A service data automatic vectorization and semantic retrieval method and system

Abstract

The application relates to the technical field of semantic retrieval, and discloses a business data automatic vectorization and semantic retrieval method, which comprises the following steps: data change capturing: sensing the adding, modifying or deleting operation of data in a business system, extracting business records and binding record identification; multi-dimensional quality assessment: performing five-dimensional quality assessment on the extracted business data in terms of integrity, consistency, timeliness, information entropy and security through an MDQA algorithm, and only allowing the quality up-to-standard data to enter the subsequent process; the retrieval quality is obviously improved through the set multi-dimensional quality assessment intelligent semantic segmentation and incremental vector updating; the MDQA algorithm filters 22% of low-quality data, the retrieval precision is improved by 19.9%, and the user satisfaction is improved by 28.1%; the SBAS algorithm ensures the completeness of segmented semantics, the recall rate is improved by 25.8%, and retrieval deviation caused by context loss is avoided.

Description

Technical Field

[0001] This invention relates to the field of semantic retrieval technology, specifically to a method and system for automatic vectorization and semantic retrieval of business data. Background Technology

[0002] Current enterprise business systems store a large amount of structured and unstructured data. Traditional data query methods rely on keyword matching or SQL statements, which are difficult to support users in directly obtaining the information they need through natural language. With the development of generative AI models, conversational information retrieval based on natural language has become the mainstream. However, the training corpus of large AI models does not include private business data of enterprises. Direct use of such data can easily lead to "illusions" and fail to meet the accuracy and security requirements of enterprise-level scenarios.

[0003] To address these issues, the industry has proposed a Retrieval Augmentation (RAG) technology architecture. This architecture transforms text into high-dimensional vectors using vector databases and embedding vectors, leveraging vector similarity for semantic retrieval and providing contextual information for the model. However, existing RAG technologies have several shortcomings for enterprise-level deployment: data updates require periodic manual script extraction, resulting in high costs and significant latency for full reconstruction; text segmentation uses fixed lengths or simple delimiters, compromising semantic integrity; there is a lack of data quality assessment mechanisms, leading to low-quality data contaminating the vector database; retrieval ranking strategies are fixed, lacking a user feedback loop; and access control relies on post-retrieval filtering, resulting in low efficiency and insufficient security.

[0004] To address the aforementioned issues, this application proposes a method and system for automatic vectorization and semantic retrieval of business data. Summary of the Invention

[0005] The purpose of this invention is to provide a subject matter that addresses the problems of insufficient timeliness of data updates, simplistic text segmentation strategies, lack of data quality control, insufficient optimization of retrieval and ranking, and coarse granularity of permission control in the prior art as described in the background section.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for automatic vectorization and semantic retrieval of business data, the method comprising the following steps:

[0007] Data change capture: detect the addition, modification or deletion of data in the business system, extract business records and bind record identifiers;

[0008] Multidimensional Quality Assessment: The extracted business data is assessed using the MDQA algorithm across five dimensions: integrity, consistency, timeliness, information entropy, and security. Only data meeting the quality standards is allowed to proceed to subsequent processes. The five-dimensional quality assessment based on the MDQA algorithm includes:

[0009] Completeness assessment: Calculate the proportion of non-empty fields, check the completeness of key fields, and halve the score if key fields are missing;

[0010] Consistency assessment: Verify the logical relationships between fields, data type matching, and compliance with business rules. The scoring formula is: Q cons =1 - (number of inconsistent items / total number of rules);

[0011] Timeliness assessment: using an exponential decay model Δt is the age of the data, λ is the decay rate, and there is an additional penalty when the data expires.

[0012] Information entropy assessment: Calculate character-level and word-level entropy values, filter out invalid data with high repetition, and halve the score when the proportion of unique characters is less than 10%;

[0013] Security assessment: Detects sensitive information and forcibly rejects data containing serious violations, such as plaintext credentials; score range: 0-1.

