Archive information extraction and intelligent management system based on self-supervised learning

Abstract

The invention discloses an archive information extraction and intelligent management system based on self-supervised learning, and the system comprises the following modules: a collection preprocessing module which is used for collecting original data of an archive and generating a layout sample data set and an original text mapping table; the self-supervision pre-training module is used for obtaining a three-mode unified encoder model; the document analysis module is used for text detection, character recognition and table structure recovery and generating a document analysis data set; the information extraction module is used for aligning pointer positioning and optimal transmission, outputting metadata and a relational data set and registering evidence entries; the alignment warehousing module is used for generating a standardized record library and an evidence link table based on the structure enhanced double-tower vector recall; and the strategy operation and maintenance module is used for authority control, desensitization, archiving and incremental updating. According to the invention, through combination of three-mode self-supervision and an evidence chain, accurate extraction, aligned warehousing and traceable management of archive elements are realized.

Description

Technical Field

[0001] This invention relates to the field of information processing and archival informatization technology, and in particular to an archival information extraction and intelligent management system based on self-supervised learning. Background Technology

[0002] With the rapid growth in the scale of electronic archives and the increasing online nature of business operations, the need for automatic identification, key information extraction, and end-to-end management of archive content from multiple sources and across different formats is becoming increasingly prominent. Existing technologies are mostly based on a pipeline of "optical recognition + layout analysis + rule configuration" or customized training on a small number of templates, but they generally suffer from the following problems in complex real-world environments:

[0003] Inconsistent scanning quality, skew distortion, seal obstruction, and handwritten annotations lead to instability in structural boundaries and text positioning, making errors easily amplified in subsequent stages. Information extraction and template adaptation typically require extensive manual annotation and frequent rule maintenance, resulting in high costs and slow deployment when migrating to new invoices, official documents, or business forms. Numerous heterogeneous spellings of dates, document numbers, units, and organization names make it difficult for traditional rules or dictionary matching to maintain global consistency and deduplication / disambiguation across pages, documents, and batches. Retrieval often remains at the text level or single-vector recall, failing to pinpoint the exact location and layout coordinates of the original text, disrupting the review, evidence collection, and verification process. Models are mostly updated offline, making it difficult to distill first-line proofreading feedback into controllable incremental improvements; cross-unit data sharing is limited, and privacy and continuous optimization are often difficult to simultaneously satisfy.

[0004] Therefore, how to provide a self-supervised learning-based archive information extraction and intelligent management system is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] One objective of this invention is to propose a self-supervised learning-based archive information extraction and intelligent management system. Compared with existing processes that rely on high annotation and rule stacking, this invention has the advantages of strong robustness to low annotation and complex layouts, high extraction and alignment accuracy, traceable and auditable evidence, high retrieval and recall efficiency, and incremental updates.

[0006] According to an embodiment of the present invention, an archival information extraction and intelligent management system based on self-supervised learning includes the following modules:

[0007] The data acquisition and preprocessing module is used to acquire the original data set of the archives, perform geometric regression correction, homomorphic denoising and self-attention page segmentation, generate page sample datasets, and establish the original text mapping table;

[0008] The self-supervised pre-training module is used to perform mask layout reconstruction and cross-view comparison self-supervised training on the layout sample dataset to obtain a three-modal unified encoder model.

[0009] The document parsing module is used to perform differentiable boundary optimized text detection, character recognition, table structure recovery and seal handwriting parsing based on the trimodal unified encoder model, generate a document parsing dataset, and associate it with the original text mapping table;

[0010] The information extraction module is used to call the information extraction model to perform pointer network span positioning, conditional random field sequence labeling and optimal transmission field alignment, output metadata and relation datasets, and register evidence entry indexes;

[0011] The alignment and storage module is used to read metadata, relational datasets, and knowledge graph libraries, build a structure-enhanced dual-tower vector recall model, establish a small world graph index and a product quantization index, and generate a standardized record library and evidence link table.

[0012] The strategy operation and maintenance module is used to implement access control, desensitization, automatic archiving, borrowing approval and due date reminders based on a standardized record library, evidence link table and original text mapping table. It also records verification and generates pseudo-tags for periodic incremental updates.

[0013] A method for extracting and intelligently managing archival information based on self-supervised learning according to an embodiment of the present invention includes the following steps:

[0014] The original dataset of the archives was collected, and geometric regression correction, homomorphic denoising and self-attention page segmentation were used to perform skew correction and segmentation to obtain the page sample dataset and generate the original text mapping table.

[0015] Using the layout sample dataset as input, a three-modal unified encoder model is obtained by performing representation learning through self-supervised joint pre-training of mask layout reconstruction and cross-view comparison.

[0016] The layout sample dataset is processed based on a three-modal unified encoder model. Cascaded detection and recognition, graph neural network table parsing are used for localization and reconstruction to obtain a document parsing dataset. The original text mapping table is then used for backtracking.

[0017] Taking the document parsing dataset as input, the information extraction model is invoked, and the pointer network and optimal transmission are used for joint decoding to perform entity localization and key-value alignment, thereby obtaining metadata and relation datasets.

[0018] Using metadata, relational datasets, and knowledge graph libraries as input, a structure-enhanced dual-tower vector recall model is constructed. Global vectors and field anchor vectors are used, with inner product and anchor matching as the metric. Small world graph index and product quantization index are combined to solve for alignment, disambiguation, and consistency, resulting in a standardized record library and evidence link table.

[0019] Based on a standardized record library, and using an evidence linking table and a policy rule engine, the system implements access control, desensitization, automatic archiving, borrowing approval and due date reminders, and records are verified and pseudo-labels are generated for feedback.

[0020] Optionally, the original dataset of the collected archives is subjected to geometric regression correction, homomorphic denoising, and self-attention page segmentation to perform skew correction and segmentation, resulting in a page sample dataset, and generating a text mapping table, specifically including:

[0021] Collect scanned copies of paper documents, electronic documents, and structured forms, unify them into a parsable format, and record the source number and timestamp to form the original dataset of the archives;

[0022] Geometric regression correction is performed on the page image, and homomorphic denoising and adaptive binarization are applied in the logarithmic domain to obtain the corrected page image and correction parameter log.

[0023] A self-attention page segmentation network is constructed. Line segment candidates are generated by combining the corrected page image with connected components and vertical projection. The text prior is formed by overlaying the OCR character box rasterization. The input consists of the corrected page image, text prior, 2D relative coordinate embedding and hierarchical path embedding. The network adopts a hierarchical self-attention encoder and a multi-scale fusion neck. Multi-head decoding is set for block category, boundary vertex regression, hierarchical label and reading order score. The training objective is the weighted sum of classification cross-entropy, boundary IoU or DIoU, hierarchical sequence cross-entropy and order consistency loss. Data augmentation with coordinate perturbation and random occlusion is applied.

