File data content accurate and deep analysis and interpretation method based on AI

Abstract

The invention belongs to the technical field of artificial intelligence, and particularly relates to an AI-based file data content accurate and deep analysis and interpretation method, which comprises the following steps: acquiring multi-format file data and file meta-information, constructing an AI analysis network, extracting text semantic vectors, image visual features, table structure information and document layout features, and constructing a multi-dimensional semantic map. According to a user query intention, semantic extension is performed in combination with a domain knowledge base, an enhanced semantic description vector is generated through a graph attention mechanism, semantic reasoning and relation mining are performed by adopting an improved knowledge distillation Transform model, and a deep analysis conclusion is generated through a multi-hop reasoning model in combination with file data complexity, information density and user requirements. And generating a personalized interpretation report in combination with a user role and a task scene, and outputting an analysis result through a visual interface. Therefore, the problems of poor understanding ability, poor file adaptability and the like in the prior art are solved.

Description

Technical Field

[0002] This invention belongs to the field of artificial intelligence technology, specifically relating to an AI-based method for precise and in-depth analysis and interpretation of file data content. Background Technology

[0003] With the accelerated advancement of digital transformation, the market urgently needs AI-based methods for accurate and in-depth analysis and interpretation of document data, requiring multi-format data compatibility, cross-modal semantic fusion, and dynamic deep reasoning capabilities. However, existing technologies, due to the lack of deep fusion of multi-source document perception networks, the absence of cross-modal semantic collaborative reasoning frameworks, and dynamic parsing accuracy balancing mechanisms, struggle to support accurate interpretation in complex professional scenarios (such as scientific research literature, government composite documents, and enterprise multimodal reports), and fall significantly short of the technical requirements for constructing full-dimensional semantic association graphs and real-time adaptation of parsing granularity.

[0004] However, traditional document analysis methods have key shortcomings: rule engines lack sufficient parsing accuracy for unstructured documents (such as composite documents containing mixed text, images, and tables), and are susceptible to interference from format variations and ambiguities in technical terms; the models lack dynamic robustness adjustment mechanisms, resulting in significant inconsistencies in analysis results across different parsing nodes when faced with fluctuations in document complexity (such as long document logical hierarchies and high-density technical information); data dimensions are limited to single-modal features (such as pure text semantics or pure image visual information), failing to form a multimodal fusion perceptual network and lacking cross-modal attention mechanisms for the collaborative processing of text semantics and image / table features, leading to semantic fragmentation of different types of document data. With the accelerated advancement of digital transformation, the market urgently needs in-depth interpretation of highly accurate and efficient document data, but existing technologies, due to poor comprehension capabilities, poor document adaptability, and limited semantic expression capabilities, struggle to support accurate analysis applications in complex professional scenarios. Summary of the Invention

[0005] This application provides an AI-based method for accurate and in-depth analysis and interpretation of file data content to address the problems of poor comprehension and poor file adaptability in existing technologies.

[0006] The first aspect of this application provides a method for accurate and in-depth analysis and interpretation of file data content based on AI, comprising the following steps: acquiring multi-format file data and file metadata; constructing an AI file perception and parsing network based on the multi-format file data and file metadata; extracting text semantic vectors, image visual features, table structure information, and document layout features based on the AI ​​file perception and parsing network; constructing a multi-dimensional semantic graph that integrates contextual relationships based on the text semantic vectors, image visual features, table structure information, and document layout features; semantically expanding the multi-dimensional semantic graph according to the user's query intent and combining it with a domain knowledge base; focusing on key information nodes through a graph attention mechanism to generate enhanced semantic description vectors; performing semantic reasoning and relationship mining using an improved knowledge distillation Transformer model based on the enhanced semantic description vectors; generating in-depth analysis conclusions through a multi-hop reasoning model by combining the complexity, information density, and user needs of the current file data; and generating a personalized interpretation report by configuring corresponding tone styles and content granularities based on the in-depth analysis conclusions, combined with the user role and task scenario, and outputting the analysis results through a visual interface.

[0007] Preferably, an improved knowledge distillation Transformer model is used for semantic reasoning and relationship mining. Combining the complexity, information density, and user needs of the current file data, a multi-hop reasoning model is used to generate in-depth analysis conclusions. This includes: constructing an improved knowledge distillation Transformer model and a multi-hop reasoning model; inputting an enhanced semantic description vector into the improved knowledge distillation Transformer model to perform entity relationship mining and semantic logic derivation; combining the complexity, information density, and user needs of the current file data to generate preliminary model reasoning results; inputting the preliminary model reasoning results into the multi-hop reasoning model; starting from key information nodes, traversing related nodes and edges in the graph through multi-step associations to supplement implicit relationships and deep logic; integrating the multi-hop reasoning results to generate in-depth analysis conclusions that include core data conclusions, relationship analysis, and potential value mining.

[0008] Preferably, generating a personalized interpretation report includes: constructing a user profile model; based on the user profile model, combined with user roles and task scenarios, configuring corresponding timbre styles and content granularities, converting in-depth analysis conclusions into natural language text, and adjusting the language style according to the timbre style; generating a personalized interpretation report based on the natural language text, and outputting it through a visualization interface.

[0009] Preferably, after converting the in-depth analysis conclusions into natural language text, the following steps are taken: A three-level reporting mechanism is established. When the document complexity is low, the information density is less than 0.5, and the user's need is browsing, it is classified as a Level 1 report, triggering summary generation and outputting only key conclusions and simple charts. When the document complexity is medium, the information density is greater than 0.5 but less than 0.8, and the user's need is analysis, it is classified as a Level 2 report, linking multimodal data to generate a detailed interpretation, and annotating the data source and confidence level. When the document complexity is high, the information density is greater than 0.8, and the user's need is decision-making, it is classified as a Level 3 report, pushing an interactive report to the user, annotating the reasoning process with the relevant domain knowledge base, and providing suggestions for comparing multiple solutions.

[0010] Preferably, generating an enhanced semantic description vector includes: constructing a multidimensional semantic graph; based on the multidimensional semantic graph, semantically expanding the graph nodes by combining a domain knowledge base, focusing on key information nodes through a graph attention mechanism, calculating the importance weight of the nodes, and generating an enhanced semantic description vector.

[0011] Preferably, the graph attention mechanism formula is:

[0012] in, To output the attention result; It is a set of entity nodes in a semantic graph; It is the set of edges connecting nodes; The query vector is generated from the features of entity nodes; The key vector is generated from the features of the associated edges; Key vector The transpose of the matrix; Key vector The dimension; A value vector generated from the features of entity nodes; The scaled query-key dot product score; This is to normalize each row of the score matrix.

[0013] The second aspect of this application provides an AI-based system for precise and in-depth analysis and interpretation of file data content, comprising: an acquisition module for acquiring multi-format file data and file metadata; an extraction module for constructing an AI file perception and parsing network based on the multi-format file data and file metadata, and extracting text semantic vectors, image visual features, table structure information, and document layout features based on the AI ​​file perception and parsing network; an extension module for constructing a multi-dimensional semantic graph that integrates contextual relationships based on the text semantic vectors, image visual features, table structure information, and document layout features, semantically extending the multi-dimensional semantic graph according to the user's query intent and combining it with a domain knowledge base, focusing on key information nodes through a graph attention mechanism, and generating enhanced semantic description vectors; a reasoning module for performing semantic reasoning and relationship mining based on the enhanced semantic description vectors using an improved knowledge distillation Transformer model, and generating in-depth analysis conclusions through a multi-hop reasoning model, combining the complexity and information density of the current file data with user needs; and a generation module for generating personalized interpretation reports based on the in-depth analysis conclusions, configuring corresponding tone styles and content granularities according to user roles and task scenarios, and outputting the analysis results through a visual interface.

[0014] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement an AI-based method for accurate and in-depth analysis and interpretation of file data content as described in the above embodiments.

[0015] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement an AI-based method for accurate and in-depth analysis and interpretation of file data content as described in the above embodiments.

[0016] Therefore, this application has the following beneficial effects: By acquiring multi-format file data and metadata, and combining it with an AI file perception and parsing network, this application can simultaneously extract text semantics, image vision, table structure, and document layout features, solving the problem of incomplete analysis of single formats or single features. It performs full-domain coverage parsing of complex file data. The multi-dimensional semantic graph, combined with a domain knowledge base and graph attention mechanism, can establish cross-feature contextual associations and accurately focus on key information nodes. Furthermore, through an improved knowledge distillation Transformer model and multi-hop inference, it can generate in-depth conclusions based on file complexity, information density, and user needs, significantly improving the accuracy and depth of analysis and interpretation, avoiding information fragmentation or shallow reasoning. It can also configure tone style and content granularity according to user roles and task scenarios, and output personalized reports through visualization, balancing professional needs and ease of use, allowing different users to efficiently obtain suitable information and significantly reducing information acquisition costs. Thus, it solves the problems of poor comprehension and poor file adaptability in the prior art.

[0017] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0018] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating an AI-based method for precise and in-depth analysis and interpretation of file data content, provided in an embodiment of this application. Figure 2 This is a schematic diagram of a joint-stock bank in an intelligent credit approval scenario according to an embodiment of this application; Figure 3 This is a schematic diagram of a tertiary hospital in a pneumonia auxiliary diagnosis scenario according to an embodiment of this application; Figure 4 This is a schematic diagram of a research institution using intelligent document analysis according to an embodiment of this application; Figure 5 This is a schematic diagram illustrating the generation of a personalized investment analysis report in the field of bank wealth management according to an embodiment of this application; Figure 6 This is a schematic diagram of an AI-based method for precise and in-depth analysis and interpretation of file data content, according to an embodiment of this application. Figure 7 This is a schematic diagram of the structure of an AI-based system for precise and in-depth analysis and interpretation of file data content, according to an embodiment of this application. Figure 8This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0019] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0020] The following describes an AI-based method for precise and in-depth analysis and interpretation of file data content, with reference to the accompanying drawings. Addressing the issue of poor comprehension mentioned in the background, this application provides an AI-based method for precise and in-depth analysis and interpretation of file data content. This method acquires multi-format file data and metadata, and combines it with an AI file perception and parsing network to simultaneously extract text semantics, image vision, table structure, and document layout features. This solves the problem of incomplete analysis of single formats or single features, enabling comprehensive parsing of complex file data. A multi-dimensional semantic graph, combined with a domain knowledge base and graph attention mechanism, establishes cross-feature contextual relationships and accurately focuses on key information nodes. Further, through an improved knowledge distillation Transformer model and multi-hop inference, in-depth conclusions can be generated based on file complexity, information density, and user needs, significantly improving the accuracy and depth of analysis and interpretation, avoiding information fragmentation or shallow reasoning. The method also configures tone style and content granularity based on user roles and task scenarios, and outputs personalized reports through visualization, balancing professional needs with ease of use. This allows different users to efficiently obtain suitable information, significantly reducing information acquisition costs. Thus, it solves the problems of poor comprehension and poor file adaptability in existing technologies.

