Inference system for enterprise unstructured data based on private knowledge world model

Abstract

The application discloses an inference system for enterprise unstructured data based on a private knowledge world model and relates to the technical field of data management; the system is composed of a multi-modal data access layer, a knowledge extraction and vectorization module, a knowledge graph and private knowledge world model module, a data non-out-domain privacy calculation training module, an industry mechanism and AI fusion inference module, and a business prediction and risk early warning application module; the system uniformly accesses and processes multiple types of unstructured data, sequentially completes knowledge extraction, vectorization, knowledge graph construction and private knowledge world model modeling, completes model training and iteration in the private domain of an enterprise, realizes fusion inference in combination with industry mechanism and AI, and further completes business trend prediction, risk early warning, compliance review and experience reuse; the inference system for enterprise unstructured data based on the private knowledge world model is suitable for various enterprise scenes with high supervision and high confidentiality requirements.

Description

Technical Field

[0001] This invention relates to the field of data management technology, and in particular to a reasoning system for enterprise unstructured data based on a private knowledge world model. Background Technology

[0002] Enterprises often store massive amounts of knowledge and business data in unstructured formats such as documents, tables, rules and regulations, business processes, personnel experience, images, voice recordings, and system logs. This results in fragmented knowledge, difficulties in data aggregation, and low management efficiency. Traditional enterprise knowledge bases only have basic keyword search functions and cannot uncover logical connections, causal relationships, and business rules between data. They also lack intelligent reasoning, trend prediction, risk identification, and decision support capabilities.

[0003] Using public large models to process confidential and sensitive business data within enterprises poses a significant risk of data leakage and fails to meet the compliance and control requirements of heavily regulated industries. At the same time, existing technologies struggle to transform internal enterprise systems, business experience, logical relationships, and causal rules into machine-readable, computable, and reasonable digital models, resulting in the inability to accumulate, reuse, and pass on tacit knowledge, thus limiting the level of intelligent decision-making within enterprises.

[0004] Based on the shortcomings of the existing technologies, this invention proposes a reasoning system for enterprise unstructured data based on a private knowledge world model. Under the premise of ensuring that the data does not leave the enterprise's private domain throughout the process, it can complete unstructured data governance, knowledge modeling and intelligent reasoning applications. Summary of the Invention

[0005] The purpose of this invention is to provide a reasoning system for enterprise unstructured data based on a private knowledge world model. It constructs an enterprise-specific private knowledge world model and, under the security premise that the data does not leave the domain, realizes unified modeling of enterprise unstructured knowledge, intelligent logical reasoning, business trend prediction, automatic risk warning, and reuse of business experience, providing stable and reliable intelligent decision support for enterprise autonomous systems.

[0006] To achieve the above objectives, this invention provides a reasoning system for enterprise unstructured data based on a private knowledge world model, including a multimodal data access module, a knowledge extraction and vectorization module, a knowledge graph and private knowledge world model module, an industry mechanism and AI fusion reasoning module, a data privacy computing training module that does not leave the domain, and a business prediction and risk warning application module. The multimodal data access layer collects various types of unstructured data from enterprises, completes preprocessing, classification and security level labeling, and stores the data in the enterprise's private domain; The knowledge extraction and vectorization module communicates with the multimodal data access module to extract knowledge elements and generate semantic vectors, thus building a private knowledge vector library. The knowledge graph and private knowledge world model module connects to the knowledge extraction and vectorization module to complete the construction of the enterprise knowledge graph and private knowledge world model; The Data Without Leaving the Domain Privacy Computing Training Module is responsible for training, parameter tuning, and incremental iteration of the private knowledge world model within the enterprise's private domain, and for achieving rapid learning from samples and continuous iterative optimization of the model based on the foundation. The industry mechanism and AI fusion reasoning module adopts a dual-engine fusion architecture of industry mechanism engine and AI reasoning engine. Based on the trained private knowledge world model, it combines the two engines to carry out intelligent reasoning, rule verification and logical analysis. The business forecasting and risk warning application module is designed for real-world business scenarios. Based on the reasoning results, it enables trend prediction, risk warning, business review, and decision support, and outputs corresponding business forecast results.

