Data security compliance detection method and system for large model output content

Abstract

The invention relates to a data security compliance detection method and system for large model output content, and belongs to the technical field of data content compliance detection. The method comprises the following steps: acquiring output content data of a large model, establishing a data risk assessment matrix, and analyzing a deviation ratio of semantic vector distribution associated with the output content; constructing a target content detection model, and inputting the large model output content data into the target content detection model for data content compliance detection; performing privacy compliance-scale simulation detection on the output content of the large model through a compliance filtering algorithm, optimizing the structure of a content sensitive element in a content detection process in combination with entity data of a data compliance knowledge graph, and obtaining compliance detection information; and when the compliance detection information cannot pass the data content compliance detection, taking the output content sensitive element as a keyword, storing the keyword into the data content detection library, and generating a data security compliance detection report.

Description

Technical Field

[0001] This invention belongs to the field of data content compliance detection technology, specifically relating to a data security compliance detection method and system for large model output content. Background Technology

[0002] With the rapid development of Large Language Models (LLMs) and Generative AI, they have been widely applied in fields such as intelligent question answering, text generation, coding, content creation, and data analysis. However, large models often pose potential data security and compliance risks during content generation. On the one hand, the model training process may involve massive amounts of public or semi-public data, including private information, sensitive corpora, or protected data content. If these are not rigorously screened or filtered in compliance with regulations, they may be "reproduced" or "leaked" by the model during the output stage. On the other hand, the generated content may also contain politically sensitive information, false or inaccurate descriptions, discriminatory remarks, and semantic expressions that violate ethical norms, resulting in output results that fail to meet the compliance requirements of laws, regulations, and industry standards.

[0003] Existing large-scale model output detection techniques largely rely on static rule matching or keyword filtering. These methods have limited effectiveness when dealing with complex semantic structures, implicit semantic references, and cross-domain knowledge reasoning, making it difficult to accurately identify violations at the semantic level and within contextual relationships. Furthermore, traditional detection methods lack dynamic risk assessment and semantic bias correction mechanisms, failing to adaptively adjust detection strategies based on the characteristics of content generated by different models, resulting in low precision and recall rates. In addition, existing solutions often lack integration with semantic enhancement technologies such as knowledge graphs, hindering the intelligent quantitative analysis of compliance risks in a multi-dimensional information space.

[0004] Therefore, there is an urgent need for a data security compliance detection method for the output content of large models. This method should comprehensively utilize techniques such as semantic vector analysis, content parameter correction, multi-level semantic fusion, and compliance knowledge graph reasoning to fully model and intelligently detect semantic risks in the output content. The method should possess dynamic adaptive capabilities, optimizing the model structure based on semantic bias rate and risk correction parameters during the detection process. This would enable the coordinated operation of privacy information identification, sensitive element structure optimization, and compliance confidence calculation, thereby effectively improving the credibility and controllability of the output content of large models in terms of data security and legal compliance, and ensuring the safe, usable, and compliant operation of generative artificial intelligence systems. Summary of the Invention

[0005] To address the aforementioned problems in existing technologies, this invention provides a method for detecting data security and compliance of large model output content. The objective of this invention can be achieved through the following technical solutions: S1: Obtain the output content data of the large model, establish a data risk assessment matrix based on the output content data of the large model, and analyze the deviation rate between the semantic vector distribution and the output content through the content parameter detection and correction mechanism to obtain risk correction parameters; S2: Construct a target content detection model based on the risk correction parameters, analyze the data risk assessment matrix through a multi-level semantic fusion network, and input the output content data of the large model into the target content detection model to perform data content compliance detection; S3: The data content compliance detection uses a compliance filtering algorithm to perform privacy compliance simulation detection on the output content of the large model, and optimizes the structure of content-sensitive elements in the content detection process based on the deviation constraint content function and the entity data of the data compliance knowledge graph to obtain compliance detection information; S4: Construct a data content detection library based on the compliance detection information. When the compliance detection information fails the data content compliance detection, store the sensitive elements of the output content as keywords in the data content detection library and generate a data security compliance detection report.

[0006] Specifically, the method for obtaining the large model output content data is as follows: the model output content is context-annotated to adjust the detection range of the output content, and semantic features of the output content are extracted in combination with the detection range to obtain the large model output content data.