[0021] Specifically, Figure 1 This is a flowchart illustrating an AI-based method for precise and in-depth analysis and interpretation of file data content, provided in an embodiment of this application.

[0022] like Figure 1 As shown, this AI-based method for precise and in-depth analysis and interpretation of file data includes the following steps: In step S101, multi-format file data and file metadata are obtained.

[0023] Multi-format file data refers to a comprehensive data collection that originates from different types of files such as documents, images, and tables, and exists in various forms such as text, images, and structured tables.

[0024] It is understood that the embodiments of this application acquire comprehensive text, image, and structured table data from various carriers such as documents, pictures, and tables, avoiding the omission of key information due to a single format, achieving full coverage of file data, and obtaining file meta-information (such as creation time, author, file type, etc.) can assist AI in quickly identifying file attributes and structure, providing an initial basis for subsequent construction of AI file perception and parsing network and accurate feature extraction, reducing invalid parsing steps, and improving overall analysis efficiency and accuracy.

[0025] In step S102, an AI file perception and parsing network is constructed based on multi-format file data and file metadata. Based on the AI ​​file perception and parsing network, text semantic vectors, image visual features, table structure information, and document layout features are extracted.

[0026] Among them, the AI ​​document perception and parsing network is an AI-driven network model that can perceive and parse multi-format file data and file metadata, thereby extracting text semantic vectors, image visual features, table structure information and document layout features.

[0027] It is understood that the embodiments of this application utilize an AI-driven file perception and parsing network to overcome the barriers to parsing multi-format files and file metadata. Through AI-driven intelligent perception and deep parsing capabilities, it accurately extracts text semantic vectors, image visual features, table structure information, and document layout features. This can replace the tedious manual processing of multi-format files, significantly shortening the parsing cycle and reducing labor costs. At the same time, the precise recognition capabilities of AI reduce human error, ensuring the accuracy and completeness of extracted features. The extracted multi-dimensional features can provide high-quality data support for downstream tasks such as intelligent document retrieval, cross-modal data analysis, automated classification and archiving, and intelligent content generation, enabling rapid mining and efficient utilization of the information value of multi-format files.

[0028] It should be noted that, based on multi-format file data and file metadata, the construction of the AI ​​file perception and parsing network must be centered on multi-format file data and file metadata. First, the multimodal preprocessing module, feature mapping unit, and metadata extraction tool at the file processing end serve as the core processing nodes, collecting basic information such as the original content, format features, and data structure of different types of files in real time. At the same time, dedicated processing devices such as heterogeneous model fusion components, cross-modal attention modules, and multi-task learning units are deployed at the parsing layer to collect parsing data such as file semantic features, format association information, and structured extraction results. Utilizing the multi-task processing characteristics of AI models, a network architecture with a unified data interaction protocol is connected to the core processing nodes and dedicated processing devices through a feature fusion gateway, realizing data interconnection between the file processing end and the parsing layer devices. Computational resources are allocated in real time according to file complexity and parsing requirements to ensure the processing efficiency of high-priority parsing tasks, thus constructing an AI file perception and parsing network that covers all file format types and the entire parsing process.

[0029] For example, such as Figure 2 As shown, a joint-stock bank utilizes an AI document perception and parsing network in its intelligent credit approval process to efficiently handle various formats of materials submitted by borrowers. The network first processes PDF loan contracts, scanned financial statements, and image-based guarantee documents through a multimodal preprocessing module, simultaneously embedding metadata such as document format and creation time as auxiliary features. Then, it extracts semantic features from contracts and tabular structure features from reports using a heterogeneous model fusion architecture. After fusion via a cross-modal attention mechanism, a multi-task learning module accurately identifies document types and extracts key structured information such as loan amount, repayment period, and financial indicators. Finally, the parsing results are converted into a unified format and pushed to the credit approval system. This not only reduces the processing time for a single document from 2 hours to 15 minutes but also automatically identifies hidden risk clauses, increasing the risk identification accuracy to 98%, significantly improving the efficiency and compliance of credit approval.

[0030] In step S103, a multidimensional semantic graph that integrates contextual associations is constructed based on text semantic vectors, image visual features, table structure information, and document layout features. According to the user's query intent, the multidimensional semantic graph is semantically expanded in conjunction with the domain knowledge base. Key information nodes are focused through a graph attention mechanism to generate enhanced semantic description vectors.

[0031] Among them, the multidimensional semantic graph is a knowledge organization model that integrates multi-dimensional information such as multiple attributes of entities, context, spatiotemporal or cross-domain information on the basis of the traditional semantic graph (which is composed of entities and relations to form a knowledge structure) in order to achieve more comprehensive semantic expression, deep knowledge association and intelligent reasoning.

[0032] It is understood that the embodiments of this application, by utilizing multidimensional semantic graphs, can not only break down multimodal information barriers and achieve unified knowledge organization and deep association of fragmented data, but also semantically expand the knowledge related to user query intent by combining domain knowledge bases, filling the knowledge gaps of single information sources; at the same time, by accurately focusing on key information nodes through graph attention mechanisms and avoiding interference from redundant information, the generated enhanced semantic description vectors can more profoundly capture the essence and association of information, greatly improving the accuracy of understanding user query intent, providing more comprehensive and accurate knowledge support for subsequent tasks such as intelligent retrieval, question-and-answer interaction, and decision support, and effectively reducing the problems of weak information association and one-sided expression under traditional semantic organization methods.

[0033] It should be noted that, based on text semantic vectors, image visual features, table structure information, and document layout features, constructing a multi-dimensional semantic graph that integrates contextual relationships requires first structuring the four core information categories: text, images, tables, and document layout. For text, a pre-trained language model is used to transform the text content into text semantic vectors containing semantic relationships, capturing the logical relationships and contextual information between sentences. For images, a computer vision model is used to extract visual features, such as object outlines, color distribution, and scene elements, transforming image information into machine-understandable feature vectors. For tables, table structure recognition and content parsing are used to extract table headers, row / column data, and the relationships between data, forming structured table information. For document layout, the positional distribution, hierarchical relationships, and layout logic of text blocks, image blocks, and table blocks in the document are analyzed to obtain document layout features and clarify the contextual relationships of each information module. Building upon this foundation, with entities as the core link, key entities (such as concepts in text, objects in images, and data subjects in tables) are extracted from various features through entity recognition. Then, relationships between entities are defined by combining contextual association rules (such as "causal relationship" in text, "explanatory relationship" between images and text, and "positional association" between tables and document layouts). At the same time, text semantic vectors, image visual features, table structure information, and document layout features are embedded into the graph as multi-dimensional attributes of entities. This ultimately forms a multi-dimensional semantic graph that includes a core knowledge structure of entities and relationships, and integrates multi-modal features and contextual associations, providing comprehensive knowledge support for subsequent semantic expansion and key information focusing.

[0034] Building a domain knowledge base requires following a complete workflow logic of data foundation-knowledge processing-structured organization-dynamic maintenance. First, the specific domain scope (e.g., healthcare, finance, education) and core knowledge boundaries must be clearly defined. Then, authoritative data from multiple sources within that domain should be systematically collected—including structured data (e.g., standard tables in industry databases, business system logs), semi-structured data (e.g., domain specification documents, product manuals, XML / JSON format interface data), and unstructured data (e.g., academic papers, technical reports, expert interview texts, industry conference minutes). Simultaneously, low-quality and irrelevant data should be filtered out to ensure the professionalism and accuracy of the data sources. Next, the collected raw data should be preprocessed to clean up duplicate, erroneous, or redundant information. Unstructured data should be transformed into structured fragments using natural language processing techniques (e.g., word segmentation, part-of-speech tagging, entity recognition). Semi-structured data should be standardized in format and key information extracted, laying the foundation for knowledge processing. The core knowledge extraction phase then begins, employing adaptive methods for different data types: From structured data, pre-defined entities and relationships are extracted using SQL queries and ETL tools; from pre-processed unstructured data, core domain concepts (such as "disease" and "drug" in the medical field), inter-concept relationships (such as "drug-indication" relationships), and attribute information (such as "contraindications" and "dosage" for drugs) are extracted using relationship extraction, event extraction, and attribute extraction; from semi-structured data, knowledge in fixed formats (such as the "model-parameter" correspondence for products) is extracted through rule matching and template parsing. Following this, knowledge fusion is performed to unify the domain's terminology system (e.g., standardizing "myocardial infarction" and "heart attack" as the same concept), resolve conflicts between knowledge from different data sources (e.g., calibrating contradictory information using authoritative domain standards or expert consensus), and connect scattered knowledge fragments to form logically coherent knowledge units. Then, a structured framework is constructed through knowledge organization. Ontology modeling is used to define the concept hierarchy, relationship types, and constraint rules within the domain (such as the hierarchical relationship of "course-prerequisite courses" in the education field), or knowledge graphs are used to present the entity-relationship-attribute network in a graph structure. At the same time, a domain rule base is established (such as "risk rating judgment rules" in the financial field) to give the knowledge system a clear logical structure and reusability. Finally, an appropriate storage solution is selected (relational databases for structured knowledge, graph databases for graph structured knowledge, and document databases for unstructured knowledge), and a dynamic update and maintenance mechanism is established—new domain achievements (such as the latest academic research and industry policies) are collected regularly to supplement knowledge, feedback from domain experts is received to correct erroneous knowledge, and the completeness and accuracy of the knowledge base are monitored through quality testing tools. Ultimately, a domain knowledge base that covers the core knowledge of the domain, is logically rigorous, and dynamically adaptable is formed.

[0035] For example, such as Figure 3As shown, in the scenario of auxiliary diagnosis of pneumonia, a tertiary hospital integrates symptom description text from electronic medical records, visual features of CT images, tabular structure data from laboratory reports, and layout information of various documents to construct a basic atlas. Natural language processing is used to extract symptom entities such as "cough frequency" and "number of days with fever" and generate semantic vectors. Computer vision technology is used to annotate visual feature nodes such as "ground-glass opacity" and "solid region" in the images. Structured indicators such as "white blood cell count" and "C-reactive protein" are extracted through table parsing. At the same time, the "adjacency relationship between symptom paragraphs and laboratory data" in the document layout is embedded into the atlas as a contextual association, and medical relationships such as "symptom-image mapping" and "indicator threshold association" are defined. When a clinician enters a query about a patient's "cough with fever for 3 days," the graph, combined with a respiratory medicine knowledge base, performs semantic expansion, automatically associating professional knowledge such as "ground-glass opacity is common in viral pneumonia" and "bacterial infections are often accompanied by elevated white blood cell counts." Then, through a graph attention mechanism, it focuses on the image node "ground-glass opacity in the lower lobe of the left lung," the table node "abnormal white blood cell count," and the corresponding symptom text node, which are strongly related to the chief complaint. Finally, it generates an enhanced semantic description vector that integrates multimodal evidence, providing doctors with reasoning basis that links "symptoms-images-indicators." This increases the accuracy of pneumonia diagnosis suggestions from 82% to 94%, significantly shortening diagnosis time and reducing the risk of misdiagnosis.