[0014] Intelligent semantic segmentation: For newly added quality-compliant data, the SBAS algorithm is used to perform four-layer progressive semantic boundary detection to generate semantically complete text segments;

[0015] Incremental vector update: For modified data that meets the quality standards, the IVDU algorithm is used to analyze the differences between the old and new texts, and the update strategy of incremental update, hybrid update or full reconstruction is automatically selected to generate the updated vector;

[0016] Vector-level permission embedding: The VPEA algorithm is used to select a permission embedding scheme that is suitable for the scenario, and the permission information is embedded into a vector or index structure.

[0017] Vector storage and retrieval: Vectors embedding permission information are stored in a vector database. After receiving user natural language queries and quantizing them, semantic retrieval with permission verification is performed based on vector similarity. The authorized results are returned for GenAI to generate the final answer.

[0018] Furthermore, the four-layer progressive semantic boundary detection based on the SBAS algorithm includes:

[0019] Sentence level: Identify true sentence boundaries and exclude false boundaries caused by decimal points or abbreviations;

[0020] Paragraph layer: Extract topic vectors from adjacent sentences, calculate cosine similarity, and select sentences with similarity below a preset threshold θ. para It is determined to be a paragraph boundary at that time;

[0021] Semantic layer: The candidate segments are scored using a four-dimensional semantic integrity assessment, which includes syntactic structure, reference resolution, terminology completeness, and / or dependency relations. The scoring formula is as follows:

[0022] Score = 0.3 × Syntax + 0.25 × Reference + 0.25 × Terminology + 0.2 × Dependency;

[0023] Decision-making level: Under the premise of satisfying the maximum length limit, select the option with a semantic integrity score ≥ θ. semantic The optimal split point.

[0024] Furthermore, the selection of the update strategy based on the IVDU algorithm includes:

[0025] Difference analysis: Calculate the edit distance and segment cosine similarity between the old and new texts to identify local, structural, or global modification types;

[0026] Strategy decision: When the average similarity is ≥ θ high When the modification is local, incremental update is performed; when the average similarity is high, hybrid update is performed; when the average similarity is low, a hybrid update is performed. low At that time, a full rebuild will be performed;

[0027] Quality verification: Vector quality is verified using three metrics: semantic consistency, retrieval quality, and relational maintenance. A score below θ indicates a problem. quality It will automatically downgrade to a full rebuild.

[0028] Furthermore, the permission embedding scheme based on the VPEA algorithm includes:

[0029] Option 1: Metadata Attachment: Store vector and permission metadata separately, and perform permission filtering after retrieval;

[0030] Option 2: Vector Space Expansion: Encode permissions into a 4-dimensional vector (owner, department ID normalized, tenant ID normalized, permission level normalized), expand the original vector dimensions and store it, and complete permission verification and similarity calculation simultaneously during retrieval;

[0031] Option 3: Bloom filter prediction: Create Bloom filters for each permission group. During retrieval, quickly predict the permissions by using the filters first, and then perform detailed permission checks.

[0032] Option 4: Differential Privacy Protection: Add Laplace noise to highly sensitive data, specifically:

[0033] v′=v+Laplace(0,sensitivity / ε);

[0034] ε represents the privacy budget.

[0035] Furthermore, the selection criteria for the permission embedding scheme include data size, query frequency, data sensitivity, and permission complexity, specifically:

[0036] If the data size is greater than 1 million and the query frequency is greater than 1000, choose option 3;

[0037] When the data sensitivity level is ≥4, choose option 4;

[0038] When the permission complexity is simple, choose option 2;

[0039] For other scenarios, choose option 1.

[0040] Furthermore, an automatic vectorization and semantic retrieval system for business data is proposed, the system comprising:

[0041] Data Change Awareness Module: Used to capture the addition, modification, or deletion of business data, extract business records, and bind identifiers;

[0042] Multidimensional Quality Assessment Module: Deploys the MDQA algorithm, performs five-dimensional data quality assessment and filtering, and also generates improvement suggestions for low-quality data;

[0043] Intelligent segmentation module: Deploys the SBAS algorithm to achieve semantically complete text segmentation and supports adjustment of segmentation parameters;

[0044] Incremental update module: Deploys the IVDU algorithm, intelligently selects vector update strategies, and also records update strategy selection and quality scores;

[0045] Permission embedding module: Deploys the VPEA algorithm, provides multiple permission embedding schemes, and is also used to maintain a sensitive information detection pattern library;

[0046] Vector storage module: Used to store vector data with embedded permission information, supporting flexible switching between mainstream vector databases;

[0047] Semantic retrieval module: Receives user natural language queries and performs vector similarity retrieval with permission verification;

[0048] GenAI Interaction Module: Generates the final answer based on the search results and returns it to the user.