[0007] Specifically, the data risk assessment matrix is ​​based on the semantic control points extracted from the output content data of the large model, and combines the semantic feature deviations within the detection range of the output content data of the large model with the analyzed semantic deviations and the constrained content.

[0008] Specifically, the content parameter detection and correction mechanism calculates the correlation deviation rate based on the semantic vector of the output content and the semantic vector of the reference knowledge base, and applies feature constraints to each parameter set. By reducing the nonlinear semantic offset error, the deviation rate is weighted and corrected.

[0009] Specifically, the method for generating the risk correction parameters is as follows: Based on the local content data vector grid extracted from the output content of the large model, the semantic distribution curve of the content is calculated, and the content deviation rate is discretely fitted in combination with data compliance parameters to obtain deviation variable data based on vector security compliance. Based on the deviation variable data, the functional relationship between the content semantic gradient and the corresponding deviation vector offset is correlated, and the deviation cost including entity association deviation and semantic offset is analyzed based on the content semantic distribution curve to obtain the weight coefficient of the deviation variable on the compliance benchmark. Content deviation rate is extracted by analyzing the entity association distribution of the local content data vector grid. The content deviation rate is combined with the weight ratio of the intrinsic and extrinsic parameters of the risk assessment matrix to generate correction polynomial coefficients. The content elements are then remapped using vector interpolation to obtain risk correction parameters.

[0010] Specifically, the method for constructing the content target detection model is as follows: Extract multi-scale semantic features from the output content data of large models, and transform unstructured content data into a vector set of risk candidate regions. Combine this with a multi-level semantic fusion network to associate content risk types and generate a content risk detection feature matrix. Based on the content risk detection feature matrix, the risk correction parameters and multimodal risk assessment matrix are received to process the content monitoring features, and the feature fusion results are converged through an adaptive mechanism. A content compliance detection scheme is generated based on the adaptive filtering algorithm. The content compliance detection scheme is implemented, and the parameter weights of the content risk detection are dynamically corrected by gradient descent under the constraint of the deviation constraint energy function. The risk type constraint is used as the external mapping parameter to construct the content target detection model.

[0011] Specifically, the multi-level semantic fusion network is designed with multiple parallel feature extraction layers. One layer focuses on low-level semantic features such as word vectors and syntax, while another layer extracts entity and security intent knowledge features. By analyzing the multimodal risk assessment matrix, the low-level semantic features are fused with the security intent knowledge features, and the detection of risks in the output content of the large model is optimized based on skip connections to generate enhanced semantic feature data.

[0012] Specifically, the method for performing privacy compliance simulation detection on the output content of the large model is as follows: based on the compliance filtering algorithm, sensitive content regions are isolated in the output content sequence, and a rule deviation map is calculated according to the deviation constraint content. Combined with the discontinuity of the minimized elements in the output content of the large model, privacy compliance simulation detection is performed.

[0013] Specifically, the method for obtaining the compliance detection information is as follows: by performing multi-dimensional semantic analysis on the content feature results output by the target content detection model, feature vectors containing sensitive words, policy and regulation related items and potential violation contexts are extracted, and knowledge-enhanced reasoning is performed in combination with the regulatory nodes and semantic related edges in the data compliance knowledge graph to obtain the compliance detection information.

[0014] Specifically, the sensitive elements of the output content simulate the semantic and compliance changes of the output content of the large model in the data content detection library based on dynamic rules.

[0015] Specifically, the method for generating the compliance risk assessment report is as follows: the compliance detection information is structured and encoded to obtain multi-dimensional report fields, and the multi-dimensional report fields are detected based on a templated report framework. When a risk parameter is detected to deviate from the threshold, a risk detection alarm is triggered, and a compliance risk assessment report is output.