[0024] The self-attention page segmentation network outputs block categories, boundary polygons, hierarchical labels, and reading order scores. It then performs page-block segmentation to generate a page sample dataset containing page numbers, block identifiers, page coordinates, hierarchical paths, reading order tags, and content hashes.

[0025] A source mapping table is established, with fields including source number, file path, page number, block identifier, page coordinates, hierarchical path, reading order marker, content hash, and correction parameter log index. Based on the source mapping table, a one-to-many binding is established between the original archive dataset, the corrected page diagram, and the layout sample dataset.

[0026] Optionally, the step of using a layout sample dataset as input and performing self-supervised joint pre-training of mask layout reconstruction and cross-view comparison to learn representations and obtain a three-modal unified encoder model specifically includes:

[0027] The page sample dataset is aggregated with page and block identifiers according to the original text mapping table to form training samples of text sequence, page image slice, two-dimensional relative coordinates and hierarchical path. The reading order label is retained as a regularization constraint, and the same page three modalities are positive pairs, and cross-page or cross-file pairs are negative pairs.

[0028] A pre-trained backbone for a trimodal unified encoder model is constructed, comprising a text sub-encoder, a visual sub-encoder, and a coordinate embedding module. The three features are aligned and aggregated in the fusion layer, and the output is a page-level vector and a block-level vector in the same representation space.

[0029] Two types of self-supervised tasks are set up: mask layout reconstruction and cross-view contrast alignment. The categories, boundaries and contents of the masked page blocks and characters are reconstructed, the positive and negative pairs are contrasted and learned, and the reading order consistency constraint is applied.

[0030] We optimize the loss by weighted sum of reconstruction loss and contrast loss, and combine in-batch negative sampling and hard negative sampling. During training, we apply Gaussian perturbation to page coordinates and perform random occlusion enhancement on page blocks, while keeping the page and block identifiers in the original text mapping table unchanged.

[0031] After completing the joint pre-training, the pre-training parameters of the trimodal unified encoder model are generated and the model version is registered.

[0032] Optionally, the step of processing the layout sample dataset based on the three-modal unified encoder model, using cascaded detection and recognition, graph neural network table parsing, for localization and reconstruction to obtain the document parsing dataset, and backtracking according to the original text mapping table, specifically includes:

[0033] The page layout sample dataset is loaded with page and block identifiers according to the original text mapping table. The affine and perspective parameters of the correction parameter log are read to complete the page correction. The reading order mark and two-dimensional relative coordinates are combined, and the three-modal unified encoder model is called to generate page-level and block-level features while maintaining the consistency of the identifiers.

[0034] A text detection method with differentiable boundary optimization is adopted to simultaneously predict the text probability map and threshold map. A soft binary map is obtained by using a differentiable threshold approximation function. Connectivity extraction, centroid offset correction, and polygon fitting are performed. Boundary polygons and block confidence are registered and associated with block identifiers.

[0035] Candidate text blocks are cropped according to the log inverse transform of the correction parameters and normalized with a fixed height. Homomorphic denoising and adaptive binarization are performed. Convolutional features are extracted and unfolded into a temporal sequence along the reading direction. Character probabilities are calculated and decoded using a connection-time classification bundle search. In the bundle search stage, the scores are re-scored and format constraints are applied based on a restricted dictionary and regular template. The output is a character sequence with character confidence and word confidence. Character coordinates and word coordinates are generated by back-projection of temporal position and boundary polygon.

[0036] Connectivity detection and line segment detection are used to generate candidate cell boundaries for the candidate table region. A relationship graph is constructed with cells as nodes and adjacency and alignment relationships as edges. Under row and column uniqueness constraints and merging consistency constraints, row and column ownership and merging relationships are jointly solved. Cross-page headers are inherited with structural consistency constraints. A table structure graph and the mapping of cells to page coordinates and text are generated.

[0037] Pixel-level masks and outlines are obtained by instance region segmentation for the stamp area and handwritten area. Learnable sharpening items are set at the boundaries. Text sequence decoding is used to parse the annotation content. The results are registered as independent blocks and the page identifier, block identifier, page coordinates and hierarchical path are kept consistent.

[0038] The boundary polygon, text baseline, and cell vertex coordinates are inversely transformed to the page coordinate system according to the calibration parameter log. The block-level and cell-level confidence scores are calculated by integrating geometric consistency, character confidence, and word confidence. The hierarchical path and reading order scores are generated based on proximity, inclusion relationship, and reading order constraints. The page-level and block-level results are summarized to form a document parsing dataset, and the backtracking relationship is established using the original text mapping table.

[0039] Optionally, the step of taking the document parsing dataset as input, calling the information extraction model, using pointer networks and optimal transmission for joint decoding, performing entity localization and key-value alignment, and obtaining metadata and relation datasets specifically includes:

[0040] The document parsing dataset and the original text mapping table are aggregated according to page identifier and block identifier, including text within the block, page coordinates, block type, table structure diagram and reading order label, and field dictionary, format template and required constraints are generated from the pre-set knowledge graph library as extraction priors;

[0041] An information extraction model is constructed, the encoder is initialized with a three-modal unified encoder model, the backbone parameters are frozen and a low-rank adaptation layer is superimposed to obtain block-level representation and context window representation;

[0042] The starting point probability and ending point probability are calculated using a pointer network. A set of candidate spans is generated based on threshold, span length and block boundary constraints. Evidence page coordinates and block identifiers are registered for each candidate span.

[0043] Conditional random fields are used for sequence labeling, and BIO tag sequences are output. Consistency constraints are applied to the BIO tag sequences and candidate span sets to generate entity candidate sets. The entity sets and entity categories are determined according to conflict resolution and format template constraints, and the source candidate span identifier and page coordinates are retained for each entity.

[0044] Based on the field dictionary, a field anchor point representation is generated for each target field. Candidate value representations are extracted from the entity set and table structure diagram. The corresponding double affine score is calculated to form a cost matrix. Under the constraints of mandatory field, uniqueness, and format template, the field value mapping and pairing confidence are obtained by optimal transmission decoding.

[0045] An entity relationship graph is constructed based on the entity set. Adjacency within the same paragraph and adjacency across blocks are set. Message passing and joint classification are performed to obtain entity relationship triples. Time standardization, unit conversion and document number structure parsing are performed. Cross-block deduplication and consistency verification are performed according to reading order and proximity relationship to form metadata and relationship dataset.