[0007] Preferably, the specific contents of the multimodal data access module are as follows: Unstructured data is collected using a localized interface, and the unstructured data does not leave the enterprise intranet throughout the entire process; Clean the unstructured data, remove invalid garbled characters, duplicate files, blank content, and standardize the data format; Data is classified and archived according to business department, data type, and security level; hierarchical access permissions are set; and basic data security control is completed.

[0008] The preferred content of the knowledge extraction and vectorization module is as follows: For text and tabular data, extract knowledge elements, including entities, entity relationships, business rules, business processes, business experience, and risk factors. For image-based data, OCR recognition technology is used to complete image and text parsing and extract useful knowledge; For voice-based data, speech-to-text transcription is performed before knowledge element extraction. All extracted knowledge elements are converted into semantic vectors of a unified dimension, a private knowledge vector library is constructed, and the private knowledge vector library is set to be stored and accessed only within the enterprise's private domain.

[0009] Preferably, the specific content of building the knowledge graph in the knowledge graph and private knowledge world model module is as follows: Obtain the knowledge elements output by the knowledge extraction and vectorization module, complete data cleaning, error correction and format normalization, and obtain a standardized knowledge dataset; Based on enterprise business scenarios and business rules, define the knowledge graph schema architecture, divide entity types, entity attributes, and relationships between entities, and unify naming rules and association constraints; Using business entities as nodes and entity relationships as edges, and binding entity attributes, business tags, and data source information, the entire set of entity nodes and related edges is constructed to form a basic knowledge network. Based on a private knowledge vector library, entity disambiguation and duplicate node merging are completed, and implicit business relationships between entities are mined to complete the knowledge links and form a preliminary knowledge graph. The initial knowledge graph undergoes logical and business rule verification to correct errors and logical conflicts, ultimately generating a standard knowledge graph.

[0010] Preferably, the specific content of constructing the private knowledge world model in the knowledge graph and private knowledge world model module is as follows: The validated standard knowledge graph and private knowledge vector library are loaded into the model framework to serve as the underlying static knowledge base and semantic support of the model. Import industry mechanism rules, external regulatory regulations, internal corporate systems, compliance clauses, and standard business processes to establish a rule knowledge base, and associate and bind the rules with entities and relationships in the knowledge graph; Access the enterprise's historical business cases, risk handling records, and business experience data to build a case library and experience library, and link them to the corresponding business scenarios; Based on knowledge networks, rule knowledge bases, and experience bases, we sort out business causal relationships, risk evolution paths, and scenario evolution logic to complete the logical modeling of all business scenarios. By calling upon the industry mechanism and AI fusion reasoning module, data interaction channels at all levels are opened up to achieve the integration of rule determination, semantic parsing, and logical deduction functions; The model is divided into multiple scenario sub-models according to business scenarios and business departments, and independent access permissions and data isolation strategies are configured. Complete the overall model debugging and functional verification, enable the incremental data docking interface, and support the continuous updates of knowledge, rules, and cases.

[0011] Preferably, the specific content of the data-without-the-domain privacy computing training module is as follows: Set up a private training environment, cut off public network access channels, and complete all training operations and data interactions within the enterprise intranet; It uses locally generated knowledge data, graph data, and vector data as training samples, without calling external public datasets or sending enterprise data back to the outside world; Complete the initial training and parameter optimization of the private knowledge world model. When the system adds new business data, knowledge, rules, or cases, it will automatically trigger incremental training. For business scenarios with few or no samples, the model learns new business rules autonomously based on its generalization capabilities; at the same time, it encrypts and controls access permissions for the private knowledge world model parameters and training logs to prevent data and model leakage.