[0016] Specifically, a data security compliance detection system for the output content of a large model is characterized by comprising: Data risk assessment module: acquires the output content data of the large model, establishes a data risk assessment matrix based on the output content data of the large model, and analyzes the deviation rate between the semantic vector distribution and the output content through a content parameter detection and correction mechanism to obtain risk correction parameters; Target content detection model construction module: Based on the risk correction parameters, a target content detection model is constructed. The data risk assessment matrix is ​​analyzed through a multi-level semantic fusion network. The output content data of the large model is then input into the target content detection model for data content compliance detection. Compliance simulation detection module: The data content compliance detection uses a compliance filtering algorithm to perform privacy compliance simulation detection on the output content of the large model, and optimizes the structure of content-sensitive elements in the content detection process based on the deviation constraint content function and the entity data of the data compliance knowledge graph to obtain compliance detection information; Compliance report generation module: Constructs a data content detection library based on the compliance detection information. When the compliance detection information fails the data content compliance detection, the sensitive elements of the output content are stored as keywords in the data content detection library to generate a data security compliance detection report.

[0017] The beneficial effects of this invention are as follows: This invention provides a data security compliance detection method for the output content of large models, which has significant technical advantages and practical application effects. First, by establishing a data risk assessment matrix based on semantic vector distribution and introducing a content parameter detection and correction mechanism, multi-dimensional semantic risk quantification of the output content of large models is achieved. Compared with traditional keyword filtering or template matching methods, this invention can identify potential privacy information, illegal expressions, and biased semantics at the semantic level, significantly improving the accuracy and robustness of risk detection.

[0018] By introducing a multi-level semantic fusion network, a target content detection model is constructed, which integrates features from the semantic layer, context layer, and knowledge layer in a multi-dimensional manner. This enables a more comprehensive capture of the deep semantic logic and potential risk relationships within the generated content. This design overcomes the limitations of traditional single semantic matching, achieving intelligent identification and dynamic detection of privacy information, discriminatory expressions, and false content in complex contexts.

[0019] A detection mechanism combining deviation-constrained content functions and data compliance knowledge graphs can automatically identify and optimize the structure of sensitive elements during the detection process, achieving dynamic correction of semantic deviation rates and optimization of risk energy constraints. This mechanism not only effectively reduces detection errors but also performs semantic mapping of regulatory clauses, industry standards, and sensitive entities through entity relationship reasoning in the knowledge graph, making the detection results more interpretable and traceable.

[0020] By constructing a data content detection library, this invention can store keywords and continuously learn from sensitive elements that fail detection, enabling the detection model to self-optimize and accumulate knowledge. As the data detection library dynamically expands, the system can continuously improve the coverage and accuracy of content risk identification, forming a closed-loop compliance detection system.

[0021] In summary, this invention achieves fully automated detection from semantic understanding and risk modeling to intelligent compliance judgment, effectively preventing data leakage, privacy infringement, and illegal generation issues in the output content of large models. It significantly improves the controllability and credibility of artificial intelligence systems in terms of data security, content compliance, and social ethics, and has broad application prospects and industrial promotion value. Attached Figure Description

[0022] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0023] Figure 1 This is a schematic diagram of the data security compliance detection method and system for large model output content according to the present invention.

[0024] Figure 2 This is a schematic diagram illustrating the overall technical flow of a data security compliance detection method and system for large model output content according to the present invention. Detailed Implementation

[0025] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0026] Please see Figure 1 A method for data security compliance testing of large model output content: S1: Obtain the output content data of the large model, establish a data risk assessment matrix based on the output content data of the large model, and analyze the deviation rate between the semantic vector distribution and the output content through the content parameter detection and correction mechanism to obtain risk correction parameters; S2: Construct a target content detection model based on the risk correction parameters, analyze the data risk assessment matrix through a multi-level semantic fusion network, and input the output content data of the large model into the target content detection model to perform data content compliance detection; S3: The data content compliance detection uses a compliance filtering algorithm to perform privacy compliance simulation detection on the output content of the large model, and optimizes the structure of content-sensitive elements in the content detection process based on the deviation constraint content function and the entity data of the data compliance knowledge graph to obtain compliance detection information; S4: Construct a data content detection library based on the compliance detection information. When the compliance detection information fails the data content compliance detection, store the sensitive elements of the output content as keywords in the data content detection library and generate a data security compliance detection report.

[0027] Specifically, the method for obtaining the large model output content data is as follows: the model output content is context-annotated to adjust the detection range of the output content, and semantic features of the output content are extracted in combination with the detection range to obtain the large model output content data.