[0036] In this embodiment of the application, generating an enhanced semantic description vector includes: constructing a multidimensional semantic graph; based on the multidimensional semantic graph, semantically expanding the graph nodes in conjunction with a domain knowledge base, focusing on key information nodes through a graph attention mechanism, calculating the importance weight of the nodes, and generating an enhanced semantic description vector.

[0037] Domain knowledge bases are knowledge sets built around specific domains (such as healthcare, finance, and education), integrating authoritative knowledge such as core concepts, rules, cases, and data within that domain. After being organized in a structured or semi-structured manner, they are used to support intelligent decision-making, question-and-answer interaction, and other applications in that domain.

[0038] It is understood that the embodiments of this application, by utilizing a domain knowledge base, can provide precise domain-specific support for multidimensional semantic graphs. This can supplement the deep association knowledge of graph nodes within specific domains, fill the knowledge gaps caused by data limitations in the basic graph, and make the semantic expansion of nodes more aligned with domain logic. It avoids generalization or associations deviating from the professional scope, and can provide a domain-specific standard for graph attention mechanisms to judge the importance of nodes. This helps to accurately select information nodes that are key to user queries or task objectives, reduce the interference of redundant and non-core nodes on vector generation, and the enhanced semantic description vectors generated by the domain knowledge base can not only more profoundly and accurately carry the knowledge connotations within the domain, but also enable the vectors to output more professional and credible results in subsequent applications such as intelligent question answering and decision support, effectively improving the professionalism and reliability of the overall task.

[0039] For example, in a corporate credit risk control scenario, a commercial bank utilizes a financial knowledge base. This knowledge base is built around credit business and integrates authoritative structured and semi-structured knowledge, including credit policy terms issued by regulatory authorities, data on corporate default cases from the past five years, financial health indicators and standards for various industries (such as the asset-liability ratio threshold for manufacturing and the revenue growth rate benchmark for the service industry), and post-loan risk warning rules. When a company submits a loan application, the system first uses a multi-dimensional semantic graph to analyze the company's financial statement data, textual descriptions of its operating status, and guarantee document information in the application materials. Then, it calls the domain knowledge base to semantically expand nodes such as "corporate revenue" and "asset-liability ratio" in the graph—comparing the company's actual financial data with industry standard indicators in the knowledge base, while also associating it with historical cases of "default characteristics of highly indebted companies" in the knowledge base. Through a graph attention mechanism, it focuses on key risk nodes such as "asset-liability ratio exceeding the standard by 30%" and "revenue declining for six consecutive months," and calculates node weights according to the risk rating rules of the knowledge base. The resulting enhanced semantic description vector not only accurately presents the creditworthiness of enterprises, but also automatically marks risk warnings that "meet the characteristics of subprime loans". This reduces the time for risk identification in credit approval from 4 hours to 1.5 hours and lowers the false judgment rate from 12% to 5%. At the same time, it ensures that all approval basis complies with regulatory policy requirements, effectively balancing approval efficiency and risk control rigor.

[0040] In this embodiment, the graph attention mechanism formula is as follows:

[0041] in, To output the attention result; It is a set of entity nodes in a semantic graph; It is the set of edges connecting nodes; The query vector is generated from the features of entity nodes; The key vector is generated from the features of the associated edges; Key vector The transpose of the matrix; Key vector The dimension; A value vector generated from the features of entity nodes; The scaled query-key dot product score; This is to normalize each row of the score matrix.

[0042] It is understood that the embodiments of this application utilize graph attention mechanisms to dynamically quantify the association weights between multimodal nodes and extended domain knowledge nodes within the graph and the user's query intent. This accurately filters redundant nodes unrelated to the query, focuses on key information nodes, and avoids diluting core semantics with irrelevant information. It can adapt to the feature differences of multimodal nodes and the complex graph structure after semantic expansion. There is no need to manually preset weight rules. By calculating the similarity of node features and the relevance to the task, the weights are flexibly adjusted to adapt to different query scenarios. This allows the generated enhanced semantic description vectors to centrally carry core knowledge strongly related to the query, improving the accuracy of the vectors in representing user intent and providing more accurate semantic support for subsequent tasks such as intelligent question answering and information retrieval.

[0043] For example, such as Figure 4 As shown, a research institution, in its intelligent literature analysis, utilizes a graph attention mechanism. It first integrates the textual semantic vectors of academic papers, the table structure of experimental data, the visual features of research results diagrams, and document layout information to construct a multi-dimensional semantic graph that incorporates contextual relationships. This graph uses text entities such as "research methods" and "experimental conclusions," "data indicators" and "statistical results" in tables, and "trend curves" and "comparison charts" in images as nodes, defining connections between nodes using terms like "supporting relationships" and "corresponding relationships." When researchers query "mechanical property testing methods and results of a new type of material," the graph is semantically expanded using a materials science knowledge base, adding related nodes such as "testing standards" and "data on similar materials." Then, the graph attention mechanism is activated: by calculating the similarity between the features of each node and the query intent, it automatically assigns weights as high as 0.8-0.9 to the text node "tensile strength testing methods," the image node "stress-strain curve," and the table node "specific values ​​of elongation at break," while reducing the weights of redundant nodes such as "author biographies" and "published journal information" to below 0.1. The key nodes that are ultimately focused on are fused to generate an enhanced semantic description vector, which accurately presents the core process and key results of the material's mechanical performance test. This reduces the time researchers spend locating effective information from massive amounts of literature from 2 days to 3 hours, and increases the information extraction accuracy from 65% to 92%, significantly improving the efficiency and accuracy of literature analysis.

[0044] In step S104, based on the enhanced semantic description vector, the improved knowledge distillation Transformer model is used for semantic reasoning and relation mining. Combining the complexity, information density and user needs of the current file data, a deep analysis conclusion is generated through a multi-hop reasoning model.

[0045] Among them, the improved knowledge distillation Transformer model is based on the traditional Transformer architecture. By optimizing the knowledge transfer strategy, it efficiently transfers the core information such as deep semantic knowledge and attention patterns of the large teacher model to the lightweight student model. While significantly compressing the number of parameters, reducing inference latency and computational cost, it retains the performance of the original model to the maximum extent and is an optimized Transformer model that is suitable for resource-constrained scenarios.

[0046] It is understood that the embodiments of this application, through the improved knowledge distillation Transformer model, based on enhanced semantic description vectors, not only accurately capture complex relationships in the data to empower relationship mining, but also adapt to file data with different complexities and information densities through two-stage distillation. At the same time, it can proactively combine the complexity, information density, and user needs of the current file data, and link multi-hop inference models to advance the analysis. Through the improvement of knowledge distillation, it takes into account both the lightweight nature of the model and the core performance, greatly improving the efficiency of semantic processing and relationship mining, and can accurately match data characteristics and user needs, avoiding analysis from deviating from the target, helping to generate in-depth analytical conclusions, and fully mining the inherent relationships and core value behind the file data.

[0047] It should be noted that the improved knowledge distillation Transformer model is as follows:

[0048] in, KL divergence loss for soft labels in teacher and student models; For the student model, use hard-label cross-entropy loss; The loss is used to fit the attention matrix; The intermediate layer feature alignment loss; This is the loss weighting coefficient.

[0049] Multi-hop inference model:

[0050] in, Let be the embedding vector of entity v in the l-th layer of the graph neural network; It is an aggregate function; For the first The embedding vector of entity v in the layer; For update functions; For the first The embedding vector of the neighboring entity u in the layer; The relationship between entity u and entity v; Let v be the set of neighbors of entity v; Let entity u be a neighbor of v.

[0051] For example, in the intelligent analysis of medical literature, when using the improved knowledge distillation Transformer model, the multi-source heterogeneous medical record texts and medical guideline data are first transformed into enhanced semantic description vectors. The teacher model (a Transformer with a large number of parameters) is used to mine the deep associations between diseases, symptoms, and treatment plans (such as the multi-hop relationship of "diabetes → neuropathy → foot ulcer"). Then, through an improved distillation strategy—not only distilling the inference results of the output layer, but also specifically distilling the attention weight distribution and entity relationship feature map of the intermediate layer of the teacher model—the student model (a lightweight version of the Transformer) can still accurately capture the semantic dependencies of complex medical knowledge and efficiently generate deep association analysis conclusions of "symptom-cause-intervention measures" even with a 70% reduction in parameter size, thus meeting the low-latency requirements of real-time clinical decision support.

[0052] In this embodiment, an improved knowledge distillation Transformer model is used for semantic reasoning and relationship mining. Combining the complexity, information density, and user needs of the current file data, a multi-hop reasoning model is used to generate in-depth analysis conclusions. This includes: constructing an improved knowledge distillation Transformer model and a multi-hop reasoning model; inputting enhanced semantic description vectors into the improved knowledge distillation Transformer model to perform entity relationship mining and semantic logic derivation; combining the complexity, information density, and user needs of the current file data to generate preliminary model reasoning results; inputting the preliminary model reasoning results into the multi-hop reasoning model; starting from key information nodes, traversing related nodes and edges in the graph through multi-step associations to supplement implicit relationships and deep logic; integrating the multi-hop reasoning results to generate in-depth analysis conclusions that include core data conclusions, relationship analysis, and potential value mining.

[0053] Among them, the multi-hop reasoning model is a model that mines indirect relationships between entities from data such as knowledge graphs and text through multiple iterations, thereby achieving relationship prediction or deep conclusion reasoning.

[0054] It is understood that the embodiments of this application, by employing a multi-hop reasoning model, take over the preliminary results generated by the improved knowledge distillation Transformer model. Starting from key information nodes, it iteratively traverses the nodes and edges in the knowledge graph and text data through multiple steps to mine indirect relationships between entities. This effectively supplements implicit relationships, sorts out deep logic, and solves the problem of information fragmentation in high-complexity, high-information-density data. It not only solves the bottleneck of traditional single-hop reasoning in capturing implicit facts, but also integrates discrete information fragments through explicit multi-step reasoning paths. This makes the analysis conclusions more in-depth on the basis of clear core conclusions and clear relationships, while mining the potential value in the data that is not directly presented, and accurately adapting to the user's needs for in-depth insights.

[0055] In step S105, based on the in-depth analysis conclusions, and combined with the user role and task scenario configuration, a personalized interpretation report is generated and the analysis results are output through a visual interface.

[0056] It is understood that this application's embodiments, by matching user roles with task scenarios and customizing corresponding timbre styles and content granularities, transform abstract in-depth analysis conclusions into concrete information tailored to the user, while presenting the analysis results through a visual interface. This not only solves the information mismatch problem existing in traditional standardized reports, avoiding comprehension barriers caused by inconsistencies between content style, granularity, and user needs, but also reduces the user's cognitive load, allowing users to quickly grasp the core value without additional processing. Visual presentation further improves the efficiency and accuracy of information acquisition, and the scene-appropriate timbre style can enhance the user's willingness to accept information, ensuring the practical application value of in-depth conclusions, while improving the user's task completion efficiency and user experience.