[0012] The preferred module for integrating industry mechanisms with AI inference has the following specific tasks: It connects to the private knowledge world model, knowledge graph, private knowledge vector library, rule library and case library, completes engine initialization and parameter configuration, receives external business data to be inferred and performs pre-processing; The inference mode is automatically scheduled according to the complexity of the business scenario, and either single-engine independent inference or dual-engine joint inference is selected. The task is divided into four categories: rule matching reasoning, associative logic reasoning, experience reuse reasoning, and scenario simulation reasoning. Cross-validation, conflict correction, and integration of the dual-engine outputs are performed to generate a unified reasoning conclusion. Forward the inference results to the business forecasting and risk warning application module, and encrypt and retain the inference logs throughout the entire process.

[0013] The preferred application module for business forecasting and risk warning includes the following features: Based on the output of the industry mechanism and AI fusion reasoning module, business trend prediction, risk warning, business review, and decision support are completed, and business prediction results are output. Automatically identify anomalies, violations, and potential risks in business data, classify risk levels, and trigger risk warnings; For business scenarios such as contract review, compliance self-inspection, intelligent Q&A, and management decision-making, it outputs structured reports, risk labels, modification suggestions, and visual analysis content; It retains complete operation logs, reasoning records, and business results throughout the entire process, supporting data traceability and auditing.

[0014] Therefore, the reasoning system for enterprise unstructured data based on the private knowledge world model described above, as presented in this invention, has the following advantages compared to existing technologies: 1. This application completes the unified digital modeling of fragmented and unstructured enterprise knowledge, realizing the long-term accumulation, intelligent reasoning, reuse and intergenerational inheritance of enterprise knowledge; 2. Relying on a private knowledge world model and fusion reasoning capabilities, it can provide a dedicated intelligent decision-making brain for enterprise autonomous execution systems, improving the level of enterprise business intelligence; it has small-sample and zero-sample learning capabilities, and can quickly adapt to new business and temporary rules of enterprises, reducing model maintenance and manual annotation costs; 3. All data, models, and computations throughout the entire process are completed within the enterprise's private domain. Combined with privacy computing technology, this completely avoids the risk of data leakage and meets the compliance requirements of highly regulated industries such as finance, government, and central enterprises.

[0015] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0016] Figure 1 This is an overall structural diagram of the reasoning system for enterprise unstructured data based on the private knowledge world model of this invention; Figure 2 This is a visual interface diagram of the reasoning system for enterprise unstructured data based on the private knowledge world model of this invention; Figure 3 This is a diagram of the contract review interface of the reasoning system for enterprise unstructured data based on the private knowledge world model of this invention. Detailed Implementation

[0017] In the description of this invention, it should be noted that the terms "upper," "lower," "inner," "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of this invention is usually placed when in use. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0018] Example like Figures 1-3 As shown, the reasoning system for enterprise unstructured data based on a private knowledge world model of the present invention includes a multimodal data access module, a knowledge extraction and vectorization module, a knowledge graph and private knowledge world model module, an industry mechanism and AI fusion reasoning module, a data privacy computing training module that does not leave the domain, and a business prediction and risk warning application module.

[0019] The multimodal data access layer collects various types of unstructured data from enterprises, completes preprocessing, classification and security level labeling, and stores the data in the enterprise's private domain; The specific contents of the multimodal data access module are as follows: Unstructured data is collected using a localized interface, and the unstructured data does not leave the enterprise intranet throughout the entire process; Clean the unstructured data, remove invalid garbled characters, duplicate files, blank content, and standardize the data format; Data is classified and archived according to business department, data type, and security level; hierarchical access permissions are set; and basic data security control is completed.