[0028] Specifically, the data risk assessment matrix is ​​based on the semantic control points extracted from the output content data of the large model, and combines the semantic feature deviations within the detection range of the output content data of the large model with the analyzed semantic deviations and the constrained content.

[0029] Specifically, the content parameter detection and correction mechanism calculates the correlation deviation rate based on the semantic vector of the output content and the semantic vector of the reference knowledge base, and applies feature constraints to each parameter set. By reducing the nonlinear semantic offset error, the deviation rate is weighted and corrected.

[0030] This embodiment provides a data security and compliance detection system for large model output content. It is deployed in the cloud service cluster and local edge security node of the artificial intelligence content generation platform. Through large model output content collection, semantic feature parsing, risk assessment modeling, and knowledge graph constraint reasoning, it realizes real-time detection, judgment, and report generation of the data security and compliance status of the generated content.

[0031] The overall system architecture includes a data acquisition module, a semantic analysis module, a risk assessment module, a compliance model testing module, and a report generation module, forming a complete closed-loop testing process of "content acquisition → semantic parsing → risk identification → compliance verification → report generation".

[0032] First, the system continuously acquires text, code, or structured output data generated by the large model through content acquisition interfaces (such as HTTP API, intermediate message queue, or file listening service). The data undergoes format parsing, encoding standardization, abnormal character cleaning, and sensitive symbol normalization by the preprocessing engine. At the same time, time tags and task IDs are used to associate the data source with the context to ensure the traceability of the detection process.

[0033] Subsequently, the semantic analysis module utilizes natural language processing and vector embedding techniques to perform deep feature extraction on the collected content. This module employs HanLP, Jieba, and BERT-based tokenizers for word segmentation and named entity recognition, extracts syntactic dependencies and behavioral relationships through AllenNLP semantic role labeling (SRL), and combines the Sentence-BERT embedding model to map semantic information to a high-dimensional semantic vector space, generating a semantic feature vector set.

[0034] The risk assessment module constructs a data risk assessment matrix based on the semantic features and calculates the semantic bias rate and risk correction parameters using a content parameter detection and correction mechanism. This module employs a multi-layer attention mechanism network implemented in the PyTorch framework, combined with a bias-constrained energy function, to perform quantitative risk analysis on different types of outputs (such as privacy information, violation descriptions, and ambiguous expressions).

[0035] During the compliance model detection phase, the system introduces a data compliance knowledge graph (built using Neo4j / JanusGraph), where nodes include legal clauses, sensitive word entities, ethical norms, and contextual rule edges. The detection module, based on the BERT / ERNIE semantic fusion model and knowledge graph inference engine, performs semantic association inference and legal matching on the output content. The compliance filtering algorithm (implemented by the Drools rule engine) scores and labels detected violation risks and generates rectification suggestions based on preset thresholds and weight rules.

[0036] When the risk confidence level in the detection results exceeds the set threshold, the system automatically triggers adaptive optimization logic: based on incremental learning and rolling optimization mechanisms, the embedding parameters, rule weights and knowledge graph relationships of the model are dynamically fine-tuned to achieve continuous optimization and intelligent iteration of the detection model.

[0037] Finally, the report generation module calls Jinja2, python-docx, and ReportLab components to generate a structured data security compliance detection report based on the detection results. The report includes violation categories, confidence distribution, knowledge graph tracing paths, and correction suggestions. At the same time, sensitive elements that fail the detection are written into the FAISS index and ElasticSearch suggestion dictionary to support rapid matching and risk reuse in subsequent detection tasks.

[0038] Through the above design, this embodiment realizes an automated processing flow from content acquisition, semantic understanding, risk modeling to compliance judgment and report generation. It can accurately identify privacy leaks, illegal expressions and compliance risk content in complex semantic scenarios, and significantly improve the security, compliance and credibility of the large model output.

[0039] Specifically, the method for generating the risk correction parameters is as follows: Based on the local content data vector grid extracted from the output content of the large model, the semantic distribution curve of the content is calculated, and the content deviation rate is discretely fitted in combination with data compliance parameters to obtain deviation variable data based on vector security compliance. Based on the deviation variable data, the functional relationship between the content semantic gradient and the corresponding deviation vector offset is correlated, and the deviation cost including entity association deviation and semantic offset is analyzed based on the content semantic distribution curve to obtain the weight coefficient of the deviation variable on the compliance benchmark. Content deviation rate is extracted by analyzing the entity association distribution of the local content data vector grid. The content deviation rate is combined with the weight ratio of the intrinsic and extrinsic parameters of the risk assessment matrix to generate correction polynomial coefficients. The content elements are then remapped using vector interpolation to obtain risk correction parameters.