[0049] Furthermore, the improvement suggestions generated by the multi-dimensional quality assessment module include specific problem dimensions of low-quality data and remediation guidelines; the information recorded by the incremental update module includes update time, strategy type, number of API calls, quality score and reason for downgrade; the sensitive information detection pattern library maintained by the permission embedding module supports the addition of industry-specific sensitive rules.

[0050] Furthermore, it supports adjusting core algorithm parameters via configuration files, including MDQA parameters, SBAS parameters, IVDU parameters, VPEA Bloom filter false positive rate, and differential privacy budget ε; it also supports replacing embedding models and vector databases without modifying the core process code.

[0051] Compared with the prior art, the beneficial effects of the present invention are:

[0052] This invention significantly improves retrieval quality through multi-dimensional quality assessment, intelligent semantic segmentation, incremental vector updates, vector-level permission embedding, and vector storage and retrieval: the MDQA algorithm filters 22% of low-quality data, improving retrieval accuracy by 19.9% ​​(from 68.5% to 82.1%) and user satisfaction by 28.1% (from 3.2 / 5.0 to 4.1 / 5.0); the SBAS algorithm ensures semantic integrity of segments, improving recall by 25.8% and avoiding retrieval bias caused by context loss; operational costs are significantly reduced: the IVDU algorithm reduces API call costs by 80%, and update latency from 2.3 seconds to 0.4 seconds, improving the real-time performance of business data; the SBAS algorithm reduces the average number of segments by 37%, lowering vector storage and API call costs; security and controllability are enhanced: the VPEA algorithm reduces permission control latency by more than 95% (from 152ms to 0.6ms), and Top-K accuracy is improved. It achieves 100% accuracy, avoiding the insufficient results caused by traditional post-filtering; the MDQA algorithm automatically detects sensitive information, and the VPEA differential privacy solution provides strong privacy protection, meeting the compliance requirements of industries such as finance and healthcare; the entire process is automated: from data change awareness → quality assessment → segmentation / update → permission embedding → vector storage → retrieval, no manual intervention is required, zero-intrusion deployment, reducing manual operation and maintenance costs by 70%; it supports real-time data change capture, avoiding the delay problem of traditional periodic extraction; it has good flexibility and scalability: the modular design supports selective activation of core algorithms (such as MDQA can be deployed separately if only quality control is needed); parameters are configurable (such as thresholds, weights, model paths) to adapt to different business scenarios; it supports flexible switching between vector databases (Milvus / Pinecone / Weaviate) and embedding models (OpenAI / localized), reducing the risk of technology selection binding. Attached Figure Description

[0053] Figure 1 This is a diagram illustrating the overall architecture of an automatic vectorization and semantic retrieval system for business data according to the present invention.

[0054] Figure 2 This is a data writing architecture diagram for an automatic vectorization and semantic retrieval method for business data according to the present invention;

[0055] Figure 3 This is a flowchart illustrating the data writing process of an automatic vectorization and semantic retrieval method for business data according to the present invention.

[0056] Figure 4 This is a flowchart illustrating the retrieval and query process of a business data automatic vectorization and semantic retrieval method according to the present invention.

[0057] Figure 5The flowchart of the SBAS semantic boundary-aware segmentation algorithm for an automatic vectorization and semantic retrieval method for business data according to the present invention is shown below.

[0058] Figure 6 This is a flowchart of the IVDU incremental vector differential update algorithm for an automatic vectorization and semantic retrieval method for business data according to the present invention.

[0059] Figure 7 This is a flowchart of the MDQA multidimensional data quality assessment algorithm for an automatic vectorization and semantic retrieval method for business data according to the present invention.

[0060] Figure 8 This is a flowchart of the VPEA vector-level permission embedding algorithm for an automatic business data vectorization and semantic retrieval method according to the present invention.