[0046] Optionally, the constructed structure-enhanced dual-tower vector recall model uses global vectors and field anchor vectors, with inner product and anchor matching as the metric. It combines small-world graph indexes and product quantization indexes to perform alignment, disambiguation, and consistency calculations, resulting in a standardized record library and evidence linking table. Specifically, this includes:

[0047] Read metadata and relational datasets, knowledge graph library and original text mapping table, load field anchor representations, candidate value representations, entity sets and cost matrices, and associate evidence entry indexes with page identifiers and block identifiers;

[0048] A structure-enhanced dual-tower vector recall model is constructed. Both the query encoder and the candidate encoder are self-attention-based transformer encoders with non-shared parameters. The input consists of text tags, two-dimensional relative coordinates, and block type embeddings. The sequence representation is obtained through multi-head attention and feedforward network. Then, a global vector is generated by weighted common tag pooling. At the same time, a field anchor sequence is generated using field name or field template as seed and the field anchor vector is obtained through the same path. All vectors are processed by layer normalization and norm normalization while maintaining page coordinates and block identifier references.

[0049] Define a weighted matching score, which is the weighted sum of the global vector inner product score and the optimal transmission matching score of the field anchor point as the pairing score. The weight coefficients are denoted as the first coefficient and the second coefficient.

[0050] Establish a vector retrieval index, construct a hierarchical navigable small world graph index for global vectors, construct a product quantization index for field anchor vectors, register the index handle and establish a reference with the page identifier and block identifier in the original text mapping table;

[0051] Entity and field candidates are retrieved from the small-world graph index and the product quantization index based on inner product similarity. A weighted matching score is calculated by combining the cost matrix, format template and mandatory constraints to generate a candidate pairing set.

[0052] The field consistency constraint, time sequence consistency constraint, and document number structure constraint are modeled as integer linear programming. The candidate pairing set is solved to obtain entity alignment results, same name disambiguation results, and field value mapping, and the solution log and unsatisfied constraint list are output.

[0053] The global vector is optimized by contrastive learning loss, and the transmission cost between field anchors and candidate values ​​is minimized by anchor matching loss. For hard examples, hard example sampling and temperature scaling calibration based on pairwise scores are used to learn the first and second coefficients and keep them consistent with the cost matrix.

[0054] Time is standardized according to a time template, quantity and unit are standardized according to a unit conversion table, institution and region are standardized according to a coding table, and document number is parsed and its constituent elements are recorded according to a structural template, generating standardized field values ​​and writing them into a standardized record library, while also generating an evidence link table.

[0055] Optionally, the system, based on a standardized record library and an evidence linking table and policy rule engine, implements access control, data anonymization, automatic archiving, borrowing approval, and due date reminders. It also verifies records and generates pseudo-tags for feedback. Specifically, this includes:

[0056] Read the standardized record library, evidence link table, original text mapping table and index handle, and obtain the page coordinates, block identifiers and character coordinates according to the record identifier;

[0057] The policy rule engine performs access control, desensitization, and process processing. Visible fields are clipped based on RBAC and access control tables. Desensitization is implemented according to regular expression templates, sensitive word libraries, and coordinate masking. Automatic archiving and borrowing approval are completed by a directed state machine. Expiration reminders are generated based on time templates and timers.

[0058] Based on the evidence link table, return the record identifier, page coordinates, block identifier, and character coordinates, and output the source field;

[0059] Record manual proofreading, generate pseudo-label sample set, filter field values ​​and relationships using consistency rules, format templates and thresholds, and register evidence entry index through conflict filtering and deduplication;

[0060] The parameters of the low-rank adaptation layer of the trimodal unified encoder model, information extraction model and structure-enhanced dual-tower vector recall model are adjusted, and federated average aggregation and differential privacy noise injection are adopted, and the privacy budget and model version are recorded.

[0061] Write operations to the operation log and audit trail, and associate new records, pseudo-label samples, model version numbers, task results and timestamps with the original text mapping table.

[0062] The beneficial effects of this invention are:

[0063] Compared to existing "OCR plus rule" pipelines, this invention offers the following advantages: It significantly improves robustness to complex layouts such as tilt, perspective distortion, stamp occlusion, and handwritten annotations by learning robust representations of layout, text, and coordinates under low-annotation conditions through a trimodal self-supervised unified encoder; it reduces error propagation caused by block boundaries and text line breaks by employing differentiable boundary-optimized layout parsing and connection-time classification; it reduces mis-extraction and mismatching while also considering table structure backfilling by using pointer networks and conditional random field consistency constraints for optimal transmission of field alignment; it introduces structure-enhanced dual-tower vector recall and consistency solving to achieve globally optimal decisions for entity alignment and name disambiguation; it establishes a bidirectional traceable link from records to original text coordinates through an evidence link table, facilitating verification and auditing; it combines a policy rule engine with a standardized record library to complete access control, desensitization, and automatic archiving, forming a closed-loop iteration with pseudo-label reflow and efficient parameter fine-tuning, continuously improving accuracy and reducing rule maintenance and manual annotation costs; and it employs federated aggregation and differential privacy to balance cross-unit data compliance and continuous optimization. Attached Figure Description

[0064] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0065] Figure 1 This is a schematic diagram of the structure of a self-supervised learning-based intelligent management system for extracting and managing archival information proposed in this invention.

[0066] Figure 2 This is a flowchart of a self-supervised learning-based method for extracting and intelligently managing archival information, as proposed in this invention.

[0067] Figure 3 This is a framework diagram of the structure-enhanced dual-tower vector recall model in a self-supervised learning-based method for extracting and intelligently managing archival information proposed in this invention. Detailed Implementation

[0068] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0069] refer to Figure 1 A self-supervised learning-based intelligent management system for extracting and managing archival information includes the following modules:

[0070] The data acquisition and preprocessing module is used to acquire the original data set of the archives, perform geometric regression correction, homomorphic denoising and self-attention page segmentation, generate page sample datasets, and establish the original text mapping table;

[0071] The self-supervised pre-training module is used to perform mask layout reconstruction and cross-view comparison self-supervised training on the layout sample dataset to obtain a three-modal unified encoder model.

[0072] The document parsing module is used to perform differentiable boundary optimized text detection, character recognition, table structure recovery and seal handwriting parsing based on the trimodal unified encoder model, generate a document parsing dataset, and associate it with the original text mapping table;

[0073] The information extraction module is used to call the information extraction model to perform pointer network span positioning, conditional random field sequence labeling and optimal transmission field alignment, output metadata and relation datasets, and register evidence entry indexes;

[0074] The alignment and storage module is used to read metadata, relational datasets, and knowledge graph libraries, build a structure-enhanced dual-tower vector recall model, establish a small world graph index and a product quantization index, and generate a standardized record library and evidence link table.

