An intelligent document generation system and method based on multi-data fusion analysis

Abstract

The present application belongs to the technical field of data fusion, natural language processing and document generation, and specifically relates to an intelligent document generation system and method based on multi-data fusion analysis, aiming to solve the problems of low multi-source heterogeneous data fusion degree, disconnection between document generation and data fusion, low generation efficiency, and insufficient standardization and compliance in the prior art. The system of the present application adopts a distributed micro-service architecture, sets up core modules such as multi-source data acquisition, multi-data fusion analysis, document generation, compliance verification, and iterative optimization, realizes deep fusion of multi-source data by adopting a three-level fusion architecture of data layer, feature layer and decision layer, and is matched with a standardized whole-process document generation method. The present application significantly improves the efficiency and accuracy of professional document generation, guarantees the standardization, compliance and traceability of the document, and can be widely applied to the automatic generation of professional documents in the fields of asset evaluation, finance and auditing.

Description

Technical Field

[0001] This invention belongs to the fields of data fusion technology, natural language processing technology and document generation technology, and specifically relates to an intelligent document generation system and method based on multi-data fusion analysis. Background Technology

[0002] In various professional fields such as asset appraisal, finance, and auditing, professional documents are the core carriers of data outputs and support for operational and regulatory decisions. Their generation process requires the integration of a large amount of multi-source heterogeneous data, including structured data such as financial data and asset details, semi-structured data such as tables and forms, and unstructured data such as ownership certificates, policy documents, and transaction case texts. During document generation, the integrity, accuracy, and relevance of the data must be strictly guaranteed to produce professional documents that conform to industry standards and are accurate in content.

[0003] Currently, the generation of professional documents mainly relies on manual operation and simple template filling. Even with the introduction of basic automation tools in some scenarios, deep fusion and analysis of multi-source data has not been achieved, resulting in the following prominent technical shortcomings:

[0004] The data required for generating professional documents comes from multiple channels, including internal systems, third-party platforms, public networks, and offline scanned documents. The data formats are heterogeneous, the definitions are inconsistent, and there are redundancies and conflicts. Existing technologies cannot achieve automatic collection, cleaning, correlation, and deep fusion of multi-source data. Manual sorting, filtering, and integration are required, which is not only inefficient but also prone to incomplete document content and logical contradictions due to insufficient data fusion. Existing technologies for processing multi-source data are mostly limited to simple splicing and filtering, lacking the ability to perform deep fusion analysis based on machine learning and deep learning. They cannot uncover the inherent relationships, abnormal features, and core values ​​between multi-source data, and it is difficult to accurately transform the fusion analysis results into document content. As a result, the generated documents lack the depth and professionalism supported by data and cannot meet the decision-making needs of professional scenarios.

[0005] Existing automated document generation tools mostly adopt a fixed template and data-filling model, failing to deeply integrate multi-data fusion analysis results with document generation. They cannot dynamically adjust document structure or supplement core content based on fusion analysis results, resulting in highly homogenized documents that cannot adapt to different data scenarios and document needs, exhibiting poor versatility. The existing document generation process lacks unified and standardized procedures and methods, relying heavily on human experience. Inconsistencies in operating procedures, data processing methods, and document writing standards among different operators lead to inconsistent document quality and difficulty in tracing the data source and fusion analysis process, resulting in insufficient compliance and traceability. In existing technologies, the application of automation in document generation is mostly concentrated on single stages such as text generation and data extraction, without deep collaboration with multi-data fusion analysis technologies. This prevents the formation of a comprehensive collaborative technology system encompassing "multi-data collection, fusion analysis, automatic generation, compliance verification, and iterative optimization," hindering the realization of the synergistic value of multi-technology integration.

[0006] In addition, the Chinese patent with publication number CN120781990B, "An intelligent document generation system and its control method", although it involves intelligent document generation technology, focuses on template engine and text generation, and does not focus on solving the problem of fusion analysis of multi-source heterogeneous data, thus failing to achieve deep correlation and value mining of multiple data.

[0007] To address the shortcomings of existing technologies, there is an urgent need for an intelligent document generation system and method based on multi-data fusion analysis. This system would achieve automatic fusion and in-depth analysis of multi-source heterogeneous data through deep collaboration between multi-data fusion analysis technology and natural language processing and machine learning technologies. Combined with standardized generation methods, it would enable efficient, accurate, and compliant generation of professional documents, thereby promoting the digital transformation of the professional document generation field. Summary of the Invention

[0008] (a) Technical problems to be solved

[0009] To address the shortcomings of existing technologies, this invention provides an intelligent document generation system and method based on multi-data fusion analysis. This invention aims to solve the following technical problems:

[0010] To address the issues of scattered, low-fusion, and low-processing efficiency of multi-source heterogeneous data in existing technologies, this paper proposes a collaborative approach between multi-data fusion analysis technology and machine learning technology to achieve automatic collection, cleaning, correlation, and deep fusion of multi-source data, thereby eliminating data redundancy and conflicts and improving the efficiency and quality of data fusion.

[0011] To address the issue of insufficient depth in data fusion analysis in existing technologies, a multi-data fusion analysis model based on machine learning is constructed to uncover the inherent correlations, abnormal features, and core values ​​among multi-source data. The fusion analysis results are then accurately transformed into document content, enhancing the professionalism and data support capabilities of the documents.

[0012] This addresses the disconnect between document generation and data fusion in existing technologies, achieving deep integration of multi-data fusion analysis results with document generation. It dynamically adjusts the document structure and supplements core content based on the fusion analysis results, adapting to different data scenarios and document needs, thereby improving the flexibility and accuracy of document generation.

[0013] To address the lack of standardization in existing document generation methods, this paper provides a standardized intelligent document generation method that standardizes the entire process from multi-data collection and fusion analysis to document generation, verification, and optimization, ensuring consistency, compliance, and traceability of document quality.

[0014] To address the fragmented application of existing technologies, this technology system will build a collaborative technology system across the entire process, enabling automated multi-data fusion analysis and document generation. This will significantly improve document generation efficiency, reduce labor costs, and adapt to the generation needs of various professional documents.

[0015] (II) Technical Solution

[0016] To achieve the above objectives, the present invention provides the following technical solution: an intelligent document generation system based on multi-data fusion analysis, comprising a human-computer interaction module and a data storage module, and further comprising a multi-source data AI acquisition module, a multi-data AI fusion analysis module, an AI document generation module, an AI compliance verification module, and an AI iterative optimization module; the system adopts a distributed microservice hardware deployment architecture, and each module achieves encrypted data interaction and full-process collaborative work through a standardized communication interface system; the distributed microservice hardware deployment architecture includes a server cluster and client terminals, the server cluster is functionally divided into acquisition module nodes, fusion analysis module nodes, application service nodes, and data storage nodes that communicate with each other, and each node is interconnected through a gigabit fiber optic switch, supporting horizontal expansion; the standardized communication interface system uses HTTP / HTTPS, gRPC, and MQTT protocols to achieve inter-module communication, all data transmission uses TLS 1.3 encryption, the interface uniformly follows the RESTful API specification, and request and return parameters use JSON format; the multi-source data AI acquisition module is used for multi-channel multi-... The system automatically collects heterogeneous source data, adapts its types, and controls its collection quality, outputting the initial collected dataset to the multi-data AI fusion analysis module. This module, the core of the system, performs data cleaning, three-level deep fusion, data analysis, and fusion result verification on the initial collected dataset, outputting fusion analysis results that meet preset accuracy requirements to the AI ​​document generation module. Based on the fusion analysis results and user needs, the AI ​​document generation module adapts document templates, generates document content, and optimizes document format, outputting the final document draft to the AI ​​compliance verification module. This module performs full-process verification of the final document draft for content compliance, logical compliance, and traceability compliance, outputting compliance verification results and correction suggestions. The AI ​​iterative optimization module collects user feedback data to perform adaptive optimization and effect verification of the fusion analysis model and document generation model. The data storage module provides encrypted storage, backup, and metadata management for the entire process. The human-computer interaction module handles user input, data upload, process preview, document export, and feedback submission.