[0061] Figure 9 This is a user query flowchart for an automatic vectorization and semantic retrieval method for business data according to the present invention; Detailed Implementation

[0062] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0063] Example 1

[0064] Please see Figures 1-9 This invention provides a technical solution: a method for automatic vectorization and semantic retrieval of business data, which includes the following steps:

[0065] Data change capture: detect the addition, modification or deletion of data in the business system, extract business records and bind record identifiers;

[0066] Multidimensional Quality Assessment: The extracted business data is assessed using the MDQA algorithm across five dimensions: integrity, consistency, timeliness, information entropy, and security. Only data meeting the quality standards is allowed to proceed to subsequent processes. The five-dimensional quality assessment based on the MDQA algorithm includes:

[0067] Completeness assessment: Calculate the proportion of non-empty fields, check the completeness of key fields, and halve the score if key fields are missing;

[0068] Consistency assessment: Verify the logical relationships between fields, data type matching, and compliance with business rules. The scoring formula is: Q cons =1 - (number of inconsistent items / total number of rules);

[0069] Timeliness assessment: using an exponential decay model Δt is the age of the data, λ is the decay rate, and there is an additional penalty when the data expires.

[0070] Information entropy assessment: Calculate character-level and word-level entropy values, filter out invalid data with high repetition, and halve the score when the proportion of unique characters is less than 10%;

[0071] Security assessment: Detects sensitive information and forcibly rejects data containing serious violations, such as plaintext credentials; score range: 0-1.

[0072] Intelligent semantic segmentation: For newly added quality-compliant data, the SBAS algorithm is used to perform four-layer progressive semantic boundary detection to generate semantically complete text segments;

[0073] Incremental vector update: For modified data that meets the quality standards, the IVDU algorithm is used to analyze the differences between the old and new texts, and the update strategy of incremental update, hybrid update or full reconstruction is automatically selected to generate the updated vector;

[0074] Vector-level permission embedding: The VPEA algorithm is used to select a permission embedding scheme that is suitable for the scenario, and the permission information is embedded into a vector or index structure.

[0075] Vector storage and retrieval: Vectors embedding permission information are stored in a vector database. After receiving user natural language queries and quantizing them, semantic retrieval with permission verification is performed based on vector similarity. The authorized results are returned for GenAI to generate the final answer.

[0076] The four-layer progressive semantic boundary detection based on the SBAS algorithm includes:

[0077] Sentence level: Identify true sentence boundaries and exclude false boundaries caused by decimal points or abbreviations;

[0078] Paragraph layer: Extract topic vectors from adjacent sentences, calculate cosine similarity, and select sentences with similarity below a preset threshold θ. para It is determined to be a paragraph boundary at that time;

[0079] Semantic layer: The candidate segments are scored using a four-dimensional semantic integrity assessment, which includes syntactic structure, reference resolution, terminology completeness, and / or dependency relations. The scoring formula is as follows:

[0080] Score = 0.3 × Syntax + 0.25 × Reference + 0.25 × Terminology + 0.2 × Dependency;

[0081] Decision-making level: Under the premise of satisfying the maximum length limit, select the option with a semantic integrity score ≥ θ. semantic The optimal split point.

[0082] The selection of the update strategy based on the IVDU algorithm includes:

[0083] Difference analysis: Calculate the edit distance and segment cosine similarity between the old and new texts to identify local, structural, or global modification types;

[0084] Strategy decision: When the average similarity is ≥ θ high When the modification is local, incremental update is performed; when the average similarity is high, hybrid update is performed; when the average similarity is low, a hybrid update is performed. low At that time, a full rebuild will be performed;

[0085] Quality verification: Vector quality is verified using three metrics: semantic consistency, retrieval quality, and relational maintenance. A score below θ indicates a problem. quality It will automatically downgrade to a full rebuild.

[0086] The permission embedding scheme based on the VPEA algorithm includes:

[0087] Option 1: Metadata Attachment: Store vector and permission metadata separately, and perform permission filtering after retrieval;

[0088] Option 2: Vector Space Expansion: Encode permissions into a 4-dimensional vector (owner, department ID normalized, tenant ID normalized, permission level normalized), expand the original vector dimensions and store it, and complete permission verification and similarity calculation simultaneously during retrieval;

[0089] Option 3: Bloom filter prediction: Create Bloom filters for each permission group. During retrieval, quickly predict the permissions by using the filters first, and then perform detailed permission checks.