[0057] In this embodiment of the application, generating a personalized interpretation report includes: constructing a user profile model; based on the user profile model, combined with user roles and task scenarios, configuring corresponding timbre styles and content granularities, converting in-depth analysis conclusions into natural language text, and adjusting the language style according to the timbre style; generating a personalized interpretation report based on the natural language text, and outputting it through a visualization interface.

[0058] Among them, the user profile model is a data analysis model that constructs a three-dimensional image of a user based on multi-dimensional user data through integration, analysis, and tagging, in order to support the needs of personalized services and accurate decision-making.

[0059] It is understood that this application embodiment integrates and analyzes multi-dimensional user data and completes tagging processing to construct a three-dimensional image that reflects the core characteristics of the user. This provides a clear basis for configuring appropriate tone style and content granularity based on user roles and task scenarios, ensuring that the conversion of in-depth analysis conclusions into natural language text always revolves around user needs. It effectively avoids the information misalignment problem caused by the lack of user-specificity in traditional reports, making the language style and content detail of natural language text more in line with user cognitive habits and actual needs. This enables the generated personalized interpretation report to accurately convey core value. When combined with the output of the visualization interface, it further reduces the cost of user information reception and understanding, improves the readability and practicality of the report, helps users efficiently obtain and utilize the analysis results, and fully releases the application value of in-depth analysis conclusions.

[0060] For example, such as Figure 5As shown, taking the generation of personalized investment analysis reports in the field of bank wealth management as an example, the process of using a user profiling model clearly demonstrates its practical value: The bank first integrates multi-dimensional user data, including basic demographic data such as age, occupation, and region; transaction behavior data such as historical wealth management product purchase records and fund transfer frequency; and demand data such as risk tolerance and investment knowledge obtained through questionnaire feedback and customer service interaction. After cleaning and integration, the data is tagged—labeling corporate executives as "high asset size, medium-to-high risk tolerance, focus on long-term returns, and fast decision-making pace"; generating labels for middle-aged salaried users as "medium income, conservative risk appetite, focus on steady growth, and need easy-to-understand explanations"; and constructing labels for young investors as "small and diversified investments, flexible risk tolerance, focus on emerging fields, and preference for..." The model is characterized by its "concise and efficient expression." Based on these multi-dimensional profiles, when transforming in-depth analysis of market trends and asset allocation into interpretive reports, the model precisely adapts to different users: for corporate executives, it adopts a professional and concise tone, focusing on core asset portfolio recommendations and industry trend analysis, while downplaying explanations of basic concepts; for middle-aged salaried workers, it uses a friendly and easy-to-understand language style, focusing on breaking down the return logic and risk points of low-risk products, supplemented with case analogies; for young investors, it uses clear and concise expression, highlighting investment opportunities in emerging sectors and small-scale participation methods; the final personalized reports not only fit the cognitive habits and actual needs of each user, but also reduce the user's understanding cost through precise content adaptation, helping different groups efficiently obtain investment information that meets their own needs, significantly improving the accuracy of wealth management services and user satisfaction.

[0061] In this embodiment, after converting the in-depth analysis conclusions into natural language text, the following steps are taken: a three-level reporting mechanism is set up. When the file complexity is low, the information density is less than 0.5, and the user's need is browsing, it is classified as a Level 1 report, triggering summary generation and outputting only key conclusions and simple charts. When the file complexity is medium, the information density is greater than 0.5 but less than 0.8, and the user's need is analysis, it is classified as a Level 2 report, linking multimodal data to generate detailed interpretations and labeling data sources and confidence levels. When the file complexity is high, the information density is greater than 0.8, and the user's need is decision-making, it is classified as a Level 3 report, pushing an interactive report to the user, linking the domain knowledge base to label the reasoning process, and providing suggestions for comparing multiple solutions.

[0062] It is understandable that this application's embodiments dynamically and accurately adapt reports based on file characteristics (complexity, information density) and user needs (browsing, analysis, decision-making). By triggering differentiated content presentation and function configurations through hierarchical adjustments, the delivery of in-depth analysis conclusions is made more aligned with actual application scenarios. This avoids the problems of redundant information for browsing needs and lack of key support for decision-making needs in the traditional single-report model—Level 1 reports meet the needs of quick browsing with concise key conclusions and simple charts, reducing the information reception cost in low-complexity scenarios; Level 2 reports provide rigorous evidence for in-depth analysis needs through detailed interpretation, data source annotation, and confidence level explanations, ensuring the accuracy and traceability of information in medium-complexity scenarios; Level 3 reports provide comprehensive support for decision-making needs through interactive functions, reasoning processes associated with domain knowledge bases, and multi-solution comparison suggestions, assisting in scientific judgment in high-complexity scenarios—and further enhance the practical value and user experience of reports through hierarchical adaptation, allowing users in different scenarios to efficiently obtain information that suits their needs, reducing the troubles of information overload or insufficient information, and providing more sufficient evidence for the decision-making process.

[0063] This application proposes an AI-based method for precise and in-depth analysis and interpretation of file data content. By acquiring multi-format file data and metadata, and combining it with an AI file perception and parsing network, it can simultaneously extract text semantics, image vision, table structure, and document layout features. This solves the problem of incomplete analysis based on a single format or feature, enabling comprehensive parsing of complex file data. A multi-dimensional semantic graph, combined with a domain knowledge base and graph attention mechanism, can establish cross-feature contextual relationships and accurately focus on key information nodes. Further, through an improved knowledge distillation Transformer model and multi-hop inference, it can generate in-depth conclusions based on file complexity, information density, and user needs, significantly improving the accuracy and depth of analysis and interpretation. This avoids information fragmentation or superficial reasoning. The method also allows for configuration of tone style and content granularity based on user roles and task scenarios, and outputs personalized reports through visualization, balancing professional needs with ease of use. This enables different users to efficiently obtain suitable information and significantly reduces information acquisition costs. Therefore, it solves the problems of poor comprehension and poor file adaptability in existing technologies.

[0064] The following will illustrate a specific example of an AI-based method for accurate and in-depth analysis and interpretation of file data content. Figure 6 As shown, it includes: Taking a multi-dimensional analysis scenario of a company's annual financial report as an example, when processing multi-format financial report files from core users within the company, the first step is to establish a multi-source file acquisition channel, integrating the company's internal data system with external disclosure platforms: Extracting the 2023 annual profit statement, balance sheet, and detailed cash flow statement in Excel format from the company's ERP system (including values, debit / credit directions, and accounting dates for first-level to fourth-level accounts); downloading the 2023 annual financial report in PDF format (including chapters such as "Management Discussion and Analysis," "Notes to Financial Statements," and "Explanation of Significant Events," totaling 128 pages) and the PDF audit report issued by a third-party auditing firm from the stock exchange disclosure platform; obtaining PNG format "2023 Annual Revenue Quarterly Trend Chart," "Asset Structure Ratio Pie Chart," and "Profit Contribution Bar Chart for Each Business Line" (15 images in total, 300dpi resolution), as well as scanned copies of large-value raw material purchase vouchers (JPG format, 86 documents in total, including handwritten signatures and printed text) from the finance department's shared drive. Synchronously collect file metadata and establish a metadata database: For each file, label it with "Source Channel" (ERP / Exchange / Shared Drive), "Data Dimension" (Revenue / Cost / Assets / Cash Flow), "Time Range" (January 1, 2023 - December 31, 2023), "Related Department" (Finance Department / Audit Department / Business Department), and "Data Credibility Level" (ERP data is Level A, exchange-disclosed data is Level A, scanned vouchers are Level B, manually entered supplementary data is Level C). For unstructured files (such as PDF text and scanned documents), preprocessing is performed: Adobe Acrobat Pro is used to remove watermarks, blank pages, and duplicate headers and footers from PDFs; scanned vouchers are converted into editable text using the Tesseract OCR engine (while retaining the original image as supporting evidence); and Excel tables have standardized field naming conventions (e.g., unifying "Revenue Amount" and "Operating Income" to "Operating Income - Current Period Amount") to ensure consistency in subsequent parsing.

[0065] Based on the processed file data, an AI file perception and parsing network with four collaborative modules of "text-image-table-layout" is constructed. The FinBERT pre-trained model (a BERT variant pre-trained on financial text) is selected and fine-tuned based on the company's historical financial report text from 2018 to 2022 (a total of 5 million words). By adding "financial entity recognition tasks" (such as recognizing entities such as "operating revenue", "operating cost", and "net profit") and "financial relationship extraction tasks" (such as extracting the causal relationship between "revenue growth of 15%" and "caused by net profit growth of 8%), the model's understanding of semantics in the financial domain is optimized. The text in the PDF financial report, such as "Management Discussion" and "Financial Notes", is divided into 512-token text segments by chapter. These segments are then input into a fine-tuned FinBERT model, which outputs a text semantic vector with a dimension of 768. The vector contains core semantic features such as "Reasons for Revenue Growth", "Cost Control Measures", and "Risk Warnings". For example, the vector corresponding to "In 2023, raw material costs increased by 12% year-on-year due to the rise in commodity prices" will strengthen the semantic weight of keywords such as "raw material costs", "commodity prices", and "year-on-year growth". For financial charts (trend charts, pie charts, bar charts), a dedicated ChartOCR chart parsing model is used. First, the image is preprocessed using OpenCV (grayscale conversion, edge detection) to identify the chart type and axis information (e.g., X-axis for "quarters," Y-axis for "revenue (ten thousand yuan)"). Then, the ChartOCR model directly extracts data points, category labels, and numerical relationships from the chart, generating structured chart data (e.g., "Q1: 5200, Q2: 5800, Q3: 6200, Q4: 5900") and a 500-dimensional visual feature vector—this vector represents the numerical relationships in the chart (e.g., the upward slope of a line chart, the height difference of a bar chart) and category information. For example, for the "2023 Q1-Q4 Revenue Trend Chart," the model can extract structured data such as "Q3 revenue peak (62 million yuan)" and "Q4 quarter-on-quarter decrease of 5%" and associate them with the corresponding text descriptions. Using the TableTransformer table recognition and parsing model, for Excel detailed reports, it first identifies the row / column boundaries and merged cells (e.g., the "Operating Costs" row in the profit statement contains three sub-columns: "Direct Materials," "Direct Labor," and "Manufacturing Expenses"). Then, it uses a rule engine to verify the calculation relationships between cells (e.g., verifying whether the difference between "Operating Revenue - Current Period Amount" and "Operating Costs - Current Period Amount" equals "Gross Profit") to ensure the consistency of numerical logic. Finally, it outputs structured information of "Subject Name - Value - Related Subject - Calculation Logic" and a table semantic vector with a dimension of 512 (e.g., the vector corresponding to "Operating Revenue - 2023 - 520 million yuan" will be associated with the historical data features of "2022 Operating Revenue 450 million yuan").The LayoutLMv3 multimodal document layout analysis model is used to perform page layout analysis on PDF financial reports, and to identify the coordinates and semantic relationships (such as "") of elements such as "chapter titles", "body paragraphs", "chart insertion positions" and "data table areas". Figure 3-2 The "2023 Revenue Trend" is located after the third paragraph of the "2023 Operating Results Analysis" section. The output layout feature vector (dimension 256) establishes a correlation between "content location" and "semantic importance" (e.g., layout features corresponding to chapter titles have higher weights than those in the main text, and the main text paragraphs surrounding charts have higher weights than other areas). The extracted text semantic vector, image visual features, table structure information, and document layout features are weighted and concatenated (weights are assigned according to feature importance) to form an initial fusion feature with dimension 2048, providing a foundation for subsequent semantic graph construction.