[0075] The strategy operation and maintenance module is used to implement access control, desensitization, automatic archiving, borrowing approval and due date reminders based on a standardized record library, evidence link table and original text mapping table. It also records verification and generates pseudo-tags for periodic incremental updates.

[0076] refer to Figure 2-3 A method for extracting and intelligently managing archival information based on self-supervised learning includes the following steps:

[0077] The original dataset of the archives was collected, and geometric regression correction, homomorphic denoising and self-attention page segmentation were used to perform skew correction and segmentation to obtain the page sample dataset and generate the original text mapping table.

[0078] Using the layout sample dataset as input, a three-modal unified encoder model is obtained by performing representation learning through self-supervised joint pre-training of mask layout reconstruction and cross-view comparison.

[0079] The layout sample dataset is processed based on a three-modal unified encoder model. Cascaded detection and recognition, graph neural network table parsing are used for localization and reconstruction to obtain a document parsing dataset. The original text mapping table is then used for backtracking.

[0080] Taking the document parsing dataset as input, the information extraction model is invoked, and the pointer network and optimal transmission are used for joint decoding to perform entity localization and key-value alignment, thereby obtaining metadata and relation datasets.

[0081] Using metadata, relational datasets, and knowledge graph libraries as input, a structure-enhanced dual-tower vector recall model is constructed. Global vectors and field anchor vectors are used, with inner product and anchor matching as the metric. Small world graph index and product quantization index are combined to solve for alignment, disambiguation, and consistency, resulting in a standardized record library and evidence link table.

[0082] Based on a standardized record library, and using an evidence linking table and a policy rule engine, the system implements access control, desensitization, automatic archiving, borrowing approval and due date reminders, and records are verified and pseudo-labels are generated for feedback.

[0083] In this embodiment, the original dataset of the collected archives is subjected to geometric regression correction, homomorphic denoising, and self-attention page segmentation to perform skew correction and segmentation, resulting in a page sample dataset, and generating a text mapping table, specifically including:

[0084] Collect scanned copies of paper documents, electronic documents, and structured forms, unify them into a parsable format, and record the source number and timestamp to form the original dataset of the archives;

[0085] Geometric regression correction is performed on the page image, and homomorphic denoising and adaptive binarization are applied in the logarithmic domain to obtain the corrected page image and correction parameter log.

[0086] A self-attention page segmentation network is constructed. Line segment candidates are generated by combining the corrected page image with connected components and vertical projection. The text prior is formed by overlaying the OCR character box rasterization. The input consists of the corrected page image, text prior, 2D relative coordinate embedding and hierarchical path embedding. The network adopts a hierarchical self-attention encoder and a multi-scale fusion neck. Multi-head decoding is set for block category, boundary vertex regression, hierarchical label and reading order score. The training objective is the weighted sum of classification cross-entropy, boundary IoU or DIoU, hierarchical sequence cross-entropy and order consistency loss. Data augmentation with coordinate perturbation and random occlusion is applied.

[0087] The self-attention page segmentation network outputs block categories, boundary polygons, hierarchical labels, and reading order scores. It then performs page-block segmentation to generate a page sample dataset containing page numbers, block identifiers, page coordinates, hierarchical paths, reading order tags, and content hashes.

[0088] A source mapping table is established, with fields including source number, file path, page number, block identifier, page coordinates, hierarchical path, reading order marker, content hash, and correction parameter log index. Based on the source mapping table, a one-to-many binding is established between the original archive dataset, the corrected page diagram, and the layout sample dataset.

[0089] This implementation generates a high-quality page layout sample dataset through geometric correction, homomorphic denoising, and self-attention page segmentation, and solidifies the relationship between pages, blocks, and the original text with a mapping table, which significantly improves the stability of subsequent recognition and extraction and the availability of training samples.

[0090] In this embodiment, the step of using a layout sample dataset as input, employing self-supervised joint pre-training of mask layout reconstruction and cross-view comparison, and performing representation learning to obtain a three-modal unified encoder model specifically includes:

[0091] The page sample dataset is aggregated with page and block identifiers according to the original text mapping table to form training samples of text sequence, page image slice, two-dimensional relative coordinates and hierarchical path. The reading order label is retained as a regularization constraint, and the same page three modalities are positive pairs, and cross-page or cross-file pairs are negative pairs.

[0092] A pre-trained backbone for a trimodal unified encoder model is constructed, comprising a text sub-encoder, a visual sub-encoder, and a coordinate embedding module. The three features are aligned and aggregated in the fusion layer, and the output is a page-level vector and a block-level vector in the same representation space.

[0093] Two types of self-supervised tasks are set up: mask layout reconstruction and cross-view contrast alignment. The categories, boundaries and contents of the masked page blocks and characters are reconstructed, the positive and negative pairs are contrasted and learned, and the reading order consistency constraint is applied.

[0094] We optimize the loss by weighted sum of reconstruction loss and contrast loss, and combine in-batch negative sampling and hard negative sampling. During training, we apply Gaussian perturbation to page coordinates and perform random occlusion enhancement on page blocks, while keeping the page and block identifiers in the original text mapping table unchanged.

[0095] After completing the joint pre-training, the pre-training parameters of the trimodal unified encoder model are generated and the model version is registered.

[0096] This implementation method learns a unified representation of layout, text, and coordinates under unlabeled or poorly labeled conditions through three-modal self-supervised pre-training of mask layout reconstruction and cross-view comparison, thereby enhancing robustness across templates and low-quality scans and reducing manual annotation and migration costs.

[0097] In this embodiment, the process of processing the layout sample dataset based on the three-modal unified encoder model, using cascaded detection and recognition, graph neural network table parsing, for localization and reconstruction, to obtain the document parsing dataset, and backtracking according to the original text mapping table, specifically includes:

[0098] The page layout sample dataset is loaded with page and block identifiers according to the original text mapping table. The affine and perspective parameters of the correction parameter log are read to complete the page correction. The reading order mark and two-dimensional relative coordinates are combined, and the three-modal unified encoder model is called to generate page-level and block-level features while maintaining the consistency of the identifiers.

[0099] A text detection method with differentiable boundary optimization is adopted to simultaneously predict the text probability map and threshold map. A soft binary map is obtained by using a differentiable threshold approximation function. Connectivity extraction, centroid offset correction, and polygon fitting are performed. Boundary polygons and block confidence are registered and associated with block identifiers.

[0100] Candidate text blocks are cropped according to the log inverse transform of the correction parameters and normalized with a fixed height. Homomorphic denoising and adaptive binarization are performed. Convolutional features are extracted and unfolded into a temporal sequence along the reading direction. Character probabilities are calculated and decoded using a connection-time classification bundle search. In the bundle search stage, the scores are re-scored and format constraints are applied based on a restricted dictionary and regular template. The output is a character sequence with character confidence and word confidence. Character coordinates and word coordinates are generated by back-projection of temporal position and boundary polygon.