[0090] Option 4: Differential Privacy Protection: Add Laplace noise to highly sensitive data, specifically:

[0091] v′=v+Laplace(0,sensitivity / ε);

[0092] ε represents the privacy budget.

[0093] The selection criteria for the permission embedding scheme include data size, query frequency, data sensitivity, and permission complexity, specifically:

[0094] If the data size is greater than 1 million and the query frequency is greater than 1000, choose option 3;

[0095] When the data sensitivity level is ≥4, choose option 4;

[0096] When the permission complexity is simple, choose option 2;

[0097] For other scenarios, choose option 1.

[0098] Please see Figure 1-9 Corresponding to Embodiment 1 above, this embodiment also proposes an automatic business data vectorization and semantic retrieval system, which includes:

[0099] The data change awareness module captures new, modified, or deleted operations on business data, extracts business records and binds identifiers. After a successful business operation at the service layer, it detects the new, modified, or deleted data event and generates an asynchronous notification containing the table name, record identifier, and operation type. The receiving end retrieves the latest data, filters sensitive fields (such as passwords and API keys), binds record identifiers, and generates semantic text to be sent to subsequent processes. For deletion operations, it accurately reclaims the vector segments of the corresponding record through a vector database interface, preventing invalid data from consuming storage resources. This module achieves real-time data change capture through a low-intrusion design (based on business operation callbacks) without modifying existing business system code.

[0100] Multidimensional Quality Assessment Module: Deploys the MDQA algorithm to perform five-dimensional data quality assessment and filtering, and also generates improvement suggestions for low-quality data; as the system's "quality gatekeeper," it intercepts low-quality data at the data entry point. Its core principle is: constructing a five-dimensional quality assessment model, determining the data quality level through weighted comprehensive scoring, and executing an "accept / reject" decision based on the principle of prioritizing security. Assessment dimensions and weights: completeness (25%), consistency (25%), timeliness (15%), information entropy (20%), and security (15%); Decision rule: forced rejection when security is 0; comprehensive score ≥ θ high Furthermore, a safety score ≥ 0.9 indicates high quality, and immediate vectorization is required; a comprehensive score ≥ θ low Furthermore, a security score of ≥0.7 indicates medium quality, which is conditionally vectorized; otherwise, the data is rejected and improvement suggestions are generated. This is the first time that data quality assessment has been introduced into the RAG vectorization process. The five-dimensional model comprehensively covers the dimensions of data quality, and the assessment accuracy rate reaches 94.2%. The security detection adopts a zero-tolerance strategy, and plaintext credential data is directly rejected to reduce the risk of data leakage.

[0101] It should be understood that its output, in addition to quality decisions, also generates detailed scoring reports and improvement suggestions, such as "Incompleteness: The key field 'Order Number' is missing and needs to be added and resubmitted," to help data administrators optimize the quality of source data.

[0102] Intelligent Segmentation Module: Deploys the SBAS algorithm to achieve semantically complete text segmentation, supports segmentation parameter configuration and adjustment, solves the semantic integrity problem of long text segmentation, and ensures that each segment is an independent semantic unit. Its core principle is: through a four-layer progressive detection mechanism, semantic boundaries are located from coarse-grained to fine-grained, dynamically balancing segment length and semantic integrity; key steps: sentence boundary detection eliminates false boundaries, paragraph boundary detection is based on topic similarity, semantic integrity scoring covers four-dimensional indicators, and adaptive decision-making selects the optimal segmentation point; parameter configuration: θ para The default value is 0.6, θ semanticThe default value is 0.7, and the maximum segment length is 512 characters. It supports business scenario adaptation and adjustment. Compared with fixed-length segments, the recall rate is improved by 25.8% (from 68.5% to 93.4%), the semantic integrity is improved by 35.7% (from 52.3% to 89.2%), and the average number of segments is reduced by 37% (from 8.2 to 4.1), reducing the cost of subsequent vector generation and storage.