[0066] Based on the aforementioned multi-dimensional features, a "multi-dimensional semantic graph of corporate financial statements" integrating contextual relationships is constructed using the Neo4j graph database. The graph is then optimized by combining user query intent with a domain knowledge base to generate enhanced semantic description vectors. The definition of nodes and edges in the semantic graph must adhere to the principle of "comprehensive coverage and clear associations" to ensure a complete representation of the entities, attributes, relationships, and context of the financial data. Entity nodes are subdivided into four categories: "subject entities" (e.g., "Company A", "Auditing Firm B", "Supplier C"), "financial entities" (e.g., "Operating Revenue", "Operating Costs", "Net Profit"), "external influence entities" (e.g., "Copper Price", "Crude Oil Price", "Industry Average"), and "time entities" (e.g., "2023 Annual Report" "2023 Q3"). Each entity category is labeled with a unique ID and type tag (e.g., "Company A" has the ID "ENT001" and the type "Subject Entity - Company"). Attribute nodes are designed with differentiated attribute items for different entity types. For example, the attributes of financial entities include "value", "unit", "year-on-year growth rate", and "credibility level" (e.g., the attribute node for "operating revenue" is "value: 520 million yuan", "unit: 10,000 yuan", "year-on-year growth rate: 15%", "credibility level: A"). The attributes of principal entities include "industry", "registered capital", and "business scope" (e.g., the attribute node for "Company A" is "industry: home appliance manufacturing" and "registered capital: 1 billion yuan"). Relationship edges are divided into five categories: "attribution relationship" (e.g., "belongs to"), "calculation relationship" (e.g., "calculation dependency"), "influence relationship" (e.g., "influencing factor"), "audit relationship" (e.g., "audit opinion"), and "comparison relationship" (e.g., "industry comparison"). Each type of relationship is labeled with the calculation rules for relationship strength (e.g., the strength of "influence relationship" is calculated based on a weighted average of semantic similarity and data credibility). Context nodes include two types: "document chapters" (such as "management discussion chapter" and "financial notes chapter") and "data dimensions" (such as "Q3 quarterly data" and "East China regional data"), used to associate entities with data generation scenarios. The definition of edges corresponds to the node type. Association edges are used to connect entity nodes with entity nodes (such as "operating revenue - belongs to - 2023" "net profit - influencing factors - copper price"), attribute edges are used to connect entity nodes with attribute nodes (such as "operating revenue - attribute - value: 520 million yuan"), and context edges are used to connect entity nodes with context nodes (such as "operating revenue - included in - management discussion chapter"). Each edge is marked with source information (such as the edge "operating revenue - belongs to - 2023" is marked "source: ERP system profit statement") to ensure the traceability of relationships.For example, the "Operating Revenue of 520 Million Yuan" in the Excel spreadsheet (entity node "Operating Revenue" + attribute node "Value: 520 Million Yuan") and the "2023 Operating Revenue Increased by 15% Year-on-Year" in the PDF text (entity node "Operating Revenue" + attribute node "Year-on-Year Growth Rate: 15%)" are connected by a "contextual association edge". The edge is labeled "Source: ERP System + Financial Report Text" and associated with the context node "Management Discussion Section", forming a complete association chain of "context-entity-attribute". The fusion of contextual associations needs to achieve "semantic alignment across data formats" and eliminate data fragmentation through multi-dimensional feature linkage: In terms of text and table association, the multimodal similarity between FinBERT text semantic vectors and TableTransformer table semantic vectors is calculated (the threshold is set to 0.75 through experimental calibration). If the similarity is higher than the threshold, a "contextual association edge" is established. For example, the text vector of "2023 revenue growth mainly came from the East China market" in PDF text has a multimodal similarity of 0.82 with the table vector of "East China region revenue of 180 million yuan (accounting for 34.6%)" in Excel table, which is higher than the threshold. Therefore, an association edge is established between the two and labeled "Association basis: multimodal similarity 0.82". Regarding image-text association, the CLIP multimodal model is used to directly calculate the similarity between chart images and text paragraphs (with a threshold of 0.7). If the similarity is higher than the threshold, a "trend association edge" is established—for example, the CLIP similarity between the "2023 Q1-Q4 Revenue Trend Chart" and the text "Q3 revenue hit a new annual high due to peak season demand" is 0.76, thus establishing an association edge to achieve semantic alignment of the image and text data. Regarding layout-content association, based on the layout semantic relationships output by LayoutLMv3, the "chart insertion position" is connected to the adjacent "text paragraph" through "layout association edges" (e.g., ...). Figure 3-2The "2023 Revenue Trends" and the third paragraph of the "2023 Operating Results Analysis" section are semantically adjacent, so a link is established to ensure the graph reflects the semantic positional relationships of the content and avoids contextual breaks. Combining user query intent with semantic expansion of the domain knowledge base, a dual optimization of "user demand orientation" and "domain knowledge support" needs to be achieved: User query intent collection adopts a combination of "proactive research + historical behavior analysis + real-time feedback." Questionnaires were distributed to senior management, analysis, and execution levels (120 valid questionnaires were collected), and user query records in the financial system over the past year (a total of 5000 records) were analyzed. Combined with user click feedback on recommended content, the user intent model is dynamically updated. Three types of users are identified. The core intent is as follows: Senior management focuses on "strategic-level conclusions" (such as reasons for declining net profit, industry comparison gaps, risk warnings, and decision-making recommendations), with the most frequently searched keywords being "net profit," "industry average," and "decision-making recommendations." The analytical level focuses on "professional-level breakdowns" (such as detailed cost structure, profit contribution from business lines, and data calculation processes and sources), with the most frequently searched keywords being "cost breakdown," "business line," and "calculation logic." The execution level focuses on "operational-level verification" (such as matching raw material cost details with vouchers, and the reasons for and handling of data deviations), with the most frequently searched keywords being "voucher matching," "deviation," and "pending processing." The domain knowledge base needs to cover the entire financial analysis process, including a "financial indicator library" (containing 120 indicators). The system includes: calculation formulas and industry standard values ​​for core financial indicators, such as "Gross Profit Margin = (Revenue - Cost) / Revenue, average for the home appliance manufacturing industry is 18%"; an "Industry Database" (containing financial data of 50 leading companies in the home appliance manufacturing industry in 2023, such as "Net Profit Growth Rate of Leading Company C in the Same Industry -8%" and "Industry Average Net Profit Growth Rate -5%"); a "Cost Influencing Factors Library" (containing 20 factors affecting costs, such as raw material prices, interest rates, and tax policies, and their related logic, such as "Rising Copper Prices → Increased Raw Material Costs in the Home Appliance Industry, Elasticity Coefficient 0.3"); a "Voucher Verification Rule Library" (containing 15 verification rules for matching purchase vouchers with cost data, such as "The deviation between the voucher amount and the cost details amount must be ≤3%"); and a knowledge base. Data is connected to authoritative data sources via API interfaces to achieve semi-automatic monthly updates, ensuring timeliness. Semantic expansion based on user intent and domain knowledge base requires precise matching: For executives' intent regarding "declining net profit," the "net profit" entity node is split into three sub-nodes: "operating net profit," "net profit attributable to parent company," and "net profit excluding non-recurring items" (meeting executives' needs for detailed dimensions of net profit). Influencing factor nodes such as "operating costs," "financial expenses," and "asset impairment losses" are linked from the domain knowledge base (based on a cost influencing factor database). Industry comparison nodes such as "industry average net profit growth rate -5%" and "leading company C's net profit growth rate -8%" are added (based on an industry database), establishing a "net profit - industry comparison - industry average" relationship.For the execution layer's intent of "matching raw material cost details with vouchers," verification rule nodes (such as "deviation ≤ 3%) from the "voucher verification rule base" are linked from the domain knowledge base. The "raw material cost details" node is then broken down into sub-nodes such as "copper cost," "plastic cost," and "steel cost," and associated with the corresponding "purchase voucher" nodes to meet the execution layer's verification requirements. The graph attention mechanism focuses on key information nodes and enhances vector generation, requiring "on-demand focusing" and "feature optimization": The core of GATv2 (Improved Graph Attention Network) is to assign differentiated weights to nodes under different user intents through learnable attention weight calculation. The weight calculation adopts a three-dimensional weighted formula of "semantic relevance + data credibility + user attention": Attention weight = α × semantic relevance + β × data credibility + γ × user attention (where α, β, and γ are learnable parameters that are automatically optimized through training, rather than manually preset). For example, regarding executive intent, the semantic relevance of the "net profit" node (matching intent with "net profit decline" 0.95), data credibility (A level, corresponding to 0.9), and user attention (executives query the most frequently, corresponding to 0.9) are calculated to have an attention weight of 0.92 using GATv2; the semantic relevance of the "number of employees" node (matching intent 0.1), data credibility (B level, corresponding to 0.7), and user attention (executives query the least frequently, corresponding to 0.2) are calculated to have a weight of 0.15, achieving focus on key nodes. In the enhanced semantic description vector generation stage, the key nodes with the top 30% attention weight and their associated edge features are first extracted (such as the nodes and associated edges of "net profit", "operating cost", "copper price" and "industry average" under the executive's intent). Then, the feature vectors of these nodes (text, table, image, layout fusion features) and the relationship features of the edges (association strength, source credibility) are subjected to gated attention pooling to generate an enhanced semantic description vector with a dimension of 1024. This vector not only contains the core features of the key nodes (such as "net profit (value: 0.3 billion yuan, year-on-year -20%)"), information of associated nodes (such as "operating cost (+12%), copper price (+18%), industry average (-5%)"), and relationship strength (such as 0.88, 0.82, 0.78), but also marks the credibility level of each node (such as "net profit" data credibility level A, "copper price" data credibility level B) and source channel (such as "net profit" source ERP system + audit report), ensuring the integrity and reliability of the vector and providing accurate input for subsequent deep reasoning.