[0017] Furthermore, the server cluster nodes are configured as follows: the data acquisition module nodes consist of two independent servers, each equipped with an Intel Xeon Silver 4310 processor, 32GB of RAM, and a 512GB SSD, deployed in the core area of ​​the enterprise intranet, with each node supporting 100 concurrent data acquisition tasks; the fusion analysis module nodes consist of four high-performance computing servers, each equipped with an Intel Xeon Platinum 8358 processor, 128GB of RAM, a 2TB SSD, and an NVIDIA A100 graphics card, deployed in the dedicated computing area, employing a round-robin + weighted load balancing strategy, with fusion tasks accounting for 70% of the load. The analysis task accounts for 30% of the weight; the application service node is shared by the AI ​​document generation module, AI compliance verification module, and AI iteration optimization module, and is deployed on 3 application servers, configured with Intel Xeon Gold 5320 processors, 64GB memory, and 1TB SSD. It is deployed in the application service area and adopts dynamic load balancing, automatically allocating tasks when the CPU utilization threshold is 80%; the data storage node is deployed on 2 distributed storage servers + 1 backup server, configured with Intel Xeon Silver 4310 processors, 64GB memory, and 8TB HDD. It is deployed in the data security area and adopts a primary-backup redundant storage strategy.

[0018] Furthermore, the multi-source data AI acquisition module includes a multi-channel data acquisition unit, a data type adaptation unit, and an acquisition quality control unit. The multi-channel data acquisition unit integrates web crawling, optical character recognition, and API interface adaptation technologies, respectively used for data crawling from public network channels, structured data integration between internal systems and third-party platforms, and text extraction from unstructured data such as images and scanned documents. The data type adaptation unit uses semantic analysis and format recognition algorithms to automatically identify the structured, semi-structured, and unstructured types of the acquired data and adapt them to the corresponding acquisition rules and extraction methods. The acquisition quality control unit uses an anomaly detection algorithm to automatically identify and remove invalid and duplicate data, and automatically re-acquire abnormal data through a retry mechanism, while also supporting manual data supplementation by users.

[0019] Furthermore, the multi-data AI fusion analysis module includes a data cleaning unit, a multi-data fusion unit, a data analysis unit, and a fusion result verification unit. The data cleaning unit is used to remove redundancy, correct outliers, unify data definitions, and convert formats in the initial collected dataset. The multi-data fusion unit adopts a three-level fusion architecture: data layer, feature layer, and decision layer. The data layer performs weighted fusion of data from the same source, the feature layer extracts key features and performs weighted fusion, and the decision layer handles conflicts between multi-source data while simultaneously mining the inherent relationships between multi-source data through semantic association algorithms. The data analysis unit performs trend analysis, anomaly analysis, and core value extraction on the fused data, generating a standardized fusion analysis report. The fusion result verification unit uses a comparative verification algorithm to compare and verify the fusion analysis results with the original data source and industry standard data. If the deviation exceeds a preset threshold, the result is returned to the corresponding unit for reprocessing; the preset accuracy requirement is a deviation ≤ 3%.

[0020] Furthermore, the data cleaning unit employs the Isolation Forest algorithm for outlier detection, and the outlier score is calculated as follows: in, For the sample Path length, This represents the expected path length. , For harmonic series, The total number of samples; the fixed threshold for outlier scores is 0.7, which can be dynamically adjusted according to data type, with a threshold range of 0.65-0.75 for price data and 0.7-0.8 for financial data, with an adjustment step size of 0.05; missing value completion uses the LSTM interpolation algorithm; the data layer fusion of the multi-data fusion unit uses the Kalman filter algorithm, and the weighting coefficients are adjusted according to the measurement noise. Inversely proportional, for asset price data, process noise covariance Measure noise covariance Regarding financial data, , For text feature data, , The feature layer fusion uses the PCA algorithm, retaining 95% of the cumulative variance contribution rate; the decision layer fusion uses the DS evidence theory, with a conflict coefficient threshold of 0.8.

[0021] Furthermore, the AI ​​document generation module includes a document template adaptation unit, a document content generation unit, and a document format optimization unit. The document template adaptation unit constructs a multi-type professional document template library based on a knowledge graph, automatically matches the optimal template according to user needs and fusion analysis results, and dynamically adjusts the template structure through natural language processing technology. The document content generation unit uses a large language model combined with retrieval-enhanced generation technology to extract the core data and conclusions from the fusion analysis results, automatically writes the document text according to the template structure, generates corresponding data visualization charts, and embeds them into the document. The document format optimization unit uses a fusion algorithm of natural language processing and computer vision to automatically verify and correct document format deviations, optimize language expression, and support the export of documents in multiple formats.

[0022] Furthermore, the AI ​​compliance verification module includes a compliance knowledge graph construction unit, a content compliance verification unit, a logic compliance verification unit, and a source traceability compliance verification unit. The compliance knowledge graph construction unit constructs a compliance knowledge graph based on knowledge graph technology, covering laws and regulations, industry standards, and document specifications for various industries, and supports real-time updates of compliance clauses. The content compliance verification unit uses semantic analysis algorithms to identify violations in document content. The logic compliance verification unit uses logical reasoning algorithms to verify the logical consistency of document content. The source traceability compliance verification unit uses blockchain technology to store all process data on the blockchain, constructing an immutable source traceability link. The AI ​​iterative optimization module includes a feedback data collection unit, a fusion analysis model optimization unit, a document generation model optimization unit, and an optimization result verification unit. The feedback data collection unit collects user feedback data and extracts core optimization requirements through semantic analysis algorithms. The fusion analysis model optimization unit and the document generation model optimization unit respectively complete parameter optimization and logical adjustment of their corresponding models. The optimization result verification unit uses a comparative verification algorithm to verify the optimization effect, automatically readjusting model parameters when the preset target is not met.

[0023] This invention also provides an intelligent document generation method based on multi-data fusion analysis, comprising the following steps:

[0024] Step 1: Requirements Input and System Initialization. The user inputs a document to generate requirements and core specifications, uploads basic data, and the system loads the corresponding hardware resources, data collection rules, and template adaptation rules to complete the initialization.

[0025] Step 2: Multi-source heterogeneous data acquisition. Automatically collect heterogeneous data from multiple channels and sources, complete data type adaptation and acquisition quality control, generate an initial collection dataset, and push it to the multi-data AI fusion analysis module.

[0026] Step 3: Multi-data fusion analysis. The initial collected dataset is sequentially cleaned, three-level deep fusion is performed, and data analysis is performed. The accuracy of the fusion analysis results is verified. If the deviation exceeds the preset threshold, it is returned to the corresponding step for reprocessing until the accuracy requirements are met. The valid fusion analysis results are then output to the AI ​​document generation module.

[0027] Step 4: Intelligent document generation. Based on user needs and fusion analysis results, template adaptation is completed, the fusion analysis results are extracted to generate the document text and embed visualization charts, the format is optimized, and the final document is generated and pushed to the AI ​​compliance verification module.

[0028] Step 5: Compliance verification and correction. Perform a full-process compliance verification on the final draft of the document, identify violations and provide correction suggestions. After correction, complete a second verification until the document is fully compliant.

[0029] Step 6: Document export and data storage. After the user confirms that the document is correct, the document is exported in the corresponding format. All data in the process is encrypted, stored, and backed up in real time.

[0030] Step 7: Iterative optimization, collect user feedback data, extract core optimization requirements, automatically optimize the system's core algorithms and model parameters, and complete the system iterative update after verification.

[0031] Furthermore, step 3, the multi-data fusion analysis, specifically includes:

[0032] Step 3.1: Use the Isolation Forest algorithm to identify outliers, use the LSTM interpolation algorithm to complete missing data, and perform redundancy removal, outlier correction, standardization and format conversion on the initial collected dataset to generate a standardized dataset;

[0033] Step 3.2: A three-level fusion architecture of data layer-feature layer-decision layer is adopted. The data layer completes the weighted fusion of data from the same source through the Kalman filter algorithm. The feature layer completes the feature fusion by combining the PCA algorithm with information entropy weighting. The decision layer handles the conflicts of multi-source data, mines the inherent correlation of data, and generates a structured fusion dataset through DS evidence theory.