[0103] It should be understood that: θ can be adjusted based on business data characteristics. para (Paragraph segmentation threshold), θ semantic (Semantic integrity threshold) and max_length (maximum segment length). For long texts such as product manuals, max_length can be appropriately increased, while for short texts such as work order records, the threshold can be decreased to ensure semantic independence.

[0104] The incremental update module deploys the IVDU algorithm, intelligently selects vector update strategies, and records update strategy selection and quality scores. For data modification scenarios, it intelligently selects the optimal update strategy to reduce vectorization costs. Its core principle is based on text difference quantification analysis to achieve "on-demand updates" rather than "full reconstruction," balancing cost and quality. Difference dimensions include: edit distance (character level), segment similarity (vector level), and change type (structural level). It quantifies the differences between old and new text from three dimensions, dynamically selecting incremental update (similarity ≥ 0.98), hybrid update (0.95 ≤ similarity < 0.98), or full reconstruction (similarity < 0.95) strategies. A built-in quality verification mechanism ensures that the vector quality does not decline after updating through three-dimensional indicators: semantic drift, retrieval quality, and vector relationship preservation. If the quality does not meet the standards, it automatically downgrades. Strategy effects: incremental updates save 80% of costs, hybrid updates save 40-60% of costs, full reconstruction ensures quality, and the overall vector quality loss is only 2.1%.

[0105] Permission Embedding Module: Deploys the VPEA algorithm, providing multiple permission embedding schemes. It also maintains a sensitive information detection pattern library, elevating permission control from "post-retrieval filtering" to "vector-level embedding," solving efficiency and security issues in permission control. Its core principle is: providing four permission embedding schemes to adapt to different scenario requirements, enabling simultaneous completion of permission verification and retrieval; Scheme characteristics: Scheme 2 (vector space expansion) has a latency of 0.6ms and Top-K accuracy of 100%; Scheme 3 (Bloom filter) has a latency of 0.8ms and is suitable for millions of data points; Scheme 4 (differential privacy) provides strong privacy protection; Scheme 1 (metadata) has good compatibility; Encoding logic: The 4-dimensional permission vector includes the owner identifier (1.0 = owner, 0.0 = non-owner), the normalized value of the department ID (dept_id / MAX_DEPARTMENTS), the normalized value of the tenant ID (tenant_id / MAX_TENANTS), and the normalized value of the permission level (level / 5.0), ensuring that the permission information is compactly embedded without affecting the original vector semantics.

[0106] It should be understood that: vector-level permission verification filters out unauthorized data from the source of retrieval, avoiding the risk of sensitive data feature exposure that may be caused by traditional "post-retrieval filtering"; differential privacy scheme can achieve strong privacy protection with only a 2% loss in retrieval accuracy when privacy budget ε=0.1.

[0107] Vector storage module: Used to store vector data embedded with permission information, supporting flexible switching between mainstream vector databases; Semantic retrieval module: Receives user natural language queries and performs vector similarity retrieval with permission verification. Supports storage in mainstream vector databases. After receiving user natural language queries, it performs semantic retrieval with permission verification, where:

[0108] Search process: User query → Embedding model vectorization → Adapt query vector according to VPEA scheme (e.g., extend 4-dimensional permission dimension) → Vector database similarity retrieval → Permission filtering (e.g., Bloom filter prediction, metadata verification) → Return authorization results;

[0109] Multi-tenancy support: Through tenant ID encoding using the VPEA algorithm, data isolation between tenants is ensured. Users can only retrieve data from their own tenant and data they have permission to access, meeting the security requirements of enterprise-level multi-tenant deployments.

[0110] It should be noted that the system workflow is as follows:

[0111] A. Data entry process: Business data change → MDQA quality assessment → Adding data and executing SBAS segmentation / Modifying data and executing IVDU update → Vector generation → VPEA permission embedding → Vector library storage;

[0112] B. Search and query process: User natural language query → Query vectorization → Vector similarity retrieval (permission verification completed simultaneously) → Return of search results → GenAI generates final answer.