[0067] Preliminary semantic reasoning is achieved through an improved knowledge distillation Transformer model, which is then combined with a multi-hop reasoning model to supplement implicit relationships, ultimately generating in-depth analysis conclusions tailored to user needs, balancing reasoning accuracy and efficiency. The construction of the improved knowledge distillation Transformer model needs to address the balance between "accuracy of a large model and efficiency of a small model," employing a "teacher-student" distillation architecture: the teacher model uses DeBERTa-V3-large (approximately 700M parameters), which performs excellently in financial text understanding and can handle non-linear relationships in the financial domain (such as "cost increases exceeding revenue growth leads to a decline in net profit" and "copper price increases and interest rate hikes jointly affect net profit"). To ensure the teacher model's suitability for the financial domain, it is necessary to use data from 100 home appliance companies within the industry. The financial report data from 2018 to 2023 (a total of 10 million words of text and 500,000 tables) was fine-tuned. The fine-tuning tasks were set as "financial relationship reasoning" (e.g., input "revenue increased by 15%, cost increased by 12%", output "gross profit increased by 20%") and "conclusion generation" (e.g., input "net profit declined by 20%, industry average declined by 5%", output "net profit declined by more than 15 percentage points above the industry average"). After fine-tuning, the teacher model achieved an accuracy of 96.8% on the financial relationship reasoning task and a BLEU score of 0.87 on the conclusion generation task. The student model used TinyBERT (approximately 30M parameters), whose lightweight architecture can be adapted to the company's existing server resources (CPU is Intel Xeon Gold 6338, GPU is NVIDIA A10, 16GB of VRAM), avoiding inference latency caused by excessive model size (target inference time controlled within 8 seconds). The distillation process employs a "temperature scaling + knowledge transfer" strategy: the temperature parameter is set to 8 (balancing the weights of hard and soft labels), and the soft labels (e.g., "the probability of a correlation between a decline in net profit and a rise in copper prices is 0.92") and hard labels (e.g., "correlated / unrelated") of the teacher model for financial reasoning tasks are combined as the training target for the student model; at the same time, a "financial domain loss function" is added, including "numerical calculation accuracy loss" (using the mean absolute error of MAE to calculate the difference between the student model and the teacher model in the calculation results of financial indicators, such as the error in the calculation value of "gross profit") and "relational reasoning loss" (using cross-entropy to calculate the difference between the student model and the teacher model in relation judgment, such as "affect / not affect"), to ensure that the student model can accurately transfer the financial reasoning ability of the teacher model. The student model was trained using historical financial data from 2018 to 2022 (2 million words of text and 100,000 tables). The training run consisted of 50 rounds, a batch size of 64, and a learning rate of 5e-5. The final student model achieved an accuracy of 91.5% on the "cost structure analysis" and "profit attribution" tasks, which is 16.2% higher than the original TinyBERT. The inference time was reduced to 6 seconds, meeting the efficiency requirements of enterprises.Based on preliminary reasoning combining data characteristics and user needs, it is necessary to achieve "quantitative data assessment" and "precise matching of needs": Data complexity quantification adopts a "three-dimensional scoring method," which calculates the complexity from three dimensions (each with a weight of 1 / 3): "file type complexity" (including 4 file types, score 0.9), "field hierarchy complexity" (four levels of subjects, score 0.8), and "association complexity" (cross-file association, score 0.85). The final 2023 financial report data complexity score is (0.9 + 0.8 + 0.85) / 3 ≈ 0.85 (out of 1.0). Information density quantification adopts the "core field proportion method." There are 320 core financial fields (revenue, cost, profit, assets, liabilities, etc.) and a total of 444 fields. The information density = number of core fields / total number of fields ≈ 320 / 444 ≈ 0.78, which belongs to medium-high information density data. The preliminary inference conclusions should be generated according to the priority of user needs: For senior management (decision-making needs), priority should be given to inferring "strategic core conclusions", focusing on three core points: "decline in net profit", "industry comparison" and "risk warnings", to generate the following preliminary conclusion: "Net profit attributable to the parent company in 2023 was 0.3 billion yuan, a year-on-year decrease of 20%. The preliminary related factors are that the operating cost increased by 12% year-on-year (3.8 billion yuan), and the revenue growth rate of 15% (5.2 billion yuan) failed to cover the cost increase; the industry average net profit growth rate was -5%, and the company's decline was 15 percentage points higher than the industry average, indicating a risk that its cost control capabilities are weaker than those of its peers." For the analysis layer (analytical requirements), prioritize reasoning for "professional-level breakdown conclusions," focusing on the two core points of "business line profit contribution" and "cost structure," generating preliminary conclusions: "In 2023, the profit of the home appliance business was 0.18 billion yuan (accounting for 60%), a year-on-year decrease of 18%; the profit of the new energy business was 0.09 billion yuan (accounting for 30%), a year-on-year increase of 25%; direct materials accounted for 78% (3.0 billion yuan) of operating costs, a year-on-year increase of 15%, and direct labor accounted for 15% (0.57 billion yuan), a year-on-year increase of 5%." For the execution layer (operational requirements), prioritize reasoning for "operational-level verification conclusions," focusing on the two core points of "raw material cost details" and "voucher matching," generating preliminary conclusions: "In 2023, raw material costs were 3.0 billion yuan (direct materials), of which copper costs were 1.2 billion yuan (accounting for 40%), a year-on-year increase of 18%; among 86 large-amount purchase vouchers, 7 voucher amounts deviated from the cost details by more than 3% (verification rule threshold), requiring further review." Preliminary conclusions all indicate the data source (e.g., "Net profit data source: ERP system profit statement + audit report") and confidence level (e.g., "The correlation between the decline in net profit and the increase in costs has a confidence level of 92%)", providing a basis for subsequent multi-hop inference.The multi-hop inference model supplements implicit relationships and deep logic, and needs to achieve "implicit association mining" and "conclusion integration and optimization": The multi-hop inference model adopts an iterative strategy of beam search + semantic similarity filtering, with the beam width set to 3 and the maximum number of hops for inference set to 5 (to avoid redundant inference). The screening threshold for each hop association node is semantic similarity ≥ 0.75. For the "declining net profit" node that senior management is concerned about, the first jump starts from this node and filters related nodes with semantic similarity ≥ 0.75 to obtain the "increased operating costs" node (association strength 0.88, source: preliminary inference conclusion + cost details data); the second jump starts from the "increased operating costs" node and filters related nodes to obtain the "increased raw material costs" node (association strength 0.85, source: cost structure breakdown data); the third jump starts from the "increased raw material costs" node and, combined with the domain knowledge base "cost influencing factors library", filters related nodes to obtain the "increased copper prices" node (association strength 0.82, source: industry database copper price data, average copper price increase of 18% in 2023); the fourth jump starts from the "increased copper prices" node and, combined with the industry knowledge base, filters related nodes to obtain the industry node "average increase of 18% in the commodity market copper price in 2023" (association strength 0.78, source: industry database), forming an implicit relationship chain: "increased copper prices → increased raw material costs → increased operating costs → declining net profit". Meanwhile, in the association screening of the second jump node "increased operating costs", the node "increased financial expenses" was also found (association strength 0.76, source: cash flow statement data, financial expenses increased by 8% year-on-year). Starting from this node in the third jump, combined with the domain knowledge base, the node "interest rate increase" was obtained (association strength 0.75, source: central bank interest rate data, loan interest rates increased by an average of 0.5 percentage points in 2023), forming a secondary relationship chain: "interest rate increase → financial expenses +8% → net profit decline", supplementing the secondary reasons for the decline in net profit. Regarding the "rising costs in the home appliance business" node that the analysis layer focuses on, the multi-hop inference process is as follows: The first hop is associated with the "raw material costs in the home appliance business" node (association strength 0.86), the second hop is associated with the "increased plastic costs" node (association strength 0.83, plastic costs in the home appliance business increased by 12% year-on-year), and the third hop combines the domain knowledge base "cost influencing factors library" to associate with the "rising crude oil prices" node (association strength 0.79, the average increase in crude oil prices in 2023 was 10%, and plastics are a downstream product of crude oil), forming an implicit relationship chain: "rising crude oil prices → increased plastic costs → rising raw material costs in the home appliance business → rising costs in the home appliance business", explaining the underlying reasons for the rising costs in the home appliance business.For the "voucher deviation" node that the execution layer is concerned about, the multi-hop inference process is as follows: the first hop is associated with the "voucher OCR recognition error" node (association strength 0.77, 3 deviation vouchers caused OCR recognition errors due to blurred handwritten signatures), the second hop is associated with the "cost details entry error" node (association strength 0.76, 4 deviation vouchers caused by decimal point misalignment during manual entry), and the specific reasons for the voucher deviation are supplemented. When finally integrating the results of multi-hop inference, the principle of "prioritizing core relationship chains, supplementing secondary relationship chains, and refining the reasons for deviations" should be followed to generate in-depth analysis conclusions: The senior management conclusions include "core reasons for the decline in net profit (increased raw material costs due to rising copper prices, and increased financial expenses due to interest rate hikes), industry comparison (the decline exceeded the industry average by 15 percentage points), risk warnings (weaker cost control capabilities than peers, requiring optimization of the supply chain and financing structure), and decision-making recommendations (promoting centralized procurement to reduce costs and negotiating lower loan interest rates)." The analysis layer conclusions include "cost structure breakdown for each business line (material costs account for 80% of the home appliance business and 70% of the new energy business), reasons for differences in profit contribution (the new energy business benefits from policy subsidies, with profit growth exceeding that of the home appliance business), and Q4 cost optimization measures." The implementation results (after the implementation of centralized procurement in Q4, raw material costs decreased by 3% quarter-on-quarter), data calculation process (e.g., home appliance business profit = home appliance business revenue of 250 million yuan - home appliance business cost of 232 million yuan = 18 million yuan)); the execution level conclusions include "raw material cost details (copper 120 million yuan, plastic 80 million yuan, steel 60 million yuan), voucher matching results (7 out of 86 vouchers were inaccurate, 3 had OCR errors, and 4 had data entry errors), pending matters (3 OCR-inaccurate vouchers need to be rescanned and recognized, and 4 data entry-inaccurate vouchers need to have their detailed data corrected, to be completed within 3 working days), and verification progress tracking (20 vouchers have been verified, and the remaining 66 need to be completed within 5 working days)", ensuring that the conclusions not only meet the core needs of users in each role, but also contain sufficient in-depth logic and detailed support.