[0034] Step 3.3: Perform trend analysis, anomaly analysis, and core value extraction on the structured fusion dataset. Combine retrieval enhancement generation technology and knowledge graph to generate a standardized fusion analysis report.

[0035] Step 3.4: Compare the fusion analysis results with the original data source and industry standard data. If the deviation is ≤3%, it meets the accuracy requirements. If the deviation is >3%, return to the cleaning or fusion stage for reprocessing.

[0036] Furthermore, the full-process compliance verification in step 5 includes content compliance verification, logical compliance verification, and source traceability compliance verification. Content compliance verification compares the compliance knowledge graph with the fusion analysis results to identify violations such as missing data support, contradictory conclusions, and incorrect clause citations. Logical compliance verification verifies the logical consistency of the document content and the matching of data and conclusions. Source traceability compliance verification verifies the full-process traceability of the document content through blockchain-on-chain data. The iterative optimization in step 7 specifically involves collecting user feedback on the fusion analysis results and generated documents, extracting core optimization points through semantic analysis algorithms, automatically adjusting Kalman filter parameters, isolated forest anomaly thresholds, and model weights, verifying the optimization effect through comparison, and automatically readjusting parameters when the preset target is not met, continuously iterating and optimizing.

[0037] (III) Beneficial Effects

[0038] Compared with existing technologies, this invention provides an intelligent document generation system and method based on multi-data fusion analysis, which has the following beneficial effects:

[0039] This intelligent document generation system and method based on multi-data fusion analysis, through a three-level fusion architecture of data layer, feature layer, and decision layer and the collaboration of multiple technologies, combined with reproducible algorithms such as Kalman filter dynamic weighting and isolated forest accurate anomaly detection, realizes the automatic collection, cleaning, association and deep fusion of multi-channel and multi-format data, eliminates data redundancy and conflict, and explores the inherent correlation and core value of data, solving the pain points of data dispersion and insufficient fusion in traditional technologies, and providing accurate and comprehensive data support for document generation.

[0040] The entire process is automated, from multi-data collection and fusion analysis to document generation and compliance verification. There is no need for manual data organization and document writing, which shortens the generation cycle of a single professional document from several days to several hours, improving efficiency by more than 85%. At the same time, the fusion analysis results are deeply integrated with document generation. The algorithm and model can stably reproduce the fusion and generation effects, ensuring that the document content is accurate and the data support is sufficient, avoiding errors caused by manual operation and improving document quality.

[0041] The system clearly defines hardware deployment, load balancing, client requirements, and inter-module interface specifications, allowing those skilled in the art to directly build the system based on the technical solution. The core algorithm includes complete calculation formulas, parameter values, and training details, and its fusion analysis and document generation effects are 100% reproducible, solving the technical challenge of unreproducible algorithms in the industry. It provides a standardized document generation method, standardizing the entire process and ensuring consistent document quality and compliance with industry standards across different operators. Through compliance verification and blockchain traceability technology, it achieves compliance verification and full-process traceability of document content, meeting regulatory requirements and reducing compliance risks.

[0042] It supports the generation of various professional documents, including asset valuation, financial analysis, and auditing documents. Adapting to multi-source heterogeneous data scenarios, it meets the personalized needs of different industries and users through adaptive template adjustments and model optimization, significantly improving its versatility and practicality. Through an AI-based iterative optimization module, combined with user feedback and historical data, it automatically optimizes the multi-data fusion analysis model and document generation model without manual upgrades. System performance continuously improves with increased usage, adapting to changes in industry policies and user needs. It deeply applies multi-data fusion analysis technology with natural language processing and machine learning to the field of document generation, breaking the limitations of traditional manual operation modes and achieving automation and intelligence in data processing and document generation. This provides efficient and accurate technical support for the generation of professional documents in various industries, driving the digital transformation of these industries. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the overall modules of the intelligent document generation system based on multi-data fusion analysis of the present invention;

[0044] Figure 2 This is a schematic diagram of the workflow of the multi-data AI fusion analysis module of the present invention;

[0045] Figure 3 This is a schematic diagram of the three-level data fusion architecture of the present invention, namely "data layer - feature layer - decision layer";

[0046] Figure 4 This is a schematic diagram of the overall process of the intelligent document generation method based on multi-data fusion analysis of the present invention;

[0047] Figure 5 This is a schematic diagram of the optimization logic of the AI ​​iterative optimization module of the present invention;

[0048] Figure 6 This is a schematic diagram of the hardware deployment architecture of the present invention;

[0049] Figure 7 This is a schematic diagram of the Kalman filtering process of the present invention;

[0050] Figure 8 This is a schematic diagram of the isolated forest anomaly detection method of the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Please see Figures 1 to 8This invention discloses an intelligent document generation system based on multi-data fusion analysis. In this embodiment, a distributed microservice hardware deployment architecture is adopted, distinguishing between the server cluster and the client terminal. The specific deployment rules are as follows:

[0053] Server-side hardware node configuration and deployment: Multi-source data AI acquisition module node: Deploy 2 independent servers, configured with Intel Xeon Silver 4310 processor, 32GB memory, 512GB SSD, deployed in the core area of ​​the enterprise intranet, responsible for web crawling, OCR recognition, API data retrieval, and a single node supports 100 concurrent acquisition tasks;

[0054] Multi-data AI fusion analysis module node: Deploy 4 high-performance computing servers, configured with Intel Xeon Platinum 8358 processors, 128GB memory, 2TB SSD + NVIDIA A100 graphics cards, deployed in the computing power zone, and adopting a load balancing strategy (round-robin + weight allocation), with the core fusion task weight accounting for 70% and the analysis task weight accounting for 30%, to ensure computing power allocation.

[0055] AI document generation module / AI compliance verification module / AI iterative optimization module nodes: Share 3 application servers, configured with Intel Xeon Gold 5320 processor, 64GB memory, 1TB SSD, deployed in the application service area, adopt dynamic load balancing, and automatically allocate tasks according to the system CPU utilization (threshold 80%).

[0056] Data storage module nodes: Deploy 2 distributed storage servers + 1 backup server, configured with Intel Xeon Silver 4310 processors, 64GB memory, and 8TB HDD, deployed in the data security zone, and adopt a primary-backup redundant storage strategy;

[0057] Overall cluster: Nodes are interconnected through gigabit fiber optic switches, supporting horizontal scaling. A single cluster can support the simultaneous generation of up to 2,000 documents.

[0058] Client hardware requirements:

[0059] The terminal devices support Windows 10 and above, macOS 12 and above, and Linux Ubuntu 20.04 and above operating systems;

[0060] Minimum hardware requirements: Intel i5 processor, 8GB RAM, 100GB available hard drive, and network connectivity.

[0061] Recommended configuration: Intel i7 processor, 16GB RAM, 500GB available hard drive.

[0062] Inter-module interface specifications and communication rules

[0063] Communication protocols: Modules communicate using three protocols: HTTP / HTTPS (interface calls), gRPC (high-performance data transmission), and MQTT (lightweight message notification). All data transmissions are encrypted using TLS 1.3.

[0064] Interface standards: All interfaces conform to the RESTful API specification. The interface request parameters are in JSON format, and the returned parameters include code (status code), msg (description), and data (business data). The data transmission format is uniformly UTF-8, and large data streams are transmitted in chunks (10MB in size).

[0065] Interface timeout and retry: The timeout period is 30 seconds, and it will automatically retry 3 times. If the retry fails, an alarm will be triggered and the log will be recorded.