[0101] Connectivity detection and line segment detection are used to generate candidate cell boundaries for the candidate table region. A relationship graph is constructed with cells as nodes and adjacency and alignment relationships as edges. Under row and column uniqueness constraints and merging consistency constraints, row and column ownership and merging relationships are jointly solved. Cross-page headers are inherited with structural consistency constraints. A table structure graph and the mapping of cells to page coordinates and text are generated.

[0102] Pixel-level masks and outlines are obtained by instance region segmentation for the stamp area and handwritten area. Learnable sharpening items are set at the boundaries. Text sequence decoding is used to parse the annotation content. The results are registered as independent blocks and the page identifier, block identifier, page coordinates and hierarchical path are kept consistent.

[0103] The boundary polygon, text baseline, and cell vertex coordinates are inversely transformed to the page coordinate system according to the calibration parameter log. The block-level and cell-level confidence scores are calculated by integrating geometric consistency, character confidence, and word confidence. The hierarchical path and reading order scores are generated based on proximity, inclusion relationship, and reading order constraints. The page-level and block-level results are summarized to form a document parsing dataset, and the backtracking relationship is established using the original text mapping table.

[0104] This implementation method accurately recovers block boundaries, character and table structures through differentiable boundary detection, connection time sequence classification and recognition, and relationship graph table parsing. It outputs character and word coordinates and confidence scores, reduces line breaks, adhesion and misalignment, and improves parsing accuracy and evidence location capabilities.

[0105] In this embodiment, the step of taking the document parsing dataset as input, calling the information extraction model, using pointer networks and optimal transmission for joint decoding, performing entity localization and key-value alignment, and obtaining metadata and relation datasets specifically includes:

[0106] The document parsing dataset and the original text mapping table are aggregated according to page identifier and block identifier, including text within the block, page coordinates, block type, table structure diagram and reading order label, and field dictionary, format template and required constraints are generated from the pre-set knowledge graph library as extraction priors;

[0107] An information extraction model is constructed, the encoder is initialized with a three-modal unified encoder model, the backbone parameters are frozen and a low-rank adaptation layer is superimposed to obtain block-level representation and context window representation;

[0108] The starting point probability and ending point probability are calculated using a pointer network. A set of candidate spans is generated based on threshold, span length and block boundary constraints. Evidence page coordinates and block identifiers are registered for each candidate span.

[0109] Conditional random fields are used for sequence labeling, and BIO tag sequences are output. Consistency constraints are applied to the BIO tag sequences and candidate span sets to generate entity candidate sets. The entity sets and entity categories are determined according to conflict resolution and format template constraints, and the source candidate span identifier and page coordinates are retained for each entity.

[0110] Based on the field dictionary, a field anchor point representation is generated for each target field. Candidate value representations are extracted from the entity set and table structure diagram. The corresponding double affine score is calculated to form a cost matrix. Under the constraints of mandatory field, uniqueness, and format template, the field value mapping and pairing confidence are obtained by optimal transmission decoding.

[0111] An entity relationship graph is constructed based on the entity set. Adjacency within the same paragraph and adjacency across blocks are set. Message passing and joint classification are performed to obtain entity relationship triples. Time standardization, unit conversion and document number structure parsing are performed. Cross-block deduplication and consistency verification are performed according to reading order and proximity relationship to form metadata and relationship dataset.

[0112] This implementation combines pointer positioning with conditional random field consistency and optimal transmission alignment to form entity sets, field value mappings, and relation triples, reducing erroneous extraction and mispairing, supporting table backfilling and cross-block merging, and directly producing structured results that can be stored in the database.

[0113] In this embodiment, the construction of the structure-enhanced dual-tower vector recall model uses global vectors and field anchor vectors, with inner product and anchor matching as the metric. It combines small-world graph indexes and product quantization indexes to perform alignment, disambiguation, and consistency calculations, resulting in a standardized record library and evidence linking table. Specifically, this includes:

[0114] Read metadata and relational datasets, knowledge graph library and original text mapping table, load field anchor representations, candidate value representations, entity sets and cost matrices, and associate evidence entry indexes with page identifiers and block identifiers;

[0115] A structure-enhanced dual-tower vector recall model is constructed. Both the query encoder and the candidate encoder are self-attention-based transformer encoders with non-shared parameters. The input consists of text tags, two-dimensional relative coordinates, and block type embeddings. The sequence representation is obtained through multi-head attention and feedforward network. Then, a global vector is generated by weighted common tag pooling. At the same time, a field anchor sequence is generated using field name or field template as seed and the field anchor vector is obtained through the same path. All vectors are processed by layer normalization and norm normalization while maintaining page coordinates and block identifier references.

[0116] Define a weighted matching score, which is the weighted sum of the global vector inner product score and the optimal transmission matching score of the field anchor point as the pairing score. The weight coefficients are denoted as the first coefficient and the second coefficient.

[0117] Establish a vector retrieval index, construct a hierarchical navigable small world graph index for global vectors, construct a product quantization index for field anchor vectors, register the index handle and establish a reference with the page identifier and block identifier in the original text mapping table;

[0118] Entity and field candidates are retrieved from the small-world graph index and the product quantization index based on inner product similarity. A weighted matching score is calculated by combining the cost matrix, format template and mandatory constraints to generate a candidate pairing set.

[0119] The field consistency constraint, time sequence consistency constraint, and document number structure constraint are modeled as integer linear programming. The candidate pairing set is solved to obtain entity alignment results, same name disambiguation results, and field value mapping, and the solution log and unsatisfied constraint list are output.

[0120] The global vector is optimized by contrastive learning loss, and the transmission cost between field anchors and candidate values ​​is minimized by anchor matching loss. For hard examples, hard example sampling and temperature scaling calibration based on pairwise scores are used to learn the first and second coefficients and keep them consistent with the cost matrix.

[0121] Time is standardized according to a time template, quantity and unit are standardized according to a unit conversion table, institution and region are standardized according to a coding table, and document number is parsed and its constituent elements are recorded according to a structural template, generating standardized field values ​​and writing them into a standardized record library, while also generating an evidence link table.

[0122] This implementation achieves high recall and high precision alignment of entities and fields by using structurally enhanced dual-tower recall, small-world graph, product quantization index, and consistency constraint solution; at the same time, it retains evidence coordinates and index handles, supporting massive scale and audit traceability.

