowledge units. S15: Perform global deduplication and entity normalization verification on standardized knowledge units generated from all data formats to complete the entire structured preprocessing process.

9. The method for constructing and integrating private knowledge bases with large models for industry scenarios as described in claim 1, characterized in that, In step S2, the fine-tuning construction process of the large model after industry fine-tuning specifically includes the following steps: S21: Construct an industry-specific fine-tuning training dataset, collect labeled business corpora, standardized business rule documents, historical business question-and-answer pairs, and industry-specific entity and relation labeled data for the target industry scenario, perform format standardization, noise data removal, positive and negative sample balancing on the training dataset, and divide it into training set, validation set and test set; S22: Perform pre-trained base adaptation, load the general large model pre-trained base, fix all parameters of the backbone transformer network of the pre-trained base, and only unlock the trainable parameters of the semantic encoding layer, entity relationship extraction head, and scene classification head. S23: Perform phased supervised fine-tuning. The first phase is based on industry-labeled entity and relation data. Fine-tuning training is performed on the entity and relation extraction head until the extraction accuracy reaches the preset accuracy threshold. The second stage involves performing comparative learning and fine-tuning on the semantic encoding layer based on industry business corpus and business rule documents to complete the adaptation of the industry semantic space; the third stage involves performing fine-tuning training on the scenario classification head based on business question and answer data to complete the adaptation of business scenario tags. S24: Perform fine-tuning effect verification. Based on the test set, perform indicator verification on the fine-tuned large model for entity extraction accuracy, relation extraction recall, and scene classification accuracy. When all indicators reach the preset threshold, the model verification is completed. S25: Execute parameter fixation on the large model that has passed the verification to generate a large model with industry fine-tuning, which is used for the extraction of entities, attributes and business relationships in step S2.

10. The method for constructing and integrating private knowledge bases with large models for industry scenarios as described in claim 1, characterized in that, In step S5, the construction and operation of the knowledge integration interface layer with privacy isolation mechanism specifically includes the following steps: S51: Construct the basic communication framework of the interface layer, establish a communication channel that supports both RESTful and gRPC protocols, adapt the communication protocols of the large model inference engine and the private knowledge base respectively, and complete the construction of the bidirectional data transmission link. S52: Construct a privacy isolation module at the interface layer. Based on a preset privacy budget and fault tolerance probability, a differential privacy mechanism is used to add noise to the knowledge embedding vector output by the private knowledge base. At the same time, sensitive information is desensitized on the request content input by the large model inference engine to block the direct exposure of private raw data to the outside world. S53: Construct a hierarchical permission control module for the interface layer, pre-set a multi-level user permission system, configure corresponding private knowledge base access scope, knowledge granularity permissions, and large model reasoning function call permissions for different permission levels, and establish a mapping relationship between user identity and permission level; S54: Execute access authentication request. When receiving an inference request, first perform identity verification on the request initiator. If the verification is successful, match the corresponding user's permission level, generate an access token with the corresponding permission, and configure the knowledge base access scope and large model call permission for this request based on the access token. S55: Performs end-to-end control of bidirectional calls, performs permission verification on every bidirectional call operation between the large model inference engine and the private knowledge base based on access tokens, intercepts call requests that exceed the permission scope, and performs end-to-end data forwarding and protocol conversion on legitimate call operations; S56: Construct a compliance audit module for the interface layer, which performs full-process logging of all bidirectional call operations. The logged content includes the identity of the request initiator, the request time, the call content, the access scope, and a summary of the reasoning result. At the same time, it generates a compliance audit report according to a preset period, thus completing the full-process control of the interface layer.

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