[0020] The knowledge extraction and vectorization module communicates with the multimodal data access module to extract knowledge elements and generate semantic vectors, thus building a private knowledge vector library. The specific content of the knowledge extraction and vectorization module is as follows: For text and tabular data, extract knowledge elements, including entities, entity relationships, business rules, business processes, business experience, and risk factors. For image-based data, OCR recognition technology is used to complete image and text parsing and extract useful knowledge; For voice-based data, speech-to-text transcription is performed before knowledge element extraction. All extracted knowledge elements are converted into semantic vectors of a unified dimension, a private knowledge vector library is constructed, and this private knowledge vector library is configured to be stored and accessed only within the enterprise's private domain; the specific formula is as follows: ;in, For the first The knowledge elements (entities / relationships / rules / clauses / experiences, etc.) obtained from the extraction. Deploy a multimodal encoder model for private use (private large-scale encoder). This is a private model parameter set (stored entirely on the company's intranet and not exported externally). For dimension Private semantic vectors, all Constructing a private knowledge vector library , The dimension of the vector is unified (the system is globally fixed to ensure that the vector is computable).

[0021] The knowledge graph and private knowledge world model module connects to the knowledge extraction and vectorization module to complete the construction of the enterprise knowledge graph and private knowledge world model; The specific details of building the knowledge graph in the Knowledge Graph and Private Knowledge World Model module are as follows: Obtain the knowledge elements output by the knowledge extraction and vectorization module, complete data cleaning, error correction and format normalization, and obtain a standardized knowledge dataset; Based on enterprise business scenarios and business rules, define the knowledge graph schema architecture, divide entity types, entity attributes, and relationships between entities, and unify naming rules and association constraints; Using business entities as nodes and entity relationships as edges, and binding entity attributes, business tags, and data source information, the entire set of entity nodes and related edges is constructed to form a basic knowledge network. Based on a private knowledge vector library, entity disambiguation and duplicate node merging are completed, and implicit business relationships between entities are mined to complete the knowledge links and form a preliminary knowledge graph. The semantic similarity between two entities is calculated to determine whether they are the same entity and whether there is any implicit business relationship. The specific formula is as follows: ; in, These are the semantic vectors corresponding to the two entities. For vector dot product, It is the L2 norm (Euclidean modulus). The similarity score indicates a stronger semantic / business relevance; Entity disambiguation: Set entity merging threshold ,like If it is determined to be a duplicate entity, node merging will be performed; Implicit Link Completion: Setting Association Thresholds ,like Automatically supplement implicit business relationship edges between entities; The initial knowledge graph undergoes logical and business rule verification to correct errors and logical conflicts, ultimately generating a standard knowledge graph. The formula for quantifying the business association density of entity nodes and detecting broken links and isolated nodes is as follows: ;in, For a specific entity node in a knowledge graph, For nodes The degree (total number of connected edges). This represents the total number of entity nodes in the knowledge graph. For nodes Topological correlation If the node is identified as an isolated node or a broken link node, link repair will be triggered.

[0022] The specific content of constructing the private knowledge world model in the Knowledge Graph and Private Knowledge World Model module is as follows: The validated standard knowledge graph and private knowledge vector library are loaded into the model framework to serve as the underlying static knowledge base and semantic support of the model. Import industry mechanism rules, external regulatory regulations, internal corporate systems, compliance clauses, and standard business processes to establish a rule knowledge base, and associate and bind the rules with entities and relationships in the knowledge graph; Access the enterprise's historical business cases, risk handling records, and business experience data to build a case library and experience library, and link them to the corresponding business scenarios; Based on knowledge networks, rule knowledge bases, and experience bases, we sort out business causal relationships, risk evolution paths, and scenario evolution logic to complete the logical modeling of all business scenarios. By calling upon the industry mechanism and AI fusion reasoning module, data interaction channels at all levels are opened up to achieve the integration of rule determination, semantic parsing, and logical deduction functions; The model is divided into multiple scenario sub-models according to business scenarios and business departments, and independent access permissions and data isolation strategies are configured. Complete the overall model debugging and functional verification, enable the incremental data docking interface, and support the continuous updates of knowledge, rules, and cases.