[0123] In this embodiment, the implementation of access control, data anonymization, automatic archiving, borrowing approval, and due date reminders, based on a standardized record library, evidence linking tables, and a policy rule engine, along with record verification and pseudo-tag resubmission, specifically includes:

[0124] Read the standardized record library, evidence link table, original text mapping table and index handle, and obtain the page coordinates, block identifiers and character coordinates according to the record identifier;

[0125] The policy rule engine performs access control, desensitization, and process processing. Visible fields are clipped based on RBAC and access control tables. Desensitization is implemented according to regular expression templates, sensitive word libraries, and coordinate masking. Automatic archiving and borrowing approval are completed by a directed state machine. Expiration reminders are generated based on time templates and timers.

[0126] Based on the evidence link table, return the record identifier, page coordinates, block identifier, and character coordinates, and output the source field;

[0127] Record manual proofreading, generate pseudo-label sample set, filter field values ​​and relationships using consistency rules, format templates and thresholds, and register evidence entry index through conflict filtering and deduplication;

[0128] The parameters of the low-rank adaptation layer of the trimodal unified encoder model, information extraction model and structure-enhanced dual-tower vector recall model are adjusted, and federated average aggregation and differential privacy noise injection are adopted, and the privacy budget and model version are recorded.

[0129] Write operations to the operation log and audit trail, and associate new records, pseudo-label samples, model version numbers, task results and timestamps with the original text mapping table.

[0130] This implementation method achieves location tracking by using a policy rule engine-driven permission, de-identification, and process control, combined with an evidence link table; it also uses pseudo-label feedback and efficient parameter fine-tuning to form online incremental iteration, continuously improving accuracy and reducing operation and maintenance costs.

[0131] Example 1:

[0132] To verify the feasibility of this invention in practice, it was applied to the archive management and transformation of an e-government sharing platform. The platform covers paper scans, electronic documents, and structured forms from four commissions and bureaus: Development and Reform Commission, Finance Bureau, Human Resources and Social Security Bureau, and Market Supervision Bureau. During implementation, geometric regression correction and homomorphic denoising were first used to improve image quality. Then, self-attention layout segmentation was used to establish a text mapping table. Self-supervised pre-training of a three-modal unified encoder was completed using mask layout reconstruction and cross-view comparison. Document parsing data was generated based on differentiable boundary optimization of text detection, table graph parsing, and seal / handwriting parsing. Finally, "pointer network + CRF + optimal transmission" was used for field location and pairing, and "structure-enhanced dual-tower + HNSW / PQ + integer programming constraints" were used to complete alignment, disambiguation, and consistency solutions. All fields can be traced back to page / block / character coordinates, and the strategy engine implements desensitization, approval, and expiration reminders.

[0133] Table 1. Benchmark Evaluation Sampling Set

[0134] System / Configuration End-to-end field accuracy Table Structure F1 Character recognition CER Average approval time (hours) Manual proofreading time per thousand pages (hours) Evidence traceability Rules + General OCR Baseline 0.552 0.724 0.073 57.3 162 0.822 This invention (v1 joint pre-training) 0.742 0.842 0.038 26.1 92.4 0.942 This invention (final version: trimodal + dual tower + ILP) 0.833 0.894 0.026 8.4 58.7 0.983

[0135] As can be seen from the comparison in Table 1, this invention improves the end-to-end field accuracy from 0.552 to 0.833; reduces the average approval time to 8.4 hours; reduces proofreading time to 58.7 hours / thousand pages; reduces CER from 0.073 to 0.026, significantly stabilizes the parsing of cross-page headers and merged cells; and achieves an evidence traceability rate of 0.983, meeting the requirements for audit trailing.

[0136] Table 2 Monthly Operational Data Table

[0137] month Processing Pages System field accuracy False alarm rate underreporting rate Borrowing approval SLA (hours) Save labor hours Jun-25 421,380 0.82 0.054 0.071 48.6 6,218 Jul-25 452,910 0.85 0.051 0.067 32.4 6,984 Aug-25 468,230 0.87 0.043 0.061 18.5 7,812

[0138] Table 2 shows that with the return of false labels and incremental updates for low-rank adaptation, the accuracy rate increased from 0.82 to 0.87; the false alarm / missed alarm rate decreased simultaneously, and the approval SLA decreased from 48.6 hours to 18.5 hours; more than 20,000 hours of manpower were saved in three months (including other units), significantly reducing operation and maintenance costs while maintaining high accuracy.

[0139] To verify the value of key components, ablation experiments were conducted, and the results are shown in Table 3:

[0140] Table 3 Ablation Data of Key Components

[0141] Configuration End-to-end field accuracy Field alignment accuracy Same Name Disambiguation F1 Manual proofreading time per thousand pages (hours) Complete model (control) 0.833 0.911 0.875 58.7 Differentiable boundary removal optimization 0.789 0.881 0.847 72.3 Remove optimal transport decoding 0.755 0.856 0.831 78.1 Remove dual towers + indexes 0.743 0.867 0.816 93.6 Remove Integer Programming Consistency 0.781 0.881 0.847 71.2

[0142] As can be seen from Table 3, differentiable boundary optimization directly affects detection robustness; optimal transmission ensures correct field-level pairing; dual-tower retrieval and indexing are the foundation for recall and efficiency under large-scale candidate scenarios; integer programming transforms business constraints into globally consistent solutions, significantly suppressing mismatches that appear to be correct locally.

[0143] This invention achieves highly robust extraction and alignment, significantly improving end-to-end accuracy while simultaneously reducing approval and verification costs; evidence links enable coordinate-level traceability, meeting compliance audit requirements; through self-monitoring and lightweight incremental updates, the system maintains stable performance even with changes in business distribution, achieving "more accurate extraction, more stable alignment, auditable management, and adaptable operation and maintenance."

[0144] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A self-supervised learning-based archival information extraction and intelligent management system, characterized in that, Includes the following modules: The data acquisition and preprocessing module is used to acquire the original data set of the archives, perform geometric regression correction, homomorphic denoising and self-attention page segmentation, generate page sample datasets, and establish the original text mapping table; The self-supervised pre-training module is used to perform mask layout reconstruction and cross-view comparison self-supervised training on the layout sample dataset to obtain a three-modal unified encoder model. The document parsing module is used to perform differentiable boundary optimized text detection, character recognition, table structure recovery and seal handwriting parsing based on the trimodal unified encoder model, generate a document parsing dataset, and associate it with the original text mapping table; The information extraction module is used to call the information extraction model to perform pointer network span positioning, conditional random field sequence labeling and optimal transmission field alignment, output metadata and relation datasets, and register evidence entry indexes; The alignment and storage module is used to read metadata, relational datasets, and knowledge graph libraries, build a structure-enhanced dual-tower vector recall model, establish a small world graph index and a product quantization index, and generate a standardized record library and evidence link table. The strategy operation and maintenance module is used to implement access control, desensitization, automatic archiving, borrowing approval and due date reminders based on a standardized record library, evidence link table and original text mapping table. It also records verification and generates pseudo-tags for periodic incremental updates.