[0066] Interface definition:

[0067] Interface 1: Acquisition Module → Fusion Analysis Module

[0068] API address: / api / data / collect

[0069] Request method: POST

[0070] Request parameters:

[0071] {

[0072] "taskId":"Unique identifier for the task",

[0073] "userId":"User ID",

[0074] "docType":"Document Type (Evaluation / Finance / Audit)",

[0075] "dataList":[

[0076] {

[0077] "dataId":"Data ID",

[0078] "sourceType":"Data source type (Network / Internal System / OCR)",

[0079] "dataType":"Data format (structured / semi-structured / unstructured)",

[0080] "content":"Data content",

[0081] "collectTime":"Collection Time",

[0082] "filePath": "File storage path (optional)"

[0083] } ]

[0085] }

[0086] Return result:

[0087] {

[0088] "code":200,

[0089] "msg":"Data received successfully",

[0090] "data":{

[0091] "receiveId":"Receive Task ID",

[0092] "status":"Receive status",

[0093] "dataCount": "Number of data entries received"

[0094] }

[0095] }

[0096] Interface 2: Fusion Analysis Module → AI Document Generation Module

[0097] API address: / api / data / fusion

[0098] Request method: POST

[0099] Request parameters:

[0100] {

[0101] "fusionId":"fusion task ID",

[0102] "taskId":"Original Task ID",

[0103] "docType":"Document Type",

[0104] "fusionData":"fused structured data",

[0105] "analysisResult":"Analysis Results Text",

[0106] "checkResult":"Check result (pass / fail)",

[0107] "deviation":"deviation rate",

[0108] "traceIdList":["Tracing ID1","Tracing ID2"]

[0109] }

[0110] Return result:

[0111] {

[0112] "code":200,

[0113] "msg":"Fusing result successfully received",

[0114] "data":{

[0115] "generateId":"Document generation task ID",

[0116] "status":"Start generating"

[0117] }

[0118] }

[0119] Interface 3: Full Module → Data Storage Module

[0120] API address: / api / data / storage

[0121] Request method: POST

[0122] Request parameters:

[0123] {

[0124] "operateType":"Operation type (write / read / backup)",

[0125] "dataType":"Data type (collection / cleaning / fusion / document)",

[0126] "dataContent":"Data Content",

[0127] "encrypt": "Whether to encrypt (true / false)",

[0128] "traceId":"Source ID"

[0129] }

[0130] Return result:

[0131] {

[0132] "code":200,

[0133] "msg":"Storage successful",

[0134] "data":{

[0135] "fileId":"File ID",

[0136] "savePath":"storage path",

[0137] "encryptStatus": "Encryption status"

[0138] }

[0139] }

[0140] This system includes: a multi-source data AI acquisition module, a multi-data AI fusion analysis module, an AI document generation module, an AI compliance verification module, an AI iterative optimization module, a data storage module, and a human-computer interaction module. These modules are interconnected through the aforementioned hardware nodes, communication protocols, and standard interfaces to collaboratively complete multi-data fusion analysis and intelligent document generation.

[0141] Multi-source data AI acquisition module: This module integrates AI web crawling, optical character recognition (OCR), API interface adaptation, and other technologies to achieve automatic acquisition of multi-source heterogeneous data, providing comprehensive data input for subsequent multi-data fusion analysis. Specifically, it includes:

[0142] Multi-channel data acquisition unit: Employing AI web crawler algorithms based on deep learning, it automatically crawls publicly available data from public online channels (such as industry databases, policy platforms, and trading websites); it connects to internal enterprise systems (such as financial systems and asset management systems) and third-party data service providers through API interfaces to obtain internal structured data; and it extracts text and data from offline scanned documents and image-based unstructured data (such as ownership certificates and contract documents) through the AIOCR deep learning algorithm, achieving multi-channel, comprehensive data acquisition.

[0143] Data type adaptation unit: Employing AI semantic analysis and format recognition algorithms, it automatically identifies the type of collected data (structured data: financial statements, asset details; semi-structured data: tables, forms; unstructured data: text, images, PDFs), adapts the collection rules and extraction methods for different types of data, and ensures the integrity of data collection;

[0144] Data Acquisition Quality Control Unit: Employs an AI anomaly detection algorithm to automatically identify invalid and duplicate data during the acquisition process, provides real-time feedback on acquisition anomalies (such as missing data or acquisition failure), and automatically re-acquires data through an AI retry mechanism to ensure the initial quality of the acquired data; it also supports users to manually supplement acquired data through a human-computer interaction module, improving the flexibility of data acquisition.

[0145] Multi-Data AI Fusion Analysis Module: This is the core module of the system, integrating technologies such as data cleaning, data fusion, and AI deep analysis. It achieves standardized processing, deep fusion, and value mining of multi-source heterogeneous data, including the complete computational logic of core algorithms, parameter definitions, and dynamic adjustment rules. It provides accurate fusion analysis results for document generation, specifically including:

[0146] Multi-Data AI Cleaning Unit: Employs machine learning-based AI data cleaning algorithms to standardize collected multi-source heterogeneous data, including: removing redundant data, correcting outliers, standardizing data definitions, and converting data formats. Simultaneously, it utilizes AI knowledge graph technology to construct a data cleaning rule base, automatically adapting cleaning rules to different data types to ensure the accuracy and consistency of the cleaned data. Outlier Detection Algorithm Implementation: Employs the Isolation Forest algorithm for outlier identification. Core computational logic:

[0147] Anomaly score calculation method:

[0148] in: For the sample Path length, This represents the expected path length. , For harmonic series, The total number of samples;

[0149] Anomaly threshold: Fixed threshold of 0.7. Determination basis: Based on training and verification of 100,000 sets of asset valuation / financial data, when the anomaly score is ≥0.7, the anomaly identification accuracy rate is ≥98%.

[0150] Threshold adjustment logic: Supports dynamic fine-tuning based on data type. The threshold range for price data is 0.65-0.75, and the threshold range for financial data is 0.7-0.8, with an adjustment step size of 0.05.

[0151] The missing value completion algorithm is implemented as follows: The missing data is completed using the LSTM interpolation algorithm. The input dimension is 128, the number of hidden layer nodes is 64, the activation function is ReLU, the optimizer is Adam, the learning rate is 0.001, the training batch size is 32, and the number of iterations is 50.

[0152] Multi-data AI fusion unit: It adopts a three-level fusion architecture of data layer, feature layer and decision layer, combined with AI deep learning algorithm to achieve deep fusion of multi-source data; during the fusion process, the inherent relationship between different data sources is explored through AI semantic association algorithm to form a structured fusion data system;

[0153] Complete implementation of the Kalman filter algorithm:

[0154] Data layer fusion employs the Kalman filter algorithm. The core computational process and dynamic adjustment rules are as follows:

[0155] Core formula: State prediction:

[0156] Covariance prediction:

[0157] Kalman gain:

[0158] Status Update:

[0159] Covariance update:

[0160] Formula for dynamic adjustment of weighting coefficients: The weighting coefficients are inversely proportional to the measurement noise R; the higher the noise, the lower the weight.

[0161] Differentiated values ​​for Q / R parameters:

[0162] Asset price data: process noise covariance Measure noise covariance ;

[0163] Financial data: Process noise covariance Measure noise covariance ;

[0164] Text feature data: process noise covariance Measure noise covariance .

[0165] Feature layer / decision layer fusion algorithm:

[0166] Feature layer fusion: The PCA algorithm retains 95% of the cumulative variance contribution rate, and the feature weights adopt the information entropy weighting method; Decision layer fusion: The basic probability allocation function of DS evidence theory adopts a combination of expert experience and data statistics, with a conflict coefficient threshold of 0.8. If the threshold is exceeded, the evidence correction rule is activated.

[0167] The AI ​​Deep Analysis Unit employs machine learning, deep learning, and RAG (Retrieval Augmentation) technologies to perform deep analysis on the fused data, including trend analysis, anomaly analysis, and core value extraction. Simultaneously, it combines AI knowledge graphs to perform semantic parsing and relational reasoning on the fused data, generating standardized fusion analysis reports. Furthermore, it uses similarity threshold judgment and clustering algorithms to deduplicate the fused data, combined with a query-based dynamic weighting strategy, to ensure the comprehensiveness and accuracy of the fusion analysis results.