[0040] Specifically, the method for constructing the content target detection model is as follows: Extract multi-scale semantic features from the output content data of large models, and transform unstructured content data into a vector set of risk candidate regions. Combine this with a multi-level semantic fusion network to associate content risk types and generate a content risk detection feature matrix. Based on the content risk detection feature matrix, the risk correction parameters and multimodal risk assessment matrix are received to process the content monitoring features, and the feature fusion results are converged through an adaptive mechanism. A content compliance detection scheme is generated based on the adaptive filtering algorithm. The content compliance detection scheme is implemented, and the parameter weights of the content risk detection are dynamically corrected by gradient descent under the constraint of the deviation constraint energy function. The risk type constraint is used as the external mapping parameter to construct the content target detection model.

[0041] This embodiment provides a data security compliance detection system for the output content of a large model. It is deployed in the security control module of a cloud-based content generation platform and achieves real-time security assessment and compliance detection of the content generated by the large model through semantic vector analysis, deviation constraint function calculation, and knowledge graph reasoning.

[0042] The system comprises four core units: data acquisition and preprocessing unit, semantic feature extraction unit, risk assessment and deviation correction unit, and compliance reasoning and report generation unit.

[0043] First, the data acquisition and preprocessing unit is responsible for acquiring the generated content in real time from the API output interface of the large model. This content includes text, image descriptions, or code snippets. The system uses regular expression matching (re library) and natural language preprocessing tools (such as HanLP and Jieba) to perform sentence segmentation, stop word removal, and feature standardization on the data. Let the output content corpus be: C = {c1, c2, ..., c n}, where each c i For each independent generated result, the semantic feature extraction unit calculates its semantic representation using the BERT vectorization model: , , Among them, v i ∈R d Statement c i The d-dimensional semantic vector. Further calculation of the output content and the semantic center v of the compliance knowledge graph. k Cosine similarity: When the similarity S i When the threshold δ1 is exceeded, the content is determined to involve potentially sensitive topics or regulatory entities, and enters the risk assessment stage.

[0044] In the risk assessment and deviation correction unit, the system constructs a data risk assessment matrix R∈R m×n Its elements are defined as: , , Among them, v ij For the components on semantic dimension j, v j As a reference for the semantic mean of knowledge, α i The deviation weighting coefficient is used. The deviation energy constraint is calculated by obtaining the matrix energy function to measure the degree to which the semantic output deviates from the compliance space.

[0045] The compliance reasoning and report generation unit utilizes the Drools rule engine and Neo4j knowledge graph, combined with deviation correction parameters, to match and reason about non-compliant entity nodes. When the risk confidence level of the output content is detected... When this happens, the system marks the content as "non-compliant" and automatically generates rectification suggestions.

[0046] The detection results are output in a structured report format, including a semantic bias matrix visualization, risk confidence distribution, and knowledge graph tracing path. The report generation uses Jinja2 templates and ReportLab rendering mechanism, and sensitive keywords are stored in the ElasticSearch suggestion dictionary to enable knowledge reuse and model self-learning for subsequent detection tasks.

[0047] Through the above embodiments, this system realizes an automated data security compliance detection process based on semantic feature calculation, energy function constraints and knowledge reasoning. It can dynamically identify privacy leaks, illegal expressions and potential risky content, and achieve high-precision and interpretable security compliance control of large model output.

[0048] Specifically, the multi-level semantic fusion network is designed with multiple parallel feature extraction layers. One layer focuses on low-level semantic features such as word vectors and syntax, while another layer extracts entity and security intent knowledge features. By analyzing the multimodal risk assessment matrix, the low-level semantic features are fused with the security intent knowledge features, and the detection of risks in the output content of the large model is optimized based on skip connections to generate enhanced semantic feature data.