[0113] The GenAI interaction module generates the final answer based on the search results and returns it to the user. Based on the retrieved authorized results, it constructs a GenAI context, calls a large language model to generate an accurate and concise answer, and returns the search criteria (including record ID and similarity score), improving the traceability of results and user trust. It supports adaptation to different LLM models (such as GPT-3.5 / 4 and localized open-source models), and can be switched via configuration files to meet different enterprise needs regarding model cost and privacy.

[0114] The improvement suggestions generated by the multi-dimensional quality assessment module include specific problem dimensions of low-quality data and remediation guidelines; the information recorded by the incremental update module includes update time, strategy type, number of API calls, quality score and reason for downgrade; the sensitive information detection pattern library maintained by the permission embedding module supports the addition of industry-specific sensitive rules.

[0115] Supports adjusting core algorithm parameters via configuration files, including θ for MDQA. high θ low λ, θ of SBAS para θ semantic max_length, θ of IVDU low θ high θ quality VPEA boasts a high Bloom filter false positive rate and differential privacy budget ε; it also supports the replacement of Embedding models and vector databases without requiring modifications to the core process code.

[0116] It should be understood that the software parameters are configured as follows:

[0117] MDQA Algorithm: θ high =0.75, θ low =0.50, λ=0.001, the sensitive information detection pattern library includes rules for ID cards, passwords, API keys, etc.;

[0118] SBAS algorithm: θ para =0.6, θ semantic =0.7, maximum segment length = 512 characters, topic extraction uses BERTopic model;

[0119] IVDU algorithm: θ low =0.95, θ high =0.98, θ quality =0.85, and the similarity was calculated using cosine similarity.

[0120] VPEA algorithm: Bloom filter false positive rate = 0.01, capacity = 1 million records / filter, differential privacy ε = 0.1 (high sensitivity) to 1.0 (low sensitivity).

[0121] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

Claims

1. A method for automatic vectorization and semantic retrieval of business data, characterized in that: The method includes the following steps: Data change capture: detect the addition, modification or deletion of data in the business system, extract business records and bind record identifiers; Multidimensional Quality Assessment: The extracted business data is assessed using the MDQA algorithm across five dimensions: integrity, consistency, timeliness, information entropy, and security. Only data meeting the quality standards is allowed to proceed to subsequent processes. The five-dimensional quality assessment based on the MDQA algorithm includes: Completeness assessment: Calculate the proportion of non-empty fields, check the completeness of key fields, and halve the score if key fields are missing; Consistency assessment: Verify the logical relationships between fields, data type matching, and compliance with business rules. The scoring formula is: Q cons =1 - (number of inconsistent items / total number of rules); Timeliness assessment: using an exponential decay model Δt is the age of the data, λ is the decay rate, and there is an additional penalty when the data expires. Information entropy assessment: Calculate character-level and word-level entropy values, filter out invalid data with high repetition, and halve the score when the proportion of unique characters is less than 10%; Security assessment: Detects sensitive information and forcibly rejects data containing serious violations, such as plaintext credentials; score range: 0-1. Intelligent semantic segmentation: For newly added quality-compliant data, the SBAS algorithm is used to perform four-layer progressive semantic boundary detection to generate semantically complete text segments; Incremental vector update: For modified data that meets the quality standards, the IVDU algorithm is used to analyze the differences between the old and new texts, and the update strategy of incremental update, hybrid update or full reconstruction is automatically selected to generate the updated vector; Vector-level permission embedding: The VPEA algorithm is used to select a permission embedding scheme that is suitable for the scenario, and the permission information is embedded into a vector or index structure. Vector storage and retrieval: Vectors embedding permission information are stored in a vector database. After receiving user natural language queries and quantizing them, semantic retrieval with permission verification is performed based on vector similarity. The authorized results are returned for GenAI to generate the final answer.

2. The method for automatic vectorization and semantic retrieval of business data according to claim 1, characterized in that: The four-layer progressive semantic boundary detection based on the SBAS algorithm includes: Sentence level: Identify true sentence boundaries and exclude false boundaries caused by decimal points and / or abbreviations; Paragraph layer: Extract topic vectors from adjacent sentences, calculate cosine similarity, and select sentences with similarity below a preset threshold θ. para It is determined to be a paragraph boundary at that time; Semantic layer: The candidate segments are scored using a four-dimensional semantic integrity assessment, which includes syntactic structure, reference resolution, terminology completeness, and / or dependency relations. The scoring formula is as follows: Score = 0.3 × Syntax + 0.25 × Reference + 0.25 × Terminology + 0.2 × Dependency; Decision-making level: Under the premise of satisfying the maximum length limit, select the option with a semantic integrity score ≥ θ. semantic The optimal split point.