[0023] The Data Without Leaving the Domain Privacy Computing Training Module is responsible for training, parameter tuning, and incremental iteration of the private knowledge world model within the enterprise's private domain, and for achieving rapid learning from samples and continuous iterative optimization of the model based on the foundation. The specific content of the data-without-domain privacy computing training module is as follows: Set up a private training environment, cut off public network access channels, and complete all training operations and data interactions within the enterprise intranet; It uses locally generated knowledge data, graph data, and vector data as training samples, without calling external public datasets or sending enterprise data back to the outside world; Complete the initial training and parameter optimization of the private knowledge world model. When the system adds new business data, knowledge, rules, or cases, it will automatically trigger incremental training. The basic loss function for initial training and parameter optimization is as follows: ; The set of trainable parameters for a private knowledge world model. This represents the total number of local training samples for the enterprise (internal network data only, no external public data). For the first Training samples (a combination of knowledge graph data, vectors, and business rule features). The sample contains real labels (risk category type, rule matching results, business conclusions, etc.). To predict the output value for the model, Cross-entropy loss (general for classification tasks, suitable for risk classification and rule matching). For L2 regularization terms, This is a regularization coefficient to prevent the model from overfitting. The incremental training loss function is: ;in, These are parameters that have already been trained in the previous version. To preserve coefficients for historical parameters and control for differences between the old and new models. For business scenarios with few or zero samples, the model learns new business rules autonomously by leveraging its generalization capabilities; at the same time, the parameters of the private knowledge world model and training logs are encrypted and access controlled to prevent data and model leakage. Few-shot learning loss function: ;in, This is the generalized distillation coefficient. For knowledge distillation functions, reuse the common business logic of the model base and adapt to a small number of new samples.

[0024] The industry mechanism and AI fusion reasoning module adopts a dual-engine fusion architecture of industry mechanism engine and AI reasoning engine. Based on the trained private knowledge world model, it combines the two engines to carry out intelligent reasoning, rule verification and logical analysis. The specific tasks of the industry mechanism and AI fusion reasoning module are as follows: It connects to the private knowledge world model, knowledge graph, private knowledge vector library, rule library and case library, completes engine initialization and parameter configuration, receives external business data to be inferred and performs pre-processing; The inference mode is automatically scheduled according to the complexity of the business scenario, and either single-engine independent inference or dual-engine joint inference is selected. The task is divided into four categories: rule matching reasoning, associative logic reasoning, experience reuse reasoning, and scenario simulation reasoning. Cross-validation, conflict correction, and integration of the dual-engine outputs are performed to generate a unified reasoning conclusion. By integrating the industry mechanism engine and the AI ​​engine scores, the comprehensive inference confidence formula is generated as follows: ; in, To assess the confidence level of the overall reasoning, The weight of the industry mechanism engine is relatively high (compliance scenarios have a higher weight). The weighting of AI inference engines is higher for scenarios involving experience reuse and implicit data mining. For industry mechanism engine rule matching score and , This represents the total number of enterprise / industry rules corresponding to the current business scenario. This represents the number of rules that the data to be inferred has successfully matched. Score the semantic reasoning of the AI ​​engine and ; The dynamic weighting strategy design (inference scheduling logic in the matching scheme) for different scenarios is as follows: Lightweight compliance scenarios: The primary focus is on rule-based validation. Complex risk / decision-making scenarios: Balanced integration of the two engines; Experience reuse / case matching scenarios: It mainly uses AI semantic reasoning; The specific formulas for cross-validation and conflict correction of dual-engine results are as follows: ;in Set a conflict threshold for the score difference between the two engines. ,like If the judgment results conflict, manual review and secondary rule verification will be initiated. The judgment results are consistent and do not require review. Forward the inference results to the business forecasting and risk warning application module, and encrypt and retain the inference logs throughout the entire process.

[0025] The specific details of engine initialization and data integration for the industry mechanism and AI fusion inference module are as follows: It loads underlying data sources in real time, including pre-trained private knowledge world models, enterprise knowledge graphs, private knowledge vector libraries, industry mechanism rule libraries, enterprise compliance system libraries, historical risk case libraries, and business experience libraries, establishing a stable data interaction channel between the engine and various databases and models.