[0049] Specifically, the method for performing privacy compliance simulation detection on the output content of the large model is as follows: based on the compliance filtering algorithm, sensitive content regions are isolated in the output content sequence, and a rule deviation map is calculated according to the deviation constraint content. Combined with the discontinuity of the minimized elements in the output content of the large model, privacy compliance simulation detection is performed.

[0050] Specifically, the method for obtaining the compliance detection information is as follows: by performing multi-dimensional semantic analysis on the content feature results output by the target content detection model, feature vectors containing sensitive words, policy and regulation related items and potential violation contexts are extracted, and knowledge-enhanced reasoning is performed in combination with the regulatory nodes and semantic related edges in the data compliance knowledge graph to obtain the compliance detection information.

[0051] Specifically, the sensitive elements of the output content simulate the semantic and compliance changes of the output content of the large model in the data content detection library based on dynamic rules.

[0052] Specifically, the method for generating the compliance risk assessment report is as follows: the compliance detection information is structured and encoded to obtain multi-dimensional report fields, and the multi-dimensional report fields are detected based on a templated report framework. When a risk parameter is detected to deviate from the threshold, a risk detection alarm is triggered, and a compliance risk assessment report is output.

[0053] Specifically, a data security compliance detection system for the output content of a large model is characterized by comprising: Data risk assessment module: acquires the output content data of the large model, establishes a data risk assessment matrix based on the output content data of the large model, and analyzes the deviation rate between the semantic vector distribution and the output content through a content parameter detection and correction mechanism to obtain risk correction parameters; Target content detection model construction module: Based on the risk correction parameters, a target content detection model is constructed. The data risk assessment matrix is ​​analyzed through a multi-level semantic fusion network. The output content data of the large model is then input into the target content detection model for data content compliance detection. Compliance simulation detection module: The data content compliance detection uses a compliance filtering algorithm to perform privacy compliance simulation detection on the output content of the large model, and optimizes the structure of content-sensitive elements in the content detection process based on the deviation constraint content function and the entity data of the data compliance knowledge graph to obtain compliance detection information; Compliance report generation module: Constructs a data content detection library based on the compliance detection information. When the compliance detection information fails the data content compliance detection, the sensitive elements of the output content are stored as keywords in the data content detection library to generate a data security compliance detection report.

[0054] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for detecting data security compliance of large model output content, characterized in that, include: S1: Obtain the output content data of the large model, establish a data risk assessment matrix based on the output content data of the large model, and analyze the deviation rate between the semantic vector distribution and the output content through the content parameter detection and correction mechanism to obtain risk correction parameters; S2: Construct a target content detection model based on the risk correction parameters, analyze the data risk assessment matrix through a multi-level semantic fusion network, and input the output content data of the large model into the target content detection model to perform data content compliance detection; S3: The data content compliance detection uses a compliance filtering algorithm to perform privacy compliance simulation detection on the output content of the large model, and optimizes the structure of content-sensitive elements in the content detection process based on the deviation constraint content function and the entity data of the data compliance knowledge graph to obtain compliance detection information; S4: Construct a data content detection library based on the compliance detection information. When the compliance detection information fails the data content compliance detection, store the sensitive elements of the output content as keywords in the data content detection library and generate a data security compliance detection report.

2. The method according to claim 1, characterized in that, The method for obtaining the output content data of the large model is as follows: the output content of the model is context-annotated to adjust the detection range of the output content, and the semantic features of the output content are extracted in combination with the detection range to obtain the output content data of the large model.

3. The method according to claim 1, characterized in that, The data risk assessment matrix is ​​based on the semantic control points extracted from the output content data of the large model, and combines the semantic feature deviations within the detection range of the output content data of the large model with the analyzed semantic deviations and associates the analyzed semantic deviations with the constraint content.

4. The method according to claim 1, characterized in that, The content parameter detection and correction mechanism calculates the correlation deviation rate based on the semantic vector of the output content and the semantic vector of the reference knowledge base, and applies feature constraints to each parameter set. By reducing the nonlinear semantic offset error, the deviation rate is weighted and corrected.