[0168] AI model training details:

[0169] RAG model: Llama3-7B base, Chroma vector library, vector dimension 1536, top 5 in retrieval recall, BGE-reranker-base reranking model; K-Means clustering algorithm, number of clusters... ( (sample size), similarity threshold 0.85.

[0170] Fusion Result Verification Unit: Employing an AI comparison and verification algorithm, the fusion analysis results are compared with the original data source and industry standard data to verify the accuracy and rationality of the fusion results. If there is a deviation, the data is automatically returned to the data cleaning or fusion unit for reprocessing until the fusion results meet the preset accuracy requirements (deviation ≤3%).

[0171] AI document generation module:

[0172] This module integrates generative AI, natural language processing (NLP), and template adaptive adjustment technologies to achieve a deep binding between fusion analysis results and document generation. It automatically generates professionally compliant documents, including complete training details of the generation model, specifically:

[0173] Document Template AI Adaptation Unit: Based on AI knowledge graph technology, a multi-type document template library is built (covering asset appraisal reports, financial analysis reports, audit reports, etc.), and the templates conform to the standards of various industries; according to the document type and generation requirements input by the user, combined with the core features of the multi-data fusion analysis results, AI automatically matches the optimal template, and dynamically adjusts the template structure through NLP technology (such as adding fusion analysis related modules and adjusting content layout) to adapt to the presentation requirements of fusion analysis results;

[0174] The document content AI generation unit employs a generative AI algorithm based on a large language model, combined with RAG technology, to automatically extract fused data and in-depth analysis conclusions from the multi-data AI fusion analysis module. Following the structure requirements of the adapted template, it automatically writes the main body of the document, including data source explanations, the fusion analysis process, core conclusions, and supporting data details. The core conclusions section automatically links the fused analysis data to generate data visualization charts (such as line charts and bar charts), which are then embedded in the document, enhancing its readability and professionalism. It also supports user-defined document content focus, ensuring the document content aligns with user needs.

[0175] Details of generative AI model training:

[0176] Base model: ChatGLM3-6B, fine-tuning dataset contains 100,000 professional documents (asset valuation, finance, auditing), fine-tuning method is LoRA, rank r=8, α=16, learning rate 2e-5, training batch 8, iteration 100 rounds, generation parameters: temperature 0.3, top_p=0.95, maximum generation length 4096;

[0177] Document Format AI Optimization Unit: Employs a fusion algorithm of NLP and Computer Vision (CV) to automatically verify the format compliance of documents and automatically correct format deviations; it also uses AI algorithms to optimize the language expression of documents; in addition, it supports exporting documents in multiple formats.

[0178] AI compliance verification module:

[0179] This module integrates AI semantic analysis, knowledge graphs, logical reasoning, and other technologies. Combined with the results of multi-data fusion analysis, it performs end-to-end compliance verification on the generated documents to ensure the compliance of document content, format, and logic. Specifically, this includes:

[0180] Compliance Knowledge Graph Construction Unit: Based on AI knowledge graph technology, construct compliance knowledge graphs for various industries, covering relevant laws and regulations, industry standards, and document specifications. The knowledge graph can use AI crawler technology to capture updated industry policy information in real time and automatically update compliance clauses to ensure the timeliness of compliance verification.

[0181] Content Compliance AI Verification Unit: Employing NLP semantic analysis algorithms, the unit analyzes document content sentence by sentence, compares it with a compliance knowledge graph, and combines the results of multi-data fusion analysis to automatically identify content violations (such as missing data support, contradictions between conclusions and fusion analysis results, and incorrect citation of compliance clauses). It also marks the type and location of the violation and provides AI compliance correction suggestions.

[0182] Logical Compliance AI Verification Unit: Employs AI logical reasoning algorithms to automatically verify the logical consistency of document content (such as the correlation between the integrated analysis process and core conclusions, the matching of data and conclusions, and the logical coherence of each module in the document), identify logical contradictions, and automatically prompt users to make corrections;

[0183] Traceability and Compliance Verification Unit: Through AI blockchain technology, the entire process of data collection, fusion analysis, and document generation is stored on the blockchain to build an immutable traceability link, ensuring that the document content is traceable (such as the data source and fusion analysis process corresponding to each conclusion) and meets compliance and regulatory requirements.

[0184] AI Iterative Optimization Module:

[0185] This module integrates technologies such as deep learning and machine learning. By analyzing user feedback and historical data, it achieves adaptive optimization of multi-data fusion analysis models and document generation models, thereby improving system performance. Specifically, it includes:

[0186] AI-powered feedback data collection unit: Through the human-computer interaction module, it collects user feedback on the fusion analysis results and generated documents, as well as their suggestions for improvement and optimization. Using NLP semantic analysis algorithms, it automatically extracts the core feedback requirements (such as insufficient fusion accuracy, inaccurate document content, and non-standard format), and stores them in the feedback database.

[0187] Fusion Analysis Model Optimization Unit: Based on user feedback data and historical fusion analysis data, it uses deep learning algorithms to automatically optimize the fusion algorithm and parameter settings (such as fusion weight, deduplication threshold, and weighting strategy) of the multi-data AI fusion analysis module, thereby improving the accuracy and efficiency of multi-data fusion and reducing fusion bias.

[0188] Document generation model optimization unit: Based on user feedback and historical document generation data, optimize the text generation logic and template adaptation rules of the generative AI model to improve the accuracy of document content and the standardization of format, so that the generated documents are more in line with user needs and industry standards.

[0189] Optimization Result Verification Unit: Using an AI comparative verification algorithm, the optimized model is compared with historical data to verify the optimization effect (such as the improvement in fusion accuracy and the improvement in document generation efficiency). If the preset target is not achieved, the model parameters are automatically readjusted and continuously iterated and optimized.

[0190] Data storage module:

[0191] Employing AI-powered distributed storage technology, a secure and efficient data storage system is constructed to store multi-source collected data, cleaned data, fused analysis data, document templates, compliance knowledge graphs, user feedback data, and traceability data. Simultaneously, AI-integrated data encryption algorithms (such as AES encryption) are used to encrypt and store sensitive data (such as corporate financial data and asset ownership information) to prevent data leakage. AI-powered automatic backup algorithms enable real-time data backup and fault recovery, ensuring data security and integrity. Furthermore, a unified metadata center is built to automatically collect metadata from various source systems, forming an enterprise-level data catalog for easy data management and traceability.

[0192] Human-computer interaction module:

[0193] Provides a visual user interface based on client hardware requirements, supports Windows / macOS / Linux systems, and allows users to upload data, input requirements, preview fusion analysis results, preview documents, and submit feedback. Integrates an AI-powered intelligent question-answering robot based on NLP technology to automatically answer common user questions during system use. Allows users to manually intervene in fusion analysis results and document content; after modification, AI automatically performs compliance and logical checks on the modified parts. Also supports visual display of the fusion analysis process and document generation process for easy real-time monitoring by users.

[0194] Based on the above system, this invention provides an intelligent document generation method based on multi-data fusion analysis, including the following steps, to achieve full automation and standardization of the multi-data fusion analysis and document generation process:

[0195] Step 1: Requirements Input and System Initialization. The user inputs a document to generate requirements and core specifications, uploads basic data, and the system loads the corresponding hardware resources, data collection rules, and template adaptation rules to complete the initialization.

[0196] Step 2: Multi-source heterogeneous data acquisition. Automatically collect heterogeneous data from multiple channels and sources, complete data type adaptation and acquisition quality control, generate an initial collection dataset, and push it to the multi-data AI fusion analysis module.

[0197] Step 3: Multi-data fusion analysis. The initial collected dataset is sequentially cleaned, three-level deep fusion is performed, and data analysis is performed. The accuracy of the fusion analysis results is verified. If the deviation exceeds the preset threshold, it is returned to the corresponding step for reprocessing until the accuracy requirements are met. The valid fusion analysis results are then output to the AI ​​document generation module.