[0026] Complete engine parameter configuration: bind industry regulatory rules, industry-specific physical / business mechanisms, and mandatory enterprise process rules; set inference accuracy thresholds, risk assessment standards, semantic matching thresholds, and business scenario permission scopes; differentiate between general inference mode, high-precision inference mode, and lightweight fast inference mode.

[0027] Receive data requests from upstream businesses, including contract texts, policy documents, business processes, operational data, and other inference objects. Perform data format adaptation, feature extraction, and pre-filtering to remove invalid and interfering information.

[0028] The intelligent scheduling of inference tasks automatically allocates inference execution entities based on the type, complexity, and real-time requirements of the tasks to be processed, enabling dual-engine division of labor and collaboration or joint computation: Lightweight routine scenarios (simple compliance comparison, basic clause retrieval): Only the industry mechanism engine is activated to perform rule reasoning, ensuring computational efficiency; Complex business scenarios (comprehensive contract review, risk tracing, decision support, and implicit relationship mining): Initiate dual-engine joint reasoning, with the mechanism engine controlling compliance bottom lines and the AI ​​reasoning engine completing in-depth semantic and logical analysis; Experience reuse scenarios (historical case matching, business experience reference): The AI ​​reasoning engine is the main engine, supplemented by the mechanism engine, and the case library is used to complete analogical reasoning.

[0029] The business forecasting and risk warning application module is designed for real-world business scenarios. Based on the reasoning results, it enables trend forecasting, risk warning, business review, and decision support, and outputs the corresponding business forecast results. The specific content of the business forecasting and risk warning application module is as follows: Based on the output of the industry mechanism and AI fusion reasoning module, business trend prediction, risk warning, business review, and decision support are completed, and business prediction results are output. Automatically identify anomalies, violations, and potential risks in business data, classify risk levels, and trigger risk warnings; Formula for calculating comprehensive risk score: ;in, As a risk score, The larger the size, the higher the risk; Risk level classification rules: ; This is a high-risk threshold; This is a medium-risk threshold (enterprises can customize it according to regulatory requirements); For business scenarios such as contract review, compliance self-inspection, intelligent Q&A, and management decision-making, it outputs structured reports, risk labels, modification suggestions, and visual analysis content; It retains complete operation logs, reasoning records, and business results throughout the entire process, supporting data traceability and auditing.

[0030] In the specific implementation process, intelligent contract review is used as a specific application scenario. Based on the reasoning system described in this invention, automated contract review, risk identification, and suggestion output are realized. Users upload contract documents to the multimodal data access layer, and the document data directly enters the enterprise's private environment. The system completes document parsing, format adaptation, and pre-cleaning. The knowledge extraction and vectorization module parses the contract document, extracts core knowledge elements such as the contract subject, cooperation amount, performance period, rights and responsibilities clauses, liability for breach of contract, and special risk clauses, and completes the vectorization process. The knowledge world model retrieves internal compliance rules, historical risk cases, and industry regulatory rules, and compares them with the extracted contract elements. It receives the extracted and vectorized contract elements (contract parties, amount, term, liability for breach of contract, special clauses, etc.), and the underlying knowledge base unit matches the contract-related entities, business links, and semantic vectors.

[0031] Access contract compliance systems, financial / industry regulatory provisions, historical contract risk cases, and past review experience to identify specific binding rules for contracts. The contract risk evolution model is activated to analyze the compliance of each contract clause with the rules and to deduce the potential legal risks, performance risks, and financial risks after the clauses are executed. Formula for quantifying the risk of a single contract clause: ; in, Risk value for a single contract clause. These are respectively the rule risk coefficient and the case matching risk coefficient. ; Formula for calculating the overall risk value of the entire contract: ;in, This refers to the total number of terms in the contract. For the first Risk value of each clause Substitute the values ​​into the risk level formula above to determine the risk level of the entire contract; By linking the industry mechanism engine (compliance rule verification) and the AI ​​inference engine (hidden risk discovery), a risk list and risk level determination criteria are generated. The contract review sub-model operates independently, accurately marking high, medium, and low-risk clauses and matching them with historical rectification plans for similar risks; The model summarizes all conclusions, generates and outputs review reports and modification suggestions, and synchronously archives contract data and review logs; it identifies new clauses and new risk types in contracts, triggering incremental learning of the model. The industry mechanism and AI fusion reasoning module conduct joint reasoning to automatically determine the risk type and risk level, and locate and mark high-risk clauses in the contract; The business forecasting and risk warning application module generates a complete contract review report, simultaneously outputs suggestions for clause modification, and archives all operation records and inference data locally, thus completing the contract review process.