5. The method according to claim 2, characterized in that, The method for generating the risk correction parameters is as follows: Based on the local content data vector grid extracted from the output content of the large model, the semantic distribution curve of the content is calculated, and the content deviation rate is discretely fitted in combination with data compliance parameters to obtain deviation variable data based on vector security compliance. Based on the deviation variable data, the functional relationship between the content semantic gradient and the corresponding deviation vector offset is correlated, and the deviation cost including entity association deviation and semantic offset is analyzed based on the content semantic distribution curve to obtain the weight coefficient of the deviation variable on the compliance benchmark. Content deviation rate is extracted by analyzing the entity association distribution of the local content data vector grid. The content deviation rate is combined with the weight ratio of the intrinsic and extrinsic parameters of the risk assessment matrix to generate correction polynomial coefficients. The content elements are then remapped using vector interpolation to obtain risk correction parameters.

6. The method according to claim 5, characterized in that, The method for constructing the content target detection model is as follows: Extract multi-scale semantic features from the output content data of large models, and transform unstructured content data into a vector set of risk candidate regions. Combine this with a multi-level semantic fusion network to associate content risk types and generate a content risk detection feature matrix. Based on the content risk detection feature matrix, the risk correction parameters and multimodal risk assessment matrix are received to process the content monitoring features, and the feature fusion results are converged through an adaptive mechanism. A content compliance detection scheme is generated based on the adaptive filtering algorithm. The content compliance detection scheme is implemented, and the parameter weights of the content risk detection are dynamically corrected by gradient descent under the constraint of the deviation constraint energy function. The risk type constraint is used as the external mapping parameter to construct the content target detection model.

7. The method according to claim 4, characterized in that, The multi-level semantic fusion network is designed with multiple parallel feature extraction layers. One layer focuses on low-level semantic features such as word vectors and syntax, while another layer extracts entity and security intent knowledge features. By analyzing the multimodal risk assessment matrix, the low-level semantic features are fused with the security intent knowledge features, and the detection of risks in the output content of the large model is optimized based on skip connections to generate enhanced semantic feature data.

8. The method according to claim 2, characterized in that, The method for performing privacy compliance simulation detection on the output content of the large model is as follows: based on the compliance filtering algorithm, sensitive content regions are isolated in the output content sequence, and a rule deviation map is calculated according to the deviation constraint content. Combined with the discontinuity of the minimized elements in the output content of the large model, privacy compliance simulation detection is performed.

9. The method according to claim 4, characterized in that, The method for obtaining the compliance detection information is as follows: by performing multi-dimensional semantic parsing on the content feature results output by the target content detection model, feature vectors containing sensitive words, policy and regulation related items and potential violation contexts are extracted, and knowledge-enhanced reasoning is performed in combination with the regulatory nodes and semantic related edges in the data compliance knowledge graph to obtain the compliance detection information.

10. The method according to claim 4, characterized in that, The sensitive elements of the output content are based on dynamic rules that simulate the semantic and compliance changes of the output content of the large model in the data content detection library.

11. The method according to claim 7, characterized in that, The method for generating the compliance risk assessment report is as follows: the compliance detection information is structured and encoded to obtain multi-dimensional report fields, and the multi-dimensional report fields are detected based on a templated report framework. When a risk parameter is detected to deviate from the threshold, a risk detection alarm is triggered, and a compliance risk assessment report is output.

12. A data security compliance detection system for the output content of a large model, characterized in that, include: Data risk assessment module: acquires the output content data of the large model, establishes a data risk assessment matrix based on the output content data of the large model, and analyzes the deviation rate between the semantic vector distribution and the output content through a content parameter detection and correction mechanism to obtain risk correction parameters; Target content detection model construction module: Based on the risk correction parameters, a target content detection model is constructed. The data risk assessment matrix is ​​analyzed through a multi-level semantic fusion network. The output content data of the large model is then input into the target content detection model for data content compliance detection. Compliance simulation detection module: The data content compliance detection uses a compliance filtering algorithm to perform privacy compliance simulation detection on the output content of the large model, and optimizes the structure of content-sensitive elements in the content detection process based on the deviation constraint content function and the entity data of the data compliance knowledge graph to obtain compliance detection information; Compliance report generation module: Constructs a data content detection library based on the compliance detection information. When the compliance detection information fails the data content compliance detection, the sensitive elements of the output content are stored as keywords in the data content detection library to generate a data security compliance detection report.

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