2. The self-supervised learning-based archival information extraction and intelligent management system according to claim 1, characterized in that, The modules are connected in the following way: The original dataset of the archives was collected, and geometric regression correction, homomorphic denoising and self-attention page segmentation were used to perform skew correction and segmentation to obtain the page sample dataset and generate the original text mapping table. Using the layout sample dataset as input, a three-modal unified encoder model is obtained by performing representation learning through self-supervised joint pre-training of mask layout reconstruction and cross-view comparison. The layout sample dataset is processed based on a three-modal unified encoder model. Cascaded detection and recognition, graph neural network table parsing are used for localization and reconstruction to obtain a document parsing dataset. The original text mapping table is then used for backtracking. Taking the document parsing dataset as input, the information extraction model is invoked, and the pointer network and optimal transmission are used for joint decoding to perform entity localization and key-value alignment, thereby obtaining metadata and relation datasets. Using metadata, relational datasets, and knowledge graph libraries as input, a structure-enhanced dual-tower vector recall model is constructed. Global vectors and field anchor vectors are used, with inner product and anchor matching as the metric. Small world graph index and product quantization index are combined to solve for alignment, disambiguation, and consistency, resulting in a standardized record library and evidence link table. Based on a standardized record library, and using an evidence linking table and a policy rule engine, the system implements access control, desensitization, automatic archiving, borrowing approval and due date reminders, and records are verified and pseudo-labels are generated for feedback.

3. The self-supervised learning-based archival information extraction and intelligent management system according to claim 2, characterized in that, The original dataset of the collected archives is subjected to geometric regression correction, homomorphic denoising, and self-attention page segmentation to perform skew correction and segmentation, resulting in a page sample dataset, and generating a text mapping table, specifically including: Collect scanned copies of paper documents, electronic documents, and structured forms, unify them into a parsable format, and record the source number and timestamp to form the original dataset of the archives; Geometric regression correction is performed on the page image, and homomorphic denoising and adaptive binarization are applied in the logarithmic domain to obtain the corrected page image and correction parameter log. A self-attention page segmentation network is constructed. Line segment candidates are generated by combining the corrected page image with connected components and vertical projection. The text prior is formed by overlaying the OCR character box rasterization. The input consists of the corrected page image, text prior, 2D relative coordinate embedding and hierarchical path embedding. The network adopts a hierarchical self-attention encoder and a multi-scale fusion neck. Multi-head decoding is set for block category, boundary vertex regression, hierarchical label and reading order score. The training objective is the weighted sum of classification cross-entropy, boundary IoU or DIoU, hierarchical sequence cross-entropy and order consistency loss. Data augmentation with coordinate perturbation and random occlusion is applied. The self-attention page segmentation network outputs block categories, boundary polygons, hierarchical labels, and reading order scores. It then performs page-block segmentation to generate a page sample dataset containing page numbers, block identifiers, page coordinates, hierarchical paths, reading order tags, and content hashes. A source mapping table is established, with fields including source number, file path, page number, block identifier, page coordinates, hierarchical path, reading order marker, content hash, and correction parameter log index. Based on the source mapping table, a one-to-many binding is established between the original archive dataset, the corrected page diagram, and the layout sample dataset.

4. The self-supervised learning-based archival information extraction and intelligent management system according to claim 2, characterized in that, The process involves using a layout sample dataset as input, performing self-supervised joint pre-training of mask layout reconstruction and cross-view comparison, and then performing representation learning to obtain a three-modal unified encoder model, specifically including: The page sample dataset is aggregated with page and block identifiers according to the original text mapping table to form training samples of text sequence, page image slice, two-dimensional relative coordinates and hierarchical path. The reading order label is retained as a regularization constraint, and the same page three modalities are positive pairs, and cross-page or cross-file pairs are negative pairs. A pre-trained backbone for a trimodal unified encoder model is constructed, comprising a text sub-encoder, a visual sub-encoder, and a coordinate embedding module. The three features are aligned and aggregated in the fusion layer, and the output is a page-level vector and a block-level vector in the same representation space. Two types of self-supervised tasks are set up: mask layout reconstruction and cross-view comparison alignment. The categories, boundaries and contents are reconstructed for the masked page blocks and characters, the positive and negative pairs are compared and learned, and the reading order consistency constraint is applied. We optimize the loss by weighted sum of reconstruction loss and contrast loss, and combine in-batch negative sampling and hard negative sampling. During training, we apply Gaussian perturbation to page coordinates and perform random occlusion enhancement on page blocks, while keeping the page and block identifiers in the original text mapping table unchanged. After completing the joint pre-training, the pre-training parameters of the trimodal unified encoder model are generated and the model version is registered.

5. The self-supervised learning-based archival information extraction and intelligent management system according to claim 2, characterized in that, The process of processing the layout sample dataset based on the three-modal unified encoder model involves using cascaded detection and recognition, graph neural network table parsing, to locate and reconstruct the document, resulting in a document parsing dataset. This is followed by backtracking based on the original text mapping table, specifically including: The page layout sample dataset is loaded with page and block identifiers according to the original text mapping table. The affine and perspective parameters of the correction parameter log are read to complete the page correction. The reading order mark and two-dimensional relative coordinates are combined, and the three-modal unified encoder model is called to generate page-level and block-level features while maintaining the consistency of the identifiers. A text detection method with differentiable boundary optimization is adopted to simultaneously predict the text probability map and threshold map. A soft binary map is obtained by using a differentiable threshold approximation function. Connectivity extraction, centroid offset correction, and polygon fitting are performed. Boundary polygons and block confidence are registered and associated with block identifiers. Candidate text blocks are cropped according to the log inverse transform of the correction parameters and normalized with a fixed height. Homomorphic denoising and adaptive binarization are performed. Convolutional features are extracted and unfolded into a temporal sequence along the reading direction. Character probabilities are calculated and decoded using a connection-time classification bundle search. In the bundle search stage, the scores are re-scored and format constraints are applied based on a restricted dictionary and regular template. The output is a character sequence, character confidence, and word confidence. Character coordinates and word coordinates are generated by back-projection of temporal position and boundary polygon. Connectivity detection and line segment detection are used to generate candidate cell boundaries for the candidate table region. A relationship graph is constructed with cells as nodes and adjacency and alignment relationships as edges. Under row and column uniqueness constraints and merging consistency constraints, row and column ownership and merging relationships are jointly solved. Cross-page headers are inherited with structural consistency constraints. A table structure graph and the mapping of cells to page coordinates and text are generated. Pixel-level masks and outlines are obtained by instance region segmentation for the stamp area and handwritten area. Learnable sharpening items are set at the boundaries. Text sequence decoding is used to parse the annotation content. The results are registered as independent blocks and the page identifier, block identifier, page coordinates and hierarchical path are kept consistent. The boundary polygon, text baseline, and cell vertex coordinates are inversely transformed to the page coordinate system according to the calibration parameter log. The block-level confidence and cell-level confidence are calculated by integrating geometric consistency, character confidence, and word confidence. The hierarchical path and reading order score are generated based on proximity, inclusion relationship, and reading order constraints. The page-level and block-level results are summarized to form a document parsing dataset, and the backtracking relationship is established using the original text mapping table.