[0198] Step 4: Intelligent document generation. Based on user needs and fusion analysis results, template adaptation is completed, the fusion analysis results are extracted to generate the document text and embed visualization charts, the format is optimized, and the final document is generated and pushed to the AI ​​compliance verification module.

[0199] Step 5: Compliance verification and correction. Perform a full-process compliance verification on the final draft of the document, identify violations and provide correction suggestions. After correction, complete a second verification until the document is fully compliant.

[0200] Step 6: Document export and data storage. After the user confirms that the document is correct, the document is exported in the corresponding format. All data in the process is encrypted, stored, and backed up in real time.

[0201] Step 7: Iterative optimization, collect user feedback data, extract core optimization requirements, automatically optimize the system's core algorithms and model parameters, and complete the system iterative update after verification.

[0202] In this solution, step 3, the multi-data fusion analysis, specifically includes:

[0203] Step 1: Use the Isolation Forest algorithm to identify outliers, use the LSTM interpolation algorithm to complete missing data, and perform redundancy removal, outlier correction, standardization and format conversion on the initial collected dataset to generate a standardized dataset.

[0204] Step 2: A three-level fusion architecture of data layer-feature layer-decision layer is adopted. The data layer completes the weighted fusion of data from the same source through the Kalman filter algorithm. The feature layer completes the feature fusion by combining the PCA algorithm with information entropy weighting. The decision layer handles the conflicts of multi-source data and mines the inherent correlation of data through DS evidence theory to generate a structured fusion dataset.

[0205] Step 3: Perform trend analysis, anomaly analysis, and core value extraction on the structured fusion dataset. Combine retrieval enhancement generation technology and knowledge graph to generate a standardized fusion analysis report.

[0206] Step 4: Compare the fusion analysis results with the original data source and industry standard data. If the deviation is ≤3%, it meets the accuracy requirements. If the deviation is >3%, return to the cleaning or fusion stage for reprocessing.

[0207] In this solution, step 5, the full-process compliance verification, includes content compliance verification, logical compliance verification, and source traceability compliance verification. Content compliance verification compares the compliance knowledge graph with the fusion analysis results to identify violations such as missing data support, contradictory conclusions, and incorrect clause citations. Logical compliance verification verifies the logical consistency of the document content and the matching of data and conclusions. Source traceability compliance verification verifies the full-process traceability of the document content through blockchain-based data. Step 7, iterative optimization, specifically involves collecting user feedback on the fusion analysis results and generated documents, extracting core optimization points through semantic analysis algorithms, automatically adjusting Kalman filter parameters, isolated forest anomaly thresholds, and model weights, verifying the optimization effect through comparison, and automatically readjusting parameters when the preset target is not met, continuously iterating and optimizing.

[0208] Example 1: Intelligent generation of asset appraisal reports based on multi-data fusion analysis.

[0209] Step 1: Requirements Input and System Initialization

[0210] Users, staff of asset appraisal agencies, input document generation requirements through the human-computer interaction module: the document type is a corporate real estate asset appraisal report, the purpose of generation is mortgage appraisal, and the core requirements are "content focuses on valuation data support and the format complies with the 'Asset Appraisal Practice Standards'"; at the same time, they upload basic data such as the company's business license, real estate ownership certificate (scanned copy), and asset detail table; the system initializes, schedules the resources of 4 servers of the fusion analysis module, calls the data collection rules, standard interfaces and report template adaptation rules related to real estate asset appraisal, and completes the preliminary preparation.

[0211] Step 2: Multi-source heterogeneous data acquisition

[0212] The multi-source data AI acquisition module is activated, collecting heterogeneous data from multiple sources using the following methods:

[0213] Using a deep learning-based web crawler algorithm, we automatically retrieved 50 similar office building transaction cases (structured data) from the local real estate transaction platform over the past 6 months, local macroeconomic indicators (GDP growth rate, real estate price index, structured data), and real estate industry mortgage assessment policy documents (unstructured text).

[0214] By connecting to the enterprise's internal asset management system via API, we can obtain real estate depreciation data and maintenance cost data (structured data), and connect to the local real estate registration center system to obtain real estate registration information (structured data).

[0215] By using OCR deep learning algorithms, text data is extracted from the uploaded scanned copies of real estate ownership certificates, including location, land use right type, and building area, thus completing the conversion of unstructured data into structured data.

[0216] During the data collection process, the data collection quality control unit automatically identified and removed 3 duplicate transaction cases and 1 invalid price data. It also automatically re-collected 1 policy document that failed to be collected and pushed the initial data collection dataset to the multi-data AI fusion analysis module through the / api / data / collect interface.

[0217] Step 3: Multi-data fusion analysis

[0218] 3.1 The data cleaning unit standardizes and cleans the initial collected dataset: unifies the monetary format (retaining 2 decimal places) and the date format (YYYY-MM-DD), uses the Isolation Forest algorithm to calculate the anomaly score \(s(x,n)\), identifies 2 transaction price data with anomaly scores ≥0.7, and uses the LSTM interpolation algorithm (input dimension 128, learning rate 0.001) to complete 1 missing maintenance cost data, transforming the policy document text into standardized structured data and generating a standardized dataset;

[0219] 3.2 The data fusion unit adopts a three-level fusion architecture of "data layer - feature layer - decision layer" to deeply fuse standardized datasets: The data layer uses the Kalman filter algorithm, configuring Q=1e-4 and R=1e-3 for asset price data, and calculates the fusion weight through a dynamic adjustment formula of weighting coefficients to perform weighted fusion of price data of similar transaction cases; the feature layer uses the PCA algorithm to extract key features from each data source and performs feature-weighted fusion; the decision layer uses DS evidence theory to handle the conflict between transaction case data and policy data, forming a structured fused dataset; at the same time, semantic association algorithms are used to explore the inherent relationship between real estate area and transaction price, and between policy requirements and valuation methods.

[0220] 3.3 The deep analysis unit performs in-depth analysis on the structured fusion dataset: It uses machine learning algorithms to analyze the trend of real estate price fluctuations, identifies one abnormal price deviation (15% deviation from the industry average), marks the cause of the abnormality and corrects it; it extracts core data such as comparable case prices, discount rates, and newness rates, and combines RAG technology (vector dimension 1536, recall top 5) with asset valuation knowledge graph to generate a fusion analysis report, which clarifies the core conclusion of real estate valuation as RMB 19.04 million, and provides corresponding complete data supporting details;

[0221] 3.4 The fusion result verification unit compares the fusion analysis results with historical similar asset appraisal data and industry standard data. The calculated deviation is 0.42%, which is lower than the preset threshold of 3%, confirming that the fusion analysis results are valid. The results are then pushed to the AI ​​document generation module through the / api / data / fusion interface.

[0222] Step 4: Intelligent Document Generation

[0223] 4.1 The document template adaptation unit automatically matches the corresponding standard template based on the requirements of the "Real Estate Mortgage Appraisal Report" and the results of the fusion analysis. It adds a "Multi-Data Fusion Analysis Description" module through NLP technology, adjusts the template structure, and adapts to the presentation requirements of the fusion analysis results.

[0224] 4.2 The document content generation unit uses a finely tuned ChatGLM3-6B generative AI algorithm to extract the core data and conclusions from the fusion analysis report and automatically write the main body of the report, including client information, valuation object, valuation basis (explanation of multi-source data sources), multi-data fusion analysis process, valuation method, valuation conclusion, etc.; at the same time, it generates a line chart comparing transaction case prices and a bar chart of key parameters, which are embedded in the document to complete the first draft of the report;

[0225] 4.3 The document format optimization unit optimizes the format of the initial report draft, unifying the font to SimSun, the body font size to 12pt, the line spacing to 1.5, adding page numbers and a table of contents, correcting the layout of attachments, optimizing the language, generating a standardized final report, and pushing it to the AI ​​compliance verification module.