[0068] By configuring personalized reports and providing a visual interface, abstract, in-depth analytical conclusions are transformed into information directly usable by users in various roles, enhancing the practicality of the analysis results and the user experience. The configuration of personalized reports needs to achieve "style matching the role and granularity matching the scenario," ensuring that different users can efficiently understand and use the conclusions. For senior management (role: decision-making manager, scenario: strategic meeting presentation, approximately 5 minutes per report reading time), the tone and style should be "professional and concise," with language that is simple, dignified, and highlights the core information, avoiding redundant details (such as "Net profit in 2023 declined by 20% year-on-year, the core driving factor being the rise in raw material copper prices (+18%) leading to cost pressure, coupled with an interest rate increase (+0.5 percentage points) pushing up financial expenses; compared with the industry average (-5%), the decline exceeded 15 percentage points, reflecting weaker cost control capabilities than peers." Recommendation: First, reduce raw material costs through centralized procurement of the supply chain (expected cost reduction of 5%), and at the same time negotiate with banks to lower loan interest rates to alleviate financial pressure. The content focuses on "macroeconomic conclusions + core data support + decision-making recommendations". The report structure is set as "Executive Summary (1 page) - Core Conclusions (2 pages) - Industry Comparison (1 page) - Decision-making Recommendations (1 page)". The data calculation process and detailed fields are omitted, and only key data are marked in the core conclusions (such as "net profit of 0.3 billion yuan, revenue of 5.2 billion yuan, and cost of 3.8 billion yuan"). Industry comparison data is supplemented (such as "industry average net profit growth rate of -5%, and growth rate of leading company C of -8%)", which helps senior executives quickly grasp strategic information.For the analysis layer (role: professional analyst, scenario: quarterly financial review meeting, each report reading time is approximately 15 minutes), the tone and style should be "rigorous and detailed," with clear logic, detailed data, and a complete reasoning process (e.g., "In 2023, the net profit of the home appliance business was 0.18 billion yuan (accounting for 60%), a year-on-year decrease of 18%, mainly due to a 15% year-on-year increase in raw material costs for the home appliance business (from 2.6 billion yuan to 3.0 billion yuan), of which plastic costs accounted for 27% (0.8 billion yuan), a year-on-year increase of 12%, affected by the transmission of upstream crude oil price increases (+10%); the net profit of the new energy business was 0.09 billion yuan (accounting for 30%), a year-on-year increase of 25%, benefiting from the new energy subsidy policy (receiving subsidies of 0.03 billion yuan), which offset some of the cost increase pressure"). The content granularity should focus on "detailed data + calculations". The report structure is set as follows: "Business Line Profit Analysis (3 pages) - Cost Structure Breakdown (4 pages) - Data Calculation Explanation (2 pages) - Abnormal Data Investigation (1 page)". It details the revenue, cost, and profit of each business line (e.g., regional revenue of home appliance business: East China 0.8 billion yuan, South China 0.6 billion yuan, North China 0.5 billion yuan, other 0.6 billion yuan). It marks the source of each data point (e.g., "Data source of East China region revenue of home appliance business: ERP system sales module, credibility level A") and the calculation logic (e.g., "Gross profit margin of home appliance business = (2.5 billion yuan - 2.32 billion yuan) / 2.5 billion yuan = 7.2%)). It analyzes the reasons for abnormal data (e.g., Q2 home appliance business revenue decreased by 8% quarter-on-quarter) (e.g., "Q2 was affected by the epidemic, and logistics in East China was blocked") to meet the professional analysis needs of analysts. For the execution level (role: grassroots financial staff, scenario: monthly account reconciliation, each report reading time is approximately 20 minutes), the voice style should be "plain and intuitive," the language should be concise and easy to understand, avoiding technical jargon, and using conversational expressions to convey operational instructions (e.g., "In 2023, the total cost of raw materials was 300 million yuan, of which 120 million yuan was spent on copper, 18% more than last year; and 80 million yuan was spent on plastics, 12% more than last year. Of the 86 large purchase vouchers, 7 did not match the cost details: 3 were because the handwritten signatures on the vouchers were too blurry and the machine could not recognize them correctly; 4 were because the decimal point was misplaced when the data was entered."These 7 vouchers need processing: 3 blurry ones need to be rescanned, and 4 incorrectly entered ones need to have their cost details corrected. This must be completed within 3 days. The content focuses on "detailed item comparison + verification results + pending tasks + operation guidelines." The report structure is set as follows: "Raw material cost detail comparison (2 pages, 2022 vs 2023) - Voucher verification results (3 pages, including voucher number, deviation amount, and reason for deviation) - To-do list (1 page, including task content, completion deadline, and responsible person) - Operation guidelines (1 page, steps for rescanning and detail correction)." The cost detail comparison is clearly displayed in tabular form (e.g., "Copper: 102 million yuan in 2022 vs 120 million yuan in 2023, difference +18 million yuan"). Specific operation steps are marked for each pending task (e.g., "Rescan voucher: Open scanning software → Select 'High Definition Mode' → Scan both sides of the voucher"). (Save and upload to the system) to ensure that the execution layer can directly complete the work according to the report's instructions. The visual interface design and output need to realize "role-customized entry and interaction adaptation requirements," improving the efficiency of users obtaining information through intuitive graphical displays and convenient interactive functions: The interface technical architecture uses Vue.js as the front-end framework (to realize component-based development and facilitate subsequent function iteration), ECharts as the visualization library (supporting rich chart types and interactive effects), and the SpringBoot framework for the back-end (handling data queries and interaction requests). Data transmission uses JSON format (to ensure data transmission efficiency). Three independent entry points are divided according to user roles. The entry page is labeled with the role name and core functions (such as "Executive Entry: Core Indicator Monitoring and Decision Suggestions"). Users are automatically matched to the corresponding entry point based on their account permissions, without the need for manual switching.The executive interface is designed around a "core indicator dashboard," with a three-column layout: the left column (30%) displays year-on-year / month-on-month trend line charts for the three core indicators: net profit, revenue, and gross profit margin (X-axis represents "month," Y-axis represents "amount (hundred million yuan)" or "ratio (%)"; the line charts support hover to display specific values, such as hovering over "December 2023 net profit" to display "0.03 billion yuan, year-on-year -18%"); the middle column (40%) contains "core conclusion cards," divided into four categories: "net profit analysis," "industry comparison," "risk warning," and "decision-making suggestions," each using a different color. The report is divided into sections (e.g., "Risk Warning" in orange, "Decision Recommendations" in green). Each card contains the core conclusions of the executive report. Clicking on a card expands to view a simplified reasoning process (e.g., clicking the "Net Profit Analysis" card expands to show a simplified relationship chain: "Rising Copper Prices → Increased Raw Material Costs → Declining Net Profit"). The right sidebar (30% of the screen) displays an "Industry Comparison Radar Chart" (including five dimensions: net profit growth, gross profit margin, cost control rate, revenue growth, and debt-to-equity ratio; blue represents the company's own data, and red represents the industry average; the radar chart allows clicking on dimension names to display specific values) and a "Decision Recommendation Tracking Table" (displaying the recommendation name, responsible department, planned completion time, and current progress). The interface supports two main interactive functions: first, "Time Dimension Switching," allowing users to switch between "Annual / Quarterly / Monthly" data using the top filter controls (e.g., switching to the "Quarterly" dimension changes the X-axis of the trend chart to "Q1-Q4"); second, "Report Export," where clicking the "Export PDF" button in the upper right corner generates a PDF report containing only the core conclusions and charts (removing the reasoning process and detailed data), meeting the needs of executive meetings. The analysis layer interface is designed around the core concept of "data decomposition and reasoning process," and its layout adopts a "top-bottom + left-right" structure: the upper half (accounting for 20%) is the "business line filter bar," which supports drop-down selection of "home appliance business / new energy business / other businesses," and the charts and tables below update the data synchronously after selection; the lower half on the left (accounting for 40%) displays a "stacked bar chart of business line profit contribution" (X-axis is "month," Y-axis is "profit (ten thousand yuan)," and the stacked items are "revenue, cost, profit") and a "cost structure pie chart" (categorized by "direct materials / direct labor / manufacturing overhead / other," supporting hover display of percentage and amount); the lower half on the right (accounting for 60%) is the "detailed data table," which includes seven columns: "item name, 2023 value, 2022 value, year-on-year growth rate, data source, credibility level, and confidence level." The table supports sorting (e.g., sorting in descending order by "year-on-year growth rate") and filtering (e.g., filtering for "credibility level A" data).The core interactive function of the interface is "Viewing the Reasoning Process": Clicking on a row of data in the table (such as "Home Appliance Business Costs -232 Million Yuan") will bring up a "Reasoning Process Tree" pop-up window on the right, displaying the reasoning path of the data in a tree structure (such as "Home Appliance Business Costs = Direct Materials 200 Million Yuan + Direct Labor 23 Million Yuan + Manufacturing Expenses 9 Million Yuan → Direct Materials 200 Million Yuan = Copper 80 Million Yuan + Plastics 70 Million Yuan + Others 50 Million Yuan → Plastics 70 Million Yuan increased by 12% year-on-year due to rising crude oil prices"). Each node in the reasoning process tree is labeled with the data source and confidence level, meeting the analyst's needs for data traceability and logical verification. It also supports "Excel Export". Clicking the "Export Excel" button will generate an Excel file containing detailed data, calculation process, and reasoning path, which is convenient for analysts to conduct in-depth analysis and write reports. The execution layer interface is designed around "task processing and voucher verification," with a two-column layout: the left column (50%) is a "Raw Material Cost Details Comparison Table," containing six columns: "Raw Material Type, 2022 Amount, 2023 Amount, Difference Amount, Difference Rate, and Number of Related Vouchers." Rows with a difference rate exceeding 5% are highlighted in red (e.g., "Copper Difference Rate 17.6%"). Clicking on the value in the "Number of Related Vouchers" column (e.g., "Copper Related Vouchers 32") simultaneously displays a list of vouchers for that type of raw material in the right column. The right column (50%) is divided into a "Voucher Verification Result List" and a "Voucher Preview Area." The table contains eight columns: "Voucher Number, Voucher Date, Supplier Name, Voucher Amount, Detailed Amount, Deviation Amount, Deviation Reason, and Processing Status." Processing status is categorized as "Pending / Processing / Completed," and filtering by status is supported (e.g., filtering for "Pending" vouchers). The voucher preview area allows clicking on a voucher number in the list to display the OCR text of that voucher and the original scanned image (compared in left and right columns). Below the preview area are "Operation Buttons" (e.g., "Rescan," "Correct Details," "Mark as Complete"). Execution-level users can directly complete voucher processing operations on the interface (e.g., clicking the "Correct Details" button brings up a detail correction pop-up window; entering the correct amount and submitting). The interface supports "Batch Processing of To-Do Tasks": selecting multiple "Pending" vouchers and clicking the "Batch Export To-Do List" button generates an Excel list containing voucher numbers, processing requirements, and completion deadlines. It also supports "Progress Statistics," displaying a statistical card at the top showing "Total Vouchers: 86; Verified: 20; Pending Verification: 66; Deviation Vouchers: 7," helping execution-level users monitor work progress in real time. In addition, all three roles' interfaces support the "message reminder" function: when new analysis conclusions are generated (such as the completion of monthly financial report analysis) or pending tasks are overdue (such as the overdue processing of execution layer vouchers), a red reminder icon pops up in the upper right corner of the interface. Clicking it will display the reminder content, ensuring that users receive important information in a timely manner.The interface also features a responsive design, supporting adaptive display on different devices such as computers and tablets (e.g., adjusting the three-column layout to a two-column layout on tablets), enhancing the multi-device user experience. By combining personalized reports with a visual interface, not only are in-depth analysis conclusions more easily understood and used by users in various roles, but user work efficiency is also significantly improved (e.g., executives' time to obtain decision-making information is reduced from 30 minutes to 5 minutes, and the efficiency of voucher processing at the execution level is increased by 40%), fully unleashing the practical application value of AI analysis.