3. The method for automatic vectorization and semantic retrieval of business data according to claim 1, characterized in that: The selection of the update strategy based on the IVDU algorithm includes: Difference analysis: Calculate the edit distance and segment cosine similarity between the old and new texts to identify local, structural, or global modification types; Strategy decision: When the average similarity is ≥ θ high When the modification is local, incremental update is performed; when the average similarity is high, hybrid update is performed; when the average similarity is low, a hybrid update is performed. low At that time, a full rebuild will be performed; Quality verification: Vector quality is verified through three dimensions: semantic consistency, retrieval quality, and / or relation maintenance. A score below θ indicates a failure to verify vector quality. quality It will automatically downgrade to a full rebuild.

4. The method for automatic vectorization and semantic retrieval of business data according to claim 1, characterized in that: The permission embedding scheme based on the VPEA algorithm includes: Option 1: Metadata Attachment: Store vector and permission metadata separately, and perform permission filtering after retrieval; Option 2: Vector Space Expansion: Encode permissions into a 4-dimensional vector, which includes normalized owner, department ID, tenant ID, and permission level. Store the vector after expanding its dimensions. During retrieval, permission verification and similarity calculation are completed simultaneously. Option 3: Bloom filter prediction: Create Bloom filters for each permission group. During retrieval, quickly predict the permissions by using the filters first, and then perform detailed permission checks. Option 4: Differential privacy protection: Add Laplace noise to highly sensitive data.

5. The method for automatic vectorization and semantic retrieval of business data according to claim 4, characterized in that: The selection criteria for the permission embedding scheme include data size, query frequency, data sensitivity, and permission complexity, specifically: If the data size is greater than 1 million and the query frequency is greater than 1000, choose option 3; When the data sensitivity level is ≥4, choose option 4; When the permission complexity is simple, choose option 2; For other scenarios, choose option 1.

6. A business data automatic vectorization and semantic retrieval system, characterized in that, The system employs the automatic vectorization and semantic retrieval method for business data as described in any one of claims 1 to 5, and the system comprises: Data Change Awareness Module: Used to capture the addition, modification, or deletion of business data, extract business records, and bind identifiers; Multidimensional Quality Assessment Module: Deploys the MDQA algorithm, performs five-dimensional data quality assessment and filtering, and also generates improvement suggestions for low-quality data; Intelligent segmentation module: Deploys the SBAS algorithm to achieve semantically complete text segmentation and supports adjustment of segmentation parameters; Incremental update module: Deploys the IVDU algorithm, intelligently selects vector update strategies, and also records update strategy selection and quality scores; Permission embedding module: Deploys the VPEA algorithm, provides multiple permission embedding schemes, and is also used to maintain a sensitive information detection pattern library; Vector storage module: Used to store vector data with embedded permission information, supporting flexible switching between mainstream vector databases; Semantic retrieval module: Receives user natural language queries and performs vector similarity retrieval with permission verification; GenAI Interaction Module: Generates the final answer based on the search results and returns it to the user.

7. The business data automatic vectorization and semantic retrieval system according to claim 6, characterized in that: The improvement suggestions generated by the multi-dimensional quality assessment module include specific problem dimensions of low-quality data and remediation guidelines; the information recorded by the incremental update module includes update time, strategy type, number of API calls, quality score and reason for downgrade; the sensitive information detection pattern library maintained by the permission embedding module supports the addition of industry-specific sensitive rules.

8. The business data automatic vectorization and semantic retrieval system according to claim 6, characterized in that: It supports adjusting core algorithm parameters via configuration files, including MDQA parameters, SBAS parameters, IVDU parameters, VPEA Bloom filter false positive rate, and differential privacy budget ε; it supports replacing embedding models and vector databases without modifying the core process code.

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