[0226] Step 5: Compliance Verification and Rectification

[0227] 5.1 The AI ​​compliance verification module performs compliance verification on the final report: the content compliance verification identifies the violation point of "insufficient description of the multi-data fusion analysis process", marks the location and provides correction suggestions (supplementing the fusion weight, deduplication and weighting process of each data source); the logic compliance verification finds no logical contradictions; the traceability compliance verification confirms that the data of the entire process is traceable;

[0228] 5.2 Based on the correction suggestions, the user supplements the details of the fusion analysis process, triggers a secondary verification, and confirms that the report is fully compliant.

[0229] Step 6: Document Export and Data Storage

[0230] After the user confirms that the report is correct, the asset appraisal report in PDF format can be exported through the human-computer interaction module. The data storage module is based on distributed storage nodes and uses the AES-256 encryption algorithm to automatically encrypt and store multi-source collected data, standardized data, fused analysis data, final report, verification records, and traceability data in real time, completing the data retention of the entire process.

[0231] Step 7: Iterative Optimization

[0232] User feedback suggested: "We hope to add the integration of rental data for similar assets during the fusion analysis process to improve valuation accuracy." The AI ​​iteration optimization module extracted the core requirements, automatically adjusted the Kalman filter Q / R parameters, increased the weight of rental data, optimized the collection rules and document generation model, and improved the fusion accuracy by 6% after optimization, thus completing the system iteration optimization.

[0233] Example 2: Intelligent generation of financial analysis reports based on multi-data fusion analysis.

[0234] The difference between this embodiment and Embodiment 1 is that the document type is an annual financial analysis report of an enterprise, the hardware deployment and interface specifications are consistent with Embodiment 1, and the Kalman filter parameters are adapted to the financial data during data fusion: Q=1e-5, R=1e-4. The isolated forest anomaly threshold is adjusted to 0.75. The specific implementation process is as follows:

[0235] Users input their requirements through the human-computer interaction module: the document type is the company's annual financial analysis report, the core requirement is "focusing on financial data trend analysis and correlation analysis", and they upload the company's annual financial statements (Excel format), industry financial benchmark data, and macroeconomic data;

[0236] The multi-source data AI acquisition module automatically collects multi-source data: balance sheets, profit and loss statements, and cash flow statements from the enterprise's internal financial system (structured data), industry financial benchmark data (structured data), macroeconomic indicators (structured data), and enterprise financial audit reports (unstructured text).

[0237] The multi-data AI fusion analysis module cleans and deeply integrates multi-source financial data. It adopts Kalman filtering and isolated forest algorithms adapted to financial data and uses a three-level fusion architecture of "data layer - feature layer - decision layer". It integrates enterprise financial data with industry benchmark data and macroeconomic data, explores the correlation between financial data, and generates fusion analysis results such as financial trend analysis, anomaly analysis, and core indicator evaluation through in-depth analysis.

[0238] The AI ​​document generation module matches the financial analysis report template, calls the finely tuned generation model to automatically write the report text, generate financial data visualization charts, and optimize the document format.

[0239] The AI ​​compliance verification module performs compliance verification on financial analysis reports to ensure that financial data is accurate, analytical logic is compliant, and financial reporting standards are met.

[0240] After the user confirms that everything is correct, the report is exported. The data storage module completes encrypted storage and backup based on distributed nodes. The AI ​​iteration and optimization module optimizes the financial data fusion algorithm based on user feedback to improve the accuracy of trend analysis.

[0241] As can be seen from the above embodiments, the intelligent document generation system and method based on multi-data fusion analysis of the present invention has feasible hardware deployment, standard communication interfaces and reproducible core algorithms. It can adapt to the generation needs of various types of professional documents. Through deep fusion analysis of multi-data and collaboration with artificial intelligence technology, combined with a standardized generation process, it can achieve efficient, accurate and compliant document generation, greatly improve document generation efficiency and quality, reduce labor costs, and has strong practicality and promotion value. It can be widely used in multiple professional fields such as asset appraisal, finance, and auditing, and promote the digital and intelligent transformation of the professional document generation field.

Claims

1. An intelligent document generation system based on multi-data fusion analysis, comprising a human-computer interaction module and a data storage module, characterized in that, It also includes a multi-source data AI acquisition module, a multi-data AI fusion analysis module, an AI document generation module, an AI compliance verification module, and an AI iterative optimization module. The system adopts a distributed microservice hardware deployment architecture, and each module achieves encrypted data interaction and full-process collaborative work through a standardized communication interface system. The distributed microservice hardware deployment architecture includes a server cluster and client terminals. The server cluster is functionally divided into acquisition module nodes, fusion analysis module nodes, application service nodes, and data storage nodes that communicate with each other. Each node is interconnected through a gigabit fiber optic switch, supporting horizontal expansion. The standardized communication interface system uses HTTP / HTTPS, gRPC, and MQTT protocols to achieve inter-module communication. All data transmission is encrypted using TLS 1.3, and the interfaces uniformly follow the RESTful API specification for requests and responses. The parameters are in JSON format. The multi-source data AI acquisition module is used for automatic acquisition, type adaptation, and acquisition quality control of heterogeneous data from multiple channels and sources, and outputs the initial acquired dataset to the multi-data AI fusion analysis module. The multi-data AI fusion analysis module is the core module of the system, used for data cleaning, three-level deep fusion, data analysis, and fusion result verification of the initial acquired dataset, and outputs fusion analysis results that meet the preset accuracy requirements to the AI ​​document generation module. The AI ​​document generation module is used to complete document template adaptation, document content generation, and document format optimization according to the fusion analysis results and user needs, and outputs the final document to the AI ​​compliance verification module. The AI ​​compliance verification module is used to perform full-process verification of the final document for content compliance, logical compliance, and traceability compliance, and outputs compliance verification results and correction suggestions. The AI ​​iterative optimization module is used to collect user feedback data and complete the adaptive optimization and effect verification of the fusion analysis model and document generation model; the data storage module is used for encrypted storage, backup and metadata management of the entire process data; the human-computer interaction module is used for user requirement input, data upload, process preview, document export and feedback submission.

2. The intelligent document generation system based on multi-data fusion analysis according to claim 1, characterized in that, The server cluster is configured as follows: The data acquisition module nodes consist of two independent servers, each equipped with an Intel Xeon Silver 4310 processor, 32GB of RAM, and a 512GB SSD, deployed in the core area of ​​the enterprise intranet, with each node supporting 100 concurrent data acquisition tasks. The fusion analysis module nodes consist of four high-performance computing servers, each equipped with an Intel Xeon Platinum 8358 processor, 128GB of RAM, a 2TB SSD, and an NVIDIA A100 graphics card, deployed in a dedicated computing zone, employing a round-robin + weighted load balancing strategy, with fusion tasks accounting for 70% of the weight and analysis tasks... The workload weight accounts for 30%; the application service node is shared by the AI ​​document generation module, AI compliance verification module, and AI iteration optimization module, and is deployed on 3 application servers, configured with Intel Xeon Gold 5320 processors, 64GB memory, and 1TB SSD. It is deployed in the application service area and adopts dynamic load balancing, automatically allocating tasks when the CPU utilization threshold is 80%; the data storage node is deployed on 2 distributed storage servers + 1 backup server, configured with Intel Xeon Silver 4310 processors, 64GB memory, and 8TB HDD. It is deployed in the data security area and adopts a primary-backup redundant storage strategy.

3. The intelligent document generation system based on multi-data fusion analysis according to claim 1, characterized in that, The multi-source data AI acquisition module includes a multi-channel data acquisition unit, a data type adaptation unit, and an acquisition quality control unit. The multi-channel data acquisition unit integrates web crawling, optical character recognition, and API interface adaptation technologies, respectively used for data crawling from public network channels, structured data integration between internal systems and third-party platforms, and text extraction from unstructured data such as images and scanned documents. The data type adaptation unit uses semantic analysis and format recognition algorithms to automatically identify the structured, semi-structured, and unstructured types of the acquired data and adapt them to the corresponding acquisition rules and extraction methods. The acquisition quality control unit uses an anomaly detection algorithm to automatically identify and remove invalid and duplicate data, and automatically re-acquire abnormal data through a retry mechanism, while also supporting manual data supplementation by users.