[0032] Therefore, this invention adopts the above-mentioned reasoning system based on a private knowledge world model for enterprise unstructured data, and constructs an enterprise-specific private knowledge world model. Under the premise of data security without leaving the domain, it realizes unified modeling of enterprise unstructured knowledge, intelligent logical reasoning, business trend prediction, automatic risk warning, and reuse of business experience, providing stable and reliable intelligent decision support for enterprise autonomous systems.

[0033] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A reasoning system for enterprise unstructured data based on a private knowledge world model, characterized in that: It includes a multimodal data access module, a knowledge extraction and vectorization module, a knowledge graph and private knowledge world model module, an industry mechanism and AI fusion reasoning module, a data privacy computing training module that does not leave the domain, and a business prediction and risk warning application module; The multimodal data access layer collects various types of unstructured data from enterprises, completes preprocessing, classification and security level labeling, and stores the data in the enterprise's private domain; The knowledge extraction and vectorization module communicates with the multimodal data access module to extract knowledge elements and generate semantic vectors, thus building a private knowledge vector library. The knowledge graph and private knowledge world model module connects to the knowledge extraction and vectorization module to complete the construction of the enterprise knowledge graph and private knowledge world model; The Data Without Leaving the Domain Privacy Computing Training Module is responsible for training, parameter tuning, and incremental iteration of the private knowledge world model within the enterprise's private domain, and for achieving rapid learning from samples and continuous iterative optimization of the model based on the foundation. The industry mechanism and AI fusion reasoning module adopts a dual-engine fusion architecture of industry mechanism engine and AI reasoning engine. Based on the trained private knowledge world model, it combines the two engines to carry out intelligent reasoning, rule verification and logical analysis. The business forecasting and risk warning application module is designed for real-world business scenarios. Based on the reasoning results, it enables trend prediction, risk warning, business review, and decision support, and outputs corresponding business forecast results.

2. The reasoning system for enterprise unstructured data based on a private knowledge world model according to claim 1, characterized in that: The specific contents of the multimodal data access module are as follows: Unstructured data is collected using a localized interface, and the unstructured data does not leave the enterprise intranet throughout the entire process; Clean the unstructured data, remove invalid garbled characters, duplicate files, blank content, and standardize the data format; Data is classified and archived according to business department, data type, and security level; hierarchical access permissions are set to complete basic data security control.

3. The reasoning system for enterprise unstructured data based on a private knowledge world model according to claim 2, characterized in that: The specific content of the knowledge extraction and vectorization module is as follows: For text and tabular data, extract knowledge elements, including entities, entity relationships, business rules, business processes, business experience, and risk factors. For image-based data, OCR recognition technology is used to complete image and text parsing and extract useful knowledge; For voice-based data, speech-to-text transcription is performed before knowledge element extraction. All extracted knowledge elements are converted into semantic vectors of a unified dimension, a private knowledge vector library is constructed, and the private knowledge vector library is set to be stored and accessed only within the enterprise's private domain.