[0069] In summary, this invention integrates multi-format financial report data (Excel, PDF, PNG, JPG) through multi-source collection, combining standardized preprocessing and metadata annotation technology (annotating credibility and source) to solve the problem of fragmented financial report data while laying a foundation for high-quality analysis. It relies on a four-module AI parsing network of "text-image-table-layout" to accurately mine implicit relationships in financial data, and combines Neo4j semantic graphs with FinBERT and ChartOCR-specific models to form a "context-entity-attribute" association chain, improving the depth and accuracy of data parsing. Through a user intent adaptation mechanism (executive decision-making, analysis layer decomposition, execution layer verification), it links knowledge distillation Transformer and multi-hop reasoning to generate differentiated conclusions. Coupled with personalized reports and role-customized visualization interfaces, it meets the needs of users at different levels and avoids information overload. Ultimately, it effectively shortens the time for executives to obtain decision-making information, improves the efficiency of voucher processing at the execution layer, optimizes the user experience (real-time message reminders, responsive design), fully releases the application value of AI in financial report analysis, and helps enterprises efficiently carry out cost control, strategic decision-making, and accounting management.

[0070] Next, referring to the accompanying drawings, an AI-based system for precise and in-depth analysis and interpretation of file data content is described according to an embodiment of this application.

[0071] Figure 7 This is a schematic diagram of the structure of an AI-based system for precise and in-depth analysis and interpretation of file data content, according to an embodiment of this application.

[0072] like Figure 7 As shown, the AI-based file data content accurate and in-depth analysis and interpretation system 10 includes: an acquisition module 100, an extraction module 200, an extension module 300, an inference module 400, and a generation module 500.

[0073] The module comprises the following components: Acquisition module 100 acquires multi-format file data and file metadata; Extraction module 200 constructs an AI file perception and parsing network based on the multi-format file data and file metadata, and extracts text semantic vectors, image visual features, table structure information, and document layout features based on the AI ​​file perception and parsing network; Extension module 300 constructs a multi-dimensional semantic graph integrating contextual relationships based on text semantic vectors, image visual features, table structure information, and document layout features, and semantically extends the multi-dimensional semantic graph according to the user's query intent and the domain knowledge base, focusing on key information nodes through a graph attention mechanism to generate enhanced semantic description vectors; Reasoning module 400 performs semantic reasoning and relationship mining based on the enhanced semantic description vectors using an improved knowledge distillation Transformer model, and generates in-depth analysis conclusions through a multi-hop reasoning model, taking into account the complexity, information density, and user needs of the current file data; and Generation module 500 generates personalized interpretation reports based on the in-depth analysis conclusions, combined with user roles and task scenarios, configuring corresponding tone styles and content granularities, and outputting the analysis results through a visual interface.

[0074] It should be noted that the foregoing explanation of an embodiment of a method for accurate and in-depth analysis and interpretation of file data content based on AI also applies to an AI-based system for accurate and in-depth analysis and interpretation of file data content in this embodiment, and will not be repeated here.

[0075] This application proposes an AI-based system for precise and in-depth analysis and interpretation of file data. By acquiring multi-format file data and metadata, and combining it with an AI file perception and parsing network, it can simultaneously extract text semantics, image vision, table structure, and document layout features. This solves the problem of incomplete analysis based on a single format or feature, enabling comprehensive parsing of complex file data. A multi-dimensional semantic graph, combined with a domain knowledge base and graph attention mechanism, can establish cross-feature contextual relationships and accurately focus on key information nodes. Further, through an improved knowledge distillation Transformer model and multi-hop inference, it can generate in-depth conclusions based on file complexity, information density, and user needs, significantly improving the accuracy and depth of analysis and interpretation. This avoids information fragmentation or superficial reasoning. The system allows for configuration of tone style and content granularity based on user roles and task scenarios, and outputs personalized reports through visualization, balancing professional needs with ease of use. This enables different users to efficiently obtain suitable information and significantly reduces information acquisition costs. Therefore, it solves the problems of poor comprehension and poor file adaptability in existing technologies.

[0076] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 801, the processor 802, and the computer program stored on the memory 801 and capable of running on the processor 802.

[0077] When the processor 802 executes the program, it implements the AI-based method for accurate and in-depth analysis and interpretation of file data content provided in the above embodiments.

[0078] Furthermore, electronic devices also include: Communication interface 803 is used for communication between memory 801 and processor 802.

[0079] The memory 801 is used to store computer programs that can run on the processor 802.

[0080] The memory 801 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0081] If the memory 801, processor 802, and communication interface 803 are implemented independently, then the communication interface 803, memory 801, and processor 802 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0082] Optionally, in a specific implementation, if the memory 801, processor 802, and communication interface 803 are integrated on a single chip, then the memory 801, processor 802, and communication interface 803 can communicate with each other through an internal interface.

[0083] The processor 802 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0084] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described AI-based method for accurate and in-depth analysis and interpretation of file data content.

[0085] In the description of this specification, the references to "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0086] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0087] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0088] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0089] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0090] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. An AI-based file data content accurate deep analysis interpretation method, characterized in that, The method comprises the following steps: acquiring multi-format file data and file meta information; constructing an AI file perception analysis network based on the multi-format file data and file meta information, extracting a text semantic vector, an image visual feature, table structure information, and document layout features based on the AI file perception analysis network; constructing a multi-dimensional semantic graph that fuses context association based on the text semantic vector, the image visual feature, the table structure information, and the document layout features, performing semantic expansion on the multi-dimensional semantic graph based on a user query intent and in combination with a domain knowledge base, focusing on key information nodes through a graph attention mechanism, and generating an enhanced semantic description vector; based on the enhanced semantic description vector, performing semantic reasoning and relationship mining using an improved knowledge distillation Transformer model, generating a deep analysis conclusion through a multi-hop reasoning model in combination with the complexity, information density, and user demand of current file data; based on the deep analysis conclusion, configuring a corresponding timbre style and content granularity in combination with a user role and a task scenario, generating a personalized interpretation report, and outputting analysis results through a visual interface.

2. The AI-based file data content accurate deep analysis and interpretation method according to claim 1, characterized in that, The improved knowledge distillation Transformer model is used for semantic reasoning and relationship mining, and the deep analysis conclusion is generated through the multi-hop reasoning model in combination with the complexity, information density, and user demand of the current file data, including: constructing an improved knowledge distillation Transformer model and a multi-hop reasoning model; inputting the enhanced semantic description vector into the improved knowledge distillation Transformer model to perform entity relationship mining and semantic logic deduction, generating a model preliminary reasoning result in combination with the complexity, information density, and user demand of the current file data; inputting the model preliminary reasoning result into the multi-hop reasoning model, starting from the key information node, traversing related nodes and edges in the graph through multiple steps of association, supplementing implicit relationships and deep logic, integrating multi-hop reasoning results, and generating a deep analysis conclusion containing data core conclusions, relationship analysis, and potential value mining.

3. The AI-based file data content accurate deep analysis and interpretation method according to claim 1, characterized in that, The improved knowledge distillation Transformer model includes: ； wherein, is the KL divergence loss of the teacher model and the student model soft labels; is the student model hard label cross-entropy loss; is the attention matrix fitting loss; is the intermediate layer feature alignment loss; is the loss weight coefficient.

4. The AI-based file data content accurate deep analysis and interpretation method according to claim 1, characterized in that, generating a personalized interpretation report, including: constructing a user portrait model; based on the user portrait model, configuring a corresponding timbre style and content granularity in combination with a user role and a task scenario, converting the deep analysis conclusion into natural language text, and adjusting the language style according to the timbre style; based on the natural language text, generating a personalized interpretation report and outputting it through a visual interface.

5. The AI-based file data content accurate deep analysis and interpretation method according to claim 4, characterized in that, After converting the deep analysis conclusion into natural language text, including: A three-level reporting mechanism is set up. When the file complexity is low, the information density is less than 0.5, and the user demand is browsing, it is divided into a first-level report, triggering the generation of an abstract, and only outputting key conclusions and simple charts. When the file complexity is medium, the information density is greater than 0.5 and less than 0.8, and the user demand is analysis, it is divided into a second-level report, and a detailed interpretation is generated by linking multiple modal data, and the data source and confidence are marked. When the file complexity is high, the information density is greater than 0.8, and the user demand is decision-making, it is divided into a third-level report, and an interactive report is pushed to the user, the reasoning process is marked by associating with a domain knowledge base, and multiple scheme comparison suggestions are provided.

6. The AI-based file data content accurate deep analysis and interpretation method according to claim 1, characterized in that, The enhanced semantic description vector is generated, including: A multi-dimensional semantic graph is constructed; Based on the multi-dimensional semantic graph, the semantic extension of the graph nodes is performed in combination with a domain knowledge base, the key information nodes are focused through a graph attention mechanism, the node importance weight is calculated, and the enhanced semantic description vector is generated.

7. The AI-based file data content accurate deep analysis and interpretation method according to claim 1, characterized in that, The formula of the graph attention mechanism includes: ； wherein, is the attention output result for the graph; is the set of entity nodes in the semantic graph; is the set of inter-node association edges; is the query vector generated by the entity node features; is the key vector generated by the association edge features; is the key vector is the transpose matrix of is the key vector is the dimension of is the value vector generated by the entity node features; is the scaled query-key dot product score; is the normalization processing on each row of the score matrix.

8. An AI-based file data content accurate deep analysis and interpretation system, characterized in that, An acquisition module is configured to acquire multi-format file data and file meta information; An extraction module is configured to construct an AI file perception analysis network based on the multi-format file data and file meta information, extract a text semantic vector, an image visual feature, table structure information, and document layout features based on the AI file perception analysis network; An extension module is configured to construct a multi-dimensional semantic graph that fuses context association based on the text semantic vector, the image visual feature, the table structure information, and the document layout features, perform semantic extension on the multi-dimensional semantic graph in combination with a domain knowledge base according to a user query intent, focus on key information nodes through a graph attention mechanism, and generate an enhanced semantic description vector; A reasoning module is configured to perform semantic reasoning and relationship mining by using an improved knowledge distillation Transformer model based on the enhanced semantic description vector, generate a deep analysis conclusion through a multi-hop reasoning model in combination with the complexity, information density, and user demand of current file data; A generation module is configured to generate a personalized interpretation report and output an analysis result through a visual interface in combination with a user role, a task scene configuration, a corresponding timbre style, and a content granularity according to the deep analysis conclusion. A computer program or instructions are executed to implement the AI-based file data content accurate deep analysis interpretation method of any one of claims 1-7.

9. An electronic device, comprising: A computer program or instructions are executed to implement the AI-based file data content accurate deep analysis interpretation method of any one of claims 1-7.

10. A computer readable storage medium having stored thereon a computer program or instructions, characterized in that, ​

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