4. The intelligent document generation system based on multi-data fusion analysis according to claim 1, characterized in that, The multi-data AI fusion analysis module includes a data cleaning unit, a multi-data fusion unit, a data analysis unit, and a fusion result verification unit. The data cleaning unit removes redundancy, corrects outliers, standardizes data definitions, and converts formats in the initial collected dataset. The multi-data fusion unit adopts a three-level fusion architecture: data layer, feature layer, and decision layer. The data layer performs weighted fusion of data from the same source, the feature layer extracts key features and performs weighted fusion, and the decision layer handles conflicts between multi-source data while using semantic association algorithms to uncover inherent relationships between the data. The data analysis unit performs trend analysis, anomaly analysis, and core value extraction on the fused data, generating a standardized fusion analysis report. The fusion result verification unit uses a comparative verification algorithm to compare the fusion analysis results with the original data source and industry standard data. If the deviation exceeds a preset threshold, the result is returned to the corresponding unit for reprocessing; the preset accuracy requirement is a deviation ≤ 3%.

5. The intelligent document generation system based on multi-data fusion analysis according to claim 4, characterized in that, The data cleaning unit uses the isolated forest algorithm to detect outliers, and the outlier score is calculated as follows: in, For the sample Path length, This represents the expected path length. , For harmonic series, The total number of samples; the fixed threshold for outlier scores is 0.7, which can be dynamically adjusted according to data type, with a threshold range of 0.65-0.75 for price data and 0.7-0.8 for financial data, with an adjustment step size of 0.05; missing value completion uses the LSTM interpolation algorithm; the data layer fusion of the multi-data fusion unit uses the Kalman filter algorithm, and the weighting coefficients are adjusted according to the measurement noise. Inversely proportional, for asset price data, process noise covariance Measure noise covariance Regarding financial data, , For text feature data, , The feature layer fusion uses the PCA algorithm, retaining 95% of the cumulative variance contribution rate; the decision layer fusion uses the DS evidence theory, with a conflict coefficient threshold of 0.

8.

6. The intelligent document generation system based on multi-data fusion analysis according to claim 1, characterized in that, The AI ​​document generation module includes a document template adaptation unit, a document content generation unit, and a document format optimization unit. The document template adaptation unit constructs a multi-type professional document template library based on a knowledge graph, automatically matches the optimal template according to user needs and fusion analysis results, and dynamically adjusts the template structure using natural language processing technology. The document content generation unit uses a large language model combined with retrieval-enhanced generation technology to extract the core data and conclusions from the fusion analysis results, automatically writes the document text according to the template structure, generates corresponding data visualization charts, and embeds them into the document. The document format optimization unit uses a fusion algorithm of natural language processing and computer vision to automatically verify and correct document format deviations, optimize language expression, and support the export of documents in multiple formats.

7. The intelligent document generation system based on multi-data fusion analysis according to claim 1, characterized in that, The AI ​​compliance verification module includes a compliance knowledge graph construction unit, a content compliance verification unit, a logical compliance verification unit, and a source tracing compliance verification unit. The compliance knowledge graph construction unit uses knowledge graph technology to build a compliance knowledge graph covering laws, regulations, industry standards, and document specifications for various industries, and supports real-time updates of compliance clauses. The content compliance verification unit uses semantic analysis algorithms to identify violations in document content. The logical compliance verification unit uses logical reasoning algorithms to verify the logical consistency of document content. The source tracing compliance verification unit uses blockchain technology to store all process data on the blockchain, constructing an immutable source tracing link. The AI ​​iterative optimization module includes a feedback data collection unit, a fusion analysis model optimization unit, a document generation model optimization unit, and an optimization result verification unit. The feedback data collection unit collects user feedback data and extracts core optimization requirements through semantic analysis algorithms. The fusion analysis model optimization unit and the document generation model optimization unit respectively complete the parameter optimization and logic adjustment of their respective models; the optimization result verification unit uses a comparative verification algorithm to verify the optimization effect, and automatically readjusts the model parameters when the preset target is not achieved.

8. A method for generating intelligent documents based on multi-data fusion analysis, characterized in that, The system implementation based on claim 1 includes the following steps: Step 1: Requirements Input and System Initialization. The user inputs a document to generate requirements and core specifications, uploads basic data, and the system loads the corresponding hardware resources, data collection rules, and template adaptation rules to complete the initialization. Step 2: Multi-source heterogeneous data acquisition. Automatically collect heterogeneous data from multiple channels and sources, complete data type adaptation and acquisition quality control, generate an initial collection dataset, and push it to the multi-data AI fusion analysis module. Step 3: Multi-data fusion analysis. The initial collected dataset is sequentially cleaned, three-level deep fusion is performed, and data analysis is performed. The accuracy of the fusion analysis results is verified. If the deviation exceeds the preset threshold, it is returned to the corresponding step for reprocessing until the accuracy requirements are met. The valid fusion analysis results are then output to the AI ​​document generation module. Step 4: Intelligent document generation. Based on user needs and fusion analysis results, template adaptation is completed, the fusion analysis results are extracted to generate the document text and embed visualization charts, the format is optimized, and the final document is generated and pushed to the AI ​​compliance verification module. Step 5: Compliance verification and correction. Perform a full-process compliance verification on the final draft of the document, identify violations and provide correction suggestions. After correction, complete a second verification until the document is fully compliant. Step 6: Document export and data storage. After the user confirms that the document is correct, the document is exported in the corresponding format. All data in the process is encrypted, stored, and backed up in real time. Step 7: Iterative optimization, collect user feedback data, extract core optimization requirements, automatically optimize the system's core algorithms and model parameters, and complete the system iterative update after verification.

9. The intelligent document generation method based on multi-data fusion analysis according to claim 8, characterized in that, The multi-data fusion analysis in step 3 specifically includes: Step 3.1: Use the Isolation Forest algorithm to identify outliers, use the LSTM interpolation algorithm to complete missing data, and perform redundancy removal, outlier correction, standardization and format conversion on the initial collected dataset to generate a standardized dataset; Step 3.2: A three-level fusion architecture of data layer-feature layer-decision layer is adopted. The data layer completes the weighted fusion of data from the same source through the Kalman filter algorithm. The feature layer completes the feature fusion by combining the PCA algorithm with information entropy weighting. The decision layer handles the conflicts of multi-source data, mines the inherent correlation of data, and generates a structured fusion dataset through DS evidence theory. Step 3.3: Perform trend analysis, anomaly analysis, and core value extraction on the structured fusion dataset. Combine retrieval enhancement generation technology and knowledge graph to generate a standardized fusion analysis report. Step 3.4: Compare the fusion analysis results with the original data source and industry standard data. If the deviation is ≤3%, it meets the accuracy requirements. If the deviation is >3%, return to the cleaning or fusion stage for reprocessing.

10. The intelligent document generation method based on multi-data fusion analysis according to claim 8, characterized in that, The full-process compliance verification in step 5 includes content compliance verification, logic compliance verification, and source traceability compliance verification. The content compliance verification compares the compliance knowledge graph with the fusion analysis results to identify violations such as missing data support, contradictory conclusions, and incorrect clause citations; the logical compliance verification verifies the logical consistency of the document content and the matching of data and conclusions; the traceability compliance verification verifies the full-process traceability of the document content through blockchain-on-chain data; the iterative optimization in step 7 specifically involves collecting user feedback on the fusion analysis results and the generated document, extracting core optimization points through semantic analysis algorithms, automatically adjusting Kalman filter parameters, isolated forest anomaly thresholds, and model weights, verifying the optimization effect through comparison, and automatically readjusting parameters when the preset target is not met, continuously iterating and optimizing.

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