4. The reasoning system for enterprise unstructured data based on a private knowledge world model according to claim 3, characterized in that: The specific details of building the knowledge graph in the Knowledge Graph and Private Knowledge World Model module are as follows: Obtain the knowledge elements output by the knowledge extraction and vectorization module, complete data cleaning, error correction and format normalization, and obtain a standardized knowledge dataset; Based on enterprise business scenarios and business rules, define the knowledge graph schema architecture, divide entity types, entity attributes, and relationships between entities, and unify naming rules and association constraints; Using business entities as nodes and entity relationships as edges, and binding entity attributes, business tags, and data source information, a basic knowledge network is formed by building all entity nodes and related edges. Based on a private knowledge vector library, entity disambiguation and duplicate node merging are completed, and implicit business relationships between entities are mined to complete the knowledge links and form a preliminary knowledge graph. The initial knowledge graph undergoes logical and business rule verification to correct errors and logical conflicts, ultimately generating a standard knowledge graph.

5. The reasoning system for enterprise unstructured data based on a private knowledge world model according to claim 4, characterized in that: The specific content of constructing the private knowledge world model in the Knowledge Graph and Private Knowledge World Model module is as follows: The validated standard knowledge graph and private knowledge vector library are loaded into the model framework to serve as the underlying static knowledge base and semantic support of the model. Import industry mechanism rules, external regulatory regulations, internal corporate systems, compliance clauses, and standard business processes to establish a rule knowledge base, and associate and bind the rules with entities and relationships in the knowledge graph; Access the enterprise's historical business cases, risk handling records, and business experience data to build a case library and experience library, and link them to the corresponding business scenarios; Based on knowledge networks, rule knowledge bases, and experience bases, we sort out business causal relationships, risk evolution paths, and scenario evolution logic to complete the logical modeling of all business scenarios. By calling upon the industry mechanism and AI fusion reasoning module, data interaction channels at all levels are opened up to achieve the integration of rule determination, semantic parsing, and logical deduction functions; The model is divided into multiple scenario sub-models according to business scenarios and business departments, and independent access permissions and data isolation strategies are configured. Complete the overall model debugging and functional verification, enable the incremental data docking interface, and support the continuous updates of knowledge, rules, and cases.

6. The reasoning system for enterprise unstructured data based on a private knowledge world model according to claim 5, characterized in that: The specific content of the data-without-domain privacy computing training module is as follows: Set up a private training environment, cut off public network access channels, and complete all training operations and data interactions within the enterprise intranet; It uses locally generated knowledge data, graph data, and vector data as training samples, without calling external public datasets or sending enterprise data back to the outside world; Complete the initial training and parameter optimization of the private knowledge world model. When the system adds new business data, knowledge, rules, or cases, it will automatically trigger incremental training. For business scenarios with few or no samples, the model learns new business rules autonomously based on its generalization capabilities; at the same time, it encrypts and controls access permissions for the private knowledge world model parameters and training logs to prevent data and model leakage.

7. The reasoning system for enterprise unstructured data based on a private knowledge world model according to claim 6, characterized in that: The specific tasks of the industry mechanism and AI fusion reasoning module are as follows: It connects to the private knowledge world model, knowledge graph, private knowledge vector library, rule library and case library, completes engine initialization and parameter configuration, receives external business data to be inferred and performs pre-processing; The inference mode is automatically scheduled according to the complexity of the business scenario, and either single-engine independent inference or dual-engine joint inference is selected. The task is divided into four categories: rule matching reasoning, associative logic reasoning, experience reuse reasoning, and scenario simulation reasoning. Cross-validation, conflict correction, and integration of the dual-engine outputs are performed to generate a unified reasoning conclusion. Forward the inference results to the business forecasting and risk warning application module, and encrypt and retain the inference logs throughout the entire process.

8. The reasoning system for enterprise unstructured data based on a private knowledge world model according to claim 7, characterized in that: The specific content of the business forecasting and risk warning application module is as follows: Based on the output of the industry mechanism and AI fusion reasoning module, business trend prediction, risk warning, business review, and decision support are completed, and business prediction results are output. Automatically identify anomalies, violations, and potential risks in business data, classify risk levels, and trigger risk warnings; For business scenarios such as contract review, compliance self-inspection, intelligent Q&A, and management decision-making, it outputs structured reports, risk labels, modification suggestions, and visual analysis content; It retains complete operation logs, reasoning records, and business results throughout the entire process, supporting data traceability and auditing.

Images