6. The self-supervised learning-based archival information extraction and intelligent management system according to claim 2, characterized in that, The process involves taking a document parsing dataset as input, calling an information extraction model, using a pointer network and optimal transport for joint decoding, performing entity localization and key-value alignment, and obtaining metadata and relation datasets. Specifically, this includes: The document parsing dataset and the original text mapping table are aggregated according to page identifier and block identifier, including text within the block, page coordinates, block type, table structure diagram and reading order label, and field dictionary, format template and required constraints are generated from the pre-set knowledge graph library as extraction priors; An information extraction model is constructed, the encoder is initialized with a three-modal unified encoder model, the backbone parameters are frozen and a low-rank adaptation layer is superimposed to obtain block-level representation and context window representation; The starting point probability and ending point probability are calculated using a pointer network. A set of candidate spans is generated based on threshold, span length and block boundary constraints. Evidence page coordinates and block identifiers are registered for each candidate span. Conditional random fields are used for sequence labeling, and BIO tag sequences are output. Consistency constraints are applied to the BIO tag sequences and candidate span sets to generate entity candidate sets. The entity sets and entity categories are determined according to conflict resolution and format template constraints, and the source candidate span identifier and page coordinates are retained for each entity. Based on the field dictionary, a field anchor point representation is generated for each target field. Candidate value representations are extracted from the entity set and table structure diagram. The corresponding double affine score is calculated to form a cost matrix. Under the constraints of mandatory field, uniqueness, and format template, the field value mapping and pairing confidence are obtained by optimal transmission decoding. An entity relationship graph is constructed based on the entity set. Adjacency within the same paragraph and adjacency across blocks are set. Message passing and joint classification are performed to obtain entity relationship triples. Time standardization, unit conversion and document number structure parsing are performed. Cross-block deduplication and consistency verification are performed according to reading order and proximity relationship to form metadata and relationship dataset.

7. The self-supervised learning-based archival information extraction and intelligent management system according to claim 2, characterized in that, The constructed structure-enhanced dual-tower vector recall model uses global vectors and field anchor vectors, with inner product and anchor matching as the metric. It combines small-world graph indexes and product quantization indexes to perform alignment, disambiguation, and consistency calculations, resulting in a standardized record library and evidence linking table. Specifically, this includes: Read metadata and relational datasets, knowledge graph library and original text mapping table, load field anchor representations, candidate value representations, entity sets and cost matrices, and associate evidence entry indexes with page identifiers and block identifiers; A structure-enhanced dual-tower vector recall model is constructed. Both the query encoder and the candidate encoder are self-attention-based transformer encoders with non-shared parameters. The input consists of text tags, two-dimensional relative coordinates, and block type embeddings. The sequence representation is obtained through multi-head attention and feedforward network. Then, a global vector is generated by weighted common tag pooling. At the same time, a field anchor sequence is generated using field name or field template as seed and the field anchor vector is obtained through the same path. All vectors are processed by layer normalization and norm normalization while maintaining page coordinates and block identifier references. Define a weighted matching score, which is the weighted sum of the global vector inner product score and the optimal transmission matching score of the field anchor point as the pairing score. The weight coefficients are denoted as the first coefficient and the second coefficient. Establish a vector retrieval index, construct a hierarchical navigable small world graph index for global vectors, construct a product quantization index for field anchor vectors, register the index handle and establish a reference with the page identifier and block identifier in the original text mapping table; Entity and field candidates are retrieved from the small-world graph index and the product quantization index based on inner product similarity. A weighted matching score is calculated by combining the cost matrix, format template and mandatory constraints to generate a candidate pairing set. The field consistency constraint, time sequence consistency constraint, and document number structure constraint are modeled as integer linear programming. The candidate pairing set is solved to obtain entity alignment results, same name disambiguation results, and field value mapping, and the solution log and unsatisfied constraint list are output. The global vector is optimized by contrastive learning loss, and the transmission cost between field anchors and candidate values ​​is minimized by anchor matching loss. For hard examples, hard example sampling and temperature scaling calibration based on pairwise scores are used to learn the first and second coefficients and keep them consistent with the cost matrix. Time is standardized according to a time template, quantity and unit are standardized according to a unit conversion table, institution and region are standardized according to a coding table, and document number is parsed and its constituent elements are recorded according to a structural template, generating standardized field values ​​and writing them into a standardized record library, while also generating an evidence link table.

8. The self-supervised learning-based archival information extraction and intelligent management system according to claim 2, characterized in that, The system, based on a standardized record library and grounded in an evidence linking table and a policy rule engine, implements access control, data anonymization, automatic archiving, borrowing approval, and due date reminders. It also verifies records and generates pseudo-tags for data return. Specifically, this includes: Read the standardized record library, evidence link table, original text mapping table and index handle, and obtain the page coordinates, block identifiers and character coordinates according to the record identifier; The policy rule engine performs access control, desensitization, and process processing. Visible fields are clipped based on RBAC and access control tables. Desensitization is implemented according to regular expression templates, sensitive word libraries, and coordinate masking. Automatic archiving and borrowing approval are completed by a directed state machine. Expiration reminders are generated based on time templates and timers. Based on the evidence link table, return the record identifier, page coordinates, block identifier, and character coordinates, and output the source field; Record manual proofreading, generate pseudo-label sample set, filter field values ​​and relationships using consistency rules, format templates and thresholds, and register evidence entry index through conflict filtering and deduplication; The parameters of the low-rank adaptation layer of the trimodal unified encoder model, information extraction model and structure-enhanced dual-tower vector recall model are adjusted, and federated average aggregation and differential privacy noise injection are adopted, and the privacy budget and model version are recorded. Write operations to the operation log and audit trail, and associate new records, pseudo-label samples, model version numbers, task results and timestamps with the original text mapping table.

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